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DEWEY
HB31
.M415
Massachusetts Institute of Technology
Department of Economics
Working Paper Series
INTERNET RETAIL DEMAND: TAXES, GEOGRAPHY,
AND ONLINE-OFFLINE COMPETITION
Glenn Ellison
Sara Ellison
Working Paper 06-1
April 28, 2006
Room
E52-251
50 Memorial Drive
Cambridge,
MA 021 42
This paper can be downloaded without char ge from the
Social Science Research Network Paper Collection at
http;//ssrn,com/abstract=901 852
MASSACHUSETTS INSTITUTE
OF TECHNOLOGV
JUN
2
2006
LIBRARIES
Internet Retail
Demand:
Taxes, Geography, and Online-Offline Competition^
Glenn Ellison
and NBER
MIT
and
Sara Fisher Ellison
MIT
April 2006
^We would
thank Nathan Barczi, Eric Moos, and Andrew Sweeting for excellent research
Goebel and Steve Ellison for assistance with data issues, and Austan Goolsbee
and John Morgan for helpful comments. We also thank the Center for Advanced Studies in the Behavioral Sciences, the Hoover Institute, the Institute for Advanced Study, the Toulouse Network for
Information Technology, and the National Science Foundation (SES-02 19205) for financial support.
like to
assistance, Patrick
Abstract
memory modules
Data on
sales of
There
a strong relationship between e-retail sales to a given state and sales tax rates that
is
apply to purchases froin
between online and
e-retail activity.
are used to explore several aspects of e-retail
offline retailers.
offline retail,
This suggests that there
is
demand.
substantial substitution
and tax avoidance may be an important contributor to
Geography matters
in
two ways: we find some evidence that consumers
prefer purchasing from firms in nearby states to benefit
from
faster shipping times as well as
evidence of a separate preference for buying from in-state firms. Consumers appear fairly
rational in
some ways, but boundedly
rational in others.
1
Introduction
The
recent growth of Internet retail (e-retail) has attracted a great deal of attention in the
academic
literature
and popular
press.-'
While we have learned a
of these markets the past few years, the remarkable swings in the
lot
about the structure
market value of
companies provides ample evidence that our understanding of the industry's future
limited.^
This future of e-retail
Intellectually, e-retail
is
is
of interest for
a great case study:
it
e-retail
is
quite
both intellectual and practical reasons.
provides an opportunity to reexamine our
understanding of consumer and firm behavior and suggests new questions. Practically,
retail
its
could have significant effects on the economy.
current growth rate:
of the
it
impact on traditional
has grown steadily at about
And
dot-com "bubble."
retail,
It will
e-
not remain small for long at
25%
per year since the collapse
even a small e-retail industry could have a substantial
which employs
as
many Americans
all
manufacturing industries
combined.'^
In this paper
we
investigate aspects of
consumer behavior that
impact on the future of Internet and traditional
we examine
We
prove more
eflficient in
close substitutes, though, will have a dramatic
have a substantial
focus on three issues.
substitution betweeen Internet and traditional retail.
e-retail or traditional retail will
channel.
retail.
will
It is
the long run.
First,
not clear whether
Whether the two are
impact on the future of the
less efficient
Second, we examine the extent to which the success e-retail has had
is
due to
the de facto tax-free status of most e-retail purchases in the U.S.'* This bears on the relative efficiency of e-retail,
and
is
important to understanding what
are able to tax online sales, with the Internet
^See, for example, Goolsbee (2000),
may happen
Tax Nondiscrimination Act
if
states
set to expire in
Smith (2001), Chevalier and Goolsbee (2003), and Ellison and Ellison
(2005).
^Amazon's market value, for example, grew from $2 billion at the start of 1998 to $35 bilhon in the
middle of 1999, and fell back to S3 biUion in 2001 before reaching $25 bilhon for a second time in 2003.
U.S. e-retail sales are now approximately $100 billion per year, which is 3% of total retail sales.
Forty five U.S. states levy sales taxes on traditional retail purchases. Each of these states also has laws
its residents make from out-of-state firms. However, the Supreme
Court ruled in Quill vs. North Dakota (1992) that absent new federal law, a state could not compel a firm
without substantial physical "nexus" in that state to collect use taxes on its behalf. The 1998 Internet Tax
Nondiscrimination Act makes explicit that web presence alone does not constitute nexus. While consumers
are obligated to self-report use-tax liability, few do in practice. Note that states are able to collect sales
taxes on e-retailers' in-state sales.
assessing "use taxes" on purchases that
2007.^
Third, we examine the geography of
graphic differentiation
is
e-retail.
It is
commonly supposed that
an important factor allowing traditional
retail stores to
geo-
maintain
the markups over marginal cost they need to survive. Branding, obfuscation, or other factors
may
allow e-retailers to survive even without geographic differentiation, but knowing
whether geographic differentiation
what market structure might
more generally
as
really eliminated
is
also
important
for
understanding
evolve.^
Although everything we study
of
is
is
in the context of e-retail, this
paper can also be thought
an empirical analysis of consumer informedness and sophistication. The
standard fully-informed, rational analysis would make a number of simple predictions,
consumers should compare firms on the basis
on the basis
of the tax-inclusive prices
of the prices charged at the time
to at other times).
when
the purchase
is
e.g.
and make decisions
made
(as
opposed
Alternate predictions could be obtained in a number of ways.
For
instance, one could use rational models with information acquisition costs, rational models
with information processing costs, or models with "irrational" consumers.
several of the standard predictions appear not to hold in our data
We
find that
and discuss what
this
suggests about the form of our consumers' bounded rationality.'^
The environment we study
at
consumers shopping
for
is
that examined in Ellison and Ellison (2004):
computer memory modules using the Pricewatch.com search
engine. For a period of approximately one year,
we have hourly data on the twelve lowest
prices listed on Pricewatch for each of several products.
e-retailer listing each price
and unusually bad
we look
is
in another.
located.
Our quantity data
The bad
part
is
that
the fisted websites (both located in California), so
purchased from other websites (or from traditional
We know
is
the state in which the
unusually good in one respect
we only observe purchases from two
of
we do not know how many consumers
retailers).
The good
part
is
that the
^There are two ways in which the de facto tax-free status of Internet purchases in the U.S. might be
threatened in the near future. The expiration of the Internet Tax Nondiscrimination Act in 2007 could
have implications for the legal definition of nexus. In addition, eighteen states have joined the Streamlined
Sales Tax Project in an attempt to simplify and harmonize their sales tax laws. The Project's goals are to
encourage online retailers to agree to collect use taxes for sales made in those eighteen states and, eventually,
to pave the way to federal legislation requiring collection of use taxes.
^See Brynjolfsson and Smith (2001), Chevalier and Goolsbee (2003), Baye and Morgan (2004), Ellison
(2005), and Ellison and Ellison (2004).
^Hossain and Morgan (2006) 's analysis of bidding on eBay provides compelling evidence of limitations
in
consumer
rationality in that environment.
data are at the individual order
The
level
and include each consumer's location.
structure of the data provides several nice opportunities for examining consumer
we observe the
preferences and behavior. First, the fact that
is
state in
which each consumer
located creates an opportunity to look at geography, taxes, and online-ofHine substitu-
tion:
we can quantify
sales taxes
retailers
watch
—taxes
the extent to which our websites
both
in
competing against other California
against e-retailers in
New
substantial turnover in the Price-
list
and others
which they are competing
in
effects.
volatile. In the highly
Third, wholesale prices for
quickly.
week
Many
at the price
gap
online-offline price
Sunday, and in other weeks
it is
how
another way
memory modules
are re-
traditional retailers, in contrast, keep
they advertised in the latest Sun-
sales circular. This creates a interesting source of variation
some weeks the
is
at
competitive Pricewatch universe, wholesale price increases
prices fixed over the course of each
prices: in
Looking
hour are affected by the competitors' locations
geography and tax
and
of the twelve lowest-priced
hours in which our two websites are mostly
e-retailers,
and decreases are passed through very
day
many
is
Jersey, Illinois, Oregon, etc. with similar prices.
state-specific sales in a given
markably
Second, there
terms of which websites make the
in their price ranking. Hence, there are
to identify
in states that levy higher
primarily affect the firm's competitive position relative to traditional
— and to states that are nearby.
lists,
more
sell
much
higher.
is
How
between online and
much lower on Friday than
consumers react to
it
this price
offline
was on
gap
will
again be informative on online-offline substitution and consumer awareness of up-to-date
price information.
The paper
variation.
is
organized around two analyses designed to exploit different sources of
In Section 3
we
exploit the time-invariant factors
differences in state-to-state shipping times
section regressions examining the total
the course of the year.
important motivation
—
in the simplest
number
—state-level
way
possible.
of orders received
These regressions provide
tax rates and
We
run cross-
from each state over
clear evidence that tax savings are
an
for online shopping: our e-retailer's sales are substantially greater in
high-tax states than in low-tax states.
We
can provide an additional piece of supporting
evidence to bolster the case that the differences are due to taxes and not due to unobserved
consumer heterogeneity: our
e-retailer sells
much
less in
California than in comparable
(This would be expected under the tax hypothesis because our e-retailer must
states.
charge sales tax on sales to California residents.) These cross-section regressions provide
some weak evidence that geography matters
are
somewhat more
for
shipping-time reasons and that consumers
more expensive products
sensitve to differences in tax rates for
which the tax difference
in dollars
Section 4 applies standard
is
larger).
demand
the hourly variation in the data:
estimation techniques in an unusual
we estimate
discrete choice
The nonstandard
data on consumer purchases from two of the
choice models to datasets containing
firms'
all
part of the application
listed firms.
way
to exploit
models that use as their
dependent variable the number of orders of a given product from consumers
state in a particular hour.^
(for
is
in a particular
that
we only have
Normally, one applies discrete
market shares. Having data on
all
firms
is,
however, not necessary to identify the model given that we have substantial intertemporal
variation in the characteristics of the competitors.
about substitution between
and so
prices
and locations change.
The
discrete-choice analysis provides
differences in the taxes
between
how much
retailers
significant effect:
variation that helps us learn
attention consumers pay to geography,
simply by looking at how our firm's sales go up and down as
taxes,
forth,
e-retailers,
It is this
between
some evidence that consumers pay attention to
e-retailers.
due to differences
There
is little
in shipping times.
evidence of geographic differences
Geography does appear
consumers are estimated to have a strong preference
e-retailers located in their
own
rivals'
for
to have one
purchasing from
state (after controlling for differences in shipping times
and
sales taxes).
A
far
general theme that emerges from the discrete-choice analysis
from the fully-informed, fully-rational
differences in settings
e-retailers.
less
They
strongly
still,
that consumers seem
Consumers react very strongly
where the price comparisons are
easy,
hardly at
all,
to price
such as between competing
react less strongly to tax differences of a similar magnitude.
similar magnitude.
is
ideal.
is
to transitory variation between online
and
They
react
offline prices of a
These findings are roughly consistent with models of costly information
These regressions include dummy variables for each state so that the results derive from variation that
independent of the variation that identifies the cross-section regressions of Section 3.
acquisition or rule-of-thumb reasoning.
Our work
related to a
is
ternet taxation
is
number
Goolsbee (2000).
It
of previous papers.
The standard
reference on In-
examines a 1997 survey in which 25,000 consumers
were asked (among many other things) whether they had ever bought products online.
Consumers
living in states with higher sales tax rates are
bought products
online.
The
big-picture conclusion
tion could reduce online sales by 24%.
found to be more
likely to
have
that subjecting e-retailers to taxa-
is
One motivation
for the the
an
to address a couple potential concerns about Goolsbee's work:
tax part of our paper
elasticity derived
is
from
analyzing whether consumers ever purchase anything on the Internet could be very different
from the
elasticity of total quantity
with respect to taxes (which
will reflect
much more
the
behavior of intensive Internet shoppers); and one could also worrry that some of the tax
effects
he finds could be due to differences in unobserved consumer characteristics across
states (driven, for example,
by California and Washington having high
as populations inchned to use the Internet).^
literature
retail, e.g.
A
on the
results also relate, of course, to the
on location and consumer behavior in traditional
Fox (1986) and Walsh and Jones (1988).
number
of e-retail
effects of sales taxes
Our tax
sales taxes as well
of other papers have used data from price search engines to
examine aspects
demand. Brynjolfsson and Smith (2001) examines consumers who
Better. com in 1999.
It
has a puzzling finding on taxes:
visited
consumers are estimated to be
twice as sensitive to differences in taxes as they are to differences in item prices. -^^
finds strong evidence that
One hmitation
is
consumers prefer branded
Even-
e-retailers over lesser
that they do not actually have any quantity data.
imputed by assuming that that consumers purchased from the
also
firms.
The quantity data
e-retailer
Elhson and Elhson (2004) examines the same Pricewatch data as
known
It
they visited
is
last.
this paper. It notes that
websites attracting customers via Pricewatch.com have extremely price-elastic demand, and
Despite the examples of California and Washington, sales taxes in the U.S. are, in
correlated with the demographic controls for computer usage
fact,
we employ. For example,
not positively
Louisiana, Ten-
Oklahoma, and Alabama each have both one of the eight highest average tax rates in the country
and a below average fraction of households with home Internet access. Goolsbee casts doubt on the unobserved heterogeneity explanation for his results by using extensive household-level demographic controls, by
including MSA dummies, and by showing that tax rates are not correlated with ownership of computers.
nessee,
This could be explained as an artifact of price endogeneity
unobserved quality whereas higher taxes are not.
if
higher prices are associated with higher
investigates
how
it is
that firms are able to maintain nontrivial markups.
The primary
ob-
servations on this count are that firms engage in a great deal of obfuscation, and that an
adverse selection disincentive for price cutting,
pears to be present. Baye, Gatti,
on consumers shopping
price sensitivity
other questions:
for
PDAs
like
that described in Ellison (2005), ap-
Kattumen and Morgan
through the Kelkoo.com search engine
and take advantage of the structure of
how
(2005) examine clickstream data
price-sensitivity varies with the
and price-rank separately influence demand;
in 2003.
of listed firms;
in the
how
of
screen-
etc.
Several papers have addressed onhne-offline competition with limited data.
Goolsbee (2001) find that
find
number
their data to address a
number
They
mid 1990's term
demographic groups whose members were more
life
Brown and
insurance rates dropped more for
likely to
have Internet access.
Goolsbee
(2001) constructs a measure of the competitiveness of local retail markets using survey data
on the prices paid
retail
consumers and shows that consumers
for
markets are more
onhne and
likely to
buy computers
offline substitutability of
online. ^^
in less competitive traditional
Prince (2005) also examines
personal computer purchases using the same measure of
competitiveness of traditional retail markets. Chiou (2005) examines consumer's decisions
on where to purchase
We
number
DVDs
using a dataset that includes both online and offline purchases.
are not aware of any other
of papers have
examined
work on
spatial differentiation
between
e-retailers.
A
spatial differentiation in traditional retail, including Weis-
brod, Parcells and Kern (1984), Chiou (2005), and Davis (2006).
2
Data
In this paper
we examine
sales of four different types of
memory
128MB PC133, 256MB PClOO, and 256MB PC133.12 Qur
downloading the
first
''These results could
(or first
modules,
128MB PClOO,
price data were obtained
and second) screens from Pricewatch's memory price
in part reflect
by
lists
unobserved heterogeneity: the high prices paid for computers in an
who are more computer savvy and purchase computers
area could also reflect the presence of consumers
that are of higher quality in the unobserved dimensions.
'^As described in Ellison and Ellison (2004), our e-retailer sells three versions of each of these types of
three versions are clearly ranked in quality. In this paper, we restrict our attention
to the lowest quality "generic" version of each type of memory module. This is the only quality level for
which one can easily use Pricewatch to identify competitors' prices. Low quality memory also accounts for
memory modules. The
the majority of our firm's
sales.
on an hourly basis from from
128MB modules
May
2000 to
2001 (with some gaps).
Our data on the
include information on the twenty four lowest-priced websites listed on
Pricewatch. The data on
websites. There
May
is
a fair
256MB modules
amount
include information on the twelve lowest-priced
of turnover
day (and even from hour to hour
in
some
and
reshuffling of the price lists
from day to
Over the course of the year there
periods).
a dramatic decrease in prices. For example, in the space of a year the price of a
modules
fell
home
128MB
from about $120 to about $20.
Pricewatch does not calculate sales taxes
the
is
state of each retailer so that a
for
consumers on these pages, but
consumer who knew the tax rate
(and understood that sales taxes will apply
if
and only
firm) could take sales tax differences into account.
We
if
in his
it
does hst
home
state
he or she buys from an in-state
downloaded the state locations as
well.
We
obtained quantity data for these products from an Internet retailer that gets most of
its traffic
from Pricewatch.
It
operates two similar websites, which typically have different
The quantity data again
prices for the products studied. ^'^
with some gaps. The raw data are
cover
May
2000 to
at the level of the individual order.
We
May
2001
use data on
approximately 15,000 orders. The available data on each order include the website from
which the customer made the order,
Our
is
e-retailer
is
just one of
many
detail
listing
on what was ordered, and the shipping address.
products
for sale
on Pricewatch.
A rough
estimate
that 100,000 other consumers visited Pricewatch during our sample period and purchased
a corresponding product from one of the e-retailers for which
We
also use a few state-level variables.
average sales tax rate.
we do not have quantity
The most important
Sales tax rates vary by county
and
of these
locality in
is
many
data.
the state's
states.
Our
data are averages across the various jurisdictions within a state computed by a private firm.
We
collected data on
UPS
ground shipping times by querying the
UPS
website. These data
include both shipping times from our e-retailer's zip code to each state, and a state-to-state
shipping time matrix.
-^"^
Our other
state level variables
come from Census Bureau
datasets:
^ There
are several possible motivations for having multiple websites; they may be given different looks
and consumers may have heterogeneous reactions; it allows the websites to be more specialized (which
seems to be attractive to some consumers); it facilitates experimentation; it may help promote private-label
branded products; The firm may occupy multiple places on the Pricewatch screen.
^ UPS provides
these data on a zip code to zip code basis and there can be some within-state variation.
the fraction of households with
home
Internet access as reported in a 2001 survey, the
population of each state in the 2000 census, and the number of computer stores and gas
stations reported in the 1997 Census of Retail Industries.
Analysis of aggregate state-level sales
3
In this section
we
take the most straightforward approach to examine
variables in our dataset
We
—
sales tax rates
construct measures of the total
and shipping times
number
—
than
Summary
3.1
The
more
in states
and use
with high
with low sales taxes.
statistics
We use two primary dependent variables:
128MB modules
Quantity 128
is
the
number
of orders
received over the course of the year from a given state; Quantity256
the corresponding
is
consumer demand.
regressions in this section will have 51 observations: one for each state and the District
of Columbia.
for
in states
affect
the time invariant
of orders received from each state,
regressions to, for example, look at whether our e-retailer sells
sales taxes
how
number
for
gressions are presented in Table
256MB
1.
Our
Summary
modules. ^^
e-retailer sells
204
statistics for the basic re-
128MB memory modules
to the
average state over the course of the year. This ranges from a low of 19 in the District of
Columbia
as large.
taxes.
to a high of 762 in Texas. Unit sales of
The average
sales tax rate
5.7 percent.
The UPS ground shipping time from our
days.''^
The percentage
in the District of
0.041 in
of households with
Columbia
230 computer stores.
We
is
The
West Virginia
to a high of
ratio of
home
70.2%
256MB memory modules
are about half
Four states have no state or local
retailer to the average state is
sales
about 4
Internet access varies from a low of 40.6%
in
New
computer stores
Hampshire. The average state has
to gas stations ranges
from a low of
to a high of 0.184 in California.
some
we took an
typically collected data using one zip code from the the largest population center in the state. In
cases where a state did not have one dominant population center and the shipping time varied
average of the times for the two largest population centers.
^^Note that in doing
this
the two speeds of each size
we are summing both over the two websites for which we have data and over
memory module: PCIOO and PC133. We do this because there is no reason to
expect that taxes or geography would have a different impact across websites or speeds.
'®The minimum value of 1..5 days reflects that shipping times are one day for shipments to Southern
California and two days for shipments to Northern California.
—
Although prices axe not used
pretation of
price of a
some
results.
in this state-level anaysis,
The mean
256MB memory module
is
then, adds 70 cents on average to a
price of a
A
$139.
they are relevant for the inter-
128MB memory module
is
$70.
one percentage point difference
128MB module but
$1.39 to a
256MB
The mean
in tax rates,
module.
Basic results
3.2
To analyze how the number
of orders received from state s
we estimate a negative binomial
Quantiti/s
~
Poisson(/is)
log{fis)
=
/?o
regression model,
i.e.
+ /^o + /?i OfftineSalesTaxRate^ +
^
-Fp4
—
Computer
Storess
—
-;
:
^
h
,
is
we assume
P2Californias
.
pzlnternet Access s
GasbtationSs
where the
ts
meters by
maximum
random
are independent
likelihood.''^
variables with e^"
One can
related to the state's tax rate
~
+
r,
^
+ P3S hippingTimes
/-r^
\
1
Pe logi Populations)
+
e^,
V{9,6), and estimate the para-
think of this as similar to estimating a linear
regression with logQs as the dependent variable.'^
Table 2 presents coefficients obtained from estimating the regression above on the total
unit sales to each of the 51 states.
the dependent variable.
effect
on online
sales.
The
The
The
first
column uses 128MB memory module
sales as
results are strongly suggestive that sales taxes have a large
5.94 coefficient estimate on OfftineSalesTaxRate indicates that
a one percentage point increase in a state's sales tax increases the number of orders our
e-retailer receives
from that state by about 6%. The average sales tax rate
in
our data
is
5.7%. Hence, in a typical state, online purchases would be predicted to decrease by about
30%
'
it is
if
the offline sales tax were eliminated.
The Poisson
common
regression model
is
Goolsbee argues that
this
the special case of the negative binomial with 6
to find that a specification test can reject the Poisson
model
is
=
a good forecast
appUed work
models that allow
00. In
in favor of other
more dispersion. The particular assumption that the errors are distributed like the logarithm of a gamma
random variable (as opposed to being normally distributed for example) is motivated by the fact that a
relationship between Poisson and gamma random variables allows the likelihood to be evaluated without
a numerical integration. The distribution of Qs turns out to be negative binomial which is what gives the
model its name. Section 19.9.4 of Greene (1997) provides a clear description of the model. Hausman, Hall
and Griliches (1984) discuss a number of models for count data.
The advantage of the negative binomial regression is that the model can be estimated in the same way
regardless of whether some of quantities are zero. All quantities are positive in our base analysis, but there
will be some zeros in later analyses of quantities sold during particular time periods. The third and fourth
colimins of table 2 show coefficient estimates from regressions with \og(Qs) as a dependent variable for
comparison. The results are similar.
for
impact of taxing online
for the
between online and
sales
offline retail
—the implicit assumption
is
that achieving tax parity
should have a similar effect regardless of whether
it
is
achieved by increasing online taxes or by decreasing offline taxes.
The extra customers our
ple be
coming from three
retailers,
firm attracts in states with high sales tax rates could in prinici-
sources:
from other online
they might otherwise have purchased from traditional
retailers, or
not at
Few
all.
of our e-retailer's online competitors
are in any particular state (other than California), so very
be taken from online
home
retailers in the customer's
little
state.
most could be people who otherwise would have chosen not
would require a highly
elastic aggregate
demand
must be substantial substitution between online
The
coefficient
on the California
what we have estimated
erogeneity.
What would we
OfflineSalesTaxRate
to brick
and mortar
would expect
covariates.
is
its
is
dummy
for
retail
to
of the
added demand could
It also
seems unlikely that
buy memory, because
and
provides additional support for the view that
a tax effect and not an artifact of unobserved state-level hetpredict about our firm's sales to California
stores in California
—
its
when
selling in California.
The estimate on
still
sales.
The estimated
67%
states
that this
coefficient
on
implausible that an effect of this magnitude could be due to an
for online
shopping on the part of Californians.
the ShippingTime variable provides some
shipping to the destination state
coefficients
—we
lower than sales to
weak evidence that geography
matters on the Internet. Sales are estimated to be reduced by about 10%
The
sales tax
One would expect
California indicates that sales to California customers are about
unobserved distaste
must pay
relative
Second, our firm has a disadvantage
disadvantage would lead to an additional reduction in
It is
the coefflcient on
no tax advantage
California customers
(California sales are taxed at 7.25%.)
states.
if
be about 35% lower than one would otherwise predict given state
relative to non-California e-retailers
comparable
conclude that there
offline retail.
truly a tax effect? First, since our firm has
sales to
We
memory. -^^
this
is
UPS ground
one day longer.
on the other control variables seem reasonable.
where the fraction of residents with Internet access
^^The regressions of quantity on the log of the lowest
suggest that the elasticity of aggregate
if
demand with
listed price
respect to price
10
is
higher.
Sales are higher in
We
cannot
reject that
reported in Ellison and Ellison (2004)
may be
close to one.
the coefficient
home
of people with
ratio
one, which would correspond with sales being proportional to the
is
Internet access.
The
might be expected to have either
coefficient
sign:
on the computer store-gas station
both interest in computers and
reflects
it
The estimated
the availability of computer parts at traditional retail stores.
Population
positive but not statistically significant.
aggregate
Potential reasons
sales.
population
is
an imperfect proxy
why
is
number
coefficient is
obviously a strong determinant of
the coefficient might be less than one include that
market
for the potential
size
(which
is
affected
business activity, and other factors), and that larger population states
by income,
may have
better
offiine retail.
The second column
of Table 2 presents coefficient estimates
256MB memory modules
for
as the
from a regression with orders
dependent variable. These results are very
similar: sales
are substantially higher in states that levy higher sales taxes on traditional retail purchases;
sales are
sales;
notably lower in California; there
is
weak evidence that shipping times may
affect
the effects of the other demographic variables are similar.
The
third
and fourth columns of Table
2 report
demand
regressions with \og{Quantity) s as the depedent variable.
estimates obtained via
The
OLS
results are quite similar to
those from the negative binomial regressions.
Demand
3.3
The
fact that
at different price levels
we have data on goods
sold at different prices provides an additional oppor-
tunity to gain insights into consumer behavior:
on
sales of expensive
we can compare the
effects of sales taxes
and inexpensive products. What would we expect to
find in such a
comparison?
A good way to think about
this is in
terms of a discrete-choice model with heterogeneous
preferences for online vs. offline shopping: suppose a consumer of type 9 in market
utility Vi^on
that the
—
Pi,on
CDF
if
she buys online and
of 6 in
market
i
is
Fi.
rate increases the effective offline price
Pi,offh{Pi,off)dt.
differ across
The answer
is
Vi^off
—
Pi,off{^
+
t)
+
6
if
i
gets
she buys offline and
In such a model, a dt increase in the offline tax
by
pi^ojjdt
and thereby increases online demand by
therefore that estimated coefficients on the tax rate
products, and whether they do should reflect
11
how
may
the distribution of consumer
preferences for ofHine vs.
inexpensive.
How we would
come from. For example,
use vs.
9
online shopping compares for the two products, expensive
if
expect these distributions to compare depends on where the 9s
6
was primarily determined by a consumer's taste
for
computer
most natural assumption would be that the distribution of
driving, then the the
would not vary across products.
offline
and
In this case, the sensitivity of
onhne demand to the
tax rate should be proportional to the product price. Alternatively, the distribution
of 9 could arise from heterogeneity in consumers' willingness-to-pay to get the product
immediately.
In such a case,
across products
is
it
would be more natural to assume that what
the percentage of the product price that a consumer
avoid waiting for the product to arrive.
In this case, the density of 9
proportional to the product price, and the sensitivity of online
rate
sources of variation that
can compare the demand
mean
sales-weighted
128MB
module.
coefficient estimate
for
invariant
willing to
is
pay to
would be inversely
demand
to the offline tax
would be constant across products.
Our dataset has two
a
is
128MB
for
128MB modules
price of a
Looking
us
at the first
The standard
make such comparisons.
with the demand for
256MB memory module
for
256MB
First,
of Table 2
256MB modules
modules. The
is
we note that the
shghtly larger than that
errors, however, are sufficiently large so that
reject neither that they are equal nor that they differ
we
about 60% higher than that of
is
and second columns
on OjflineSalesTaxRate
modules.
let
we can
by 60%.
Second, we can exploit the substantial time-series variation in prices by comparing de-
mand
in different
the time period
they were
when they were
much
The estimates
time periods. For example, we look at the
cheaper.
in the first
The
demand
for
over $100 compared with a period a few
first
two columns of Table
128MB modules
months
when
3 contain such a comparison.
two columns indicate that quantities were more
ferences in tax rates in the period
later
when 128MB modules were more
in
^°
sensitive to dif-
expensive, suggestive
that consumers' channel preferences are more likely fixed and not dependent on item price.
Again, though, the estimates are not sufficiently precise to allow us to reject equality.
The
is at the end of our data, whereas the latter is mostly in the summer of 2000. Each of
again estimated via a negative binomial regression run on a cross-section containing 51 observations.
^°The former period
these
is
By "obtained from two separate time periods" we mean that the first dependent variable is obtained by
summing the hourly sales to each state over the set of hours during which the lowest price on Pricewatch
was between 20 and
.50
dollars.
12
evidence in the third and fourth column
less conclusive.
is
larger in the high-price period, but the differences are far
The
on the California
results
California
dummy
is
dummy
larger in the period
differences are not significant.
are similar.
when
The
point estimate
from
about
is
20%
significant.
In Table 3 the coefficient on the
more expensive, but the
the products are
In Table 2 the point estimates are not larger for the
more
expensive product.
In summary, the comparisons across products and across times provide quite limited
demand being more
evidence of
items.
It
sensitive to differences in tax rates for
could be that the effect
Alternatively,
be explained
it
in
with the item
is
more expensive
there and data limitations prevent us from seeing
could be that the tax-sensitivity does not vary much. Such a result could
two ways.
price, as
First,
it is
possible that channel preferences
mentioned above.
A
may be
"irrational" explanation
would be that consumers may follow
For example, the ones
high tax states
online
in
and save on the
may have
sales tax,
A
itive conditions.
simple rules of thumb.
generally a good idea to
on more expensive items.
This variation provides a nice opportunity to gain additional insight into
demand and consumer
effects of
The
it is
behavior.
In this section
geography and
sales taxes,
retail,
we use
discrete-choice models to
substitution between e-retailers, the
and consumer sophistication.
Motivation
analysis in this section
is
designed to exploit two sources of short-term variation in our
data: turnover in the relative price rankings and changes in price levels that are
most
is
second
exhibits an unusual degree of short-term variation in compet-
explore substitution between online and offline
4.1
learned that
fairly
A
Discrete-Choice Analysis
The Pricewatch environment
e-retail
up
but have not developed more sophisticated
rules recognizing that the tax savings are larger
4
scaling
primary source of onhne-ofHine differentiation
could be heterogeneity in the disutility for waiting for online purchases to arrive.
buy products
it.
firms.
and why
We
it
briefly discuss each of these to provide
should be useful.
13
some
intuition for
common
to
what the variation
Turnover
4.1.1
Figure
we
in price rankings
a particularly clean example of the type of turnover in the relative price rankings
1 is
As
see multiple times a day.
such,
this turnover to estimate aspects of
listed
on the
August
Memory
consumer behavior.
screen of Pricewatch's
It
of the e-retailers
of
New
Jersey,
made
shows the twelve
128MB PClOO memory page
over the top
e-retailers'
slot.
The
of the
9am
three columns
first
at $128, reduced
list
at
its
names, their locations, and their
prices.
The
California, respectively,
Recall that
we observe
would pay
sales for
if
sales
price
its
of Virginia,
price to $111
site
and took
but which consumers could compute
in
New
Jersey, Virginia,
e-retailers.^^
two websites. However, we observe not
fact,
crucial for our estimation strategy.
just total sales
along with the turnover in relative price
To
illustrate, consider first the case
where
by every firm into every state every hour. One could assess whether consumers
pay attention to tax differences by looking
sales into
New
Jersey at
at
11am.
A
at
whether UpgradePlanet was making many
11am than Coast-to-Coast was making
whether Coast-to-Coast was making more
was making
9am, raised
they purchased from each of the
but sales into each state at each hour. This
is
Coast-to-Coast
fourth through the sixth columns
from the given information: the tax-inclusive prices that customers
more
e-retailers
show information presented on Pricewatch: the
contain numbers not presented on the Pricewatch
we had
exploit
9am and 11am on
from the top twelve by 11am. UpgradePIanet.com
which was on the second page
rankings,
at
price changes between these two times.
which offered the lowest price of $112
sufficiently so as to disappear
and
how we can
2000.
1,
Two
first
provides a nice illustration of
it
sales into Virginia at
at
9am, and at
9am then UpgradePlanet
preference for buying from nearby firms could, of course, offset
the tax disadvantage.
Geographic preference, however, could be separately identified in
many
look at whether UpgradePlanet
ways.
One could
in states bordering
New
is
or
is
than Coast-to-Coast had
Jersey and more in states bordering North Carolina.
use data from other points in time to look at
an Oregon firm
sells less
how market
not present at the top of the
^'A consumer, of course, would need to know
are only assessed on in-state sales to
make
list.
shares in Oregon change
(Oregon has no
his or her local sales tax rate
this calculation.
14
One could
and the
sales tax.)
when
One
fact that sales taxes
Price
Price
Information on Pricewatch
Price
into NJ
into VA
State
Website
Pricewatch ranking at 9:01am EDT
112
118.72
112
NJ
Coast-to-Coast Memory113
CA 113
113
Connect Computers
114
114
114
FL
Computer Craft
115
CA 115
115
Advanced PCBoost
116
116
CA 116
1st Choice Memory
117
CA 117
117
Jazz Technology
117
Memplus.com
CA 117
117
CA 119
119
119
Portatech
CA 120
120
120
Augustus Technology
120
120
EconoPC
IL
120
Advanced Vision
Computer Super Sale
CA
UpgradePlanet.com
Connect Computers
VA
121
121
122
122
IL
Pricewatch ranking at 11:01am
Computer Craft
Advanced PCBoost
Augustus Technology
EconoPC
IL
Advanced Vision
Computer Super
CA
1st
Choice
Memory
Memplus.com
Portatech
1:
FL
CA
CA
CA
CA
CA
CA
Jazz Technology
Figure
CA
Sale
IL
111
113
114
115
116
117
117
119
120
120
121
122
121
122
into
CA
112
121.64
114
123.80
124.87
125.95
125.95
128.10
129.18
120
130.26
122
EDT
111
115.99
113
114
115
116
113
114
115
116
117
117
119
120
120
121
122
117
117
119
120
120
121
122
Price
111
121.64
114
123.80
124.87
125.95
125.95
128.10
129.18
120
130.26
122
Sample Pricewatch rankings: 128MB PClOO memory modules on August
15
1,
2000
could also examine tax effects controlling for geographic preferences by looking at relative
magnitudes: does the $6.72 tax that Coast-to-Coast must levy
effect
than the $4.99 tax that UpgradePlanet must levy
Our quantity data
all
sumers react to
limited:
we observe
in Virginia?
sales for only
in
different competitive enviromnents: the reactions
changes in the demand
To think about how
puters.^^
more
Jersey have a larger
two websites rather than
This limitation, however, should not prevent us from examining how con-
websites.
be reflected
are
New
in
this works,
mentioned above would
which we do have data.
for the websites for
suppose that our sales data were from Connect Com-
At 9am Connect Computers'
tax-inclusive price for
New
Jersey residents
than that of any other website. At 11am Connect Comptuters' tax-inclusive price
Jersey residents
taxes
at
is
only the second lowest. Accordingly,
we would expect Connect Computers'
11am. Similarly,
its
sales into Virginia
estimate tax effects controlling for a
increase in Connect's Virginia sales.
A
New
Jersey to be higher at
state preference
New
lower
for
New
consumers pay attention to sales
would be higher
home
tude of the 9am-llam drop in Connect's
sales into
if
is
at
11am than
by looking
at
9am.
how
at
can
9am-llam
sales at
11 am will teach us about substitution between retailers: shipping times from
We
the magni-
Jersey sales compares with the
comparison of Connect's California
9am than
9am and
New
Jersey
and Virginia to California are the same, so the comparison should help us learn how many
from the second-lowest to the low-priced firm when the low-priced firm
consumers
shift
reduces
its
price by one dollar.
4.1.2
Within-week changes
The second
in price levels
useful source of variation in our data
e-retail prices. Traditional retailers
is
common
prices
to advertise prices for
do not change
is
within-week changes in the
their prices frequently. In particular, it
memory modules
in
weekly sales circulars, so that the
remain constant each week from Sunday-Saturday. The
in contrast, are highly volatile.
Many
level of
prices listed
e-retailers hold essentially
on Pricewatch,
no inventory and pass on
wholesale price changes almost immediately.
Figure 2 provides an illustration.
Connect Computers
is,
in fact,
The
thin line
is
the lowest price on Pricewatch for
not one of the websites from which we have data.
16
a
128MB PClOO memory
module. ^'^
There are many instances where the price changes
substantially over the course of a week.
between Sunday, September
from $89 to $78.
If
17,
Usually these are price decreases. For example,
2000 and Saturday, September 23, 2000, the price dropped
consumers are rational and
are close substitutes), then one
fully
informed (and online and
would expect that an online
retailer
Saturday than they had on the previous Sunday. Prices also
between Monday, November
increased from $42 to $53.
20,
would
sell
rise at times.
offline retail
more on
this
For example,
2000 and Friday, November 24, 2000, the onhne price
In such a week, one
would expect that online
retailers
do worse on Friday and Saturday than they had on Sunday and Monday.
would
Our data
are
well-suited to examine such predicitons.
Online and Offline Prices:
128MB PC100 Memory
140
120
100
-
U
May-00
Jul-OG
Sep-00
Nov-00
Jan-01
Mar-01
May-01
Date
Low Pricewatch
Figure
2:
Price
Online and Offline Prices:
BestBuy Price
128MB PClOO memory modules
To make the two price series more comparable, the shipping and handhng fee that
Pricewatch, $11, has been added to the listed price.
17
is
standard on
Toward the end
of our
sample period we collected price data
from the largest traditional electronics
of the figure
a graph of these prices.
is
(and data from more
retailers),
we could
we had
prices
and employ a
similar data for our entire sample period
try to estimate online-ofHine substitution by using
We
the price gap as a primary explanatory variable.
sample period, however, so we
a comparable product
BestBuy.^^ The bold line on the right side
retailer,
If
for
do not have these data
will just estimate the effect of
for
most
of our
within-week changes in online
set of time trends to control for longer-term trends in the online-offline
price gap.
4.2
Methodology
Let Nsht be the number of consumers in state
module
in
hour h of day
s
from the twenty-four
t
whose prices we observe. Assume that consumer
purchasing a particular type of
memory
256MB modules)
websites
(or twelve for
/c's
utility
if
he purchases from website
i
is
Uiksht
=
PiiPricCiht
+ P^SalesTaXisht) + PsShippingTimeis
jiiH omeStateis
where SalesTax
is
of results,
is
and
a
Cifc,
the sales tax in dollars due on the purchase, ShippingTime
ground shipping time, HomeState
SecondScreen
+ P^SecondScreeriiht +
dummy
eik is
is
a
dummy
variable for whether website
indicating whether website
a logit
random
i
hand
variable independent of the right
rn^gi
i
formula
conditional on the total
number
Our dataset only contains
number
^""The
of
for the
number
of
consumers
in state s
product,
is
in state s,
side variables
this expression,
we
buying from website
of purchases Nght'-
sales
from two particular websites.
It
does not contain the
consumers purchasing from other websites, from traditional
BestBuy
UPS
introduced below).
Writing Xght for the vector of attributes on the right hand side of
logit
is
the
only appears on the second screen
(and of the additional right hand side variables and the error
have the familiar
i
is
a branded product that
may be
the onhne price data.
18
retailers, or
not at
of higher quality than the products covered
by
all.
The
total
number
of consumers buying through Pricewatch
by a number
affected
is
there are clear day-of-week and hour-of-day effects; Internet use
of factors:
is
climbing
over our sample period; there are substantial price declines that should increase aggregate
demand; there
is
price effects with the size of the potential
by past
prices.
Our data
approach we take
demand
AT-
^^ sht
—
where
is
is
—
will not allow us to separately identify all of these effects.
simply to specify a
flexible functional
form
for the
we assume
YiMinPTiceiit+'y2SundayPriceht+13Weekendt+'i4TimeTrendlt + ---+^7TiTneTrend4t
i
'
^5 is a state fixed effect to
be estimated, g^
Sunday, Weekendt
is
a weekend
dummy,
is
an hour-of-day fixed
Sunday Priccht
the
is
TimeTrend
trends with slopes changing every ninety days, and
rjfist
is
The
aggregate Pricewatch
^sHh^
the lowest price listed on Pricewatch,
Vhst,
Minpriceht
effect,
the price on the most recent
variables allow for linear time
a
have mean zero conditional on the right hand side variables
We
intertemporal
at a given time being affected
consumer pool
that could reflect each of the effects. Specifically,
r
may be
variation in the online-offline price gap; and there
random
error
term assumed to
in this equation.
^^
estimate the model via nonlinear least squares, using hour-website-destination state
sales as the
dependent variable. The model could
in principal
be estimated on the 800,000
observation datasets obtained by using sales to each of the fifty-one states in each of the
approximately 7900 hours by each of the two websites as the observations. Some states,
however, account for a very small portion of the sales in our dataset. Other states account
for a nontrivial
Pricewatch.
number
Data on
because consumers
paying sales tax.
preferences,
and
It
of purchases, but rarely or never have an in-state firm listed
on
sales to such states will not help us in estimating tax sensitivities
in these states
can purchase from any websites on the
would provide information on interfirm price
online-offline substitution,
but the
first
elasticities,
list
without
home-state
two of these can be estimated
^^Note that we do not include an "outside good" in the discrete-choice set eis one might do to attempt
on aggregate demand. We are thus implicitly assuming, for
example, that the total sales by Pricewatch e-retailers to state s are not affected by the states in which
the e-retailers are located and the difference between the n"" loweset price and the lowest price. We do
this because we have httle data to estimate such effects, think they must be small, and prefer a more
parsimonious model in which fewer coefficients are used to capture aggregate demand effects. Reasons why
any inclusive- value effects would be hard to find include that prices on Pricewatch are almost always tightly
bunched, and that, in any state other than California, having more than one or two e-retailers on the list
from that state is extremely rare.
to estimate the effect of a logit-inclusive value
19
precisely with sales to a smaller set of states.
We
decided to reduce the computational
burden by carrying out our analysis on a smaller dataset containing hourly
websites in just ten states: Alabama, Florida, Georgia,
Illinois,
sales
by our two
Ohio, Oregon, Pennsylvania,
Texas, Virginia, and Wisconsin.
We
carry out the estimation four times to obtain independent estimates using data on
128MB PCIOO
each of the four products:
modules, and
256MB PC133
hand
why
it is
we think
this
With regard
is
that a substantial part of price variation
is
particular hour on a particular day being a
seventh lowest price.
With regard
to relative prices in the choice-
a very reasonable assumption and a major
is
the Pricewatch environment
reason
256MB PClOO
modules,
not necessary to use instruments for the prices on
side of the above equations.
between-retailers equation,
128MB PC133
modules.
Note that we are assuming that
the right
modules,
We
a nice one to study.
find
it
implausible
driven by information the firms have about a
good time
to have the third as opposed to the
to the prices in the aggregate
demand
equation, one
could worry more about endogeneity. These estimates are not our primary focus, however,
so
we
are willing to think of
than as demand
Table 4 reports
The
as coefficients
Statistics
summary
unit of observation
is
statistics separately for
number
particular hour to one particular state
The dramatic
maximums
in question.
about $70
memory modules.
sell
for
of
is
128MB PClOO modules
0.013."^ Price
is
sold
by a website
128MB modules and about
Our
firm's
MinPrice
128MB
average rank on the Pricewatch
is
$140
for the
256MB
one
modules.
minimums and
the lowest price listed on Pricewatch in the hour
prices are about $2 to $4 higher than this
list is
in
the price charged by our websites.
price declines that occurred over the year are visible in the
for this variable.
memory
zero
in a typical hour, average sales figures at this level are quite
low. For example, the average
prices are
each of the four types of
an hour-state- website. Given that our websites
modules to a typical state
Mean
on reduced-form control variables rather
elasticities.
Summary
4.3
them
sixth.
on average.
The average gap between our
We count a single order of multiple memory modules as having quantity one.
period, our firm limited purchases of memory modules to one per order.
20
firm's
Its
256MB
For most of our time
price
and the lowest available price
is
Much
larger.
Our
firm offered these modules at a very low price.
PSunday
the average value of
is
MinPrice — PSunday
statistics for
mean
is
The mean
price difference for
PC133. Again, there
a
maximum
We
is
of $36 for
is
due to a period when one
firm's average rank
the most recent Sunday.
is
256MB modules
is
sales tax
PC133 modules! (These
3%
Approximately
would be paid
if
This
minimum
in our estimation, so
of the listed websites are in-state
buying from an in-state firm
-$2.07 for
of -$51
and
figures are correct.)
consumers
they bought from an in-state firm (except
if
The summary
PClOO and
-$1.39 for
in the ten states
considering would not need to pay sales tax to buy from our websites.
pay
sixth.
well under a dollar but the standard deviation
a lot of variation around those means, with a
do not include California
about
is still
give a feel for the within-week price volatility.
128MB modules
price difference for
large.
MinPrice on
of this
is
They would need
who
those
for
we are
live in
to
Oregon).
The average tax that
on average.
$7.04.
Basic Results
4.4
Table 5 presents coefficient estimates obtained by performing separate nonlinear least
128MB PClOO, 128MB
squares estimations on the data for each of the four products:
PC133, 256MB PClOO, and 256MB PC133. In many ways, the four
sets of results are quite
similar.
The most
(as
basic fact about the Pricewatch environment
we previously noted
in
is
that
-0.82.
The estimate
for
128MB PClOO memory
example, corresponds to an own-price elasticity of -33 (holding
sample means). The estimates are extraordinarily
when our
The
firm raises
coefficients
its
price (or
is
watch over our sample period. The
coefficient
that that overall
demand was growing
in the first three
months
of our
all
at
significant.
undercut)
on the time-trend variables
are obtained by adding
intensely competitive
Elhson and Ellison (2004)). The coefficients on Price
columns range from -0.47 to
occurs
it is
is
The
illustrate the
2%
modules, for
variables fixed at their
decrease in
demand
growth (and decline) of Pricein the first
per day (equivalent to
sample (May- August, 2000). Growth rates
column
60%
indicates
per month)
for later periods
of the earlier coefficients. This suggests that sales decreased
21
that
so large as to be impossible to miss.
on TimeTrendl
about
all
in the four
40%
per
month
in the fall of 2000,
per
month
in the spring of 2001.
were
the winter 2000-01, and
flat in
Growth
an additional 20%
fell
rates for the other three products are similar,
suggesting these patterns are not just product-specific fluctuations.
Figure 3 presents a graph of the hour dummies. ^^
picks
They
up substantially between 7am and 11am, continues
past the normal workday, remains at about
off substantially until
availability
may be
80%
of
at
peak value
indicate that online shopping
approximately the 11am
until midnight,
level
and then drops
Gam. The large number of late-night purchases suggests that greater
an important factor differentiating
e-retail
from traditional
retail.
Intraday Sales Pattern:
128MB PC1 00 Modules
0.30
Time
Figure
4.5
3:
Intraday Sales Pattern;
128MB PClOO memory modules
Taxes
Recall that in our
the basis of Price
of one on the
demand
specification
+ P2SalesTax,
SalesTax
consumers are assumed to evaluate products on
with SalesTax measured
coefficient
would correspond
which consumers care only about their
in dollars.
Hence, an estimate
to the standard rational
total expenditure
and an estimate
model
of zero
in
would
^''Recall that we simply set these to the sample mean quantities for each hour rather than making them
part of the nonlinear least squares estimation. Sample means are computed on a time-zone adjusted basis
with the times of all purchases being recorded from the consumer's perspective.
22
'
correspond to consumers
The most
who
are entirely insensitive to tax differences.'^^
general conclusion
we draw from the four
sets of results
that consumers pay
is
attention to sales ta:xes than the standard rational model predicts.
less
the four columns are 0.05
Note that the
first
(s.e.
0.12), 0.38 (s.e. 0.15), 0.10 (s.e. 0.09),
The
estimates in
and 1,27
(s.e.
0.64).
three are significantly different from unity while the second and last are
significantly different
from
zero.
attention to taxes but not as
We
much
interpret this as evidence that consumers are paying
as price differences of a similar magnitude.
These
effects
are not very precisely estimated, though.
It is
important to note that the fact that consumers pay
than to price differences does not imply that
sales taxes are not important.
if
variable was 0.3, our estimates would be that a firm that
must
its sales
collect a
6%
sales tax
would
decline by about 60%.
Geography
4.6
Geography enters our demand model
possiblity that consumers
will
Our consumers
the coefficient on the SalesTax
are extraordinarily sensitive to price differences, so even
have
tax differences
less attention to
may
in
prefer to
two ways.
buy from
First,
ShippingTime allows
e-retailers in
have faster delivery times with standard ground shipping.
such an
effect in these regressions:
these
significant.
is
If
price coefficients, one
for the
nearby states because they
We
fail
to find evidence of
only two of the four estimates are negative; only one of
one thinks about the magnitudes of these estimates relative to the
would conclude that any geographic
effects are small.
A
coefficient
of 0.1
on the ShippingTime variable would mean that reducing the shipping time by one
day
comparable to reducing the price by about 20
is
regressions provided
some evidence
cents. Recall that our cross sectional
of a shipping time effect, in contrast to
what we
find
here.
Second, we included the HomeState
may have an
dummy
to allow for the possibility that
additional preference for buying from in-state firms. Here,
we
consumers
get consistent
^^There are clearly other "rational" models in which the coefficient would be greater than or less than
An example of the former is if price is a signal of quality so that a high price-zero tax offer is preferable
to a low price-high tax offer with the same total expenditure. Examples of the latter would be a model in
which consumers benefit from taxes collected by their state government or have nonselfish preferences and
thereby gain from payments to local firms and/or governments.
one.
23
results
showing that geography does matter.
and
positive
significant.
The magnitudes
home-state preference will roughly
consumers pay
differences in prices,
also
it
a two dollar price difference.
Price coefhcient
imphes that the home-state preference
then the home-state preference
or less.
The
finding that the
HomeState
the
-0.5,
is
coefHcient
will
outweigh the sales-
if
the SalesTax coefficient
is
1.0
and the tax rate
is
is
6%,
outweigh the tax disadvantage on items costing $100
will
home
In light of our
attention to difference in sales taxes than to
less
tax disadvantage on moderately priced items. For example,
0.33, the
coefficient estimates are
of the coefficient estimates indicate that the
offset
earlier estimates that
Three of the four
state preference
nearly strong enough to outweigh the
is
tax-disadvantage of buying from an in-state firm contrasts with our earlier finding that our
firm
sells
much
less in
California than in other states.
home
state preference while others
4.7
Online-Offline Substitution
As noted above, the within-week
examine
do
possible that
is
when
will
offline prices are
constant over the course of a
move one-for-one with changes
MinPrice — PSunday.
separately in the equation for the
The MinPrice
tion.
two
Note that
rccisons:
consumers
it
variable
may
aggregate
Our estimates
demand
(as
Our
number
of online consumers.
number
will
of
consumers buying memory on Pricewatch
be higher when the price
opposed of
offline)
when
of this coefficient are highly significant
about
We
demand
-2 for the
in the lowest price available
specification includes these two variables
uct classes: a one-dollar decrease in the price of a
the total
in the online price. In
the lowest price available on Pricewatch in the hour in ques-
affect the
buy online
will
is
states enjoy
not.
terms of the variables defined above, the within-week change
on Pricewatch
some
variation in the online price provides an opportunity to
online-offline substitution:
week, the online-offline price gap
It is
at Pricewatch retailers by
128MB modules and about
is
lower;
and a higher share of
the online-offline price gap
is
is
estimated to increase
about 3%. This corresponds with an
256MB modules
(at the
elasticity
mean
take this result as suggestive of substantial online-offline substitution, because
unlikely that the aggregate
demand
for
memory modules
24
wider.
and consistent across the four prod-
memory module
-4 for the
for
is
so elastic.
One
prices).
it
seems
piece of evidence
to this effect
memory
in
result in a
is
prices
much
P Sunday is
DRAM manufacturers led to a rougly 400%
that collusion between
May
between December 2001 and
smaller price increase
aggregate
if
of 2002.^^
demand was
increase
Optimal collusion would
so elastic.
One would
the lowest price hsted on Pricewatch on the most recent Sunday.
expect higher values of this variable to be associated with higher sales through Pricewatch
for
two reasons:
it
should be associated with a higher
interested in buying
memory
at the current price
Our estimates on the 128MB modules provide
significant in
Among
two
of the four estimations, but
offline price;
and the pool
may be larger when past
of
consumers
prices were higher.
PSunday
httle evidence of either effect.
one of the significant estimates
is
is
negative.
the potential explanations for our inability to find the predicted effects are that our
data are limited, that consumers
may
not be reacting strongly to transient differences in
onhne-ofHine prices because they are unaware of them, and that there
of a customer-pool effect because few
consumers consider purchasing
may
not be
much
at multiple points in
time.
Conclusion
5
In this paper
we have examined
a cross-sectional analysis of
demand
at
Internet retail
demand
demand using two
in different states
different approaches:
and a discrete-choice analysis of
an hourly frequency. The two analyses exploit separate sources of variation in
the data: the state-level analysis ignores
all
of the variation in competitive conditions;
and
the discrete-choice analysis uses state fixed effects to absorb any persistent factors like tax
rates.
Our most
activity.
Our
basic conclusion on sales taxes
state-level regressions
show
is
that they are an important driver of e-retail
clearly that sales are higher in states that levy
higher sales taxes on traditional retail purchases.
so
little in
some
California
artifact of
in that
is
strong evidence that what
The
we
fact that the websites
are picking
up
is
unobserved heterogeneity. The environment we study
consumers are highly savvy and price-sensitive, but
in this
sell
a tax effect and not
is
somewhat unusual
environment
^^The largest manufacturers reached agreements with the DOJ in 2004 to 2006.
milhon in fines and jail time for senior executives at Infineon, Hynix, and Samsung.
25
we study
at least,
These included $700
we would agree with Goolsbee's
(2001) conclusion that applying sales taxes to e-retail sales
demand by
could reduce e-retail
one-quarter or more.
inconsistent with this conclusion, but
We
find that
consumers do not pay
to differences in pre-tax prices
is
as
a
little
much
Our
discrete-choice analysis
The
effects
attention to differences in taxes as they do
when choosing between
Taxes do matter to
e-retailers.
do
state-level analysis indicates that
they can
geography
matters in
still
e-retail.
The
websites
sales to states that are closer to California in a shipping-time metric.
do not find an analogous
effect,
however, in our discrete-choice analysis.
find consistently in the discrete-choice analysis
buying from in-state
We
e-retailers.
geographic differentiation.
It
think this
is
is
One
thing
we
that consumers have a preference for
an interesting result on the sources of
has implications for market structure that would
what one would obtain from thinking about shipping
times.
about purchasing from their home state could lead to a
many
are,
on consumer behavior.
we study make more
We
not
surprising in light of our earlier results.
consumers, though, and given how tightly distributed prices in this market
have large
is
less
A
differ
from
world where consumers care
concentrated e-retail sector with
small firms, whereas a world where consumers do not have a home-state preference
but do care about shipping times could lead to a sector dominated by a few large firms
that effectively use distributed warehouses to minimize both shipping times and sales tax
liabilitites.
A
couple of our results suggest that there
offline retail.
Most
away from
we
identify in the state-level analysis
traditional retail. Similarly,
of prices on the total Pricewatch
effects
substantial substitution between online and
Accordingly, most of
states have few e-retailers listed on Pricewatch.
the tax-demand relationship
substitution
is
demand
and hence must be coming
we think
of our estimate of the effect
as being too large to be
at least in part
must be coming from
due to market expansion
from online-offline substitution.
We
do
not, however, find convincing evidence of short-term online-offline substitution from our
analysis of recent past prices.
Taken together we
rationality.
see our results as suggesting that there are hmitations to
In each of the dimensions
we analyze, there
quite easy for consumers to recognize and
some that
26
are
are
consumer
some considerations that are
more subtle
or
would
entail
more
A
costly information aquisition.
general theme that appears to emerge from our analysis
is
that consumers are closer to the traditional rational ideal on the easier tasks.
In terms of sales taxes, for example,
quite easy for consumers, in high tax states,
it is
buying things online saves on taxes, and we find
especially, to learn the general principle that
clear evidence sales being higher in high tax states.
more expensive items
is
larger for
is
a
little
more
more expensive items
indicates that consumers do not
prices
and
sales taxes.
subtle,
in our
It is intuitive
might lead to such a result
if
the state in which each firm
is
Our
less clear.
rate-sensitivity
discrete-choice analysis also
one-for-one tradeoff between differences in item
that the
way
that data
is
presented on Pricewatch
consumers are boundedly rational. Pricewatch's
is
be larger on
will
and the evidence on whether tax
data
make a
That the tax benefit
located so that a consumer
who understood
lists
includes
that taxes would
only be assessed on in-state purchases (and knew his local sales tax rate) could compute the
sales taxes,
but taxes are not presented to the consumer and the
the basis of the pre-tax price. In this vein
it is
lists
are always sorted
on
noteworthy that Brynjolfsson and Smith's
—they find that react twice as strongly to tax differences as they
do to item price differences — were obtained
an environment
which taxes are explicity
(2001) contrasing estimates
in
presented to consumers in a
list
that
is
in
sorted on the basis of tax-inclusive prices. ^'^
Similar patterns exist in the other dimensions of behavior
ences
we study
are stable over time. This should
tax states to learn that purchasing online
memory module
prices
on Pricewatch
is
is
make
it
a good idea.
highly unusual.
we
consider.
The tax
differ-
very easy for consumers in high
The high-frequency
We
find
it
volatility of
plausible that
we may
have a harder time finding consumer reactions to these short run price movements because
(a)
"rational"
consumers with information acquisition costs may not have invested
in
up-
to-date infomation on the evolution of the online-offline price gap over the last few days, or
(b)
"boundedly rational" consumers
stores' "stale" prices
when
may
not have figured out that they can exploit retail
there has been a very recent run up in online prices.
The environment we study
is
somewhat
special.
The consumers shopping through
Hossain and Morgan (2006) find that consumers do not fully take shipping costs into account in a
field experiment involving selling items on eBay. A commonality between shipping costs in
their experiment and tax diff'erences on Pricewatch is that the shipping cost differences were easily available
in the item descriptions, but some effort would have been required to learn the differences.
neatly-designed
27
Pricewatch are more technically savvy than most and we presume
They
are presented with
more complete data on competing
fact that
fact that
it
effects that are
on
offline prices
not so different from those
on consumer rationality may also make
easier to generalize in a
model where a
may
we would
it
can be avoided,
see in other environments. Limitations
easier to generalize
fraction of
is
standard.
What
our suggestion that discrete-choice models
come buy. Quantity data
and the
from our
results:
it is
much
consumers always buy online to save on
way on
the
number
of dollars
for instance.
Technically, our analysis
quantity data for one firm.
in sales taxes
lead to tax- and online-offline substitution
taxes than in a model where behavior depends in a complex
in taxes that
price offers than are usually
Pricewatch does not compute or highlight differences
lacks information
price sensitive.
environment might not be so unrepresentative.
available. In other dimensions, however, the
The
much more
Price data for
are
much harder
could perhaps be more broadly useful
may be
all
usefully
apphed
of the firms in a
to obtain.
is
to datasets containing
market are
fairly
easy to
There may, however, be many other
situations like ours where quantity data could be obtained from one firm. (This could even
be done in a
field
experiment.)
Our example
explore interfirm competition.
28
suggests that this
may
be a
fruitful
way
to
References
Kattuman, and John Morgan (2005). "Estimating
Comparison Site: Accounting for Shoppers and the Number
of Competitors" University of California-Berkeley, mimeo.
Baye, Michael, Rupert
Firm-Level
Demand
J.
Gatti, Paul
at a Price
,
Morgan (2004). "Price Dispersion in the Lab and on
Theory and Evidence," Rand Journal of Economics, 35 (3), 449-466.
Baye, Michael, and John
the Internet:
Brown, Jeffrey and Austan Goolsbee (2002). "Does the Internet Make Markets More ComEvidence from the Life Insurance Industry," Journal of Political Economy, 110
petitive?
(3),
481-507.
"Consumer Decision-making
Brynjolfsson, Erik and Michael Smith (2001).
Shopbot," Journal of Industrial Economics, 49
(4),
at
an Internet
541-558.
Chevaher, Judith and Austan Goolsbee (2003). "Price Competition Online: Amazon Versus
And Noble," Quantitative Marketing and Economics, 1 (2), 203-222.
Barnes
Chiou, Leshe (2005). "Empirical Analysis of Retail Competition: Spatial Differentiation at
Walmart, Amazon.com, and Their Competitors," Occidental College, mimeo.
Davis, Peter (2006).
"Spatial Competition in Retail Markets:
Movie Theaters,"
RAND
Journal of Economics, forthcoming.
Ellison,
(2),
Glenn
(2005).
"A Model
Add-on
of
Pricing," Quarterly Journal of Economics, 120
585-637.
Ellison,
Glenn and Sara Fisher Ellison (2004). "Search, Obfuscation, and Price
NBER Working Paper 10570.
Elasticities
on the Internet,"
Glenn and Sara Fisher Ellison (2005). "Lessons about Markets from the Internet,"
Journal of Economic Perspectives, 19 (2), 139-158.
Ellison,
Fox, William (1986).
"Tax Structure and the Location of Economic Activity Along State
Border," National Tax Journal, 39
(4),
387-401.
Goolsbee, Austan (2000). "In a World without Borders:
Commerce," Quarterly Journal
The Impact
of
Taxes on Internet
of Economics, 115 (2), 561-576.
Goolsbee, Austan (2001). "Competition in the Computer Industry:
Journal of Industrial Economics, 49
(4),
Onhne Versus
Retail,"
487-499.
Greene, William H. (1997): Econometric Analysis. Prentice Hall.
Hausman, Jerry, Bronwyn Hall and Zvi Griliches (1984); "Economic Models for Count
Data with an Application to the Patents-R&D Relationip," Econometrica 52, 909-938.
Hossain, Tanjim and John
(Non) Equivalence
in Field
Morgan
(2006):
"...Plus Shipping and Handling:
Experiments on eBay," Advances in Economic Analysis
29
Revenue
& Policy,
6,
Article
3.
"Substitutability between Online and Retail Personal Computers:
Changes over Time and the 'Online Research' Component," mimeo, Cornell University.
Prince, Jeffrey (2005):
Walsh, Michael and Jonathan Jones (1988). "More Evidence on the 'Border Tax' Effect:
of West Virginia," National Tax Journal, 41 (2), 261-265.
The Case
Weisbrod,
C,
R. Parcells and C.
Kern
(1984).
Shopping Area Market Attraction," Journal of
30
"A Disaggregate Model
Retailing, 60 (1), 65-83.
for
Predicting
Variable
Mean
St.Dev
Min
Max
Quantityl28
Quantity256
OfftineSales TaxRate
Internet Access
203.5
176.0
19.0
762.0
85.6
84.3
5.0
391.0
0.057
0.021
0.000
0.084
0.57
0.07
0.41
0.70
3.89
0.92
1.50
5.00
CcrmputeT Stores
GasStations
0.092
0.034
0.041
0.184
\og{Population)
15.02
1.04
13.11
17.33
ShippingTime
Table
1;
Summary
statistics for state-level regressions
31
Product/Estimation Method
OfftineSales TaxRate
California
Shipping Time
Internet Access
Computer Stores
GasStations
\og{ Population)
128MB
256MB
128MB
256MB
5.96
6.33
6.08
7.47
(2.16)
(2.59)
(2.39)
(2.71)
-1.03
-0.84
-1.01
-0.85
(4.01)
(3.21)
(3.18)
(2.46)
-0.10
-0.07
-0.09
-0.05
(2.04)
(1.32)
(1.64)
(0.82)
1.89
1.04
1.86
0.91
(2.62)
(1.25)
(2.32)
(1.06)
1.90
4.39
1.94
5.05
(1.11)
(2.29)
(0.98)
(2,36)
0.85
0.89
0.87
0.91
(20.54)
(18.98)
(18.51)
(17.88)
OLS
OLS
Neg. Binomial
Estimation
Observations
51
51
R^
Note:
The
51
51
0.93
0.93
two columns report estimates from negative binomial regressions with
first
Quantity 1 28 a,nd Quantity 25 6 as dependent variables. The third and fourth columns present
estimates from related
OLS
regressions with the \og{Quantity) as the dependent variable,
i-statistics in parentheses.
Table
2:
State-level regressions
Product/Sample Period
Product:
Price-based sample
SalesTaxRate
California
ShipTime
Internet Access
Computer Stores
GasStations
Log {Population)
Note:
The
variable
is
256MB
128MB
$20-$50
$100-h
$20-$50
$100-)-
4.22
7.32
7.43
8.89
(1.74)
(2.95)
(2.26)
(2.62)
-0.92
-1.39
-0.62
-0.74
(3.26)
(6.29)
(2.24)
(2.37)
-0.10
-0.15
-0.00
0.01
(1.88)
(2.79)
(0.01)
(0.13)
1.13
3.67
-0.69
1.97
(1.38)
(4.32)
(0.62)
(1.63)
1.05
1.61
5.59
6.25
(0.55)
(0.88)
(2.24)
(2.38)
0.85
0.94
0.80
0.85
(18.05)
(19.26)
(13.20)
(12.91)
table reports estimates from negative binomial regressions.
the
number
The dependent
when the
of orders received from each state during the time period
lowest price listed on Pricewatch was in the specified range, i-statistics in parentheses.
Table
3:
State-level regressions examining sales in different time periods
32
Mean
Variable
Min
St.Dev
Max
Mean
128MB PCIOO
St.Dev
Min
Max
128MB PC133
Quantity
0.013
0.118
4
0.010
0,105
Price
65.52
34.56
21
123
74.11
36.70
21
131
MinPrice
61.71
33.46
20
122
71.72
37.27
20
131
6.5
4.1
1
21
6.0
4.5
1
21
3.54
-16
16
Rank
MinPrice —
P Sunday
-0.24
3.30
Number
-13
13
-0.33
Num.
of Obs.: 154070
256MB PCIOO
Quantity
Price
0.004
0.068
129.98
65.10
MinPrice
120.19
5.9
-1.39
Rank
MinPrice — PSunday
4:
Summary
Obs.: 133310
256MB PC133
3
0.007
0.091
43
258
146.17
79.27
39
291
58.29
43
215
135.24
75.54
39
269
3.1
1
12
6.3
3.05
1
12
5.12
-18.75
35
-2.07
8.21
-51
36
Number
Table
2
of Obs.: 120420
Num.
Obs.: 124520
statistics for individual-level regressions
33
4
Product
128MB
PCIOO
128A'IB
256MB
256MB
PC133
PCIOO
PC133
Variables affecting choices between sites
Price
SalesTax
HomeState
ShippingTime
SecondScreen
-0.53
-0.82
-0.49
-0.47
(33.16)
(28.64)
(20.39)
(28.37)
0.05
0.38
0.10
1.27
(0.37)
(2.52)
(1.07)
(1.98)
1,24
1.31
1.13
1.23
(3.69)
(2.92)
(2.31)
(1.19)
0.14
-0.02
-0.06
-0.11
(2.34)
(0.30)
(0.74)
(2.01)
-1.58
0.61
(1.05)
(0.18)
Variables affecting total Pricew^atch
Weekend
-0.43
-0.34
demand
-0.31
-0.80
(10.88)
(7.36)
(4.11)
(11.41)
-0.03
-0.03
0.00
-0.03
(5.51)
(4.47)
(0.19)
(9.27)
0.001
0.003
-0.033
0.015
(0.17)
(0.49)
(4.67)
(3.29)
0.02
0.02
0.02
0.02
(5.29)
(6.93)
(4.71)
(3.37)
-0.03
-0.02
-0.03
-0.04
(4.89)
(3.98)
(4.25)
(3.68)
0.02
0.00
0.00
0.01
(4.78)
(0.17)
(0.53)
(1.50)
TimeTrendA
-0.01
-0.01
0.01
0.01
(5.84)
(2.85)
(6.36)
(8.23)
Observations
154070
133310
120420
124520
0.04
0.03
0.02
0.04
MinPrice
PSunday
TimeTrendl
TimeTrend2
TimeTrendS
i?2
Note: Dependent variables are number of distinct customers in each of ten states ordering
from each of websites A and B in each of approximately 7900 hours.
contain state and website dummies, ^-statistics are in parentheses.
Table
5:
Discrete-choice model of hourly sales of
(}(}k
kS
34
memory modules
Regressions also
in ten states
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