Econometrica, Vol. 77, No. 2 (March, 2009), 427–452
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES
ON THE INTERNET
BY GLENN ELLISON AND SARA FISHER ELLISON1
We examine the competition between a group of Internet retailers who operate in an
environment where a price search engine plays a dominant role. We show that for some
products in this environment, the easy price search makes demand tremendously pricesensitive.
Retailers, though, engage in obfuscation—practices that frustrate consumer
search or make it less damaging to firms—resulting in much less price sensitivity on
some other products. We discuss several models of obfuscation and examine its effects
on demand and markups empirically.
KEYWORDS: Search, obfuscation, Internet, retail, search engines, loss leaders, addon
pricing, demand elasticities, frictionless commerce.
1. INTRODUCTION
WHEN INTERNET COMMERCE first emerged, one heard a lot about the promise
of “frictionless commerce.” Search technologies would have a dramatic effect
by making it easy for consumers to compare prices at online and offline merchants.
This paper examines an environment where Internet price search plays
a dominant role: small firms selling computer parts through Pricewatch.com.
A primary observation is that the effect of the Internet on search frictions is not
so clear-cut: advances in search technology are accompanied by investments by
firms in obfuscation.
We begin with a brief discussion of some relevant theory. One way to think
about obfuscation is in relation to standard search-theoretic models in which
consumers do not learn all prices in equilibrium. Obfuscation can be thought
of as an action that raises search costs, which can lead to less consumer learning
and higher profits. Another way to think about obfuscation is in relation
to Ellison (2005), which describes how sales of “add-ons” at high unadvertised
prices can raise equilibrium profits in a competitive price discrimination model.
Designing products to require add-ons can thereby be a profit-enhancing obfuscation
strategy even when consumers correctly infer all prices.
Pricewatch is an Internet price search engine popular with savvy computerparts
shoppers. Dozens of small, low-overhead retailers attract consumers just
1
We would like to thank Nathan Barczi, Jeffrey Borowitz, Nada Mora, Youngjun Jang, Silke
Januszewski, Caroline Smith, Andrew Sweeting, and Alex Wolitzky for outstanding research assistance.
We also thank Patrick Goebel for a valuable tip on Internet data collection, Steve Ellison
for sharing substantial industry expertise, and Drew Fudenberg, the co-editor, and three anonymous
referees for their comments. This work was supported by NSF Grants SBR-9818524, SES-
0219205, and SES-0550897. The first author’s work was supported by fellowships from the Center
for Advanced Study in the Behavioral Sciences and the Institute for Advanced Study. The second
author’s work was supported by fellowships from the Hoover Institute and the Institute for
Advanced Study.
© 2009 The Econometric Society DOI: 10.3982/ECTA5708
428 G. ELLISON AND S. F. ELLISON
by keeping Pricewatch informed of their low prices. Although atypical as a retail
segment, Pricewatch retail has many of the features one looks for as a
setting for an empirical industrial organization study: it is not too complicated,
there is unusually rich data, and the extreme aspects of the environment should
make the mechanisms of the theory easier to examine.
We present an informal evidence section describing various practices that
can be thought of as forms of obfuscation. Some of these are as simple as making
product descriptions complicated and creating multiple versions of products.
We particularly call attention to the practice of offering a low-quality
product at a low price to attract consumers and then trying to convince them
to pay more for a superior product. We refer to this as a “loss-leader strategy”
even though it sometimes differs from the classic loss-leader strategy in two
respects: it involves getting consumers to upgrade to a superior product rather
than getting them to buy both the loss leader and a second physical good, and
the loss leader may be sold for a slight profit rather than at a loss.
The majority of the paper is devoted to formal empirical analyses. We
analyze demand and substitution patterns within four categories of computer
memory modules. Data come from two sources. We obtained yearlong
hourly price series by repeatedly conducting price searches on Pricewatch. We
matched this to sales data obtained from a single private firm that operates
several computer parts websites and derives most of its sales from Pricewatch
referrals.
Our first empirical result is a striking confirmation that price search technologies
can dramatically reduce search frictions. We estimate that the firm
faces a demand elasticity of −20 or more for its lowest quality memory mod-
ules!
Our second main empirical result is a contribution to the empirics of loss
leaders. We show that charging a low price for a low-quality product increases
our retailer’s sales of medium- and high-quality products. Intuitively, this happens
because one cannot ask a search engine to find “decent-quality memory
module sold with reasonable shipping, return, warranty, and other terms.”
Hence, many consumers use Pricewatch to do what it is good at—finding websites
that offer the lowest prices for any memory module—and then search
within a few of these websites to find products that better fit their preferences.
Other empirical results examine how obfuscation affects profitability. We examine
predictions of the two obfuscation mechanisms mentioned above. In the
search-theoretic model, obfuscation raises profits by making consumers less informed.
In Ellison’s (2005) add-on pricing model, obfuscation raises profits by
creating an adverse-selection effect that deters price-cutting. We find evidence
of the relevance of both mechanisms.
Finally, we examine an additional data source—cost data—for direct evidence
that retailers’ obfuscation strategies have been successful in raising
markups beyond the level that would otherwise be sustainable. Given the extreme
price sensitivity of the demand for low-quality products, a naive application
of single-good markup rules would suggest that equilibrium price–cost
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 429
margins might be just 3% to 6%. We find that the average markup on the memory
modules sold by the firm that provided us with data is about 12%.
A few previous papers have examined price search engines empirically.
Brynjolfsson and Smith (2001) used a data set containing the click sequences
of tens of thousands of people who conducted price searches for books on
Dealtime to estimate several discrete-choice models of demand. Baye, Gatti,
Kattuman, and Morgan (2006) examined an extensive data set on the Kelkoo
price comparison site and noted that there is a big discontinuity in clicks at
the top, in line with clearinghouse models. One advantage of our data set relative
to others we are aware of is that we observe actual quantities sold and not
just “clickthroughs.” A large number of studies have documented online price
dispersion.2
The one study we know of that reports price elasticities obtained
from quantity data in an online retail sector is Chevalier and Goolsbee (2003).
Some other studies that provide evidence related to Internet search and price
levels are Brown and Goolsbee (2002) and Scott Morton, Zettelmeyer, and
Silva-Risso (2001, 2003). Our paper has also spawned a broader literature on
obfuscation.3
2. THEORY OF SEARCH AND OBFUSCATION
Our most basic observation is that it is not a priori obvious that the Internet
will lead us toward frictionless commerce. Price search engines and other Internet
tools will help consumers to find and to process information, but retailers
may simultaneously harness the power of the Internet to make information
processing problems more formidable and/or to make consumer informedness
less damaging to their profits. In this section we quickly sketch two ways in
which one might think about obfuscation.4
2.1. Incomplete Consumer Search
A number of authors have developed models in which consumer search costs
affect market efficiency and firm profits. Stahl (1989, 1996), for example, considered
a model in which some consumers incur a search cost every time they
incur a price quote, whereas other consumers do not. The model has a mixed
strategy equilibrium: retailers randomize over prices in some interval; fully informed
consumers purchase from the lowest priced firm; other consumers often
stop searching before finding the lowest priced firm. Firm profits are in-
2
See Baye, Morgan, and Scholten (2004) for one such study and Baye, Morgan, and Scholten
(2007) for a survey.
3
See Ellison (2005), Gabaix and Laibson (2006), Spiegler (2006), and Brown, Hossain, and
Morgan (2007).
4
See Ellison and Ellison (2004) for a longer discussion of search engines and search and obfuscation;
see Baye and Morgan (2001, 2003) for two formal models of search engines and their
effects on prices and firm profits.
430 G. ELLISON AND S. F. ELLISON
creasing in the fraction of consumers with positive search costs and in the level
of the search costs.
One could regard obfuscation as an action that raises search costs and/or
the fraction of consumers who incur search costs. Such actions would increase
average markups and the fraction of consumers buying from relatively highpriced
firms. Developing such a formal model for our application is well beyond
the scope of this paper: one would want all consumers’ searches to be
directed by the Pricewatch list, whereas Stahl’s consumers search in a random
manner; one would want to extend the model to include multiple products per
firm; and one would also want to make search costs firm-specific so that obfuscation
could be an action taken by individual firms and not by firms as a
whole.5
Nonetheless, the basic intuition from search models that obfuscation
might lead to higher profits by making consumer learning less complete seems
useful to explore empirically.
2.2. Add-Ons and Adverse Selection
Ellison (2005) provided a model with a somewhat different flavor—add-on
pricing schemes can raise retailers’ profits even if consumers correctly infer
all prices in equilibrium. We develop this idea in more generality below to
illustrate how it would work in an empirically relevant setting.6
Suppose two firms i = 1 2 can each produce two versions of a good j = L H
at constant marginal costs cL and cH . They post prices piL for their low-quality
goods on a price comparison site and simultaneously choose nonposted prices
piH for their high-quality products. Consumers who visit the price comparison
site learn both low-quality prices. At a time cost of s, consumers can visit a
firm’s website, learn its high-quality price, and buy or not buy. They can then
visit the second firm’s site at an additional cost of s if they so desire. We assume,
however, that consumers wish to buy at most one unit.
As in Diamond (1971), the incremental price of the “upgrade” from good L
to good H is priced at the ex post monopoly price in any pure strategy equilibrium.
The argument is that at any lower price the firm will always be tempted
to raise its upgrade price by ε. For ε < s, no consumer will switch to the other
firm, because that would require incurring s again and the other firm’s product
was less attractive at the prices that the consumer anticipated. Formally, if we
write piU ≡ piH − pIL for the upgrade price, cU = cH − cL for the cost of the
5
Another difficulty with the application is that the mixed strategy nature of the equilibrium is
awkward.
6
Ellison (2005) used several special assumptions. The population consists of two types, demand
for the low-quality good is linear, and all consumers of the same type have an identical willingness
to pay to upgrade to the high-quality good.
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 431
upgrade, and x(piU piL p−iL) for the fraction of consumers who choose to
upgrade, Diamond’s argument implies that the equilibrium price p∗
iU satisfies
p∗
iU (piL p−iL) = pm
iU (piL p−iL) ≡ Argmax
p
(p − cU )x(p piL p−iL)
Write x∗
(p1L p2L) for x(p∗
iU (piL p−iL) piL p−iL).
Write D1(p1 p2) for the number of consumers who visit firm 1.7
Assume that
this function is smooth, strictly decreasing in p1, and otherwise well behaved.
Firm 1’s profits when it sets price p1L and the other firm follows its equilibrium
strategy are given by
π1(p1L p∗
2L) = p1L − cL + x∗
(p1L p∗
2L)(pm
1U (p1L p∗
2L) − cU )
× D1(p1L p∗
2L)
The first-order condition implies that the equilibrium prices satisfy
p∗
1L + x∗
(p∗
1L p∗
2L)pm
1U − cL − x∗
(p∗
1L p∗
2L)cU
p∗
1L + x∗(p∗
1L p∗
2L)pm
1U
(1)
= −
1
ε
1 + (pm
1U − cU )
∂x∗
∂p1L
+ x∗
(p∗
1L p∗
2L)
∂pm
1U
∂p1L
where
ε =
∂D1
∂p1L
p∗
1L + x∗
(p∗
1L p∗
2L)pm
1U
D1(p∗
1L p∗
2L)
The left-hand side of this expression is the firm’s revenue-weighted average
markup. The right-hand side is the product of a term that is like the inverse of
a demand elasticity and a multiplier.
Suppose first that the fraction of firm 1’s customers who buy the upgrade at
any given price p1U is independent of p1L.8
Then the last two terms in the multiplier
are zero. Hence, the average markup satisfies an inverse elasticity rule.
If total demand is highly sensitive to the low-quality price, then markups will be
low. It does not matter whether the firm earns extremely high profits on addon
sales: these are fully “competed away” with below-cost prices if necessary
in the attempt to attract consumers.
Although the constant-upgrade-fraction assumption might seem natural and
has been made with little comment in many papers on competitive price discrimination,
Ellison (2005) argued that it is not compelling. One way in which
7
In any pure strategy equilibrium, all consumers who visit firm i will buy from firm i. Otherwise
they would be better off not visiting.
8
For example, suppose that the optimal price for good H is always $25 above the price of good
L and that 50% of consumers upgrade at this price differential.
432 G. ELLISON AND S. F. ELLISON
real-world consumers will be heterogeneous is in their marginal utility of income.
In this case, price cuts disproportionately attract “cheapskates” who
have a lower willingness to pay for upgrades. This suggests that it may be more
common that both ∂pm
1U /∂p1L > 0 and ∂x∗
/∂p1L > 0. Ellison (2005) refered to
such demand systems as having an adverse-selection problem when add-ons
are sold. With such demand, sales of add-ons will raise equilibrium profit margins
above the inverse-elasticity benchmark. The factor by which profit margins
increase is increasing in both the upgrade price and the fraction of consumers
who upgrade. Hence, taking a low-cost, high-value feature out of the
low-quality good and making it available in the high-quality good may be a
profit-enhancing strategy.
3. THE PRICEWATCH UNIVERSE AND MEMORY MODULES
We study a segment of e-retail shaped by the Pricewatch price search engine.
It is composed of a large number of small, minimally differentiated firms selling
memory upgrades, central processing units (CPUs), and other computer
parts. The firms do little or no advertising, and receive most of their customers
through Pricewatch.
Pricewatch presents a menu that contains a set of predefined categories.
Clicking on one returns a list of websites sorted from cheapest to most expensive
in a twelve listings per page format. The categories invariably contain heterogeneous
offerings: some include products made by higher and lower quality
manufacturers, and all include offers with varying return policies, warranties,
and other terms of trade. Figure 1 contains the first page of a typical list, that
for 128MB PC100 memory modules from October 12, 2000.
There is substantial reshuffling in the sorted lists, making Pricewatch a nice
environment for empirical study. For example, on average three of the twentyfour
retailers on the first two pages of the 128MB PC100 list change their prices
in a given hour. Each price change can move several other firms up or down
one place. Some websites regularly occupy a position near the top of the Pricewatch
list, but there is no rigid hierarchy.
Several factors contribute to the reshuffling. One of these is the volatility
of wholesale memory prices: wholesale price changes will make firms want to
change retail prices. Memory prices declined by about 70% over the course of
the year we study, but there were also two subperiods during which prices rose
by at least 25%. A second complementary factor is a limitation of Pricewatch’s
technology: Pricewatch relied on retailers updating their prices in its data base.
Most or all of the retailers were doing this manually in the period we study
and would probably reassess each price one or a few times per day.9
When
wholesale prices are declining, this results in a pattern where each firm’s price
tends to drift slowly down the list until the next time it is reset.
9
A retailer may have dozens or hundreds of products listed in various Pricewatch categories.
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 433
FIGURE 1.—A sample Pricewatch search list: 128MB PC100 memory modules at 12:01pm
ET on October 12, 2000.
Our sales and cost data come from a firm that operates several websites,
two of which regularly sell memory modules.10
We have data on products in
four Pricewatch categories of memory modules: 128MB PC100, 128MB PC133,
256MB PC100, and 256MB PC133. PC100 versus PC133 refers to the speed
with which the memory communicates with the CPU. They are not substitutes
for most retail consumers because the speed of a memory module must match
the speed of a computer’s CPU and motherboard. The second part of the
10
We will call these Site A and Site B.
434 G. ELLISON AND S. F. ELLISON
FIGURE 2.—A website designed to induce consumers to upgrade to a higher quality memory
module.
product description is the capacity of the memory in megabytes. The 256MB
modules are about twice as expensive. Each of our firm’s websites sells three
different quality products within each Pricewatch category. They are differentiated
by the quality of the physical product and by contract terms. Figure 2
illustrates how a similar quality choice is presented to consumers on a website
that copied Site A’s design. Making comparisons across websites would be
much harder than making within-website comparisons because many sites contain
minimal technical specifications and contractual terms are multidimen-
sional.
4. OBSERVATIONS OF OBFUSCATION
Pricewatch has made a number of enhancements to combat obfuscation.
Practices that frustrate search nonetheless remain commonplace.
One of the most visible search-and-obfuscation battles was fought over shipping
costs. In its early days Pricewatch did not collect information on shipping
costs and sorted its lists purely on the basis of the item price. Shipping charges
grew to the point that it was not uncommon for firms to list a price of $1 for
a memory module and inform consumers of a $40 “shipping and handling”
fee at check out. Pricewatch fought this with a two-pronged approach: it mandated
that all firms offer United Parcel Service (UPS) ground shipping for a
fee no greater than a Pricewatch-set amount ($11 for memory modules); and
it added a column that displayed the shipping charge or a warning that cus-
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 435
tomers should be wary of stores that do not report their shipping charges.11
Many retailers adopted an $11 shipping fee in response, but uncertainty about
the cost of UPS ground shipping was not completely eliminated: a number of
retailers left the column blank or reported a range of charges. The meaning
of “UPS ground shipping” was also subject to manipulation: one company explicitly
stated on its website that items ordered with the standard UPS ground
shipping were given lower priority for packing and might take two weeks to
arrive. More recently, Pricewatch mandated that retailers provide it with shipping
charges and switched to sorting low-price lists based on shipping-inclusive
prices. This appears to be working, but is only fully satisfactory for customers
who prefer ground shipping: those who wish to upgrade to third-, second-, or
next-day air must search manually through retailers’ websites.
One model of obfuscation we discussed involved firms trying to increase customers’
inspection costs and/or reduce the fraction of customers who will buy
from the firm on the top of the search engine’s list. We observed several practices
that might serve this purpose. The most effective seems to be bundling
low-quality goods with unattractive contractual terms, like providing no warranty
and charging a 20% restocking fee on all returns. Given the variety of
terms we observed, it would seem unwise to purchase a product without reading
the fine print. Another practice is making advertised prices difficult to find.
In 2001 it took us quite a bit of time to find the prices listed on Pricewatch
on several retailers’ sites. In a few cases, we never found the listed prices. Several
other firms were explicit that Pricewatch prices were only available on
telephone orders. Given that phone calls are more costly for the retailers, we
assume that firms either wanted people to waste time on hold or wanted to
make people sit through sales pitches. Pricewatch has fought these practices
in several ways. For example, it added a “buy now” button, which (at least in
theory) takes customers directly to the advertised product.
The second obfuscation mechanism we discussed is the adoption of a lossleader
or add-on pricing scheme: damaged goods are listed on the search engine
at low prices and websites are designed to convince customers attracted
by the low prices to upgrade to a higher quality product. Such practices are
now ubiquitous on Pricewatch. Figure 2 is one example. Customers who tried
to order a generic memory module from Buyaib.com at the price advertised
on Pricewatch.com were directed to this page. It illustrates several ways in
which the low-priced product is inferior to other products the company sells (at
higher markups). Figure 3 is another example. A consumer who tried to order
a generic module from Tufshop.com was taken to this page, on which a number
of complementary products, upgrades, and services were listed. The figure
shows the webpage as it initially appeared, defaulting the buyer to several
11
Our empirical work is based on data from the period when these policies were in effect.
436 G. ELLISON AND S. F. ELLISON
FIGURE 3.—Another website designed to induce consumers to upgrade and/or buy add-ons.
upgrades. To avoid purchasing the various add-ons, the consumer must read
through the various options and unclick several boxes. After completing this
page, the customer was taken to another on which he or she must choose from
a long list of shipping options. These include paying $15.91 extra to upgrade
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 437
FIGURE 3.—(Continued.)
from UPS ground to UPS 3-day, $30.96 extra to upgrade to UPS 2-day, and
$45.96 extra to upgrade to UPS next day.12
Our impression is that the practices are also consistent with the add-on pricing
model in terms of the low-priced goods being of inefficiently low quality.
In Pricewatch’s CPU categories all of the listings on the first few pages were
“bare” CPUs without fans attached. This seems highly inefficient: an experienced
installer can attach a fan in less than a minute, whereas there is a non-
12
The incremental costs to Tufshop of the upgraded delivery methods were about $4, $6, and
$20.
438 G. ELLISON AND S. F. ELLISON
trivial probability that a novice will snap off a pin and ruin a $200 chip. We were
also told that most of the generic memory modules at the top of Pricewatch’s
memory lists are poor quality products that are much more likely to have problems
than are other modules that can sometimes be purchased wholesale for
just $1 or $2 more. We know that the wholesale price difference is occasionally
so small as to induce the retailer from which we got our data to ship mediumquality
generic modules to customers who ordered low-quality modules (without
telling the customers) because it felt the time cost and hassle of dealing
with returns was not worth the cost savings.
Obfuscation could presumably take many forms in addition to those we outlined
in our theory section. One is that firms could try to confuse boundedly
rational consumers. Presumably, this would involve either tricking consumers
into paying more for a product than it is worth to them or altering their utility
functions in a way that raises equilibrium profits. Our impression is that many
Pricewatch retailers’ sites are intentionally confusing. For example, whereas
several sites will provide consumers with product comparison lists like that in
Figure 2, we did not see any that augmented such a comparison with a description
of what “CAS latency” means to help consumers think about whether they
should care about it.
Pricewatch requires that retailers enter their prices into a data base. An alternate
technology for running a price comparison site is to use shopbots to
gather information automatically from retailers’ sites. The shopbot approach
may be even more prone to obfuscation. In 2001, for example, the Yahoo!
Shopping search engine should have had a much easier time gathering information
than a general search engine because it only searched sites hosted by
Yahoo. Yahoo collected a royalty on all sales made by merchants through Yahoo!
Shopping, so there must have been some standardization of listing and
ordering mechanics. Nonetheless, when we typed “128MB PC100 SDRAM
DIMM” into the search box, the five lowest listed prices were from merchants
who had figured out how to get Yahoo! Shopping’s search engine to think the
price is zero even though a human who clicks over to the retailer can easily
see the price (and see that it is 50–100% above the Pricewatch price). The next
hundred or so cheapest items on Yahoo’s search results were also either products
for which Yahoo’s search engine had misinterpreted the price or misclassified
items.
5. DATA
Our price data were collected from Pricewatch.com. They contain information
on the twelve or twenty-four lowest price offerings within each of the four
predefined categories mentioned above.13
They are at hourly frequency from
May 2000 to May 2001.
13
We collected the twenty-four lowest prices for the 128MB PC100 and 128MB PC133 categories
and the twelve lowest prices for the other two.
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 439
In addition to the price data for these low-quality products, we obtained
price and quantity data from an Internet retailer who operates two websites
that sell memory modules. The data contain the prices and the quantities sold
for all products that fit within the four Pricewatch categories. The websites
usually offer three different quality products in each category. We aggregate
data on individual orders to produce daily sales totals for each product–website
pair.14
Our primary price variables are the average transaction prices for sales
of a given product on a given day.15
We also record the daily average position
of each website on Pricewatch’s price-ranked list.
The same Internet retailer also provided us with data on wholesale acquisition
costs for each product.
Websites A and B have identical product lineups: they sell three products
within each memory module category, which we refer to as the low-, the
medium-, and the high-quality module. Our data set contains between 575
and 683 observations in each category.16
Summary statistics for the 128MB
PC100 category are given in Table I.17
The data are at the level of the website
day, so the number of days covered is approximately half of the number
TABLE I
SUMMARY STATISTICS FOR MEMORY MODULE DATA (128MB PC100 MEMORY MODULES;
683 WEBSITE DAY OBSERVATIONS)
Variable Mean Stdev Min Max
LowestPrice 62 98 33 31 21 00 120 85
Range 1–12 6 76 2 52 1 00 13 53
PLow 66 88 34 51 21 00 123 49
PMid 90 71 40 10 35 49 149 49
PHi 115 19 46 37 48 50 185 50
log(1 + PLowRank) 1 86 0 53 0 69 3 26
QLow 12 80 17 03 0 163
QMid 2 44 3 33 0 25
QHi 2 02 3 46 0 47
14
Here, “product” also includes the quality level, for example, a high-quality 128MB PC100
module.
15
Transaction prices are unavailable for products which have zero sales on a given day. These
are filled in using the data collected from Pricewatch or imputed using prices on surrounding days
and prices charged by the firm’s other websites.
16
Data are occasionally missing due to failures of the program we used to collect data and
missing data in the files the firm provided. The 256MB prices are missing for most of the last six
weeks, so we chose to use mid-March rather than May as the end of the 256MB samples.
17
Summary statistics for the other categories are presented in Ellison and Ellison (2004, 2004).
We will present many results for the 128MB PC100 category and only discuss how the most important
of these extend to the other categories. One reason for this choice is that the 128MB PC100
data are available for the longest time period and demand is less time-varying, which allows for
more precise estimates.
440 G. ELLISON AND S. F. ELLISON
of observations. LowestPrice is the lowest price listed on Pricewatch (which is
presumably for a low-quality memory module).18
Range 1–12 is the difference
between the twelfth lowest listed price and the lowest listed price. Note that
the price distribution is fairly tight. PLow, PMid, and PHi are the prices for
the three qualities of memory modules at the two websites. QLow, QMid, and
QHi are the average daily quantities of each quality of module sold by each
website. The majority of the sales are the low-quality modules. PLowRank is
the rank of the website’s first entry in Pricewatch’s sorted list of prices within
the category.19
This variable turns out to allow us to predict sales much better
than we can with simple functions of the cardinal price variables.
We have not broken the summary statistics down by website. Website A’s
prices are usually lower than website B’s, but there is no rigid relationship. In
the 128MB PC100 category, website A has a lower low-quality price on 251
days and accounts for 70% of the combined unit sales.
6. DEMAND PATTERNS
In this section we estimate demand elasticities and examine how consumers
substitute between low-, medium-, and high-quality products. We do this both
to provide descriptive evidence on search-engine-influenced e-retail and to
provide empirical evidence on theories of obfuscation.
6.1. Methodology for Demand Estimation
Assume that within each product category c, the quantity of quality q products
purchased from website w on day t is
Qwcqt = eXwct βcq
uwcqt
with
Xwctβcq = βcq0 + βcq1 log(PLowwct) + βcq2 log(PMidwct)
+ βcq3 log(PHiwct) + βcq4 log(LowestPricect)
+ βcq5 log(1 + PLowRankwct) + βcq6Weekendt
+ βcq7SiteBw +
12
s=1
βcq7+sTimeTrendst
18
The Pricewatch data are hourly. Daily variables are constructed by taking a weighted average
across hours using weights that reflect the average hourly sales volumes of the websites we study.
19
We only know a site’s Pricewatch rank if it is among the twelve or twenty-four lowest priced
websites. When a site does not appear on the list, we impute a value for PLowRank using the
difference between the site’s price and the highest price on the list. In the 128MB category this
happens for fewer than 1% of the observations. In the 256MB category this happens for 3% of
the Site A observations and 14% of the Site B observations.
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 441
The effect of PLowRank on demand is of interest for two reasons: it will
contribute to the own-price elasticity of demand for low-quality memory and
it provides information on how the Pricewatch list is guiding consumers who
buy other products. The price variables PLow, PMid, and PHi are used to
estimate elasticities. We think of the other variables mostly as important controls.
An important part of our estimation strategy is the inclusion of the TimeTrend
variables, which allow for a piecewise linear time trend with a slope that
changes every 30 days.
We estimate the demand equations via generalized method of moments.
Specifically, for most of our estimates we assume that the multiplicative error
term uwcqt satisfies E(uwcqt|Xwct) = 1 so that we can estimate the models
using the moment condition
E(Qwcqte−Xwct βcq
− 1|Xwct) = 0
These estimates are done separately for each product category and each quality
level. Standard errors use a Newey–West style approach to allow for serial
correlation.
This estimation approach presumes that the price variables and PLowRank
are not endogenous. In the case of PLowRank we think this is a very good
assumption: our e-retailer has little information on demand fluctuations and
little analytic capability to assess whether idiosyncratic conditions affect the
relative merits of different positions on the Pricewatch list. The person who
sets prices told us that he checks some of the Pricewatch lists a few times a
day and might change prices for a few reasons: if a rank has drifted too far
from where he typically leaves it, if there has been a wholesale prices change;
or occasionally if multiple employees have failed to show up for work and he
needs to reduce volume.
The price variables are more problematic. The obvious endogeneity concern
is that prices may be positively correlated with demand shocks and/or
rivals’ prices, which would bias estimates of own-price elasticities toward zero.
The idea behind our base estimates, however, is that the unusual time-series
properties of the variables may let us address this at least in part without instruments.
The unusual aspect of the data is that our retailer tends to leave
medium- and high-quality prices fixed for a week or two and then to change
prices by $5–10. Our hope is that demand shifts and rivals’ prices are moving
sufficiently smoothly so that much of the variation in them can be captured
by the flexible time trends. The effect of our firm’s prices on demand may be
picked up in the periods around the discontinuous changes. In the next section
we will see that we have some success with this approach, but in several
categories it does not work very well.
We present alternate estimates derived from using two distinct sets of instruments
for the price variables in Section 6.5.
442 G. ELLISON AND S. F. ELLISON
6.2. Basic Results on Demand
Table II presents demand estimates from the 128MB PC100 memory module
category. The first column of the table contains estimates of the demand equation
for low-quality modules. The second and third columns contain estimates
of the demand for medium- and high-quality modules.
Our first main empirical result is that demand for low-quality modules at a
website is extremely price-sensitive. Most of this is due to the effect of Pricewatch
rank on demand. The rank effect is very strong: the coefficient on the
log(1 + PLowRank) variable in the first column implies that moving from first
to seventh on the list reduces a website’s sales of low-quality modules by 83%.
The estimates are highly significant—we get a t-statistic of 10.9 in a regression
with only 683 observations. Table III presents demand elasticities derived from
the coefficient estimates.20
The upper left number in the upper left matrix indicates
that the combination of the two price effects in the model results in an
own-price elasticity of −24.9 for low-quality 128MB PC100 modules.
TABLE II
DEMAND FOR 128MB PC100 MEMORY MODULESa
Dep. Var.: Quantities of Each Quality Level
Independent Variables Low q Mid q High q
log(1 + PLowRank) −1 29∗
−0 77∗
−0 51∗
(10 9) (4 6) (2 9)
log(PLow) −3 03 −0 59 1 49
(2 3) (0 4) (0 9)
log(PMid) 0 68 −6 74∗
2 38
(0 8) (5 9) (1 7)
log(PHi) 0 17 2 72 −4 76∗
(0 2) (1 8) (3 3)
SiteB −0 25∗
−0 31∗
−0 59∗
(3 5) (2 9) (5 6)
Weekend −0 49∗
−0 94∗
−0 72∗
(8 4) (8 3) (5 8)
log(LowestPrice) 1 20 0 83 −0 14
(1 1) (0 6) (0 1)
Number of obs. 683 683 683
aAbsolute value of t-statistics in parentheses. Asterisks (*) denote significance at the 5% level.
20
Elasticities with respect to changes in the low-quality price are a sum of two effects: one
due to changes in the PLow variable and one due to changes in the PLowRank variable. We
estimate the latter by treating PLowRank as a continuous variable and setting the derivative of
PLowRank with respect to PLow equal to the inverse of the average distance between the twelve
lowest prices, and setting the rank and other variables equal to their sample means.
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 443
TABLE III
PRICE ELASTICITIES FOR MEMORY MODULES: THREE QUALITIES IN EACH OF
FOUR PRODUCT CLASSESa
128MB PC100 Modules 128MB PC133 Modules
Low Mid Hi Low Mid Hi
PLow −24 9∗
−12 5∗
−7 2∗
−33 1∗
−11 2∗
−4 9∗
PMid 0 7 −6 7∗
2 4 0 8 −3 6∗
0 5
PHi 0 2 2 7 −4 8∗
0 2 −4 8∗
−4 8∗
256MB PC100 Modules 256MB PC133 Modules
Low Mid Hi Low Mid Hi
PLow −17 4∗
−8 1∗
−4 1 −24 8∗
−12 5 −6 6
PMid 5 7 −7 8 −4 1 0 3 3 3 3 9∗
PHi 0 7 6 4 −3 8 −0 9 −7 2 −0 8
aAsterisks (*) denote significance at the 5% level.
A second striking empirical result in Table II is that low-quality memory is an
effective loss leader. The coefficients on log(1+PLowRank) in the second and
third columns are negative and highly significant. This means that controlling
for a site’s medium- and high-quality prices and other variables, a site sells
more medium- and high-quality memory when it occupies a higher position on
Pricewatch’s (low-quality) list. The effect is very strong. The −0.77 coefficient
estimate indicates that moving from first to seventh on the Pricewatch list for
low-quality 128MB PC100 memory reduces a website’s sales of medium-quality
128MB PC100 memory by 66%. The −0.51 coefficient estimate indicates that
moving from first to seventh on the Pricewatch list for low-quality memory
would reduces high-quality memory sales by 51%.21
A potential concern about this result is that PLowRank might be significant
not because Pricewatch’s low-quality list is guiding consumers’ searches,
but rather because of an omitted variable problem in our analysis: PLowRank
might be correlated with a ranking of our firm’s medium- and high-quality
prices relative to its competitors’ prices for comparable goods. We think that
this is unlikely given what we know of the time-series behavior of the different
series: Pricewatch ranks change frequently, whereas medium- and high-quality
prices are left unchanged for substantial periods of time, so that most of the
variation in the attractiveness of our firm’s medium- and high-quality prices
will occur around the occasional price changes. One’s first reaction to this concern
would be to want to address it by including within-category rank variables
21
Although it is common in marketing to talk about loss leaders, the empirical marketing literature
on the effectiveness of loss leaders has produced mixed results (Walters (1988), Walters
and McKenzie (1988), Chevalier, Kashyap, and Rossi (2003)). We are not aware of any evidence
nearly as clear as our results.
444 G. ELLISON AND S. F. ELLISON
PMidRank or PHiRank analogous to PLowRank. This is, however, not pos-
sible.22
We can, however, provide a test robust to this concern by looking at
choices conditional on buying from one of our websites. We discuss this and
present results in Section 6.3.
A third noteworthy result is that the coefficients on the Site B dummy are
negative and significant in all three regressions. Site B is particularly less successful
at selling high-quality memory. This could indicate that website design
is important.23
Alternative explanations would include that people may prefer
to buy memory from Site A because it specializes in memory and that there
may be reputational advantages we cannot directly observe.
We report elasticity matrices for the other memory categories in Table III,
but to save space we have not included full tables of demand estimates.24
The elasticity tables reveal that our findings that low-quality products have
highly elastic demand and that there are loss-leader benefits from selling lowquality
goods at a low price are consistent across categories. The estimated
own-price elasticities for low-quality modules range from −33.1 in the 128MB
PC133 category to −17.4 in the 256MB PC100 category. The one way in which
the results for the 128MB PC100 category are unusual is that the own-price
elasticities of medium- and high-quality memory are precisely estimated. This
problem is particularly severe in the 256MB categories where the effective
sample size is reduced by the fact that most of the memory is sold toward the
end of the data period.
6.3. The Mechanics of Obfuscation: Incomplete Consumer Search
One way to think about the obfuscation discussed in Section 2 is as an increase
in search costs that made search less complete. We noted in Section 6.2
that the finding that PLowRank affects medium- and high-quality sales suggests
that consumers are conducting a meaningfully incomplete search with
the omissions being influenced by Pricewatch’s list, but that an alternate explanation
for the finding could be that PLowRank is correlated with the rank of a
site’s higher quality offerings. In this section we note that the structure of our
22
We did not collect data on other firms’ full product lines. Even if we had done so, mediumand
high-quality memory are not sufficiently well defined concepts to make within-quality rank a
well defined concept: every website has a different number of offerings with (often undisclosed)
technical attributes and service terms that do not line up neatly with the offerings of our retailer.
23
Site A and Site B are owned by the same firm. They share customer service and packing
employees. A few attributes should make Site B more attractive: it had slightly lower shipping
charges for part of the sample, it offers more products other than memory, and at the time it
had a higher customer feedback rating at ResellerRatings.com, which was probably the most
important reputation-posting site for firms like this.
24
Significance levels in the other categories are generally similar to those in the 128MB PC100
category. The log(1 + PLowRank), Weekend, and Site B variables are usually highly significant.
The other variables are usually insignificant.
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 445
TABLE IV
EVIDENCE OF INCOMPLETE CONSUMER LEARNING: CONDITIONAL SITE CHOICES OF
CONSUMERS OF MEDIUM- AND HIGH-QUALITY MEMORYa
Dependent Variable: Dummy for Choice of Site A
Independent Variables Medium Quality High Quality
log(1 + PLowRank) −0 64∗
−0 31∗
(4 2) (4 0)
log(PMid) −3 08∗
1 48
(2 2) (1 4)
log(PHi) −1 43 −5 73∗
(1 2) (3 4)
Number of obs. 4118 6768
aThe table presents estimates of logit models. The dependent variable for the transaction-level data set is a dummy
for whether a consumer chose to buy from Site A (versus Site B). The samples are all purchases of medium- or highquality
modules from Site A or Site B. Absolute values of z-statistics in parentheses. Asterisks (∗) denote significance
at the 5% level. The regressions also include unreported category dummies, a linear time trend, and the difference
between dummies for appearing on Pricewatch’s first screen.
data set provides an opportunity to avoid this confounding. Our two websites
offer identical products. If all consumers learned about all prices, then conditioning
on a consumer’s decision to purchase from one of our sites, the relative
position of the two sites on Pricewatch’s list should not help predict which site
a consumer will purchase from.25
To provide a straightforward analysis of conditional choices, we estimate
simple logit models on the consumer-level data using a dummy for whether
each consumer chose to buy from Site A (versus Site B) as the dependent
variable. As explanatory variables we include the difference across sites in
log(1 + PLowRank), log(PMid), and log(PHi), and a set of time trends.
The two columns of Table IV report estimates from the sample of all
consumers who purchased medium- and high-quality memory, respectively.26
The significant coefficients on the log(PMid) variable in the first column and
on the log(PHi) variable in the second column indicate that consumers are
influenced by the prices of the product they are buying. Interestingly, however,
the significant coefficients on log(1 + PLowRank) in both columns indicate
that consumers are also more likely to purchase from the site with a lower lowquality
price. Considering the standard deviations of the two variables, we find
that the rank of a firm’s low-quality product has about as much influence on
25
This would be exactly true in a discrete-choice model with the IIA property. In a randomcoefficients
model where consumers had preferences over websites and over quality levels, one
would expect PLowRank to have the opposite effect from the one we find: when Site A has a
low price for low-quality memory, then fewer consumers with a strong Site A preference will buy
medium-quality memory, which makes the pool of consumers buying medium-quality memory
tilted toward Site B.
26
We have pooled observations from all four memory categories.
446 G. ELLISON AND S. F. ELLISON
consumer decisions as the price of the product consumers are buying. Overall,
the regressions support the conclusion that consumer learning about prices is
incomplete.
6.4. The Mechanics of Obfuscation: Add-Ons and Adverse Selection
The second model in Section 2 noted that creating inferior versions of products
to advertise could raise equilibrium markups by creating an adverse selection
problem. More concretely, this occurs if a decrease in a firm’s low-quality
price decreases the fraction of consumers who buy upgrades. In other words, if
the elasticity on the low-quality memory is larger (in absolute value) than that
for medium- or high-quality memory, there is evidence of adverse selection.
This feature is present in all four of our elasticity matrices.27
An alternate way to get intuition for the magnitude of this adverse-selection
effect without relying on the functional form assumptions is to look at the firm’s
quality mix using sample means. For example, we find that when one of our
sites is first on one of the Pricewatch lists for 256MB memory, 63% of its unit
sales are low-quality memory. On days when one of them is in tenth place,
only 35% of the unit sales are low-quality memory.
6.5. Instrumental Variables Estimates
We noted above that an obvious source of potential difficulty for our elasticity
estimates (especially with respect to changes in medium- and high-quality
prices) is that our price variables may be correlated with demand shocks, rival
firms’ prices, or both. In this section we present two sets of instrumental
variables (IV) estimates.
Our first set of instruments are cost-based. We instrument for PLow, PMid,
and PHi with our firm’s acquisition costs for each product. Many textbooks
use costs as the prototypical example of an instrument for price in a demand
equation. In retail, however, the case for instrumenting with acquisition costs
is tenuous: “costs” are really wholesale prices and will therefore be affected by
broader demand shocks, and they may be correlated with retail prices charged
by our firm’s rivals. Two features of the memory market make correlation with
demand shocks less of a worry than they would be in other retail industries:
(i) sales of aftermarket memory are small compared to the use of memory
in new computers, so aftermarket memory prices will not be much affected
by aftermarket demand shocks; (ii) some of the variation in wholesale prices
in the period we study is due to collusion among memory manufacturers.28
The correlation with rival’s prices is clearly a potential problem.
27
See Ellison and Ellison (2004) for additional evidence on this point, including similar estimates
from CPUs.
28
Demand shocks in the new computer and memory upgrades markets may be correlated, of
course, if both are driven by the memory requirements of popular applications. Samsung, Elpida,
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 447
TABLE V
INSTRUMENTAL VARIABLES ESTIMATES OF PC100 128MB MEMORY DEMAND MODELa
Dependent Variables: Quantity of 128MB PC100
Memory Modules of Each Quality Level
Cost Instruments Other Speed Instruments
Independent Variables Low Mid Hi Low Mid Hi
log(1 + PLowRank) −1 99∗
−1 06∗
−1 19∗
−1 75∗
−0 25 0 02
(3 8) (2 2) (3 1) (3 0) (0 4) (0 0)
log(PLow) 0 94 3 54 12 75∗
2 68 −1 58 2 39
(0 1) (0 6) (2 3) (0 6) (0 4) (0 5)
log(PMid) 12 61 −6 88 0 76 −1 72 −7 14∗
−0 50
(1 6) (0 9) (0 1) (1 1) (4 1) (0 1)
log(PHi) 6 41 6 38 −4 69 5 21∗
2 03 −3 45
(1 3) (1 4) (1 4) (2 5) (0 8) (0 9)
SiteB −0 40∗
−0 36∗
−0 59∗
−0 25∗
−0 51∗
−0 66∗
(2 6) (3 0) (4 4) (2 1) (2 8) (2 6)
Weekend −0 55∗
−0 95∗
−0 76∗
−0 48∗
−0 95∗
−0 64∗
(5 1) (8 0) (4 7) (7 3) (8 2) (4 6)
log(LowestPrice) −5 91 −2 47 −8 26 −3 89 2 17 2 37
(1 0) (0 5) (1 6) (1 1) (0 7) (0 7)
Number of obs. 683 683 683 608 608 608
aAbsolute value of t-statistics in parentheses. Asterisks (∗) denote significance at the 5% level.
The first three columns of Table V report estimates of the demand equations
for 128MB PC100 memory modules (comparable to those in Table II)
obtained using the cost-based instruments for PLow, PMid, and PHi.29
Our
primary results about own-price elasticities and loss-leader benefits are robust
to this change: The effect of PLowRank on sales remains large, negative, and
significant in all three categories. The biggest difference between the IV estimates
and our earlier estimates is that the cross-price terms are all positive
and many are much larger. The standard errors, however, are also generally
larger in these regressions so few of the own-price and cross-price estimates
are significant.
We refer to our second set of instruments as the “other-speed” set. We instrument
for PLow, PMid, PHi, and log(1+PLowRank) in the 128MB PC100
category using a website’s prices for low-, medium-, and high-quality 128MB
PC133 modules and its rank in this category.30
These may be useful in identifyInfineon,
and Hynix pled guilty in separate cases to collusion charges covering the period from
April 1999 to June 2002. Executives from these companies and a Micron sales representative
were also prosecuted individually and received jail sentences.
29
First-stage regressions are presented in Ellison and Ellison (2009). The cost of mediumquality
memory has less predictive power than one might hope.
30
Ellison and Ellison (2009) present first-stage regressions showing that the instruments are
not weak, although predictive power is better for the prices than for the rank.
448 G. ELLISON AND S. F. ELLISON
ing exogenous shifts in medium- and high-quality prices if these tend to occur
in both categories simultaneously either because prices are reviewed sporadically
or if prices are adjusted in response to unexpected labor shortages. Another
attractive aspect of this strategy is that the availability of the other-speed
rank gives us a fourth instrument, whereas in our cost-based instrument set
we had to maintain the assumption that log(1 + PLowRank) was exogenous.
There are still potential concerns. For example, prices in the other category
may not be completely orthogonal to demand conditions if demand in both categories
is driven by a common shock, like the memory requirements of popular
software applications.
The second three columns of Table V present estimates from the other-speed
instruments. Instrumenting for log(1 + PLowRank) makes the standard errors
on the estimates much larger. Two of the estimates become more negative
and one becomes less negative. The cross-price effects between low- and highquality
memory are much larger than in our noninstrumented results. Standard
errors on all the price effects are also much larger. Overall, we see the
IV results as indicating that cross-price terms probably are larger than in our
noninstrumented results. There is nothing to cause concern about any of our
main results, although the limited quality of the instruments does not let us
provide strong additional support either.
7. MARKUPS
This section examines price–cost margins. It is intended both to provide descriptive
evidence on price search-dominated e-commerce and to give insight
on how obfuscation affects markups.
Table VI presents revenue-weighted average percentage markups for each
of the four categories of memory modules.31
In the two 128MB memory categories,
the markups for low-quality products are slightly negative. Prices have
not, however, been pushed far below cost by the desire to attract customers
who can be sold upgrades. Markups are about 16% for medium-quality modules
and about 27% for high-quality modules. Averaging across all three quality
levels, markups are about 8% and 12% in the two categories. This corresponds
to about $5 for a PC100 module and $10 for a PC133 module.
The firm’s average markups in the 256MB memory categories were higher:
13% and 16% in the two categories. Part of the difference is due to the fact
that a higher fraction of consumers buy premium quality products, but the
31
The percentage markup is the percentage of the sale price, that is, 100(p − mc)/p. Dollar
markups were obtained by adding the standard shipping and handling charge to the advertised
item price, and then subtracting the wholesale acquisition cost, credit card fees, an approximate
shipping cost, an estimate of marginal labor costs for order processing, packing, and returns, and
an allowance for losses due to fraud. The labor and shipping costs were chosen after discussions
with the firm, but are obviously subject to some error.
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 449
TABLE VI
MEAN PERCENTAGE MARKUP IN SIX PRODUCT CLASSESa
Product Category
128MB Memory 256MB Memory
PC100 PC133 PC100 PC133
Actual low markup −0 7% −2 5% 4 3% 2 9%
Actual mid markup 17 3% 15 6% 16 2% 19 9%
Actual hi markup 27 3% 26 9% 24 3% 24 9%
Overall markup 7 7% 11 5% 12 7% 15 8%
Overall elasticity ε −23 9 −27 7 −16 0 −21 2
1/ε 4 2% 3 6% 6 3% 4 7%
Adverse selection multiplier 2 0 3 5 1 7 2 4
Predicted markup 8 3% 12 8% 10 9% 11 4%
aThe table presents revenue-weighted mean percentage markups for products sold by websites A and B in each of
four product categories along with predicted markups as described in Sections 2.2 and 7.
largest part comes from the markups on low-quality memory being substantially
higher.
It is interesting to examine how the actual markups compare to what one
would expect given the overall demand elasiticity and the strength of the adverse
selection effect. The sixth row of the the table reports the inverse demand
elasiticity 1/ε defined in Section 2.2. Absent any adverse selection effects,
these would be the expected markups. They range from 3.6% to 6.3%
across the categories. Although these are small numbers and we have emphasized
that demand is highly elastic, one channel by which obfuscation may be
affecting markups is by preventing elasticities from being even higher than they
are. We do not know how elastic demand would be absent the obfuscation, but
it is perhaps informative to note that our estimates imply that fewer than onethird
of consumers are buying from the lowest priced firm. If Pricewatch ads
were more standardized and consumers did not need to worry about restocking
policies, etcetera, then one might imagine that many more consumers would
buy from the lowest priced firm and demand could be substantially more elas-
tic.
The second mechanism by which we noted that obfuscation could affect
markups is through the adverse selection effect that arises when firms sell addons.
The seventh row reports the markup multiplier we would expect given
the degree of adverse selection we have estimated to be present. Specifically,
we report an estimate of the rightmost term in parentheses in equation 1,
obtained by assuming ∂πm
1U /∂p1L = 0 and computing the multiplier term as
1 + ∂x∗
/∂p1L(p1U − c1U ).32
The multipliers range from 1.7 to 3.5 across the
32
The effect of the low-quality price on the fraction upgrading comes from the demand system
and the markup on the upgrade is set to its sample mean.
450 G. ELLISON AND S. F. ELLISON
four categories. This indicates that the adverse selection we have identified is
sufficiently strong so that one would expect it to have a substantial effect on
equilibrium markups.
The actual and predicted markups are roughly consistent. In three of
the four categories the actual markups are within two percentage points
of the predicted markups. This implies, for example, that prices are within
$2 of what we would predict on a $100 product. The actual and predicted
markups are both lowest in the 128MB PC100 category. The difference between
the actual and predicted markups is largest in the 256MB PC133 category,
where actual markups are four percentage points higher than the prediction.
Looking further into the data we note that the positive average markups
for low-quality 256MB modules are entirely attributable to two subperiods:
low-quality 256MB modules were sold at about $10 above cost in September–
October 2000 and at about $5 above cost in February–March 2001. We think
we understand what happened in the former period. A small number of retailers
found an obscure supplier willing to sell them 256MB modules at a price
far below the price offered by the standard wholesale distributors.33
As a result,
there were effectively six or fewer retailers competing in these two months
rather than dozens.
8. CONCLUSION
In this paper we have noted that the extent to which the Internet will reduce
consumer search costs is not clear. Although the Internet clearly facilitates
search, it also allows firms to adopt a number of strategies that make search
more difficult. In the Pricewatch universe, we see that demand is sometimes
remarkably elastic, but that this is not always what happens.
The most popular obfuscation strategy for the products we study is to intentionally
create an inferior quality good that can be offered at a very low price.
Retailers could, of course, avoid the negative impact of search engines simply
by refusing to let the search engines have access to their prices. This easy
solution, however, has a free rider problem: if other firms are listing, a firm
will suffer from not being listed. What may help make the obfuscation strategy
we observe popular is that it is hard not to copy it: if a retailer tries to advertise
a decent quality product with reasonable contractual terms at a fair price,
it will be buried behind dozens of lower price offers on the search engine’s
list. The endogenous-quality aspect of the practice makes it somewhat different
from previous bait-and-switch and loss-leader models, and it seems that it
would be a worthwhile topic for research.34
We would also be interested to see
33
The first retailer to have found the supplier appears to have found it on July 10. On that day,
when the firm that supplied us with data bought modules wholesale for $270, PC Cost cuts its
retail price to $218—a full $51 below the next lowest price.
34
Simester’s (1995) model seems to be the most similar to the practice. We would imagine,
however, that what makes the low prices on Pricewatch have advertising value is that the offerings
SEARCH, OBFUSCATION, AND PRICE ELASTICITIES 451
more work integrating search engines into models with search frictions, exploring
other obfuscation techniques (such as individualized prices), and trying to
understand why adoption of price search engines has been slow.
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Dept. of Economics, Massachusetts Institute of Technology, E52-380, 50 Memorial
Drive, Cambridge, MA 02142-1347, U.S.A. and NBER; gellison@mit.edu
and
Dept. of Economics, Massachusetts Institute of Technology, E52-274B,
50 Memorial Drive, Cambridge, MA 02142-1347, U.S.A.; sellison@mit.edu.
Manuscript received February, 2005; final revision received October, 2007.