Entry and Exit Behavior at a Shopbot: E-sellers as Kirznerian
Entrepreneurs
Michelle Haynes*
&
Steve Thompson†
August 2008
Paper prepared for submission to the Journal of Economics and Management Strategy special
issue on the Economics of the Entrepreneur.
*
Nottingham University Business School, Nottingham, NG8 1BB, UK. Phone 44 (0) 115
9515483, Fax 44 (0) 115 8466667, E-mail Michelle.Haynes@nottingham.ac.uk
†
Address for correspondence: Nottingham University Business School, Nottingham,
NG8 1BB, UK. Phone 44 (0) 115 8466629, Fax 44 (0) 115 8466667, E-mail
Steve.Thompson@nottingham.ac.uk
Acknowledgements: The authors would like to thank Victor Porcar for programming
assistance and Peter Wright for helpful comments on this research.
1
Abstract
The shopbot, with its absence of sunk costs and resource requirements for entry, has created an arbitrage
arena for Kirznerian entrepreneurs. This paper presents a preliminary investigation of the behavior of
these firms using a unique data set obtained from daily visits to NexTag.com over a 134 day interval. It
is shown that entry/exit responds systematically to variations in expected profit, as determined by the
lagged mark-up and lagged sales, in such a way as to act as an equilibrating force in shopbot markets.
We also find evidence of considerable seller heterogeneity with smaller participants apparently favoring a
hit-and-run strategy involving lower entry prices and shorter forays into the market than their large,
established rivals. The results also suggest that the presence of very large sellers, such as Amazon, reduces
the equilibrium number of participants consistent with a moderate entry-deterrence effect. The removal of
Amazon also reveals a much greater sensitivity of net entry to expected profits among the (non-Amazon)
potential sellers, consistent with the bifurcation of sellers into established retailers and Kirznerian
arbitrageurs.
Key words: Kirznerian entrepreneurs, entry and exit, market adjustment
JEL codes: L81, M13, L11
2
1.
Introduction
The price/product comparison site (hereafter “shopbot”) is among the more important
innovations of the digital revolution. In economic terms, shopbots have evolved from
being specialized search engines to become two-sided markets visited by potential buyers
seeking accessible product and price information and by potential sellers attracted by the
opportunity of transacting with the former. Shopbots such as NexTag.com provide the
platform where this interaction occurs, whilst seeking to ensure the reliability of
information flows and seller conduct and thus maintain the integrity of the whole. Unlike
earlier, more passive two-sided markets, such as newspapers or TV stations, the shopbot
typically provides free access to potential sellers and collects fees based on actions of
potential buyers in clicking through to the sellers’ own sites.
Therefore it has an
incentive to safeguard the interests of buyers as well as sellers: for example, by the
provision of seller reputation data to consumers, including consumer-generated feedback
on seller performance, and market data to sellers. The availability of information and the
absence of restrictions on entry and exit has led to the shopbot-mediated market (SMM)
being characterized as “nearly perfect” (Brynjolfsson et al., 2008).
Modern entrepreneurship literature contrasts Schumpeter’s (1934) entrepreneur, the
innovator whose novel contribution disrupts existing markets and technologies, and the
Kirznerian entrepreneur who is alert to arbitrage opportunities and thus acts to return
markets towards equilibrium (Douhan et al. 2007). While the two functions produce
opposing effects and, indeed, probably require very different qualities (Kirzner, 1999,
p13), they are intrinsically linked insofar that major innovations create market
opportunities. The shopbot, like other business model/portal forms that have emerged
in e-commerce, including the search engine and the auction site, is both a disruptive
1
Schumpeterian innovation, in its implications for traditional selling practices1, and the
creator of new scope for entrepreneurial activity. As an innovation it achieves a drastic
lowering of transaction costs by facilitating consumer search such that information on all
aspects of an intended transaction, including price, delivery terms, product quality and
seller reliability can be retrieved with little effort. However, by bringing together
informed consumers and would-be sellers it offers profitable opportunities to any of the
latter that can enter the market at an appropriate price.
Unlike previous work on SMMs, which has been largely concerned with consumer
behavior (e.g. Brynjolfsson et al. 2008, and references therein) and pricing (e.g. Baye et al.
2004; Haynes and Thompson, 2008), this paper explores entry and exit by potential
sellers as entrepreneurial responses to changing market opportunities. In comparison to
conventional markets, SMM selling involves operating within a set of rules that are tightly
defined by the shopbot as “gatekeeper” (Baye and Morgan, 2001). Sellers offer a
homogeneous good, typically specified by the manufacturer’s unique product code (upc),
and use standardized displays which appear on the shopbot’s screens and in which the
seller is required to provide specified information. While sellers clearly differ in other
respects, including their own web site displays, logistics and dealings with suppliers, their
participation in the SMM is essentially constrained to a small number of decisions with
regard to entry/exit, price and display position. This, together with the fluidity of
shopbot markets, in which change occurs with a daily frequency, makes these markets a
particularly useful laboratory to study the behavior of Kirznernian entrepreneurs.
1
Shopbots tend to be stronger in the markets for services and goods with high value-weight ratios (e.g.
consumer electronics, computers) than in cheap and/or bulky household goods. Goldmanis et al. (2008)
have recently demonstrated the impact e-commerce has had on several such sectors.
2
This paper uses a unique panel dataset based on the markets for 295 models of digital
camera obtained from daily visits to NexTag.com over a period of 134 days. This allows
us to explore the micro-behavior of sellers in ways not previously undertaken. Our data
confirm that shopbot markets experience very high rates of entry and exit, orders of
magnitude higher than those reported for traditional markets. They also reveal the
existence of a large reservoir of potential participants, members of which make
(frequently short-lived) forays into the product markets. There appear to be systematic
differences in behavior between larger and smaller sellers, with the latter tending to use a
“hit-and-run” entry strategy based on low pricing and rapid exit. By contrast, the larger
sellers tend to enter with a price close to the prevailing level and to remain in the market
for a longer period. We then estimate entry and exit to the product markets using a panel
error correction model. It is shown that sellers behave as would be expected of
Kirznerian entrepreneurs, entering and leaving the market in response to changes in
expected profitability. Research on e-commerce has suggested that large, established
sellers, such as Amazon.com, both enjoy disproportionate share of sales and a price
premium over unbranded retailers. We find evidence that the presence of a major
national retailer reduces the equilibrium number of sellers consistent with an entry
deterrent effect. Moreover, excluding Amazon.com from the seller count sharply increases
the sensitivity of entry/exit behavior to expected profits.
The paper is structured as follows: Section 2 discusses the operation of shopbot markets
and outlines the determination of our model. Data collection and the data’s
characteristics are described in Section 3, followed by the results in Section 4 and a brief
conclusion.
3
2.
Entry and Exit at a Shopbot: Discussion and Model
2.1
Seller Participation at a Shopbot
Entry to an SMM differs in two important respects from entry to conventional product
markets as studied in the literature: first, is the near-total absence of sunk costs, with
their implied barriers to entry and exit; and second is the minimal specific resource
requirement for SMM participation. These characteristics, together with the transparency
of pricing by sellers at a shopbot allow us to model seller behavior at an SMM as a form
of arbitrage behavior in which entrants emerge from (return to) a reservoir of potential
sellers in response to the appearance (disappearance) of profitable opportunities created
by market circumstances. These attributes are considered in turn:
SMMs, such as that operated by NexTag.com, offer a close approximation to a completely
free entry (i.e. zero sunk costs) market. Participants pay no up-front fee for a listing and
gain access to software that enables them to advertise their wares and to monitor interest
shown by potential buyers. Each entrant does need its own web site capable of processing
sales transactions but since this may be able to handle a large number of products, it
seems reasonable to take it as a given for individual product market entry2. Of course,
each entry/exit decision does require some effort – although even this could be
automated and triggered by price and positional data – but then any economic act
presumably needs some prior optimizing behavior on the part of the agent. SMM
2
Latcovich and Smith (2001) comparing on-line only retailers and their bricks and mortar rivals in
bookselling suggest that the former experience higher sunk costs and the latter lower variable costs.
However, small on-line sellers operating via shopbots would appear to be able to reduce set-up costs
very substantially; not least by free-riding on the existing product information provision.
4
participation is not entirely free of explicit sunk costs in that entrants incur click-through
fees irrespective of whether the clicks generate subsequent sales. Since the conversion
rate, variously estimated between 0.03 and 0.53, is well below unity it seems reasonable to
expect that most sellers will incur at least some fees at the shopbot prior to any sale;
although their total cost per product offered will be small.
Sellers may incur the basic click-through fee, currently 40c per click, or opt to offer a
higher rate in anticipation of securing a superior position in the shopbot’s default listing.
Ranking here is not based upon the bid alone but may reflect other seller features. Search
engines, such as Google, acknowledge giving weight to factors such as seller reputation
and while shopbots do not reveal their ranking algorithms, they probably do likewise.
Position, as well as price, is a major determinant of the probability of securing a click
through to the selling page. Baye et al. (2007a), using UK data from Kelkoo.com find a
ceteris paribus decline in clicks of 17% per ranking position. However, they report two
important caveats: first, there is a large discontinuity between positions one and two in
the rankings, with an associated 40% fall in clicks; and second, the positional effect is
sensitive to the number of sellers and to any movement between successive screen pages.
Baye et al. (2007a) report a similar discontinuity with respect to price associated with a
move between lowest and next-lowest prices.
Price and display position are then each important in determining clicks, and therefore
sales, and in neither case is the potential entrant at any necessary disadvantage with
3
Estimates of the conversion rate for clicks into sales vary, perhaps reflecting shopbots’ reluctance to
release this data. Baye et al. (2004) report a 50% conversion rate on Dealtime.com, with little variation
across retailers, but two more recent estimates have put the conversion rate at 0.05 and 0.03,
respectively (Baye et al., 2007a,b).
5
respect to incumbents. However, ab initio entrants do face the disadvantage of being
unknown to consumers. Since the rise of e-commerce it has been generally apparent that
seller reputation is important in electronic markets; not least is this manifested in the
ability of market leaders, such as Amazon.com, to attract a price premium for an otherwise
homogeneous product (Clay et al., 2001). Waldfogel and Chen (2006) reason this
advantage should decay as consumers gain familiarity with electronic transacting, but
nevertheless they report it continues to operate.
Of course, in any individual product
SMM most entrants are not ab initio newcomers, but are already operating in other
product markets and may well have offered this model in the recent past. These entrants
will bring their reputation, including any shopbot summary statistics of consumer
feedback, with them and hence are not necessarily at any disadvantage with respect to
current sellers of the product.
One consequence of the absence of either non-trivial explicit sunk costs or specific
resource requirements of entry is that the SMM is conjectured here to be a vehicle for
pure entrepreneurial activity in the meaning of Kirzner (1973). That is alertness to
profitable opportunities giving rise to arbitrage activity. Here it is assumed that potential
sellers will tend to enter where they perceive an opportunity for profit at the prevailing
levels of price and marginal cost and will tend to exit in the event of lowered prices and
hence expected profits. In this way agents migrating between the reservoir of potential
sellers and the market act as an equilibrating force.
The treatment of market entry as a disequilibrium phenomenon (discussed in detail in
Caree and Thurik, 1999) is implicit in an extensive empirical literature which extends via
Geroski (1991, 1995) back to Orr (1974). However, what distinguishes the SMM from
markets previously studied is its absence of sunk costs and its minimal resource
6
requirements for entry. In manufacturing, for example, international studies suggest the
irrecoverable costs associated with investment in plant and equipment, growing sales,
reputation building, etc act as measurable barriers to entry (e.g. Geroski and Schwalbach
1991, Fotoupolos and Spence, 1999). Moreover, the importance of sunk costs shapes the
entry process. It helps to explain why the entry/exit response to changing profit
opportunities is relatively sluggish (Caree and Thurik, 1999) and why new entrants are
typically much smaller than average incumbents (Geroski, 1995).
Economics normally distinguishes the entrepreneurial function of being alert to
profitable opportunities from the function of resource economizing, or ensuring that
relevant factors are employed in their most valuable use, even where both functions are
combined in the same individual. Decisions to enter new markets ordinarily involve both
functions, and impediments to resource mobility are widely interpreted as a barrier to
entry and, its corollary, a source of sustainable rents for incumbents. Indeed the resourcebased view of the firm, with roots going back to Penrose (1959), explicitly links the
possession of a sustainable competitive advantage, or the firm’s ability to earn a greater
return than its rivals, with inter-firm resource heterogeneity that arises inter alia from
path-dependent firm evolution and is correspondingly difficult to overcome (Barney,
2001). However, sellers at a SMM are almost pure entrepreneurs in that minimal
resources are required for participation. Clearly, some e-tailers using SMMs have
substantial physical, human and organizational resources which may deliver benefits on
the demand or supply sides. However, such resources do not appear to be a necessary
condition for SMM participation. Indeed, the SMM is most unusual in that it provides a
7
forum in which, at least in principle, Joe Doe operating from his spare room can
compete against worldwide sellers such as Amazon.com4.
The SMM also displays a very high degree of transparency in pricing. Shopbots such as
NexTag.com reveal full pre- and post-tax pricing data for every seller in the market. While
e-sellers in general have been criticized for obfuscation over prices (Ellison and Ellison,
2004), especially the use of “bait and switch” tactics, the SMM both polices such
behavior and provides an incentive for truthful price quotation5. This transparency
stands in contrast to many traditional markets where sellers find it costly to observe
rivals’ transaction prices, increasing the uncertainty attaching to their own pricing
decisions.
Given the characteristics of (almost) zero sunk costs, minimal resource requirements for
market participation and transparency of pricing it is unsurprising that SMM markets
exhibit a high degree of churn, with rates of entry and exit believed to be far higher than
those observed in traditional markets. We are unaware of any published information on
this, but the data used here and described in Section 3 below confirms this contention.
On average across our sample, over 35% of market participants are replaced in the
course of a week. This compares to, say, between 5 and 10% per year in traditional retail
markets (Caree and Thurik, 1999). This churn is consistent with the existence of a
4
NexTag appears to be particularly supportive to smaller sellers and offers a program for sellers with
less than 100 products: see Yin and Scholten (2005).
5
“Bait and switch” involves quoting low prices to attract visitors who are then informed of the
unavailability of the items specified and referred to higher priced alternatives. Since the seller pays per
click, irrespective of whether any sale results, she bears the cost of any reduction in the conversion rate
consequent upon non-availability of advertised goods.
8
substantial reservoir of potential sellers some of which enter each product market as
opportunities arise.
It is conjectured that the high rate of churn may also reflect the sellers’ operation across
multiple markets. Most e-sellers participate in more than one SMM and many also
operate traditional bricks and mortar sales outlets. However, while each shopbot market
develops its own price structure, Yin and Scholten (2005), for example, report a five
percent mean difference in price between Cnet and NexTag for identical items, most
sellers do not maintain separate prices in each market. Therefore entry and exit represent
an alternative to re-pricing in the event of changed market circumstances.
2.2
Model
We follow the entry literature in assuming that sellers respond to opportunities such that
net entry is a function of expected profitability, itself determined by the price markup
and expected sales6. Such an approach is consistent with entrepreneurial behavior acting
as an equilibrating mechanism driving market membership towards its equilibrium level.
Caree and Thurik (1999) borrow the term “carrying capacity” from evolutionary
economics to describe the optimal number of firms that is supportable given existing
local demand and supply conditions. Using pooled data from Netherlands retailing, they
report adjustment to disequilibrium to be relatively slow, with an adjustment rate of
6
The naïve assumption that current profitability provides an unbiased estimate of post-entry
profitability is widely deployed in the entry literature, despite a recognition that adjustment to
newcomers is likely to disturb the existing market circumstances (Geroski, 1995). However, in the
present context it seems rather less naïve in that the absence of sunk costs allows the entrants the option
of rapid exit as conditions change.
9
about 40% per year such that the process is effectively complete in, say, five years.
Furthermore, they report an asymmetric adjustment process: in markets dominated by
smaller stores exit is particularly sluggish, with equilibrating occurring largely via a decline
in the gross entry rate.
A number of features of virtual markets, including those operated by shopbots, make
their “carrying capacity” more difficult to define than is the case for bricks and mortar
retailing. First and most obviously, the spatial dimensions that define the boundaries of
traditional markets are unimportant. Second, unlike conventional retail markets where
sellers must secure sales sufficient to cover substantial operating costs, SMM
membership carries little or no operating cost per se7. Third, those pricing theories which
endogenize the number of sellers – and some do not – typically assume a symmetric
(zero profit) equilibrium. However, as noted above, the emerging evidence from SMMs
strongly suggests that market outcomes could be extremely asymmetric with sales
(unevenly) concentrated among a small proportion of the sellers in each market.
Iyer and Pazgal (2003) do present an explicit model of seller participation at a shopbot,
but it addresses rather different issues to us. The authors’ principal concern is why some
sellers chose to use SMMs and others remain outside. They develop an insider-outsider
model, following the lead of Varian (1980) and Rosenthal (1980), in which sellers either
join the institutional market, where a mixed strategy equilibrium with randomized pricing
obtains, or chose to target loyal (and hence price insensitive) customers without the
SMM. However, in the present context their model has two serious disadvantages: First,
7
A potential seller whose product is sufficiently highly priced as to attract no clicks through from
potential buyers may suffer incremental reputation damage if the latter perceive it to be a high-priced
seller. Most sellers will be offering other products via the SMM and/or operate other selling outlets.
10
whilst it does derive an optimal number of inside participants, this comes at the cost of
endogenizing the “reach” of the market in terms of the participants’ characteristics.
Among other things this treats the shopbot as a single market rather than a gatekeeper to
n product markets, each with its distinctive circumstances, as in our research. Second, as
with other insider-outsider models its force depends on average prices within the SMM
increasing with the number of sellers. This contradicts the empirical evidence which
overwhelmingly suggests prices fall with n (Baye et al 2004; Haynes and Thompson, 2008,
and references therein).
Therefore we adopt an approach which requires no explicit priors on the carrying
capacity or equilibrium number of firms, but one that is consistent with three observed
empirical regularities in shopbot markets. These are: entry and exit rates are substantially
higher than those observed in traditional retail markets; the numbers of market
participants are typically greater than in these markets8 and prices generally fall with the
number of participants. Instead it is simply assumed that there is a reservoir of potential
sellers of product i some of which enter (leave) that market in response to increases
(decreases) in profitable opportunities. Since a count across our sample of 295 digital
cameras models sold on NexTag.com revealed 161 separate sellers over the period of
investigation, this assumption appears benign. Each potential seller is assumed to be
aware of their own marginal costs of selling i via a SMM. Cost heterogeneity across the
pool is admissible, perhaps arising from economies of scale or scope, purchasing
economies, differences in technical efficiency or differential access to wholesale supplies
as a result of differences in supply arrangements, foresight or chance. Potential seller j
8
Carree and Thurik (1999, p.995) suggest that for a range of speciality stores in geographically distinct
markets, the optimal number of participants is typically small, normally between two and five.
11
anticipating price Pi and marginal cost Cij is attracted to using the SMM if Pi – Cij >0. This
leads us to a basic error correction model with the following form:
∆N it = β 1 ∆PCM it −1 + φ1 ∆Lit − (1 − α )[ N it −1 − γ 1 − γ 2 PCM it −1 − γ 3 Lit −1 ] + ε it
…(1)
Where N is the number of sellers in the market, PCM is average price cost margin in the
market, L is the number of leads, used here as a proxy for sales9 and ε it is an error term.
All variables are in logs. Thus the current change in the number of sellers is proportional
to the change in average PCM and leads, and a correction to take account of the extent
to which Nit-1 deviated from its equilibrium value (as given by the term in square
brackets). Error correcting behavior requires that the coefficient on the error-correction
term is negative. Thus, ceteris paribus, if the number of sellers is above its long run
predicted value, ∆N it will fall in order to move it back towards its long rum equilibrium
value, and vice versa. Providing that the variables co-integrate all terms in the ECM are
stationary, so that standard critical values of t and F distributions apply. We generate our
baseline estimating equation by multiplying out the square bracketed term to get:
∆N it = β 0 + β 1 ∆PCM it −1 + φ1 ∆Lit − (1 − α ) N it −1 + (1 − α )γ 2 PCM it −1 + (1 − α )γ 3 Lit −1 + ε it
…(2)
where β 0 = (1 − α )γ 1 . Since, the variables are in logs, the short run elasticities are given
by the coefficients β1 and φ1 . The long run parameters are estimated by dividing the
coefficients on the lagged levels variables by the adjustment coefficient.
9
Leads, or clicks through to the seller’s website, is widely used as a quantity proxy in shopbot
research: see Baye et al.(2007a).
12
Having estimated the basic model the paper undertakes some further experiments with
the data to explore market adjustment and entry deterrence: First, since inactive SMM
membership appears to carry no explicit costs we allow for the possibility of asymmetric
behavior between market expansion and market contraction.
We use a fall in the
(lagged) number of sellers to make the distinction between a growing, stagnant and
declining market10. We then estimate separate adjustment parameters to compare the
respective rates of expansion and contraction in response to changes in expected
profitability.
Second, the parity in exposure across an SMM, where every seller is allocated a
standardized display, conceals enormous heterogeneity in size and reputation among
participants. It has been noted that some early movers appear to retain market leadership,
even in e-commerce where low consumer search costs might be expected to erode it. We
examine a previously unexplored aspect of this dominance by looking at the entry
deterrence effect of market membership by Amazon. We augment the baseline model
alternatively with variables denoting Amazon entry/exit and presence and re-estimate
after adjusting the seller number to remove Amazon brand sellers.
10
For a robustness check, we also used the change in the number of leads to classify a market. The
general pattern of results did not change. These results are available from the authors.
13
3.
Data: Collection, Characteristics and Sample
3.1
Data: Collection and Characteristics
NexTag.com, typical of the multi-product price comparison sites that have emerged since
the late 1990s, lists a wide range of goods and services but is particularly strong in high
value-to-weight products such as consumer electronics. The digital camera, the product
selected for our research, is both representative of such goods and carries the advantage
of usually being purchased singly and therefore not being the subject of bulk discounts,
as might apply to, say, CDs. NexTag provides buyers and sellers with continuously
updated data on the pre- and post-tax prices of listing sellers, delivered prices, feedback
on seller reputation and limited information on model characteristics for each camera
listed. While an alternative listing may be specified by the user, the default ranking of
sellers for those searching by product model is determined by the shopbot and displayed
on the screen as illustrated in Figure A1 in Appendix 1. Additional information available
includes a diagram of the product’s price history and a histogram showing the number of
leads – or clicks through to seller – on a monthly basis for the previous 17 months.
We used a java script program to interrogate NexTag.com daily to extract data from the
screen11 display. The program was run between November 19th 2007 and March 31st
2008. Daily visits (at 2.00 am EST) were used to capture the apparent high frequency of
entry and exit. The target sample was updated weekly to allow for new model entry.
Models were identified by their unique product code (upc)12. We excluded cameras that
11
Although collection was automated, screen shot originating data did require some cleaning before
use and time costs prohibited more frequent visits.
12
The upc originally appeared on Nextag’s screen display but is currently not available.
14
were introduced prior to 2005, assumed to be discontinued, and kits where the camera
came bundled with complementary products, since these sometimes changed
composition. In addition, we excluded all models posting prices below $50 to reduce the
likelihood of including refurbished models or misreported prices.
A second java
program was run to capture the leads data corresponding to the changing target sample.
In addition, a search of digital camera preview sites was undertaken to determine the
manufacturer’s recommended selling price (MRSP) by upc. Non-availability of leads
and/or MRSP data and the exclusion of models where demand, proxied by leads, failed
to reach 10013 per month reduced the final sample to 295 models.
Scrutiny of the raw data immediately confirms two of our prior conjectures on SMMs:
first, these markets are used, at least intermittently, by large numbers of sellers; and
second, SMMs exhibit very high rates of entry and exit. These findings are considered in
turn:
In total we identified 161 different sellers who participated in the 295 sample NexTag.com
camera model markets over the 134 day interval of scrutiny. The average individual
market membership on any one day was 12 sellers, with an average of 71 separate sellers
participating daily across all markets in the sample. This is consistent with the existence
of a substantial reservoir of potential sellers, some of which enter each product market as
opportunities arise. Sellers ranged from very large on-line retailers such as Amazon.com,
who participated in 95% of the model markets at some time during the investigation, and
large traditional retailers (e.g. Radioshack.com) to 37 sellers with five products or less. This
13
We used a cut-off of 100 leads since we were interested in studying behaviour in active markets.
15
inequality of participation by product count is illustrated in the Lorenz curve (Gini
coefficient = 0.65) in Figure 1.
0
.2
% of Camera Products
.4
.6
.8
1
Figure 1. Lorenz Curve Showing Inequality of Sellers
0
.2
.4
.6
.8
1
% of Sellers
Across the 295 products the mean numbers of entrants (including re-entrants) and exits
(including temporary exits) was each approximately 189 per day. This is equivalent to
0.64 per market per day’s inclusion in sample. Given an average of 12 sellers per market,
this represents approximately 37% leaving and being replaced each week. This is a far
higher rate of churn than observed in conventional retail markets where perhaps 5 to
10% per year would be normal. The daily profile of entry, exit and net entry is shown in
Figures 2, 3 and 4 respectively.
16
0
Number of Entries
200
400
600
Figure 2. Number of Daily Entries
01dec2007
01jan2008
01feb2008
Date
01mar2008
01apr2008
0
100
Number of Exits
200
300
400
500
Figure 3. Number of Daily Exits
01dec2007
01jan2008
01feb2008
Date
17
01mar2008
01apr2008
-400
-200
Net Entry
0
200
400
Figure 4. Daily Net Entry
01dec2007
01jan2008
01feb2008
Date
01mar2008
01apr2008
When retailer size was approximated either by sales or the extent of shopbot
involvement in digital camera markets, there was a clear indication that entry/exit
strategies differed between larger and smaller retailers. Denoting as “large” those retailers
which figured in the Dealerscope leading 100 US electronics goods sellers for 2007 and as
“small” those that did not, it appeared that smaller sellers were involved for fewer days
but were more likely to make temporary forays into an SMM. Large sellers tended to
remain in each market for a longer period. For example, across the sample, ignoring
movements in and out, large sellers were present for a mean of 56 days (median 49) and
small sellers for a mean of 41 days (median 25). Since large sellers on average participate
in many more markets than smaller sellers, they still account for 35.5% of all entries and
37.3% of all exits. Both groups tended to engage in temporary entry; although while large
sellers stayed on average for 12 continuous days (median six), small sellers averaged nine
18
continuous days stay (median three). Thus a high proportion of small firm entry, in
particular, appears to represent a hit-and-run response to market conditions.
Differences are also apparent in pricing strategy. Subtracting the seller’s entry price from
the previous day’s mean price yields -$8.73 for large sellers (median $2.11) and $35.16
(median $21.47) for their smaller rivals. The mean difference is highly significant, as
shown in Table 1 and displayed graphically in Figure 5. This finding is also suggestive of
smaller sellers engaging in hit-and-run entry, in which they temporarily undercut the
prices of incumbent sellers before exiting. By contrast, the larger sellers do not appear to
offer price discounts over incumbents suggesting they hope to sell on reputation.
Table 1. Test of the Difference between Mean Entry Price of Small and Large Sellers
Mean
S.D.
No. of Obs
Small Sellers
-8.735848
88.53022
9,414
Large Sellers
35.15724
130.149
16,750
Test Statistic [p-value]
-32.3249 [0.000]
Thus scrutiny of the raw data suggests that large and small sellers may be using the SMM
in somewhat different ways. Large sellers tend to offer a much wider range of models
over a greater proportion of the interval studied. While they exhibit high rates of entry
and exit compared to traditional markets, their forays into each market tend to last
substantially longer than those of the small firms.
19
-100
-50
0
50
100
Figure 5: Mean Market Price-Entry Price for Large and Small Sellers
01dec2007
01jan2008
01feb2008
Date
large sellers
3.2
01mar2008
01apr2008
small sellers
Sample Characteristics
Table 2 provides summary statistics, across markets and time, for the 295 models in our
final sample. NexTag provides both net and post-sales tax prices and the price inclusive
of shipping costs. We used the net price in our analysis. The right skewness of the price
variables reflects the small number of high quality SLR cameras among the larger
numbers of compact and ultra-compact point-and-shoot models.
20
Table 2. Summary Statistics
Price
Minimum Price
MRSP
PCM
Minimum PCM
Sellers
Leads
Mean
Median
S.D.
Min
Max
426.72
372.64
510.03
172.59
117.62
11.96
356.40
249.88
215
300
88.17
54.01
11
156
802.79
720.34
863.25
383.21
319.60
7.32
637.90
60.81
60.81
79.99
-74.53
-171
1
0
9,999.99
7999.99
8,000
4997
4000.49
39
10500
No. of
Obs
28234
28234
28234
28234
28234
28234
28234
The marginal cost to the retailer – i.e. the wholesale price – is directly unobservable.
However, the initial MRSP was collected and the marginal cost was assumed to be
0.5xMRSP14. From this we constructed a measure of the price-cost margin (PCM) as net
price minus marginal cost. We used the average PCM on each day in each market in our
analysis. This was negative in a very small proportion of cases, an unsurprising finding
insofar as inventory concerns for a short life cycle product may lead to discounting.
Mark-ups appeared to be greater on the high quality-low volume models than on the
cheaper, high volume models. Thus using the industry’s own classification of camera
types, the average mark-ups for compact ($86.26) and subcompact ($90.45) models were
similar but considerably less than that of the high quality SLR models ($629.31).
Weekly updating of the target sample to accommodate new model entry and
discontinued model exit generated an unbalanced panel, whose dimensions are given in
Table 3 below. The number of sellers per model ranged from one to 39, with a mean just
below 12. It will be recalled that for inclusion in the analysis we required that a model
experienced a period of active trading, which we took to be a minimum of 100 leads
clicks in a single month. Inevitably not all models sustained this level of demand over the
14
After discussions with retail industry sources suggested a gross mark-up of approximately 100% to
be a reasonable estimate.
21
period of inquiry so that the range of leads ran between zero and 10,500, with a mean of
approximately 35615.
Table 3. Balance of Panel
Number of Days
Number of Cameras
Under 20
21-40
41-60
61-80
81-100
101-120
Over 120
19
18
21
17
12
29
179
Notes: 64 cameras are observed over the entire 134 day period
4.
Results
Prior to running the error correction model, we tested for stationarity. Since this
methodology is standard, a brief discussion of these results is given in Appendix 2. After
this confirmation of the satisfactory time series properties the data, the error correction
model was estimated and the results are reported in Table 4. In all equations, the error
term is free from autocorrelation and heteroskedasticity and an F-test on the joint
significance of the regressors is overwhelmingly significant.
15
Only 7 cameras saw their number of leads fall to zero over the period of inquiry.
22
Table 4. Error Correction Model
Intercept
∆PCM it −1
∆L
N it −1
(1)
0.02737
(0.00600)***
0.06464
(0.00661)***
0.00317
(0.00056)***
-0.02889
(0.00148)***
N it −1 *Increasing
N it −1 *Decreasing
PCM it −1
Lit −1
0.00237
(0.00113)**
0.00499
(0.00058)***
(2)
0.02747
(0.00594)***
0.04454
(0.00656)***
0.00297
(0.00055)***
-0.03547
(0.00169)***
-0.00451
(0.00106)***
0.02363
(0.00107)***
0.00307
(0.00111)***
0.00446
(0.00057)***
Amazon_Entry
Amazon_Exit
(3)
0.01730
(0.03119)
0.44634
(0.10180)***
0.02324
(0.01057)**
-0.06718
(0.00637)***
(4)
0.05261
(0.03274)*
0.45266
(0.10237)***
0.02441
(0.01048)**
-0.06837
(0.00642)***
0.01723
(0.00654)***
0.03104
(0.00618)***
-0.1329
(0.06097)**
0.1497
(0.05869)**
0.01424
(0.00649)**
0.03348
(0.00635)***
Amazon_Presence
-0.07985
(0.01577)***
F-test
[p-value]
101.26
[0.000]
195.80
[0.000]
17.84
[0.0000]
20.97
[0.0000]
No. of cameras
No. of Observations
295
28234
295
28234
295
28234
295
28234
Robust standard errors are given in parentheses below the estimated coefficients: *** p<0.01, ** p<0.05, *
p<0.1.
The results from equation (2) are given in column (1). All the principal parameter
estimates are significant with the expected signs16. The two variables assumed to
determine expected profitability, namely average (lagged) price-cost margins and (lagged)
sales, as proxied by leads, each have a positive and significant effect on the number of
sellers in a camera market. This confirms our conjecture that inward and outward
movement to and from the SMM responds systematically to profit opportunities.
16
Perhaps surprisingly, the inclusion of a dummy variable to indicate the Xmas period had no
significant effect on entry.
23
Examining the coefficients suggests a one point increase in price cost margins leads to an
immediate 0.06 increase in seller numbers. The long-run coefficient is 0.08292. A one
point increase in sales leads to an immediate 0.003 increase in seller numbers. The long
run coefficient is much higher (0.17013). Since the sales distribution for camera models
appears highly skewed, like that for many electronics products, this is important in
explaining the wide variation in seller numbers observed in our data and shown in Table
3 above. Recalculating the price mark-up using the minimum price rather than the mean
made no material difference to the results.
The error correction coefficient is negative and significant as expected. The magnitude
of this coefficient indicates that about 3% of any disequilibrium is corrected for each day.
Or, equivalently, 40% of any disequilibrium is eliminated in 17 days. This confirms our
conjecture that SMM markets adjust far more quickly than traditional retailing where, it
may be recalled, Caree and Thurik (1999) reported 40% adjustment per year.
It was noted in Section 2 above that inactive sellers in shopbot markets, unlike their
counterparts in traditional retailing, do not appear to face explicit costs of continuing
market membership; although they might face implicit costs arising from their joint
participation in the SMM and other markets and reputation damage from posting
uncompetitive prices. In consequence of the lack of explicit costs, it was conjectured that
the incentive to exit from SMMs may be lowered giving rise to an asymmetry in
adjustment. Column (2) reports the results of re-estimating the baseline model with
separate adjustment coefficients for markets with increasing and decreasing membership.
It is evident that adjustment is slower in declining markets than those that are growing (0.0118 compared to -0.03994).
24
It was noted above, following Waldfogel and Chen (2006) that early mover advantages
have been found to be surprisingly persistent in electronic markets. Leading sellers, such
as Amazon.com, have been shown to enjoy a price premium and enjoy a disproportionate
share of sales. It was conjectured that the presence of such a seller would tend to
discourage participation by lesser retailers, either because the leading seller would take a
disproportionate share of the price insensitive consumers, thus lowering the
attractiveness of the pool of consumers left, or because of a leader-follower effect in
which the lesser brands perceive a lower residual demand. The raw data were consistent
with an Amazon entry deterrence effect. There was a mean market membership of 12.69
where Amazon was present compared with 9.64 where it was not, the difference being
significant at the one per cent level (z=33.51).
Since market leaders (like their followers) would be expected to be attracted by
anticipated profitability, we re-estimate the ECM model, in Columns (3) and (4), after
removing Amazon (and Amazon Mall) from the seller count but including Amazon entry
and exit dummies and Amazon presence variables, respectively. It can be seen in Column
(3) that the entry of Amazon has a significant negative impact on the number of
participants, whilst exit has a significant positive effect. The impact coefficients suggest
that the entry of Amazon leads to an immediate fall of about 13% in the number of nonAmazon participants, a reduction that appears to be reversed on Amazon’s exit. In an
average market this equates to a drop of between one and two sellers. The specification
with an Amazon presence variable, in Column (4), confirms the significant negative
effect. This is suggestive of a modest strategic entry barrier effect through which the
entry of Amazon displaces one or two smaller sellers. Since research elsewhere on
shopbot markets suggests that price falls monotonically with the number of sellers, this
result is also suggestive of a welfare loss associated with dominant firm presence.
25
Another effect of omitting Amazon from the seller count is to increase substantially the
size of the coefficients on the variables determining expected profitability, as seen in
Columns (3) and (4) and the adjustment parameter. This suggests that smaller retailers
are more price and quantity sensitive in their entry behavior than Amazon, whose sales
presumably rest on reputation to a much greater extent. Taken together with the entry
deterrence effect, this result is suggestive of a bifurcation of market participants into one
or more dominant players and a competitive fringe, with the latter acting as the
equilibrating force entering and exiting in response to expected profits. Of course, a full
exploration of this conjecture would require sales (or at least leads) data at the seller level
as well as price data.
5.
Conclusion
This paper has presented some preliminary results of an investigation of entry behavior
at a shopbot-mediated market. It was suggested that standardized market access,
obviating the need for specific resources for market participation, and an absence of
sunk costs of entry creates an arena in which Kirznerian entrepreneurs operate, entering
and leaving the market in response to arbitrage opportunities. We used a specially
constructed panel of 295 camera model markets mediated via NexTag.com to estimate an
error correction model of the determinants of net entry. Specification followed the entry
literature in assuming that sellers act in response to changes in expected profitability, as
proxied by lagged mark-up and lagged sales (leads).
Having established that the variables were co-integrated and that appropriate diagnostics
obtained, it was found that the error correction model was well determined with all the
26
principal change and levels variables and the adjustment coefficient being statistically
significant with the anticipated signs. It was also apparent that there exists a substantial
reservoir of potential entrants who make forays, often of a very short duration, into the
SMM. The performance of the model suggests these visits function as an equilibrating
force, with participants entering and leaving the market in response to changes in
expected profit. The adjustment parameter confirmed that the number of participants
adjusts much more rapidly to changes in profit opportunities than the equivalent in
bricks and mortars retailing or other traditional markets. However, there appears to be an
asymmetry between inward and outward movement, with more rapid adjustment during
expansions than contractions. This probably reflects the absence of explicit costs
associated with market membership, certainly in comparison with traditional markets.
It was clear that there exists considerable heterogeneity across the set of sometime
participants in this shopbot market, with large sellers such as Amazon.com selling as many
as 95% of the 295 sample products at some time across the interval of inquiry, through
to just over a quarter of the sometime sellers offering five or less of the models. The data
were also suggestive of size-related behavioral differences with smaller participants
apparently more likely to use hit-and-run tactics involving shorter, lower-priced forays
into the product markets. Large sellers were more likely to enter close to the prevailing
mean price and, once entered, to remain for longer periods. Re-estimating the error
correction model after controls for the presence of Amazon.com indicated a significant
negative impact on the number of market participants. This exceeded the displacement
effect. It was suggestive of an entry deterrence effect. Whether this reflected a reduced
attraction to a market in which Amazon.com might be expected to capture a
disproportionate share of price insensitive buyers, or whether it reflects a perceived
reduction in residual demand could not be determined.
27
Re-estimating the model with controls for the Amazon effect also required that we drop
that company’s brands from the count of sellers in each market. This had the effect of
sharply increasing the sensitivity of net entry to the variables determining expected
profitability and raising the speed of market adjustment. That is non-Amazon sellers
appeared to be more responsive to market conditions than the set of sellers which
included the Amazon brands. This reinforced the conjecture of a bifurcation in shopbot
markets with smaller sellers operating as hit-and-run arbitrageurs, true Kirznerian
entrepreneurs, while larger sellers relied on their reputation to maintain a stable market
position.
28
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Appendix 1
Figure A1. Nextag Screen Output
33
Appendix 2: Time Series Properties
The error correction formulation requires that the variables are stationary and cointegrated. Hence we need to test for unit roots in our data series before we estimate
equation (2). We use the augmented Dickey-Fuller test to test for non-stationarity for
our variables in levels. Given that our panel contains a large number of cameras the
majority of which are observed over a large number of time periods, we tested the null
hypothesis of unit roots by estimating individual augmented Dickey-Fuller tests for all
markets exhibiting a long time period17. The results from these tests using the variables
expressed in levels are summarized in Figures A2-A418. For our number of sellers and
PCM variables the unit root test is rejected at the 5% significance level for approximately
35 cameras. For our leads series it is rejected for 13 cameras. Therefore, it seems
reasonable to regard the series as borderline I(1) variables. The results from the unit root
tests using the variables expressed in differences are summarized in Figures A5-A6. For
the number of sellers and PCM variables the unit root test is rejected for all cameras.
Therefore, there is overwhelming evidence to suggest that the series are stationary19.
In order to examine whether the series are co-integrated we test for non-stationarity in
the residuals of a cointegrating equation. We follow the Engle-Granger (Engle and
17
We defined the time period as long if we had more than 50 time periods for that market.
18
The results are shown for a model with one lag which was the ADF regression with the fewest
parameters that is free from serial correlation. We experimented with a different number of lags on the
differenced variable but these did not alter our unit root inference.
19
The leads data is slightly more problematic due to the nature of its frequency. However, given that
we find that the variables are cointegrated and we find a statistically significant coefficient on the error
correction term in our ECM regression we are justified in using the ECM approach.
34
Granger, 1987) two-step procedure. In the first step we estimate the static long-run
regression model (the cointegrating regression). The second step of the procedure is to
use an augmented Dickey-Fuller test on the residuals obtained from the first step. The
results from the unit root tests in individual camera markets are reported in Figure A7.
The unit root null corresponds to ‘no cointegration’ between the variables. It is not
rejected for only 2 cameras therefore co-integration analysis indicates that the three
variables, number of sellers, average PCM and number of leads are co-integrated.
In addition to the individual unit root tests we estimated a Fisher-type test on the pooled
sample of cameras which had a small number of time periods. The test combines the
significance levels from N independent unit root tests for each cross section i, as
developed by Maddala and Wu (1999). One advantage of this test is that it does not
require a balanced panel. The test-statistic for non-stationarity using our variables in
levels was not significant for all our variables. The test-statistic for non-stationarity using
our variables in differences was significant for all our variables. In addition, the Fishertype test for co-integration was significant. For a robustness check, we also run the
Fisher-type test on the whole sample pooled together. These results again supported
our findings of stationarity and cointegration20.
20
These results are available from the authors.
35
0
.1
.2
Density
.3
.4
.5
Figure A2. Unit Root Tests for log N (Number of Sellers)
-6
-4
-2
Test Statistic
0
2
Notes: Vertical line represents 5% significance level
0
.1
Density
.2
.3
.4
Figure A3. Unit Root Tests for log PCM (Average Price-Cost Margin)
-4
-2
0
Test Statistic
Notes: Vertical line represents 5% significance level
36
2
4
0
.2
Density
.4
.6
Figure A4. Unit Root tests for Log L (Leads)
-4
-3
-2
Test Statistic
-1
0
Notes: Vertical line represents 5% significance level
0
.05
.1
Density
.15
.2
.25
Figure A5. Unit Root Tests for ∆N (Number of Sellers)
-15
-10
Test Statistic
Notes: Vertical line represents 5% significance level
37
-5
-1
0
.05
Density
.1
.15
.2
Figure A6. Unit Root Tests for ∆PCM (Average Price-Cost Margin)
-15
-10
-5
0
Test Statistic
Notes: Vertical line represents 5% significance level
0
.05
Density
.1
.15
.2
Figure A7. Cointegration Tests for log N, log PCM and log L
-15
-10
-5
Test Statistic
Notes: Vertical line represents 5% significance level
38
0