entry and exit behavior at a shopbot: e-sellers as .../file/steve_thomps… · michelle haynes*...

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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 [email protected] †Address for correspondence: Nottingham University Business School, Nottingham, NG8 1BB, UK. Phone 44 (0) 115 8466629, Fax 44 (0) 115 8466667, E-mail [email protected] Acknowledgements: The authors would like to thank Victor Porcar for programming

assistance and Peter Wright for helpful comments on this research.

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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

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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

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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.

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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.

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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.

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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).

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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

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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 resource-

based 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

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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.

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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.

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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.

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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.

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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:

ititititititit LPCMNLPCMN εγγγαφβ +−−−−−∆+∆=∆ −−−− ])[1( 131211111 …(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 itε 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, itN∆ 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:

ititititititit LPCMNLPCMN εγαγααφββ +−+−+−−∆+∆+=∆ −−−− 131211110 )1()1()1(

…(2)

where 10 )1( γαβ −= . Since, the variables are in logs, the short run elasticities are given

by the coefficients 1β and 1φ . 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).

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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.

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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.

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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

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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.

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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

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We used a cut-off of 100 leads since we were interested in studying behaviour in active markets.

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inequality of participation by product count is illustrated in the Lorenz curve (Gini

coefficient = 0.65) in Figure 1.

Figure 1. Lorenz Curve Showing Inequality of Sellers

0.2

.4.6

.81

% o

f C

am

era

Pro

ducts

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.

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Figure 2. Number of Daily Entries

02

00

400

600

Num

ber

of E

ntr

ies

01dec2007 01jan2008 01feb2008 01mar2008 01apr2008Date

Figure 3. Number of Daily Exits

01

00

200

300

400

500

Num

ber

of E

xits


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Figure 4. Daily Net Entry

-400

-200

02

00

400

Net E

ntr

y


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

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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

Small Sellers Large Sellers Test Statistic [p-value] Mean -8.735848 35.15724 -32.3249 [0.000] S.D. 88.53022 130.149 No. of Obs 9,414 16,750

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.

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Figure 5: Mean Market Price-Entry Price for Large and Small Sellers

-100

-50

05

01

00


large sellers small sellers

3.2 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.

21

Table 2. Summary Statistics Mean Median S.D. Min Max No. of

Obs Price 426.72 249.88 802.79 60.81 9,999.99 28234 Minimum Price 372.64 215 720.34 60.81 7999.99 28234 MRSP 510.03 300 863.25 79.99 8,000 28234 PCM 172.59 88.17 383.21 -74.53 4997 28234 Minimum PCM 117.62 54.01 319.60 -171 4000.49 28234 Sellers 11.96 11 7.32 1 39 28234 Leads 356.40 156 637.90 0 10500 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.

22

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 19 21-40 18 41-60 21 61-80 17 81-100 12 101-120 29 Over 120 179 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.

23

Table 4. Error Correction Model

(1) (2) (3) (4) Intercept 0.02737

(0.00600)*** 0.02747 (0.00594)***

0.01730 (0.03119)

0.05261 (0.03274)*

1−∆ itPCM 0.06464 (0.00661)***

0.04454 (0.00656)***

0.44634 (0.10180)***

0.45266 (0.10237)***

L∆ 0.00317 (0.00056)***

0.00297 (0.00055)***

0.02324 (0.01057)**

0.02441 (0.01048)**

1−itN -0.02889 (0.00148)***

-0.03547 (0.00169)***

-0.06718 (0.00637)***

-0.06837 (0.00642)***

1−itN *Increasing -0.00451 (0.00106)***

1−itN *Decreasing 0.02363 (0.00107)***

1−itPCM 0.00237 (0.00113)**

0.00307 (0.00111)***

0.01723 (0.00654)***

0.01424 (0.00649)**

1−itL 0.00499 (0.00058)***

0.00446 (0.00057)***

0.03104 (0.00618)***

0.03348 (0.00635)***

Amazon_Entry -0.1329 (0.06097)**

Amazon_Exit 0.1497 (0.05869)**

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 295 295 295 295 No. of Observations 28234 28234 28234 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.

24

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).

25

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 non-

Amazon 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.

26

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

27

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.

28

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.

29

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33

Appendix 1

Figure A1. Nextag Screen Output

34

Appendix 2: Time Series Properties

The error correction formulation requires that the variables are stationary and co-

integrated. 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.

35

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 Fisher-

type 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.

36

Figure A2. Unit Root Tests for log N (Number of Sellers)

0.1

.2.3

.4.5

Den

sity

-6 -4 -2 0 2Test Statistic

Notes: Vertical line represents 5% significance level

Figure A3. Unit Root Tests for log PCM (Average Price-Cost Margin)

0.1

.2.3

.4D

en

sity

-4 -2 0 2 4Test Statistic


37

Figure A4. Unit Root tests for Log L (Leads)

0.2

.4.6

Den

sity

-4 -3 -2 -1 0Test Statistic


Figure A5. Unit Root Tests for N∆ (Number of Sellers)

0.0

5.1

.15

.2.2

5D

en

sity

-15 -10 -5 -1Test Statistic


38

Figure A6. Unit Root Tests for PCM∆ (Average Price-Cost Margin)

0.0

5.1

.15

.2D

en

sity

-15 -10 -5 0Test Statistic


Figure A7. Cointegration Tests for log N, log PCM and log L

0.0

5.1

.15

.2D

en

sity

-15 -10 -5 0Test Statistic


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