ORGANIZATIONAL FORM AND PRODUCT MARKET COMPETITION:
ARE FOCUSED FIRMS WEAK COMPETITORS?
NAVEEN KHANNA and SHERI TICE*
July 2002
First Draft: Feb 2002
*Khanna is from the Eli Broad College of Business, Michigan State University and Tice is from the A.B.
Freeman School of Business, Tulane University. We thank Rob Hansen, Paul Spindt, Tom Noe, Suman
Banerjee, Ted Fee, Charlie Hadlock, Long Chen and Zsuzsanna Fluck for their comments. Tice gratefully
acknowledges research support from a Freeman Faculty Research Fellowship at the A.B. Freeman School
of Business. Address correspondence to Sheri Tice, A.B. Freeman School of Business, Tulane University,
New Orleans, LA 70118, or email: stice@tulane.edu.
ORGANIZATIONAL FORM AND PRODUCT MARKET COMPETITION:
ARE FOCUSED FIRMS WEAK COMPETITORS?
ABSTRACT
We contribute to the debate whether organizational form matters from a different angle. We
examine product market behavior of diversified and focused retailers to determine if focused
firms behave as weak competitors and are perceived as such. We do so in three distinct ways. We
first test pricing predictions of a simple switching cost model of market competition, that weaker
firms charge higher prices in equilibrium (and sacrifice market share). Not surprisingly, we find
higher average prices in cities with a larger proportion of high debt and low efficiency firms.
Surprisingly, the same result holds also for cities with larger proportion of focused firms. Our
second test documents that firms with these characteristics are also more likely to exit after facing
a negative shock to their city, again suggesting that like high debt and low efficiency firms,
focused firms are weak competitors. Given that certain characteristics are associated with
weakness, and these characteristics are observable, stronger competitors should be able to take
advantage of firms with such characteristics. To test for this, we model the optimal location
decision of a new entrant into a city with existing incumbent stores. The prediction is the entrant
locates closer to a weaker incumbent. We document a new entrant locates closer to high debt as
well as focused firms suggesting that at least in the retail industry, focused firms are perceived as
weak competitors.
JEL D43, G31, G32
2
The presence of a diversification discount has been a troublesome puzzle since documented by
Lang and Stulz (1994) and Berger and Ofek (1995). Given the compelling advantages of
diversification, and that much of the economic activity in the U.S. is conducted by diversified
firms, there is a natural resistance to the notion that diversified firms destroy value. Not
surprisingly, a number of recent papers have issued a serious challenge to the existence of the
discount and the arguments in support of it. The challenges have come on two fronts.
Researchers like Fluck and Lynch (1999), Maksimovic and Phillips (2002), Campa and Kedia
(2002) and Graham Lemmon and Wolf (2002) argue that for endogeneity and self-selection
reasons, comparing diversified firms to stand-alones is inappropriate. If weaker firms are more
likely to diversify or if acquired firms are on average weaker, this benchmark consisting of standalones is too stringent. Researchers like Whited (2001), Mansi and Reeb (2002), and Villalonga
(2002) argue that the documented inefficient cross-subsidization as well as the documented
diversification discount result from measurement problems. When corrected for, either
organizational form is irrelevant, or arguably there is a diversification premium.
We contribute to the debate from a different angle. Instead of focusing on differences in
valuation of diversified and stand-alone firms, we focus on differences in their equilibrium
decisions in the product market. This not only permits us to circumvent some of the perceived
shortcomings of previous tests, but also provides new insights about how firms compete in these
markets. If organizational form is irrelevant, not only should both diversified and stand-alone
firms make similar decisions, but their competitors should perceive the firms to be similar and
respond similarly. Since these responses are conditioned also on competitors’ own organizational
forms, equilibria across markets with different mixes of diversified and focused firms should be
similar. However, if either decisions or responses depend on own and/or competitors’
organizational form, organizational form would appear to play an important role in product
market competition.1
We are not the first to compare decisions of focused and diversified firms. Papers like Shin and
Stulz (1998), Rajan, Sarvaes and Zingales (2000), and Scharfstein (2002?) document inefficient
capital redistribution by diversified firms, while Khanna and Tice (2001) and Maksimovic and
Phillips (2002) document that diversified firms invest more efficiently. The contribution of this
1
The literature is appropriately concerned whether such results can be attributed to organizational form, or
if there is an unobservable variable driving both organizational form and differences in decisions and
responses. We discuss this issue later.
3
paper is two fold. One, we look at a different set of decisions to establish whether they too are
affected by organizational form. Two, we circumvent the need for measuring a firm’s prospects
directly by inferring them through the equilibrium behavior of a firm and its competitors. The
switching cost model we use provides predictions about how weak firms are likely to behave viz
a viz stronger ones. Then by observing how different firms behave we can identify which
consider their prospects good and which weak. This allows us to avoid using measure like
profitability, productivity or industry Q as proxies for prospects.
We use city level observations for the discount department industry in our study. This industry is
particularly suited to our tests because of the heterogeneity of incumbent characteristics both
within and across cities. Cities differ not only in the total number of competing stores, but also in
the mix of firms owning these stores. While some cities are served by only one firm, others have
six or seven firms competing. The firms themselves are quite heterogeneous, differing on
variables like size, profitability, leverage, and organizational form. Another benefit of this
industry is Wal-Mart. Wal-Mart, expanded dramatically across the United States during this
period fanning out from its base in Arkansas. Since the merchandise sold is relatively
homogeneous, entry by a technologically superior and low cost firm puts pressure on all
incumbents, particularly weaker ones.
We examine the link between organizational form and firm weakness in three distinctly different
ways. We first test pricing predictions using a simple switching cost model of market
competition. The main insight from this model is that weaker firms charge higher prices in
equilibrium and consequently sacrifice market share. Thus, markets with one or more weak
competitors have softer competition, allowing other incumbents to also charge higher prices.2
This results in higher average prices for such markets.3
As predicted, variables traditionally used as proxies for weakness are positively correlated with
equilibrium prices. Cities with a larger proportion of high debt and low efficiency firms have
2
See Appendix I for our simple model demonstrating this result.
This may not hold if there is an attempt to force exit of weaker incumbents through aggressive, even
predatory pricing. Since this forces the rival incumbent to also lower its price, the average price in a
market with a weak incumbent could be lower. However, in markets with switching costs, firms are
localized monopolies and make positive profits. Thus, the option to run out a rival by charging a very low
price is quite expensive. It requires either that the rival can be ousted quickly or that the resulting market
share after ousting the rival is very valuable. We later provide evidence that predatory pricing does not
appear to occur in our data set.
3
4
higher prices. However, prices are also positively correlated with the fraction of focused firms in
the city. The focus variable appears to have the most explanatory power and is persistently
significant in all the specifications tested. It remains significant when we include low efficiency,
high debt, small division size, and small parent firm size in our specifications. The results are
consistent with focused firms being weak competitors.
If a firm is weak, it should be especially vulnerable when there is new entry as that increases
aggregate supply and pushes prices lower. For our second test, we examine the exit decisions of
incumbent stores within four years after Wal-Mart’s entry into a city. 4 We find that having low
efficiency, high debt and the focused organizational form increase the probability of exit from a
city within four years after Wal-Mart’s entry. The focus variable is a strong predictor of exit even
after controlling for other traditional measures of weakness. As in Chevalier (1995), by using
city level observations, the potential endogeneity of firm characteristics as a response to a
negative shock to a city is less of a concern than in other settings. Interestingly, we do not find
evidence of predatory pricing, as prices are highest in cities with the largest amount of subsequent
exit.
For our third test we look at competitor decisions. Given that certain characteristics are
associated with a firm being weak, and these characteristics are observable, stronger competitors
should be able to take advantage of firms with such characteristics. Our pricing data consists of
average prices at the city level so we cannot use firm level prices to directly examine this.
However, there is a test we can perform which explicitly documents that strong firms do
condition on characteristics associated with weak incumbents. When Wal-Mart enters a city it
knows the location and characteristics of all incumbents. An important decision it makes is
where to locate its store relative to the existing stores in the city. Its location affects not only the
prices and market share of its immediate competitors but those of all other incumbents. Thus, it
chooses that location which maximizes its own profits after accounting for the equilibrium
responses of all incumbents.
To examine this, we build a simple extension of the Klemperer (1987, 1995) model of pricing and
spatial competition. Our model predicts that in equilibrium the new entrant should locate closer,
4
This test is similar to one in Zingales (1998) to test the impact of debt on firm survival when trucking
firms faced a deregulation shock.
5
but not too close to the weaker incumbents.5 It locates closer to a weak incumbent, because such
incumbents charge higher prices, allowing the new entrant to also charge higher prices. It does
not locate too close though, because locating closer decreases the switching costs for consumers,
forcing both firms to lower prices. Thus, where Wal-Mart locates should provide information
about its perception of incumbent weakness. Using a sub-sample, we find that when Wal-Mart
enters a city it places its first store closest to stores owned by high debt and focused firms
suggesting that Wal-Mart perceives these firms to be the weaker incumbents. On average it
locates 1.7 miles away from the closest incumbent consistent with the model predictions of not
locating too close.
If a particular organizational form reflects weakness and competitors base their decisions on it, a
natural question is whether the firm should change its organizational form. This is not unlike the
endogeneity issue driving the current debate. Since organizational form is a decision variable, it
is endogenously and optimally determined. Thus, identifying a benefit associated with it does not
imply the firm would gain from changing it. Other costs may outweigh the benefits of changing
organizational form. This is especially relevant for our data set where the same firm operates in
numerous local markets. If it decides to change organizational form in response to what happens
in one or few of its markets, it must consider the effect of the change it all its other markets and,
thus, on total efficiency. Another question is whether it is organizational form that is important or
some unobservable factor is driving both organizational form and firm weakness. As in the
literature, we try to handle this concern by controlling for a number of potentially relevant
variables in our tests. Two of these are financial leverage and operating efficiency. If the
unobservable factor is important, it is likely to impact at least one of these variables. Thus, we
can suggest the following policy: firms should switch to the diversified organizational form to
gain the benefits of being tough only if they can do so without increasing debt or lowering overall
efficiency, as these too are attributes of weak competitors.
The rest of the paper proceeds as follows. Section I describes our data and sample selection.
Section II contains pricing and exit tests. Section III contains the new entrant location tests.
Section IV concludes.
5
Our model demonstrating this result is located in Part B of Appendix I.
6
I. City Level Data and Sample
A. Pricing Data
The quarterly price data used in this study comes from the American Chamber of Commerce
Researchers Association (ACCRA) Cost of Living Index. Under the guidelines of the ACCRA,
local chambers of commerce offices collect quarterly prices on a variety of items. The cities
included in the survey are those where chambers of commerce or similar organizations have
volunteered to participate. The number of respondents providing prices varies from quarter to
quarter. ACCRA stringently reviews all prices reported each quarter and attempts to eliminate
errors and noncompliance with specifications. The Cost of Living Index generally includes only
cities with populations over 40,000.6 The composition of the items in the Cost of Living Index
changed substantially between 1981 and 1982. To standardize results, we only use prices in 1982
and beyond in our tests.
The Miscellaneous Goods & Services Index is one of the indexes in the Cost of Living Index. It
contains several items that are sold in discount department stores. We divide the items contained
in the Miscellaneous Goods & Services Index into three groups: (1) Discount Items Group: Seven
items sold by discount department stores; (2) Non-Discount Items Group: Ten items not sold by
discount department stores; (3) Alcoholic Beverage Group. Another index available in the Cost
of Living Index is a Grocery Items Index. We add three non-food items from the Grocery Items
Index to the discount items group, as they are items likely to be sold in discount department stores
over the period of the study. The items are “facial tissue” (Kleenex), “washing powder” (Tide,
Bold or Cheer) and “soft drink” (2 liter Coca Cola). The Grocery Items Index also consists of
many food items. In general, discount department stores chains have gradually added food items
in their stores during the 1980’s and 1990’s. However, this has been done at different paces and
times with various discount chains. To keep the introduction of noise to a minimum, food items
from the Grocery Items Index are not added to the discount item group or the non-discount item
group.7
6
According to ACCRA, there are, however, a small number of special case exceptions where communities
have proven their ability to provide data coverage.
7
The prices of alcoholic beverage prices are not used, as several reporting cities are apparently located in
dry counties and do not report prices of alcoholic beverages.
7
We use the discount items group to measure discount price levels and the non-discount items
group to control for overall price levels in a city caused by unobservables. A detailed listing of
the composition of the discount and non-discount item groups is displayed in Table I.
During our sample period ACCRA switched Johnson’s Baby Shampoo with Alberto VO5 in the
Miscellaneous Goods & Services Index. We adjust the price of the item after the switch to reflect
this change. The details of the adjustment methodology used are provided in Appendix II.
B. Store Location Data
Two trade journals are used to identify industry participants. Using trade journals to define an
industry is a cleaner way to identify industry participants than using the Compustat Industrial
Segment Data where firm managers have discretion with respect to how they aggregate their
activities into segments. We first make a list of all discount department store chains that are on
the “Leading Discounters” list in Discount Merchandiser for at least one year during the 1975 –
1996 time period. This step eliminates very small chains of stores. The Directory of Discount
Department Stores is then used to determine store locations for each chain for each year in which
they operate in the industry during the 1982 – 1996 time period.8 A discount department store
chain must be in both of these trade journals to be included in our tests. The trade journals show
the name of the firm that owns each chain. Sometimes a firm may own more than one discount
department store chain. If this is the case, the details with respect to the chains are combined
within the firm for our tests.
C. The Sample
The Directory of Discount Departments Stores lists store locations at the beginning of each year.
Therefore, there is ambiguity regarding the quarter in which the new store actually opens. For
example, according to the Directory of Discount Department Stores a Wal-Mart store is first
located in Covington, Louisiana at the beginning of 1984. This store could have opened anytime
between the beginning of 1983 and the beginning of 1984. We define the beginning of the
calendar year in which Wal-Mart enters a city as quarter zero, with the realization that Wal-Mart
enters the city sometime between quarter zero and quarter plus four.
8
After 1995 discounters are pooled with other general merchandise retailers, and firm specific details are
no longer provided. Thus, our sample is constrained on the lower end at 1982 due to pricing data
limitations and at the upper end at 1996 due to firm characteristic limitations.
8
Wal-Mart enters 1,588 cities during the 1984 – 1996 time period. For each of these cities we
identify whether prices are available at quarter minus one. Due to the increase in spatial
competition that occurs with new entry, we measure prices in quarter minus one to ensure that
prices are measured before Wal-Mart enters a city. If prices are not available for a city at quarter
minus one we go back to quarter minus two. Cities are dropped from the sample if there are no
incumbent stores in the city when Wal-Mart enters, if there are any stores owned by a foreign
firm, or if there are any stores owned by a franchise operation. This results in 180 cities for our
first set of tests. There are 43 different incumbent firms in the 180 sample cities, and stores in
any one city are owned by between one to seven incumbent firms.9 Kmart is an incumbent in all
but seven of the 180 cities and is the largest incumbent firm based on the number of stores or
sales over the sample period.
To minimize potential endogeneity arising directly from the effect of Wal-Mart’s entry on firm
characteristics, we measure firm characteristics for the fiscal year that ends around quarter zero.
However, we do not feel that this type of endogeneity is likely given the multi-market structure of
this industry. A time line illustrating the basic empirical set-up is shown below:
Incumbent Firm
Characteristics
Measured
Wal-Mart’s
1 st Store Observed
in City j
Incumbent Exit
Measured City j
---------------------------------------------------------------------------------------------
Qtr – 1
Qtr 0
Qtr +4
Qtr +20
Wal-Mart Enters City j
Between Qtr 0 and Qtr +4
Prices Measured
In City j
II. Pricing and Exit Tests
A. Determinants of Market Prices
9
If a firm spins-off a division, the spin-off company is treated as a new firm.
9
A standard finding in the industrial organization literature and a result that emerges in a simple
switching cost model (see Appendix I), is that in equilibrium, high cost firms charge higher
prices. In their extension of the Klemperer (1987, 1995) switching cost model, Chevalier and
Scharfstein (1996) show that equilibrium prices will also be higher in markets with a higher
fraction of high debt firms. High debt firms have a higher probability of exit making future
market share less important. Thus, these firms are more likely to charge higher current prices at
the expense of market share. Since both high costs and high leverage have traditionally been
associated with weakness, our hypothesis is that cities with a higher fraction of weak incumbents
will charge higher prices.
Our dependent variable is defined as:
10
Relative Discount Prices j,- 1
 Pd,j,-1
d=1
 ----------------------10
 Pnd j ,-1
nd = 1
where P is defined as an item’s price, j indexes cities, d indexes discount items, nd indexes nondiscount items.
Prices of discount items are measured relative to those of non-discount items in the same city at
the same point in time. Relative discount prices (sum of ten discount item prices over sum of ten
non-discount item prices) control for local cost of living differences and other unobservables that
may impact prices at the city level. This is important as observations are pooled across cities and
time in our tests.
Documenting higher average prices in cities with a larger fraction of weak incumbents is a
general result that should hold independent of when or whether Wal-Mart enters a particular
market. However, this finding is likely to be strongest around Wal-Mart’s entry. Given that WalMart is a cost effective and technologically superior firm, its entry is likely to hurt the prospects
of the weakest firms the most. With deteriorating prospects, these firms are likely to care less
about future market share and charge higher current prices. 10 For this reason we test this
hypothesis with prices measured immediately before Wal-Mart’s entry into a city.11
Wal-Mart expands through a ‘hub and spoke’ distribution system. It opens a distribution center and fills
the surrounding area with stores. When one center is over-extended, another center is opened in an
10
10
The independent variables used in our tests are defined below:
Discount Sales Per Square Foot
Sales-per-square foot is frequently used as a measure of operating costs/efficiency in the discount
department stores industry. It measures firm performance using what is known as the retailing
productivity loop. If a retailer has low cost of goods sold or low SG&A expenses, they are able
to charge lower prices and remain profitable. When prices are lowered, they pick up additional
volume leveraging fixed costs further. The leveraging of fixed costs provides the ability to
further lower prices and capture higher volume leading to an even lower expense ratio. How
good a firm is at exploiting the retailing productivity loop is typically measured in the industry
through sales per square foot. We follow the industry standard of measuring firm
costs/productivity with discount sales per square foot. Firm level sales-per-square foot is
available for the discount divisions of both public and private firms in our sample from The
Directory of Discount Department Store.12
Parent Firm Size
Parent firm size could matter for many reasons. It may be a measure of efficiency, as efficient
firms may be more likely to grow large. Larger firms may also have better access to external
financing.
adjoining area to service the fringe stores of the first center and open new stores further away. Once a
distribution center is opened, incumbents in surrounding/adjoining markets are put on notice that Wal-Mart
intends to enter their markets soon. There is a potential endogeneity problem if entry is anticipated, as
incumbents may choose to change their characteristics (right hand side variables). However, as shown in
Khanna and Tice (2000), firms do not alter their characteristics around their first interaction with Wal-Mart.
This is probably due to their presence in multiple heterogeneous markets. What happens in one market or
in a few of their markets is unlikely to cause them to change firm characteristics, as this would impact all of
their other markets as well. Given heterogeneity of markets, the total impact of such a change will be hard
to gauge.
11
It is possible that the effect will be even stronger after Wal-Mart enters. However, a test with post entry
prices is confounded by a large exodus of incumbent firms in response to Wal-Mart’s entry. The exit starts
within a year and goes on for some time. Given the extent of changes in spatial competition due to entry
and exit during this period, it would be hard to disentangle the pricing effects due to spatial competition
from those due to changes in market characteristics. Attributing price effects caused by changes in spatial
competition to attributes is a well-documented problem in industrial organization.
12
In a few cases, discount sales per square foot for a specific fiscal year is missing for some firms in the
Directory of Discount Department Stores. In these instances, the value for the prior year for the firm is
used instead. In the case of one firm, sales per square foot is not shown for several years, but firm sales and
the number of discount stores is shown for each fiscal year. For this firm, the average square footage of the
11
Discount Size
Like parent firm size, the size of a firm’s discount department store operations is used in the
specification for many reasons. The size of a firm in the industry may be a proxy for operating
costs due to volume discounts when purchasing merchandise. It may also proxy for efficiency, as
those firms that are efficient in this business are more likely to grow large.
Firm Debt
Firm indebtedness is a commonly used proxy for financial constraints. The level of firm debt has
been found to be relevant for investment and pricing decisions in the product market literature.
We measure this using a firm’s total debt ratio defined as: Total Assets (COMPUSTAT item A6)
minus stockholder equity (COMPUSTAT item A216) all divided by total assets (COMPUSTAT
item A6).13 Unfortunately, this information is not available for the privately held firms in our
sample. We first run our tests for cities with both public and private firms excluding this
variable. To address the omitted variable issue present with this specification, we subsequently
re-run our tests including the debt variable using the sub-set of cities where debt is known for all
incumbents.
Focused Organizational Form
Firms with the diversified organizational form may be tougher competitors for several reasons.
They may have lower costs due to synergies, more access to capital due to a higher borrowing
capacity, financing flexibility due to an internal capital market, and a lower probability of
bankruptcy. However, firms with the diversified organizational form may be weaker competitors.
This can happen if agency conflicts due to the more complex organizational form create high
agency costs paid out as extra managerial compensation or wasted in rent-seeking behavior by
division managers, if there is less access to external funds due to lower transparency, or if there is
less managerial effort due to an inability to offer division managers incentive contracts linked
directly to division stock price performance. 14,15
firms stores is calculated for the last year it is shown, and implied sales per square foot is calculated
assuming average store size remains unchanged over the subsequent four years.
13
In a few cases, this information is not available in Compustat for a specific fiscal year. In these
instances, the value for the prior year for the firm is used instead. In one case, this was not available, so the
value for the following fiscal year is used. One firm is not in Compustat. Information available in Moody’s
Industrial Manuals is used to calculate the debt ratio for this firm.
14
For papers discussing the theoretical benefits of diversification see Lewellen (1971), and Stein (1997).
For papers discussing the theoretical disadvantages see Meyer, Milgram and Roberts (1992), Scharfstein
and Stein (1998), Rajan, Servaes and Zingales (2000).
12
Following the capital expenditure literature, focused firms are defined as those with more than 90
percent of their total firm sales originating from the discount department store industry. Discount
department store sales come from the Directory of Discount Department Stores. Occasionally
this data is missing. In these instances, discount department store sales come from Discount
Merchandiser. For most of the sample, total firm net sales come from COMPUSTAT. If not
available there, the data comes from the Million Dollar Directory, or Wards Business Directory.16
Privately Held Equity
It is possible that private firms have less access to capital and consequently may be weaker than
firms with access to public equity markets.
The prices in our data set are available at the city level. Due to this, measures of weakness have to
be measured at the city level for our tests. The explicit definitions of the independent variables
used in the first set of tests are:
Frlowssq: The fraction of firms in city j with CPI inflation adjusted discount sales per square
foot of less than $177 (in 1996 dollars). This is approximately the mean/median industry sales
per square foot documented for the discount department store industry in Khanna and Tice
(2001).
FrSmFirmSize: The fraction of stores in a city j owned by firms with CPI adjusted total firms
sales of less than 1.475 billion (in 1996 dollars). This results in half of the firms in the sample
being classified as small.
FrSmSegmentSize: The fraction of stores in a city j owned by firms with CPI adjusted discount
department store sales of less than 1.475 billion (in 1996 dollars). This is the same size used to
identify small firm size.
15
It is unclear if inefficient cross-subsidization via an internal capital market would make firms tougher or
weaker competitors. In these models good prospect divisions receive too little funding and bad prospect
divisions receive too much funding vis-à-vis stand-alone firms in the same industries.
16
In a few cases, this ratio was not available for a particular firm in a particular fiscal year. In this cases
the focus variable is measured one year earlier.
13
Frhighdebt: The fraction of stores in city j owned by firms with a total debt to total assets ratio
of at least 70%. A debt ratio cut-off of 70% causes around 50% of the cities to be classified as
having at least one store owned by a high debt incumbent firm.
FrFocused: The fraction of firms in city j with more than 90% of firm sales attributed to the
discount department store industry.
FrPrivate: The fraction of stores in city j owned by firms without publicly traded stock.
Herf: This is the city Herfindahl measure. It is calculated as the sum of the squares of the market
share held by each incumbent firm i in city j at the beginning of the event window. It is used as a
control variable for market concentration.
The summary statistics for the variables are shown in Table II, while regression results are shown
in Table III. The dependent variable in Table III is relative discount item prices measured
immediately prior to Wal-Mart’s entry. Sometimes prices are not available in a particular quarter
as the city failed to report them. If pricing data is not available at quarter minus one, prices are
measured at quarter minus two if available.
We find that relative discount prices are higher the larger the fraction of low sales per square foot
incumbents, and the larger the fraction of focused incumbents in a city. The results on the focus
variable are also economically significant. Using the coefficient estimates shown in column 1, if
the fraction of focused firms in a city increases by 20%, the coefficient implies an increase of
.0135 in relative discount prices. At the sample average relative discount price ratio of .8680, this
translates to a 1.55% increase in discount item prices holding non-discount item prices fixed. The
finding of higher prices in cities with a large fraction of less efficient stores is not surprising,
given firms with higher costs would be expected to charge higher prices. The finding of higher
prices in cities with a larger fraction of focused firms is more striking given the extensive
literature on the diversification discount. It also turns out to be robust to the inclusion of other
variables that measure firm weakness, making it an important determinant of whether a firm is
weak. Total parent size, discount operation size and whether a firm is privately held are not
determinants of relative discount prices before the shock. The coefficient on the Herfindahl
measure is positive and significant in some specifications providing some evidence that prices are
higher in cities with less competition.
14
Previous studies have documented that firm financial leverage is an important determinant of
prices.17 Thus, there is a potential omitted variable bias present in our results. We next test
whether the documented results continue to hold after including firm financial leverage in the
specifications. We drop all cities in the sample that have any incumbent firms without debt
information. This results in a sub-sample of cities that is 75 percent the size of the original
sample. The same tests are run using this sub-sample and are shown in Table IV. The fraction of
stores owned by high debt firms in a city is positive and statistically significant indicating that the
higher the fraction of high debt stores in a city, the higher the discount prices. The coefficient on
the financial leverage variable is also economically significant and of a similar magnitude to the
focus coefficient. For example, using the coefficient estimates shown in column 1, if the fraction
of high debt firms in a city increases by 20%, the coefficient implies an increase of .0136 in
relative discount prices. At the sample average relative discount price ratio of .8680, this
translates to a 1.56% increase in discount item prices holding non-discount item prices fixed.
These results are consistent with those obtained by Chevalier (1995) who showed that
supermarkets that had recently undergone a LBO charge higher prices than their non-LBO rivals.
It is also noteworthy that the fraction of stores in a city owned by focused firms continues to be
positive and significant indicating that cities with a larger fraction of focused firms in them have
higher prices. Using this sub-sample, the fraction of low sales per square foot incumbents is not a
significant determinant of relative prices. This is probably due to a high correlation between
leverage and sales per square foot. As with the previous tests using all cities, firm size, and
discount size are not significant independent variables.
B. Firm Characteristics That Predict Exit
Weak firms are more likely to exit especially after a negative shock. During the period of our
study, one such shock to incumbent firms is Wal-Mart’s entry into their markets. The severity of
this shock is reflected in the extent of exit that occurs after it. 34% of incumbent stores exit by
quarter +20 where Wal-Mart enters the city between quarter 0 and quarter +4. This provides a
good opportunity to check whether the variables associated with higher prices in the previous test
are also associated with a higher probability of exit. This test is consistent with the prediction in
Chevalier and Scharfstein (1996), that prices are likely to be higher in cities with firms having a
higher probability of exit.
17
See Chevalier (1995), Phillips (1995), Chevalier and Scharfstein (1996), Zingales (1998), and Campello
(2002).
15
We start with the 180 city observations used in Table III. We drop all observations for cities with
quarter zero occurring after 1991, as store location data is needed for twenty quarters after quarter
zero and our store location data ends in 1996. This results in a sub-sample of 115 city
observations for the exit tests. The dependent variable is the fraction of incumbent stores at
quarter zero that exit city j by quarter plus twenty. We estimate this as a latent variable or using a
Tobit Model: y* = o + x +  where /x  Normal (0, 2) and Fraction Exit j,
0 to +20
 max (0,
y*). Formally
if #Strs n,j, +20  #Strs n, j, 0
then y = Fraction Exit j , 0
to + 20
 0
if #Strs n,j,+ 20  #Strs n, j, 0
then y = y* = Fraction Exit j, 0
to +20
N
 [Net Reduction #Strs n ,j] 0 to +20
n=1
 ---------------------------------------------------N
 #Strs n ,j 0
n=1
where [Net Reduction #Strs n ,j] 0 to +20 = [#Strs n, j, 0  #Strs n, j, +20], #Strs is the number of stores, j
indexes cities, n indexes incumbent firms in city j and Wal-Mart enters city j between quarter 0
and quarter +4.
The estimation results are shown in Table V.18 The fraction of incumbent stores that exit a city
after WalMart’s entry is larger the higher the fraction of stores owned by low sales per square
foot firms, and the higher the fraction of stores owned by focused firms. Therefore, incumbent
characteristics that lead to higher equilibrium prices in their cities, also predict more exit after
new entry. Consistent with the pricing results, total firm size, firm discount operations size, and
access to public equity markets are not important predictor’s of firm exit. We then repeat these
tests using the sub-sample of cities where debt is known for all incumbents and then include debt
levels as an explanatory variable. We find that the fraction of incumbent stores that exit the city
18
If a diversified firm spins off its discount division and continues to operate a store in a market this is not
treated as an exit. Similarly, if a firm goes private and continues to operate a store in a city this is not
16
is greater the higher the fraction of stores owned by low sales per square foot firms, the higher the
fraction of stores owned by focused firms, and the higher the fraction of stores owned by high
debt firms.19 These results are consistent with low efficiency (high cost) firms, high debt firms,
and focused firms being weaker competitors as they have a higher exit rate after the negative
shock. We can perhaps infer something about the direction of causality with this test. As in
Chevalier (1995), by using city level observations, the potential endogeneity of firm
characteristics as a response to a negative shock to a city is less of a concern than in other
settings. This suggests that not only are focused firms weak, but that the focused organizational
form is likely the cause of the observed weakness in the product market rather than the reverse.
We do not find evidence of predation by stronger incumbents against weaker incumbents as the
fraction of firms that exit after the negative shock is higher in markets that have the highest prices
immediately before the shock. These results are shown in Table VI. If predation exists, one
would expect higher exit in markets with lower prices. However, in markets with switching
costs, incumbents are localized monopolists and make positive profits. Thus, attempts to drive
out a rival by charging low prices are probably justified if either the rival can be driven out
quickly, or gaining market share is very valuable. Neither of these is likely in our sample. Since
competition takes place at city level and the same firms do not compete in every city, the effect of
lowering prices in only some of the rival’s markets may not result in quick exit. Also, given low
barriers to entry, exit by one rival may make entry by another more likely, reducing the
importance of gaining market share. Hence, it is not surprising that we do not find evidence of
predation. Consistent with this, we find prices are the lowest in markets that have all strong
competitors and, consequently, the least likelihood of exit. In our sample, twenty cities have only
diversified, low debt, and high efficiency competitors, and the average relative discount item
prices in those cities is .82 versus .87 for the other one hundred and sixty cities. This difference is
statistically significant at the 2% level. This suggests prices are lower because of stronger
competition rather than the existence of predatory pricing.
We next examine the firm specific characteristics of the firms that retrench to verify that it is the
“weak” firms that are exiting cities after Wal-Mart’s entry. The 115 cities in Table V are again
used for this robustness test. For each of the 115 cities, we record if each firm i has less stores in
treated as an exit. However, if an incumbent firm is acquired by another firm, or an incumbent store is sold
to a competitor, this is treated as exit by the incumbent firm.
19
The exit results for financial leverage are consistent with those found by Zingales (1998).
17
city j at quarter plus twenty than it has at quarter zero (recall that Wal-Mart enters the city
sometime between quarter 0 and quarter plus four). This results in 307 firm i, city j pairs of
observations. A difference in the mean test is done to see if there are statistically significant
differences in the average firm characteristics of the firms that retrench in a city versus those that
do not retrench after Wal-Mart’s entry. The results are shown in Table VII. Retrenching firms
tend to have the focused organizational form, high debt levels, low operating efficiency (or high
operating costs), be smaller industry players, and be smaller firms. It appears firms with “weak”
characteristics are the ones exiting after Wal-Mart’s entry, and focus is one of these
characteristics.
III. New Entrant Location
We also test for firm weakness from a new and different angle. In the two previous tests, we
investigated how incumbent firms’ decisions relate to their characteristics. Now we study
whether and how incumbent firms’ characteristics affect a new competitor’s decisions. If certain
observable characteristics of incumbents reveal weakness, an entrant should condition on it when
making its own decisions. One such decision is to determine where to locate its new store in
relation to existing incumbents’ stores. To fix our hypotheses, we build a simple model where an
entrant observes heterogeneous incumbents’ characteristics and locations and determines a
location that maximizes its own profits after accounting for the equilibrium responses of both
incumbents. The model, provided in Section B of Appendix I, suggests that the new entrant
(Wal-Mart) will locate closer (but not too close) to the weaker incumbent. The reason is that a
weaker incumbent charges higher prices in equilibrium. By locating closer to the weaker
incumbent, the entrant can charge higher prices than it could if it located closer to the stronger
incumbent. The new entrant would not locate too close, though, because switching costs become
smaller the closer the new entrant locates to the weaker incumbent, putting downward pressure on
prices.
The 1987 & 1995 Directory of Discount Department Stores contain detailed store location data
and were available to us. Using these two volumes of store location data, we identify all cities
entered by Wal-Mart for the first time during each of these years. We drop all cities with less
than two incumbent firms with stores in the city at the time of Wal-Mart’s entry. We also drop
cities with any stores owned by a foreign firm, or a franchised firm, as no firm level data is
18
available for these firms. Cities with multiple Wal-Mart stores entering at the same time are
dropped, as measuring the distance from each incumbent store to the Wal-Mart stores is complex.
We next determine the driving distance in miles between the new entrant (Wal-Mart) and each of
the incumbent stores in the city using the point-to-point driving directions on Yahoo.com.20 If
any incumbent store address cannot be found on Yahoo.com or Mapquest.com, the city in which
the store is located is dropped from the sample.21 Sometimes instead of listing the address of the
new Wal-Mart store, the 1987 store location book identifies the new Wal-Mart as a “projected
new store” in the city. In these cases, the Wal-Mart address shown in the 1995 volume is used as
long as there is only one Wal-Mart store in the city in 1995, and the square footage of the store
matches.
This process results in 124 cities and 408 firm i - city j pairs of observations for our tests.
Consistent with model predictions, we find that Wal-Mart locates close, but not too close to the
closest incumbent when it enters a city. The average driving distance between the new Wal-Mart
store and the closest incumbent store is 1.7 miles. The average distance between the new WalMart and the other incumbents in each city is 6.1 miles.
We next test to see if Wal-Mart locates its stores closest to stores owned by “weak” incumbents
when it enters new cities. The regressions consists of firm i – city j pairs of observations. The
dependent variable is the distance (in miles) from the new entrant’s store to incumbent store i.
All of the following independent variables are measured for firm i for the fiscal year ending when
Wal-Mart enters: (1) Focus Dummy: dummy = 1 if discount department store sales are > 90% of
firm sales (2) Total Debt Ratio: Total Debt over Total Assets (only available for firms that have
publicly traded stock) (3) Natural log of Inflation Adjusted Discount Sales (’96 dollars) (4)
Inflation Adjusted Discount Sales per Square Foot (’96 dollars) (5) Natural log of Inflation
Adjusted Parent Sales (’96 dollars) (6) The number of incumbent stores in the city when WalMart enters is included as a control variable to adjust for city size.
The regression results are shown in Table VIII. Columns 1 & 3 consist of only those firm i-city j
pairs where debt is know for firm i while Columns 2 & 4 consist of all firm i- city j pairs of
20
If there is a tie in the distance for the two incumbents closest to Wal-Mart we break the tie using point-topoint driving directions from mapquest.com, as decimal places are carried further.
21
For example, sometimes a shopping mall or shopping center will be named rather than a street address.
If an internet search doesn’t lead to a street address, the city was dropped from the sample.
19
observations. It appears that Wal-Mart locates its stores closer to focused firms and high debt
firms when it enters a city, controlling for firm efficiency, discount size, total firm size and the
size of the city. This is consistent with Wal-Mart perceiving high debt and focused firms as
weaker competitors.
IV. Conclusion
This paper contributes to the debate whether organizational form matters from a different angle.
Instead of focusing on differences in valuation of diversified and stand-alone firms, we focus on
differences in their equilibrium decisions in the product market. This not only permits us to
circumvent many of the perceived shortcomings of previous tests, but also provides new insights
about how firms compete in these markets. If organizational form is irrelevant, not only should
both diversified and stand-alone firms make similar decisions, but their competitors should
perceive the firms to be similar and respond similarly. Since these responses are conditioned also
on competitors’ own organizational forms, equilibria across markets with different mixes of
diversified and focused firms should be similar. However, if either decisions or responses depend
on own or competitors’ organizational form, this would suggest that either organizational form or
an unobservable driving organizational form plays a role in product market competition.
Using data from the discount department store industry, we examine the link between
organizational form and firm weakness in the product market in three distinctly different ways.
We first test pricing predictions of a simple switching cost model of market competition,
demonstrating that weaker firms charge higher prices in equilibrium (and sacrifice market share).
Not surprisingly, we find higher average prices in cities with a larger proportion of high debt and
low efficiency firms. Surprisingly, the same result holds also for cities with larger proportion of
focused firms. Our second test documents that firms with these characteristics are also more
likely to exit after facing a negative shock to their city, again suggesting that like high debt and
low efficiency firms, focused firms are weak competitors. Given that certain characteristics are
associated with weakness, and these characteristics are observable, stronger competitors should
be able to take advantage of firms with such characteristics. We model the optimal location
decision of a new entrant into a city with existing incumbent stores. The prediction is the entrant
locates closer to a weaker incumbent. We document a new entrant locates closer to high debt as
well as focused firms suggesting that in the retail industry, focused firms are perceived as weak
competitors.
20
If the focused organizational form reflects weakness and competitors base their decisions on it, a
natural question is whether the firm should change its organizational form. This is a difficult
question and one we address only indirectly. The reason is organizational form is endogenously
determined. Thus, identifying a benefit associated with the diversified form does not imply the
firm would gain from changing to it. Other costs may outweigh the benefit. So it is important to
look at the net effect through the expected impact on profitability or efficiency. If this is positive,
changing organizational form may be reasonable. Another unresolved issue is whether some
unobservable factor is driving both organizational form and firm weakness. We try to address
this by controlling for a number of potentially relevant variables. Two of these are leverage and
efficiency. If the unobservable factor is important, it is likely to impact at least one of these
variables. Thus, we suggest that firms should switch to the diversified organizational form only if
they can do so without increasing debt or lowering operating efficiency, as these too are attributes
of weak competitors.
This study uses data for the discount department store industry, an industry for which extensive
detailed data is available. It would be interesting to see whether the results hold more generally.
Given that Maksimovic and Phillips (2002) use manufacturers and find similar results to Khanna
and Tice (2001) who use the discount department store industry, the results of this study could
well extend to manufacturers too. If not, an investigation of potential differences would increase
our understanding of why organizational form matters for some industries and not others. We
leave these and other issues to future research.
21
Table I
Discount and Non-Discount Item Descriptions
ACCRA descriptions of the items from the Cost of Living Index used in this study are shown below. All
items are from the ACCRA Miscellaneous Goods & Services list except, facial tissue, washing powder and
soft drink, which are from the ACCRA Grocery Items list.
Discount Department Store Items Group
Non-Discount Items Group
Toothpaste:
Hamburger Sandwich:
6 oz – 7 oz tube, Crest or Colgate
¼ lb patty with cheese, McDonalds, if available
Shampoo:
Pizza:
11 oz bottle Johnson’s Baby Shampoo
12” – 13” thin crust, cheese, Pizza Hut or Pizza Inn, if
(Switches to 11 oz bottle of Alberto VO5 in 1991 Qtr 4 )
available. (11” – 12” size starting in 1994 Qtr 4)
Man’s Dress Shirt:
Fried Chicken:
White, cotton/poly blend, long sleeves
Thigh and drumstick, with or without extras, whichever
(Various combinations of brands used over time
is less expensive, Church’s or Kentucky Fried Chicken,
including a change to 100% cotton pinpoint Oxford, long
if available.
sleeves in 1994 Qtr 4)
Man’s Denim Jeans:
Major Appliance Repair:
Levi brand, size 28/30 – 34/36
Home service call, clothes washing machine; minimum
(Specific Levis jean changes: Levi’s straight leg, Levi’s
labor, excluding parts.
500 series, Levi’s 501s or 505s)
Boy’s Underwear:
Beauty Salon:
Package of 3 cotton briefs, lowest price
Woman’s shampoo, trim and blow dry.
Tennis Balls:
Dry Cleaning:
Wilson or Penn brand, can of 3 extra-duty yellow.
Man’s two-piece suit.
Board Game:
Haircut:
Parker Brothers “Monopoly” No. 9 standard edition
Man’s barbershop haircut, no styling.
Facial Tissue:
Newspaper Subscription:
175 count box Kleenex brand
Monthly cost of daily and Sunday home delivery.
Washing Powder:
Movie:
42 oz or 49 oz Tide, Bold or Cheer
First-run, indoor, evening rate, no discount.
Soft Drink:
Bowling:
2 liter Coca Cola excluding any deposit
Price per game, evening rate.
22
Table II
Summary Statistics for Variables
This table contains means and standard deviations for the variables used in the subsequent specifications.
The 180 cities used have prices available in qtr 1 (or qtr –2 if prices are not available in qtr –1). Wal-Mart
enters city j between qtr 0 and qtr +4. Cities have at least one incumbent firm at the beginning of the event
window, and no foreign owned or franchised stores. The variables measure the characteristics of the
incumbent firms in city j for the fiscal year that ends at the beginning of qtr 0. FrFocused is the fraction of
incumbent stores with more than 90% of sales coming from the discount department store industry,
FrLowssq is the fraction of incumbent stores owned by firms with inflation-adjusted sales per square foot
of less than $177 in 1996 dollars, FrSmFirmSize is the fraction of incumbent stores owned by firms with
total firm inflation adjusted sales of less than 1.475 billion in 1996 dollars, FrSmSegmentSize is the
fraction of incumbent stores owned by firms with discount department store inflation-adjusted sales of less
than $1.475 billion in 1996 dollars, FrPrivate is the fraction of incumbent stores owned by firms that do not
have publicly traded stock, Herf is the Herfindahl measure for the city and is defined as the sum of the
squares of the market share held by each incumbent firm i in city j at the beginning of the event window,
Relative Disc Prices is the sum of the prices of the 10 discount items divided by the sum of the prices of the
10 non-discount items measured at quarter –1 (or qtr –2 if qtr –1 prices are not available). 43 firms are
represented in the sample.
Variable
Mean
Std Dev.
FrFocused
.3442
.3250
FrLowssq
.3145
.3314
FrSmFirmSize
.1214
.1924
FrSmSegmentSize
.1545
.2274
FrPrivate
.0736
.1513
Herf
.4858
.2413
Relative Disc Prices
.8680
.0951
23
Table III
Determinants of Prices
The dependent variable is the relative prices of discount items in city j defined formally as:
10
 Pd ,j ,-1
d=1
Relative Discount Prices j, -1 

10

Pnd ,j ,-1
nd = 1
where P is defined as an item’s price, j indexes cities, d indexes discount items, nd indexes non-discount
items. The 180 cities used have prices available in qtr 1 (or qtr –2 if prices are not available in qtr –1).
Wal-Mart enters city j between qtr 0 and qtr +4. Cities have at least one incumbent firm at the beginning of
the event window, and no foreign owned or franchised stores. The variables measure the characteristics of
the incumbent firms in city j for the fiscal year that ends at the beginning of qtr 0. FrFocused is the fraction
of incumbent stores with more than 90% of sales coming from the discount department store industry,
FrLowssq is the fraction of incumbent stores owned by firms with inflation-adjusted sales per square foot
of less than $177 in 1996 dollars, FrSmFirmSize is the fraction of incumbent stores owned by firms with
total firm inflation adjusted sales of less than 1.475 billion in 1996 dollars, FrSmSegmentSize is the
fraction of incumbent stores owned by firms with discount department store inflation-adjusted sales of less
than $1.475 billion in 1996 dollars, FrPrivate is the fraction of incumbent stores owned by firms that do not
have publicly traded stock, Herf is the Herfindahl measure for the city and is defined as the sum of the
squares of the market share held by each incumbent firm i in city j at the beginning of the event window.
43 different firms are represented in the sample. A time dummy representing the 1980’s is included, but
the estimate of the coefficient is not shown. The estimations shown use White’s adjustment for
heteroskedasticity. P-values are in parentheses.
1
2
3
4
FrLowssq
.0441**
(.044)
.0476*
(.053)
.0458*
(.053)
.0405*
(.070)
FrFocused
.0676***
(.001)
.0709***
(.001)
.0689***
(.001)
.0624***
(.002)
.0175
(.671)
FrSmFirmSize
.0074
(.819)
FrSmSegment Size
FrPrivate
Herf.
# Observations
R-Squared
.0504
(.273)
.0428
(.124)
.0405
(.141)
.0414
(.133)
.0481*
(.079)
180
180
180
180
.1008
.1017
.1011
.1062
***, **, or * denote t-statistics significant at the one, five and ten percent respectively
24
Table IV
Determinants of Prices: Incumbent Debt Known
The dependent variable is the relative prices of discount items in city j defined formally as:
10
10
Relative Discount Prices j,-1 
 Pd ,j ,-1 

Pnd ,j ,-1
d=1
nd = 1
where P is defined as an item’s price, j indexes cities, d indexes discount items, nd indexes non-discount
items, and q indexes the specific quarter. The 134 cities have prices available in quarter 1 (or quarter –2).
Wal-Mart enters city j between qtr 0 and qtr +4. All cities used in each panel have at least one incumbent
firm at the beginning of the event window, and no foreign owned or franchised stores. The variables
measure the characteristics of the incumbent firms in city j for the fiscal year that ends at the beginning of
qtr 0. FrFocused is the fraction of incumbent stores with more than 90% of sales coming from the discount
department store industry, FrLowssq is the fraction of incumbent stores owned by firms with inflationadjusted sales per square foot of less than $177 in 1996 dollars, FrHighDebt is the fraction of incumbent
stores owned by firms with a total debt to total assets ratio of at least 70%, FrSmFirmSize is the fraction of
incumbent stores owned by firms with total firm inflation adjusted sales of less than 1.475 billion in 1996
dollars, FrSmSegmentSize is the fraction of incumbent stores owned by firms with discount department
store inflation-adjusted sales of less than $1.475 billion in 1996 dollars, Herf is the Herfindahl measure for
the city and is defined as the sum of the squares of the market share held by each incumbent firm i in city j
at the beginning of the event window. A time dummy representing the 1980’s is included, but the estimate
of the coefficient is not shown. P-values are in parentheses. The estimations shown use White’s adjustment
for heteroskedasticity.
1
2
3
FrLowssq
.0210
(.447)
.0193
(.524)
.0204
(.501)
FrFocused
.0680***
(.002)
.0663***
(.006)
.0676***
(.003)
.0632*
(.070)
.0634*
(.076)
.0633*
(.076)
FrHighDebt
FrSmFirmSize
.0128
(.823)
FrSmSegment Size
Herf.
# Observations
R-Squared
.0034
(.933)
.0289
(.370)
.0300
(.369)
.0294
(.374)
134
134
134
.1121
.1124
.1121
***, **, or * denote t-statistics significant at the one, five and ten percent respectively
25
Table V
Determinants of Exit
The dependent variable is the fraction of incumbent stores that exit city j by qtr +20. Wal-Mart enters city j
between qtr 0 and qtr +4. We estimate this as a Tobit Model: y* = o + x +  where /x  Normal (0, 2)
and Fraction Exit j, 0 to +20  max (0, y*). If #Strs n,j, +20  #Strs n, j, 0 then y = Fraction Exit j ,0 to +20  0.
If #Strs n,j,+20  #Strs n, j, 0 then y = y* = Fraction Exit j, 0 to +20 where
N
 [Net Reduction #Strs n ,j] 0 to +20
n=1
Fraction Exit j, 0 to +20)  ---------------------------------------------------N

#Strs n ,j ,0
n=1
and [Net Reduction #Strs n ,j] 0 to +20 = [#Strs n, j, 0  #Strs n, j, +20], #Strs is the number of stores, , j indexes
cities, n indexes incumbent firms in city j.
Only observations with quarter zero equal to ’91 or earlier are used, as data through quarter +20 is needed.
The 115 city observations used in Panel A have prices available in quarter 1 (or qtr –2 if qtr –1 is not
available), at least one incumbent firm at the beginning of the event window, no foreign owned or
franchised stores. The dependent variable is in decimal form. The independent variables are measured for
each city j using data for the year ending at qtr 0. A time dummy representing the 1980’s is included, but
the estimate of its coefficient is not shown. The 86 city observations used in Panel B are the observations
where debt is known for all incumbents in the city. NOTE: The Tobit coefficient estimates are not the
partial effects of the conditional expectations. P-values are in parentheses.
Panel A: Fraction of Incumbent Stores Exiting by Quarter +20: All Cities
1
2
3
4
FrLowssq
.7197***
(.000)
.7371***
(.000)
.7255***
(.000)
.7363***
(.000)
FrFocused
.2653***
(.010)
.2854***
(.000)
.2705**
(.014)
.2900***
(.007)
 .0834
(.668)
FrSmFirmSize
 .0220
(.890)
FrSmSegmentSize
.1657
(.460)
FrPrivate
Herf.
Pseudo R-Squared
# Observations
.4914***
(.002)
.5077***
(.002)
.4967***
(.003)
.5015***
(.002)
.4061
.4073
.4062
.4096
115
115
115
115
***, **, or * denote t-statistics significant at the one, five and ten percent respectively
26
Table V
(continued)
Panel B: Fraction of Incumbent Stores Exiting by Quarter +20: Debt Known for All Incumbents
1
2
3
FrLowssq
.6691***
(.000)
.6214***
(.000)
.6315***
(.000)
FrFocused
.3029**
(.022)
.2656**
(.048)
.2802**
(.035)
FrHighDebt
.5770***
(.002)
.6344***
(.001)
.6151***
(.002)
FrSmFirmSize
.3118
(.298)
FrSmSegmentSize
Herf.
Pseudo R-Squared
# Observations
.1912
(.365)
.5140**
(.016)
.4401**
(.044)
.4517**
(.040)
.4328
.4413
.4392
86
86
86
27
Table VI
Price Levels and Subsequent Exit
The dependent variable is the fraction of incumbent stores that exit city j by qtr +20. Wal-Mart enters city j
between qtr 0 and qtr +4. We estimate this as a Tobit Model: y* = o + x +  where /x  Normal (0, 2)
and Fraction Exit j, 0 to +20  max (0, y*). If #Strs n,j,+20  #Strs n, j, 0 then y = Fraction Exit j , 0 to +20  0.
If #Strs n,j,+20  #Strs n, j, 0 then y = y* = Fraction Exit j, 0 to +20 where
N
 [Net Reduction #Strs n ,j] 0 to +20
n=1
Fraction Exit j, 0 to +20  ---------------------------------------------------N

#Strs n ,j ,0
n=1
and [Net Reduction #Strs n ,j] 0 to +20 = [#Strs n, j, 0  #Strs n, j, + 20], #Strs is the number of stores, , j indexes
cities, n indexes incumbent firms in city j.
Only observations with quarter zero equal to ’91 or earlier are used, as data through quarter +20 is needed.
The 115 city observations used have prices available in quarter 1 (or qtr –2 if qtr –1 is not available), at
least one incumbent firm at the beginning of the event window, no foreign owned or franchised stores. The
dependent variable is in decimal form. Relative Prices for city j are measured at quarter –1; The Herfindahl
measure for city j is measured at quarter 0. A time dummy representing the 1980’s is included, but the
estimate of its coefficient is not shown. NOTE: The Tobit coefficient estimates are not the partial effects of
the conditional expectations. P-values are in parentheses.
Relative Prices City j
Herfindahl City j
#Observations
Pseudo R-Squared
1.0205**
(.018)
 .7568***
(.000)
115
.1246
***, **, or * denote t-statistics significant at the one, five and ten percent respectively
28
Table VII
Difference in Mean Characteristics of Retrenching vs Non-Retrenching Firms
The 180 cities used have prices available in quarter 1 (or quarter –2), have at least one incumbent firm at
the beginning of the event window, and no foreign owned or franchised stores. Wal-Mart enters city j
between qtr 0 and qtr +4. If prices are not available in quarter 1, the city is included if prices are available
in quarter 2. The sample consists of 307 firm i - city j observations. In 104 cases firm i has less stores in
city j at the end of the event window. In the remaining 203 observations, no reduction in stores occurred
for firm i, in city j over the event window. Two-sample t-tests with equal variances assumed are shown
below to test if the characteristics of retrenching firms differ from those that do not retrench. Each firm
characteristic is measured at quarter 0. These are defined as: a Focus dummy = 1 if discount sales  90%
of firm sales; total debt to total assets for firm i (not available for the private firms); inflation adjusted
discount sales per square foot for firm i (1996 dollars); inflation adjusted discount sales for firm i in
millions (1996 dollars); inflation adjusted parent sales for firm i in millions (1996 dollars); a dummy =1 if
the firm does not have publicly traded stock (private dummy).
Variable
#Firm i - City j
Pairs
Mean for
Firm i - City j
Pairs
#Firm i - City j
Pairs
Mean for
Firm i - City j
Pairs
Focus Dummy
Firm
Retrenches
104
Firm
Retrenches
.404
Firm Does
Not Retrench
203
Firm Does
Not Retrench
.246
t-stat
Ho:
Mean (diff) = 0
2.88***
Debt Ratio
89
.723
185
.611
6.09***
Inf. Adj. S/Sq ft
(’96 dollars)
Inf. Adj.Disc.
Sales (000’s)
(’96 dollars)
Inf. Adjusted
Parent Sales
(000’s)
(’96 dollars)
Private Dummy
104
$154.26
203
$203.86
8.49***
104
$4518.60
203
$15876.85
8.33***
104
$7824.89
203
$20414.68
8.65***
104
.144
203
.123
.605
***, **, or * denote t-statistics significant at the one, five and ten percent respectively
29
Table VIII
New Entrant Distance to Incumbents
The dependent variable is the distance in miles between the new entrant’s store and the incumbent store in
city j. This sample contains cities Wal-Mart entered in 1987 and 1995. 124 cities and 408 firm i – city j
pairs are represented in the tests below. The independent variables are measured for firm i for the fiscal
year ending when Wal-Mart enters: (1) Focus Dummy: Equals 1 if discount sales  90% of firm sales for
firm i , otherwise = 0; (2) Total Debt Ratio: total debt to total assets for firm i (not available for some firms
as they are privately held); (3) LnDiscSales: natural log of inflation adjusted discount department store
sales for firm i (’96 dollars); (4) Inf. Adj. Sales/SqFt: Inflation Adjusted Sales per Square Foot for firm i
(’96 dollars); (5) LnFirmSales: natural log of inflation adjusted firm sales for firm i (’96 dollars); (6)
#Incumbent Stores: The number of stores in city j when Wal-Mart enters (is a proxy for the size of city j)
All estimations include a 1995 year dummy, however, the coefficient estimate is not shown. The
estimations shown use White’s adjustment for heteroskedasticity. Columns 1 & 3 consist of the subsample of incumbent firms where the debt ratio is known (ie: public).
Public Only
Public & Private
Public Only
Public & Private
1
2
3
4
Focus
Dummy
 1.024*
(.083)
 1.1767*
(.053)
 1.373*
(.079)
 1.373*
(.073)
Total Debt
Ratio
 3.752*
(.068)
LnDiscSales
 0.2566
(.144)
 0.2317
(.164)
0.0009
(.859)
 0.0001
(.977)
Inf Adj.
Sales/SqFt
 3.863*
(.064)
LnFirmSales
#Incumbent
Stores
#Observations
R-Squared
0.0009
(.985)
 0.0009
(.843)
 0.319
(.215)
 0.2341
(.291)
0.5661***
(0.000)
.5581***
(0.000)
0.5672***
(0.000)
0.5610***
(0.000)
372
408
372
408
.4148
.4028
.4138
.4017
***, **, or * denote t-statistics significant at the one, five and ten percent respectively
30
Appendix I
A. Model Base Case
We start with the original model developed by Hotelling (1929). Assume a “linear city” of length
1, with consumers uniformly distributed over its length. There are two stores belonging to
different chains located at the extremes of the city; store A at x = 0, and store B at x = 1.22 The
stores sell homogeneous goods, but one can have lower unit costs than the other. Let CA and CB
represent their respective one unit costs. Consumers incur transportation cost t per unit of length.
Since we use a variant of Hotelling’s model by assuming quadratic instead of linear costs, a
consumer living at x incurs a cost tx2 to go to store A and a cost of t(1-x)2, to go to B. Thus, if
prices charged by the two stores are PA and PB, total price to this customer is PA + tx2 to buy from
A and PB + t(1-x)2 to buy from B. Each consumer demands one unit of the good per period and
has a reservation price of R. As a starting point, we use the two period model of Klemperer
(1987). Since these models assume switching costs in the second period, firms retain the
customers they attract in the first period and can charge them up to their reservation price, R, in
the second period.
Assuming quadratic transportation/switching costs, and that firms charge prices PA and PB
simultaneously in period one and R in the second period, we look for the Nash equilibrium in
prices. We also assume prices are close together so that both firms capture positive market share
and they are low enough so all customers buy.
Proposition 1: The equilibrium period-1 prices are :
PA = t + 4/3 CA + 2/3 CB – R and
PB = t + 4/3 CB + 2/3 CA – R.
Proof: A consumer located at x is indifferent between shopping at A or B if his total price
including transportation costs is equal across stores. That is:
PA + tx2 = PB + t(1-x)2 or
x = 1/2 + (PB – PA)/2t.
22
With the cost function we use in this model, locating at the extremes is an equilibrium outcome. See
d’Aspremont, Gabszewicz, and Thisse (1979) and Economides (1986) among others.
31
Given this formulation of the problem, x represents the portion of the market captured by store A.
Thus A captures a larger portion of the market if PA < PB. If PA = PB, both split the market
equally in the first period, and because of switching costs in the second period retain that share.
Thus A’s two period profit, A, is:
A = (PA – CA)(1/2 + (PB – PA)/2t) + (R – CA)(1/2 + (PB – PA)/2t)
(1)
Equating A /PA to zero, gives the reaction curve of PA to a given PB, results in:
2PA = t + PB +2CA –R
(2)
Similarly, the reaction curve of PB to a given PA is:
2PB = t + PA +2CB –R
(3)
Substituting PB from (3) into (2) gives the desired expression for PA. The desired expression for
PB is by symmetry.
QED
The proposition shows that an incumbent’s equilibrium price is positively related to its own cost
and that of its competitor and is negatively related to a customer’s reservation price in the second
period. Thus, markets with an inefficient (high cost) competitor display higher prices in
equilibrium. A firm with higher costs charges a higher price. This permits its more efficient
competitor to raise prices and increase its own profits. Thus weaker competition results in higher
average prices in such markets.23 The reason equilibrium prices are negatively related to R is a
result of the following trade off in a two period model. Since this model assumes stores retain
their first period customers for the second period and charge them their reservation price, higher
R makes the second period profit more attractive. To maximize this profit opportunity, firms try
to capture a larger share of the market in the first period. However, this necessitates a lower
price. Thus, as R increases, prices in the first period decrease.
23
Similar results occur if we also introduce a probability of exit for incumbents as in Chevalier and
Scharfstein’s (1996). Average prices are higher in markets with higher probability of exit. Reasons are
similar to markets with a high cost or low efficiency incumbent. A firm with a higher probability of exit is
more inclined to sacrifice future market share and charges a higher current price. This allows its
competitors to also raise price, making the average price higher. Since high leverage is related to higher
probability of exit, markets with high debt firms should see higher average prices.
32
B. New Entry
Next, we allow for new entry. A store belonging to firm C, with cost CC per unit enters the
market with the two firms A and B located as above.24 C has to make two decisions, where to
locate, and given the location what price to charge assuming that A and B will respond optimally
by changing their prices to maximize their profits given C’s location.25 In effect we are assuming
that prices are again determined simultaneously as a Nash equilibrium, after location is observed.
We solve for the equilibrium by identifying, for every location, y, the PA, PB, and PC which
maximize each store’s profits given the other stores’ reaction function. The y we are interested in
gives C the highest profits over all other y.
It is instructive to draw a diagram of the linear city.
0______ xCA__ y______xCB__________1
A
C
B
A and B are at 0 and 1 as before. C locates at some y between them. Now consumers lying
between 0 and y, choose between A and C, while those lying between y and 1 choose between C
and B. Given PA, PB, PC and y, we refer to xCA as the consumer who is indifferent between
shopping at C and A, and xCB as the consumer indifferent between C and B. Thus, xCA will
represent portion of the market between 0 and y captured by C, and xCB as the portion between C
and B captured by C. Thus the total market share captured by C will be xCA + xCB for the given
prices and location. Since xCA is indifferent between C and A, as before:
2
PC + txCA
= PA + t(y - xCA)2 . This gives:
xCA = y/2 + (PA – PC)/2ty,
which is similar to the expression for market share without entry except for y. By symmetry, A’s
market share now is:
xA = y/2 + (PC – PA)/2ty.
It can similarly be shown, that
xCB = (1 – y)/2 + (PB – PC)/2t(1 – y),
xB = (1 – y)/2 + (PC – PB)/2t(1 – y).
24
We do not study the entry decision of C, only its location decision after the entry decision has been made.
However, this decision should be the same whether or not the entry decision is considered.
25
Here we are restricting A and B to react only through prices and not through spatial competition either
through a change in location or by adding more stores. For quadratic cost functions, it remains optimal to
maintain maximal dispersion. Thus neither A nor B are better off by leaving the extremes. Also, opening
another store reduces profits through both cannibalization and lowering of prices because of reduced
distance between stores. It can be shown that under some weak assumptions, it is also not optimal to
respond by opening another store. Proofs are available from authors on request.
33
Proposition 2: Given PA, PB and y, the price that maximizes C’s profits is given by:
2PC = ty(1 – y) + PA(1 – y) + PBy + 2CC – R.
Proof: C = (PC - CC)( xCA + xCB) + (R - CC)( xCA + xCB), and
(xCA + xCB) = ½ + (PA – PC)/2ty + (PB – PC)/2t(1 – y)
= (1/2ty(1-y))(ty(1 – y) + PA(1 – y) – PC + PBy).
Thus, C = (1/2ty(1-y))(ty(1 – y) + PA(1 – y) – PC + PBy) (PC + R – 2CC).
Taking f.o.c. of C with respect to PC, and simplifying results in the required expression for the
optimal PC. This is C’s reaction function for different PB.
QED
Like in Proposition 1, the equilibrium PC is positively related to its own cost and competitor
prices, which are functions of their own cost as is made explicit in the next proposition. It is also
dependent on y in an intuitive way. As y increases, C locates closer to B and B’s price becomes
more important in determining PC. Similarly PA becomes more important when C locates closer
to A. Proposition-1 shows that equilibrium prices are more sensitive to own costs than other’s
prices/costs. Thus, the store with higher costs will charge higher prices. Let this be incumbent B.
This suggests that C would benefit by locating closer to B, as it would enable it to charge a higher
price to capture the same portion of the market between them, than it would need against A.
However, locating closer to B has a downside too. As C locates closer to B, switching costs
between B and C decrease, putting downward pressure on prices. Equilibrium prices and location
result from balancing these tradeoffs. Propositions 3 and 4 formalize this intuition.
Proposition 3: Given y and PC as above, the equilibrium prices, PC, PA and PB as functions
of only exogenous variables are:
PC = ty(1 – y) + 2/3 CA(1 – y) + 2/3 CBy + 4/3 CC – R,
PA = ty/2 + 4/3 CA + 1/3 y(CB - CA) + 2/3 CC – R, and
PB = t(1 – y)/2 + 1/3 CA + CB + 1/3 y(CB – CA) + 2/3 CC – R.
Proof : A’s profit in both periods comes from the portion of the market it captures from C in the
first period. Like B and C it charges R to its customers in the 2nd period. Therefore:
A = (PA – CA)(y/2 + (PC – PA)/2ty) + (R – CA)(y/2 + (PC – PA)/2ty).
Taking the f.o.c. with respect to PA and simplifying gives,
34
2PA = ty2 + PC + 2CA –R.
This is the reaction function for A for different PC. To get the equilibrium PA, we need only
substitute for PC. Before that, though, we need to express PC as a function of only the exogenous
variables. We achieve that by substituting the above expression for PA and the following
expression for PB into the expression for PC in Proposition-2. The reaction curve for B, given PC
and y can be shown to be:
2PB = t(1-y)2 + PC + 2CB – R.
Substituting for PA and PB gives us the necessary expression for PC:
PC = ty(1 – y) + 2/3 CA(1 – y) + 2/3 CBy + 4/3 CC – R.
Substituting this expression for PC into above expressions for PA and PB gives us the equilibrium
PA and PB to be:
PA = ty/2 + 4/3 CA + 1/3 y(CB - CA) + 2/3 CC – R, and
PB = t(1 – y)/2 + 1/3 CA + CB + 1/3 y(CB – CA) + 2/3 CC – R.
QED.
As before, the equilibrium prices are positively related to own and competitor costs, are more
sensitive to own costs, and are negatively related to R. They are also positively related to
switching costs and to how far C locates in relation to the incumbents.
The next proposition determines the optimal location of the entrant as a function of the exogenous
variables, CA, CB, CC, t, and R.
Proposition 4: The optimal location, y, is a solution to:
2ty3 – 3ty2 + ty – 2/3 CA + 2/3 yCA + 2/3 yCB + 2/3 CC – 4/3 yCC = 0.
Proof: The optimal location is one that maximizes C’s profits given optimal price responses by
incumbents A and B to the choice of y and PC by C. From Proposition 2,
C
= (1/2ty(1-y))(ty(1 – y) + PA(1 – y) – PC + PBy) (PC + R – 2CC), and from
Proposition 3, the optimal PA and PB, given y and PC are given by:
2PA = ty2 + PC + 2CA –R, and
2PB = t(1-y)2 + PC + 2CB – R .
Substituting for PA and PB in equation for C gives us:
C = (1/4ty(1-y))[ty(1 – y) + (ty2 + PC + 2CA –R )(1 – y) – PC + (t(1-y)2 + PC + 2CB –
R)y](PC + R – 2CC), or
C = (1/4ty(1-y))[3ty – 3ty2 + 2CA(1-y) + 2CBy – PC – R](PC + R – 2CC).
35
Substituting for PC from Proposition 3 reduces this to:
C = (1/2ty(1-y))[ty(1 – y) + 2/3 CA(1 – y) + 2/3 CBy – 2/3 CC]2 .
Taking f.o.c. of C with respect to y gives:
4ty(1 – y)(t – 2ty – 2/3 CA + 2/3 CB) – (2t – 4ty)[ty(1-y) + 2/3 CA(1 – y) + 2/3 CBy – 2/3 CC] = 0,
which simplifies to the cubic equation
2ty3 – 3ty2 + ty – 2/3 CA + 2/3 yCA + 2/3 yCB + 2/3 CC – 4/3 yCC = 0.
QED
The solution to this equation is messy. However, we can find the first derivative of y with respect
to CB to see where the new entrant locates as CB increases. To do so we find the total derivative
of the cubic, and set dCA and dCC = 0. Then, we get:
dy/dCB = (- 1/3 y)/[3ty(y – 1) + t/2 + 1/3 CA + 1/3 CB – 2/3 CC].
This derivative is positive as long as the firms’ costs do not differ by too much, an assumption we
have needed all along, and y is not close to the extremes. Under these conditions, y is increasing
in CB. That implies, C chooses to locate closer to B as CB increases. That is, C locates closer to a
weaker incumbent.
A number of interesting observations can be made with the help of a numerical example. For
instance, assume the following: t = .20; CA = .85; CB = .86; CC = .83; R = 1.3., making B the
weaker incumbent. As can be seen from the table below, C’s profit, C, is maximized at y = .55,
closer (though not too close) to the weaker incumbent, B, with the higher cost. PC also has an
interior maximum, though not necessarily at the same y. PA is monotonically increasing and PB
monotonically decreasing in y. This is as expected, since who ever C locates closer to has to
charge a lower price because of lower switching costs. The weaker incumbent, B, charges a
similar price to A at the equilibrium location for C, i.e., y = .55. This occurs even though B has
higher costs, because C has located closer to it, reducing its ability to charge higher. It is also
trivial to show that prices are lower after C enters than before. This is because with three
competitors, switching costs are lower for consumers, reducing the ability of firms to charge
higher prices. Since C is the most efficient, it makes the highest profits. Though not shown, C
also captures the largest market share. The results hold over a reasonable parameter space and as
long as costs are close enough so all firms capture positive market shares.
36
Two Period Model with Quadratic Costs: Entry Decision of C at Optimal y
t = .20; CA = .85; CB = .86; CC = .83; R = 1.3.
Y
0.1
0.20
0.3
0.4
0.5
0.53
0.55
0.57
0.6
0.7
0.8
0.9
P*c
0.392
0.407
0.417
0.424
0.427
0.427
0.427
0.426
0.425
0.420
0.411
0.397
P*a
P*b
0.397 0.487
0.407 0.477
0.418 0.468
0.428 0.458
0.438 0.448
0.441 0.445
0.444 0.444
0.446 0.442
0.449 0.439
0.459 0.429
0.469 0.419
0.480 0.410
c
0.02844
0.03403
0.03913
0.04267
0.04444
0.04463
0.04467
0.04464
0.04446
0.04286
0.04011
0.03872
a
0.0002
0.0007
0.0026
0.0049
0.0073
0.0081
0.0086
0.0091
0.0099
0.0124
0.0150
0.0176
b
0.0125
0.0103
0.0081
0.0060
0.0040
0.0034
0.0031
0.0027
0.0022
0.0007
0.0000
0.0027
37
Appendix II
Adjustments for ACCRA Discount Items Replacement
In the fourth quarter of 1991 the ACCRA replaced Johnson’s Baby Shampoo with Alberto VO5
shampoo. We adjust the prices of the items after the switch to reflect these changes by replacing
the new price with the price of the old item had no switch occurred. Formally,
9
 [(P j,i ,q  P j,i,q-1 )/ P j,i,q-1]
i=1
DPC j,q  Average Discount Item Price Change j,q = ------------------------------------9
where P represents item price, j indexes the city, and i indexes the nine discount items in city j not
switched during quarter q.
Price Old Item j, q– 1 (1+ DPC j,q )
Adj. Price New Item j, q + n = ----------------------------------------- X Price New Item j, q + n
Price New Item j, q
where q is the quarter that the item is switched, and n indexes the number of quarters after the
switch.
NOTE: In some cases we had to go forward to quarter q+2, or backwards to quarter q-3 to get
prices before and after the switch. For a couple of cities, prices before and after the switch are
not available. In these cases, we use the average multiplier for the other cities defined as:
[Price Old Item j, q– 1 (1+ DPC j,q )]/ Price New Item j, q .
38
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