Radius Restrictions and the Similarity of Neighboring Shopping Centers
by
Andrew Eckert and Douglas S. West*
Department of Economics
University of Alberta
Edmonton, Alberta T6G 2H4
Email: Andrew Eckert: aeckert@ualberta.ca
Douglas West: Douglas.west@ualberta.ca
January 2006
Abstract
In Canada and the U.S., shopping center developers may impose “radius restrictions” on
tenants in their shopping centers, prohibiting tenants in a particular shopping center from
opening another store within a certain radius. Whether a radius restriction is imposed on
a chain will depend upon the relative bargaining positions of the chain and the developer.
This paper presents an empirical analysis of regional shopping center composition in the
five westernmost provinces in Canada, using variables that reflect the bargaining power
of retail chains and shopping center developers. We find that large, well established
chains are more likely to enter neighboring malls, consistent with the hypothesis that
whether a chain enters neighboring malls depends upon its bargaining power. As well, we
find that a chain is more likely to enter neighboring malls owned by large developers,
consistent with large developers trying to keep retail chains out of the malls of small
developers.
*The authors thank Janene Taylor and Benjamin Atkinson for research assistance, and
the University of Alberta for financial assistance.
1
1 Introduction
In the United States, it is well known that shopping center developers have
imposed “radius restrictions” on some of the tenants in their shopping centers. These
restrictions are described in shopping center leases, and usually prohibit a retail tenant in
a particular shopping center from opening another store within a certain radius (e.g. five
miles) of the center. The restriction might apply to the entire term of the lease, but it will
exclude from consideration a retailer’s stores that were already established prior to
signing the lease containing the restriction.1 Radius restrictions have attracted the
attention of competition authorities.2
Radius restrictions, in economic terms, are vertical restraints, and like other
vertical restraints, there are alternative explanations for their use.3 For example, stores in
shopping centers confer demand externalities on one another, either because of
multipurpose or comparison shopping on the part of consumers. Developers, in making
their tenant choices, presumably take into account the strength of these externalities
because of the impact they can have on rental income. However, a retail firm will choose
its store locations to maximize profits given the demand externalities imposed on its store
by established tenants, but will not necessarily be concerned with the effect that its
location decision has on other tenants (e.g., the diminished demand externality imposed
by the store in one mall if an identical store opens in a neighboring mall). A radius
restriction can eliminate this dilution of the demand externality. On the other hand, radius
1
See Kampler and Pomerantz (2003).
See Kampler and Pomerantz (2003) and Lentzner (1977).
3
Radius restrictions are similar to the particular vertical restraint of exclusive dealing, in that a retail chain
that wishes to lease retail space from one mall is excluded from leasing additional space from a
geographically close competing mall. For a discussion of the competitive effects of exclusive dealing
restraints, see for example Mathewson and Winter (1987).
2
2
restrictions could be used to make it more difficult for rival centers to attract high quality
tenants.
It is possible that for certain retail chains, the interests of the developer and the
chain will coincide: both the developer and the chain may wish the chain to locate in only
one or two malls in a city. However, the interests of the developer and retail chain need
not coincide. Some retail chains will see it in their interests to try and locate stores in
every mall in a city. The ability of a developer to impose a radius restriction that would
prevent this from happening will depend upon the bargaining power of the developer and
the retail chain. The purpose of this paper is to carry out a spatial empirical analysis of
regional shopping center composition in the five westernmost provinces in Canada using
variables that reflect the bargaining power of retail chains and shopping center
developers. Our analysis will allow us to identify some important characteristics of
chains that tend to appear in neighboring malls, and characteristics of shopping centers
associated with chains entering neighboring centers.
In terms of results, it is found that certain retail chains are more likely to be
present in neighboring malls when both malls are owned by large developers than when
one of the malls is owned by a small developer. This result is consistent with large
developers trying to keep retail chains out of the malls of small developers. New retail
chains are less likely to be in neighboring malls, and this is consistent with the radius
restriction clause being imposed on new rather than established tenants. Chains are more
likely to be in neighboring malls when the malls are farther apart, but are less likely to be
in neighboring malls in large cities. The size of retail chain, as measured by the number
of cities containing members of the chain, is also positively associated with whether a
3
chain is in neighboring malls, and this is consistent with bargaining power increasing
with the size of the chain. Certain other variables, such as whether a chain sells private
label merchandise and is part of a multi-chain firm, also play a role in explaining whether
a chain store is in neighboring malls.
This paper undertakes a systematic examination of factors that could affect the
ability of the developer to exercise market power. Developers with market power would
likely exercise it by raising rents to their retail tenants, and their ability to do this could
depend on whether their tenants have space in neighboring centers. While there is a
variety of factors that can contribute to the explanation of shopping center rents, our
empirical work suggests that market power considerations cannot be dismissed.
In the next section, the radius restriction literature is briefly reviewed along with
the shopping center lease and shopping center similarity literature. There is little
consideration in this literature of which party has the power in the shopping center/tenant
relationship. Certain variables that may affect or reflect bargaining power, and whether a
retail chain will occupy space in neighboring shopping centers, are then discussed. In
Section 3, the data sources to be used in the empirical examination are described. Section
4 presents the regression model to be estimated, which essentially involves explaining
whether a chain store that is in one shopping center is also in a neighboring shopping
center. Section 5 presents summary statistics, while the results of econometric estimation
are discussed in Section 6. Section 7 provides a summary and some concluding remarks.
4
2
Radius Restrictions and Shopping Center Similarity
2.1 Radius Restriction Clauses in Shopping Center Leases
Radius restriction clauses in shopping center leases have been used for many
years in the U.S. Lentzer (1977) has provided the most detailed discussion of these
clauses, their impact, their history, and antitrust treatment. Lentzer (1977, 10-11) reported
that in a survey of 34 regional shopping centers and five specialty centers in Los Angeles
in 1976, 88 percent of the responding landlords reported the use of radius restrictions in
their centers for all but the largest and most desirable tenants. These restrictions
generally prohibit the retail tenant from opening up another store within a certain radius
of the center.4 Eighty-four percent of the respondents indicated a radius restriction that
extended between three and five miles from the center. Sixty-seven percent of the
respondents imposed the restriction for the duration of the lease.
Lentzer (1977, 12) has written that landlords have advanced two arguments in
favor of radius restrictions: (1) radius restrictions are necessary to protect percentage
rental income paid by the tenant, and (2) the radius restriction “enables the landlord to
combat the ‘unfair competition’ of his own tenants that might destroy the economic
vitality of the center itself.”5 Without the radius restriction, a tenant’s sales in one center
might be reduced by its presence in a neighboring center, thereby reducing or eliminating
the percentage rent that the landlord would otherwise have collected.
4
Jones, Addison, and Davidson (2001, 57-64) provide examples of typical radius and exclusive use
provisions in shopping center leases. The latter type of provision generally restricts the landlord from
renting space in the center to competing retailers.
5
Shopping center leases generally require tenants to pay a fixed minimum monthly rent, and a percentage
of sales rent once sales exceed some specified threshold. See Murray (2001) for a detailed discussion of
percentage rent provisions in shopping center leases. Lee (1995) provides an economic analysis of the fixed
rent/percentage rent shopping center lease.
5
A survey of retail tenants, whose results are also summarized by Lentzer (1977,
18) indicated that single store and small chain operators do not have sufficient bargaining
power to challenge the radius restriction, whereas larger firms were able to negotiate
radius restrictions which reduced the size of the territorial restriction and its duration.
With increased scrutiny of radius restrictions by the Federal Trade Commission, Lentzer
(1977, 23) stated that landlords have reduced radius restrictions to a two to five mile
radius, and put them in effect for the first five years of the lease. The latter change
suggests that newer retail chains and tenants are more likely to be affected by the radius
restriction than long established chains.
In addition to the ample evidence of the continuing use of radius restrictions in
U.S. shopping center leases, there is some evidence of their use by Canadian developers.
For example, in a report on discontinued cases for the years 2000-2002, the Competition
Bureau discusses a complaint that Iberville Development was using certain shopping
center lease provisions that violated the market restriction provisions of the Competition
Act. “Although the 16-km distance used in the radius clauses adopted by Iberville
Development Ltd. is higher than the distances usually used in the industry, the Bureau
concluded, following an in-depth and detailed study of the facts, that the radius clauses
used by Iberville Development Ltd. are not likely to substantially lessen competition in
the Sherbrooke area.”6 Some Canadian shopping center developers apparently use radius
restrictions in their shopping center leases.7
See Competition Bureau, “Discontinued Cases 2002-2001-2000”, available on the Competition Bureau’s
website at http://www.competitionbureau.gc.ca/internet/index.cfm?itemID=1192&1g=e, last accessed on
August 26, 2005.
7
Additional evidence of the use of radius restrictions by Canadian shopping centers is provided by a report
of a Canadian law firm, Jones, Addison and Davidson (2001). This report contains a discussion of the
appropriate wording and drafting of radius restrictions. Radius restrictions also are discussed by the
International Council of Shopping Centers (2001), which describes them on page 145 as “the shopping
6
6
2.2 Recent Economic Studies of Shopping Centers
There have been some studies of shopping centers in the economics literature,
but none that focus on the radius restriction and whether chain stores locate in
neighboring centers. Much of the literature tries to explain shopping center rents.
Benjamin, Boyle, and Sirmans (1990) construct a model of retail leases that predicts that
minimum fixed rents are lower with higher percentage rents and are higher with higher
threshold sales levels. Benjamin, Boyle, and Sirmans (1992) find empirical support for a
shopping center model’s predictions that developers use tenant characteristics, such as the
probability of default and customer traffic-generating potential, to set shopping center
rents in a discriminatory fashion.
Pashigian and Gould (1998) wish to examine whether shopping center rents
reflect demand externalities among stores in the center. They find that the external
economies generated by anchor stores are reflected in the lower rents paid by the anchors;
the authors report on page 115 that their estimates suggest that “anchors receive a per
foot rent subsidy of no less than 72 percent that which nonanchor stores pay.” In a later
paper, Gould, Pashigian, and Prendergast (2005) use data on the rent, sales, and
contractual provisions of over 2500 stores in large shopping malls in the U.S. to show
that (1) the increasing presence of anchor stores in a mall generates higher sales, and
consequently higher rents, for non-anchor stores, and (2) “stores with a national name
brand, which also tend to generate mall traffic, receive significant rent discounts”.8 The
Gould, Pashigian, and Prendergast (2005) study is interesting in that, like the present
center landlord’s effort to assure itself that the tenant will devote its energy to the operation of its business
at the shopping center, generate the greatest possible sales from the premises, and not be induced by
competing landlords.”
8
See Gould, Pashigian, and Prendergast (2005, p. 411).
7
study, it uses store-specific information in the empirical analysis. However, they do not
examine whether possible radius restrictions are related to the observed rent differences
in their sample.
Other papers in the shopping center literature have focused on the shopping center
space allocation problem. Brueckner (1993) and Miceli, Sirmans, and Stake (1998)
construct models of space allocation in shopping centers when inter-store externalities are
present. Neither paper addresses the problem of shopping center allocation of space to
specific store brands when those brands might be in neighboring centers. Mejia and Eppli
(1999) look at the effects of allocating space to large stores of particular types on the
sales per square foot of smaller stores of the same type, and generally find these effects to
be positive and significant.
In a different but related vein, Eppli and Shilling (1996) use a retail gravity model
and data from 38 U.S. regional shopping centers to estimate the retail sales potential of
regional shopping centers. They find that actual retail sales at regional shopping centers
are largely determined by center size (in square feet), and to a lesser extent by distance to
competitors. They conclude that large regional shopping centers may dominate smaller
regional centers. There are of course a number of shopping center characteristics (such as
store variety and the attractiveness of the shopping center’s store brands) that could be
related to center size that are not included in Eppli and Shilling’s analysis.
There have been two papers that have compared the store brand compositions of
regional shopping centers. West (1992) used regional shopping center data from Calgary
and Edmonton to show that malls located in the same city will be more similar (in certain
store types) than malls located in different cities, malls owned by the same firm will be
8
more similar than malls owned by different firms, and malls will be more similar in
certain store types dominated by multi-chain firms than in store types that are not.
Golosinski and West (1995) extended the analysis of shopping center similarity
by studying the internal compositions of 97 shopping centers owned by the eight largest
shopping center developers in the five westernmost Canadian provinces. They also
provided a double moral hazard hypothesis for the similarity of shopping centers owned
by the same developer. That is, to reduce the incentive to shirk on the part of each party
to the shopping center contract, a multi-center developer will promote the growth of retail
chains by offering space in all of its centers to members of the same chain, while retail
chains seek locations in the centers owned by a multi-center developer. Golosinski and
West found that shopping centers owned by the same developer were more similar than
centers owned by different developers and that centers in the same city and same
province were more similar than centers in different cities and provinces. Shopping
centers owned by large developers tended to be more similar than shopping centers
owned by small developers. These results support the double moral hazard explanation
for shopping center similarity.
While West (1992) and Golosinski and West (1995) considered various factors
that might help explain the similarity of shopping centers, neither paper attempted to
explain the degree of similarity specifically among neighboring centers, or to consider the
role of proximity in determining similarity, other than examining the effect of being in
the same city. As will become clear below, the bargaining power of the developer
compared to that of the retail chain could be very important in determining whether a
chain will be able to acquire space in neighboring shopping centers.
9
2.3 Chain Store Presence in Neighboring Shopping Centers
Lentzer (1977) suggests that whether a retail chain will be in neighboring shopping
centers depends on the bargaining power of the developer and the retail chain, a
suggestion consistent with other available documents and analyses pertaining to radius
restrictions. Before proceeding to analyze relative bargaining power further, we must
define what is meant by the phrase “neighboring shopping centers”.
It will be assumed that each city contains a hierarchy of shopping centers with
two important characteristics: (1) a large number of small centers and a small number of
large centers, and (2) centers at a given level of the hierarchy contain all of the store types
that locate in lower level centers, plus additional store types as well. The shopping center
hierarchy that has been analyzed in the past contains at least four levels of centers:
neighborhood centers, community centers, regional centers, and the central retail district.
Shopping centers can also be planned or unplanned, where a planned center is one that is
under the control of a single owner/developer that controls entry into the center. For
purposes of this paper, the focus will be on planned regional shopping centers. The
reason is that certain store types (e.g. clothing and shoe stores) are more likely to be
found in regional rather than lower level shopping centers, and these store types tend to
be those for which developers that control entry may wish to impose radius restrictions.9
Once attention is focused on regional shopping centers, there are a number of
ways one can define neighboring centers. The fact that lease restrictions are based on a
fixed radius leads us to define neighboring centers as centers within such a radius.
9
Empirical affirmation of the hierarchal structure is provided in West, Von Hohenbalken, and Kroner
(1985).
10
Because reference is made in Lentzer (1977) to a five mile radius, in this paper we will
consider two centers to be neighbors if they are within five miles of each other.
The incentive that developers have to impose radius restrictions is related to the
drawing power or catchment area of the center. If regional shopping centers were
identical, then consumers would have an incentive to patronize the closest center to them
in order to minimize transportation costs. A regional center’s catchment area would then
consist of the set of points closer to that center than to any other center in the market.
Given that regional centers are not identical, some centers will have greater attractive
power than others, in part based on their quality level and tenant list, drawing consumers
from greater distances. One would expect the sales and profits of the developer of and
tenants in more attractive centers to be higher as a result. The presence of a high profile
chain in a shopping center can have external effects on the other tenants in the center, in
that its presence in the center increases the number of people frequenting the shopping
center and doing business with the other tenants. As a result, the decision of a chain to
enter a neighboring mall will have external effects on the shopping center and its tenants
not taken into account by the chain, and which the shopping center may wish to address
with a radius restriction. As well, a radius restriction imposed on a chain that benefits a
developer and some of its retail tenants also may not be in the interest of the retail chain
since the chain forgoes locations in neighboring centers. For these reasons, the interests
of developers and their retail chains may or may not coincide, and the ability of a
developer to impose a radius restriction will depend on a number of factors as elaborated
below.
11
First, the developer will not necessarily want to impose a radius restriction on all
of the retail tenants in its regional centers. Rather, it will wish to impose it on store types
that figure most prominently in the consumer’s decision as to which center to patronize.
Regional shopping centers contain a variety of different store types, some catering to
multipurpose shoppers and selling convenience goods (e.g., grocery and drug stores), and
some, catering to multipurpose/comparison shoppers, that sell comparison shopping
goods.10 Given that it is the latter type of stores (“C” stores) that largely distinguish
regional centers from centers at lower levels of the hierarchy, one would expect
comparison shopping store types to be the primary target of radius restrictions.11 Among
C stores, there are some that sell brands that tend to be advertised either as private brands
for the retailer or as brands that are exclusive to a retailer in a certain geographic area.
The combination of offering exclusive brands and attractive brands will make these stores
candidates for radius restrictions on the part of the developer.
One would also expect newer retail chains to be subject to radius restrictions to a
greater extent that older chains. Shopping center developers lease space to retailers for
fixed terms, and with lease renewals that may be at the developer’s discretion. This
allows the developer the opportunity to replace tenants whose leases expire with new
retailers that have greater sales potential. The new retailers may be ones that are selling
new products and services that are in high demand, and that can attract consumers to the
10
Golosinski and West (1995) refer to the latter such stores as C stores, and define them as follows: C
stores sell merchandise that is (a) highly differentiated in both horizontal and vertical dimensions and for
which branding is important, and (b) sufficiently costly that there are perceived positive net returns to
search across stores for price and quality. C store types include women’s clothing, shoe and jewelry stores.
11
For example, Cambridge Shopping Centres Limited (1999) states on page 6 that regional malls “derive
about 50% of their sales from the fashion and footwear segments…” and on page 8 that “when it comes to
categories such as fashion apparel, footwear and accessories, established regional malls will continue to
enjoy a competitive advantage.”
12
shopping center. It would be in the interests of the developer to keep such retailers out of
neighboring shopping centers.12
Another potential determinant of whether a developer can impose a radius
restriction is the developer’s size. As mentioned above, many C store types seek locations
in regional shopping centers precisely because of the comparison shopping opportunities
that they facilitate.13 Developers that own more than one or two regional shopping centers
(“large developers”) may wish to impose a radius restriction on a particular retail chain
and can make access to other centers that the developer owns conditional on accepting
the restriction.14 However, the restriction may not be imposed in the same way on all C
store chains, and there may be differences in whether the restriction is enforced against a
neighboring large developer’s as opposed to small developer’s center. Large developers
could attempt to use the radius restriction to keep retail chains out of the small
developers’ centers, making them less attractive and less profitable. Ultimately, they
could fail and exit the market.
Of course, the developer will not always be successful at imposing a radius
restriction. Certain retail chains may be highly desired as tenants by a developer and have
sufficient bargaining power to reject the restriction. In particular, large retail chains will
presumably be attractive to developers to the extent that their size signals profitable
expansion. In addition, a large chain has less incentive to shirk due to the impact that can
12
See again Lentzer (1997, 23). In a Canadian context, Jones, Addison and Davidson (2001, 58) state that
standard exemptions to radius restriction clauses include “existing tenants of the shopping centre” and
“major stores and specifically named department stores.”
13
Whether stores selling the same or similar goods will cluster in space depends on the strength of two
forces: the competition effect, which generally pulls the stores apart, and the market area effect, which
drives them to locate together. See Stahl (1987) for a discussion and analysis of these effects.
14
For certain store types, there may be a much larger number of retail chains than there are shopping
centers in a city within which to locate. Competition among retail chains for space in regional shopping
centers, especially for chains in store types that consider alternative locations to be unacceptable, could tip
the bargaining power balance towards the developer.
13
have on the chain’s reputation, and this too will be attractive to developers. Furthermore,
a large attractive chain might exercise bargaining power by threatening not to take space
in any of a developer’s centers, or by threatening to withdraw from all of a developer’s
centers. Some large retail chains may see it in their interests (and have the bargaining
power) to obtain locations in as many regional shopping centers as possible, given that
many consumers will patronize the closest regional center, and will not necessarily travel
a greater distance to patronize certain retail chains.
Besides the size of the chain, two other possible indicators of a chain’s bargaining
power are whether it is part of a multi-chain firm and whether it carries private label
merchandise. If a chain is part of a multi-chain firm, where each chain is viewed by the
developer as a desirable tenant, then the firm may be able to make leasing multiple stores
from the developer conditional on the absence of a radius restriction. However, it is also
the case that some multi-chain firms (e.g., those that specialize in women’s clothing)
would place a high value on acquiring multiple store locations in a shopping center so
that the firm can capture the sales of comparison shoppers regardless of which store they
patronize. It is then an empirical question whether the developer’s or multi-chain firm’s
preferences regarding the radius restriction will be satisfied.
If a chain sells private label merchandise, it may also have bargaining power
provided the private label is one that is in demand by consumers. As compared to
retailers that stock multiple non-exclusive brands, a retailer selling an exclusive private
label brand may be in a better position to reject a radius restriction if the developer has a
strong preference for having the brand represented in its center. There are, however,
certain retail chains that have such strong private brands that they believe that they can
14
attract consumers to their stores even if they are not in every regional center. For such
retail chains, the interests of the developer and retail chain can coincide. It is once again
an empirical question as to whether, on balance, chains selling private label merchandise
are more or less likely to have stores in neighboring regional shopping centers.
From what has been reported in the radius restriction literature, the radius used in
the restriction can vary. It is likely then that the greater the distance between two regional
shopping centers, the less likely a radius restriction will actually apply.
It is apparent then that if a chain tends not to be in neighboring centers, that can
be consistent with developer bargaining power as well as with a chain with strong brand
preference that does not need to be located in all centers to maximize its sales and profits.
If a chain tends to be in neighboring centers, that can be consistent with chain bargaining
power, as well as with the lack of implementation of radius restrictions for store types or
chains for which the developer perceives little net benefit. In the empirical analysis to
follow, we will seek to determine whether the conjunction of the results point to greater
bargaining power being exercised by retail chains or developers.
3 Data Description
3.1 Shopping Centers
This paper uses data on planned regional shopping centers (i.e. malls) collected
from the 2002 Canadian Directory of Shopping Centres. Regional shopping centers from
the Canadian provinces of British Columbia, Alberta, Saskatchewan, Manitoba, and
Ontario are considered. Initially, all shopping centers with a reported store capacity of 75
were included in the data set. It was then required that the actual store count of a center
15
be at least 60 in order for a center to be included in the data set.15 Further, shopping
centers were dropped from the data set if the percentage of stores that were chain stores
was 50 percent or less. This restriction primarily eliminated malls with a square footage
of less than 200,000, and malls lacking a major anchor store.
Finally, because one of the objectives of this paper is to explain when chain stores
appear in neighboring shopping centers, the data set includes only centers that have at
least one other mall in the sample within five miles of their locations. This yields a final
sample of 85 shopping centers. Of these, 89 percent have no more than five other
shopping centers within five miles, and 61 percent have only one or two other centers
within five miles. The malls in the sample produce 109 pairs of shopping centers within
five miles of each other. These will henceforth be referred to as neighboring shopping
centers.
As noted in Section 2, whether a chain is in both of two neighboring centers could
depend on the number of centers owned and/or controlled by the developer. Table 1
presents the number of malls owned and/or controlled by each company owning or
controlling at least two centers in the sample.16 The final row in the table lists the number
of companies that operate only one mall in the sample. Table 1 shows that 62 percent of
the sample malls are managed by the five largest companies: Cadillac Fairview
Corporation Limited, Ivanhoe Cambridge, 20 Vic Management Inc, Oxford Properties
Group Inc, and Morguard Investments Limited.
15
This allows for the elimination of centers from the data set that have high vacancy rates or that have
failed to completely report their tenant list to the directory’s publisher.
16
Some malls in the sample are owned by pension funds, but managed by professional management
companies. Since the management company is presumably responsible for leasing, malls are classified by
management company when owner and manager differ. All companies owning and/or managing malls will
be referred to as “developers”.
16
For this study, a developer is defined to be small if it operates one or two sample
shopping centers. A developer is large if it operates more than two sample centers. By
this definition, 64 of the 85 sample shopping centers (or 75 percent) are considered large.
Of the 109 pairs of neighboring shopping centers in the sample, nine are pairs operated
by the same large developer, 47 involve two large developers, 46 involve one large and
one small developer, and only seven pairs involve two small developers.
3.2 Retail Stores and Chains
The 2002 Canadian Directory of Shopping Centres reports the tenant lists of
shopping centers in Canada. In the 85 sample malls, there are 10,217 retail stores, where
a retail store is defined as a seller of goods or services that might be purchased on a
multipurpose shopping trip.17 Of these stores, 7,960, or 78 percent, are members of
chains, where a chain is defined as having at least two stores with a common name in the
sample.18 There are 930 chains in the data set.
The stores in the sample can be divided into a number of categories, according to
the goods or services sold by the store. This categorization is made by the 2002 Canadian
Directory of Shopping Centers. Not all categories were included in the analysis. First, we
excluded categories for which the total number of stores in the sample was 75 or less.
Chains in categories with low store counts are unlikely to be found in neighboring centers
because of the low store count in the category. Second, because the focus of this paper is
on whether radius restrictions are applied to chain stores, store categories for which
17
Certain locations in a mall are occupied by non-retail tenants, such as doctors, lawyers, real estate agents,
and financial consultants. These mall tenants are excluded from the store list for each mall.
18
Note that in a small number of instances, a chain operated more than one store in the same mall. Because
our interest is in whether a chain is present in neighboring malls, and not in how often it appears in the
malls, the second occurrence of a chain in a mall is dropped from the data.
17
attracting a chain store is unlikely to be important were dropped. It is assumed that such
categories would be those for which the fraction of stores that are chain stores is less than
50 percent.19 Finally, the restaurants/fast food and specialty food/drink categories were
dropped because it seems unlikely that a shopping center would have an incentive to
impose a radius restriction on these businesses. Shopping center developers include these
types of stores to keep hungry/thirsty shoppers from leaving the mall, but their presence
in a particular mall probably does not motivate the choice of mall for most shoppers.
Table 2 presents a list of the remaining store type categories used in this study,
along with the number of stores of each type in the sample, the percentage of the sample
accounted for by each store type category, and the percentage of the category composed
of chains. As Table 2 illustrates, the category with the most stores is ladies’ wear (1428
stores), representing 14 percent of all stores in the sample malls. Other large categories
include unisex clothing (811 stores), footwear and leather accessories (727 stores), and
jewelry (599 stores). The percentage of stores in a category that are chain stores ranges
from 53 percent in furniture/home décor to 96 percent in department stores.
4 Econometric Model and Variable Definitions
The dependent variable for the regression analysis is Innbri,jk, which is defined for
a pair of neighboring malls j and k, and equals one if chain i is in both malls, and zero if
the chain is in only one – the variable is not defined if the chain is not in either mall.
19
The categories dropped by these two criteria are: supermarkets, dry cleaners, florists, other groceries,
hairstyling, hardware, hobby/craft, specialty merchandise, theatres, toys, draperies and blinds, home
appliances, automotive, printing, small department stores, other services, liquor, shoe repair, and video
rental.
18
There is therefore one observation for each pair of malls and for each chain that is in at
least one mall in the pair.
In the econometric specification, the probability that a chain that is in one
shopping center of a pair is in both shopping centers is estimated as a function of chain
characteristics, mall characteristics, and location characteristics. Specifically, it is
assumed that:
Prob (Innbri,jk = 1) = Φ(β` Xi,jk),
where Xi, jk is a vector of characteristics for observation i,jk, β is a vector of coefficients,
and Φ( ) is the cumulative distribution function for a standard normal random variable.
Hence, a probit model is estimated. 20
The variables in the econometric model are divided into three groups: chain store
variables, shopping center variables, and location variables. Their definitions and the
expected signs of their coefficients are as follows:
Chain Store Variables
The following variables control for the characteristics of a particular chain:
ncities:
= the number of census metropolitan areas in which a retail chain has at
least one store;
multichain:
= 1 if the chain is part of a multi-chain firm, and 0 otherwise;
20
In principle, one could jointly estimate an equation determining whether a chain enters a particular pair
of malls, and then, given that it has entered a pair, an equation determining whether it enters one mall or
both. We have opted against this approach for several reasons. First, such an approach would require a
variable that determines whether a chain enters a particular pair, but not whether it enters one or both malls
in a pair. Given that the intended analysis in the paper is reduced form, it was not clear to us what such a
variable would be. Secondly, because of the limited dependent variables, the two equations would need to
be estimated simultaneously. Finally, the specification and estimation of a reasonable model determining
whether a chain enters a particular geographic area (one interpretation of a pair of chains) is simply beyond
the scope of this paper, but could be an interesting direction for future research.
19
label:
= 1 if the chain has a private label, and 0 otherwise;
newchain:
= 1 if the chain was not among the chains derived from the 1993
Canadian Directory of Shopping Centres (where a chain has at least two
stores with a common name in shopping centers with a reported store
count of at least 75 stores);
growth:
= the change in the number of stores in the chain from 1993 to 2002;
growth = 0 if newchain = 1.
The variable ncities measures whether the chain is a national chain present in a
large number of cities, or whether it is a local chain. Because ncities is a measure of the
size of a retail chain, and larger retail chains are expected to have greater bargaining
power than small local chains, the coefficient on ncities should have a positive sign.
The variables multichain and label measure whether a chain is part of a multichain family, and whether the chain has a private label, respectively. The variable
multichain was constructed from the 2002 Directory of Retail Chains in Canada, which
provides, for each chain listed in the directory that responded to the question, the parent
company and subsidiaries. It is anticipated that whether a chain is a member of a multichain firm could have offsetting effects on whether a chain enters both malls of a pair
(see Section 2). The expected sign of the coefficient on multichain is therefore
ambiguous.
The variable label takes on a value of one if a chain sells private label
merchandise, and a value of zero otherwise. The expected sign of the coefficient on label
is unclear. On the one hand, a chain with a recognizable private label will be more
attractive to a mall developer and thus have greater bargaining power than a chain that
20
distributes brands that can be purchased elsewhere. On the other hand, chains with highly
attractive private labels may attract consumers from a wide geographic area, and may not
desire to be in both neighboring shopping centers.
In terms of finding which chains offer private label merchandise, the 2002
Directory of Retail Chains in Canada provides this information for some of the chains in
the data set. However, because of the incompleteness of this data, other steps were taken
to obtain observations for this variable. First, it was assumed that chains that were not
indicated by the directory as having a private label, and which had less than five stores,
did not have private labels. Second, some chain stores located in shopping centers in
Edmonton, Alberta, were visited to determine whether they sold private label
merchandise. Third, internet searches were made of chain store websites to see if private
label merchandise was indicated. Fourth, for certain store type categories, the nature of
the business was such as to imply that the store would or would not be selling private
label merchandise. Finally, all chains for which a private label could not be identified
using the above methods were assumed not to have a private label.
The variable newchain measures whether the chain is at least ten years old. The
anticipated sign of newchain’s coefficient is negative. As discussed earlier, developers
are less inclined to impose radius restrictions on older chains. As well, a new chain is less
likely to have a widely recognized brand name, and may therefore have less bargaining
power. However, it may also have a brand for which a developer would like to have some
degree of exclusivity. Finally, new chains may not have had the opportunity yet to
expand into neighboring centers.
21
The variable growth is expected to have a positive sign. Chains that have been
successfully growing are anticipated to have greater bargaining power than chains whose
store counts have been dwindling over time.
Finally, to control for systematic differences across store type categories not
picked up by chain-specific variables, dummy variables are included for the different
store type categories. These variables are expected to pick up differences across
categories in the attractiveness of chains to developers, as well as differences in
concentration across the various categories.
Shopping Center Variables
The following variables control for characteristics of the neighboring shopping
centers and developers in each pair:
bothbig:
= 1 if neighboring shopping centers in a pair are operated by big
developers as defined earlier;
bothsmall:
= 1 if neighboring shopping centers in a pair are operated by small
developers;
bothsame:
= 1 if neighboring shopping centers in a pair are operated by the same
developer;
distance
= the distance between two neighboring centers, in miles
crst:
= 1 if the shopping centers in a pair are across the street from each other;
mintenants:
= the number of tenants in the smaller of the two malls;
maxage:
= the age of the older of the two malls
downtown:
= equals 1 if exactly one mall in the pair is located in its city’s downtown,
22
and 0 otherwise.
The variables bothbig and bothsmall are intended to control for differing levels of
bargaining power across large and small shopping center developers. The control group is
neighboring mall pairs in which one shopping center is operated by a large developer and
the other by a small developer. When neighboring malls are owned by small developers,
and holding other variables intended to control for shopping center quality constant, it is
expected that both malls will have little bargaining power, and therefore it is more likely
that a chain will be in neighboring malls. This predicts a positive sign on the coefficient
on bothsmall. Alternatively, if the other variables are not adequately controlling for
shopping center quality, and if lower quality shopping centers are more likely to be
operated by small developers, one would expect a negative sign on the coefficient of
bothsmall.
Controlling for the qualities of the centers, if a large developer is more likely to
use a radius restriction for predatory or anticompetitive purposes to keep a chain out of
centers owned by small developers, one would expect a positive sign on bothbig, as it
would be more likely that the chain will enter both malls when they are both owned by
big developers. This would also be consistent with a collusive outcome in which large
developers had agreed (or reached a tacit understanding) that they would not use radius
restrictions against each other. If two neighboring malls have the same developer, then
the developer has less incentive to keep a chain out of one of the malls, and we would
expect a positive sign on bothsame.
A chain will likely have less incentive to enter both malls in a pair if the two
malls are close together. Further, if the two malls are in close proximity, then the
23
developer’s incentive to impose a radius restriction is increased. To control for the
proximity of two malls in a pair, the variables distance, which measures the distance
between two malls (in miles), and crst, which equals 1 if two malls in a pair are located
across the street from each other, are included in the regression. A positive sign on the
coefficient of distance and a negative sign on the coefficient of crst are expected.
A chain will be more likely to enter both malls of a neighboring pair when both
malls have a large capacity, as measured by the number of tenants. As well, a small
shopping center will be less attractive to a chain because it may not offer sufficient
opportunities for multipurpose and comparison shopping. For these reasons, a positive
sign on mintenants is predicted.
The variables maxage and downtown are included to control for shopping center
quality and differences in the target demographic of shopping centers. The expected sign
on the coefficient of downtown is ambiguous. On the one hand, downtown is a central
retail district that can contain stores not found elsewhere (e.g., upscale department stores
or jewelers) that draw customers from the entire metropolitan area. Downtown centers
will then tend to have some tenants not found in suburban centers. On the other hand,
downtown centers also cater to office workers who may wish to shop on a lunch break or
after work, and therefore could contain stores found in suburban centers. The
determination of whether a shopping center is considered downtown was made by
consulting road maps and shopping center websites.
The sign of maxage is uncertain. If older shopping centers are less attractive to
consumers or in less desirable neighborhoods, then chains will be less attracted to these
centers, and one should expect a negative coefficient on maxage. However, if older
24
centers are more likely to be recently renovated, or are located primarily in popular
downtown areas, then chains may desire to be in both malls, and in the absence of radius
restrictions, will locate in both. Then too, older centers are also those that contain
established tenants for which radius restrictions might have less value for developers.
Location Variables
The following variables are intended to control for local competition and
differences in cities:
nother:
= the number of other malls within 5 miles of at least one of the malls in
a neighboring pair;
pop:
= the population of the census metropolitan area containing the
neighboring pair of malls, in millions;
Toronto:
=1 for all shopping centers in the Toronto census metropolitan area, and 0
otherwise.
In areas in which there are a large number of other sample malls, a chain has
many options for its locations, and it is then less likely to be in both malls of a particular
neighboring pair. Thus, a negative sign on the coeffieient of nother is predicted. The
variable pop is also expected to have a negative coefficient. First, it is anticipated that
larger cities with their larger catchment area populations are more attractive locations to
chains than smaller cities. This conveys additional bargaining power to shopping centers
in large cities, enhancing their ability to impose radius restrictions. Second, shopping
centers have a greater incentive to vigorously protect their trade area in larger cities.
Finally, nother and pop seem to be related to whether the shopping center pair is located
25
in the Toronto census metropolitan area. Therefore, to ensure that pop and nother are
picking up the effects of population and local competition and not other differences
between Toronto and the rest of the country, a dummy variable for shopping center pairs
in the Toronto census metropolitan area is included.
5 Summary Statistics
Summary statistics for the dependent variable and for all explanatory variables
(except the category dummy variables) are given in Table 3. For 17 percent of the
observations, a chain that is present in a pair of neighboring shopping centers has stores
in both centers of the pair. In the sample, four percent of observations involve two small
shopping centers, while over half of the observations involve two large centers and eight
percent involve the same developer. Very few observations involve shopping centers
across the street from each other, and on average neighboring shopping centers are
approximately three miles apart. The smaller shopping center in each pair has on average
92 tenants. Just over one-third of the observations involve one downtown shopping
center. On average, the oldest mall in a pair is 37 years old.
Forty-six percent of the observations are for shopping centers in the Toronto
census metropolitan area. The average number of competing malls within five miles of a
pair is 3.5, and the average value of pop for each observation is 2.7 million.
Regarding chains, approximately half of the observations are for multi-chain
firms, and just over half are for chains with private labels. On average, a chain is present
in approximately 15 census metropolitan areas across the sample provinces.
26
Approximately one quarter of chains are new, and the average growth of previously
existing chains from 1993 to 2002 was approximately 12 stores.
Table 4 provides the mean value of the dependent variable for each category of
store. Also included in Table 4 are the number of national chains by store type category,
and the percentage of stores accounted for by the five largest chains (CR5). For this
purpose, a chain is considered national if it is present in all five provinces in the sample.
Table 4 indicates a reasonably strong relationship between whether a chain is in
both neighboring malls and the concentration of the category – the correlation coefficient
between the second and fourth columns is 0.61. The relationship between whether a chain
is in both malls and the number of national chains is less clear. This is possibly because a
small number of national brands may reflect either a concentrated category or a narrow
category (and one for which there is not great demand).
6 Econometric Results
Results from the probit estimation are given in Table 5.21 Coefficients on store
type category dummy variables are suppressed.
Regarding which chains enter into both malls of a pair, probit estimation yields
the following results. The coefficient on ncities is positive and significant at the one
percent level, indicating as expected that chains that operate in a large number of cities
are more likely to enter both malls of a pair than are small local chains. This finding is
consistent with the hypothesis that chains with national recognition possess greater
bargaining power, and are able to command more favorable lease terms from shopping
21
Reported standard errors are not robust to heteroskedasticity. However, using robust standard errors does
not change any conclusions regarding statistical significance.
27
centers. As predicted, new chains are found significantly less likely to be in both malls of
a pair than established chains. Conditional on a chain being established, growing chains
are more likely to appear in both malls than shrinking chains, which is again consistent
with more attractive and successful chains being able to command favorable lease terms.
The coefficients on label and mulichain are both negative, although significant
only at the ten percent level. The negative coefficient on label may be driven by the fact
that, for certain chains, having a private label allows the chain to attract consumers from
a wide geographic area, making multiple nearby locations undesirable for the chain. The
negative coefficient on mulichain suggests that stores owned by a multi-chain firm tend
to locate together, implying that a shopping center developer might be imposing a radius
restriction as a condition for a multi-chain firm obtaining multiple store locations in its
center.
Regarding the characteristics of the shopping centers, the positive and significant
coefficient on distance, coupled with the negative and significant coefficient on crst,
indicate that a chain is less likely to locate in both shopping centers in a pair when the
shopping centers are close together. This result is consistent with basic spatial theory.
The positive and significant coefficient on maxage implies that a chain is more likely to
be in both malls when at least one of them is old. This result is consistent with at least
one of the malls being in an attractive older part of the city, and it might also be the result
of older malls being renovated and of higher quality. The coefficient on downtown is not
significant at any conventional significance level.
The results also show that, controlling for some shopping center characteristics, a
chain is more likely to enter both malls of a neighboring pair when they are both operated
28
by major developers, than when one is operated by a major developer and the other is
operated by a small developer. This finding is consistent with the hypothesis that large
developers use radius restrictions on their chain tenants in a possibly anticompetitive
fashion, to reduce the attractiveness of neighboring centers owned by small developers.
However, it is possible that the variables included to control for shopping center quality
are not sufficient to the task, and that large developers are more likely to operate high
quality shopping centers than are small developers. In that case, the positive and
significant coefficient on bothbig may simply be picking up the fact that when both
shopping centers are of high quality, a chain is more likely to enter both.
The coefficient on bothsmall is not significantly different from zero, for
reasonable significance levels. This suggests that for mall pairs in which both malls are
small, developers are not significantly more or less likely to have the same chain in both
malls than for mall pairs in which one mall is operated by a small developer and the other
by a large developer. Likewise, the coefficient on bothsame is not significantly different
from zero.
The coefficient on nother is not significantly different from zero at reasonable
significance levels, indicating that we have not identified a significant relationship
between whether a chain enters both malls of a given neighboring pair, and the number of
other local mall options for the chain. The coefficient on pop is negative and significant,
consistent with the interpretation that shopping centers in major cities possess greater
bargaining power, and have a greater incentive to vigorously protect their trade area in
larger cities. The coefficient on Toronto is not significantly different from zero at
standard significance levels.
29
Since the magnitudes of estimated coefficients in a probit model are not easily
interpreted, the estimated effects of key variables on the probability that a chain that is
located in at least one of the malls in a neighboring pair will be located in both have been
calculated. These effects are reported in Table 6. For purposes of computation, all
dummy variables are initially set equal to zero, and all other variables are set equal to
their sample means. For dummy variables, Table 6 reports the effect on the probability
that a chain will locate in both malls of switching the variable from zero to one, holding
everything else constant. For other variables, the reported effect is that of increasing the
variable by one standard deviation.
Table 6 shows that, for the most part, while the effects of these variables are
statistically significant, they are not large. Switching a mall pair from one large and one
small developer to two small developers increases the probability that the chain will
locate in both malls by only two percent. Switching a chain (with zero growth) from a
previously existing chain to a new chain lowers the probability it will locate in both malls
by five percent. The probability of locating in both malls increases by one percent if the
growth of the chain since 1993 is increased by one standard deviation (approximately 26
stores), and increases by two percent if the distance between the shopping centers is
increased by one standard deviation (1.1 miles). Increasing the population of the city by
one standard deviation (approximately 1.9 million) decreases the probability the chain
will locate in both malls by three percent.
The two variables that seem to have large effects on the probability that a chain
will locate in both malls are minten and ncities. Increasing the number of tenants in the
smaller of the two malls by 35.33, one standard deviation, increases this probability by
30
nine percent, while increasing the number of census metropolitan areas in which the
chain operates by twelve, also one standard deviation, increases this probability by
thirteen percent.
The estimated coefficients and effects of the store type category dummy variables
are reported in Table 7. Because the control category is ladies’ wear, statistical
significance indicates that chains in a given category are statistically more or less likely
to locate in both malls of a pair than chains in the ladies’ wear category. Likewise, the
effects given in the last column are the differences in the probability that a chain locates
in both shopping centers, comparing each category to ladies’ wear.
According to Table 7, chains in eleven store type categories are statistically more
likely (at the five percent level) to enter both malls of a neighboring pair than ladies’
wear chains, while only two categories, department stores and home furnishings, are
significantly less likely. The fact that department stores and home furnishing chains are
less likely to enter neighboring malls than ladies’ wear chains may reflect the possibility
that chains in these categories draw their customers from a wide geographic area, and so
entering neighboring malls is simply not attractive to chains in these categories.
A common feature of those categories where chains are much more likely to enter
neighboring shopping centers than ladies’ wear chains is a high five-firm concentration
ratio, as given in Table 4. Of the eight categories with concentration ratios greater than
seventy percent, all but two (department stores and books and newsstands) are
statistically more likely to enter both shopping centers in a pair than ladies’ wear chains,
31
holding other characteristics constant.22 However, certain categories appear more likely
to enter both malls than ladies’ wear chains, despite having a lower CR5. The most
notable example is the furniture category, which has a CR5 of only 25 percent.
To explore the robustness of our findings, the model was re-estimated on certain
subsamples of the data. First, one concern was that some of the results could be driven by
the assumptions that any two stores in the same category with the same name should be
considered a chain, and chains desire store locations in neighboring malls. To check this,
the model was re-estimated using only chains that were present in at least 10 census
metropolitan areas. This reduced the sample from 12652 to 7342. In terms of the
variables and results reported in Table 5, the only changes to the results of statistical tests
were that the coefficients on multichain, label, and maxage were no longer significant at
any standard significance level. With respect to the category dummy variables, while
there were some changes to rankings of categories and significance of certain
coefficients, the general pattern and results were maintained. The most noticeable change
is that the coefficient on the furniture dummy becomes negative, although it is not
significantly different from zero at a ten percent level. Therefore, it appears that the basic
conclusions are maintained when the analysis is restricted to exclude small chains.
Second, we were concerned that, despite the exclusion of a number of store type
categories, the results were being diluted by the inclusion of certain store type categories
for which the imposition of radius restrictions is of little importance to chains or
developers. To explore this possibility, the model was re-estimated using only the
clothing categories (footwear and leather accessories, menswear, ladies’ wear, unisex
22
Since the largest chain in the books category is Chapters, a big-box retailer with stand-alone stores, the
finding that chains in this category are relatively unlikely to enter both malls of a pair may perhaps also be
explained by Chapters not desiring to enter neighboring shopping centers.
32
clothing, sporting goods, and children’s wear), for which radius restrictions are more
likely to be applied.23
Again, most results remain unchanged from the original specification. There
were, however, some differences. First, the coefficient on bothsmall becomes positive
and significant at the five percent level. This result is consistent with small shopping
centers not having the bargaining power needed to impose radius restrictions and still
attract the chain. The coefficients on multichain and label remain negative but become
significantly different from zero at the five percent level. The coefficient on pop is no
longer significantly different from zero at the ten percent level. Finally, the coefficient on
downtown becomes positive and significantly different from zero at the one percent level.
This result may indicate that, at least for clothing chains, when neighboring malls are
located in different types of districts (e.g., one mall is downtown, the other is a mall in a
residential suburb), then because these malls cater to some extent to different clienteles, a
chain will be less worried about cannibalizing its business by entering both malls.
Further, a shopping center may be less worried about losing business to a neighboring
rival center when they operate in different types of areas and attract different shoppers.
7 Conclusions
Shopping center developers have included radius restriction clauses in some
Canadian and American shopping center leases for many years. These restrictions, which
specify a minimum distance around a shopping center over which a tenant cannot open a
new store, are largely designed to protect the shopping center rents of the developer and
23
These store types account for 36.4 percent of the stores in the sample malls, and are believed to include
the ones that are likely to be key determinants in a consumer’s choice of mall to patronize.
33
demand externalities of the developer’s retail tenants. They are not necessarily consistent
with the affected tenant’s interests. The ability of a developer to impose a radius
restriction depends on the relative bargaining power of retail tenants and shopping center
developers.
To examine this problem empirically, an econometric model was specified to
describe the common characteristics of those chains that tend to enter both malls in a
neighboring pair, and the common characteristics of those shopping center pairs which
are most likely to allow a chain to enter both malls of the pair. Whether the results are
consistent with a simple bargaining explanation for the use of radius restrictions is
considered, as well as the conditions under which one party or the other to the retail lease
is likely to have the bargaining power to impose or reject a radius restriction.
In terms of chain characteristics, it was found that chains that are most likely to
enter both malls of a neighboring pair are large national chains, and chains that have been
established since 1993 and growing. This description is consistent with the bargaining
hypothesis, since such chains are precisely those that would be expected to be in a strong
bargaining position with the developers. Regarding shopping center and locational
characteristics, consistent with spatial theory, the results show that a chain is more likely
to enter both shopping centers when those centers are located farther apart. A chain is
more likely to enter neighboring shopping centers when neither is small, likely reflecting
capacity constraints for retail space within a shopping center. Neighboring shopping
center pairs in which at least one mall is old are more likely to both contain a particular
chain, while shopping centers in higher population cities are less likely to both contain
the same chain.
34
Finally, it is found that, after controlling for other factors, a chain is more likely to
enter both malls when they are both operated by large developers than when one
developer is large and the other is small, although this effect on the probability of
entering both shopping centers is not large. This finding is consistent with the hypothesis
that large developers use radius restrictions in an anticompetitive fashion, particularly
against independent malls and small chains of malls. However, it could also suggest that
the malls of large developers and the malls of independents differ in a way uncontrolled
for by our explanatory variables.
Examining where shopping center tenant data are consistent with radius
restrictions being imposed is one way of attempting to assess the relative bargaining
power of shopping center developers and retail tenants. It may not be the only way, but
its value lies in relying on data that are reasonably easy to obtain. Other strategies for
assessing relative bargaining power will be explored in future research.
35
References
Benjamin, J.D., Boyle, G.W., and Sirmans, C.F. “Retail Leasing: The Determinants of
Shopping Center Rents,” AREUEA Journal, Vol. 18 (1990), 302-317.
Benjamin, J.D., Boyle, G.W., and Sirmans, C.F. “Price Discrimination in Shopping
Center Leases,” Journal of Urban Economics, Vol. 32 (1992), 299-317.
Brueckner, J.K. “Inter-Store Externalities and Space Allocation in Shopping Centers,”
Journal of Real Estate Finance and Economics, Vol. 7 (1993), 5-16.
Cambridge Shopping Centres Limited. Annual Report (1999).
Eppli, M.J. and Shilling, J.D. “How Critical Is a Good Location to a Regional Shopping
Center?,” Journal of Real Estate Research, Vol. 12 (1996), 459-468.
Golosinkski, D. and West, D.S. “Double Moral Hazard and Shopping Center Similarity
in Canada”, Journal of Law, Economics, and Organization, Vol. 11 (1995), 456478.
Gould, E.D., Pashigian, B.P, and Prendergast, C. “Contracts, Externalities, and Incentives
in Shopping Malls”, Review of Economics and Statistics, Vol. 87 (2005), 411-422.
International Council of Shopping Centres. Shopping Center Leasing. New York:
International Council of Shopping Centers (2000).
Jones, D.A., Addison, S.M., and Davidson, P.T. “Fast Effective Pain Relief…McLean &
Kerr LLP’s Update on Over-the-Counter Commercial Lease Remedies,” mimeo,
March 30, 2001.
Kampler, N.L. and Pomerantz, H.B. “Challenging Radius Restrictions in Shopping
Center Leases”, American Bar Association 14th Annual Real Property
Symposium, April 3, 2003.
36
Lee, K. “Optimal Retail Lease Contracts: The Principal-Agent Approach”, Regional
Science and Urban Economics, Vol. 25 (1995), 727-738.
Lentzner, J. “The Antitrust Implications of Radius Clauses in Shopping Center Leases”,
University of Detroit Journal of Urban Law, Vol. 55 (1977), 1-71.
Mathewson, G.F., and Winter, R. A. “The Competitive Effects of Vertical Agreements:
Comment,” American Economic Review, Vol. 77 (1987), 1057-1062.
Mejia, L.C. and Eppli, M.J. “The Effect of Merchandise Space Allocation on Retail Sales
in Enclosed Shopping Centers”, Journal of Shopping Center Research, Vol. 6
(1999), 23-40.
Miceli, T.J., Sirmans, C.F., and Stake, D. “Optimal Competition and Allocation of Space
in Shopping Centers”, Journal of Real Estate Research, Vol.16 (1998), 113-126.
Murray, J.C. “Percentage Rent Provisions in Shopping Center Leases: A Changing
World?,” Real Property, Probate and Trust Journal, Vol. 35 (2001), 1-58.
Pashigian, B.P. and Gould, E.D. “Internalizing Externalitites: The Pricing of Space in
Shopping Malls,” Journal of Law and Economics, Vol. 41 (1998), 115-142.
Stahl, K. “Theories of Urban Business Location”, in E.S. Mills (ed.), Handbook of
Regional and Urban Economics, Vol. II, Amsterdam: Elsevier Science Publishers
B.V., 1987.
West, D.S. “An Empirical Analysis of Retail Chains and Shopping Center Similarity”,
Journal of Industrial Economics, Vol. 40 (1992), 201-221.
West, D.S., Von Hohenbalken, B., and Kroner, K. “Tests of Intraurban Central Place
Theories”, Economic Journal, Vol. 95 (1985), 101-117.
37
Table 1: Developers
Developer
Cadillac Fairview Corporation Limited
Ivanhoe Cambridge
20 Vic Management Inc.
Oxford Properties Group Inc.
Morguard Investments Limited
Bentall Retail Services
Brookfield Properties Ltd.
O & Y Enterprise National Retail Services
Omers Realty Management Corp.
Revenue Properties Company Limited
Number of developers with only one center in
sample
38
Number of
Centers
20
14
9
5
5
4
4
3
2
2
17
Table 2: The size of store type categories and their chain store counts
Category
Number of Stores
1428
811
727
Percentage of all
stores in sample
malls
14.0
7.9
7.1
Percentage of
stores that are
chains
86
88
83
Ladies’ wear
Unisex clothing
Footwear and
leather accessories
Jewelry
Gifts
Menswear
Electronics
Sporting goods
Telecommunications
Variety/convenience
Furniture/home
décor
Optical
Cosmetics/toiletry
Children’s wear
Housewares
Cards/stationery
Department stores
Financial
Photo/camera
Music/instruments
Travel
Fabric and sewing
Books and
newsstands
Drugs/health and
beauty aids
599
336
321
280
248
219
218
216
5.9
3.3
3.1
2.7
2.4
2.1
2.1
2.1
66
71
82
90
89
89
77
53
208
196
194
194
167
150
145
140
128
110
109
93
2.0
1.9
1.9
1.9
1.6
1.5
1.4
1.4
1.3
1.1
1.1
0.9
72
87
86
81
90
96
89
84
91
80
59
86
86
0.8
86
39
Table 3: Summary statistics (N=12652)
Variable
Innbr
bothsmall
bothbig
bothsame
crst
multichain
mintenants
ncities
label
nother
pop
newchain
growth
downtown
maxage
Toronto
distance
Mean
0.17
0.04
0.52
0.08
0.02
0.50
92.26
15.39
0.55
3.53
2.71
0.27
11.75
0.36
37.33
0.46
3.24
St. Dev
0.37
0.20
0.50
0.28
0.14
0.50
35.33
11.98
0.50
2.54
1.88
0.45
25.50
0.48
8.07
0.49
1.19
40
Table 4: Mean values of Innbrit, the number of national chains, and CR5, by store
type category
Category
Cards/stationery
Drug/health and
beauty aids
Photo/camera
Electronics
Music/instruments
Books and
newsstands
Footwear and
leather accessories
Jewelry
Department stores
Telecommunications
Variety/convenience
Financial
Optical
Sporting goods
Ladies' wear
Cosmetics/toiletry
Unisex clothing
Menswear
Furniture/home
décor
Gift
Fabric and sewing
Housewares
Children's wear
Travel
Mean( Innbr)
Number of
National Chains
0.49 2
1
0.46
0.36 2
0.32 3
0.25 2
2
0.23
9
0.19
0.18 6
0.17 4
0.16 2
0.16 2
0.15 4
0.15 1
0.15 6
0.15 21
0.15 1
0.13 12
0.11 4
1
0.11
0.10 2
0.10 1
0.09 1
0.08 2
0.07 2
41
CR5
87
87
76
76
85
80
34
35
93
51
50
73
52
58
17
64
24
38
25
33
57
50
48
65
Table 5: Probit results (dependent variable Innbrit), N=126521
Variable
bothsmall
Coefficient (St Dev)
0.12
(0.08)
bothbig
0.10***
(0.03)
bothsame
0.02
(0.06)
crst
-1.11***
(0.18)
multichain
-0.06*
(0.04)
mintenants
0.01***
(0.0004)
ncities
0.04***
(0.002)
label
-0.07*
(0.04)
nother
-0.01
(0.01)
pop
-0.11***
(0.04)
newchain
-0.30***
(0.05)
growth
0.002***
(0.001)
downtown
0.02
(0.03)
maxage
0.005**
(0.002)
Toronto
0.16
(0.13)
distance
0.08***
(0.02)
constant
-2.90***
(0.13)
Pseudo R2
0.19
1
Coefficients on store type dummy variables are not included.
*** Indicates significance at the 1 percent level.
** Indicates significance at the 5 percent level.
* Indicates significance at the 10 percent level.
42
Table 6: Effects of selected variables*
Variable
Effect
Variable
Effect
bothbig
0.02
growth
0.01
mintenants
0.09
distance
0.02
ncities
0.13
newchain
-0.05
pop
-0.03
* For dummy variables, the reported effect is the effect on the probability that a chain
will locate in both malls of switching the variable from zero to one. For other variables,
the reported effect is that of increasing the variable by one standard deviation.
43
Table 7: Store type dummy variables: estimated coefficients and effects
Store Type Category
Drugs/health and beauty
aids
Photo/camera
Cards/stationery
Coefficient (Standard
Error)
0.88 (0.12)***
Effect1
0.68 (0.10)***
0.61(0.10)***
0.19
0.17
0.27
Electronics
0.51 (0.08)***
0.13
Telecommunications
0.46 (0.09)***
0.12
Furniture/home décor
0.41 (0.12)***
0.10
Financial
0.34 (0.10)***
0.08
Music/instruments
0.29 (0.11)***
0.07
Variety/convenience
0.27 (0.10)***
0.06
Optical
0.20 (0.10)**
0.05
Footwear and leather
0.13 (0.06)**
0.03
accessories
Books and newsstands
0.12 (0.13)
0.02
Fabric and sewing
0.09 (0.16)
0.02
Jewelry
0.06 (0.07)
0.01
Menswear
0.06 (0.08)
0.01
Gifts
0.05 (0.09)
0.01
Unisex clothing
0.05 (0.06)
0.01
Cosmetics/toiletry
-0.001 (0.10)
0.00
Sporting goods
-0.03 (0.09)
-0.01
Children’s wear
-0.10 (0.11)
-0.02
Travel
-0.13 (0.14)
-0.02
Housewares
-0.22 (0.11)**
-0.04
Department stores
-0.24 (0.10)**
-0.04
1
The reported effect is the estimated difference in the probabilities that a chain will enter
both malls, comparing each category to the control category of ladies’ wear.
*** Indicates significance at the 1 percent level.
** Indicates significance at the 5 percent level.
* Indicates significance at the 10 percent level.
44