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Empirical studies of retail distribution over the 20th-century have increasingly accumulated evidence that challenges the predictions of Central Place Theory. The single-purpose, nearest-center patronage assumption of CPT has attracted most criticism. Multiple empirical studies have shown that people do not always choose the closest shopping venues and that they often shop at multiple stores on the same trip. Using data similar to Berry’s, Rushton, Golledge, and their colleagues showed that only 35% of a rural Iowa population shopped at the nearest center (Rushton, Golledge et al. 1967). Clarke, using data from New Zealand, showed that the nearest-center patronage assumption becomes less tenable as the size of the destination center increases (Clark 1968). Whereas 63-83% of trips to small shopping centers patronized the nearest destinations, only half of the trips led to the closest destination among largest centers. The difference is explained, the authors argue, by transportation savings achieved by multipurpose shopping at the larger centers. Hanson studied explicitly whether shopping trips are multipurpose and found that 61% of all trips in his sample were multipurpose (Hanson 1980). Similarly, O’Kelly found that 63% of grocery shopping trips and 74% of non-grocery trips were multipurpose (O'Kelly 1981).

Multipurpose shopping introduces inter-store externalities to a retail location model, challenging the store distribution predictions of CPT. Transportation savings that arise in multipurpose shopping bias consumers in favor of larger centers. If a similar bundle of goods can be acquired by either patronizing disparate small stores on multiple trips or a large and heterogeneous cluster on a single trip, then the latter option often leads to a lower total cost due to savings in transportation.13 _{As a larger variety of goods draw}

a larger pool of customers, a positive feedback loop incentivizes stores to cluster in even larger

13 _{This is of course ignoring other potentially important factors of destination choice, such as emotional pleasure, which could}

lead to the patronage of multiple isolated small stores despite the additional transportation cost.

heterogeneous centers (Bacon 1971; Mullingan 1987). Eaton and Lipsey have shown how multipurpose shopping on the part of consumers and profit maximizing locational choice on the part of firms can lead to a higher concentration of clusters than predicted in CPT (Eaton and Lipsey 1982). West, Hohenbalken, and others have corroborated this effect using empirical evidence from Edmonton, Alberta:

“Our tests support the hypothesis of a hierarchy of shopping centres with properties that are more closely aligned to an Eaton and Lipsey than a Christaller-type hierarchy. In particular, we found that our shopping centre hierarchy has one important characteristic that is consistent with the predictions of the Eaton and Lipsey model, but not Christaller's, namely the replication of stores of the same type in the same shopping centre. We would expect such replications to arise naturally from the profit maximising locational behaviour of firms confronted with comparison and multipurpose shopping behaviour on the part of consumers (behaviour that is outside the Christaller model, but within those developed by Eaton and Lipsey)”.(West, Hohenbalken et al. 1985: 116).

Though multipurpose shopping has mainly been used to explain the success of shopping malls, its underlying advantages are equally applicable to traditional retail clusters in urban settings. A variety of individual stores located in close proximity allows customers to bundle multiple shopping trips into one, reducing total transportation costs for patrons and increasing demand for retailers (Figure 6). Empirical research on malls has shown that the size of a shopping center is, in fact, a considerably stronger predictor of patronage than distance to the center (Eppli and Shilling 1996). Since vehicular transportation costs associated with malls are much lower on a per mile basis than pedestrian transportation costs, we should expect a different balance of factors in urban settings where shops are often accessed on foot. Higher transportation costs make people more sensitive to distances required to reach larger centers, leading to a stronger patronage of closer and smaller stores.

Figure 6 A small cluster of complementary retailers on Highland Avenue near Davis Square in Somerville MA. The adjacent individual stores include a set of complementary establishments: a dessert store, a dairy shop, a bread store, a restaurant, and a service/catering company (Photo: Andres Sevtsuk, April 2009).

Neo-classical retail location theory suggests that multipurpose shopping also introduces a second, related effect to explain the clustering of heterogeneous and complementary stores: demand externalities. Demand externalities refer to customer spillovers that one store can produce for other stores. Given conveniently small distances between stores, customer traffic attracted to higher-order retailers can increase traffic in lower-order retailers. Unlike the transportation effect in multi-purpose shopping, where heterogeneous clusters enable customers to purchase a set of planned goods at lower total costs, demand externalities produce additional unplanned purchases in lower-order stores. A customer visiting a department store in a mall, for instance, might pay a visit to a newspaper kiosk in the same mall, thus making a purchase that he or she would avoid if a separate trip to a newspaper kiosk were required. Demand externalities are generally thought to flow in one direction — from more popular to less popular stores or from anchor stores to non-anchor stores.

Brueckner has provided a model that demonstrates how a careful manipulation of the size of stores in a shopping center, according to their level of demand externalities and their impact on the center as a whole, can maximize shopping center revenues (Brueckner 1993). In his model, the sales volume of a store i, denoted as Ri, depends on the amount of space Si that the store occupies14. In the presence of inter-store

externalities, Ri also depends on the amount of space allocated to other stores in the center. Store i’s sales

are thus given as a function of all stores’ areas in the center: Ri = Ri (S1, S2, …, Sn), where ∂ ∂ 0 ∂ ∂ 0, Equation 4 Equation 4 shows that as store i’s own space rises, then sales at store i are also expected to rise. However, if a nearby store j produces positive demand externalities for store i, then store i’s sales can also rise as store j’s floor area increases. If no externalities exist between i and j, then the marginal effect of store j’s space increase on store i’s sales can also be zero. However, if a center contains multiple stores of a given type, then competition between stores might reverse this effect. For instance, if store i and j are both shoe stores, then ∂Ri/∂Sj and ∂Rj/∂Si could both be negative as an increase in the size of store i reduces the

sales of the competing store j and vice versa. Brueckner goes on to show how a careful and coordinated manipulation of complementary store sizes can lead mall-owners to optimal profits for the center as a whole.

A large body of empirical research has studied demand externalities in shopping malls. Anikeeff provides an overview of previous attempts to measure the degree of spillovers, or ‘‘retail compatibility,’’ across different types of non-anchor stores (Anikeeff 1996). Nelson classified stores into five categories according to the percentage of customers who visit a given pair of stores (Nelson 1958). More recently, Eppli and Shilling used a new dataset and enhanced methods to re-estimate the degree of retail compatibility for a sample of stores in fifty-four regional shopping centers in the United States (Eppli and Shilling 1993).

14 _{Space is used as a proxy variable for capturing the choice of merchandise at the sore.}

They found that regional shopping centers with greater quantities of space devoted to anchor tenants have higher non-anchor tenant sales for eight out of nine merchandise types.

Theories on profitable mixing of tenants in planned shopping malls have also led numerous researchers to look for implicit evidence of demand externalities embodied in tenants’ rent contracts. In a widely popular practice, mall owners charge a discriminatory percentage of rent from tenants, depending on their impact on not only their own revenues, but also on the revenues of the mall as a whole15_{. Stores}

that constitute the primary motivation for customers’ trips to the center (i.e. department stores), generally pay low or no rent, while odd specialty stores that are not on the list of many shoppers pay high rents, as most of their traffic comes from nearby stores. This difference in rents is seen as implicit evidence of demand externalities that anchor stores generate for non-anchor stores. Empirical evidence of differential rents is given by (Benjamin, Boyle et al. 1990; Benjamin, Boyle et al. 1992).

Centrally-managed private shopping centers offer cheaper percentage rent contracts as an incentive to attract big anchor stores to the center. The positive demand externalities that such stores create for other stores are thereby returned in the form of cheaper rent payments. Eppli and Benjamin have shown that the image and fame of anchor stores (i.e. brands) play an important part in customer draw, often increasing the customers willingness to patronize otherwise distant locations (Figure 7) (Eppli and Benjamin 1994).

Figure 7 Porter Square Mall in Porter Square, Cambridge MA. A careful manipulation of demand externalities and rent contracts allows mall owner to maximize profits by orchestrating an optimal tenant mix. The part of the mall on the image contains only well- known brand stores: a Dunkin’ Donuts coffee shop, a Mexican Grill restaurant, a RadioShack electronics store, a Liquor World alcohol store, and a Zoots drycleaner.(Photo: Andres Sevtsuk, April 2009)

15 _{There is a debate over the question of whether these discriminatory rents favor mall owners or mall tenants. See Wheaton, W.}

C. (2000). "Percentage Rent in Retail Leasing: The Alignment of Landlord--Tenant Interests." Real Estate Economics 28(2): 185-204.

It is generally agreed in retail location literature that a profitable orchestration of complementary stores in planned shopping malls, coupled with discriminatory rent contracts that attract anchor stores to the center, is the primary reason why shopping malls have managed to eclipse other traditional forms of retailing in the US in the course of the 20th-century. Unlike main streets and neighborhood retailers, private shopping malls purposefully coordinate the tenant mix, prohibiting ‘unwanted’ store entry to the center and optimizing the performance of the cluster as a whole. A central management system allows owners to choose only stores that increase mall revenues by either attracting additional customers or paying higher rents for positive demand externalities, so that the positive externalities produced by higher-order stores are perfectly balanced by lower rents per square foot, thus providing all stores an equal incentive to join the center. Without subsidized rent contracts, anchor stores would face a disincentive to locate in shopping malls, since their customer draw would generate positive externalities for other stores with no compensation for the favor. Such is the case, however, in uncoordinated urban retail clusters (i.e. main streets, neighborhood centers, etc)16_{. This suggests that we should not expect the store clusters and}

agglomeration patterns found in shopping malls to appear analogous in urban settings. Instead, we expect clustering preferences to vary among different types and sizes of retailers. Non-anchor stores, such as small retailers who offer a limited choice of infrequently purchased merchandise, are more likely to value heterogeneous cluster locations, because the availability of complementary stores in a cluster enables customers to acquire a larger assortment of products from a single location, saving transportation costs and offering an incentive to visit the cluster. Large anchor stores, on the other hand, do not benefit from urban cluster locations in the same way that they do in malls. By attracting a large customer pool, they generate positive externalities for lower-order stores, without receiving rent subsidies or other types of return for the deed. We suspect that this might lead some anchor-stores to avoid cluster locations.

Retail location literature has been relatively silent on un-coordinated retail clusters17_{. The literature}

on demand externalities is especially vague about externalities in urban settings. Anecdotal evidence suggests that large anchor stores, such as supermarkets, are found equally often in free-standing locations as in neighborhood retail clusters. It is unclear if choosing a free-standing location is a result of an endogenous repellent force that drives large stores away from clusters or simply an exogenous attraction towards different types of locations, such as locations that are closer to people’s homes. Since demand externalities do not explicitly damage retailers’ own revenues, but rather just spill customers over to other stores, there is little reason to believe that a repellent force is at work. Rather, faced with a lack of rent incentives at a cluster location, anchor stores might simply drift to locations that are advantageous for exogenous reasons, such as better access to customers. At the same time, a large enough group of non-anchor stores at a cluster location could collectively produce enough positive externalities to attract a large anchor into a cluster. Such a collective positive externality challenges the currently popular assumption that demand externalities flow in only one way: from anchors to non-anchors. It is easy to conceive how the dynamics between an anchor

16 _{From an urban planning perspective, the efficiency of the planned shopping center model suggests that in order to support}

traditional forms of street retailing, merchant associations and other forms of collaborative action might aid clusters of small stores in a competition against large shopping malls.

17 _{An exception is offered by Stahl, K. (1987). Theories of Urban Business Location. Handbook of Regional and Urban}

Economics. E. S. Mills. Amsterdam: North-Holland. 2: 760-820.

store and a large set of non-anchor stores can indeed collectively produce two-way demand externalities that benefit all stores. Given the lack of theory on uncoordinated clusters, we shall try to address some of these questions empirically in Chapters Four and Five.

Another important factor that explains uncoordinated clusters is information spillovers. Caplin and Leahy have developed a search-theoretic model that explains how some potentially advantageous locations for urban retailing can remain underutilized for considerable time periods due to risk and information spillovers (Caplin and Leahy 1998). The first store that makes the decision to locate at a new location with little or no previous retail establishments must do so by internalizing a considerable risk. Should the location prove to be poor, then the penalties are carried by the risk taker alone. Should the location prove to be successful, however, then the payoffs are not only internalized by the risk taker, but will also help reveal the value of a location to potential competitors who have waited. The authors use their model to explain why Lower Sixth Avenue in New York City remained inactive among retailers for years, but witnessed a rapid turnaround after a Bed Bath & Beyond store opened shop there in 1992. Bed Bath & Beyond internalized the first mover’s risk by making a considerable investment in an uncertain location. The apparent success of the store quickly assured other retailers of the location value and resulted in a rapid retail revitalization of the area. Caplin and Leahy’s model can help us explain why some locations that appear promising due to their exogenous accessibility characteristics might remain under-utilized by retailers in our case study area.