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Journal of Retailing and Consumer Services 14 (2007) 259–268

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Black out of the blue light: An analysis of Kmart store closing decisions

Martin Shieldsa,�, Matt Kuresb

aDepartment of Economics, Colorado State University, USAbCenter for Community Economic Development, University of Wisconsin, Madison, USA

Abstract

We investigate the spatial and economic factors that influenced Kmart’s decision to close about 600 under-performing stores as part of

its Chapter 11 financial objectives review in 2002 and 2003. We develop a theoretical model of retail store location and estimate an

empirical counterpart using a Logit model to investigate the economic and spatial factors that influenced this decision, including the

degree and proximity of competition in the local market as well as local demographic characteristics. In general, our empirical results

offer statistical support for the accepted paradigm, but our model offers modest predictive ability as to which stores were actually closed.

One interesting extension is a discussion of the potential implications of store closing on the local population, especially with respect to

low-income households’ access to discount stores.

r 2006 Elsevier Ltd. All rights reserved.

1. Introduction

In March 2002, Kmart Corporation announced that itwas closing 284 stores in 40 states and Puerto Rico, cutting22,000 jobs, after filing for Chapter 11 bankruptcyprotection. Acknowledging that the initial cuts did not gofar enough, the company announced in January 2003 thatit would close an additional 323 locations, laying-off anadditional 30,000 workers. Overall, the closures reducedthe company’s store count by 30% and the workforce byabout 25%.

Prior to the meteoric rise of Wal-Mart, Kmart had longbeen the nation’s dominant discount retailer. And althoughWal-Mart’s sales had long ago passed Kmart, thecorporation had the largest discount retail network inthe US as recently as 1995, with 2161 stores. However, thegrowth of Target and Wal-Mart had relegated Kmart tothird place in sales among discount retailers by 2001.Despite its relative decline, Kmart remains a major retailer.For example, before the closings the company had $36.1billion in total annual sales (while losing $2.4 billion).

In a prepared statement announcing the 2001 closures,Chuck Conaway, Kmart’s then chief executive officer, said:‘‘The decision to close these underperforming stores, which

e front matter r 2006 Elsevier Ltd. All rights reserved.

tconser.2006.07.007

ing author.

ess: martin.shields@colostate.edu (M. Shields).

do not meet our financial requirements going forward, is anintegral part of the company’s reorganization effort.’’ Inthis paper, we take a closer look at how local marketfactors may have affected individual stores’ performance,and subsequently Kmart’s decision as to which locations toshutter. In doing so, we examine a number of economicand spatial factors that may have influenced this decision.In particular, we investigate the extent to which thedecision to close or not close an individual store dependson factors suggested by retail location theory. These factorsinclude local market demographic characteristics hypothe-sized to influence demand (e.g. per capita income andpopulation); competitive market pressures and spatialcompetition (e.g. proximity of competitors); and selectcosts of doing business (e.g. distance to distributors).An analysis of the recent Kmart closings is useful from

both a research and policy perspective. On the researchside, the closings offer an ideal framework for testing theaccepted theoretical retail location paradigm. Because theaffected stores comprise a significant share of all Kmartlocations, and because they are geographically distributedacross the US, we are positioned to conduct a robustanalysis that is typically not possible for smaller and/orregional retailers. Somewhat ghoulishly, the case of Kmartcan help forward our understanding of the location modelin that scenarios where important and geographicallydiverse businesses retreat without failing do not come up

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very often. Thus, the analysis gives us the rare opportunityto comprehensively test macro-level retail location theory,albeit in reverse.

With respect to the focus of this issue of JRCS, GISsoftware allows us to more readily define spatial marketsthan was previously possible. Both nationally and inter-nationally, retailers have recognized this ability, and theuse of GIS in retail operations has become increasinglywidespread (see Clarke (1998) for a history of GIS in retailplanning activities). Clarke et al. (1997) suggests the GISrevolution within the retail industry can be attributed to itslinkage with increasingly available geodemographic dataand the growth and refinement of spatial interactionmodeling. Combining GIS with these two componentshas allowed retailers to build custom spatial decisionsupport systems (SDSS) to be used in locational analysisand evaluation.

Historically, the focus of retail location decisions was ongrowth and expansion, with a strong concentration on thetechnical and policy issues related to the development ofnew sites and stores (Clarke et al., 1997). Given thisfocus, it is not surprising that the primary role of GISwithin a retail SDSS environment has also been retail siteselection. Modern locational planning, however, is alsohighly involved with store closures (as in the case ofK-Mart) and modifications (Bennison et al., 1995). Theimportance of these additional types of locational decisionssuggests an additional opportunity for applying GIS inretail operations.

On the policy side, there is interest in the extent to whichvarious populations are affected by the closings. Forexample, because Kmart targets moderate income shop-pers, then it is possible that the closings might eliminate animportant shopping opportunity for lower income house-holds. This result could manifest itself if the closings wereattributable to market demographics (such as low income)rather than competitive forces. This issue is importantbecause there may be real distributional impacts that needto be considered in the aftermath.

Such a concern has precedent. For example, many poor,inner-city neighborhoods have lost their supermarkets, andare now under-served. In many cases, numerous residentsof these neighborhoods have no private transportation,and thus are often resigned to shopping at small, localgrocers that may have high prices and limited selection(Nayga and Weinberg, 1999; Lavin, 2000). If Kmartclosings leave a significant number of residents with nolocal opportunities, then there may be a spatial mismatchor market failure. In such instances, policy makers maywant to offer incentives to businesses to locate in such areasin order to ensure ‘‘fairness.’’ When examining Kmart’sdecision on which stores to close, it seems reasonable toassume that Kmart chose the locations that are expected tobe the least profitable. Because we lack data on individualstore performance, our analysis is founded in a spatialmodel of retail location in an expected profit maximizationframework.

In the next section, we briefly review the fundamentalconcepts of this rather well-established framework andforward a theoretical model of location. This modelsuggests that there are a number of potentially importantfactors that can influence the closing decision, includinglocal demographics, market characteristics and spatialcompetition. From this theoretical model we then specifyan econometric model to examine the relative importanceof the hypothesized factors. Here, we use a Logit model tolook at the marginal effects of assorted local marketattributes on the fate of any particular store. In construct-ing the variables for the empirical model we draw heavilyon GIS in order to properly delineate market attributes.Following our presentation of the empirical results we

turn our attention to interpreting their meaning from twoperspectives. First, we look at how our results fit in with theretail location paradigm. Overall, we find that theparameter estimates support the hypotheses suggested bythe theoretical model with both competitive and demo-graphic factors tending to influence the closure decision.Yet the model tends to not perform well in predictingwhich locations are actually closed. Our second perspec-tive—a distributional approach—is developed in theconclusion, building on our finding that stores were morelikely to be closed in markets with relatively high povertyrates. Here, we argue that the tendency to close Kmart’s inrelatively impoverished neighborhoods may adverselyaffect these neighborhoods and may suggest a need forpolicy intervention.

2. Trends in discount retailing

Kmart’s roots lie in the S.S. Kresge Company, an 18employee ‘‘five and dime’’ initially founded in Detroit in1899. By positioning itself as a mainstream discount store,the company generated $10 million in sales through its 85stores in 1912. By 1966 the company managed 162 Kmartstores and 753 Kresge stores, with annual sales surpassing$1 billion for the first time. In 1977 the S.S. KresgeCompany officially adopted the name Kmart Corporation.While Kmart has outlived most of its early competitors,

including Woolworths and TG&Y, competition fromupstarts, primarily Wal-Mart and Target have had asubstantial impact on the company. After initially focusingexclusively on rural areas, Wal-Mart’s location strategyhad shifted by the 1990s, and it began locating in metroareas—Kmart’s primary domain. Given Wal-Mart’s strat-egy of marketing to low- and moderate-income house-holds, it soon attracted away much of Kmart’s base.Concurrently, Target Corporation was in the midst of anaggressive national expansion campaign, targeting thehigher income discount shoppers. In effect, these twobusinesses squeezed Kmart from both ends, while notnecessarily competing with each other.Fig. 1 documents trends in store counts from 1993 to

2001. Over this time, Wal-Mart expanded its US storecount from 1882 to 2624, a 39% increase. At the same time,

ARTICLE IN PRESSM. Shields, M. Kures / Journal of Retailing and Consumer Services 14 (2007) 259–268 261

Target Corporation’s store count increased 90%, from 554to 1053. By comparison, the number of US Kmartlocations actually declined 9%, from 2323 to 2114.

The effects of these trends become apparent whenlooking at recent company sales. For example, over theperiod 1997–2001, total sales at Wal-Mart and Target eachincreased by more than 60% (Fig. 2). By comparison,Kmart’s sales over this time increased only by 12%. Andthe slight decline from 2000 to 2001 was cited by manyanalysts as the impetus for the Chapter 11 declaration.

0

500

1,000

1,500

2,000

2,500

3,000

1993 1995 1997 1999 2001

K-Mart

Wal*Mart

Target

Fig. 1. Annual US store counts for Kmart, Wal-Mart and target. Source:

Corporate annual reports.

60%

80%

100%

120%

140%

160%

180%

1997 1998 1999 2000 2001

K-MartWal*MartTarget

Fig. 2. Annual sales as percent of 1997 level. Source: Corporate annual

reports.

3. Kmart closing decisions

As noted above, Kmart’s closing affected more than 600stores, about 30% of its total. It is interesting to look at thevariation in closings across regions. In Map 1 we show thelocation of the closed stores. Here, we see that nearlyevery state lost at least one store. Summarizing, we see thatsome regions were relatively more impacted than others(Table 1). For example, before the closings, the West SouthCentral Census Division had 168 stores, about 8% of thecompany total. Yet closings in this region accounted for18.6% of all stores closed, and totaled 66.1% of all storesin the Division. Conversely, the Mid-Atlantic statesaccounted for 10.6% of all Kmart’s before the closings,yet accounted for only 7.0% of all stores closed. Storeslocated in Metropolitan Statistical Areas (MSAs) seemedto be relatively susceptible to closings. Prior to closings,MSAs contained 77.6% of all Kmarts; yet 80.6% of allclosed stores were located in an MSA.

4. Why close a store? A theoretical model

GIS is a tool used to implement a specific analysisprocedure or method. Subsequently, the application of GISin the locational decision-making process is somewhatuseless without an underlying theory or technique (Her-nandez and Bennison, 2000). Retail forecasting and storelocation decisions have been a long-standing interest in theacademic literature. Reilly (1931) conducted an earlyinvestigation into the topic, culminating in the classic The

Law of Retail Gravitation. In this work, Reilly showed thatvariation in the level of retail sales across communities wasa function of the population of the communities and thedistance that separated them. While remaining influentialto this day, Reilly’s work is atheoretical in the sense that ithas no foundation in economic concepts.Over the past 80 years, economists, geographers,

regional scientists and others have built a comprehensivetheoretical framework for analyzing the geographic loca-tion of economic activity (for a recent review, see Kilkennyand Thisse, 1999). The early literature in this area looked atthe factors influencing the optimal location of single-product, profit-maximizing firms. In general this workrevolved around the incorporating the costs of transporta-tion to input- (often fixed in space) and output-markets,and resulted in notions about where firms should locateand how many plants they should include in their network.As the literature evolved, general equilibrium factors suchas land markets, which endogenized input costs, becameimportant components. Recent contributions to the theo-retical framework have incorporated notions such asimperfect competition and location decisions as a non-cooperative game.Beginning in the 1950s, marketing scientists and

geographers have made substantial theoretical and empiri-cal contributions to this field by addressing the retaillocation process (for thorough reviews, see Vandell and

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Map. 1. Locations of closed Kmart’s.

Table 1

Kmart closings by region

Census division Number of stores

prior to 2002

closings

Percent of all Kmart’s

prior to 2002 closings (%)

Number of stores

closed in division

Number of stores

closed as percent of all

stores in division (%)

Number of stores

closed as percent of all

Kmart’s closed (%)

East North Central 469 22.3 122 26.0 20.4

South Atlantic 439 20.9 127 28.9 21.2

West South Central 168 8.0 111 66.1 18.6

East South Central 149 7.1 43 28.9 7.2

Pacific 233 11.1 55 23.6 9.2

Mid-Atlantic 223 10.6 42 18.8 7.0

Mountain 160 7.6 39 24.4 6.5

West North Central 173 8.2 36 20.8 6.0

New England 68 3.2 20 29.4 3.3

Other 21 1.0 3 14.3 0.5

MSA 1633 77.6 482 29.5 80.6

Non-MSA 471 22.4 116 24.6 19.4

M. Shields, M. Kures / Journal of Retailing and Consumer Services 14 (2007) 259–268262

Carter, 1993 and Craig et al., 1984). In general, the retailliterature has followed two tracks. The first looks atconsumers’ store choices and the second looks at storelocation models. Within the store location literature, thereare several areas and accompanying methodologies ofinterest, generally focusing on either the general locationchoice among markets (macro), or siting decisions withinchosen markets (micro).

Our analysis of Kmart’s decision-making process drawson the retail firm location paradigm. In particular, we takea partial equilibrium, micro-level approach, where firmslocate and subsequently evaluate individual store perfor-mance with respect to their ability to maximize expected(utility of) profit.

Early theoretical treatments of retail location in spatiallyextensive markets begin with Beckmann (1968), whoderived a theoretical specification of consumer purchasedecisions in markets with a single retailer. Ingene and Yu(1981) expanded Beckmann’s results to include thecompetitive market case and introduced household char-acteristics into the theoretical model. The empirical modelwe forward is built on the integrated supply and demandframework offered by Ingene and Yu (1981), which wesummarize here.To begin, we layout the simplifying assumptions of

Ingene and Yu (1981): (1) demand is known, (2) allhouseholds have the same demand schedule, (3) householdsare equally spaced, (4) transportation costs are equal in all

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directions, (5) customers pay transportation costs, (6) firmsmaximize profits, and (7) there is easy entry and exit intothe market. As Ingene and Yu (1981) point out, theseassumptions are fairly typical. The only critical assumptionis that of free entry and exit, as the rest, if not met, have noreal impact on the empirical correlates.

Turning to the model proper, on the demand side, autility maximizing household is assumed to possess a lineardemand curve for the retail output of a typical store ofthe form

q ¼ aðyÞ � bp, (1)

where q is quantity, a(y) is the maximal demand quantity(as a function of household income and demographiccharacteristics y), b is the slope of the demand curveand p is the delivered price. The simplest way to introducethe notion of space into the analysis is to considertransportation costs—households will not buy from afirm too far away because transportation costs are toohigh. To explicitly introduce space, consider household i

facing price

pi ¼ p̂þ tRi, (2)

where p̂ is the store price of the retail good, Ri is thedistance from household i to the store, and t is the cost(including time) per unit of distance traveled for a round-trip. This spatial price can now be substituted into therepresentative household’s demand curve (suppressingsubscripts) to specify retail demand as a function ofdistance, among other things

q ¼ aðyÞ � bðp̂� tRÞ. (3)

Now, turn to the profit maximization problem of theretail store proper. Losch and August (1954) forwards a‘‘demand cone’’ that essentially adds a spatial componentto the typical demand curve facing a firm. Incorporatingthe spatial demand function into the ‘Loschian’ demandcone, a typical store considering locating in the center of amarket with radius r has potential total sales (Q) of

Q ¼ 2pfZ D

0

RðaðyÞ � bðp̂� tRÞÞdR, (4)

where f is the (population) density of demand. As usual,expected profits (G) are the product of the store price andquantity, less total costs, or

G ¼ 2pfðp̂� cÞR2

Z D

0

RðaðyÞ � bðp̂� tRÞÞdR� f , (5)

where f is a fixed cost of operation and ðp̂� cÞ is the grossmargin (here, c can vary from market to market due toeither store or regional differences). Integrating the profitfunction, differentiating with respect to p̂ and setting thefirst-order condition equal to zero gives the profit-maximizing price

p̂ ¼1

2

aðyÞbþ c

� ��

tr

3. (6)

Under (5) the market border beyond which consumerswill not travel without competitive pressures is

r� ¼3

4t

aðyÞb� c

� �. (7)

Ingene and Yu (1981) note that (7) is also the profit-maximizing market radius for the spatial monopolist:only competitive conditions will lead to values of r lessthan r�.Substituting (6) into the integrated value of the total

sales function gives a quantity of sales per store as

Q ¼ pfbR2 aðyÞ2b�

c

2�

tr

3

� �(40)

and profits as

G ¼ pfbR2 aðyÞ2b�

c

2�

tr

3

� �2

� f . (50)

In a perfectly competitive market, we expect firms toenter when E(G)40; and conversely, exit when E(G)o0.Given this framework, important variables are those thataffect the parameters (a(y)/b), b, c, and t, which obviouslycan vary across markets. Another important parameter isthe market radius r, which, as noted above, depends, inpart, on the degree of spatial competition. In the nextsection, we turn to the individual and regional factors thatcan influence these parameters in building our empiricalmodel.

5. An empirical model

Our next step is to provide an empirical analog to ourmodel. In general, the specification of the model followsclosely with previous econometric models of retail sales andlocation (e.g. Ingene and Yu, 1981; Ghosh and McLafferty,1987). Here, important factors of store location relate tolocal income, market size, spatial competition and agglom-eration, transportation costs and other local demographiccharacteristics.Before continuing, it is important to recognize that while

the conceptual factors are well known, the exact empiricalspecification does not fall nicely out of the model. Forexample, because Kmart is a discount retailer, it tends totarget moderate-income households. Thus, an empiricalmodel where location is a linear function of income mightnot adequately capture the realities of the corporatemarketing strategy. In order to help readers better under-stand the motivation of our exact empirical specification,we turn to Kmart’s 1997 mission statement: ‘‘Kmart willbecome the discount store of choice for middle-incomefamilies with children by satisfying their routine andseasonal shopping needs as well as or better than thecompetition.’’ Given this framework, the following factorsare included in the empirical model.

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Map. 2. Sample drive time polygon and extracted market size.

M. Shields, M. Kures / Journal of Retailing and Consumer Services 14 (2007) 259–268264

5.1. Dependent variable

Our model seeks to examine the factors related to theclosing of select Kmart locations. The dependent variabletakes a value of unity for each of the 593 Kmart’s that wereclosed in 2002 and 2003, and the value zero for the 1483locations that remained open. Store closings for wereobtained from the Kmart Corporation and open stores arefrom InfoUSA. All locations were geocoded using ESRI’sArcView 8.2. For specifics, see the Appendix A.

5.2. Explanatory variables

5.2.1. Market size

Demand theory and previous empirical work suggest theimportance of the number of potential customers in thelocation process. In our study, we used GIS to determinethe (log of the) number of households within a 15-minutedrive time polygon created around each Kmart location.The number of households was calculated using 2000Census Block Groups and a GIS overlay procedure knownas ‘‘point-in-polygon.’’ That is, those Census Block Groupswhose geographic centroids (centers) that are locatedwithin each drive time polygon were included in themarket size (households) calculations.1 These types ofoverlay procedures are common market analysis techni-ques used within a GIS environment and can be used toquantify market size or demand (Clarke, 1998). A sample15-minute drive time polygon overlying Census BlockGroups is shown on Map 2. The hypothesis is that larger

1Our empirical model was also specified with a 10-minute drive-time

polygon; the parameter estimates were quite robust, but the 15-minute

specification offered slightly better predictive ability.

markets are more likely to retain their Kmart’s, ceterisparibus. (For details on the drive time polygon construc-tion, see the Appendix A.)

5.2.2. Income

According to demand theory, income is an importantpart of the retail location process. A number of previousempirical studies support this hypothesis (e.g. Ferber, 1958;Tarpey and Bahl, 1968; Liu, 1970; Ingene and Yu, 1981).While these studies tend to look at overall markets ratherthan individual firms, they find that total income ispositively correlated with total sales and per capita incomeis positively correlated with per capita sales. Again, weused a GIS point-in-polygon analysis and 2000 Censusdata to determine the percent of households within15minute of each location with annual income between$20,000 and $50,000 (pct20k50k). Here, we expect Kmart’sare less likely to close in a market with a higher percentageof these households.

5.2.3. Spatial competition and agglomeration effects

As noted above, the rise of Wal-Mart and Target haveincreased competition in the discount market. Whileprevious work has given substantial attention to Wal-Mart’s impact on small retailers (Basker, 2005; Hicks andWilburn, 2001; Stone, 1997), little work has been done onits effects on other discounters. We use GIS to create threevariables of spatial competition/agglomeration. Here, weuse GIS to calculate the Euclidean distance from eachKmart location to the nearest (1) Wal-Mart, (2) Target,and (3) another Kmart (Map 3). While the obvioushypothesis is that Kmart’s are more likely to close wherethere is greater competition, or, in the case of anotherK-Mart, existing market coverage, there is a potential

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Map. 3. Sample drive time polygon and extracted market size.

M. Shields, M. Kures / Journal of Retailing and Consumer Services 14 (2007) 259–268 265

converse effect. Specifically, there may be agglomerationeffects that result from being near perceived competition.For example, it is well known that in many regions, similartypes of retailers tend to cluster (e.g. automobile dealers,banks), thus allowing consumers to comparison shop. Inthe context of the theoretical model developed above, theoverall effect of agglomeration is to reduce the costs anduncertainty of shopping. Craig et al. (1984) point out thesepotential contrary effects of competitive markets.

5.2.4. Transportation costs

Theory suggests that the costs of transporting productsfrom distribution centers to retail outlets can haveimportant effects on store margins. To many analysts,the size, scope and efficiency of Wal-Mart’s distributionnetwork is a primary reason for its phenomenal success(Graff, 1998). We used GIS to determine the Euclideandistance from a Kmart location to the nearest distributioncenter (transport). The hypothesis is that Kmart is lesslikely to close stores that are closer to a distribution center,ceteris paribus, as they offer higher expected margins.

5.2.5. Demographics

Finally, there may be other factors that influenceKmart’s decision. Returning to the mission statement, wesee that the company is targeting ‘‘families with children.’’Accordingly, our model incorporates the average house-hold size (US Census) within a 15minute drive (hhsize) ofthe store as a proxy, with the hypothesis that Kmart ismore likely to keep a store open with larger households inits service area.

In order to examine the potential distributional effectsof the closing decisions, we also include the average

poverty rate for households within 15minute drive of aKmart location. Here, our purpose is not so much to testthe effects of poverty on location decisions; rather it isto see if poor neighborhoods are more likely to beadversely impacted. Both the average household sizeand average poverty rate were calculated using a GISoverlay procedure and a subsequent point-in-polygonanalysis.Based on this discussion, the empirical model we

estimate can be written:

open? ¼ b0 þ b1householdsþ b2pct20 k 50 kþ b3wal

�martþ b4targetþ b5other kmart

þ b6transportþ b7hhsizeþ b8povertyþ �, ð8Þ

here e is the unobserved term normally distributed withmean ¼ 0 and variance s2 ¼ 1 The dependent variabletakes on a value of unity if the store remains open, else it iszero. As described, Eq. (8) lends itself to estimation viadichotomous choice methods—in our analysis, we estimatethe decision using maximum likelihood techniques, speci-fically the Logit model (for details of the method, seeGreene, 1997).

5.3. Descriptive statistics and results

Before providing estimation results, we provide themeans of the independent variables conditioned onwhether or not the store remained open in Table 2.Interestingly, there is not a substantial difference betweenthe means of many of the characteristics of open and closedstores. The notable differences we do see show that closedstores were, on average, in markets that had: (1) fewer

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Table 2

Conditional means for independent variables

Variable Decision

Open Close

Number of households 160,292 183,465

Percent of households with income $20K–$50K 37 37

Distance to Wal-Mart 4.5 3.2

Distance to target 21.7 19.3

Distance to nearest K-Mart 13.5 13.2

Distance to distributor 150.2 202.2

Household size 2.6 2.6

Poverty rate (%) 12 13

Table 3

Parameter estimates for logit model for Kmart closing

Variable Marginal effects P[|Z|4z]

Constant 0.082 0.45

Number of households 0.018 1.77

Percent of households with

income $20K–$50K

�0.938 �3.65

Distance to Wal-Mart �0.005 �3.07

Distance to target �0.001 �4.87

Distance to nearest K-Mart 0.001 1.49

Distance to distributor 0.001 7.29

Household size �0.045 �1.00

Poverty rate 0.403 1.77

LogL ¼ �1195.1.

M. Shields, M. Kures / Journal of Retailing and Consumer Services 14 (2007) 259–268266

households; (2) closer competitors; (3) further from adistributor; and (4) lower poverty rates.

5.4. Hypotheses tests of the model

In Table 3 we provide the results of the Logit estimation.The first column is the marginal effect and the secondcolumn is the t-ratio. In general, the signs of the parameterestimates are consistent with theory, though not alwaysstatistically significant. One important result shows thatKmart locations are less likely to be closed as thepercentage of households with income between $20,000and $50,000 increases, ceteris paribus, a finding that isconsistent with Kmart’s intention to target moderateincome families. However, the other target of theirmission—namely families with children (proxied by aver-age household size)—does not surface as statisticallyimportant in the analysis.

It is interesting to see that the number of householdslocated within 15min drive time of a store is a negative andstatistically significant (at the 10% level) factor in theclosing decision. This counters the expectation that the sizeof the potential market matters. One possible explanationis that all Kmart’s were already located in marketspreviously deemed ‘‘large enough;’’ with the subsequentvariation not mattering so much in the corporate decisionprocess.

The overall competitiveness of markets seems to matter.While the marginal effects of distance to both primarycompetitors are small, our results show that beingrelatively close to either a Wal-Mart or a Target increasesthe likelihood that a particular store will be closed.This is consistent with the notion that Kmart has beenadversely affected by the growth in these other retailers.Comparing the marginal effects, it appears that Wal-Marthas had a relatively greater effect then Target. This isnot surprising given Table 2, which shows that thedistance between a closed Kmart and the nearest Wal-Mart averaged 3.2 miles, whereas the average distancebetween a closed Kmart and the nearest Target was19.3 miles.

We also find that distance to the distributor isstatistically important. Recall Table 2, which shows theaverage closed Kmart was 50 miles further from adistributor than the stores that remained open. This seemsto support the importance of distribution costs. As notedabove, many attribute the success of Wal-Mart to its vastdistributor network, which helps it keep prices low. Ourfindings suggest that Kmart may be trying to mimic thisapproach, in part, through its ‘‘de-location’’ process.Our final notable result is the positive and statistically

significant effect of the local poverty rate on the likelihooda store being closed. While our results support the notionthat Kmart is targeting moderate-income households, theyalso support the notion that the company is less likely tokeep stores open in poor neighborhoods. This is animportant result in that it has potentially significantimpacts on poor households in terms of their access todiscount merchandisers. More will be said about this in theconcluding section.

5.5. Predictive abilities of the model

According to a likelihood ratio test of the hypothesisthat the eight coefficients are zero, the estimated model ishighly significant. Unfortunately, the goodness-of-fit sta-tistics for Logit models are not as intuitive as, say, thenotion of r2 values is for the classical regression model. Inpractice one typical test of model accuracy it its ability to‘‘correctly predict’’ the observed decision. Here, the modelcorrectly predicted 73% of the observations, based on athreshold for predicting Y ¼ 1 of 0.50; this is hardlysuggestive of no fit. The model is better at predicting storesthat stayed open (98% correct) then stores that were closed(10% correctly predicted).Unfortunately, the model only slightly outperforms the

naı̈ve model that always predicts Y ¼ 0. If one were to usesuch a model, it would ‘‘correctly predict’’ about 70% ofthe stores that remained open. While this is disappointing,it is not a complete surprise, given the nature of maximumlikelihood estimation. As Greene (1997) notes, unlikethe classical regression model, the maximum likelihood

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2As reported in the 2003 Kmart Annual Report.

M. Shields, M. Kures / Journal of Retailing and Consumer Services 14 (2007) 259–268 267

estimator is not chosen to maximize a fitting criterion on theprediction of y (such as r2). Instead, it maximizes the jointdensity of the observed dependent variables. As our resultsshow, good parameter estimates (as defined by t ratios) arenot necessarily compatible with prediction. In such instances,any evaluation of the ‘‘quality’’ of the model is subjective,though with respect to the problem at hand, prediction wasdefinitely a motivating factor for our interests.

6. Implications and conclusions

In this paper we provide an ex post analysis of the retaillocation paradigm. Our theoretical model of expectedprofit maximization in spatially competitive markets isfounded in notions of firm location and consumer demand.Our empirical results provide support for the hypothesesforwarded in the theoretical framework, especially withrespect to the relative importance of household income andspace, both in terms of competition and transport costs.

Unfortunately, our model’s predictive ability onlyslightly outperforms the naı̈ve model, suggesting that whilethe regional location paradigm is insightful, it is far fromconclusive, at least in this experiment. The model’s modestability to predict actual decisions raises the concern thatthere are other factors at play that are not captured in ourspecification. There are at least two potential notableomissions.

First, Kmart’s bankruptcy settlement noted that eachstore was evaluated on a number of criteria. Most of thesewere captured in our model (e.g. projected operating results;the impact of competition), but several more were not due tolack of data. The omitted concepts are typically at the storelevel, and include factors such as future lease liability andreal estate value; and store age, size, and capital spendingrequirements. The historical importance of the latter factorsis supported by previous research, which notes that storecharacteristics that can influence success. For example, thestore-choice literature suggests that a ‘‘pleasurable’’ shop-ping experience is a significant predictor of extra time spentin the store and increased spending (Donovan et al., 1994).Thus, we see that there are a number of location specificcharacteristics that may be of equal or greater importancethan broader regional effects.

Second, our model does not capture strategic changes incorporate priorities that may have resulted from bank-ruptcy. For example, Kmart has apparently stepped backfrom its ‘‘Super K’’ concept, which it initiated in 1991 as acombination full-service grocery and general merchandisestore. According to a 2003 Wachovia report, 72 of the 110‘‘Super Ks’’ that existed in 2002 were shuttered (66%).Unfortunately, we were unable to identify the ‘‘Super K’’stores in our data set.

While our results are mixed, they do provide insight intoa potential area of policy interest. As noted above, Kmartwas more likely to close a store in a place with a relativelyhigh poverty rate, all other things equal. We interpret thisas the possible extension of the trend of declining super-

market access in poor neighborhoods, which has resultedin substantially higher prices and transportation challenges(Nayga and Weinberg, 1999). Should this now be true inthe general merchandise category, the closing of Kmart’s inpoor areas may be having adverse distributional impacts.From a policy perspective, there is a need to better

understand how the closings are impacting individuals inthe affected markets. Should it be determined that poorfamilies have reduced access to important everyday items,then there may be a need for the public sector to offerincentives or other public support to encourage discountersto locate in these neighborhoods. This support could comein the form of site preparation/improvement, construction,financing or business support services.

Appendix A. Notes on the GIS methodology and data

sources

Given the enormous number of calculations used toanalyze Kmart closings, the spatial analysis of marketconditions would not have been possible without extensiveuse of the macro programming abilities included in modernGIS software packages. These abilities allowed for batchprocessing of transportation network analysis calculations,spatial queries and geocoding of store locations.

A.1. Store locations

Kmart locations were obtained from InfoUSA (formerlyABI). Store closings for 2002 and 2003 were obtained fromthe Kmart Corporation. All locations were geocoded usingArcGIS. The resulting data set returned the followingaccuracy levels:

�

As Puerto Rico locations were not used in the analysis,277 of the 282. 2002 store closings were identified andgeocoded (98.2%). � A total of 97.5% of the 2002 store closings were

geocoded to the block face positional accuracy level(most accurate) while the remaining 2.5% of the storeswere geocoded to ZIP+4 or ZIP+2 centroid accuracylevel. While the positional accuracy levels of these 2.5%of locations will vary, most will be within several blocks.

� A total of 316 of the 321 (323 including Puerto Rico) 2003

store closings were identified and geocoded (98.4%).

� A total of 90.0% of the 2003 store closings were geocoded

to the block face positional accuracy level (most accurate)while the remaining 10.0% were geocoded to the ZIP+4or ZIP+2 centroid accuracy level.

� A total of 1483 out of the remaining 1511 open

locations2 were used in the analysis (98.1%).

� A total of 90.6% of the 1483 open locations were

geocoded to the block face accuracy level while theremaining 9.4% were geocoded to the ZIP+4 or ZIP+2centroid accuracy level.

ARTICLE IN PRESSM. Shields, M. Kures / Journal of Retailing and Consumer Services 14 (2007) 259–268268

�

Distribution center locations were obtained from theKmart Corporation. These distribution centers containboth hardline and softline centers and omit theCorsicana, TX center closed in 2003.

Wal-Mart and Target locations were obtained from eachrespective corporation and are current as of 2nd Quarter2003. Data sets of 1190 Target Stores and 2859 Wal-Martswere included in the analysis. Each of these data sets wasgeocoded with �90.0% block face positional accuracy.

The reason for reporting the store counts and geocodingaccuracy statistics is that the geocoding process introducesthe potential for positional accuracy or omission errors.While those locations accurate to the block face level willhave minimal positional accuracy errors, stores with aZIP+4 or ZIP+2 centroid accuracy level could slightlyskew distance and demographic calculations used in theanalysis. Given the nature of ZIP+4 or ZIP+2 accuracylevels, it is likely that these errors will be minimal.However, their existence should be recognized.

A.2. Drive time polygon and demographic calculations

Ten and Fifteen-minute drive time polygons around eachKmart location were calculated using ArcGIS. Thesubsequent demographics calculated within each drive timering are based on point-in-polygon calculations using thoseUS Census Block Group centroids located within eachrespective drive time polygon. Census Block Groups areused as they are the smallest geographical units with all ofthe necessary demographic categories used in the model.Accordingly, the use of Census Block Groups avoids thescale effect associated with the Modifiable Areal UnitProblem (MAUP) often found in these types of geographiccalculations. The scale effect arises when the results of ananalysis are impacted by the aggregation of smallerreporting units into larger reporting units (i.e. aggregatingCensus Block Groups into Census Tracts). The MAUP iswell documented by Openshaw and Taylor (1979) andGreen and Flowerdew (1996).

References

Basker, E., 2005. Job Creation or Destruction? Labor Market Effects of

Wal-Mart Expansion. The Review of Economics and Statistics 87 (1),

174–183.

Beckmann, M., 1968. Location Theory. Random House, New York.

Bennison, D., Clarke, I., Pal, J., 1995. Locational decision making

in retailing: an exploratory framework for analysis. The International

Review of Retail, Distribution and Consumer Research 5 (1),

1–20.

Clarke, I., 1998. Changing methods of location planning for retail

companies. GeoJournal 45 (4), 289–298.

Clarke, I., Bennison, D., Pal, J., 1997. Towards a contemporary

perspective of retail location. International Journal of Retail and

Distribution Management 25 (2), 59–69.

Craig, C.S., Ghosh, A., McLafferty, S., 1984. Models of the retail location

process: a review. Journal of Retailing 60 (1), 5–36.

Donovan, R., Rossiter, J., Marcoolynn, G., Nesdale, A., 1994. Store

atmosphere and purchasing behavior. Journal of Retailing 70 (3),

283–294.

Ferber, R., 1958. Variations in retail sales between cities. Journal of

Marketing 22 (3), 295–303.

Ghosh, A., McLafferty, S., 1987. Location Strategies for Retail and

Service Firms. D.C. Heath, Lexington, MA.

Graff, T.O., 1998. The Locations of Wal-Mart and Kmart supercenters:

contrasting corporate strategies. Professional Geographer 50 (1),

46–57.

Greene, W., 1997. Econometric Analysis. Prentice-Hall, Englewood Cliffs,

NJ.

Hernandez, T., Bennison, D., 2000. The art and science of retail location

decisions. International Journal of Retail and Distribution Manage-

ment 28 (8), 357–367.

Hicks, M.J., Wilburn, K.L., 2001. The regional impact of Wal-Mart

entrance: a panel study of the retail trade sector in West Virginia. The

Review of Regional Studies 31 (3), 305–313.

Ingene, C., Yu, E., 1981. Determinants of retail sales in SMSAs. Regional

Science and Urban Economics 11 (4), 529–547.

Kilkenny, M., Thisse, J.F., 1999. Economics of location: a selective

survey. Computers and Operations Research 26 (4), 1369–1394.

Lavin, M., 2000. Problems and opportunities of retailing in the US inner

city. Journal of Retailing and Consumer Services 7 (1), 47–57.

Losch, P., August, P., 1954. The Economics of Location (translated by

W. Woglum and W. Stolper). Yale University Press, New Haven.

Liu, B.-C., 1970. The relationship among population, income, and retail

sales in SMSAs, 1952–66. Quarterly Review of Economics and

Business 10 (1), 25–40.

Nayga, R.M., Weinberg, Z., 1999. Supermarket access in the inner cities.

Journal of Retailing and Consumer Services 6 (3), 141–145.

Reilly, W., 1931. The Law of Retail Gravitation. Knickerbocker Press,

New York.

Stone, K.E., 1997. Impact of the Wal-Mart Phenomenon on Rural

Communities, Increasing Understanding of Public Problems and

Policies. Farm Foundation, Oak Brook, IL, pp. 189–200.

Tarpey, L., Bahl, R., 1968. Inter-city variations in retail sales:

some hypotheses revisited. Southern Journal of Business (October),

1–10.

Vandell, K.D., Carter, C.C., 1993. Retail store location and market

analysis: a review of the research. Journal of Real Estate Literature 1

(1), 13–45.