Top

Customer Needs and Solutions

Published in:

01-12-2023 | Research Article

The Agglomeration-Differentiation Tradeoff in Spatial Location Choice

Authors: Sumon Datta, K. Sudhir

Published in: Customer Needs and Solutions | Issue 1/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Retailers often co-locate spatially to draw consumers, even though it increases price competition. The paper develops a structural model of entry and location choice that isolates the agglomeration benefit of co-location, after controlling for pure differentiation rationales for co-location such as (1) high demand and/or low cost at the location, (2) zoning restrictions, and (3) format differentiation that minimizes the need for spatial differentiation. We augment the entry and location choice data used in the literature with revenue and price data to help identify the agglomeration effect. We introduce a new approach to obtaining zoning data across a large number of markets that should be of general interest for a large stream of spatial location applications. We find that agglomeration benefits explain a significant fraction of observed co-location. While zoning restrictions have a little direct impact on co-location, in combination with the agglomeration benefit, they explain a surprisingly large fraction of observed co-location.

previous article Marketing at Tuck: 120-Plus Years of Relevance, Rigor, and Impact

next article Frontline Employees’ Empathy in Service Recovery: a Systematic Literature Review and Agenda for the Future

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Some empirical evidence of the benefits of spatial co-location can be found in Fox et al. [20] and Watson [37]. Vitorino [35] finds evidence for inter-store spillovers in a particular kind of retail cluster—shopping malls. In a mall setting, however, firms only make a strategic entry decision,they do not face the tradeoff of whether to co-locate or spatially differentiate with rivals.

For example, Fox et al. [20] use data from a multi-outlet panel to study consumers’ shopping behavior and its impact on store revenues. However, their data is from a single major metropolitan market.

Some have addressed entry decisions of retail chains, considering how these chains build up their network [23],[21],[15].

Some studies that have also used post-action performance data to gain richer insights about the drivers of firms’ strategic decisions include Ellickson and Misra [17] and Draganska et al. [13].

Orhun [27] attempts to control for location-specific common profit shocks. However, with only choice data, one can only model latent profits whose errors have to be normalized for estimation. For instance, Orhun [27] assumed that the distribution of common profit shocks have a standard normal distribution.

We do not have store entry dates which are required to solve a dynamic choice game. However, our model can be extended to a dynamic set-up similar to Aguirregabiria and Vicentini [2] who have proposed a dynamic model of an oligopoly industry characterized by spatial competition.

Alternatives to likelihood-based approaches include method of moments (Thomadsen [13, 33], minimum distance or asymptotic least square estimators [28],[7],[29], and maximum score estimators (Fox and Bajari 2010; [19, 20], Ellickson et al. [15]).

See Aguirregabiria et al., [4] for a discussion on the distinction between multiple equilibria in model and multiple equilibria in data.

As described later (Eq. 15, we will consider a monotonic transformation (log(.)) of Eq. (3), which will yield an expression that has an additive form and that is conducive for estimation.

If we do not impose such a cap on the maximum distance, then the estimation becomes very cumbersome and slow as our dataset consists of several large markets that consist of large number of locations and CBGs.

We use per capita income for convenience. Alternatively, one could, of course, use other better variables such as per capita expenditure on grocery.

We use sales-weighted prices across all categories in a store as the price index of the store.

We normalize the log of profit from not entering a market (the outside option) to zero; this implies that the profit for the outside option is normalized to 1. The log transformation also implies that the profit in Eq. (3) is restricted to being non-negative.

One way to deal with this problem is to provide sufficient conditions that the parameters, θ, must satisfy to ensure a unique equilibrium (e.g., [30],[40].

Another application of the NPL approach for a static game can be found in Ellickson and Misra [17].

Many of the fixed points may be identical.

A survey of econometric methods with multiple equilibria in static games is provided by de Paula [12] and Aradillas-López [5].

Su and Judd [32] suggest using a Mathematical Programming with Equilibrium Constraints approach that finds the parameter estimates and the equilibrium CCPs simultaneously. However, like the parallel-NPL, this approach also relies on multiple runs with different starting values to find different equilibria. Hence, its ability to find the global optimum in problems that have a large action space (as in our entry and location choice problem) is unclear.

Kasahara and Shimotsu [24] suggest the following procedure for selecting the value of \(\delta\): Simulate a sequence \({\left\{{\widetilde{P}}_{n}\right\}}_{n=0}^{N}\) by iterating the transformed mapping for different values of \(\delta\), say for \(\delta \in \left\{\mathrm{0.1,0.2},...,0.9\right\}\). Then pick the value of \(\delta\) that leads to the smallest value of the mean of across n = 1,…, N.

We have weekly product category-level price index data for a one year period for 27 grocery product categories and for each store that belongs to the store chain (\({\mathrm{pr}}_{cts}=\sum\limits_{\forall i\in c}\sum\limits_{\forall u\in i}{w}_{ciuts}*p{r}_{ciuts};\) where, \({w}_{ciuts}\) is the revenue share of UPC, u, of item, i, within product category, c, for week t in store s). To construct store-level price indices, we adopt an approach similar to Chevalier et al. (2003, p. 22). That is, we aggregate over the product categories and weeks to form a store-level price index (\({\mathrm{pr}}_{s}=\sum\limits_{c=1}^{27}\sum\limits_{t=1}^{52}{w}_{cts}*{\mathrm{pr}}_{cts};\) where \({w}_{cts}\) is the dollar share of category c in week t in store s).

A comparison of the market configurations between 2001 and 2008 showed that the number of stores in these markets increased less than 10% from 399 to 438.

In this paper, distance between two points always refers to the great-circle distance.

A pixel point is one of the individual dots that make up a graphical image. Each pixel point combines red, green, and blue phosphors to create a specific color.

Note that competition between stores in neighboring locations cannot explain the absence of big-box stores in a location as we are considering big-box stores across any segment of the retail industry.

Comparing the results (not presented here) with different specifications for the maximum distance that consumers may travel for shopping, Rad, suggested that a distance of 5 mi. was sufficient. Rad values of 6 mi. and above did not change parameter estimates or increase the likelihood value significantly (vis-à-vis AIC and BIC criteria). On the other hand, Rad values of 4 mi. and below resulted in significantly different estimates for some model parameters and also gave significantly smaller likelihood values.

For this counterfactual simulation, we are counting stores within 1 mi. of a rival as a co-located store. It is plausible that the two stores belong to two neighboring 1-mi.² block retail location whose commercial centers happen to be within 1 mi. of each other.

We acknowledge that there is a potential selection bias because we only observe revenue data for locations that were chosen. Ellickson and Misra [16] propose a selection correction function in their application where supermarkets choose from one of three pricing strategies. However, their approach suffers from a curse of dimensionality in cases where the cardinality of firms’ action space is large, as is the case for firms choosing from multiple locations within a market.

Aguirregabiria V, Mira P (2005) A genetic algorithm for the structural estimation of games with multiple equilibria. Working paper

Aguirregabiria V, Vicentini G (2012) Dynamic spatial competition between multi-store firms. Working paper

Aguirregabiria V, Mira P (2007) Sequential estimation of dynamic discrete games. Econometrica 75(1):1–53CrossRef

Aguirregabiria V, Bajari P, Draganska M, Einav L, Horsky D, Misra S, Narayanan S, Orhun Y, Reiss P, Seim K, Singh V, Thomadsen R, Zhu T (2008) Discrete choice models with strategic interactions. Mark Lett 19:399–416CrossRef

Aradillas-López A (2020) The econometrics of static games. Ann Rev Econ 12(1):135–165CrossRef

Arentze TA, Oppewal OH, Timmermans HJP (2005) A multipurpose shopping trip model to assess retail agglomeration effects. J Mark Res 42(February):109–115CrossRef

Bajari P, Benkard L, Levin J (2007) Estimating dynamic models of imperfect competition. Econometrica 75(5):1331–1370CrossRef

Berry S (1992) Estimation of a model of entry in the airline industry. Econometrica 60:889–917CrossRef

Bester H (1998) Quality uncertainty mitigates product differentiation. RAND J Econ 29(Winter):828–844CrossRef

10.

Bresnahan T, Reiss P (1991) Entry and competition in concentrated markets. J Polit Econ 99:977–1009CrossRef

11.

Ciliberto F, Tamer E (2009) Market structure and multiple equilibria in airline markets. Econometrica 77(6):1791–1828CrossRef

12.

de Paula A (2013) Econometric analysis of games with multiple equilibria. Ann Rev Econ 5(1):107–131CrossRef

13.

Draganska M, Mazzeo M, Seim K (2009) Beyond plain vanilla: modeling joint product assortment and pricing decisions. Quant Mark Econ 7(2):105–146CrossRef

14.

Dudey M (1990) Competition by choice: the effect of consumer search on firm location decisions. Am Econ Rev 80(5):1092–1104

15.

Ellickson PB, Houghton S, Timmins C (2013) Estimating network economies in retail chains: a revealed preference approach. Rand J Econ 44(2):169–193CrossRef

16.

Ellickson PB, Misra S (2012) Enriching interactions: incorporating revenue and cost data into static discrete games. Quant Mark Econ 10:1–26CrossRef

17.

Ellickson PB, Misra S (2008) Supermarket pricing strategies. Mark Sci 27(5):811–828CrossRef

18.

Fischer JH, Harington JE (1996) Product variety and firm agglomeration. RAND Journal of Economics 27:281–309CrossRef

19.

Fox J (2007) Semiparametric estimation of multinomial discrete choice models using a subset of choices. RAND J Econ 38(4):1002–1019CrossRef

20.

Fox E, Postrel S, McLaughlin A (2007) The impact of retail location on retailer revenues: an empirical investigation. Working paper

21.

Holmes T (2011) The diffusion of Wal-Mart and economies of density. Econometrica 79(1):252–301

22.

Homer C, Huang C, Yang L, Wylie B, Coan M (2004) Development of a 2001 national land-cover database for the United States. Photogramm Eng Remote Sens 70(7):829–840CrossRef

23.

Jia P (2008) What happens when Wal-Mart comes to town: an empirical analysis of the discount retailing industry. Econometrica 76(6):1263–1316CrossRef

24.

Kasahara H, Shimotsu K (2012) Sequential estimation of structural models with a fixed point constraint. Econometrica 80:2303–2319CrossRef

25.

Konishi H (2005) Concentration of competing retail stores. J Urban Econ 58:488–512CrossRef

26.

Mazzeo M (2002) Product choice and oligopoly market structure. RAND J Econ 33:221–242CrossRef

27.

Orhun Y (2013) Spatial differentiation in the supermarket industry: the role of common information. Quant Mark Econ 11:3–37CrossRef

28.

Pakes A, Ostrovsky M, Berry S (2007) Simple estimators for the parameters of discrete dynamic games, with entry/exit examples. RAND J Econ 38:373–399CrossRef

29.

Pesendorfer M, Schmidt-Dengler P (2008) Asymptotic least squares estimators for dynamic games. Rev Econ Stud 75:901–928CrossRef

30.

Seim K (2006) An empirical model of firm entry with endogenous product-type choices. RAND J Econ 37(3):619–640CrossRef

31.

Stahl K (1982) Differentiated products, consumer search, and locational oligopoly. J Ind Econ 31(1–2):97–113CrossRef

32.

Su C, Judd KL (2012) Constrained optimization approaches to estimation of structural models. Econometrica 80:2213–2230CrossRef

33.

Thomadsen R (2007) Product positioning and competition: the role of location in the fast food industry. Mark Sci 26(6):792–804CrossRef

34.

Varian RH (1980) A model of sales. Am Econ Rev 70:651–659

35.

Vitorino MA (2012) Empirical entry games with complementarities: an application to the shopping center industry. J Mark Res 49(2):175–191CrossRef

36.

Vogelmann JE, Howard SM, Yang L, Larson CR, Wylie BK, Van Driel JN (2001) Completion of the 1990’s national land cover data set for the conterminous United States. Photogramm Eng Remote Sens 67(6):650–662

37.

Watson R (2005) Entry and location choice in eyewear retailing. mimeo., University of Texas-Austin.

38.

Wolinsky A (1983) Retail trade concentration due to consumers’ imperfect information. Bell J Econ 14(1):275–282CrossRef

39.

Zhu T, Singh V, Manuszak M (2009) Market structure and competition in the retail discount industry. J Mark Res 46(4):453–466CrossRef

40.

Zhu T, Singh V (2009) Spatial competition with endogenous location choices: an application to discount retailing. Quant Mark Econ 7(1):1–35CrossRef

41.

Marshall A (1920) Principles of Economics, 8th edn. Macmillan, London

Title: The Agglomeration-Differentiation Tradeoff in Spatial Location Choice
Authors: Sumon Datta
K. Sudhir
Publication date: 01-12-2023
Publisher: Springer US
Published in: Customer Needs and Solutions / Issue 1/2023
Print ISSN: 2196-291X
Electronic ISSN: 2196-2928
DOI: https://doi.org/10.1007/s40547-023-00135-w

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 1/2023

Marketing at Tuck: 120-Plus Years of Relevance, Rigor, and Impact

Marketing at UNSW Sydney: Building Marketing Capability in Australasia

Frontline Employees’ Empathy in Service Recovery: a Systematic Literature Review and Agenda for the Future

Simon Marketing

Review of Marketing Relevant Real Activity Manipulation

The Kellogg School of Marketing: Past, Present, and Future