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cannot support a big store. 23 As we do not have a precise method for identifying such locations, we use a stylized selection procedure. For each market, we find the minimum value of the total income of consumers within a 2-mile radius of the commercial locations that have big-box stores. We use this minimum as a benchmark for a commercial location in the market to be attractive enough to support at least one big-box retail store. That is, if a commercial location does not have any big-box store and the total income of consumers within a 2-mile radius of the location is less than the market benchmark then we presume that the absence of a big-box store is due to the unattractiveness of the location and not necessarily because of zoning restrictions. Hence, a commercial location with no big-box store is still treated as a potential retail location when the following condition is satisfied: Income in 2-mile radius of a commercial location that has no big-box store ≤ ⎧Income in 2-mile radius of a commercial⎫ min ⎨ ⎬ ⎩location that has a big-box store ⎭ To summarize, we use the NLCD data to construct market boundaries so that each market can be divided into a grid of 1 sq. mile locations. Then the commercial land pixel points are used to obtain the extent of commercial activity in a location and also to locate the commercial center of the location. Extant models that do not account for zoning, assume that firms are allowed to set up stores in any market location. In contrast, we account for residential zoning by excluding locations that do not have any commercial land pixel points. Finally, we account for zoning regulations particularly against big-box retailers, within commercial locations, by defining potential retail locations as those commercial locations that (1) have at least one big-box store which is either a grocery or a non-grocery store, and (2) do not have a big-box store and are in a poor neighborhood which is below the market benchmark as described above. 23 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. 34
4. Results The estimation results are presented in three parts. Table 2(a) presents the estimates for the consumer shopping location choice or the demand side of the model. Table 2(b) presents the results of the price index portion of the model. Finally, the estimates of cost and unobserved shocks are presented in Table 2(c). The demand side estimates indicate that consumers experience a negative travel cost that is convex with respect to distance (The coefficient of 2 dgl is positive and significant). 24 Consumers who are far away from the nearest retail location (That is, when the value of 35 min_dg is large), are more willing to travel long distances to get to a grocery store. Demographic characteristics seem to have very little explanatory power for consumers’ travel costs. The results show that consumers not only value economies of scope from the presence of other, non-grocery businesses at a location but they also value the agglomeration of multiple grocery stores at the location. The store agglomeration parameter ( α SA = 0.5342) is positive and significant which suggests that consumers likely visit locations with multiple grocery stores. The format agglomeration effect ( α f , FA ) is also positive and even significant for a few store formats (Supercenters, Limited Assortment stores, and Food and Drug stores). Hence, consumers are more likely to visit locations with multiple grocery stores when the cluster of stores consists of different formats. Consequently, strategic store and format agglomeration increase consumers’ propensity to shop at a location, thus increasing volume at that location. 24 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 miles was sufficient. Rad values of 6 miles 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 miles and below resulted in significantly different estimates for some model parameters and also gave significantly smaller likelihood values.
Page 1 and 2: The Agglomeration-Differentiation T
Page 3 and 4: 1. Introduction Spatial clustering
Page 5 and 6: have used this approach to study fi
Page 7 and 8: Marketing and the approach should b
Page 9 and 10: static discrete game. 7 Estimation
Page 11 and 12: 9 ( ( ⎡ ⎤ ) ) m m m m E[ πil(
Page 13 and 14: We have detailed information about
Page 15 and 16: any retail location within a radius
Page 17 and 18: The third component on the right-ha
Page 19 and 20: format (f’-format) rival and it r
Page 21 and 22: pr r c ( ω , ω , ω , θ) p Entry
Page 23 and 24: In the generalized model specificat
Page 25 and 26: * * Estimation involves finding the
Page 27 and 28: NPL iterations depends on the start
Page 29 and 30: ( ) ( ) δ 1−δ Λ P, Θ = ⎡ P,
Page 31 and 32: Pennsylvania and Ohio. This second
Page 33 and 34: esidential zone so that placing ret
Page 35: that correspond to commercial land
Page 39 and 40: (Parameter estimates not shown). Th
Page 41 and 42: the commercial zone, suggesting tha
Page 43 and 44: interdependence across markets. How
Page 45 and 46: Figure 3(a): Graphical illustration
Page 47 and 48: Figure 4(c): Using commercial land
Page 49 and 50: Notes: SM - Supermarket format; SS
Page 51 and 52: Variable 27 Travel Cost ( Tvl ) gl
Page 53 and 54: Variable 29 Supermarket (SM) Supers
Page 55 and 56: Step 0: Initial Population: Appendi
Page 57 and 58: Appendix C The following steps expl
Page 59 and 60: Dudey, M. (1990), “Competition by

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