Estimating Network Economies in Retail Chains: A Revealed Preference Approach

Many industries are characterized by a small number of rms making many entry decisions over a large choice set. Nowhere is this more true than in the case of the discount retail sector, where Target, Wal-Mart, and Kmart compete in a national market. Traditional discrete choice models of rm entry are ill-suited to this high dimensional choice problem. We instead draw upon recent innovations in the application of maximum score estimators to models of revealed preference (Bajari and Fox (2006), Fox (2007)). Unlike previous work on this sector, our approach allows us to consider any number of potential rivals, any number of stores per location, the endogeneity of the distribution network, and unobserved (to the econometrician) location attributes that might cause rms to cluster their stores. Moreover, we show how recent innovations in set identication and inference can be used to separate the role of these unobservables from observed location attributes like population. We nd that all

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