Retail Demand Interdependence and Chain Store Closures

Why do retail stores agglomerate, and what happens when those patterns of agglomeration change? I describe a model of interdependent retail demand based on multi-stop shopping, and then estimate empirical interdependence by measuring how local store foot traffic responds to national chain bankruptcies and widespread closures. I find a 4%-5% average decline in nearby store foot traffic after a Sears or K-mart closure and a 5%-6% average decline in foot traffic after a grocery closure, with considerable heterogeneity in observed declines across different nearby-store types. I fit a structural model of interdependent retail demand with parameters identified from these reduced-form estimates and then use this model to simulate a decline in in-person grocery demand, such as would occur under increased take-up of online grocery delivery. I project that while the median nearby store would lose roughly 0.08% of traffic for every 1% decline in grocery traffic, up to a quarter of adjacent hardware stores would see 0.4% or higher declines for every 1% drop in grocery traffic. The median elasticity is projected to be 20% higher if in-person grocery demand decreases only for higher-income consumers. In a separate counterfactual, I simulate the effect of remote work on retail demand and project that negatively-affected stores would lose 0.15% of traffic on average for every 1% reduction in worker density, closely in line with other empirical studies