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Working Paper
Estimating Demand Shocks from Foot Traffic: A Big-Data Approach
This study leverages high-frequency foot-traffic data from SafeGraph to estimate demandshocks in customer-facing establishments across New York City’s retail, service, and healthsectors. Recognizing that variations in foot traffic can arise from both unpredictable demandshocks and firm-driven strategies to attract customers, we present a theoretical framework that isolates establishment-level demand fluctuations from firm-level strategic choices. Implementing this empirically, we employ an unsupervised machine learning approach to classify establishments into distinct categories that are ...
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Estimating Demand Shocks from Foot Traffic: A Big-Data Approach
This study leverages high-frequency foot-traffic data from SafeGraph to estimate demand shocks in customer-facing establishments across New York City’s retail, service, and health sectors. Recognizing that variations in foot traffic can arise from both unpredictable demand shocks and firm-driven strategies to attract customers, we present a theoretical framework that isolates establishment-level demand fluctuations from firm-level strategic choices. Implementing this empirically, we employ an unsupervised machine learning approach to classify establishments into distinct categories that are ...