Estimating Demand Shocks from Foot Traffic: A Big-Data Approach

Marina Azzimonti (), David Wiczer and Yang Xuan
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Marina Azzimonti: https://www.richmondfed.org/research/people/azzimonti

No 1191, Staff Reports from Federal Reserve Bank of New York

Abstract: 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 largely orthogonal to location and sector. We find important heterogeneity in the persistence of shocks, important heterogeneity in their trends, and that estimation on a pooled sample importantly understates the variance experienced by some establishments.

Keywords: Consumer-facing; brands; service; retail trade; health; demand dynamics; demand shocks; Foot Traffic (search for similar items in EconPapers)
JEL-codes: E21 L14 L80 (search for similar items in EconPapers)
Pages: 40
Date: 2026-04-01
New Economics Papers: this item is included in nep-big
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Working Paper: Estimating Demand Shocks from Foot Traffic: A Big-Data Approach (2026)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fednsr:103026

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DOI: 10.59576/sr.1191

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