Authors: Marina Azzimonti, David Wiczer, and Yang Xuan
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Staff Reports
Estimating Demand Shocks from Foot Traffic: A Big-Data Approach
Number 1191
April 2026
April 2026
JEL classification: E21, L14, L80
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.
Author Disclosure Statement(s)
Marina Azzimonti
The author declares that she has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
David Wiczer
The author declares that he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Yang Xuan
The (co)author declares that (s)he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
The author declares that she has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
David Wiczer
The author declares that he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Yang Xuan
The (co)author declares that (s)he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Suggested Citation:
Azzimonti, Marina, David Wiczer, and Yang Xuan. 2026. “Estimating Demand Shocks from Foot Traffic: A Big-Data Approach.” Federal Reserve Bank of New York Staff Reports, no. 1191, April. https://doi.org/10.59576/sr.1191
Azzimonti, Marina, David Wiczer, and Yang Xuan. 2026. “Estimating Demand Shocks from Foot Traffic: A Big-Data Approach.” Federal Reserve Bank of New York Staff Reports, no. 1191, April. https://doi.org/10.59576/sr.1191
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