The Consumer City Revisited: Consumption Responses to Real-Time Population Presence

David Bounie (), Chloé Breton, John Galbraith and Gabrielle Gambuli
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David Bounie: IP Paris - Institut Polytechnique de Paris, CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique
Chloé Breton: Télécom Paris - IMT - Institut Mines-Télécom [Paris] - IP Paris - Institut Polytechnique de Paris
John Galbraith: McGill University = Université McGill [Montréal, Canada], CIRANO - Centre interuniversitaire de recherche en analyse des organisations [Montréal, Canada] = Center for Interuniversity Research and Analysis on Organizations [Montréal, Canada]
Gabrielle Gambuli: Télécom Paris - IMT - Institut Mines-Télécom [Paris] - IP Paris - Institut Polytechnique de Paris

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Abstract: This paper combines high-frequency mobile phone location data with card transaction records, to examine the relationship between the number of individuals present in a zone ('real-time population') and economic transactions. Using data from the metropolitan area of Lyon, France's second-largest urban area, and a Poisson Pseudo-Maximum Likelihood estimator, we estimate the elasticity of transactions with respect to a real-time count of individuals present in each of over one thousand zones. We document three key findings: (1) consumption responsiveness varies systematically across time and space, with elasticities peaking at 1.08 on Saturday midday but declining to 0.98 on Sunday and ranging from 1.04 in urban cores to 0.84 in peripheries; (2) spatial frictions reduce transaction flows by 2% per 1% increase in distance from home; and (3) sectoral heterogeneity, where essential goods (e.g., food retail) consistently outperform discretionary sectors (e.g., arts and entertainment). Together, these results underscore how temporal, spatial, and sectoral factors jointly shape economic activity.

Keywords: Transaction data; Mobile phone data; Mobility; Real-time population (search for similar items in EconPapers)
Date: 2026-04-08
Note: View the original document on HAL open archive server: https://hal.science/hal-05584665v1
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