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Is mobility a good proxy for consumption?

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  • Cepparulo, Brian

Abstract

This paper investigates the relationship between mobility and consumer expenditure using a longitudinal dataset of local-level transactions in the United Kingdom. It distinguishes between online and in-store spending and employs fixed effects models covering the period from February 2020 to March 2022. The analysis finds that retail and recreational mobility consistently serve as reliable proxies for in-store spending, while there is limited evidence of a correlation between online spending and mobility. These findings suggest that mobility data can serve as a valuable proxy for consumer activity in countries lacking high-frequency consumption data.

Suggested Citation

  • Cepparulo, Brian, 2025. "Is mobility a good proxy for consumption?," Economics Letters, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:ecolet:v:255:y:2025:i:c:s0165176525002915
    DOI: 10.1016/j.econlet.2025.112454
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    References listed on IDEAS

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    1. Kyra H. Grantz & Hannah R. Meredith & Derek A. T. Cummings & C. Jessica E. Metcalf & Bryan T. Grenfell & John R. Giles & Shruti Mehta & Sunil Solomon & Alain Labrique & Nishant Kishore & Caroline O. B, 2020. "The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
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    3. Lorenz Kueng & Scott R. Baker, 2022. "Household Financial Transaction Data," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 47-67, August.
    4. Diane Alexander & Ezra Karger, 2023. "Do Stay-at-Home Orders Cause People to Stay at Home? Effects of Stay-at-Home Orders on Consumer Behavior," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 1017-1027, July.
    5. Jérôme Coffinet & Jean-Brieux Delbos & Jean-Noël Kien & Etienne Kintzler & Ariane Lestrade & Michel Mouliom & Théo Nicolas & Vojtech Kaiser, 2023. "Tracking the economy during the Covid-19 pandemic: the contribution of high frequency indicators," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
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    More about this item

    Keywords

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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