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The Use of Scanner Data for Economics Research

Author

Listed:
  • Pierre Dubois

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse)

  • Rachel Griffith

    (University of London [London], University of Manchester [Manchester])

  • Martin O'Connell

    (University of London [London], University of Wisconsin-Madison)

Abstract

The adoption of barcode scanning technology in the 1970's gave rise to a new form of data; scanner data. Soon afterwards researchers began using this new resource, and since then a large number of papers have exploited scanner data. The data provide detailed price, quantity and product characteristic information for completely disaggregate products at high frequency and typically either track a panel of stores and/or consumers. Their availability has led to advances, inter alia, in the study of consumer demand, the measurement of market power, firms' strategic interactions and decision-making, the evaluation of policy reforms, and the measurement of price dispersion and in ation. In this article we highlight some of the pro and cons of this data source, and discuss some of the ways its availability to researchers hastransformed the economics literature.

Suggested Citation

  • Pierre Dubois & Rachel Griffith & Martin O'Connell, 2022. "The Use of Scanner Data for Economics Research," Post-Print hal-03770614, HAL.
    • Handle: RePEc:hal:journl:hal-03770614
    DOI: 10.1146/annurev-economics-051520-024949
    Note: View the original document on HAL open archive server: https://hal.science/hal-03770614v1
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    Cited by:

    1. Aiello, Darren & Bernstein, Asaf & Kargar, Mahyar & Lewis, Ryan & Schwert, Michael, 2025. "The marginal value of public pension wealth: Evidence from border house prices," Journal of Financial Economics, Elsevier, vol. 172(C).
    2. repec:ags:aaea22:335766 is not listed on IDEAS
    3. Timiryanova, Venera, 2022. "Высокочастотные Данные, Характеризующие Розничную Торговлю: Интересы Государства, Предприятий И Научных Организаций [High-frequency retail data: the interests of the state, enterprises and scientif," MPRA Paper 115681, University Library of Munich, Germany.
    4. Annette Jäckle & Jonathan Burton & Mick P. Couper, 2023. "Understanding Society: minimising selection biases in data collection using mobile apps," Fiscal Studies, John Wiley & Sons, vol. 44(4), pages 361-376, December.
    5. Afonso Rodrigues, 2025. "Consumer Choice Over Shopping Baskets: A Linear Demand Approach," Papers 2511.11846, arXiv.org.
    6. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    7. Pathak Chalise, Prayash, 2025. "Household-Level Food Price Inflation Heterogeneity: Evidence and Insights from the U.S. Consumer Panel Data (2013-2023)," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO 360870, Agricultural and Applied Economics Association.
    8. Calogero Brancatelli & Roman Inderst, 2025. "CPG consumption in times of recession: novel evidence from matched administrative data," Quantitative Marketing and Economics (QME), Springer, vol. 23(2), pages 265-289, June.
    9. Di Cosmo, Valeria & Tiezzi, Silvia, 2023. "Let them Eat Cake? The Net Consumer Welfare Impact of Sin Taxes," MPRA Paper 116214, University Library of Munich, Germany.
    10. Luca Dedola & Erwan Gautier & Chiara Osbat & Sergio Santoro, 2024. "Price Stickiness in the Euro Area," Working papers 958, Banque de France.
    11. Ezgi Cengiz & Christian Rojas, 2024. "Are food manufacturers reducing sugar content? Evidence from scanner data," Agribusiness, John Wiley & Sons, Ltd., vol. 40(3), pages 571-595, July.
    12. Rigotti, Luca & LeRoux, Matthew N. & Schmit, Todd M., 2023. "Improving Farmers Market Returns for Meat Vendors using Point-of-Sale Customer Data," 2023 Annual Meeting, July 23-25, Washington D.C. 335766, Agricultural and Applied Economics Association.
    13. Fox, William F. & Hargaden, Enda Patrick & Luna, LeAnn, 2022. "Statutory incidence and sales tax compliance: Evidence from Wayfair," Journal of Public Economics, Elsevier, vol. 213(C).

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    Keywords

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    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General

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