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The Use of Scanner Data for Economics Research

Author

Listed:
  • Martin O'Connell

    (Institute for Fiscal Studies, London, United Kingdom)

  • Pierre Dubois

    (Toulouse School of Economics, Toulouse, France)

  • Rachel Griffith

    (Institute for Fiscal Studies, London, United Kingdom)

Abstract

The adoption of barcode scanning technology in the 1970s 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 they typically 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 inflation. In this article we highlight some of the pros and cons of this data source, and we discuss some of the ways its availability to researchers has transformed the economics literature.

Suggested Citation

  • Martin O'Connell & Pierre Dubois & Rachel Griffith, 2022. "The Use of Scanner Data for Economics Research," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 723-745, August.
    • Handle: RePEc:anr:reveco:v:14:y:2022:p:723-745
    DOI: 10.1146/annurev-economics-051520-024949
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    Citations

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    Cited by:

    1. 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.
    2. Afonso Rodrigues, 2025. "Consumer Choice Over Shopping Baskets," Papers 2511.11846, arXiv.org, revised May 2026.
    3. 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.
    4. 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.
    5. 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).
    6. 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).
    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. repec:ags:aaea22:335766 is not listed on IDEAS
    9. Luca Dedola & Erwan Gautier & Chiara Osbat & Sergio Santoro, 2024. "Price Stickiness in the Euro Area," Working papers 958, Banque de France.
    10. Daniel Brunner & Florian Heiss & Anna B. Schmidt, 2026. "Dynamic Consumer Demand at Large Scale," Papers 2605.23703, arXiv.org.
    11. 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.
    12. 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.
    13. 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 scientific organizations]," MPRA Paper 115681, University Library of Munich, Germany.
    14. 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.
    15. El Hadi Caoui & Brett Hollenbeck & Matthew Osborne & Elina Page, 2026. "The impact of dollar store expansion on local market structure and food access," Quantitative Marketing and Economics (QME), Springer, vol. 24(1), pages 1-35, December.

    More about this item

    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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