IDEAS home Printed from https://ideas.repec.org/a/aea/apandp/v111y2021p321-25.html
   My bibliography  Save this article

Measuring Consumer Spending Using Card Transaction Data: Lessons from the COVID-19 Pandemic

Author

Listed:
  • Abe Dunn
  • Kyle Hood
  • Andrea Batch
  • Alex Driessen

Abstract

Pandemic-driven economic changes are measurable in days and weeks rather than months and years, underscoring a need for more timely economic data to inform the public. We summarize newly available estimates of spending using card transaction data that are available on the Bureau of Economic Analysis website and analyze correlations between card data and official series. We find that card data perform well in measuring large changes in the economy around the pandemic, although the correlations are much lower during a period of stability prior to the pandemic. This pattern is likely attributable to a shift in the signal-to-noise ratio over these periods.

Suggested Citation

  • Abe Dunn & Kyle Hood & Andrea Batch & Alex Driessen, 2021. "Measuring Consumer Spending Using Card Transaction Data: Lessons from the COVID-19 Pandemic," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 321-325, May.
    • Handle: RePEc:aea:apandp:v:111:y:2021:p:321-25
    DOI: 10.1257/pandp.20211049
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20211049
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20211049.ds
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    File URL: https://libkey.io/10.1257/pandp.20211049?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiaze Sun & Huijuan Lee & Jun Yang, 2021. "The Impact of the COVID-19 Pandemic on the Global Value Chain of the Manufacturing Industry," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    2. Tomohiro Okubo & Koji Takahashi & Haruhiko Inatsugu & Masato Takahashi, "undated". "Development of "Alternative Data Consumption Index":Nowcasting Private Consumption Using Alternative Data," Bank of Japan Working Paper Series 22-E-8, Bank of Japan.
    3. Giovanni Immordino & Tullio Jappelli & Tommaso Oliviero, 2024. "Consumption and income expectations during Covid-19," Review of Economics of the Household, Springer, vol. 22(1), pages 95-116, March.
    4. Dylan Balla‐Elliott & Zoë B. Cullen & Edward L. Glaeser & Michael Luca & Christopher Stanton, 2022. "Determinants Of Small Business Reopening Decisions After Covid Restrictions Were Lifted," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(1), pages 278-317, January.
    5. Simone Emiliozzi & Concetta Rondinelli & Stefania Villa, 2023. "Consumption during the Covid-19 pandemic: evidence from Italian credit cards," Questioni di Economia e Finanza (Occasional Papers) 769, Bank of Italy, Economic Research and International Relations Area.
    6. Abe Dunn & Mahsa Gholizadeh, 2025. "The Geography of Consumption and Local Economic Shocks: The Case of the Great Recession," American Economic Journal: Macroeconomics, American Economic Association, vol. 17(2), pages 206-244, April.
    7. Abe C. Dunn & Eric English & Kyle K. Hood & Lowell Mason & Brian Quistorff, 2024. "Expanding the Frontier of Economic Statistics Using Big Data: A Case Study of Regional Employment," BEA Papers 0128, Bureau of Economic Analysis.

    More about this item

    JEL classification:

    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • D10 - Microeconomics - - Household Behavior - - - General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:apandp:v:111:y:2021:p:321-25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.