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Payment habits during Covid-19: Evidence from high-frequency transaction data

In: Granular data: new horizons and challenges

Author

Listed:
  • Tatjana Dahlhaus
  • Angelika Welte

Abstract

The Covid-19 pandemic, in Canada and around the world, accelerated trends toward non-cash payments. In this paper, we assess the impact of high-frequency fluctuations in the severity of the pandemic on payment behaviour. These fluctuations are measured by daily changes in restrictions (“stringency”) and reported case counts. Our main measures of payment habits are the ratios of the value and transaction counts of cash withdrawals to debit card payments. Econometrically, we use local projections to estimate the effects on payment habits. We find evidence that consumer behaviour adjusted during the pandemic: consumers withdrew less cash relative to card payment and avoided frequent trips for cash withdrawals and point-of-sale purchases through higher transaction amounts. Based on our modelling, once stringency measures eased and case counts receded, cash use partially recovered, although not to pre-pandemic levels.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Tatjana Dahlhaus & Angelika Welte, 2024. "Payment habits during Covid-19: Evidence from high-frequency transaction data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
    • Handle: RePEc:bis:bisifc:61-05
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    Cited by:

    1. is not listed on IDEAS
    2. Heng Chen & Marie-Hélène Felt, 2022. "Canadians’ Access to Cash Before and During the COVID-19 Pandemic," Discussion Papers 2022-15, Bank of Canada.
    3. Dominik Höpperger & Codruta Rusu, 2022. "Payment behavior in Austria during the COVID-19 pandemic," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/21, pages 85-104.
    4. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    5. Hiroshi Fujiki, 2025. "Cashless payment methods and COVID-19: evidence from Japanese consumer panel data," The Japanese Economic Review, Springer, vol. 76(1), pages 121-162, January.
    6. Martin Brown & Matthias R. Fengler & Jonas Huwyler & Winfried Koeniger & Rafael Lalive & Robert Rohrkemper, 2023. "Monitoring consumption Switzerland: data, background, and use cases," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-16, December.
    7. Laura Felber & Simon Beyeler, 2023. "Nowcasting economic activity using transaction payments data," Working Papers 2023-01, Swiss National Bank.
    8. Christopher Henry & Matthew Shimoda & Julia Zhu, 2022. "2021 Methods-of-Payment Survey Report," Discussion Papers 2022-23, Bank of Canada.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates

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