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Analyzing Economic Effects of Extreme Events using Debit and Payments System Data

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  • John W. Galbraith
  • Greg Tkacz

Abstract

This paper uses payments system data to study the impact on personal consumption expenditure, and therefore on economic activity, of occasional extreme events. The usual quarterly data supplied by central statistical agencies are of little use to policy makers for monitoring effects of transitory events, as the impacts of events lasting a few days or weeks may be obscured in time-aggregated data. However, technological advances of the past several years have resulted in new high-frequency data sources that could potentially provide more accurate and timely information on economic activity. Here we use daily Canadian debit transaction volume data, and business-day (five times per week) debit and check transaction volume and value data, to investigate the impact on consumer expenditure of several extreme events: the September 11 2001 terrorist attacks, the SARS epidemic in the spring of 2003, and the August 2003 electrical blackout. Contrary to initial perceptions of these events, we find only small and transitory effects.

Suggested Citation

  • John W. Galbraith & Greg Tkacz, 2011. "Analyzing Economic Effects of Extreme Events using Debit and Payments System Data," CIRANO Working Papers 2011s-70, CIRANO.
  • Handle: RePEc:cir:cirwor:2011s-70
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    File URL: https://cirano.qc.ca/files/publications/2011s-70.pdf
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    References listed on IDEAS

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    1. Martin D. D. Evans, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    2. Ron Borzekowski & K. Kiser Elizabeth & Ahmed Shaista, 2008. "Consumers' Use of Debit Cards: Patterns, Preferences, and Price Response," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(1), pages 149-172, February.
    3. Judith A. Chevalier & Anil K. Kashyap & Peter E. Rossi, 2003. "Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data," American Economic Review, American Economic Association, vol. 93(1), pages 15-37, March.
    4. Jerry Hausman & Ephraim Leibtag, 2009. "CPI Bias from Supercenters: Does the BLS Know that Wal-Mart Exists?," NBER Chapters, in: Price Index Concepts and Measurement, pages 203-231, National Bureau of Economic Research, Inc.
    5. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    6. Geoffrey R. Gerdes & May X. Liu & Darrel W. Parke & Jack K. Walton, 2005. "Trends in the use of payment instruments in the United States," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), vol. 91(Spr), pages 180-201.
    7. Venkatesh Shankar & Ruth N. Bolton, 2004. "An Empirical Analysis of Determinants of Retailer Pricing Strategy," Marketing Science, INFORMS, vol. 23(1), pages 28-49, May.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Policy responses > Macroeconomic

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    More about this item

    Keywords

    debit card transactions; macroeconomic monitoring; real-time data;
    All these keywords.

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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