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Analyzing Economic Effects of September 11 and Other Extreme Events Using Debit and Payments System Data


  • John W. Galbraith
  • Greg Tkacz


Policy-makers are occasionally confronted with unusual extreme events that have the potential of affecting the national economy, which can have negative implications for such areas as employment, tax revenues, and inflation targets. In such instances, policy-makers may wish to offset the impact of the extreme event with monetary or fiscal actions. To measure the appropriate policy response, however, one must be able to gauge the severity of the extreme event in real time. The usual quarterly data supplied by central statistical agencies are of little use to policy-makers for monitoring effects of transitory events, as the data are available only with a substantial lag and 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 show that daily Canadian debit transaction volume data, and business-day debit and cheque transaction volume and value data can be used to investigate the impact on consumer expenditure of several extreme events: the terrorist attacks of 11 September 2001, 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, 2013. "Analyzing Economic Effects of September 11 and Other Extreme Events Using Debit and Payments System Data," Canadian Public Policy, University of Toronto Press, vol. 39(1), pages 119-134, March.
  • Handle: RePEc:cpp:issued:v:39:y:2013:i:1:p:119-134
    DOI: 10.3138/CPP.39.1.119

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    Cited by:

    1. John Bagnall & David Bounie & Kim P. Huynh & Anneke Kosse & Tobias Schmidt & Scott Schuh, 2016. "Consumer Cash Usage: A Cross-Country Comparison with Payment Diary Survey Data," International Journal of Central Banking, International Journal of Central Banking, vol. 12(4), pages 1-61, December.
    2. repec:gam:jsusta:v:8:y:2016:i:5:p:454:d:69575 is not listed on IDEAS
    3. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
    4. Hojin Jung & Minjae Park & Kihoon Hong & Eunjung Hyun, 2016. "The Impact of an Epidemic Outbreak on Consumer Expenditures:An Empirical Assessment for MERS Korea," Sustainability, MDPI, Open Access Journal, vol. 8(5), pages 1-15, May.
    5. Roy Verbaan & Wilko Bolt & Carin van der Cruijsen, 2017. "Using debit card payments data for nowcasting Dutch household consumption," DNB Working Papers 571, Netherlands Central Bank, Research Department.
    6. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.

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