SavedAdd your journal

Journals / Central Bank Review / 2021 / Cilt: 21 - Sayı: 3

Card spending dynamics in Turkey during the COVID-19 pandemic

Zeynep KANTUR
Gülserim ÖZCAN
Pages
71–86
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The estimated model indicates that containment measures and restrictions and fear of job/income loss mainly explain the overall impact of COVID-19 on aggregate demand.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper provides an extensive analysis of card spending during the COVID-19 pandemic in Turkey by using weekly aggregated and sectoral credit and debit card spending data from March 2014 to December 2020.
📊
Key Findings & Data:At an aggregated level, we show that aggregate demand decreases significantly at the early stages of COVID-19 and seems to reinstate its pre-COVID trend.
💡
Conclusion & Impact:The estimated model indicates that containment measures and restrictions and fear of job/income loss mainly explain the overall impact of COVID-19 on aggregate demand.
🔬Design:Modeling & Simulation
📍Scope:2014 Verileri
⚙️Variables:Domain Parameters

Özet

This paper provides an extensive analysis of card spending during the COVID-19 pandemic in Turkey by using weekly aggregated and sectoral credit and debit card spending data from March 2014 to December 2020. At an aggregated level, we show that aggregate demand decreases significantly at the early stages of COVID-19 and seems to reinstate its pre-COVID trend. However, when we include the pre-existing conditions of Turkey, the 2018 currency crisis, we observe that the recovery in demand is not that strong. To highlight the underlying reasons for structural change in aggregate demand, we estimate the model with stringency index and unemployment-related search index. The estimated model indicates that containment measures and restrictions and fear of job/income loss mainly explain the overall impact of COVID-19 on aggregate demand. We also examined sectoral data to understand aggregate demand dynamics better. Only stable and delayable sector groups have reached a trend above their pre-pandemic trajectories. However, the social and work-related sectors are far from their respective pre-pandemic trend.

🔖

Cite This Article

KANTUR, Z., ÖZCAN, G. (2021). Card spending dynamics in Turkey during the COVID-19 pandemic. Central Bank Review, Cilt: 21 - Sayı: 3, 71–86. https://ajindex.com/articles/418171

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (7)

  1. Andersen, Asger Lau, Hansen, Emil Toft, Johannesen, Niels, Adam, Sheridan, 2020. Consumer Responses to the COVID-19 Crisis: Evidence from Bank Account Transaction Data. CEPR Discussion Papers 14809, C.E.P.R. Discussion Papers. https://ideas.repec.org/p/cpr/ceprdp/14809.html.
  2. Anderson-Cook, Christine M., 2005. Experimental and quasi-experimental designs for generalized causal inference. William R. Shadish, Thomas D. Cook, and Donald T. Campbell. J. Am. Stat. Assoc. 100, 708e708. https://EconPapers.repec. org/RePEc:bes:jnlasa:v:100:y:2005:p:708-708.
  3. Andersson, Karolina, Petzold, Max Gustav, Sonesson, Christian, Lonnroth, Knut, € Anders, Carlsten, 2006. Do policy changes in the pharmaceutical reimbursement schedule affect drug expenditures?: interrupted time series analysis of cost, volume and cost per volume trends in Sweden 1986e2002. ISSN 0168- 8510 Health Pol. 79 (2), 231e243. https://doi.org/10.1016/j.healthpol.2006.01.007. https://www.sciencedirect.com/science/article/pii/ S0168851006000108. Baker, Scott R., Farrokhnia, R.A., Meyer, Steffen, Pagel, Michaela, Yannelis, Constantine, 2020. How Does Household Spending Respond to an Epidemic? Consumption during the 2020 COVID-19 Pandemic. NBER Working Papers 26949. National Bureau of Economic Research, Inc. https://ideas.repec. org/p/nbr/nberwo/26949.html. Bottomley, Christian, Scott, J. Anthony G., Isham, Valerie, 2019. Analysing interrupted time series with a control. Epidemiol. Methods 8 (1), 20180010. https:// doi.org/10.1515/em-2018-0010.
  4. Bounie, David, Camara, Youssouf, Galbraith, John, May 2020.
  5. Consumers' Mobility, Expenditure and Online- Offline Substitution Response to COVID-19: Evidence from French Transaction Data. Working Papers hal-02566443. HAL. https:// ideas.repec.org/p/hal/wpaper/hal-02566443.html. Campbell, Donald T., Stanley, Julian C., 1963. Experimental and Quasi-Experimental Designs for Research. Rand McNally College Publishing. URL. http://www. amazon.com/exec/obidos/redirect?tag¼citeulike-20/&path¼ASIN/ B000BWLQBE. Carvalho, V., Garcia, Juan R., Hansen, S., Ortiz, A., Rodrigo, T., More, J.V.R., April 2020. Tracking the COVID-19 crisis with high-resolution transaction data. In: Cambridge Working Papers in Economics 2030.
  6. Faculty of Economics, University of Cambridge. https://ideas.repec.org/p/cam/camdae/2030.html.
  7. Chen, Haiqiang, Qian, Wenlan, Wen, Qiang, July 2020. The Impact of the COVID-19 Pandemic on Consumption: Learning from High Frequency Transaction Data. Working papers, SSRN.