## wpiea2022235-print-pdf

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---

### Summary
- Objective: real-time granular analysis of consumer spending patterns during the COVID-19 pandemic in a panel of three Baltic countries using daily debit and credit card transaction data.
- Main finding: the pandemic shock (new COVID-19 infections and deaths) has a strongly negative effect on consumer spending measured by debit and credit card transactions; government containment measures reduce spending while economic support measures raise spending.
- Key magnitude (aggregate):
  - A 1 percent increase in the number of new COVID-19 infections is associated with a decline of 0.052 percent in total card transactions.
  - A 1 percent increase in the number of new COVID-19 deaths is associated with a decline of 0.022 percent in total card transactions.
- Policy contrast:
  - A 1 percent increase in the stringency index lowers card transactions by 0.171 percent.
  - A 1 percent increase in the economic support index increases card transactions by 0.021 percent.

### Data and descriptive statistics
- Debit and credit card transactions (daily):
  - Total: Observations 3,018; Mean 13,300,000; Std. dev. 4,836,900; Minimum 602,157; Maximum 34,800,000
  - Goods: Observations 3,018; Mean 9,121,104; Std. dev. 3,202,769; Minimum 485,225; Maximum 25,100,000
  - Services: Observations 3,018; Mean 4,142,733; Std. dev. 1,781,970; Minimum 102,460; Maximum 11,600,000
- COVID-19 series:
  - Infections: Observations 3,018; Mean 923; Std. dev. 1,828; Minimum 0; Maximum 15,412
  - Deaths: Observations 3,018; Mean 6; Std. dev. 90; Minimum 0; Maximum 79
- Policy indices (OxCGRT):
  - Stringency index: Observations 3,018; Mean 36.6; Std. dev. 20.7; Minimum 0; Maximum 87
  - Containment and health index: Observations 3,018; Mean 42.4; Std. dev. 16.4; Minimum 0; Maximum 76.7
  - Economic support index: Observations 3,018; Mean 53.0; Std. dev. 35.7; Minimum 0; Maximum 100.0
  - Government response index: Observations 3,018; Mean 43.7; Std. dev. 16.5; Minimum 0; Maximum 74.9
- Payment composition: goods purchases account for about 68.5 percent of card transactions on average in the Baltics during the sample period; services constitute the rest.
- Example intra-sample change: in Lithuania, cases increased from 353 in March to 1,748 by the end of June (first wave example cited).

### Empirical strategy
- Baseline panel specification (log-linear):
  - log(card_i,t) = β1 log(covid_i,t) + β2 X_i,t + η_i + μ_t + ε_i,t
  - card_i,t: spending category of debit and credit card transactions in country i and time t
  - covid_i,t: number of new COVID-19 cases or deaths
  - X_i,t: vector of health and economic policy measures (stringency index, containment and health index, economic support index, overall government response index)
  - η_i: country fixed effects; μ_t: time fixed effects
  - Robust standard errors clustered at the country level
- Unit-root testing: Variables tested for stationarity via Im-Pesaran-Shin (2003) panel unit root test; results indicate stationarity in levels after logarithmic transformation.
- Scope of estimation: Models estimated for aggregate categories (total, goods, services) and for 33 disaggregate spending categories.

### Key empirical results — aggregate and sectoral
- Aggregate (column [5] baseline including prevalence, stringency, economic support):
  - COVID-19 infections coefficient: -0.052 percent
  - COVID-19 deaths coefficient: -0.022 percent
  - Stringency index: -0.171 percent per 1 percent increase
  - Economic support index: +0.021 percent per 1 percent increase
- Goods vs. Services (disaggregate by broad category; results based on infections):
  - Goods: COVID-19 infections coefficient -0.055 percent (baseline column [5])
  - Services: COVID-19 infections coefficient -0.042 percent (baseline column [5])
  - Stringency of NPIs reduces transactions in both goods and services.
  - Economic support index has a statistically significant stimulative impact on goods (e.g., 0.027 percent in some specifications) but no significant effect on services.
- Interpretation: magnitude per 1 percent change in COVID-19 variables or indices is small individually but cumulative impacts grow as cases/deaths and policy intensities change over time.

### Disaggregate findings (33 consumption categories)
- Significant negative effects in travel-related and contact-intensive subsectors (coefficients reported where available):
  - Airports and Airlines: -0.070***
  - Travelling and Transportation: -0.074***
  - Hotels: -0.075***
  - Restaurants and Catering: -0.087***
  - Beauty and Spa: -0.097***
  - Sports Services: -0.076***
- Other negative impacts:
  - Telecommunications: -0.057***
  - Fuel: -0.071***
  - Electronics: -0.056
  - Books and Periodicals: -0.062
- Positive or insignificant responses in some subsectors:
  - Financial Institutions: COVID-19 infections coefficient 0.070 (positive and significant in some specifications)
  - Government services: COVID-19 infections coefficient 0.049
- Large negative stringency index coefficients in many subsectors (examples):
  - Hotels: -0.417***
  - Restaurants and Catering: -0.424***
  - Beauty and Spa: -0.464***
  - Electronics: -0.213***
  - Fuel: -0.297***
- Economic support index positive significant coefficients in certain goods categories (examples):
  - Fuel: 0.070
  - Food: 0.050***
  - Flowers and Gardening: 0.096***
  - Electronics: 0.037***
  - Accessories: 0.093
- Model diagnostics:
  - Number of countries = 3 in panel estimations
  - Adjusted R2 values vary by category (examples: Total adj R2 around 0.80; Goods adj R2 around 0.75; Services adj R2 around 0.85)

### Policy implications and recommendations
- Containment measures reduce consumer spending; economic support measures partially offset declines in card transactions, especially for goods.
- Economic support policies appear more effective in stimulating spending on goods than on contact-intensive services.
- High-frequency payment transaction data are valuable for policymakers to:
  - Track pace and composition of economic activity in real time.
  - Calibrate policy interventions targeting the most affected subsectors (e.g., travel, hotels, restaurants, beauty and spa).
- Caveat for policy interpretation: changes in payment habits (consumer shift from cash to card during pandemics) may inflate card-transaction measures of spending without corresponding increases in total consumption; policy design should consider potential substitution between payment instruments.

### Conclusion
- The COVID-19 pandemic had a clear negative effect on consumer spending in the Baltics measured by daily debit and credit card transactions; government policy responses shaped the extent and composition of this impact.
- Aggregate elasticities:
  - COVID-19 infections: -0.052 percent effect on total card transactions per 1 percent increase in infections
  - COVID-19 deaths: -0.022 percent effect on total card transactions per 1 percent increase in deaths
  - Stringency index: -0.171 percent effect per 1 percent increase
  - Economic support index: +0.021 percent effect per 1 percent increase
- Granular analysis reveals concentrated declines in travel and contact-intensive services and heterogeneous effects across 33 consumption categories.
- Monitoring daily payment transaction data is recommended to help policymakers respond more effectively and timely to shocks.

*Source: wpiea2022235-print-pdf - 14.5 percent, respectively. There was also variation in the pace recovery, with Lithuania taking the*

### 14.5 percent, respectively. There was also variation in the pace recovery, with Lithuania taking the

### wpiea2022235-print-pdf - 14.5 percent, respectively. There was also variation in the pace recovery, with Lithuania taking the

### Summary
- Objective: real-time granular analysis of consumer spending patterns during the COVID-19 pandemic in a panel of three Baltic countries using daily debit and credit card transaction data.
- Main finding: the pandemic shock (new COVID-19 infections and deaths) has a strongly negative effect on consumer spending measured by debit and credit card transactions; government containment measures reduce spending while economic support measures raise spending.
- Key magnitude (aggregate): a 1 percent increase in the number of new COVID-19 infections is associated with a decline of 0.052 percent in total card transactions; a 1 percent increase in the number of new COVID-19 deaths is associated with a decline of 0.022 percent in total card transactions.
- Policy contrast: a 1 percent increase in the stringency index lowers card transactions by 0.171 percent, while a 1 percent increase in the economic support index increases card transactions by 0.021 percent.

### Data and descriptive statistics
- Debit and credit card transactions (daily):
  - Total: Observations 3,018; Mean 13,300,000; Std. dev. 4,836,900; Minimum 602,157; Maximum 34,800,000
  - Goods: Observations 3,018; Mean 9,121,104; Std. dev. 3,202,769; Minimum 485,225; Maximum 25,100,000
  - Services: Observations 3,018; Mean 4,142,733; Std. dev. 1,781,970; Minimum 102,460; Maximum 11,600,000
- COVID-19 series:
  - Infections: Observations 3,018; Mean 923; Std. dev. 1,828; Minimum 0; Maximum 15,412
  - Deaths: Observations 3,018; Mean 6; Std. dev. 90; Minimum 0; Maximum 79
- Policy indices (OxCGRT):
  - Stringency index: Observations 3,018; Mean 36.6; Std. dev. 20.7; Minimum 0; Maximum 87
  - Containment and health index: Observations 3,018; Mean 42.4; Std. dev. 16.4; Minimum 0; Maximum 76.7
  - Economic support index: Observations 3,018; Mean 53.0; Std. dev. 35.7; Minimum 0; Maximum 100.0
  - Government response index: Observations 3,018; Mean 43.7; Std. dev. 16.5; Minimum 0; Maximum 74.9
- Payment composition: goods purchases account for about 68.5 percent of card transactions on average in the Baltics during the sample period; services constitute the rest.
- Example intra-sample change: in Lithuania, cases increased from 353 in March to 1,748 by the end of June (first wave example cited).

### Empirical strategy
- Baseline panel specification (log-linear):
  - log(card_i,t) = β1 log(covid_i,t) + β2 X_i,t + η_i + μ_t + ε_i,t
  - card_i,t: spending category of debit and credit card transactions in country i and time t
  - covid_i,t: number of new COVID-19 cases or deaths
  - X_i,t: vector of health and economic policy measures (stringency index, containment and health index, economic support index, overall government response index)
  - η_i: country fixed effects; μ_t: time fixed effects
  - Robust standard errors clustered at the country level
- Variables tested for stationarity via Im-Pesaran-Shin (2003) panel unit root test; results indicate stationarity in levels after logarithmic transformation (test results available upon request).
- Models estimated for aggregate categories (total, goods, services) and for 33 disaggregate spending categories.

### Key empirical results — aggregate and sectoral
- Aggregate (column [5] baseline including prevalence, stringency, economic support):
  - COVID-19 infections coefficient: -0.052 percent (i.e., a 1 percent increase in infections → -0.052 percent in total card transactions)
  - COVID-19 deaths coefficient: -0.022 percent (i.e., a 1 percent increase in deaths → -0.022 percent in total card transactions)
  - Stringency index: a 1 percent increase lowers card transactions by 0.171 percent
  - Economic support index: a 1 percent increase raises card transactions by 0.021 percent
- Goods vs. Services (disaggregate by broad category; results based on infections):
  - COVID-19 infections:
    - Goods: coefficient -0.055 percent (baseline column [5])
    - Services: coefficient -0.042 percent (baseline column [5])
  - Stringency of NPIs reduces transactions in both goods and services.
  - Economic support index has a statistically significant stimulative impact on goods (e.g., 0.027 percent in some specifications) but no significant effect on services.
- Interpretation: magnitude per 1 percent change in COVID-19 variables or indices is small individually but cumulative impacts grow as cases/deaths and policy intensities change over time.

### Disaggregate findings (33 consumption categories)
- Pandemic and containment measures have heterogeneous effects across subsectors; notable significant negative effects:
  - Travel-related: Airports and Airlines (-0.070***), Travelling and Transportation (-0.074***), Hotels (-0.075***)
  - Contact-intensive services: Restaurants and Catering (-0.087***), Beauty and Spa (-0.097***), Sports Services (-0.076***)
  - Other negative impacts: Telecommunications (-0.057***), Fuel (-0.071***), Electronics (-0.056), Books and Periodicals (-0.062)
- Some subsectors show positive or insignificant responses:
  - Financial Institutions: COVID-19 infections coefficient 0.070 (positive and significant in some specifications)
  - Government services: COVID-19 infections coefficient 0.049 (positive in the aggregated services discussion)
- Stringency index large negative coefficients observed in many subsectors (examples):
  - Hotels: -0.417***; Restaurants and Catering: -0.424***; Beauty and Spa: -0.464***; Electronics: -0.213***; Fuel: -0.297***
- Economic support index shows positive significant coefficients in certain goods categories (examples):
  - Fuel: 0.070; Food: 0.050***; Flowers and Gardening: 0.096***; Electronics: 0.037***; Accessories: 0.093
- Table-level model diagnostics:
  - Number of countries = 3 in panel estimations
  - Adjusted R2 values vary by category (examples: Total adj R2 around 0.80; Goods adj R2 around 0.75; Services adj R2 around 0.85; many disaggregate categories have adj R2 reported per table)

### Policy implications and recommendations
- Containment measures reduce consumer spending; economic support measures partially offset declines in card transactions, especially for goods.
- Economic support policies appear more effective in stimulating spending on goods than on contact-intensive services.
- High-frequency payment transaction data are valuable for policymakers to:
  - Track pace and composition of economic activity in real time
  - Calibrate policy interventions targeting the most affected subsectors (e.g., travel, hotels, restaurants, beauty and spa)
- Caveat for policy interpretation: changes in payment habits (consumer shift from cash to card during pandemics) may inflate card-transaction measures of spending without corresponding increases in total consumption; policy design should consider potential substitution between payment instruments.

### Conclusion
- The COVID-19 pandemic had a clear negative effect on consumer spending in the Baltics measured by daily debit and credit card transactions; government policy responses shaped the extent and composition of this impact.
- Aggregate elasticities:
  - COVID-19 infections: -0.052 percent effect on total card transactions per 1 percent increase in infections
  - COVID-19 deaths: -0.022 percent effect on total card transactions per 1 percent increase in deaths
  - Stringency index: -0.171 percent effect per 1 percent increase
  - Economic support index: +0.021 percent effect per 1 percent increase
- Granular analysis reveals concentrated declines in travel and contact-intensive services and heterogeneous effects across 33 consumption categories.
- Monitoring daily payment transaction data is recommended to help policymakers respond more effectively and timely to shocks.

*Source: wpiea2022235-print-pdf - 14.5 percent, respectively. There was also variation in the pace recovery, with Lithuania taking the*

### REFERENCES

### REFERENCES

### Consumer spending and transaction-data studies
- Alexander, D., and E. Karger (2020). “Do Stay-at-Home Orders Cause People to Stay at Home? Effects of Stay-at-Home Orders on Consumer Behavior,” FRBC Working Paper No. 2020-12 (Chicago: Federal Reserve Bank of Chicago).
- Andersen, A., E. Hansen, N. Johannesen, and A. Sheridan (2020). “Consumer Responses to the COVID-19 Crisis: Evidence from Bank Account Transaction Data,” CEBI Working Paper No. 18/20 (Copenhagen: Center for Economic Behavior and Inequality).
- Baker, S., R. Farrokhnia, S. Meyer, M. Pagel, and C. Yannelis (2020). “How Does Household Spending Respond to an Epidemic? Consumption During the 2020 COVID-19 Pandemic,” Review of Asset Pricing Studies, Vol. 10, pp. 834–862.
- Bounie, D., Y. Camara, and J. Galbraith (2020). “Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data,” CIRANO Working Paper No. 2020-28 (Quebec: CIRANO).
- Brinke, A., L. Fadejeva, B. Siliverstovs, and K. Vilerts (2022). “Consumer Spending in the COVID-19 Pandemic: Evidence from Card Transactions in Latvia,” Latvijas Banka Discussion Paper No. 1/2022 (Riga: Bank of Latvia).
- Cabral, S., C. Manteu, S. Serra, and C. Silva (2021). “Consumption Expenditure During the COVID-19 Pandemic: An Analysis Based on Portuguese Card Transaction Data,” Banco de Portugal Economic Studies No. 2021-13 (Lisbon: Bank of Portugal).
- Campos-Vazquez, R., and G. Esquivel (2021). “Consumption and Geographic Mobility in Pandemic Times: Evidence from Mexico,” Review of Economics of the Household, Vol. 19, pp. 353–371.
- Carvalho, M., S. Hansen, A. Ortiz, J. Garcia, T, Rodrigo, S. Mora, and J. Ruiz (2020). “Tracking the COVID-19 Crisis with High Resolution Transaction Data,” CEPR Working Paper No. 14642 (London: Center for Economic Policy Research).
- Chen, H., W. Qian, and Q. Wen (2021). “The Impact of the COVID-19 Pandemic on Consumption: Learning from High Frequency Transaction Data,” AEA Papers and Proceedings, Vol. 111, pp. 307–311.
- Chetty, R., J. Friedman, N. Hendren, and M. Stepner (2020). “How Did COVID-19 and Stabilization Policies Affect Spending and Employment? A Near Real-Time Economic Tracker Based on Private Sector Data,” NBER Working Paper No. 27431 (Cambridge, MA: National Bureau of Economic Research).
- Coibon, O., Y. Gorodnichenko, and M. Weber (2020). “The Cost of the COVID-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending,” NBER Working Paper No. 27141 (Cambridge, MA: National Bureau of Economic Research).
- Hacıoğlu, S., D. Känzig, and P. Surico (2020). “Consumption n the Time of COVID-19: Evidence from UK Transaction Data,” CEPR Discussion Paper No. 14733 (London: Centre for Economic Policy Research).
- Kapetanios, G., N. Neuteboom, F. Ritseman, and A. Ventouri (2022). “How Did Consumers React to the COVID-19 Pandemic Over Time?” Oxford Bulletin of Economics and Statistics, Vol. 84, pp. 961–993.
- Kraenzlin, S., C. Meyer, and T. Nellen (2021). “COVID-19 and Regional Shifts in Swiss Retail Payments,” Swiss Journal of Economics and Statistics, Vol. 156, pp. 1–20.
- Lewis, D., K. Mertens, and J. Stock (2020). “U.S. Economic Activity During the Early Weeks of the SARS-Cov-2 Outbreak,” NBER Working Paper No. 26954 (Cambridge, MA: National Bureau of Economic Research).
- Ludvigson, S., S. Ma, and S. Ng (2020). “COVID-19 and the Macroeconomic Effects of Costly Disasters,” NBER Working Paper No. 26987 (Cambridge, MA: National Bureau of Economic Research).

### Payments, cash demand, and payment habits
- Cevik, S. (2020). “Dirty Money: Does the Risk of Infectious Disease Lower Demand for Cash?” International Finance, Vol. 23, pp. 460–471.
- Dahlhaus, T., and A. Welte (2021). “Payment Habits During COVID-19: Evidence from High-Frequency Transaction Data,” Bank of Canada Staff Working Paper No. 2021-43 (Ottawa: Bank of Canada).
- Dunn, A., K. Hood, A. Batch, and A. Driessen (2021). “Measuring Consumer Spending Using Card Transaction Data: Lessons from the COVID-19 Pandemic,” AEA Papers and Proceedings, Vol. 111, pp. 321–325.
- Kantur, Z., and G. Özcan (2021). “Card Spending Dynamics in Turkey During the COVID-19 Pandemic,” Central Bank Review, Vol. 21, pp. 71–86.

### Firms, macroeconomics, and policy responses to epidemics
- Cevik, S., and F. Miryugin (2021). “Pandemics and Firms: Drawing Lessons from History,” International Finance, Vol. 24, pp. 276–297.
- Eichenbaum, M., S. Rebelo, and M. Trabandt (2020). “The Macroeconomics of Epidemics,” NBER Working Paper No. 26882 (Cambridge, MA: National Bureau of Economic Research).
- Fornaro, L., and M. Wolf (2020). “Covid-19 Coronavirus and Macroeconomic Policy,” CEPR Discussion Paper No. 14529 (London: Centre for Economic Policy Research).
- Hassan, T., S. Hollander, L. van Lent, and A. Tahoun (2020). “Firm-Level Exposure to Epidemic Diseases: Covid-19, SARS, and H1N1,” NBER Working Paper No. 26971 (Cambridge, MA: National Bureau of Economic Research).

### Data sources and econometric methods
- Hale, T., N. Angrist, R. Goldszmidt, B. Kira, A. Petherick, T. Phillips, S. Webster, E. Cameron-Blake, L. Hallas, S. Majumdar, and H. Tatlow (2021). “A Global Panel Database of Pandemic Policies (Oxford COVID-19 Government Response Tracker),” Nature Human Behaviour, Vol. 5, pp. 529–538.
- Im, K., M. Pesaran, and Y. Shin (2003). “Testing for Unit Roots in Heterogeneous Panels,” Journal of Econometrics, Vol. 115, pp. 53–74.
- Hassan, T., S. Hollander, L. van Lent, and A. Tahoun (2020). “Firm-Level Exposure to Epidemic Diseases: Covid-19, SARS, and H1N1,” NBER Working Paper No. 26971 (Cambridge, MA: National Bureau of Economic Research).

*References list as provided in the source PDF.*

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_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022235-print-pdf.pdf_