## wpiea2023081 - 8.9 percent in the third quarter and 0.9 percent in the fourth quarter of 2020. The recovery

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---

### Data, scope, and variables
- Panel of three Baltic countries: Estonia, Latvia, Lithuania.
- Daily debit and credit card transactions (POS transactions cover both in-store and online purchases).
- Sample period covers at least Jan-19 through Jul-22 (figures and indices plotted across this interval).
- Key COVID-19 variables:
  - COVID-19 infections: new cases, range minimum 0, maximum 15,412, mean 923.
  - COVID-19 deaths: range minimum 0, maximum 79, mean 6.
  - Vaccinations: number of new vaccinations, range minimum 1, maximum 41,195, mean 4,873 (observations for vaccinations: 1,955).
- Policy indices from OxCGRT (range 0–100 where applicable):
  - Stringency index: mean 36.6, minimum 0, maximum 87.
  - Containment and health index: mean 42.4, minimum 0, maximum 76.7.
  - Economic support index: mean 53.0, minimum 0, maximum 100.
  - Government response index: mean 43.7, minimum 0, maximum 74.9.
- Card transactions summary (Table 1, 3,018 observations):
  - Total debit and credit card transactions: Mean 13,300,000; Std. dev. 4,836,900; Minimum 602,157; Maximum 34,800,000.
  - Goods: Mean 9,121,104; Std. dev. 3,202,769; Minimum 485,225; Maximum 25,100,000.
  - Services: Mean 4,142,733; Std. dev. 1,781,970; Minimum 102,460; Maximum 11,600,000.

### Empirical strategy
- Baseline fixed-effects specification (log-linear):
  - log(card_it) = β1 log(vaccine_it) + β2 log(covid_it) + β3 X_it + η_i + μ_t + ε_it
  - X_it includes policy indices: stringency, containment and health, economic support, or overall government response.
  - Robust standard errors clustered at the country level.
  - Models estimated for total, goods, services, and 33 disaggregate spending categories.
- Dynamic analysis:
  - System GMM (Arellano and Bover, 1995; Blundell and Bond, 1998) one-step estimator used to address lagged dependent variable bias and endogeneity.
  - Strategy to limit instrument proliferation follows Roodman (2009).
  - Diagnostic results:
    - AR(1) shows high first-order autocorrelation (AR(1) p-values = 0.000 across specifications).
    - AR(2) p-values generally not significant (examples: 0.152, 0.138, 0.163, 0.152, 0.144, 0.210).
    - Hansen J-test p-values indicate validity of instruments (examples: 0.225, 0.210, 0.272, 0.254, 0.236, 0.218).

### Key empirical findings
- Vaccination effect on aggregate consumer spending:
  - A 1 percent increase in the number of COVID-19 vaccinations is associated with an increase of 0.011 percent in consumer spending (measured by the amount of debit and credit card transactions) in the baseline specification.
- COVID-19 infections:
  - In baseline reported as -0.045 (in one specification) and -0.028 to -0.035 across several columns.
- Policy measures (baseline estimates):
  - A 1 percent increase in the stringency index lowers the amount of card transactions by 0.165 percent.
  - A 1 percent increase in the economic support index brings an increase of 0.004 percent in card transactions.
  - Stringency of NPIs is associated with a significant decline in debit and credit card transactions.
  - Economic support index is associated with an increase in debit and credit card transactions (statistically significant for goods, not for services).
- Dynamic (system GMM) results:
  - Lagged consumer spending significant and positive (examples: consumer spending t-1 coefficients 0.060***, 0.036***, 0.044***, 0.031***, 0.160***, 0.194*** across panels).
  - Vaccination coefficients remain positive and statistically significant in dynamic specifications (examples: 0.017***, 0.011***, 0.021***, 0.022***, 0.013***, 0.012*** reported).
  - Infections remain negative and significant in dynamic specifications (examples: -0.045**, -0.146***, -0.016***, -0.117***, -0.034***, -0.064*** reported).

### Heterogeneity: goods, services, and subsectors
- Aggregate decline concentrated in services and goods restricted by lockdowns and infection risk.
- Goods vs services:
  - Vaccination coefficient slightly larger for goods than for services in some specifications (Table 3).
  - Economic support index significant for goods but not for services.
  - Stringency reduces transactions in both goods and services.
- Disaggregate 33-category analysis:
  - Heterogeneous effects across subsectors.
  - Vaccination and policy measures significantly affect travel-related expenditures (airlines, other transportation, hotels) and contact-intensive sectors (restaurants, beauty and spa services).
  - Positive impact of vaccination particularly greater in sectors directly restricted by lockdowns and infection risk.

### Robustness checks and limitations
- Results robust across static fixed-effects and dynamic system GMM approaches.
- Findings remain when substituting COVID-19 deaths for infections (author notes broadly similar results; full results not shown).
- Caveat: shift from cash to card payments during the pandemic could inflate card-transaction measures without reflecting a corresponding increase in total consumer spending.

### Policy implications and conclusions
- Vaccination rollout contributes positively to stabilizing and boosting consumer spending; cumulative effects grow as vaccination prevalence increases.
- Complementary measures remain important:
  - Containing the spread of the virus (public health measures).
  - Providing well-targeted economic support to households and businesses.
- Challenges noted:
  - Variation in speed of vaccination rollout across countries delays reaching critical coverage.
  - COVID-19 potentially becoming endemic could reduce vaccine effectiveness against future variants, emphasizing need for ongoing containment and support policies.

*Source: wpiea2023081 (IMF working paper content provided).*

### 8.9 percent in the third quarter and 0.9 percent in the fourth quarter of 2020. The recovery

### wpiea2023081 - 8.9 percent in the third quarter and 0.9 percent in the fourth quarter of 2020. The recovery

### Data, scope, and variables
- Panel of three Baltic countries: Estonia, Latvia, Lithuania.
- Daily debit and credit card transactions (POS transactions cover both in-store and online purchases).
- Sample period covers at least Jan-19 through Jul-22 (figures and indices plotted across this interval).
- Key COVID-19 variables:
  - COVID-19 infections: new cases, range minimum 0, maximum 15,412, mean 923.
  - COVID-19 deaths: range minimum 0, maximum 79, mean 6.
  - Vaccinations: number of new vaccinations, range minimum 1, maximum 41,195, mean 4,873 (observations for vaccinations: 1,955).
- Policy indices from OxCGRT (range 0–100 where applicable):
  - Stringency index: mean 36.6, minimum 0, maximum 87.
  - Containment and health index: mean 42.4, minimum 0, maximum 76.7.
  - Economic support index: mean 53.0, minimum 0, maximum 100.
  - Government response index: mean 43.7, minimum 0, maximum 74.9.
- Card transactions summary (Table 1, 3,018 observations):
  - Total debit and credit card transactions: Mean 13,300,000; Std. dev. 4,836,900; Minimum 602,157; Maximum 34,800,000.
  - Goods: Mean 9,121,104; Std. dev. 3,202,769; Minimum 485,225; Maximum 25,100,000.
  - Services: Mean 4,142,733; Std. dev. 1,781,970; Minimum 102,460; Maximum 11,600,000.

### Empirical strategy
- Baseline fixed-effects specification (log-linear):
  - log(card_it) = β1 log(vaccine_it) + β2 log(covid_it) + β3 X_it + η_i + μ_t + ε_it
  - X_it includes policy indices: stringency, containment and health, economic support, or overall government response.
  - Robust standard errors clustered at the country level.
  - Models estimated for total, goods, services, and 33 disaggregate spending categories.
- Dynamic analysis:
  - System GMM (Arellano and Bover, 1995; Blundell and Bond, 1998) one-step estimator used to address lagged dependent variable bias and endogeneity.
  - Strategy to limit instrument proliferation follows Roodman (2009).
  - Diagnostic results: AR(1) shows high first-order autocorrelation (AR(1) p-values = 0.000 across specifications), AR(2) p-values generally not significant (examples: 0.152, 0.138, 0.163, 0.152, 0.144, 0.210), Hansen J-test p-values indicate validity of instruments (examples: 0.225, 0.210, 0.272, 0.254, 0.236, 0.218).

### Key empirical findings
- Vaccination effect on aggregate consumer spending:
  - A 1 percent increase in the number of COVID-19 vaccinations is associated with an increase of 0.011 percent in consumer spending (measured by the amount of debit and credit card transactions) in the baseline specification.
  - COVID-19 infections have a negative effect: estimated coefficient on infections in baseline reported as -0.045 (in one specification) and -0.028 to -0.035 across several columns.
- Policy measures:
  - A 1 percent increase in the stringency index lowers the amount of card transactions by 0.165 percent (baseline estimate).
  - A 1 percent increase in the economic support index brings an increase of 0.004 percent in card transactions (baseline estimate).
  - Stringency of NPIs is associated with a significant decline in debit and credit card transactions.
  - Economic support index is associated with an increase in debit and credit card transactions (statistically significant for goods, not for services).
- Dynamic (system GMM) results:
  - Lagged consumer spending significant and positive (examples: consumer spending t-1 coefficients 0.060***, 0.036***, 0.044***, 0.031***, 0.160***, 0.194*** across panels).
  - Vaccination coefficients remain positive and statistically significant in dynamic specifications (examples: 0.017***, 0.011***, 0.021***, 0.022***, 0.013***, 0.012*** reported).
  - Infections remain negative and significant in dynamic specifications (examples: -0.045**, -0.146***, -0.016***, -0.117***, -0.034***, -0.064*** reported).

### Heterogeneity: goods, services, and subsectors
- Aggregate decline concentrated in services and goods restricted by lockdowns and infection risk.
- Goods vs services:
  - Vaccination coefficient slightly larger for goods than for services in some specifications (Table 3).
  - Economic support index significant for goods but not for services.
  - Stringency reduces transactions in both goods and services.
- Disaggregate 33-category analysis:
  - Heterogeneous effects across subsectors.
  - Vaccination and policy measures significantly affect travel-related expenditures (airlines, other transportation, hotels) and contact-intensive sectors (restaurants, beauty and spa services).
  - Positive impact of vaccination particularly greater in sectors directly restricted by lockdowns and infection risk.

### Robustness checks and limitations
- Results robust across static fixed-effects and dynamic system GMM approaches.
- Findings remain when substituting COVID-19 deaths for infections (author notes broadly similar results; full results not shown).
- Caveat: shift from cash to card payments during the pandemic could inflate card-transaction measures without reflecting a corresponding increase in total consumer spending.

### Policy implications and conclusions
- Vaccination rollout contributes positively to stabilizing and boosting consumer spending; cumulative effects grow as vaccination prevalence increases.
- Complementary measures remain important:
  - Containing the spread of the virus (public health measures).
  - Providing well-targeted economic support to households and businesses.
- Challenges noted:
  - Variation in speed of vaccination rollout across countries delays reaching critical coverage.
  - COVID-19 potentially becoming endemic could reduce vaccine effectiveness against future variants, emphasizing need for ongoing containment and support policies.

*Source: wpiea2023081 (IMF working paper content provided).*

### REFERENCES

### REFERENCES

### Dynamic panel and econometric methods
- Arellano, M., and O. Bover, 1995, “Another Look at the Instrumental Variable Estimation of Error-Components Models,” Journal of Econometrics, Vol. 68, pp. 29–51. 
- Blundell, R., and S. Bond, 1998, “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, Vol. 87, pp. 115–143. 
- Im, K., M. Pesaran, and Y. Shin (2003). “Testing for Unit Roots in Heterogeneous Panels,” Journal of Econometrics, Vol. 115, pp. 53–74. 
- Roodman, D., 2009, “How to Do xtabond2: An Introduction to Difference and System GMM in Stata,” Stata Journal, Vol. 9, pp. 86–136. 
- Tevdovski, D., P. Jolakoski, and V. Stojkoski (2022). “Testing for Unit Roots in Heterogeneous Panels,” International Journal of Health Economics and Management, Vol. 22, pp. 237–255. 

### COVID-19, vaccines, and macroeconomic effects
- Deb, P., D. Furceri, D. Jimenez, S. Kothari, J. Ostry, and N. Tawk (202). “The Effects of COVID-19 Vaccines on Economic Activity,” Swiss Journal of Economics and Statistics, Vol. 158, pp. 3–25. 
- 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). 
- Hansen, N-J., and R. Mano (2022). “COVID-19 Vaccines: A Shot in the Arm for the Economy,” IMF Economic Review, Vol. 71, pp. 148–169. 
- 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). 
- Tito, M., and A. Sexton (2022). “The Vaccine Boost: Quantifying the Impact of the COVID-19 Vaccine Rollout on Measures of Activity,” Financial and Economics Discussion Series No. 20-035 (Washington, DC: Board of Governors of the Federal Reserve System). 

### Consumer spending, payments, and firm-level exposure
- Cevik, S. (2020). “Dirty Money: Does the Risk of Infectious Disease Lower Demand for Cash?” International Finance, Vol. 23, pp. 460–471. 
- Cevik, S. (2022). “Show Me the Money: Tracking Consumer Spending with Daily Card Transaction Data During the Pandemic,” IMF Working Paper No. 22/235 (Washington, DC: International Monetary Fund).  
- Cevik, S., and F. Miryugin (2021). “Pandemics and Firms: Drawing Lessons from History,” International Finance, Vol. 24, pp. 276–297. 
- 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). 
- Gul, S., Y. Hacihasanoglu, A. Kazdal, and M. Yilmaz (2022). “COVID-19 Pandemic, Vaccination and Household Expenditures: Regional Evidence from Turkish Credit Card Data,” Applied Economic Letters. 
- 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 trackers
- 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. 

*Source: wpiea2023081 - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023081.pdf_