## CONCLUSION AND POLICY IMPLICATIONS ## Source details **Canonical URL:** [CONCLUSION AND POLICY IMPLICATIONS](https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020191-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2020/english/wpiea2020191-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2020/english/wpiea2020191-print-pdf.pdf.json) --- ### Robustness checks and containment–growth correlations - Correlation between the average Oxford Stringency Index and GDP QoQ growth in 2020:Q1 is about -0.6 (Figure 15). - Robustness checks in Appendix I produce statistically significant correlation coefficients ranging from -0.4 to -0.6. - Appendix I numeric findings: - Correlation between QoQ growth and stringency of containment (excluding China): -0.4. - Correlation between QoQ growth and stringency of containment (full sample): -0.6. - Correlation between YoY GDP growth and stringency of containment (full sample): -0.6. - Correlation between YoY GDP growth and stringency of containment (China excluded): -0.5. - Caveats: small sample size, potential confounders (infection rates, supply chain disruptions, declining exports), and preliminary nature of Q1 estimates. ### Econometric analysis of containment policies and mobility (country-day regressions) - Baseline regression specification: - Dependent variable: percentage change in cell-phone based visits to workplaces (workplaces_{i,t}). - Key regressor: Oxford Covid-19 Government Response Tracker stringency index (CC_{i,t}). - Controls (Z_{i,t}): 7-day averages of new infections and new deaths, indicators for income support, financial relief, comprehensive testing, comprehensive tracing; country-week and country-day-of-week fixed effects included. - Baseline sample: 24 EEA countries plus the UK; data as of July 16. - Main coefficient estimates (Table 2, specification (1)): - Containment policy (index): -0.510*** (standard error 0.0543). - New infections (7-day ave.): -1.076*** (0.254). - New deaths (7-day ave.): -5.628*** (1.609). - Income support: -5.875* (3.125). - Comprehensive testing and tracing coefficients not consistently significant across specifications. - Observations: 4,089; R-squared: 0.860. - Interpretation: - An increase from 0 to 100 in the stringency index implies a decline of more than 50 percentage points in presence at workplaces (hypothetical since few countries reached values above 90). - Nordic-only sample: Containment policy coefficient -0.612** (standard error 0.181); Observations: 580; R-squared: 0.796 — implying larger predicted decline in workplace presence in Nordics, plausibly reflecting higher teleworkability and stronger tradition of abiding by guidance. - New infections and new deaths significantly reduce mobility. - Availability of income support plausibly enables people to stay home (negative coefficient). ### Robustness across mobility indicators - Alternative dependent variables and key estimates (Table 3): - Transit stations (spec (1)): Containment policy -0.561*** (0.0417); New infections -0.836*** (0.150); Observations 4,089; R-squared 0.958. - Retail and recreation (spec (2)): Containment policy -0.742*** (0.0697); New infections -0.621*** (0.178); Observations 4,089; R-squared 0.946. - Apple driving indicator (spec (3)): Containment policy -0.654*** (0.0453); New infections -0.529*** (0.128); Observations 4,019; R-squared 0.977. - Apple transit indicator (spec (4)): Containment policy -0.754*** (0.0946); New infections -0.479*** (0.126); Observations 2,453; R-squared 0.979. - Robustness result: coefficient magnitudes are remarkably consistent and even larger relative to baseline when using alternative mobility measures. ### Sweden-specific deviations from regression predictions - During the most severe lockdown window (March 13 to April 12), the average decrease in presence at retail and recreation outlets in Sweden was up to 8 percentage points larger than regression-based predictions — up to a third of the actual decline depending on specification (Figure 28). - Implied behavior: voluntary reductions in activity above and beyond de jure containment measures. ### Aggregate economic outcomes and relation to containment - Simple average QoQ GDP decline across advanced economies in 2020:Q1 is 2.5 percent. - Sweden recorded GDP QoQ growth of 0.2 percent in 2020:Q1 (driven mainly by exports; private consumption and investment also performed relatively better than peers though negative). - Cross-country patterns and timing: - Sweden showed milder declines in mobility and service-sector activity during the Great Lockdown compared to many peers (Figures 16, 21, 22). - Sweden’s GDP QoQ in 2020:Q2: Sweden’s contraction was significantly larger than in other Nordics (Figure 17). - GDP HoH growth, 2020:H1: Sweden’s cumulative fall in the first six months of 2020 was smaller than other Nordic countries (Figure 18). - Interpretation: any early economic benefits of milder containment may have been eroded subsequently, partly due to higher infection rates and external demand shocks. ### Sectoral heterogeneity and structural drivers - Manufacturing: - PMI manufacturing index: In April, down by 17 points compared to February in Sweden — broadly in line with Germany and Euro Area average (Figure 19). - As of July 15, manufacturing accounted for 30 percent of all temporary layoffs while representing 15 percent of total employment (Figure 20). - Services and hospitality: - Services PMI fell by 17 points between February and April in Sweden; declines were larger in Germany, France, and Italy (Figure 21). - Hotel revenue decline in March: Sweden experienced smaller declines relative to many peers (Figure 22). - Structural features: - Sweden’s share of hospitality and recreation is relatively small (Figure 23). - Sweden has high trade openness (comparable to Germany), increasing vulnerability to external demand and supply chain disruptions. - Teleworkability: estimated 44 percent of jobs can be done from home in Sweden (Dingel and Neiman (2020)), the third-highest worldwide. ### Labor market developments and policy interventions - Jobseekers: - Increase in jobseekers in Sweden between early March and June equals almost 2 percentage points of the labor force (register-based weekly series), contrasted with lower increases in Denmark and especially Norway (Figure 24). - Data caveats: not seasonally adjusted, subject to revisions, administrative processing delays, and potential double-counting. - Short-term work schemes: - Increase in beneficiaries as a share of the labor force in Sweden amounts to around 10 percentage points (Figure 25); missing/processing applications imply true numbers may be higher. - Differences in schemes across countries (administration and timing); Sweden implemented a new short-term work scheme by early April — later than Denmark and Norway. - Objective: finance part of wages, avert permanent job losses, and facilitate rapid reactivation as crisis abates. - Interaction with mobility: - Weak correlation between weekly jobseekers and overall weekly employment, but strong correlation between workplace mobility and credit-card purchases of services (Figure 27), supporting mobility indicators as partial measures of economic activity. ### Health outcomes, demographics, and distributional patterns - Reported cases and timing: - Sweden’s reported Covid-19 cases per capita (as of mid-August) surpassed Nordic peers and some hard-hit countries like Italy, Spain, and the UK but not the U.S. (Figure 2). - Reported daily infections peaked end-June in Sweden, later than other Nordics (Figure 3); increased testing suggests actual infection peak likely occurred earlier. - Deaths and excess mortality: - Sweden’s death toll per capita is below the UK, Spain, and Italy but much higher than other Nordics (Figure 4). - Daily deaths peaked in April and have been falling since (Figure 5). - Excess mortality analysis indicates reported Covid-19 deaths explain a large share of observed excess mortality in Sweden (Figure 6). - Demographic concentration of fatalities: - Age distribution of Sweden Covid-19 deaths (Figure 9): 1.2 percent age 0-49; 9.6 percent age 50-69; 89.2 percent age 70-90 and above. - Those aged 65+ represent close to 20 percent of population but account for more than 88.2 percent of Covid-19 deaths in Sweden. - Stockholm region accounts for 50 percent of Sweden’s Covid-19 deaths while representing 23 percent of the population; within Stockholm, deaths correlate with share of residents born abroad and with average household size (Figures 11 and 12). - Nursing homes: about half of Sweden’s deaths occurred in elderly nursing facilities; timing of legally binding visiting bans varied across Nordics (Figure 10). - Immunity/seroprevalence evidence: - Swedish Public Health Authority blood donor study (mid-June): 6.3 percent had antibodies by end-May; Stockholm region highest at 10 percent. - Spain antibody study: 5 percent positive. - A subsequent Swedish survey end-June published in September found 18 percent in a Stockholm district developed antibodies. - Uncertainty: antibody prevalence varies across studies; some immunity may exist without detectable antibodies (Sekine et al., 2020); protective immunity sufficiency remains under investigation (Randolph et al., 2020). ### Conclusions and policy implications - Short-run economic effects: - During March-April, Sweden experienced milder declines in private consumption and investment in 2020:Q1 relative to many peers; mobility and service-sector decline were smaller. - Differences can be attributed to less stringent containment measures, voluntary social distancing, structural economy features (smaller hospitality sector share, high teleworkability), and export dynamics. - Medium-term uncertainty: - By 2020:Q2, Sweden experienced larger GDP contraction than Nordic peers, high jobseekers and short-term work beneficiaries, and prolonged declines in monthly activity indicators; however, cumulative 2020:H1 GDP fall remained smaller than peers and part of Q2 contraction reflected export declines. - Whether Sweden’s milder containment yields medium-term resilience (e.g., via immunity gains) remains unclear; antibody rates lower than some early projections. - Distributional and public health considerations: - High concentration of deaths among elderly and in areas with higher shares of foreign-born residents underscores challenges in protecting vulnerable groups absent stringent lockdowns. - Outcomes could be worse in countries with different demographics, institutional capacity, or social norms if they replicate Sweden’s approach. - Policy recommendations and observations: - Swift, decisive macroeconomic policy action is critical to avert worse economic outcomes. - Sweden’s policy response has been prompt, large, and well-designed; comfortable fiscal space enabled timely support via compensation schemes, guarantees, and tax deferrals. - Future research should further examine differential effects of particular containment measures and the distributional impacts of social distancing, given evidence of unequal effects from temporary layoffs and past epidemics on inequality (Furceri et al., 2020; Alstadsæter et al., 2020). - Research limitations and next steps: - Results are preliminary and subject to revisions; further work should exploit more detailed data and longer time horizons to assess medium-term trade-offs between public health and economic outcomes. *Source: IMF staff analysis — "3. ROBUSTNESS CHECKS" and "CONCLUSION AND POLICY IMPLICATIONS" from wpiea2020191-print-pdf.* ### CONCLUSION AND POLICY IMPLICATIONS ______________________________________23 ### CONCLUSION AND POLICY IMPLICATIONS ### Section headings and pagination - CONCLUSION AND POLICY IMPLICATIONS ______________________________________23 - REFERENCES ___________________________________________________________26 ### Included figures (titles as listed in source) - 1. STRICTNESS OF SOCIAL DISTANCING MEASURES __________________________________8 - 2. NUMBER OF REPORTED COVID-19 CASES (AUGUST 12, 2020) _______________________10 - 3. DAILY INFECTIONS ________________________________________________________10 - 4. TOTAL NUMBER OF REPORTED COVID-19 DEATHS (AUGUST 13, 2020) _______________10 - 5. DAILY COVID-19 DEATHS __________________________________________________11 - 6. EXCESS COVID-19 MORTALITY RATES ________________________________________11 - 7. DISTRIBUTION OF SINGLE PERSON HOUSEHOLDS _________________________________11 - 8. ELDERLY POPULATION _____________________________________________________11 - 9. SWEDEN: DEATH BY AGE GROUPS ____________________________________________12 - 10. TIMELINE ON RESTRICTION TO VISIT THE ELDERLY ______________________________12 - 11. COVID-19 DEATHS AND POPULATION WITH FOREIGN BACKGROUNDS ________________13 - 12. COVID-19 DEATHS AND HOUSEHOLD SIZE _____________________________________13 - 13. GDP QOQ GROWTH, 2020:Q1 ______________________________________________14 - 14. PRIVATE CONSUMPTION AND INVESTMENT, 2020:Q1 ____________________________14 - 15. STRINGENCY OF CONTAINMENT AND GDP QOQ GROWTH, 2020:Q1 ________________15 - 16. ECONOMIC ACTIVITY INDICATORS ___________________________________________15 - 17. GDP QOQ GROWTH (PRELIMINARY), 2020:Q2 _________________________________16 - 18. GDP HOH GROWTH, 2020:H1 ______________________________________________16 - 19. PURCHASING MANAGERS INDEX: MANUFACTURING _____________________________16 - 20. SWEDEN: SHORT-TERM LAYOFFS AND EMPLOYMENT SHARE BY SECTOR _____________16 - 21. PURCHASING MANAGERS INDEX: SERVICES ___________________________________17 - 22. EUROPE: HOTEL REVENUES IN MARCH _______________________________________17 - 23. TRADE OPENNESS AND HOSPITALITY INDUSTRY ________________________________17 - 24. JOBSEEKERS ____________________________________________________________18 - 25. JOBSEEKERS AND BENEFICIARIES OF SHORT-TERM WORK SCHEMES _________________18 - 26. NORDICS: CHANGES IN MOBILITY ___________________________________________20 - 27. NORDICS: WORKPLACE MOBILITY AND CARD TRANSACTIONS FOR SERVICES _________20 - 28. SWEDEN: CHANGES IN VISITS TO WORKPLACES ________________________________22 ### Included tables (titles as listed in source) - 1. CONTAINMENT MEASURES IN THE NORDICS _____________________________________9 - 2. EFFECTS OF CONTAINMENT ON VISITS OF WORKPLACES ___________________________22 *Source: CONCLUSION AND POLICY IMPLICATIONS (page listing and figures/tables) from the supplied PDF content.* ### 3. ROBUSTNESS CHECKS _____________________________________________________23 ### 3. ROBUSTNESS CHECKS ### Key robustness findings on containment stringency and 2020:Q1 GDP - Correlation between the average Oxford Stringency Index and GDP QoQ growth in 2020:Q1 is about -0.6 (Figure 15). - Robustness checks in Appendix I produce statistically significant correlation coefficients ranging from -0.4 to -0.6. - Caveats: small sample size, potential confounders (infection rates, supply chain disruptions, declining exports), and preliminary nature of Q1 estimates. ### Econometric analysis of containment policies and mobility (country-day regressions) - Baseline regression specification: - Dependent variable: percentage change in cell-phone based visits to workplaces (푀푀_{i,t}). - Key regressor: Oxford Covid-19 Government Response Tracker stringency index (퐶퐶_{i,t}). - Controls (푍푍_{i,t}): 7-day averages of new infections and new deaths, indicators for income support, financial relief, comprehensive testing, comprehensive tracing; country-week and country-day-of-week fixed effects included. - Baseline sample: 24 EEA countries plus the UK; data as of July 16. - Main coefficient estimates (Table 2, specification (1)): - Containment policy (index): -0.510*** (standard error 0.0543). - New infections (7-day ave.): -1.076*** (0.254). - New deaths (7-day ave.): -5.628*** (1.609). - Income support: -5.875* (3.125). - Comprehensive testing and tracing coefficients not consistently significant across specifications. - Observations: 4,089; R-squared: 0.860. - Interpretation: - An increase from 0 to 100 in the stringency index implies a decline of more than 50 percentage points in presence at workplaces (hypothetical since few countries reached values above 90). - Including only Nordic countries increases the predicted decline magnitude (Containment policy coefficient -0.612**, standard error 0.181; Observations: 580; R-squared: 0.796) — plausibly reflecting higher teleworkability and stronger tradition of abiding by guidance. - New infections and new deaths significantly reduce mobility. - Availability of income support plausibly enables people to stay home (negative coefficient). ### Robustness across mobility indicators (Table 3) - Alternative dependent variables and coefficient estimates: - Transit stations (spec (1)): Containment policy -0.561*** (0.0417); New infections -0.836*** (0.150); Observations 4,089; R-squared 0.958. - Retail and recreation (spec (2)): Containment policy -0.742*** (0.0697); New infections -0.621*** (0.178); Observations 4,089; R-squared 0.946. - Apple driving indicator (spec (3)): Containment policy -0.654*** (0.0453); New infections -0.529*** (0.128); Observations 4,019; R-squared 0.977. - Apple transit indicator (spec (4)): Containment policy -0.754*** (0.0946); New infections -0.479*** (0.126); Observations 2,453; R-squared 0.979. - Robustness result: coefficient magnitudes are remarkably consistent and even larger relative to baseline when using alternative mobility measures. ### Sweden-specific deviations from regression predictions - During the most severe lockdown window (March 13 to April 12), the average decrease in presence at retail and recreation outlets in Sweden was up to 8 percentage points larger than regression-based predictions — up to a third of the actual decline depending on specification (Figure 28). - Implied behavior: voluntary reductions in activity above and beyond de jure containment measures. ### Aggregate economic outcomes and relation to containment - Simple average QoQ GDP decline across advanced economies in 2020:Q1 is 2.5 percent. - Sweden recorded GDP QoQ growth of 0.2 percent in 2020:Q1 (driven mainly by exports; private consumption and investment also performed relatively better than peers though negative). - Cross-country patterns: - Sweden showed milder declines in mobility and service-sector activity during the Great Lockdown compared to many peers (Figures 16, 21, 22). - GDP QoQ in 2020:Q2: Sweden’s contraction was significantly larger than in other Nordics (Figure 17). - GDP HoH growth 2020:H1: Sweden’s cumulative fall in the first six months of 2020 was smaller than other Nordic countries (Figure 18). - Interpretation: any early economic benefits of milder containment may have been eroded subsequently, partly due to higher infection rates and external demand shocks. ### Sectoral heterogeneity and structural drivers - Manufacturing: - PMI manufacturing index: In April, down by 17 points compared to February in Sweden — broadly in line with Germany and Euro Area average (Figure 19). - As of July 15, manufacturing accounted for 30 percent of all temporary layoffs while representing 15 percent of total employment (Figure 20). - Services and hospitality: - Services PMI fell by 17 points between February and April in Sweden; declines were larger in Germany, France, and Italy (Figure 21). - Hotel revenue decline in March: Sweden experienced smaller declines relative to many peers (Figure 22). - Structural features: - Sweden’s share of hospitality and recreation is relatively small (Figure 23). - Sweden has high trade openness (comparable to Germany), increasing vulnerability to external demand and supply chain disruptions. - Teleworkability: estimated 44 percent of jobs can be done from home in Sweden (Dingel and Neiman (2020)), the third-highest worldwide. ### Labor market and policy interventions - Jobseekers (weekly register-based): - Increase in jobseekers in Sweden between early March and June equals almost 2 percentage points of the labor force (register-based weekly series), contrasted with lower increases in Denmark and especially Norway (Figure 24). - Data caveats: not seasonally adjusted, subject to revisions, administrative processing delays, and potential double-counting. - Short-term work schemes: - Increase in beneficiaries as a share of the labor force in Sweden amounts to around 10 percentage points (Figure 25); missing/processing applications imply true numbers may be higher. - Differences in schemes across countries (administration and timing); Sweden implemented a new short-term work scheme by early April — later than Denmark and Norway. - Objective: finance part of wages, avert permanent job losses, and facilitate rapid reactivation as crisis abates. - Interaction with mobility: - Weak correlation between weekly jobseekers and overall weekly employment, but strong correlation between workplace mobility and credit-card purchases of services (Figure 27), supporting mobility indicators as partial measures of economic activity. ### Health outcomes, demographics, and distributional patterns - Sweden’s reported Covid-19 cases per capita (as of mid-August) surpassed Nordic peers and some hard-hit countries like Italy, Spain, and the UK but not the U.S. (Figure 2). - Timing: reported daily infections peaked end-June in Sweden, later than other Nordics (Figure 3); increased testing suggests actual infection peak likely occurred earlier. - Deaths: - Sweden’s death toll per capita is below the UK, Spain, and Italy but much higher than other Nordics (Figure 4). - Daily deaths peaked in April and have been falling since (Figure 5). - Excess mortality analysis indicates reported Covid-19 deaths explain a large share of observed excess mortality in Sweden (Figure 6). - Demographic concentration: - Age: those aged 65+ represent close to 20 percent of population but account for more than 88.2 percent of Covid-19 deaths in Sweden (Figure 9: 1.2 percent age 0-49; 9.6 percent age 50-69; 89.2 percent age 70-90 and above). - Sector/local: Stockholm region accounts for 50 percent of Sweden’s Covid-19 deaths while representing 23 percent of the population; within Stockholm, deaths correlate with share of residents born abroad and with average household size (Figures 11 and 12). - Nursing homes: about half of Sweden’s deaths occurred in elderly nursing facilities; timing of legally binding visiting bans varied across Nordics (Figure 10). - Immunity/seroprevalence: - Swedish Public Health Authority blood donor study (mid-June): 6.3 percent had antibodies by end-May; Stockholm region highest at 10 percent. - Spain antibody study: 5 percent positive. - A subsequent Swedish survey end-June published in September found 18 percent in a Stockholm district developed antibodies. - Uncertainty: antibody prevalence varies across studies; some immunity may exist without detectable antibodies (Sekine et al., 2020); protective immunity sufficiency remains under investigation (Randolph et al., 2020). ### Conclusions and policy implications - Short-run economic effects: - During March-April, Sweden experienced milder declines in private consumption and investment in 2020:Q1 relative to many peers; mobility and service-sector decline were smaller. - Differences can be attributed to less stringent containment measures, voluntary social distancing, structural economy features (smaller hospitality sector share, high teleworkability), and export dynamics. - Medium-term uncertainty: - By 2020:Q2, Sweden experienced larger GDP contraction than Nordic peers, high jobseekers and short-term work beneficiaries, and prolonged declines in monthly activity indicators; however, cumulative 2020:H1 GDP fall remained smaller than peers and part of Q2 contraction reflected export declines. - Whether Sweden’s milder containment yields medium-term resilience (e.g., via immunity gains) remains unclear; antibody rates lower than some early projections. - Distributional and public health considerations: - High concentration of deaths among elderly and in areas with higher shares of foreign-born residents underscores challenges in protecting vulnerable groups absent stringent lockdowns. - Outcomes could be worse in countries with different demographics, institutional capacity, or social norms if they replicate Sweden’s approach. - Policy recommendations and observations: - Swift, decisive macroeconomic policy action is critical to avert worse economic outcomes. - Sweden’s policy response has been prompt, large, and well-designed; comfortable fiscal space enabled timely support via compensation schemes, guarantees, and tax deferrals. - Future research should further examine differential effects of particular containment measures and the distributional impacts of social distancing, given evidence of unequal effects from temporary layoffs and past epidemics on inequality (Furceri et al., 2020; Alstadsæter et al., 2020). - Research limitations and next steps: - Results are preliminary and subject to revisions; further work should exploit more detailed data and longer time horizons to assess medium-term trade-offs between public health and economic outcomes. *Source: IMF staff analysis as presented in the chapter "3. ROBUSTNESS CHECKS" from the IMF working paper (wpiea2020191-print-pdf).* ### REFERENCES ### REFERENCES ### Bibliographic entries (selected) - Acemoglu, D., Chernozhukov, V., Werning, I., & Whinston, M. D. (2020). A multi-risk SIR model with optimally targeted lockdown (NBER Working Paper No. 27102). - Alstadsæter, A., Bratsberg, B., Eielsen, G., Kopczuk, W., Markussen, S., Raaum, O., & Røed, K. (2020). The first weeks of the coronavirus crisis: Who got hit, when and why? Evidence from Norway (NBER Working Paper No. 27131). - Andersen, A. L., Hansen, E. T., Johannesen, N., & Sheridan, A. (2020). Pandemic, shutdown and consumer spending: Lessons from Scandinavian policy responses to COVID-19. arXiv preprint arXiv:2005.04630. - Aum, S., Lee, S. Y. T., & Shin, Y. (2020). COVID-19 doesn't need lockdowns to destroy jobs: The effect of local outbreaks in Korea (NBER Working Paper No. 27264). - Avery, C., Bossert, W., Clark, A., Ellison, G., & Ellison, S. F. (2020). Policy implications of models of the spread of coronavirus: Perspectives and opportunities for economists (NBER Working Paper No. 27007). - Baker, S. R., Farrokhnia, R. A., Meyer, S., Pagel, M., & Yannelis, C. (2020). How does household spending respond to an epidemic? consumption during the 2020 covid-19 pandemic (NBER Working Paper No. 26949). - Born, B., Dietrich, A., & Müller, G. J. (2020). Do lockdowns work? A counterfactual for Sweden. Covid Economics, 16, 1-22. - Cajner, T., Crane, L. D., Decker, R. A., Grigsby, J., Hamins-Puertolas, A., Hurst, E., Kurz, C., & Yildirmaz, A. (2020). The US labor market during the beginning of the pandemic recession (NBER Working Paper No. 27159). - Chen, S., Igan, D., Pierri, N., & Presbitero, A. F. (2020). Tracking the economic impact of COVID-19 and mitigation policies in Europe and the United States (IMF Working Paper 20/125). - Conyon, M. J., He, L., & Thomsen, S. (2020). Lockdowns and COVID-19 deaths in Scandinavia. Covid Economics, 26, 17-42. - Deb, P., Furceri, D., Ostry, J. D., & Tawk, N. (2020a). The economic effect of containment measures. Covid Economics, 24, 32-75. - Deb, P., Furceri, D., Ostry, J. D., & Tawk, N. (2020b). The effect of containment measures on the COVID-19 pandemic. (IMF Working Paper 20/159). - Dingel, J., & Neiman, B. (2020). How Many Jobs Can be Done at Home? (NBER Working Paper No. 26948). - Eichenbaum, M., Rebelo, S., & Trabandt, M. (2020). The macroeconomics of testing during epidemics (NBER Working Paper No. 27104). - Ellison, G. (2020). Implications of heterogeneous SIR models for analyses of Covid-19 (NBER Working Paper No. 27373). - Fang, H., Wang, L., & Yang, Y. (2020). Human mobility restrictions and the spread of the novel coronavirus (2019-ncov) in China (NBER Working Paper No. 26906). - Favero, C. A., Ichino, A., & Rustichini, A. (2020). Restarting the economy while saving lives under Covid-19 (CEPR Discussion Paper No. 14664). - Furceri, D., Loungani, P., Ostry, J. D., & Pizzuto, P. (2020). Will Covid-19 affect inequality? Evidence from past pandemics. Covid Economics, 12, 138-157. - Hale, T., Webster, S., Petherick, A., Phillips, T., & Kira., B (2020). Data use policy: Creative Commons Attribution CC BY standard. Oxford COVID-19 Government Response Tracker, Blavatnik School of Government. - IMF (2020). Pension schemes in the COVID-19 crisis: Impacts and policy considerations. Special Series on COVID-19, July. - Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the royal society of London. Series A, Containing papers of a mathematical and physical character, 115(772), 700-721. - Krueger, D., Uhlig, H., & Xie, T. (2020). Macroeconomic dynamics and reallocation in an epidemic. Covid Economics, 4, 21-55. - Maloney, W. F., & Taskin, T. (2020, May 15). Voluntary vs mandated social distancing and economic activity during COVID-19. VoxEU. https://voxeu.org/article/covid-social-distancing-driven-mostly-voluntary-demobilisation - Public Health Agency of Sweden (2020a). Demographic description of confirmed COVID-19 cases in Sweden March 13 – May 7, 2020. - Public Health Agency of Sweden (2020b). Estimates of the peak-day and the number of infected individuals during the COVID-19 outbreak in the Stockholm region, Sweden. - Public Health Agency of Sweden (2020c). Helgenomsekvensering av svenska SARS-CoV-2 som orsakar covid-19. - Randolph, H. E., & Barreiro, L.B. (2020). Herd Immunity: Understanding COVID-19. Immunity 52, no. 5, 737-741. - Riksbank (2020a). Monetary Policy Report, April 2020. - Riksbank (2020b). GDP growth in Sweden relative to other countries in the wake of Covid-19. Riksbank Economic Commentaries, June 12. - Sekine, T., Perez-Potti, A., Rivera-Ballesteros, O., Strålin, K., Gorin, J., Olsson, A., & Llewellyn-Lacey., S. (2020). Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19. Cell, forthcoming. ### Appendix I — Correlation between 2020:Q1 growth and containment policies - Analysis: Scatter plots and correlation coefficients of containment measures and GDP growth in 2020:Q1 using different samples and YoY/QoQ GDP growth. - Key numeric findings: - Correlation between QoQ growth and stringency of containment (excluding China): -0.4 (remains statistically significant). - Correlation between QoQ growth and stringency of containment (full sample): -0.6. - Correlation between YoY GDP growth and stringency of containment (full sample): -0.6. - Correlation between YoY GDP growth and stringency of containment (China excluded): -0.5. - Note: Excluding China weakens the inverse relationship between growth and stringency, possibly reflecting strict enforcement of containment measures in China. ### Appendix II — Description of variables used for econometric analysis - Containment policy (index): Index measuring the stringency of containment on scale from 0 to 100. Source: Hale et al. (2020). - New infections (7-day ave.): 7-day rolling average of new daily infections per 100,000 inhabitants. Source: Johns Hopkins University. - New deaths (7-day ave.): 7-day rolling average of new Covid-19-related deaths per 100,000 inhabitants. Source: Johns Hopkins University. - Income support: Dummy; indicates whether government provides any type of income support to individuals. Source: Hale et al. (2020). - Financial relief: Dummy; indicates whether the government provides any type of relief from financial obligations. Source: Hale et al. (2020). - Comprehensive testing: Dummy; indicates whether there is at least testing for anyone showing Covid-19 symptoms. Source: Hale et al. (2020). - Comprehensive tracing: Dummy; indicates whether there is comprehensive contact tracing for all identified cases. Source: Hale et al. (2020). - workplaces: Percentage change relative to baseline in visits to workplaces. Source: Google. - stations: Percentage change relative to baseline in visits of transit stations. Source: Google. - retail: Percentage change relative to baseline in visits of retail and recreation outlets. Source: Google. - driving: Changes in requests for driving directions relative to baseline. Source: Apple. - transit: Changes in requests for transit directions relative to baseline. Source: Apple. ### Appendix III — Announced fiscal, monetary and macro-financial policy responses as of June 2020 - Policy categories tracked: Discretionary Measures; Government Guarantees; Policy Rate Deduction; Liquidity Provision; Quantitative Easing; Swap Lines; Release CCyB/Systemic Risk Buffer; Easing Liquidity Buffers; Easing Amortization Requirement; Advice against Dividend Payouts. - Country entries (selected numeric values preserved as presented): - Sweden: 5.6 4.6 - x x x 2.5% x x x - Norway: 5.2 4.3 125 x x 1.5% x x x - Denmark: 1/5.7 7.7 * x x 1.0% x x - Finland: 2/2.9 5.4 ** x x 1.0% x x x - Notes included in source: - ** Finland is part of the Euro Area with the ECB in charge of monetary policy. - Macro-Financial Policies note: 1/ Denmark is pegged to the Euro. * Danmarks Nationalbank (DNB) is selling/buying foreign currency when the exchange rate is deviating from its bands. In March the DNB increased interest rates by 15 bsp to defend the fixed exchange rate regime. - Context: Monetary Policies and Fiscal Policies reported as (% 2019 GDP) where applicable. *Source: wpiea2020191-print-pdf - REFERENCES* --- _Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020191-print-pdf.pdf_