Abstract
The large economic costs of full-blown lockdowns in response to COVID-19 outbreaks, coupled with heterogeneous mortality rates across age groups, led to question non-discriminatory containment measures. In this paper we provide an assessment of the targeted approach to containment. We propose a SIR-macro model that allows for heterogeneous agents in terms of mortality rates and contact rates, and in which the government optimally bans people from working. We find that under a targeted policy, the optimal containment reaches a larger portion of the population than under a blanket policy and is held in place for longer. Compared to a blanket policy, a targeted approach results in a smaller death count. Yet, it is not a panacea: the recession is larger under such approach as the containment policy applies to a larger fraction of people, remains in place for longer, and herd immunity is achieved later. Moreover, we find that increased interactions between low- and high-risk individuals effectively reduce the benefits of a targeted approach to containment.
Acknowledgments
We thank, without implicating, Zhiyong An, Niccoló Comini, Philipp Engler, Fuad Hasanov, Francisco Ramírez, Kei-Mu Yi, German Cubas, Brenda Villanueva, Damiano Sandri, Nikola Spatafora, and Yunhui Zhao for their comments and suggestions. We also thank an anonymous referee for the helpful comments and constructive remarks.
Figure A.1 displays the epidemiological dynamics under a blanket and a targeted policy for the exercise featuring increased contact rates. The differences across the two scenarios are qualitatively akin to the ones in Figure 3, while magnitudes change reflecting the higher contact rates. The infection curves peak at 4.4 and 4.5 percent of initial population under a blanket policy and a targeted one, respectively, around the same week (panel A.1a). However, the death count largely favors a targeted approach, with a death rate at 0.44 percent of initial population under a blanket policy and 0.38 percent of initial population under a targeted policy (panel A.1d). As a result, there are more recovered individuals under in the blanket policy scenario (panel A.1c) and more susceptible ones under the targeted policy scenario (panel A.1b). Figure A.2 shows that, similar to the results in Figure 4, the dynamics for low-risk individuals are similar across scenarios, and that a targeted policy saves considerably more lives of high-risk individuals.
Epidemiological dynamics with higher contact rates (percent of initial population).
Notes: The figure shows the epidemiological dynamics over the first 150 weeks since the beginning of the pandemic for the no-policy scenario, the blanket policy scenario, and the targeted policy scenario.
Epidemiological dynamics of low- and high-risk individuals with higher contact rates (percent of initial population).
Notes: The figure shows the epidemiological dynamics over the first 150 weeks since the beginning of the pandemic for the blanket policy scenario and the targeted policy scenario, separating low- and high-risk individuals.
References
Acemoglu, D., V. Chernozhukov, I. Werning, and M. D. Whinston. 2020. “Optimal Targeted Lockdowns in a Multi-Group SIR Model.” In National Bureau of Economic Research Working Paper No. 27102.10.3386/w27102Search in Google Scholar
Alvarez, F., D. Argente, and L. Franceso. 2020. “A Simple Planning Problem for COVID-19 Lockdown, Testing, and Tracing.” In American Economic Review: Insights.10.3386/w26981Search in Google Scholar
Atkeson, A. 2020. “What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios.” In National Bureau of Economic Research Working Paper No. 26867.10.3386/w26867Search in Google Scholar
Baker, S. R., R. A. 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 Asset Pricing Studies 10 (4): 834–62. https://doi.org/10.1093/rapstu/raaa009.Search in Google Scholar
Baqaee, D., and E. Farhi. 2020. “Supply and Demand in Disaggregated Keynesian Economies with an Application to the Covid-19 Crisis.” In Tech. Rep. National Bureau of Economic Research Working Paper No. 27152.10.3386/w27152Search in Google Scholar
Beland, L.-P., B. Abel, and W. Taylor. 2020. “Stay-At-Home Orders and Employment: Evidence from CPS Data.” In Institute of Labor Economics DP No. 13282.10.2139/ssrn.3608531Search in Google Scholar
Brotherhood, L., P. Kircher, C. Santos, and M. Tertilt. 2020. “An Economic Model of the Covid-19 Pandemic with Young and Old Agents: Behavior, Testing and Policies.” In University of Bonn and University of Mannheim, Germany, Discussion Paper No. 175.Search in Google Scholar
Carvalho, V. M., S. Hansen, A. Ortiz, J. R. Garcia, T. Rodrigo, S. Rodriguez Mora, and P. R. de Aguirre. 2020. “Tracking the COVID-19 Crisis with High-Resolution Transaction Data.” In CEPR Discussion Paper No. DP14642.10.1098/rsos.210218Search in Google Scholar
Caselli, F. G., F. Grigoli, and D. Sandri. 2020. “Protecting Lives and Livelihoods with Early and Tight Lockdowns.” Covid Economics 66: 37–57.10.5089/9781513560434.001Search in Google Scholar
Chernozhukov, V., H. Kasaha, and S. Paul. 2020. “Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S.” Covid Economics 35: 116–75.10.1016/j.jeconom.2020.09.003Search in Google Scholar
Chetty, R., J. N. Friedman, N. Hendren, and M. Stepner. 2020. “How Did COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-Time Economic Tracker Based on Private Sector Data.” In National Bureau of Economic Research Working Paper No. 27431.Search in Google Scholar
Chronopoulos, D. K., M. Lukas, and J. O. S. Wilson. 2020. “Consumer Spending Responses to the COVID-19 Pandemic: An Assessment of Great Britain.” Covid Economics 34: 145–86.10.2139/ssrn.3586723Search in Google Scholar
Coibion, O., Y. Gorodnichenko, and M. Weber. 2020. “The Cost of the COVID-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending.” Covid Economics 20, https://www.nber.org/system/files/working_papers/w27141/w27141.pdf.10.3386/w27141Search in Google Scholar
Dave, D. M., A. I. Friedson, K. Matsuzawa, D. McNichols, and J. J. Sabia. 2020. “Did the Wisconsin Supreme Court Restart a COVID-19 Epidemic? Evidence from a Natural Experiment.” In National Bureau of Economic Research Working Paper No. 27322.10.2139/ssrn.3620628Search in Google Scholar
Eichenbaum, S. Martin, S. Rebelo, and M. Trabandt. 2020. “The Macroeconomics of Pandemics.” In National Bureau of Economic Research Working Paper No. 26882.10.3386/w26882Search in Google Scholar
Fang, H., L. Wang, and Y. Yang. 2020. “Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-ncov) in China.” In National Bureau of Economic Research Working Paper No. 26906.10.3386/w26906Search in Google Scholar
Favero, C. A., A. Ichino, and A. Rustichini. 2020. “Restarting the Economy while Saving Lives under Covid-19.” CEPR Discussion Paper No. DP 14664.10.2139/ssrn.3580626Search in Google Scholar
Ferguson, N. M., D. Laydon, G. Nedjati-Gilani, N. Imai, K. Ainslie, M. Baguelin, S. Bhatia, A. Boonyasiri, Z. Cucunubá, G. Cuomo-Dannenburg, A. Dighe, I. Dorigatti, H. Fu, K. Gaythorpe, W. Green, A. Hamlet, W. Hinsley, L. C. Okell, S. van Elsland, H. Thompson, R. Verity, E. Volz, H. Wang, Y. Wang, P. G. T. Walker, C. Walters, P. Winskill, C. Whittaker, C. A. Donnelly, S. Riley, and A. C. Ghani. 2020. “Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand.” In Imperial College London COVID-19 Response Team.Search in Google Scholar
Flood, S., M. King, R. Rodgers, S. Ruggles, and J. Robert Warren. 2020. “IPUMS-CPS.” In Integrated Public Use Microdata Series, Current Population Survey: Version 8.0 [dataset]. Minneapolis, MN, https://cps.ipums.org/cps/citation.shtml.Search in Google Scholar
Friedson, A. I., D. McNichols, J. J. Sabia, and D. Dave. 2020. “Did California’s Shelter In-Place Order Work? Early Coronavirus-Related Public Health Effects.” In National Bureau of Economic Research Working Paper No. 26992.10.3386/w26992Search in Google Scholar
Glaeser, E. L., C. S. Gorback, and J. R. Stephen. 2020. “How Much Does COVID-19 increase with Mobility? Evidence from New York and Four Other US Cities.” In National Bureau of Economic Research Working Paper No. 27519.10.3386/w27519Search in Google Scholar
Glover, A., J. Heathcote, D. Krueger, and J.-V. Rios-Rull. 2020. “Health versus Wealth: On the Distributional Effects of Controlling a Pandemic.” In Centre for Economic Policy Research DP 14606.10.3386/w27046Search in Google Scholar
Goolsbee, A., and C. Syverson. 2021. “Fear, Lockdown, and Diversion: Comparing Drivers of Pandemic Economic Decline.” Journal of Public Economics 193: 1–8. https://doi.org/10.1016/j.jpubeco.2020.104311.Search in Google Scholar
Guerrieri, V., L. Guido, L. Straub, and I. Werning. 2020. “Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?” In Tech. Rep. National Bureau of Economic Research Working Paper No. 26918.10.3386/w26918Search in Google Scholar
Gupta, S., L. Montenovo, D. N. Thuy, F. L. Rojas, I. M. Schmutte, K. I. Simon, B. A. Weinberg, and C. Wing. 2020. “Effects of Social Distancing Policy on Labor Market Outcomes.” In National Bureau of Economic Research Working Paper No. 27280.10.3386/w27280Search in Google Scholar
Imai, N., K. A. M. Gaythorpe, S. Abbott, S. Bhatia, S. van Elsland, K. Prem, Y. Liu, and N. M. Ferguson. 2020. “Adoption and Impact of Non-Pharmaceutical Interventions for COVID-19.” Wellcome Open Research 5: 1–17. https://doi.org/10.12688/wellcomeopenres.15808.1.Search in Google Scholar
Jinjarak, Y., R. Ahmed, S. Nair-Desai, W. Xin, and J. Aizenman. 2020. “Accounting for Global COVID-19 Diffusion Patterns, January–April 2020.” Economics of Disasters and Climate Change 4 (3): 515–59. https://doi.org/10.1007/s41885-020-00071-2.Search in Google Scholar
Juranek, S., and F. Zoutman (2020). “The Effect of Social Distancing Measures on Intensive Care Occupancy: Evidence on COVID-19 in Scandinavia.” Available at SSRN 3577213.Search in Google Scholar
Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society of London Series A 115 (772): 700–21.Search in Google Scholar
Lee, B. Y., S. T. Brown, P. C. Cooley, R. K. Zimmerman, W. D. Wheaton, S. M. Zimmer, J. J. Grefenstette, T.-M. Assi, T. J. Furphy, D. K. Wagener, D. S. Burke. 2010. “A Computer Simulation of Employee Vaccination to Mitigate an Influenza Epidemic.” American Journal of Preventive Medicine 38 (3): 247–57. https://doi.org/10.1016/j.amepre.2009.11.009.Search in Google Scholar
Maloney, W., and T. Taskin. 2020. “Determinants of Social Distancing and Economic Activity during COVID-19: A Global View.” In The World Bank, Policy Research Working Paper 9242.10.1596/1813-9450-9242Search in Google Scholar
Oran, D. P., and E. J. Topol. 2020. “Prevalence of Asymptomatic SARS-CoV-2 Infection: A Narrative Review.” Annals of Internal Medicine, https://www.acpjournals.org/doi/full/10.7326/M20-6976.10.7326/M20-3012Search in Google Scholar
Prem, K., A. R. Cook, and M. Jit. 2017. “Projecting Social Contact Matrices in 152 Countries Using Contact Surveys and Demographic Data.” PLoS Computational Biology 13: 9. https://doi.org/10.1371/journal.pcbi.1005697.Search in Google Scholar
Stock, J. H. 2020. “Data Gaps and the Policy Response to the Novel Coronavirus.” In National Bureau of Economic Research Working Paper No. 26902.10.3386/w26902Search in Google Scholar
WHO 2020. WHO Director-General’s Opening Remarks at the Media Briefing on COVID-19 March 2020.Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
