We use an accounting framework to evaluate the aggregate impact of a common lockdown policy for 85 countries. We find that poorer countries devote more labor to essential activities that are unaffected by the lockdown, while richer countries can more easily substitute non-essential employment with work from home. The lockdown generates an employment response that is U-shaped in income: it drops by 32% in the poorest quintile of the distribution, by 36% in the middle quintile, and by 31% in the richest quintile. Annualized GDP declines by 39% in the bottom three quintiles and by 31% in the richest quintile. Agriculture, an essential sector, is key in sustaining employment and economic activity in poorer countries.
Funding source: Leverhulme 10.13039/501100000275
Funding source: University of St. Gallen 10.13039/100009572
Funding source: ESRC-DFID
Award Identifier / Grant number: ES/L012499/1
We thank Joel Frischknecht for excellent research assistance.
Research Funding: Research funding from Leverhulme, GFF Fund of the University of St. Gallen and ESRC-DFID (ES/L012499/1) is gratefully acknowledged.
A.1 Model Derivation
Here we derive the model that underpins Equation (2) that is used to calculate GDP relative to trend. Consider a closed economy where gross output in sector i is
with parameters and such that . The sector’s TFP is z i and there are two types of production factors: is a bundle of the sector’s human and physical capital and m ij is intermediate consumption of goods from sector j. Let p i denote the price of output of sector i. Assuming perfect competition, profit maximization with respect to intermediate inputs implies , . In particular, the sector’s value added equals
The representative household chooses final consumption c i to maximize utility
with parameters such that . The optimality condition is hence , . The product market clears according to , ∀i.
Let Y denote real GDP and P ≡ 1 its normalized price so that . In equilibrium, it can be shown that GDP is
with parameter vector where I is the identity matrix and Γ is a matrix with elements . In particular, d i equals the Domar weight of sector i, . If z i is constant and the only exogenous shock occurs through the supply of x i , then where equals the (constant) aggregate value added share of sector i in the economy. GDP relative to trend is then where denotes the relative utilization of factor x i following the shock. Our final assumption is that capital and labor (l) enter homothetically into x and that they change in equal proportion following the shock, resulting in
Economies can differ in their underlying parameters, which implies that v i is country-specific.
Adams-Prassl, A., T. Boneva, M. Golin, and C. Rauh. 2020a. “Inequality in the Impact of the Coronavirus Shock: Evidence from Real Time Surveys.” Journal of Public Economics 189, https://doi.org/10.1016/j.jpubeco.2020.104245.Search in Google Scholar
Adams-Prassl, A., T. Boneva, M. Golin, and C. Rauh. 2020b. Work Tasks that can be Done from Home: Evidence on the Variation within and across Occupations and Industries, IZA Discussion Paper (13374). Bonn: IZA, Institute of Labor Economics.10.2139/ssrn.3631584Search in Google Scholar
Alipour, J. V., O. Falck, and S. Schüller. 2020. Germany’s Capacities to Work from Home, IZA Discussion Paper (13152). Bonn: IZA, Institute of Labor Economics.10.2139/ssrn.3579244Search in Google Scholar
Alon, T. M., M. Kim, D. Lagakos, and M. VanVuren. 2020. “How Should Policy Responses to the Covid-19 Pandemic Differ in the Developing World?” Covid Economics 22.10.3386/w27273Search in Google Scholar
Aum, S., S. Y. T. Lee, and Y. Shin. 2021. “Inequality of Fear and Self-Quarantine: Is there a Trade-Off between GDP and Public Health?” Journal of Public Economics.10.3386/w27100Search in Google Scholar
Baqaee, D. R., and E. Farhi. 2020. Supply and Demand in Disaggregated Keynesian Economies with an Application to the Covid-19 Crisis, NBER Working Paper (27152). Cambridge, MA: NBER, National Bureau of Economic Research.10.3386/w27152Search in Google Scholar
Boeri, T., A. Caiumi, and M. Paccagnella. 2020. “Mitigating the Work-Security Trade-Off while Rebooting the Economy.” Covid Economics 2.Search in Google Scholar
Brotherhood, L., Kircher, P., Santos, C., and Tertilt, M. 2020. An Economic Model of the Covid-19 Epidemic: The Importance of Testing and Age-Specific Policies, IZA Discussion Paper (13265). Bonn: IZA, Institute of Labor Economics.10.2139/ssrn.3618840Search in Google Scholar
del Rio-Chanona, R. M., P. Mealy, A. Pichler, F. Lafond, and D. Farmer. 2020. “Supply and Demand Shocks in the Covid-19 Pandemic: An Industry and Occupation Perspective.” Covid Economics 6.10.1093/oxrep/graa033Search in Google Scholar
Duernecker, G., and B. Herrendorf. 2016. “Structural Transformation of Occupation Employment.” Mimeo. Arizona State University.Search in Google Scholar
Fadinger, H., and J. Schymik. 2020. “The Costs and Benefits of Home Office during the Covid-19 Pandemic: Evidence from Infections and an Input-Output Model for Germany.” Covid Economics 9.Search in Google Scholar
Fana, M., S. Tolan, S. Torrejón, C. Urzi Brancati, and E. Fernández-Macías. 2020. The Covid Confinement Measures and EU Labour Markets. Luxembourg: Publications Office of the European Union.Search in Google Scholar
Farboodi, M., G. Jarosch, and R. Shimer. 2020. Internal and External Effects of Social Distancing in a Pandemic, NBER Working Paper (27059). Cambridge, MA: NBER, National Bureau of Economic Research.10.3386/w27059Search in Google Scholar
Feenstra, R. C., R. Inklaar, and M. P. Timmer. 2015. “The Next Generation of the Penn World Table.” American Economic Review 105 (10): 3150–82, https://doi.org/10.1257/aer.20130954.Search in Google Scholar
Guerrieri, V., G. Lorenzoni, L. Straub, and I. Werning. 2020. Macroeconomic Implications of Covid-19: Can Negative Supply Shocks Cause Demand Shortages?, NBER Working Paper (26918). Cambridge, MA: NBER, National Bureau of Economic Research, https://doi.org/10.3386/w26918.Search in Google Scholar
Hale, T., S. Webster, A. Petherick, T. Phillips, and B. Kira. 2020. Oxford Covid-19 Government Response Tracker, 25. Oxford, UK: Blavatnik School of Government.Search in Google Scholar
Hensvik, L., T. Le Barbanchon, and R. Rathelot. 2020. “Which Jobs are Done from Home? Evidence from the American Time Use Survey.” EPR Discussion Paper Series 14611.10.2139/ssrn.3579230Search in Google Scholar
Herrendorf, B., R. Rogerson, and A. Valentinyi. 2014. “Growth and Structural Transformation.” In Handbook of Economic Growth, 2, 855–941. Amsterdam, The Netherlands: Elsevier.10.3386/w18996Search in Google Scholar
Kissler, S. M., C. Tedijanto, E. Goldstein, Y. H. Grad, and M. Lipsitch. 2020. “Projecting the Transmission Dynamics of Sars-Cov-2 through the Postpandemic Period.” Science 368 (6493): 860–8, https://doi.org/10.1126/science.abb5793.Search in Google Scholar
Krueger, D., H. Uhlig, and T. Xie. 2020. Macroeconomic Dynamics and Reallocation in an Epidemic, NBER Working Paper Series (27047). Cambridge, MA: NBER, National Bureau of Economic Research.10.3386/w27047Search in Google Scholar
Kuznets, S. 1973. “Modern Economic Growth: Findings and Reflections.” American Economic Review 63 (3): 247–58.Search in Google Scholar
Restuccia, D., D. T. Yang, and X. Zhu. 2008. “Agriculture and Aggregate Productivity: A Quantitative Cross-Country Analysis.” Journal of Monetary Economics 55 (2): 234–50, https://doi.org/10.1016/j.jmoneco.2007.11.006.Search in Google Scholar
Saltiel, F. 2020. “Who Can Work from Home in Developing Countries.” Covid Economics 7: 104–18.Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston