Hostname: page-component-6bf8c574d5-mggfc Total loading time: 0 Render date: 2025-02-25T17:46:29.088Z Has data issue: false hasContentIssue false
  • English
  • Français

The great lockdown: information, noise, and macroeconomic fluctuations

Published online by Cambridge University Press:  04 February 2022

Michał Brzoza-Brzezina  and
Grzegorz Wesołowski
Michał Brzoza-Brzezina*
Affiliation:
SGH Warsaw School of Economics, Warszawa, Poland
Grzegorz Wesołowski
Affiliation:
University of Warsaw, Warszawa, Poland
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper argues that not only actual lockdowns can affect economies but also noisy information about them. We construct a New Keynesian model with imperfect information about how long the lockdown would last. On the one hand, a false signal about the lockdown lowers consumption, investment, employment, and output, and this effect can be quantitatively sizable. On the other hand, a true information about a lockdown being introduced can also be misinterpreted and hence cause an impact on agents’ decisions being quantitatively different from the one desired by the authorities. To the extent that the latter have less noisy information about future lockdowns than the private sector, they can reduce these undesired fluctuations by precisely communicating the lockdown policy. Importantly, under some circumstances only radical improvements in information precision are successful.

Keywords

COVID-19lockdownimperfect informationcommunication
Type
Articles
Information
Macroeconomic Dynamics , Volume 27 , Issue 4 , June 2023 , pp. 906 - 927
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Altig, D., Baker, S. R., Barrero, J. M., Bloom, N., Bunn, P., Chen, S., Davis, S. J., Leather, J., Meyer, B. H., Mihaylov, E., Mizen, P., Parker, N. B., Renault, T., Smietanka, P. and Thwaites, G. (2020) Economic Uncertainty before and During the Covid-19 Pandemic. National Bureau of Economic Research, Working Paper: No. 27418.CrossRefGoogle Scholar
Angeletos, G. M. and La’O, J. (2013) Sentiments. Econometrica 81(2), 739–779.Google Scholar
Baqaee, D. and Farhi, E. (2021) Keynesian production networks and the covid-19 crisis: A simple benchmark. AEA Papers and Proceedings 111, 272–276.CrossRefGoogle Scholar
Blanchard, O. J., L’Huillier, J.-P. and Lorenzoni, G. (2013) News, noise, and fluctuations: An empirical exploration. American Economic Review 103(7), 3045–3070.CrossRefGoogle Scholar
Boscá, J. E., Doménech, R., Ferri, J., García, J. R. and Ulloa, C. (2021) The stabilizing effects of economic policies in Spain in times of COVID-19. Applied Economic Analysis 29(85), 4–20.CrossRefGoogle Scholar
Brey, B. and Hertweck, M. S. (2020) The extension of short-time work schemes during the great recession: A story of success?. Macroeconomic Dynamics 24(2), 360–402.CrossRefGoogle Scholar
Brinca, P., Duarte, J. B. and e Castro, M. F. (2020) Measuring Labor Supply and Demand Shocks during COVID-19. Federal Reserve Bank of St. Louis, Working Papers: No. 2020-011.Google Scholar
Buera, F. J., Fattal-Jaef, R. N., Hopenhayn, H., Neumeyer, P. A. and Shin, Y. (2021) The Economic Ripple Effects of COVID-19. National Bureau of Economic Research, Inc., NBER Working Papers: No. 28704.CrossRefGoogle Scholar
Chahrour, R. and Jurado, K. (2018) News or noise? The missing link. American Economic Review 108(7), 1702–1736.CrossRefGoogle Scholar
Chang, Y., Gomes, J. F. and Schorfheide, F. (2002) Learning-by-doing as a propagation mechanism. American Economic Review 92(5), 1498–1520.CrossRefGoogle Scholar
Chudik, A., Mohaddes, K., Pesaran, M. H., Raissi, M. and Rebucci, A. (2020) A Counterfactual Economic Analysis of Covid-19 Using a Threshold Augmented Multi-Country Model. National Bureau of Economic Research, Inc., NBER Working Papers: No. 27855.CrossRefGoogle Scholar
Coibion, O., Gorodnichenko, Y. and Weber, M. (2020) The Cost of the Covid-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending. National Bureau of Economic Research, Inc., NBER Working Papers: No. 27141.CrossRefGoogle Scholar
Collard, F., Dellas, H. and Smets, F. (2009) Imperfect information and the business cycle. Journal of Monetary Economics 56(S), 38–56.CrossRefGoogle Scholar
Eichenbaum, M. S., Rebelo, S. and Trabandt, M. (2020) The Macroeconomics of Epidemics. National Bureau of Economic Research, Inc., NBER Working Papers: No. 26882.CrossRefGoogle Scholar
Ferguson, N. M., Laydon, D., Nedjati-Gilani, G., Ainslie, K., Imai, N., Baguelin, M., Bhatia, S., Boonyasiri, A., Cucunubá, Z., Cuomo-Dannenburg, G. and Dighe, A. (2020) Impact of Non-pharmaceutical Interventions (npis) to Reduce Covid-19 Mortality and Healthcare Demand. Working Paper, Imperial College COVID-19 Response Team.Google Scholar
Goolsbee, A. and Syverson, C. (2021) Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020. Journal of Public Economics 193, 1–8.CrossRefGoogle Scholar
Guerrieri, V., Lorenzoni, G., Straub, L. and Werning, I. (2020) Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages? National Bureau of Economic Research, Inc., NBER Working Papers: No. 26918.Google Scholar
Hamilton, J. D. (1994) Time Series Analysis, 1st ed. Princeton University Press, Princeton, New Jersey.CrossRefGoogle Scholar
Hürtgen, P. (2014) Consumer misperceptions, uncertain fundamentals, and the business cycle. Journal of Economic Dynamics and Control 40(C), 279–292.CrossRefGoogle Scholar
Kollmann, R. (2021) Effects of Covid-19 on Euro Area GDP and Inflation: Demand vs. Supply Disturbances. Working Papers ECARES 2021-12, ULB – Universite Libre de Bruxelles.Google Scholar
Kozlowski, J., Veldkamp, L. and Venkateswaran, V. (2020) Scarring Body and Mind: The Long-Term Belief-Scarring Effects of COVID-19. National Bureau of Economic Research, Inc., NBER Working Papers: No. 27439.Google Scholar
Lucas, R. E. (1972) Expectations and the neutrality of money. Journal of Economic Theory 4(2), 103–124.CrossRefGoogle Scholar
Mankiw, N. G. and Reis, R. (2002) Sticky information versus sticky prices: A proposal to replace the new Keynesian Phillips curve. The Quarterly Journal of Economics 117(4), 1295–1328.CrossRefGoogle Scholar
Morris, S. and Shin, H. S. (2002) Social value of public information. American Economic Review 92(5), 1521–1534.CrossRefGoogle Scholar
Smets, F. and Wouters, R. (2007) Shocks and frictions in US business cycles: A Bayesian DSGE approach. American Economic Review 97(3), 586–606.CrossRefGoogle Scholar
Suda, J. (2018) Belief-twisting shocks and the macroeconomy. Macroeconomic Dynamics 22(7), 1844–1858.CrossRefGoogle Scholar
Taylor, J. B. (1993) Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy 39(1), 195–214.CrossRefGoogle Scholar