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Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures

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  • Bonacini, Luca
  • Gallo, Giovanni
  • Patriarca, Fabrizio

Abstract

Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus’s infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.

Suggested Citation

  • Bonacini, Luca & Gallo, Giovanni & Patriarca, Fabrizio, 2020. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," GLO Discussion Paper Series 534 [pre.], Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:534pre
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    1. Wright, Austin L. & Sonin, Konstantin & Driscoll, Jesse & Wilson, Jarnickae, 2020. "Poverty and economic dislocation reduce compliance with COVID-19 shelter-in-place protocols," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 544-554.
    2. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    3. Massimiliano Bratti & Daniele Checchi & Antonio Filippin, 2007. "Geographical Differences in Italian Students' Mathematical Competencies: Evidence from Pisa 2003," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 66(3), pages 299-333, November.
    4. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    5. John M. Barrios & Yael Hochberg, 2020. "Risk Perception Through the Lens of Politics in the Time of the COVID-19 Pandemic," NBER Working Papers 27008, National Bureau of Economic Research, Inc.
    6. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    7. Allcott, Hunt & Boxell, Levi & Conway, Jacob & Gentzkow, Matthew & Thaler, Michael & Yang, David, 2020. "Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    8. Domenico Depalo, 2021. "True COVID-19 mortality rates from administrative data," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 253-274, January.
    9. Egorov, Georgy & Enikolopov, Ruben & Makarin, Alexey & Petrova, Maria, 2021. "Divided we stay home: Social distancing and ethnic diversity," Journal of Public Economics, Elsevier, vol. 194(C).
    10. Borgonovi, Francesca & Andrieu, Elodie, 2020. "Bowling together by bowling alone: Social capital and COVID-19," Social Science & Medicine, Elsevier, vol. 265(C).
    11. Andrey Simonov & Szymon Sacher & Jean-Pierre Dube & Shirsho Biswas, 2020. "The Persuasive Effect of Fox News: Non-Compliance with Social Distancing During the COVID-19 Pandemic," Working Papers 2020-67, Becker Friedman Institute for Research In Economics.
    12. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    13. Louis-Philippe Beland & Abel Brodeur & Taylor Wright, 2020. "COVID-19, Stay-at-Home Orders and Employment: Evidence from CPS Data," Carleton Economic Papers 20-04, Carleton University, Department of Economics, revised 19 May 2020.
    14. Fabio Milani, 2021. "COVID-19 outbreak, social response, and early economic effects: a global VAR analysis of cross-country interdependencies," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 223-252, January.
    15. Yun Qiu & Xi Chen & Wei Shi, 2020. "Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1127-1172, October.
    16. Lesley Chiou & Catherine Tucker, 2020. "Social Distancing, Internet Access and Inequality," NBER Working Papers 26982, National Bureau of Economic Research, Inc.
    17. Luca Bonacini & Giovanni Gallo & Sergio Scicchitano, 2021. "Working from home and income inequality: risks of a ‘new normal’ with COVID-19," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 303-360, January.
    18. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "Health and well-being in the great recession," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 26-47, April.
    19. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    20. Government of India, 2017. "National Health Policy 2017," Working Papers id:11664, eSocialSciences.
    21. Sarti, Simone & Terraneo, Marco & Tognetti Bordogna, Mara, 2017. "Poverty and private health expenditures in Italian households during the recent crisis," Health Policy, Elsevier, vol. 121(3), pages 307-314.
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    More about this item

    Keywords

    COVID-19; coronavirus; lockdown; machine learning;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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