IDEAS home Printed from https://ideas.repec.org/a/spr/jopoec/v34y2021i1d10.1007_s00148-020-00799-x.html
   My bibliography  Save this article

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

Author

Listed:
  • Luca Bonacini

    (University of Modena and Reggio Emilia)

  • Giovanni Gallo

    (University of Modena and Reggio Emilia
    National Institute for Public Policies Analysis (INAPP))

  • Fabrizio Patriarca

    (University of Modena and Reggio Emilia)

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

  • Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    • Handle: RePEc:spr:jopoec:v:34:y:2021:i:1:d:10.1007_s00148-020-00799-x
    DOI: 10.1007/s00148-020-00799-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00148-020-00799-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00148-020-00799-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    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, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    3. 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).
    4. Government of India, 2017. "National Health Policy 2017," Working Papers id:11664, eSocialSciences.
    5. 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.
    6. John Manuel Barrios & Yael V. Hochberg, 2020. "Risk Perception Through the Lens of Politics in the Time of the COVID-19 Pandemic," Working Papers 2020-32, Becker Friedman Institute for Research In Economics.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Lesley Chiou & Catherine Tucker, 2020. "Social Distancing, Internet Access and Inequality," NBER Working Papers 26982, National Bureau of Economic Research, Inc.
    12. 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.
    13. 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).
    14. 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.
    15. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    16. 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.
    17. Borgonovi, Francesca & Andrieu, Elodie, 2020. "Bowling together by bowling alone: Social capital and COVID-19," Social Science & Medicine, Elsevier, vol. 265(C).
    18. 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.
    19. 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).
    20. 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.
    21. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nicholas W. Papageorge & Matthew V. Zahn & Michèle Belot & Eline Broek-Altenburg & Syngjoo Choi & Julian C. Jamison & Egon Tripodi, 2021. "Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(2), pages 691-738, April.
    2. Maxim Ananyev & Michael Poyker & Yuan Tian, 2021. "The safest time to fly: pandemic response in the era of Fox News," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(3), pages 775-802, July.
    3. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2022. "From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1517-1550, October.
    4. 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).
    5. Abel Brodeur & Idaliya Grigoryeva & Lamis Kattan, 2021. "Stay-at-home orders, social distancing, and trust," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1321-1354, October.
    6. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    7. Deiana, Claudio & Geraci, Andrea & Mazzarella, Gianluca & Sabatini, Fabio, 2022. "Can relief measures nudge compliance in a public health crisis? Evidence from a kinked fiscal policy rule," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 407-428.
    8. Kebin Deng & Zhong Ding & Xu Liu, 2023. "Clan loyalty and COVID‐19 diffusion: Evidence from China," Health Economics, John Wiley & Sons, Ltd., vol. 32(4), pages 910-938, April.
    9. Shin, Su Hyun & Ji, Hyunjung & Lim, HanNa, 2021. "Heterogeneity in preventive behaviors during COVID-19: Health risk, economic insecurity, and slanted information," Social Science & Medicine, Elsevier, vol. 278(C).
    10. Anindya Ghose & Beibei Li & Meghanath Macha & Chenshuo Sun & Natasha Ying Zhang Foutz, 2020. "Trading Privacy for the Greater Social Good: How Did America React During COVID-19?," Papers 2006.05859, arXiv.org.
    11. 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.
    12. Ainaa, Carmen & Brunetti, Irene & Mussida, Chiara & Scicchitano, Sergio, 2021. "Who lost the most? Distributive effects of COVID-19 pandemic," GLO Discussion Paper Series 829, Global Labor Organization (GLO).
    13. Carmen Aina & Irene Brunetti & Chiara Mussida & Sergio Scicchitano, 2023. "Distributional effects of COVID-19," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 221-256, March.
    14. Leonardo Bursztyn & Aakaash Rao & Christopher Roth & David Yanagizawa-Drott, 2020. "Misinformation During a Pandemic," Working Papers 2020-44, Becker Friedman Institute for Research In Economics.
    15. Burcu Ozgun & Tom Broekel, 2024. "Saved by the news? COVID-19 in German news and its relationship with regional mobility behaviour," Regional Studies, Taylor & Francis Journals, vol. 58(2), pages 365-380, February.
    16. Burzyński, Michał & Machado, Joël & Aalto, Atte & Beine, Michel & Goncalves, Jorge & Haas, Tom & Kemp, Françoise & Magni, Stefano & Mombaerts, Laurent & Picard, Pierre & Proverbio, Daniele & Skupin, A, 2021. "COVID-19 crisis management in Luxembourg: Insights from an epidemionomic approach," Economics & Human Biology, Elsevier, vol. 43(C).
    17. Fuest, Clemens & Immel, Lea & Neumeier, Florian & Peichl, Andreas, 2023. "Does expert information affect citizens’ attitudes toward Corona policies? Evidence from Germany," European Journal of Political Economy, Elsevier, vol. 78(C).
    18. Bonacini, Luca & Gallo, Giovanni & Patriarca, Fabrizio, 2020. "Drawing policy suggestions to fight Covid-19 from hardly reliable data. A machine-learning contribution on lockdowns analysis," GLO Discussion Paper Series 534, Global Labor Organization (GLO).
    19. 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.
    20. Schettino, Francesco & Scicchitano, Sergio & Suppa, Domenico, 2024. "COVID 19 and Wage Polarization: A task based approach," GLO Discussion Paper Series 1398, Global Labor Organization (GLO).

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jopoec:v:34:y:2021:i:1:d:10.1007_s00148-020-00799-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.