IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v8y2021i1d10.1057_s41599-021-00749-2.html
   My bibliography  Save this article

What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown

Author

Listed:
  • Cyril Atkinson-Clement

    (Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière (DMU 6))

  • Eléonore Pigalle

    (Gustave Eiffel – ENPC)

Abstract

Year 2020 will mark History, with the emergence of the new Covid-19 virus, and more importantly, the consequent political decisions to apply freedom restriction at such a large-scale. Identifying the human behaviours during this extraordinary period represents a unique opportunity to both improve our fundamental knowledge and to improve future management of similar issues. Throughout almost all the duration of the French lockdown (from March 24, 2020 to May 10, 2020), we carried out an online survey on more than 12,000 individuals well distributed over the country. This online survey was performed by using both LimeSurvey and Google Forms services and was addressed to adults living in France. Statistical analyses combined classical inferential approach, mapping, clustering and text mining. The results showed that a significant part of the population moved out just before the lockdown (around 10% of our sample) and we highlighted three different profiles of participants. The results emphasised that the lockdown measures compliance was lower in two cases: (i) an unfavourable living environment (referring to social and economic inequity) associated with a high feeling of fear and a lack of trust towards Governmental measures; or (ii) the feeling that the risk was low due to the fact that others complied with the measures. In case a similar situation should occur again, it is recommended that Governments broadcast clear speeches to improve trust, limit fear and increase cooperative behaviours.

Suggested Citation

  • Cyril Atkinson-Clement & Eléonore Pigalle, 2021. "What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    • Handle: RePEc:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00749-2
    DOI: 10.1057/s41599-021-00749-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-021-00749-2
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-021-00749-2?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Ho Fai Chan & Ahmed Skali & David Savage & David Stadelmann & Benno Torgler, 2020. "Risk Attitudes and Human Mobility during the COVID-19 Pandemic," Papers 2006.06078, arXiv.org.
    2. Faheem Aslam & Tahir Mumtaz Awan & Jabir Hussain Syed & Aisha Kashif & Mahwish Parveen, 2020. "Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-9, December.
    3. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    4. Garcia, Thomas & Massoni, Sébastien & Villeval, Marie Claire, 2020. "Ambiguity and excuse-driven behavior in charitable giving," European Economic Review, Elsevier, vol. 124(C).
    5. Corinna S. Martarelli & Wanja Wolff, 2020. "Too bored to bother? Boredom as a potential threat to the efficacy of pandemic containment measures," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-5, December.
    6. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    7. Francesca Gino & Michael I. Norton & Roberto A. Weber, 2016. "Motivated Bayesians: Feeling Moral While Acting Egoistically," Journal of Economic Perspectives, American Economic Association, vol. 30(3), pages 189-212, Summer.
    8. Glenn Milligan, 1980. "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 325-342, September.
    9. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
    10. Storopoli, Jose & Braga da Silva Neto, Wilson Levy & Mesch, Gustavo S., 2020. "Confidence in social institutions, perceived vulnerability and the adoption of recommended protective behaviors in Brazil during the COVID-19 pandemic," Social Science & Medicine, Elsevier, vol. 265(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Denise R. Dunlap & Roberto S. Santos & Craig M. Lilly & Sean Teebagy & Nathaniel S. Hafer & Bryan O. Buchholz & David D. McManus, 2022. "COVID-19: a gray swan’s impact on the adoption of novel medical technologies," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    2. Akira Watanabe & Hiroyuki Matsuda, 2023. "Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures," Health Care Management Science, Springer, vol. 26(1), pages 46-61, March.
    3. Munirul H. Nabin & Mohammad Tarequl Hasan Chowdhury & Sukanto Bhattacharya, 2021. "It matters to be in good hands: the relationship between good governance and pandemic spread inferred from cross-country COVID-19 data," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    4. Mingyue Zhao & Yuqing Niu & Lei Tian & Yizhi Liu & Qiang Zhai, 2021. "Impact Measurement of COVID-19 Lockdown on China’s Electricity-Carbon Nexus," IJERPH, MDPI, vol. 18(18), pages 1-16, September.
    5. Virginia Romano & Mirko Ancillotti & Deborah Mascalzoni & Roberta Biasiotto, 2022. "Italians locked down: people’s responses to early COVID-19 pandemic public health measures," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.

    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. Weinand, J.M. & McKenna, R. & Fichtner, W., 2019. "Developing a municipality typology for modelling decentralised energy systems," Utilities Policy, Elsevier, vol. 57(C), pages 75-96.
    2. Henner Gimpel & Daniel Rau & Maximilian Röglinger, 2018. "Understanding FinTech start-ups – a taxonomy of consumer-oriented service offerings," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 245-264, August.
    3. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    4. Goethner, Maximilian & Hornuf, Lars & Regner, Tobias, 2021. "Protecting investors in equity crowdfunding: An empirical analysis of the small investor protection act," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    5. Marie Claire Villeval, 2019. "Comportements (non) éthiques et stratégies morales," Revue économique, Presses de Sciences-Po, vol. 70(6), pages 1021-1046.
    6. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    7. Johanna Mair & Julie Battilana & Julian Cardenas, 2012. "Organizing for Society: A Typology of Social Entrepreneuring Models," Journal of Business Ethics, Springer, vol. 111(3), pages 353-373, December.
    8. Öttl, Gerald & Böck, Philipp & Werpup, Nadja & Schwarze, Malte, 2013. "Derivation of representative air traffic peaks as standard input for airport related simulation," Journal of Air Transport Management, Elsevier, vol. 28(C), pages 31-39.
    9. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    10. Ja-Shen Chen & Russell K H Ching & Yi-Shen Lin, 2004. "An extended study of the K-means algorithm for data clustering and its applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 976-987, September.
    11. Michael Brusco & Douglas Steinley, 2015. "Affinity Propagation and Uncapacitated Facility Location Problems," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 443-480, October.
    12. Peter Radchenko & Gourab Mukherjee, 2017. "Convex clustering via l 1 fusion penalization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1527-1546, November.
    13. Peña-Malavera Andrea & Bruno Cecilia & Balzarini Monica & Fernandez Elmer, 2014. "Comparison of algorithms to infer genetic population structure from unlinked molecular markers," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(4), pages 1-12, August.
    14. Douglas Steinley & Michael Brusco, 2008. "Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures," Psychometrika, Springer;The Psychometric Society, vol. 73(1), pages 125-144, March.
    15. Kojadinovic, Ivan, 2010. "Hierarchical clustering of continuous variables based on the empirical copula process and permutation linkages," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 90-108, January.
    16. Zhiguang Huo & Li Zhu & Tianzhou Ma & Hongcheng Liu & Song Han & Daiqing Liao & Jinying Zhao & George Tseng, 2020. "Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 1-22, April.
    17. Tae Kyung Yoon & SoEun Ahn, 2020. "Clustering Koreans’ Environmental Awareness and Attitudes into Seven Groups: Environmentalists, Dissatisfieds, Inactivators, Bystanders, Honeybees, Optimists, and Moderates," Sustainability, MDPI, vol. 12(20), pages 1-18, October.
    18. P. (Sundar) Balakrishnan & Martha Cooper & Varghese Jacob & Phillip Lewis, 1994. "A study of the classification capabilities of neural networks using unsupervised learning: A comparison withK-means clustering," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 509-525, December.
    19. Bolívar, Fernando & Duran, Miguel A. & Lozano-Vivas, Ana, 2023. "Business model contributions to bank profit performance: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    20. Abba Krieger & Paul Green, 1999. "A cautionary note on using internal cross validation to select the number of clusters," Psychometrika, Springer;The Psychometric Society, vol. 64(3), pages 341-353, September.

    More about this item

    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:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00749-2. 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: https://www.nature.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.