IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v286y2021ics0277953621006675.html
   My bibliography  Save this article

A multi-national test on self-reported compliance with COVID-19 public health measures: The role of individual age and gender demographics and countries’ developmental status

Author

Listed:
  • Lin, Tian
  • Harris, Elizabeth A.
  • Heemskerk, Amber
  • Van Bavel, Jay J.
  • Ebner, Natalie C.

Abstract

The COVID-19 pandemic has brought far-reaching consequences on individual and societal levels. Social distancing and physical hygiene constitute effective public health measures to limit the spread of the virus. This study investigated age and gender demographics, in tandem with national levels of human development, as crucial factors influencing self-reported compliance with COVID-19-related public health measures.

Suggested Citation

  • Lin, Tian & Harris, Elizabeth A. & Heemskerk, Amber & Van Bavel, Jay J. & Ebner, Natalie C., 2021. "A multi-national test on self-reported compliance with COVID-19 public health measures: The role of individual age and gender demographics and countries’ developmental status," Social Science & Medicine, Elsevier, vol. 286(C).
  • Handle: RePEc:eee:socmed:v:286:y:2021:i:c:s0277953621006675
    DOI: 10.1016/j.socscimed.2021.114335
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953621006675
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.socscimed.2021.114335?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. Romer, Daniel & Jamieson, Kathleen Hall, 2020. "Conspiracy theories as barriers to controlling the spread of COVID-19 in the U.S," Social Science & Medicine, Elsevier, vol. 263(C).
    2. Smith, Richard D., 2006. "Responding to global infectious disease outbreaks: Lessons from SARS on the role of risk perception, communication and management," Social Science & Medicine, Elsevier, vol. 63(12), pages 3113-3123, December.
    3. Valerio Capraro & Hélène Barcelo, 2020. "The effect of messaging and gender on intentions to wear a face covering to slow down COVID-19 transmission," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 4(S2), pages 45-55, December.
    4. Jay J. Van Bavel & Katherine Baicker & Paulo S. Boggio & Valerio Capraro & Aleksandra Cichocka & Mina Cikara & Molly J. Crockett & Alia J. Crum & Karen M. Douglas & James N. Druckman & John Drury & Oe, 2020. "Using social and behavioural science to support COVID-19 pandemic response," Nature Human Behaviour, Nature, vol. 4(5), pages 460-471, May.
    5. Jean-François Daoust & Eric Bélanger & Ruth Dassonneville & Erick Lachapelle & Richard Nadeau & Michael Becher & Sylvain Brouard & Martial Foucault & Christoph Hönninge & Daniel Stegmueller, 2021. "A guilt-free strategy increases self-reported non-compliance with COVID-19 preventive measures: Experimental evidence from 12 countries," Post-Print hal-03244320, HAL.
    6. Sheung-Tak Cheng, 2009. "Generativity in Later Life: Perceived Respect From Younger Generations as a Determinant of Goal Disengagement and Psychological Well-being," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 64(1), pages 45-54.
    7. Vincenzo Galasso & Vincent Pons & Paola Profeta & Michael Becher & Sylvain Brouard & Martial Foucault, 2020. "Gender Differences in COVID-19 Related Attitudes and Behavior: Evidence from a Panel Survey in Eight OECD Countries," SciencePo Working papers Main hal-03594437, HAL.
    8. Merwan Engineer & Ian King & Nilanjana Roy, 2008. "The human development index as a criterion for optimal planning," Indian Growth and Development Review, Emerald Group Publishing Limited, vol. 1(2), pages 172-192, September.
    9. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    10. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    11. Cheng Li, 2013. "Little's test of missing completely at random," Stata Journal, StataCorp LP, vol. 13(4), pages 795-809, December.
    12. Elizabeth Stanton, 2007. "The Human Development Index: A History," Working Papers wp127, Political Economy Research Institute, University of Massachusetts at Amherst.
    13. Paul T E Cusack, 2020. "The Human Brain," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 31(3), pages 24261-24266, October.
    14. Martin Larsen & Jacob Nyrup & Michael Bang Petersen, 2020. "Do Survey Estimates of the Public’s Compliance with COVID-19 Regulations Suffer from Social Desirability Bias?," Journal of Behavioral Public Administration, Center for Experimental and Behavioral Public Administration, vol. 3(2).
    15. World Bank, 2012. "World Development Report 2012 [Rapport sur le développement dans le monde 2012]," World Bank Publications - Books, The World Bank Group, number 4391.
    16. Sobol, Małgorzata & Blachnio, Agata & Przepiórka, Aneta, 2020. "Time of pandemic: Temporal perspectives related to compliance with public health regulations concerning the COVID-19 pandemic," Social Science & Medicine, Elsevier, vol. 265(C).
    17. Connor, Jade & Madhavan, Sarina & Mokashi, Mugdha & Amanuel, Hanna & Johnson, Natasha R. & Pace, Lydia E. & Bartz, Deborah, 2020. "Health risks and outcomes that disproportionately affect women during the Covid-19 pandemic: A review," Social Science & Medicine, Elsevier, vol. 266(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. Stefanie Winter & Andrea Jesser & Thomas Probst & Yvonne Schaffler & Ida-Maria Kisler & Barbara Haid & Christoph Pieh & Elke Humer, 2023. "How the COVID-19 Pandemic Affects the Provision of Psychotherapy: Results from Three Online Surveys on Austrian Psychotherapists," IJERPH, MDPI, vol. 20(3), pages 1-15, January.
    2. Mónica Ferrín, 2022. "Reassessing Gender Differences in COVID‐19 Risk Perception and Behavior," Social Science Quarterly, Southwestern Social Science Association, vol. 103(1), pages 31-41, January.
    3. Piehlmaier, Dominik M. & Stagno, Emanuela & Nagy, Agnes, 2023. "Overconfidence at the time of COVID-19:Does it lead to laxer attitudes?," Social Science & Medicine, Elsevier, vol. 328(C).
    4. Oyenubi, Adeola & Kollamparambil, Umakrishnan, 2023. "Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?," Economic Modelling, Elsevier, vol. 120(C).
    5. Lau Lilleholt & Ingo Zettler & Cornelia Betsch & Robert Böhm, 2023. "Development and validation of the pandemic fatigue scale," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    6. Xin, Meiqi & Lau, Joseph Tak-fai & Lau, Mason M.C., 2022. "Multi-dimensional factors related to participation in a population-wide mass COVID-19 testing program among Hong Kong adults: A population-based randomized survey," Social Science & Medicine, Elsevier, vol. 294(C).
    7. Kang, Tarandeep S. & Goodwin, Robin, 2022. "Legal restrictions and mitigation strategies amongst a disabled population during COVID-19," Social Science & Medicine, Elsevier, vol. 305(C).
    8. Li, Meng-Hao & Haynes, Kingsley & Kulkarni, Rajendra & Siddique, Abu Bakkar, 2022. "Determinants of voluntary compliance: COVID-19 mitigation," Social Science & Medicine, Elsevier, vol. 310(C).

    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. Vincenzo Carrieri & Maria De Paola & Francesca Gioia, 2021. "The health-economy trade-off during the Covid-19 pandemic: Communication matters," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-25, September.
    2. Foliano, Francesca & Tonei, Valentina & Sevilla, Almudena, 2024. "Social restrictions, leisure and well-being," Labour Economics, Elsevier, vol. 87(C).
    3. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    4. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.
    5. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    6. Juvalta, Sibylle & Speranza, Camilla & Robin, Dominik & El Maohub, Yassmeen & Krasselt, Julia & Dreesen, Philipp & Dratva, Julia & Suggs, L. Suzanne, 2023. "Young people's media use and adherence to preventive measures in the “infodemic”: Is it masked by political ideology?," Social Science & Medicine, Elsevier, vol. 317(C).
    7. Borau, Sylvie & Couprie, Hélène & Hopfensitz, Astrid, 2022. "The prosociality of married people: Evidence from a large multinational sample," Journal of Economic Psychology, Elsevier, vol. 92(C).
    8. Martin, Eisele & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," MPRA Paper 57666, University Library of Munich, Germany.
    9. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    10. Dang, Hai-Anh H & Carletto, Calogero, 2022. "Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation," IZA Discussion Papers 14997, Institute of Labor Economics (IZA).
    11. Daniel Schunk, 2007. "A Markov Chain Monte Carlo Multiple Imputation Procedure for Dealing with Item Nonresponse in the German SAVE Survey," MEA discussion paper series 07121, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    12. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    13. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.
    14. F. Di Lascio & Simone Giannerini & Alessandra Reale, 2015. "Exploring copulas for the imputation of complex dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 159-175, March.
    15. Ankita Patnaik & Jeffrey Hemmeter & Arif Mamun, "undated". "Promoting Readiness of Minors with Autism Spectrum Disorder: Evidence from a Randomized Controlled Trial," Mathematica Policy Research Reports a74c93d9bdce40709ad81cdbc, Mathematica Policy Research.
    16. Jay Joseph van Bavel & Aleksandra Cichocka & Valerio Capraro & Hallgeir Sjåstad & John Nezlek & Tomislav Pavlović & Mark Alfano & Michele Gelfand & Flavio Azevedo & Michèle Birtel & Aleksandra Cislak , 2022. "National identity predicts public health support during a global pandemic: Results from 67 nations," Post-Print hal-03543504, HAL.
    17. Westermeier, Christian & Grabka, Markus M., 2016. "Longitudinal Wealth Data and Multiple Imputation: An Evaluation Study," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(3), pages 237-252.
    18. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    19. Ahfock, Daniel & Pyne, Saumyadipta & McLachlan, Geoffrey J., 2022. "Statistical file-matching of non-Gaussian data: A game theoretic approach," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    20. Koch, Michael & Park, Sarah, 2022. "Do government responses impact the relationship between age, gender and psychological distress during the COVID-19 pandemic? A comparison across 27 European countries," Social Science & Medicine, Elsevier, vol. 292(C).

    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:eee:socmed:v:286:y:2021:i:c:s0277953621006675. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    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.