IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-03953980.html
   My bibliography  Save this paper

Preferences for COVID-19 epidemic control measures among French adults: a discrete choice experiment

Author

Listed:
  • Jonathan Sicsic

    (LIRAES (URP_ 4470) - Laboratoire Interdisciplinaire de Recherche Appliquée en Economie de la Santé - UPCité - Université Paris Cité)

  • Serge Blondel

    (LIRAES (URP_ 4470) - Laboratoire Interdisciplinaire de Recherche Appliquée en Economie de la Santé - UPCité - Université Paris Cité, GRANEM - Groupe de Recherche Angevin en Economie et Management - UA - Université d'Angers - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

  • Sandra Chyderiotis

    (IP - Institut Pasteur [Paris])

  • François Langot

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CEPREMAP - Centre pour la recherche économique et ses applications - ECO ENS-PSL - Département d'économie de l'ENS-PSL - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres, UM - Le Mans Université)

  • Judith E. Mueller

    (EHESP - École des Hautes Études en Santé Publique [EHESP], IP - Institut Pasteur [Paris], METIS - Département Méthodes quantitatives en santé publique - EHESP - École des Hautes Études en Santé Publique [EHESP], ARENES - Arènes: politique, santé publique, environnement, médias - UR - Université de Rennes - Institut d'Études Politiques [IEP] - Rennes - EHESP - École des Hautes Études en Santé Publique [EHESP] - UR2 - Université de Rennes 2 - CNRS - Centre National de la Recherche Scientifique, RSMS - Recherche sur les services et le management en santé - UR - Université de Rennes - EHESP - École des Hautes Études en Santé Publique [EHESP] - INSERM - Institut National de la Santé et de la Recherche Médicale - CNRS - Centre National de la Recherche Scientifique, UR - Université de Rennes)

Abstract

In this stated preferences study, we describe for the first time French citizens' preferences for various epidemic control measures, to inform longer-term strategies and future epidemics. We used a discrete choice experiment in a representative sample of 908 adults in November 2020 (before vaccination was available) to quantify the trade-off they were willing to make between restrictions on the social, cultural, and economic life, school closing, targeted lockdown of high-incidence areas, constraints to directly protect vulnerable persons (e.g., self-isolation), and measures to overcome the risk of hospital overload. The estimation of mixed logit models with correlated random effects shows that some trade-offs exist to avoid overload of hospitals and intensive care units, at the expense of stricter control measures with the potential to reduce individuals' welfare. The willingness to accept restrictions was shared to a large extent across subgroups according to age, gender, education, vulnerability to the COVID-19 epidemic, and other socio-demographic or economic variables. However, individuals who felt at greater risk from COVID-19, and individuals expressing high confidence in the governmental management of the health and economic crisis, more easily accepted all these restrictions. Finally, we compared the welfare impact of alternative strategies combining different epidemic control measures. Our results suggest that policies close to a targeted lockdown or with medically prescribed self-isolation were those satisfying the largest share of the population and achieving high gain in average welfare, while average welfare was maximized by the combination of all highly restrictive measures. This illustrates the difficulty in making preference-based decisions on restrictions.

Suggested Citation

  • Jonathan Sicsic & Serge Blondel & Sandra Chyderiotis & François Langot & Judith E. Mueller, 2023. "Preferences for COVID-19 epidemic control measures among French adults: a discrete choice experiment," Post-Print halshs-03953980, HAL.
  • Handle: RePEc:hal:journl:halshs-03953980
    DOI: 10.1007/s10198-022-01454-w
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Emily Lancsar & Jordan Louviere, 2008. "Conducting Discrete Choice Experiments to Inform Healthcare Decision Making," PharmacoEconomics, Springer, vol. 26(8), pages 661-677, August.
    2. Hess, Stephane & Train, Kenneth, 2017. "Correlation and scale in mixed logit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 1-8.
    3. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    4. Daniel McFadden, 1986. "The Choice Theory Approach to Market Research," Marketing Science, INFORMS, vol. 5(4), pages 275-297.
    5. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    6. John Cairns & Marjon van der Pol & Andrew Lloyd, 2002. "Decision making heuristics and the elicitation of preferences: being fast and frugal about the future," Health Economics, John Wiley & Sons, Ltd., vol. 11(7), pages 655-658, October.
    7. Richard C. Ready & Patricia A. Champ & Jennifer L. Lawton, 2010. "Using Respondent Uncertainty to Mitigate Hypothetical Bias in a Stated Choice Experiment," Land Economics, University of Wisconsin Press, vol. 86(2), pages 363-381.
    8. Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth, 2019. "Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 10(1-2), pages 1-144, January.
    9. Dekker, Thijs & Hess, Stephane & Brouwer, Roy & Hofkes, Marjan, 2016. "Decision uncertainty in multi-attribute stated preference studies," Resource and Energy Economics, Elsevier, vol. 43(C), pages 57-73.
    10. Regier, Dean A. & Sicsic, Jonathan & Watson, Verity, 2019. "Choice certainty and deliberative thinking in discrete choice experiments. A theoretical and empirical investigation," Journal of Economic Behavior & Organization, Elsevier, vol. 164(C), pages 235-255.
    11. John Rose & Michiel Bliemer, 2013. "Sample size requirements for stated choice experiments," Transportation, Springer, vol. 40(5), pages 1021-1041, September.
    12. Stephane Hess & John Rose, 2012. "Can scale and coefficient heterogeneity be separated in random coefficients models?," Transportation, Springer, vol. 39(6), pages 1225-1239, November.
    13. Trine Kjær & Mickael Bech & Dorte Gyrd‐Hansen & Kristian Hart‐Hansen, 2006. "Ordering effect and price sensitivity in discrete choice experiments: need we worry?," Health Economics, John Wiley & Sons, Ltd., vol. 15(11), pages 1217-1228, November.
    14. Katrin Auspurg & Annette Jäckle, 2017. "First Equals Most Important? Order Effects in Vignette-Based Measurement," Sociological Methods & Research, , vol. 46(3), pages 490-539, August.
    15. Mandy Ryan & Verity Watson & Vikki Entwistle, 2009. "Rationalising the ‘irrational’: a think aloud study of discrete choice experiment responses," Health Economics, John Wiley & Sons, Ltd., vol. 18(3), pages 321-336, March.
    16. Thierry Blayac & Dimitri Dubois & Sebastien Duchene & Phu Nguyen-Van & Bruno Ventelou & Marc Willinger, 2021. "Population preferences for inclusive COVID-19 policy responses," Post-Print hal-03663993, HAL.
    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. Robert J. Johnston & Kevin J. Boyle & Wiktor (Vic) Adamowicz & Jeff Bennett & Roy Brouwer & Trudy Ann Cameron & W. Michael Hanemann & Nick Hanley & Mandy Ryan & Riccardo Scarpa & Roger Tourangeau & Ch, 2017. "Contemporary Guidance for Stated Preference Studies," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(2), pages 319-405.
    2. Haghani, Milad & Bliemer, Michiel C.J. & Rose, John M. & Oppewal, Harmen & Lancsar, Emily, 2021. "Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods," Journal of choice modelling, Elsevier, vol. 41(C).
    3. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods," Papers 2102.02945, arXiv.org.
    4. Ivan Tzintzun & Jonathan Sicsic & Lise Rochaix, 2023. "Into the Far West? Investigating Health Policy-Makers' Willingness to Adopt Decrementally Cost-Effective Innovations Using a DCE Approach," PSE Working Papers halshs-04154933, HAL.
    5. Lehmann, Nico & Sloot, Daniel & Schüle, Christopher & Ardone, Armin & Fichtner, Wolf, 2023. "The motivational drivers behind consumer preferences for regional electricity – Results of a choice experiment in Southern Germany," Energy Economics, Elsevier, vol. 120(C).
    6. Apurba Shee & Calum G. Turvey & Ana Marr, 2021. "Heterogeneous Demand and Supply for an Insurance‐linked Credit Product in Kenya: A Stated Choice Experiment Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(1), pages 244-267, February.
    7. Haghani, Milad & Bliemer, Michiel C.J. & Rose, John M. & Oppewal, Harmen & Lancsar, Emily, 2021. "Hypothetical bias in stated choice experiments: Part I. Macro-scale analysis of literature and integrative synthesis of empirical evidence from applied economics, experimental psychology and neuroimag," Journal of choice modelling, Elsevier, vol. 41(C).
    8. Alessandro Mengoni & Chiara Seghieri & Sabina Nuti, 2013. "The application of discrete choice experiments in health economics: a systematic review of the literature," Working Papers 201301, Scuola Superiore Sant'Anna of Pisa, Istituto di Management.
    9. Ahtiainen, Heini & Tienhaara, Annika & Pouta, Eija & Czajkowski, Mikolaj, 2017. "Role of information in the valuation of unfamiliar goods – the case of genetic resources in agriculture," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 261423, European Association of Agricultural Economists.
    10. West, Grant H. & Snell, Heather & Kovacs, Kent & Nayga, Rodolfo M., 2020. "Estimation of the preferences for the intertemporal services from groundwater," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304220, Agricultural and Applied Economics Association.
    11. Mariel, Petr & Artabe, Alaitz, 2020. "Interpreting correlated random parameters in choice experiments," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    12. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    13. Muhammad Bello & Awudu Abdulai, 2016. "Measuring heterogeneity, survey engagement and response quality in preferences for organic products in Nigeria," Applied Economics, Taylor & Francis Journals, vol. 48(13), pages 1159-1171, March.
    14. Ivan Tzintzun & Jonathan Sicsic & Lise Rochaix, 2023. "Into the Far West? Investigating Health Policy-Makers' Willingness to Adopt Decrementally Cost-Effective Innovations Using a DCE Approach," Working Papers halshs-04154933, HAL.
    15. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part I. Integrative synthesis of empirical evidence and conceptualisation of external validity," Papers 2102.02940, arXiv.org.
    16. Mariel, Petr & Meyerhoff, Jürgen, 2018. "A More Flexible Model or Simply More Effort? On the Use of Correlated Random Parameters in Applied Choice Studies," Ecological Economics, Elsevier, vol. 154(C), pages 419-429.
    17. Kassie, Girma T. & Zeleke, Fresenbet & Birhanu, Mulugeta Y. & Scarpa, Riccardo, 2020. "Reminder Nudge, Attribute Nonattendance, and Willingness to Pay in a Discrete Choice Experiment," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304208, Agricultural and Applied Economics Association.
    18. Akshay Vij & Rico Krueger, 2018. "Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions," Papers 1802.02299, arXiv.org.
    19. Mikolaj Czajkowski & Marek Giergiczny & William H. Greene, 2014. "Learning and Fatigue Effects Revisited: Investigating the Effects of Accounting for Unobservable Preference and Scale Heterogeneity," Land Economics, University of Wisconsin Press, vol. 90(2), pages 324-351.
    20. Vij, Akshay & Krueger, Rico, 2017. "Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 76-101.

    More about this item

    Keywords

    SARS-CoV-2 epidemic; COVID-19; Epidemic control measures; Preferences; Discrete choice experiment; Correlated mixed logit model; Choice certainty;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • 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:hal:journl:halshs-03953980. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.