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Population preferences for non-pharmaceutical interventions to control the SARS-CoV-2 pandemic: trade-offs among public health, individual rights, and economics

Author

Listed:
  • Axel C. Mühlbacher

    (Hochschule Neubrandenburg
    Gesellschaft Für Empirische Beratung GmbH
    Duke University)

  • Andrew Sadler

    (Hochschule Neubrandenburg)

  • Yvonne Jordan

    (Hochschule Neubrandenburg)

Abstract

Problem Policymakers must decide on interventions to control the pandemic. These decisions are driven by weighing the risks and benefits of various non-pharmaceutical intervention alternatives. Due to the nature of the pandemic, these decisions are not based on sufficient evidence regarding the effects, nor are decision-makers informed about the willingness of populations to accept the economic and health risks associated with different policy options. This empirical study seeks to reduce uncertainty by measuring population preferences for non-pharmaceutical interventions. Methods An online-based discrete choice experiment (DCE) was conducted to elicit population preferences. Respondents were asked to choose between three pandemic scenarios with different interventions and impacts of the Corona pandemic. In addition, Best–worst scaling (BWS) was used to analyze the impact of the duration of individual interventions on people’s acceptance. The marginal rate of substitution was applied to estimate willingness-to-accept (WTA) for each intervention and effect by risk of infection. Results Data from 3006 respondents were included in the analysis. The DCE showed, economic effect of non-pharmaceutical measures had a large impact on choice decisions for or against specific lockdown scenarios. Individual income decreases had the most impact. Excess mortality and individual risk of infection were also important factors influencing choice decisions. Curfews, contact restrictions, facility closures, personal data transmissions, and mandatory masking in public had a lesser impact. However, significant standard deviations in the random parameter logit model (RPL) indicated heterogeneities in the study population. The BWS results showed that short-term restrictions were more likely to be accepted than long-term restrictions. According to WTA estimates, people would be willing to accept a greater risk of infection to avoid loss of income. Discussion The results can be used to determine which consequences of pandemic measures would be more severe for the population. For example, the results show that citizens want to limit the decline in individual income during pandemic measures. Participation in preference studies can also inform citizens about potential tradeoffs that decision-makers face in current and future decisions during a pandemic. Knowledge of the population’s preferences will help inform decisions that consider people’s perspectives and expectations for the future. Survey results can inform decision-makers about the extent to which the population is willing to accept certain lockdown measures, such as curfews, contact restrictions, lockdowns, or mandatory masks.

Suggested Citation

  • Axel C. Mühlbacher & Andrew Sadler & Yvonne Jordan, 2022. "Population preferences for non-pharmaceutical interventions to control the SARS-CoV-2 pandemic: trade-offs among public health, individual rights, and economics," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(9), pages 1483-1496, December.
    • Handle: RePEc:spr:eujhec:v:23:y:2022:i:9:d:10.1007_s10198-022-01438-w
    DOI: 10.1007/s10198-022-01438-w
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    References listed on IDEAS

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    More about this item

    Keywords

    SARS-CoV-2; Population preference; Discrete choice experiments; Best–worst scaling;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • D79 - Microeconomics - - Analysis of Collective Decision-Making - - - Other

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