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Objectives:The global coronavirus disease 2019 (COVID-19) pandemic has not been well controlled, and vaccination could be an effective way to prevent this pandemic. By accommodating attribute nonattendance (ANA) in a discrete choice experiment (DCE), this paper aimed to examine Chinese public preferences and willingness to pay (WTP) for COVID-19 vaccine attributes, especially the influence of ANA on the estimated results. Methods: A DCE was designed with four attributes: effectiveness, protection period, adverse reactions and price. A random parameter logit model with an error component (RPL-EC) was used to analyse the heterogeneity of respondents' preferences for COVID-19 vaccine attributes. Two equality constraint latent class (ECLC) models were used to consider the influence of ANA on the estimated results in which the ECLC-homogeneity model considered only ANA and the ECLC-heterogeneity model considered both ANA and preference heterogeneity.Results: Data from 1,576 samples were included in the analyses. Effectiveness had the highest relative importance, followed by adverse reactions and protection period, which were determined by the attributes and levels presented in this study. The ECLC-heterogeneity model improved the goodness of fit of the model and obtained a lower probability of ANA. In the ECLC-heterogeneity model, only a small number of respondents (29.09%) considered all attributes, and price was the most easily ignored attribute (64.23%). Compared with the RPL-EC model, the ECLChomogeneity model obtained lower WTPs for COVID-19 vaccine attributes, and the ECLC-heterogeneity model obtained mixed WTP results. In the ECLC-heterogeneity model, preference group 1 obtained higher WTPs, and preference groups 2 and 3 obtained lower WTPs. Conclusions:The RPL-EC, ECLC-homogeneity and ECLC-heterogeneity models obtained inconsistent WTPs for COVID-19 vaccine attributes. The study found that the results of the ECLC-heterogeneity model considering both ANA and preference

Background Assessing the public’s willingness to pay (WTP) for the coronavirus disease 2019 (COVID-19) vaccine by the contingent valuation (CV) method can provide a relevant basis for government pricing. However, the scope issue of the CV method can seriously affect the validity and reliability of the estimation results. Aim To examine whether there are scope issues in respondents’ WTP for the COVID-19 vaccine and to further verify the validity and reliability of the CV estimate results. Method In this study, nine different CV double-bounded dichotomous choices (DBDC) hypothetical COVID-19 vaccine scenarios were designed using an orthogonal experimental design based on the vaccine’s attributes. A total of 2450 samples from 31 provinces in Mainland China were collected to independently estimate the public’s WTP in these nine scenarios with logistic, normal, log-logistic and log-normal parameter models. Based on this estimation, several external scope tests were designed to verify the validity and reliability of the CV estimate results. Results In the 20 pairs of COVID-19 vaccine scenarios, 6 pairs of scenarios were classified as negative scope issues, therefore not passing the external scope test. Of the remaining 14 pairs of scenarios, only four pairs of scenarios completely passed the external scope test, and one pair of scenarios partially passed the external scope test. Significant negative scope and scope insensitivity issues were revealed. Conclusion In the context of a dynamic pandemic environment, the findings of this study reveal that the CV method may face difficulty in effectively estimating respondents’ WTP for the COVID-19 vaccine. We suggest that future studies be cautious in applying the CV method to estimate the public’s WTP for the COVID-19 vaccine. Supplementary Information The online version contains supplementary material available at 10.1007/s40258-021-00706-9.