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# Valuing mortality risk reductions in the time of COVID-19: A stated-preference analysis

Jianhua Xu, Shiwei Fan, Jiakun Zheng

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## ► To cite this version:

Jianhua Xu, Shiwei Fan, Jiakun Zheng. Valuing mortality risk reductions in the time of COVID-19: A stated-preference analysis. Risk Sciences, 2025, 1, pp.100007. 10.1016/j.risk.2024.100007. hal-04909840

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# Valuing mortality risk reductions in the time of COVID-19:
A stated-preference analysis

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updates

Jianhua Xu<sup>a,b</sup>, Shiwei Fan<sup>c</sup>, Jiakun Zheng<sup>d,*</sup>

## ARTICLE INFO

### Keywords:

- Willingness to pay

- Mortality risk reductions

- Value per statistical life

- VSL

- COVID-19

## ABSTRACT

Lack of high-quality value per statistical life (VSL) studies in low- and middle-income countries have been recognized by scholars and analysts in the benefit-cost analysis field for decades. However, progress has been slow in addressing it. We estimated VSL in China using a stated-preference survey in the context of reducing mortality risks associated with COVID-19. The survey was administered in seven cities across China in 2022 with a purposive sampling approach, and consistency checks at different levels of stringency regarding willingness to pay (WTP) for mortality risk reductions of different magnitudes were used to screen respondents. The estimated VSL ranges from 8.0 million to 10.3 million Chinese Yuan, which is higher than previous estimates. Also previous studies found much higher VSL estimates from a subsample obtained with more stringent consistency check requiring that WTP be approximately proportional to the magnitude of mortality risk reduction, we did not find such a difference with our dataset. In addition, based on our anlaysis, respondents in first-tier cities such as Beijing, Shanghai and Guangzhou have higher VSL than those in second-tier cities such as Changchun, Chengdu, Wuhan and Xi'an; the VSL-age relationship shows a U-shaped pattern; and the collective experience of city lockdown has a negative impact on VSL. Other factors which were found to influence VSL include education, sector of work, health status, risk perception, behaviors (physical exercises, wearing face masks, getting vaccinated), knowledge, political identity, and trust in government.
JEL classification codes: I12, I18

## 1. Introduction

In making rules to reduce risks, social benefits and costs should be gauged in order to efficiently allocate resources and mitigate the influence of interest groups (Arrow et al., 1996; Sunstein, 2018). For life-saving regulations in the environmental, health and safety domains, the majority of quantified benefits come from averting mortality risks. To calculate these benefits, ideally, the value

<footnote><sup>a</sup> Department of Environmental Management, College of Environmental Sciences and Engineering, Peking University, Beijing, China</footnote>

<footnote><sup>b</sup> Institute for Global Health and Development, Peking University, Beijing, China</footnote>

<footnote><sup>c</sup> School of Government, Central University of Finance and Economics, Beijing, China</footnote>

<footnote><sup>d</sup> Aix Marseille Univ, CNRS, AMSE, Centrale Méditerranée, Marseille, France</footnote>

<footnote>* Corresponding author.
E-mail address: jiakun.zheng@outkook.com (J. Zheng).</footnote>
<figure><img src="image_7.png" /><figcaption>Fig. 1. Locations of the seven cities.</figcaption></figure>

per statistical life (VSL),<fnref n="1" /> which is the marginal rate of substitution between income and mortality risk, of those individuals who are affected by the policy should be used. However, there are myriads of risks and risk mitigation policies. Risks and populations for which VSL were estimated are often different from risks and populations in new policy contexts for which benefit-cost analysis is performed. As a result, analysts need to borrow VSL from extant studies and adjust them (i.e., do benefit transfer) in new policy contexts (OECD, 2011; Hammitt & Robinson, 2011). To do this effectively, it is important to understand what factors affect VSL and how they affect it. Also, it is preferable to borrow VSL obtained from similar social, economic, and cultural contexts to minimize the influence of unaccounted factors rooted in cultural differences if there are any (e.g., Zhai, 2011). Unfortunately, there is a lack of high-quality VSL studies in low- and middle-income countries, including China (Hoffmann et al., 2017; Robinson et al., 2019), and the factors that influence VSL are not well understood (Viscusi, 2010; Hammitt & Robinson, 2011). Although these issues have been recognized by scholars and analysts in the benefit-cost analysis field for decades, progress has been slow in addressing them. We aim to narrow these gaps by providing VSL estimates and information on their influencing factors based on a stated-preference survey conducted on samples of the general population obtained with a purposive sampling approach in seven cities across China (Fig. 1).

We chose the context of reducing mortality risks associated with COVID-19 to elicit public preference. This context is chosen for several reasons. First, COVID-19 itself is a global health issue that has claimed millions of lives and addressing it is costly. Gauging policies to deal with this health crisis requires a better understanding of people's willingness to pay (WTP) for reducing mortality risks posed by it. So far, discussion on this topic has mostly been theoretical or conceptual (e.g., Hammitt, 2020; Robinson et al., 2021; Herrera-Araujo et al., 2022), we intend to provide empirical estimates on VSL relevant to mortality risks associated with COVID-19. Second, the mortality risk posed by COVID-19 is disproportionately distributed across age groups (Wilson, 2021) and people with pre-existing conditions are more vulnerable. Existing studies on the relationship between VSL and age in this setting are mostly asymptotic or theoretical (e.g., Viscusi, 2020; Robinson et al., 2021). We intend to provide empirical evidence on the VSL-age relationship in this particular setting. Third, people in different cities had different experiences in dealing with COVID-19, which allows us to explore the influence of experience on VSL – an understudied point.

In summary, we intend to answer two questions: what are the VSL estimates in the context of reducing mortality risks associated with COVID-19? What factors affect the VSL in this context? Based on our survey data collected in 2022, we estimated the overall VSL in a Chinese setting to be between 8.0 million and 10.3 million Chinese Yuan, and the VSL in each of the seven cities to be between 7.2 million and 9.4 million Chinese Yuan. We found that factors such as income, education, sector of work, health status, knowledge, experiences, risk perception and behaviors, political identity, and trust in government all influence VSL, and the VSL-age relationship shows a U-shaped pattern.

<footnote n="1">¹ The value per statistical life (VSL) is not the monetary value of a specific life. It represents people's willingness to pay for a small reduction in mortality risk or willingness to accept for a small increase in mortality risk. If an individual is willing to pay 50 Chinese Yuan for reducing his/her risk of dying from air pollution by $10^{-5}$, then his/her VSL is 5 million Chinese Yuan. Or, to put in another way, if each individual is willing to pay 50 Chinese Yuan for reducing his/her risk of dying from air pollution by $10^{-5}$, then 100,000 similar individuals would be willing to pay 5 million Chinese Yuan aggregately to eliminate a risk that would randomly kill one person from air pollution. The 5 million Chinese Yuan is the VSL.</footnote>
Over the past two decades, approximately a dozen VSL studies have been conducted in a Chinese setting. Except for the studies of Guo and Hammitt (2009) and Qin et al. (2013), which employed revealed-preference approaches, all other studies utilized stated-preference approaches. Table 1 presents a summary of the studies that adopted stated-preference approaches and reported VSL estimates, including ours. These studies differ in scope, design, results, and quality. Our study contributes to the literature in the following aspects.

First, our study used more rigorous consistency check. An inherent problem of using stated-preference approaches to elicit WTP for mortality risk reductions is their hypothetical nature, and the associated challenge is how to help respondents understand the magnitude of changes in the probability of death and make sense of the tradeoff involved (Hammitt et al., 2019). Consistency check, which examines whether individuals are willing to pay more for larger mortality risk reduction, is often used as an indicator of the validity of stated preference studies (Johnston et al., 2017). Two types of consistency checks are suggested: internal (within individuals) and external (between individuals). The economic theory underlying stated preference approaches predicts that WTP is approximately proportional to the magnitude of risk reduction, at least locally (Hammitt & Graham, 1999). However, existing studies either didn't conduct consistency check or conducted consistency check but found that the results did not meet proportionality criteria (see Table 1). Alolayan et al. (2017) derived two criteria based on conventional economic theory: WTP should be positive and an individual's WTP should be less than but close to being proportional to the magnitude of risk reduction. They also provided methods for calculating acceptable deviation from strict proportionality. Hammitt et al. (2019) used this approach for consistency checks in their study in a Chinese setting. We adopt the same approach with slight modification. We then compare the difference in WTP between the respondents who passed the simple consistency check (i.e., WTP for a smaller mortality risk reduction should be no greater than WTP for a greater mortality risk reduction) and the respondents who passed the more stringent consistency check (the one taking proportionality into consideration).

Second, our study covers a wider geographical scope. China is a vast country with notable disparities in social and economic development across regions (Fan et al., 2011). We deliberately chose seven cities covering major regions of China. In contrast, most of the existing studies focused on a single city (e.g., Yang et al., 2016; Huang et al., 2018), with a few exceptions which covered two or three cities/areas (e.g., Hammitt & Zhou, 2006; Ohdoko et al., 2013; Hoffmann et al., 2017). The aggregate of the seven cities can better represent China than those in previous studies, and each city can better represent the region where it is located.

Third, our study provides a VSL data point at the current developmental stage in China. China has experienced fast economic growth in the past few decades, while to our knowledge the latest VSL obtained in a Chinese setting was estimated based on surveys conducted in 2016 (Hammitt et al., 2019; Jin et al., 2020). The relationship between VSL and economic development level is knowingly nonlinear, and we provide a VSL data point at the current developmental stage in China, which is conductive to further generalization of the relationship between VSL and economic development level as more data are accumulated.

In addition, we intend to stimulate discussion on a few unsettled points, regarding the relationship between such variables as age, income, and experiences and VSL.

First, income is an important factor affecting VSL and is often the only factor considered in transferring VSL across contexts, especially from high-income settings to low- and middle-income settings (e.g., Brajer & Mead, 2004; Giannadaki et al., 2018; Zhang et al., 2020). Conventional economic models predict that VSL increases with income (Hammitt, 2017), which has received empirical support (Lindhjem et al., 2011; Viscusi & Masterman, 2017; Masterman & Viscusi, 2018). However, the size of income effects on VSL, which is often measured by using income elasticity of VSL, varies greatly in empirical studies. For example, Hammitt et al. (2019) reported that the income elasticity of VSL is about 3 by comparing the estimates of VSL based on data collected in 2005 and 2016 from Chengdu, a southwestern Chinese city. Hoffmann et al. (2017) reported that the income elasticity of VSL is 0.2-0.25 based on their data collected in 2006, while Wang and Mullahy (2006) reported that the income elasticity of VSL is 1.42 based on their data collected in 1998, in a Chinese setting. The choice of income elasticity can greatly affect the magnitude of estimated benefits when conducting benefit-cost analysis. In practice, analysts often assume an income elasticity of less than 1 when transferring VSL estimates among high-income countries, which is supported by empirical evidence (Hammitt & Robinson, 2011). When transferring VSL estimates from high-income countries to low- and middle-income countries, analysts often assume an income elasticity of 1, which scholars think should be larger than 1 (Robinson et al., 2019). Income elasticity of VSL may vary with income, as some scholars found that the income elasticity of VSL decreases as income increases (Kniesner et al., 2010). However, more empirical studies are needed to derive such a relationship. By using cross-sectional data collected in seven cities in 2022, we intend to provide information on income elasticity after the income level has significantly increased in China, and provide a new data point for deriving the relationship between income elasticity of VSL and income.

Second, whether and how to discount VSL based on age has been a controversial topic, as “senior discount” means that lives with different ages are treated differently. In most benefit-cost analysis, uniform VSL across age groups was used. However, if mortality risk reductions brought about by a policy are disproportionately distributed across age groups and VSL varies greatly with age, using uniform VSL can be misleading. However, there are a few exceptions, either using life-years-saved approaches or setting an age cut point and using a smaller VSL value for individuals with age greater than that cut point. In both situations, it is desirable to know how VSL varies with age, which has been a topic attracting much attention (e.g., Hammitt, 2007; Aldy & Viscusi, 2007; Krupnick, 2007). Unlike the relationship between VSL and income which is monotonically increasing, the relationship between VSL and age is complicated. Economic theory predicts that VSL may remain constant, increase or decrease with age, or VSL and age follows an inverted-U relationship over the life cycle (Hammitt, 2007). Empirical studies provided mixed results regarding the relationship between VSL and age, with negative, none, and positive relationships found in different studies (Aldy & Viscusi, 2007; Krupnick, 2007). Krupnick (2007) attempted to explain the observed differences in results across studies, and the preliminary finding is that whether mortality risk reductions are related to health might matter, and non-health related causes (e.g., vehicular accidents) are more likely to result in senior discount. Other scholars found that the relationship between VSL and age might vary with gender (Wang & He, 2014). We speculate that the distribution of mortality risks might matter, as mortality risk and age interact and people are willing to pay more for a larger magnitude of mortality risk reduction. Our study provides further empirical evidence on VSL-age relationship when mortality risks are disproportionately born by the elderly.
<table><caption>**Table 1**
Summary of VSL studies conducted in mainland China.</caption><thead><tr><th rowspan="2">Author (Date)</th><th rowspan="2">Survey location and data year</th><th rowspan="2">Sampling method and sample size</th><th rowspan="2">Intervention</th><th colspan="2">Consistency test<sup>b</sup></th><th rowspan="2">VSL estimated<sup>d</sup></th><th rowspan="2">Income elasticity of VSL</th><th rowspan="2">VSL-age relationship</th></tr><tr><th>Size of mortality risk reduction<sup>a</sup></th><th>Internal test</th><th>External test</th></tr></thead><tbody><tr><td>Our study</td><td>Beijing, Changchun, Chengdu, Guangzhou, Shanghai, Wuhan, Xi'an; 2022</td><td>Purposive sampling; 7000 in total</td><td>Medical treatment; COVID-19 control</td><td>2/10,000 and 4/10,000</td><td>Yes</td><td>Yes</td><td>7.98 million to 9.70 million Chinese Yuan (1.19 million to 1.45 million US$)<sup>2</sup></td><td>Decrease with income; 0.05</td><td>U-shape</td></tr><tr><td>Jin et al. (2020)</td><td>Beijing, 2016</td><td>Purposive sampling, 1107</td><td>Preventive measures (air pollution context)</td><td>5/100,000, 1/10,000, and 2/10,000</td><td>Yes<sup>c</sup></td><td>—</td><td>5.54 million Chinese Yuan</td><td>—</td><td>—</td></tr><tr><td>Hammitt et al. (2019)</td><td>Chengdu, 2005, 2016</td><td>Probabilistic; 671 and 1051</td><td>Preventive treatment</td><td>3/10,000 and 5/10,000</td><td>Yes</td><td>Yes</td><td>22,000 US$ in 2005 550,000 US$ in 2016</td><td>3</td><td>Negative</td></tr><tr><td>Huang et al. (2018)</td><td>Beijing; 2010</td><td>Convenient; 475</td><td>Air pollution control</td><td>2/1000, 1.5/1000 and 1/1000</td><td>Yes<sup>c</sup></td><td>—</td><td>774,000 US$</td><td>—</td><td>No relationship</td></tr><tr><td>Hoffmann et al. (2017)</td><td>Shanghai, Nanning, and Jiujiang; 2006</td><td>Probabilistic; 980, 337 and 344</td><td>Medical, preventive, or other measures</td><td>5/10,000 and 10/10,000</td><td>Yes</td><td>Yes</td><td>1.33-1.47 million Chinese Yuan</td><td>0.1-0.3</td><td>Negative</td></tr><tr><td>Yang et al. (2016)</td><td>Nanjing; 2014-2015</td><td>Convenient; 2857</td><td>Traffic safety improvement for motorists</td><td>3/100,000</td><td>—</td><td>—</td><td>7.18 million Chinese Yuan (1.13 million US$)</td><td>—</td><td>Inverted-U relationship</td></tr><tr><td>Wang and He (2014)</td><td>Danyang, Liupanshui, and Tianjin; 2000</td><td>Convenient; 1933 in total</td><td>Vaccine for preventing cancer</td><td>~9/10,000<sup>f</sup></td><td>—</td><td>Yes</td><td>481,000-814,000 Chinese Yuan (58,000-98,000 US$)</td><td>0.42</td><td>Mixed results</td></tr><tr><td>Ohdoko et al. (2013)</td><td>Jiujiang and Changsha; 2008</td><td>Convenient; 225 and 197</td><td>Air pollution control (cancer risk)</td><td>6/100,000 ~ 4/10,000</td><td>Yes<sup>c</sup></td><td>—</td><td>353,315 US$ (Jiujiang) and 466,598 US$ (Changsha)</td><td>—</td><td>—</td></tr><tr><td>Liu and Zhao (2013)</td><td>Dalian; 2011</td><td>Convenient; 213</td><td>Traffic safety improvement</td><td>3/100,000 ~ 15/100,000</td><td>Yes<sup>c</sup></td><td>—</td><td>550,000-883,000 Chinese Yuan</td><td>—</td><td>No relationship</td></tr><tr><td>Hammitt and Zhou (2006)</td><td>Beijing, Anqing, and rural areas near Anqing; 1999</td><td>Purposive + probabilistic; 1208, 1241, and 1282</td><td>Preventive treatment (air pollution related)</td><td>10/10,000 and 20/10,000</td><td>—</td><td>Yes</td><td>4000 US$ ~ 17,000 US $</td><td>0.16-0.2</td><td>Negative</td></tr><tr><td>Wang and Mullahy (2006)</td><td>Chongqing, 1998</td><td>Probabilistic; 500</td><td>Air pollution control</td><td>5/100,000</td><td>—</td><td>—</td><td>34,458 US$</td><td>1.42</td><td>Positive</td></tr></tbody></table>

<footnote><sup>a</sup>The size of mortality risk reduction is either directly obtained from the paper or derived based on the information provided in the paper.</footnote>

<footnote><sup>b</sup>This just shows studies which conducted consistency check. It doesn't show whether the studies passed the test.</footnote>

<footnote><sup>c</sup>These studies used choice experiment approaches which implicitly forced internal consistency.</footnote>

<footnote><sup>d</sup>These VSL values are directly copied from the original papers with some using US dollar and some using Chinese Yuan. Over the past two decades, the exchange rate of the two currencies is 1 US$ = 6-8 Chinese Yuan or so.</footnote>

<footnote><sup>e</sup>Mortality risk reduction was estimated based on real life data. It rounds to 9 in 10,000.</footnote>

<footnote>By assuming an exchange rate of 1 US$ = 6.7 Chinese Yuan.</footnote>
Third, the influence of collective experience, which is shared by residents in certain geographical areas, on VSL has received scant attention so far. If experience matters, then the monetarized benefits of ex ante measures and ex post measures for reducing the same magnitude of mortality risk may differ in size. However, there is a lack of studies probing its influence on willingness to pay (WTP) for reducing mortality risks. Several studies have examined the influence of experience on WTP for market goods and other non-market goods. On balance, the conclusion is that experience matters. For instance, Liu et al. (2019) found that farmers in flooded areas have a higher WTP for index insurance than those in non-flooded areas, while Donahaue et al. (2011) found that New Yorkers are more willing to pay for terrorism prevention and detection technologies and services. We intend to examine the influence of experience, which is of the collective type, on WTP for mortality risk reductions. In our context, the experience that we study is people's experience with COVID-19 and measures for coping with COVID-19.

The remainder of this paper is organized as such. Section 2 describes the methods that were used to elicit WTP for mortality risk reductions. Section 3 presents the results on descriptive statistics, VSL estimation, and the influencing factors on VSL. Sections 4 and 5 present discussions and conclusion.

## 2. Methodology

### 2.1. Survey instrument

The questions designed to elicit respondents' WTP for mortality risk reductions are included in a general survey which aims at probing public attitude and preference towards COVID-19 and the associated policies. In this section, we only describe the parts of the survey that are relevant to our study, the details of which can be found in Supplemental Materials S1.

The part for eliciting respondents' WTP for mortality risk reductions includes questions for testing their comprehension of probabilities and scenarios designed to elicit WTP for specific levels of mortality risk reductions.

The question for testing respondents' comprehension of probabilities is designed as such. Respondents were asked to choose from two statements the correct one. The two statements are “4 in 10,000 is less than 2 in 10,000.” and “The magnitude of reducing mortality risk from 4 in 10,000 to 0 is greater than the magnitude of reducing mortality risk from 4 in 10,000 to 2 in 10,000.”

The scenarios involved a specific drug that can reduce one's mortality risk due to COVID-19. In one scenario, the mortality risk is reduced from 4 in 10,000 to zero; in the other scenario, the mortality risk is reduced from 4 in 10,000 to 2 in 10,000. Respondents were asked to express their maximum WTP for this specific drug for each scenario, assuming that this cost won't be covered by their health insurance, and with full consideration of their budget constraints when answering. To aid in communicating the magnitude of mortality risk reductions, visual aids were used. Previous studies have shown that using visual aids can improve the validity of contingent valuation (Corso et al., 2001). Respondents were presented with a field of small rectangles (100 by 100), with the red fraction representing the probability of dying from COVID-19, and the blue fraction representing the magnitude of mortality risk reductions, as shown in Supplemental Materials S1.

Payment scale techniques were utilized in this study based on the evidence indicating that payment scale presentation is more effective than open-ended questions for eliciting WTP (Donaldson et al., 1997) and performs better than dichotomous choice in a Chinese setting as the latter suffers “excessive yea-saying, or anchoring bias” (Hoffmann et al., 2017). Specifically, respondents were asked to select from a matrix of 25 predetermined values the one that is the closest to their maximum WTP for a given level of mortality risk reduction. The values ranged from 0 to 6000 Chinese Yuan which were set based on the results of an exploratory study conducted in 2021,<fnref n="2" /> and respondents who expressed a WTP greater than the upper value of this range were asked to write down the exact amount of money that they were willing to pay in the blank provided.

Other relevant questions include those for measuring demographic variables (e.g., age and gender), socioeconomic variables (e.g., education, household income, marital status, and sector of work), and other variables like health status, exercise habits, vaccination status, knowledge, risk perception, experiences, risk preference, political identity, and trust. There variables are the ones plausibly affecting people's WTP for mortality risk reductions according to the findings in previous studies (see Krupnick et al., 2002; Alberini et al., 2004; Aldy & Viscusi, 2007; Krupnick, 2007; Robinson, 2007; Bellavance et al., 2009; Lindhjem et al., 2011; Cropper et al., 2011; Robinson & Hammitt, 2015; Hoffmann et al., 2017; Viscusi & Masterman, 2017; Masterman & Viscusi, 2018). As income, age, and experience are the variables that we are particularly interested in, we single them out here for more description. *Income* was measured by using household income. *Age* was measured by using birth year. *Experience* is a loose term that needs to be clearly defined before being used. In our study, we examined the influence of experiencing city lockdown on respondents' WTP. Besides experiencing city lockdown, we measured respondents' other experience as well, such as experiencing community lockdown, economic loss, and COVID-19 infection. In addition, we asked respondents to judge the level of disturbance caused by the various governmental measures for managing the risks associated with COVID-19. We also added *the length of city lockdown* as a control variable. It is worth stressing that in a few months after we administered our survey, China switched from zero-COVID policy to live-with-COVID policy and was swept by a wave of COVID-19. Examining the influence of experiencing this wave of infection on respondents' WTP might be informative. Unfortunately, our study is not able to answer this question.

Finally, we collected respondents' zodiac sign animal for quality check purpose. Each Chinese New Year has a corresponding zodiac sign animal, for instance, 2023 is the year of rabbit. By comparing an individual's answers to the questions on his or her birth

<footnote n="2">² In the exploratory study, open-ended questions were used to elicit respondents' WTP for reducing their chance of dying from COVID-19 from 3 in 10,000 to zero and from 3 in 10,000 to 6 in 100,000, respectively. A total of 614 valid responses were collected, with mean WTP (SD) for the two mortality risk reductions of 2030 (13,798) and 1418 (10,485) and median values (with 25th and 75th percentiles) of 450 (100, 1000) and 200 (100, 600). Details of the exploratory study can be found in the Supplemental Materials S2.</footnote>
year and zodiac sign animal, we tried to discern whether he or she took the survey seriously. Chinese zodiac is part of the everyday and social life of Chinese people (Denisenko & Xu, 2021) and everybody knows his/her own zodiac sign animal. Thus, it is an ideal one for designing attention check questions.

## 2.2. Sites selection

Our survey targeted residents of seven cities: Beijing, Changchun, Chengdu, Guangzhou, Shanghai, Wuhan, and Xi'an (Fig. 1), which are either the capitals of their home provinces or municipalities directly under the central government. These cities were selected mainly based on their geographical location, level of economic development, and experience related to COVID-19.

First, these cities are located at different regions in China. Each city is quite representative of the region where it is located. Second, the level of economic development of the seven cities is quite heterogeneous. Shanghai's GDP per capita in 2021 is 173.6³ thousand Chinese yuan, ranking the highest, while Changchun's GDP per capita in 2021 is 78.2⁴ thousand Chinese yuan, ranking the lowest. The income disparity between (and within) cities allows us to explore the income elasticity of VSL using different approaches. Third, since the outbreak of the pandemic, all these cities have experienced waves (of different sizes) of outbreaks in different periods of time before China's zero-COVID policy were lifted in December 2022. Residents in different cities have different experience in terms of the timing and severity of COVID-19 outbreaks and risk management strategies, which may lead to their different assessment of the corresponding mortality risk reductions (Barnett & Breakwell, 2001). With these cities chosen, we can investigate whether experience influences VSL.

## 2.3. Survey administration

We used a purposive sampling approach. To reduce response bias associated with this nonprobability sampling approach, we tried to include as many subjects as possible. We set a target of 7000 valid responses, 1000 in each city. By valid responses, we mean the responses passed attention check, which means that their answers to birth year and zodiac sign animal are consistent. In addition, as we are particularly interested in understanding how age affects individuals' WTP for reducing mortality risks, we included respondents across a broad range of age. In each city, we considered people in four age groups, namely, [18,30], (30, 40], (40,50], and over 50, with 250 respondents in each age group and genders of equal size. Within each age-gender group, subjects were randomly reached out.

Our survey was approved by the RUC-Econlab (Approval ID: RUCecon-202207-1) at a public ethics review. All methods were performed in accordance with the guidelines of the RUC-Econlab for studies with human subjects. Prior to collecting data, we pre-tested the survey using a Chinese online survey platform named Credamo<fnref n="5" /> with 300 respondents in Beijing and Shanghai (150 each) from 21 to 24 July, 2022. This pilot study focused on the wording of the WTP questions and the WTP range. After analyzing the data from the pilot study and refining the survey, we outsourced our data collection to Credamo. Before launching the survey, we did another round of pilot study by asking the survey company to collect 50 responses from Beijing on 18 August, 2022. After minor revisions in the wording, the final survey was fielded from 26 August to 17 September, 2022. The data collected from both pilot studies were not used for our empirical analysis.

Respondents received a direct transfer of 12–15 Chinese Yuan as a compensation after their responses were confirmed as valid ones. The criterion is as such: respondents' answers to their birth year and zodiac sign animal must be consistent.<fnref n="6" /> Older respondents were provided a slightly higher compensation. The median time for completing our survey was approximately 15 mins. Subjects in each of the seven cities were reached out<fnref n="7" /> until 1000 valid responses were obtained in each of them. A total of 60,000 requests were sent out to get the targeted 7000 valid responses, and 10,363 of them responded. Among those who responded, 3363 failed the “birth year and zodiac sign consistency check” which was considered as failed to pass attention check and excluded. The percentage of respondents who failed to pass the attention check is high. However, ours is not an exception. Scholars noticed rising rates of inattentiveness in online survey responses. This is because online surveys are mostly respondent-driven instead of interviewer-driven (Alvarez et al., 2019). Respondent-driven surveys are more prone to respondent disengagement.

## 2.4. Data analysis methods

VSL was estimated by dividing average WTP by mortality risk reduction. Using the payment card technique, respondents were asked to choose the value closest to their maximum WTP for a given level of mortality risk reduction from a set of options. We assume that the deviations in both directions (i.e., those whose maximum WTP is slightly greater than the chosen value and those whose maximum WTP is slightly smaller than the chosen value) are randomly distributed and thus cancel each other out.

<footnote n="3">³ Estimated based on the data released in Statistical Communiqué of Shanghai on the 2021 National Economic and Social Development.</footnote>

<footnote n="4">⁴ Estimated based on the data released in Statistical Communiqué of Changchun on the 2021 National Economic and Social Development.</footnote>

<footnote n="5">⁵ Credamo (https://www.credamo.com/#/) is a platform for conducting surveys and experiments. The total registered sample in Credamo is close to 3 million, covering all provincial administrative regions. More details on the demographic information of the subject pool can be found on its website.</footnote>

<footnote n="6">⁶ Respondents knew before filling out the survey that they would not be paid if their survey responses were not accepted. By “not accepted”, we mean their answers to the questions on birth year and zodiac sign animal are not consistent. The respondents did not know there is such a check. The answers of those who failed to pass this attention check were not kept in the final dataset.</footnote>

<footnote n="7">⁷ Subjects were reached out based on their registered address, and their IP address was used to validate whether they are indeed from the “registered address”.</footnote>
Factors affecting WTP are identified using ordinary least squares (OLS) regression. The dependent variable is WTP, and the set of independent variables includes demographic variables, socioeconomic variables, structural variables, knowledge on COVID-19, risk perception, personal behaviors (such as vaccination status and wearing face masks), experiences and trust. The OLS regression model is as follows:

$$ \log(WTP_i + 1) = \alpha + \beta \log(r) + \Gamma \chi + \varepsilon_i $$

where $WTP_i$ is individual i's WTP, $r$ represents the magnitude of risk reduction, $\chi$ is a vector of other independent variables and $\varepsilon_i$ is the error term. These variables were fed into the OLS regression models stepwise to test their impacts on WTP. Since the data were drawn from seven cities, standard errors were clustered at the city level to rectify the bias of standard errors caused by clustered dependence.

# 3. Results

## 3.1. Descriptive statistics

The full sample was screened before being used for analysis.<fnref n="8" /> In short, out of the 7000 responses, 6258 passed the probability comprehension test. After applying the simple consistency check that WTP for a smaller mortality risk reduction should not be greater than WTP for a greater mortality risk reduction, we were left with 5219 responses. Among them, 4995 met the positivity criterion, namely, $WTP > 0$. For the proportionality criterion that WTP should be approximately proportional to (but slightly less than) the magnitude of risk reduction, we chose the ratio of WTP for mortality risk reduction from 4 in 10,000 to zero ($WTP_2$) to WTP for mortality risk reduction from 4 in 10,000 to 2 in 10,000 ($WTP_1$) within the range of 1.5–2.0 (greater than or equal to 1.5 and less than 2.0). Among the 4995 responses which met the positivity criterion, 677 had their $WTP_2/WTP_1$ ratio within this range. The choice of the range of 1.5–2.0 is somewhat arbitrary. As a robust check, we conducted sensitivity analysis on this choice, as presented in the discussion section.

The descriptive statistics of demographic, socioeconomic, and structural variables of the full sample (N=7000), the subsample which passed the simple consistency check ($WTP_2 \ge WTP_1$) (N=5219), and the subsample which passed the stringent consistency check ($WTP_2 > 0$ and $WTP_1 > 0$, and $1.5 \le WTP_2/WTP_1 < 2.0$) (N=677) are shown in Table 2. Statistical tests<fnref n="9" /> show that the two samples which passed the consistency check with different stringency levels are comparable in all terms, and the three samples are comparable in terms of respondents' risk preference, political identity (being a CPC member), physical exercises, health status, and sector (field of work). However, the full sample and the sample which passed the simple consistency check differ in terms of age, marital status, and number of children, and the full sample and the sample which passed the stringent consistency check differ in terms of gender, income, education, and physical exercises.

Descriptive statistics of the covariates are shown in Table 3. These covariates include respondents' knowledge about COVID-19, vaccination status, face mask wearing, risk perception toward COVID-19, political identity, trust in government, and experience. As this survey was administered in August and September of 2022, which was before China switched from a zero-COVID policy to a live-with-COVID policy in December 2022 when China was swept by COVID-19, 90% of the respondents reported that they themselves, their relatives or their colleagues had not been infected with COVID-19 by then. Also, during the preriod when China implemented the zero-COVID-19 policy, China implemented lockdown at different spatial scopes depending on the severity of the situation. Most of the time, community-level lockdowns were implemented, which means that only the communities where the few conformed cases lived, worked, or visited were locked down while the rest of the city functioned normally. Only on occasions when community-level lockdowns were not able to contain the spread of the virus, district-level or even city-level lockdowns were implemented.

The descriptive statistics of demographic, socioeconomic, and perceptual, attitudinal, and behavioral variables by city are presented in Supplemental Materials S5. We compared the gender, age, and education of the respondents in each of the seven cities with information obtained from the Seventh National Population Census conducted in the 2020.<fnref n="11" /> In terms of gender, we find no significant difference between our respondents and the census results in all seven cities. Regarding age, we find no significant difference between our respondents and the census results in Beijing, Changchun and Wuhan, but the mean ages of the respondents in Chengdu, Guangzhou and Xi'an are significantly greater than the census results, and the mean age of the respondents in Shanghai is significantly less than the census result. As can be expected for online surveys, our respondents have higher educational levels than the census results. For other variables, we were not able to compare their values between our samples and the population either because the census did not measure them or because the variables were measured differently.

## 3.2. WTP and VSL

The distributions of WTP for the sub-sample which passed the simple consistency check (N=5219) and the sub-sample which passed the stringent consistency check (N=677) are highly skewed towards the lower end of the value set (see Figs. 2 and 3 for visual

<footnote n="8">⁸ The details can be found in Supplemental Materials S3.</footnote>

<footnote n="9">⁹ The details of the statistical tests are presented in Supplemental Materials S4.</footnote>

<footnote n="11">¹¹ The descriptive statistics of these variables in each of the seven cities can be found in Supplemental Materials S5.</footnote>
<table><caption>**Table 2**

Descriptive statistics of demographic, socioeconomic, and structural variables<sup>c</sup></caption><thead><tr><th rowspan="2"></th><th>Full sample<br>(N=7000)</th><th>Sample passing Simple<br>consistency check<br>(N=5219)</th><th>Smaple passing stringent<br>consistency check<br>(N=677)</th></tr><tr><th>n (%)</th><th>n (%)</th><th>n (%)</th></tr></thead><tbody><tr><td>Age(years)<sup>a</sup></td><td>39(12)</td><td>39(12)</td><td>39(12)</td></tr><tr><td>Female</td><td>3500 (50.0%)</td><td>2565 (49.1%)</td><td>314 (46.3%)</td></tr><tr><td>Married</td><td>5171 (73.9%)</td><td>3737 (71.6%)</td><td>495 (73.1%)</td></tr><tr><td>Income</td><td></td><td></td><td></td></tr><tr><td>≤ 20,000</td><td>344 (4.9%)</td><td>230 (4.4%)</td><td>18 (2.7%)</td></tr><tr><td>(20,000, 50,000)</td><td>616 (8.8%)</td><td>448 (8.6%)</td><td>52 (7.7%)</td></tr><tr><td>(50,000, 100,000)</td><td>1546 (22%)</td><td>1121 (21%)</td><td>124 (18%)</td></tr><tr><td>(100,000, 200,000)</td><td>2297 (33%)</td><td>1732 (33%)</td><td>234 (35%)</td></tr><tr><td>(200,000, 300,000)</td><td>1228 (18%)</td><td>902 (17%)</td><td>132 (19%)</td></tr><tr><td>(300,000, 500,000)</td><td>556 (7.9%)</td><td>445 (8.5%)</td><td>63 (9.3%)</td></tr><tr><td>(500,000, 800,000)</td><td>259 (3.7%)</td><td>219 (4.2%)</td><td>31 (4.6%)</td></tr><tr><td>> 800,000</td><td>139 (2.0%)</td><td>111 (2.1%)</td><td>21 (3.1%)</td></tr><tr><td>Other answers</td><td>15 (0.2%)</td><td>11 (0.2%)</td><td>2 (0.3%)</td></tr><tr><td>Education</td><td></td><td></td><td></td></tr><tr><td>≤ Primary school</td><td>30 (0.4%)</td><td>21 (0.4%)</td><td>2 (0.3%)</td></tr><tr><td>Middle to professional school</td><td>2283 (32.6%)</td><td>1625 (31.1%)</td><td>184 (27.2%)</td></tr><tr><td>≥ College</td><td>4687 (67.0%)</td><td>3573 (68.5%)</td><td>491 (72.5%)</td></tr><tr><td>Sector (field of work)<sup>b</sup></td><td></td><td></td><td></td></tr><tr><td>Agriculture, forestry, animal husbandry, or fishery</td><td>156 (2.2%)</td><td>123 (2.4%)</td><td>10 (1.5%)</td></tr><tr><td>Government agency or government-affiliated institution</td><td>1076 (15.4%)</td><td>785 (15.0%)</td><td>105 (15.5%)</td></tr><tr><td>Service industry</td><td>841 (12.0%)</td><td>587 (11.2%)</td><td>73 (10.8%)</td></tr><tr><td>Manufacturing, construction, or extractive industry</td><td>1529 (21.8%)</td><td>1140 (21.8%)</td><td>149 (22.0%)</td></tr><tr><td>Transportation, warehousing, or post and<br>telecommunication industry</td><td>418 (6.0%)</td><td>302 (5.8%)</td><td>47 (6.9%)</td></tr><tr><td>Wholesale and retail industry or catering industry</td><td>666 (9.5%)</td><td>502 (9.6%)</td><td>70 (10.3%)</td></tr><tr><td>Finance, insurance, or real estate industry</td><td>471 (6.7%)</td><td>349 (6.7%)</td><td>47 (6.9%)</td></tr><tr><td>Utilities sector</td><td>256 (3.7%)</td><td>182 (3.5%)</td><td>24 (3.5%)</td></tr><tr><td>Other sectors</td><td>432 (6.2%)</td><td>329 (6.3%)</td><td>41 (6.1%)</td></tr><tr><td>Unemployed or at school</td><td>1155 (16.5%)</td><td>920 (17.6%)</td><td>111 (16.4%)</td></tr><tr><td>Health status (Do you have chronic diseases like diabetes,<br>hypertension, or cardiovascular disease?)</td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1019 (14.6%)</td><td>751 (14.3%)</td><td>105 (15.5%)</td></tr><tr><td>No</td><td>5769 (82.4%)</td><td>4320 (82.8%)</td><td>554 (81.8%)</td></tr><tr><td>Don't know</td><td>212 (3.0%)</td><td>148 (2.8%)</td><td>18 (2.7%)</td></tr><tr><td>Exercise (hours/week)<sup>a</sup></td><td>2.7(1.1)</td><td>2.7(1.1)</td><td>2.8(1.1)</td></tr><tr><td>Number of children<sup>a</sup></td><td>1.0(0.8)</td><td>1.0(0.8)</td><td>1.0(0.8)</td></tr><tr><td>Political identity (being a CPC member)</td><td>1765 (25.2%)</td><td>1295 (24.8%)</td><td>164 (24.2%)</td></tr><tr><td>Risk preference<sup>a</sup> (To what extent are you willing to take risks?<br>"Not at all" = 0, "Very much" = 10, others in between)</td><td>5.9(2.6)</td><td>5.8(2.6)</td><td>5.7(2.6)</td></tr></tbody></table>

illustration).<fnref n="12" /> The average WTP and their standard deviations for the two mortality risk reductions are presented in Table 4, along with the estimated VSL based on the average WTP. VSL ranges from 7.98 million to 9.70 million Chinese Yuan based on the subsample which passed the simple stringency test. The average WTP and VSL at the city level are also presented in Table 4. On balance, respondents in comparatively developed megacities like Beijing, Shanghai and Guangzhou are willing to pay more and thus have higher VSL; while respondents in the second-tier cities like Changchun, Chengdu, Wuhan and Xi'an are willing to pay less and thus have lower VSL.

Although we made efforts to balance our sample with the main demographic characteristics of the overall population during the research process, it is challenging to avoid biases resulting from variations in internet accessibility as our study collected responses through an online platform. To mitigate this bias, we weighted the WTP using age and education proportions of the seven cities, as reported in the Seventh National Census conducted in 2020. We used an OLS regression to model WTP on these two variables and predicted WTP. Then, we weighted the predicted WTP using the corresponding proportions derived from the population census data. Our analysis shows that, for the most part,

<footnote><sup>a</sup> Mean (SD)</footnote>

<footnote><sup>b</sup> The choice list for the variable *sector* is designed by fully considering the COVID-19 exposure risk of employers in these sectors.</footnote>

<footnote><sup>c</sup> Questions for measuring these variables can be found in Supplemental Materials S1.</footnote>

<footnote n="12">¹² Descriptive statistical analysis shows that the median, 25th and 75th percentiles of WTP for the sub-sample of size N=5219 are 800, 200 and 2000 for the mortality risk reduction of 2 in 10,000, and 1500, 600 and 4000 for the mortality risk reduction of 4 in 10,000. The median, 25th and 75th percentiles of WTP for the sub-sample of size N=677 are 1500, 600 and 3000 for the mortality risk reduction of 2 in 10,000, and 2500, 1000 and 5000 for the mortality risk reduction of 4 in 10,000.</footnote>
<table><caption>**Table 3**

Descriptive statistics of the covariates.</caption><thead><tr><th>Variables</th><th>Simple consistency check<br>(N = 5219)<br>n (%)</th></tr></thead><tbody><tr><td><strong>Experience.city.lockdown</strong> (Have you experienced city lockdown during the pandemic?)</td><td></td></tr><tr><td>Yes</td><td>3884 (74.4%)</td></tr><tr><td>No</td><td>1335 (25.6%)</td></tr><tr><td><strong>Experience community.lockdown</strong> (Have you experienced community lockdown during the pandemic?)</td><td></td></tr><tr><td>Yes</td><td>3973 (76.1%)</td></tr><tr><td>No</td><td>1246 (23.9%)</td></tr><tr><td><strong>Experience covid.infection</strong> (Have you, your relatives, or colleagues ever been infected with COVID-19?)</td><td></td></tr><tr><td>Yes</td><td>540 (10.3%)</td></tr><tr><td>No</td><td>4679 (89.7%)</td></tr><tr><td><strong>Experience-economic loss</strong> (Have you ever experienced severe economic loss (such as business closure or job loss, among others) during the pandemic?)</td><td></td></tr><tr><td>Yes</td><td>2479 (47.5%)</td></tr><tr><td>No</td><td>2740 (52.5%)</td></tr><tr><td><strong>General_covid.experience<sup>a</sup></strong> (To what extent have you been disturbed by the measures taken to cope with the pandemic? ("Not at all" = 1, "Very much" = 9, others in between))</td><td>5.4(2.2)</td></tr><tr><td><strong>Covid_knowledge_1</strong> (Do you agree with the statement that "The number of deaths due to COVID-19 is much higher than that due to flu"?)</td><td></td></tr><tr><td>Yes</td><td>3427 (65.7%)</td></tr><tr><td>No</td><td>968 (18.5%)</td></tr><tr><td>Don't know</td><td>824 (15.8%)</td></tr><tr><td><strong>Covid_knowledge_2</strong> (Do you agree with the statement that "Those who are older and have underlying medical conditions are at increased risk of severe outcomes of COVID-19 and death"?)</td><td></td></tr><tr><td>Yes</td><td>4804 (92.0%)</td></tr><tr><td>No</td><td>230 (4.4%)</td></tr><tr><td>Don't know</td><td>185 (3.6%)</td></tr><tr><td><strong>Covid_knowledge_3</strong> (Do you agree with the statement that "The probability of being reinfected with COVID-19 is much lower for those who had been infected with the virus, and even though being reinfected, the symptoms would be much milder"?)</td><td></td></tr><tr><td>Yes</td><td>2132 (40.9%)</td></tr><tr><td>No</td><td>1625 (31.1%)</td></tr><tr><td>Don't know</td><td>1462 (28.0%)</td></tr><tr><td><strong>Covid_knowledge_4</strong> (Do you agree with the statement that "Being infected with COVID-19 causes long-term health effects"?)</td><td></td></tr><tr><td>Yes</td><td>3534 (67.7%)</td></tr><tr><td>No</td><td>588 (11.3%)</td></tr><tr><td>Don't know</td><td>1097 (21.0%)</td></tr><tr><td><strong>Vaccination_status</strong> (Have you got vaccinated?)</td><td></td></tr><tr><td>No</td><td>69 (1.3%)</td></tr><tr><td>First shot done</td><td>37 (0.7%)</td></tr><tr><td>Second shot done</td><td>309 (5.9%)</td></tr><tr><td>Third shot planned</td><td>448 (8.6%)</td></tr><tr><td>Third shot done</td><td>4356 (83.5%)</td></tr><tr><td><strong>Wearing_mask</strong> (On what occasions do you wear face masks?)</td><td></td></tr><tr><td>Always wear face mask when being out</td><td>2241 (42.9%)</td></tr><tr><td>Always wear face mask when being out, but forget to wear it occasionally</td><td>2074 (39.7%)</td></tr><tr><td>Only wear face mask in crowded places</td><td>610 (11.7%)</td></tr><tr><td>Do not wear face mask unless being required to do so</td><td>283 (5.4%)</td></tr><tr><td>Others</td><td>11 (0.2%)</td></tr><tr><td><strong>Perceived_risk<sup>a</sup></strong> (To what extent are you worried about the risk of dying of COVID-19 or being severely sick after having COVID-19? ("Not at all" = 1, "Very much" = 5, others in between))</td><td>3.4(1.2)</td></tr><tr><td><strong>Trust<sup>a</sup></strong> (To what extent do you agree with the statement that "I trust in the capability of the government in public management"? ("Not at all" = 1, "Very much" = 5, others in between))</td><td>4.3(0.9)</td></tr></tbody></table>

the weighted and unweighted estimates for WTP (and thus VSL) are similar in each of the cities. However, in Guangzhou, there appears to be some overestimation of WTP in the unweighted analysis compared to the weighted one.

### 3.3. Factors influencing VSL

We examine the factors influencing VSL with stepwise regressions. Before running the regression models, we pooled the data of the two risk reduction scenarios (from 4 in 10,000 to zero and from 4 in 10,000 to 2 in 10,000) together. Mortality risk reduction (mortality_risk_reduction) became a variable. We also rescaled age by subtracting the mean from it and then dividing this difference by its standard deviation. We used rescaled age and its quadratic term in the regression models. From models1~6 in Table 5, besides mortality risk reduction, we gradually added demographic variables, socioeconomic variables, behavioral variables, structural

<footnote>a Mean(SD)</footnote>
<figure><img src="image_8.png" /><figcaption>Fig. 2. Distributions of WTP for (a) those who passed simple consistency check (N=5219), and for (b) those who passed stringent consistency check (N=677).</figcaption></figure>

variables, perceptual and attitudinal variables, and experience variables. The regression results are visually depicted in Fig. 4 which reveals that a set of demographic, socioeconomic, perceptual and attitudinal, and experiential variables are significant predictors of WTP for mortality risk reductions.

First, the magnitude of mortality risk reduction is positively associated with WTP across the models, which implies that re-
spondents were willing to pay more for larger risk reduction. This provides additional evidence on the validity of our research design
with a stated-preference approach.

Second, regarding age, income, and experience which we are particularly interested in, the results are as such. Age is not a
significant predictor of WTP, but the age-squared term is a significant predictor which implies that age is associated with WTP in a U-
shaped form. People who are at the age of around 40 (the sample mean) have the lowest WTP, other things being equal. WTP
generally increases as income increases, as expected. Income is an ordinal variable in our study, the reference level of income is less
than 20,000 RMB annually. Respondents whose income are above this level are willingness to more. However, those with the highest
<figure><img src="image_9.png" /><figcaption>Fig. 3. The distributions of WTP in the seven cities.</figcaption></figure>

<table><caption>**Table 4**
WTP and VSL at the aggregate and city level (Chinese Yuan).</caption><thead><tr><th></th><th></th><th>WTP<sub>2</sub> (for mortality risk reduction of 4/10,000)</th><th>WTP<sub>1</sub> (for mortality risk reduction of 2/10,000)</th><th>VSL estimate based on WTP<sub>2</sub> (million)</th><th>VSL estimate based on WTP<sub>1</sub> (million)</th></tr></thead><tbody><tr><td>Sub-sample (Stringent consistency check) (N = 677)<sup>a</sup></td><td></td><td>3333(4081)</td><td>2053(2472)</td><td>8.3</td><td>10.3</td></tr><tr><td>Sub-sample (simple consistency check) (N = 5219)<sup>a</sup></td><td></td><td>3192(15,231)</td><td>1939(14,197)</td><td>8.0</td><td>9.7</td></tr><tr><td rowspan="2">Beijing (N = 752)</td><td>Unweighted<sup>a</sup></td><td>3325(6397)</td><td>1869(3562)</td><td>8.3</td><td>9.4</td></tr><tr><td>Weighted</td><td>3068</td><td>1885</td><td>7.7</td><td>9.4</td></tr><tr><td rowspan="2">Changchun (N = 765)</td><td>Unweighted<sup>a</sup></td><td>2688(4313)</td><td>1662(2816)</td><td>6.7</td><td>8.3</td></tr><tr><td>Weighted</td><td>2904</td><td>1797</td><td>7.3</td><td>9.0</td></tr><tr><td rowspan="2">Chengdu (N = 762)</td><td>Unweighted<sup>a</sup></td><td>2854(4634)</td><td>1661(2438)</td><td>7.1</td><td>8.3</td></tr><tr><td>Weighted</td><td>2887</td><td>1783</td><td>7.2</td><td>8.9</td></tr><tr><td rowspan="2">Guangzhou (N = 743)</td><td>Unweighted<sup>a</sup></td><td>4331(37,112)</td><td>3181(36,908)</td><td>10.8</td><td>15.9</td></tr><tr><td>Weighted</td><td>2971</td><td>1836</td><td>7.4</td><td>9.2</td></tr><tr><td rowspan="2">Shanghai (N = 720)</td><td>Unweighted<sup>a</sup></td><td>3245(8502)</td><td>1709(2846)</td><td>8.1</td><td>8.6</td></tr><tr><td>Weighted</td><td>3006</td><td>1857</td><td>7.5</td><td>9.3</td></tr><tr><td rowspan="2">Wuhan (N = 737)</td><td>Unweighted<sup>a</sup></td><td>3113(8367)</td><td>1699(2400)</td><td>7.8</td><td>8.5</td></tr><tr><td>Weighted</td><td>3011</td><td>1851</td><td>7.5</td><td>9.3</td></tr><tr><td rowspan="2">Xi'an (N = 740)</td><td>Unweighted<sup>a</sup></td><td>2813(5501)</td><td>1799(3578)</td><td>7.0</td><td>9.0</td></tr><tr><td>Weighted</td><td>2950</td><td>1824</td><td>7.4</td><td>9.1</td></tr></tbody></table>

income level have no significant difference from the reference group in terms of WTP. Experiencing city lockdown is a negative predictor of WTP. Those who reported a higher level of being disturbed by the measures for coping with COVID-19 are willing to pay less.

Third, among other variables, respondents with an educational level of college degree or above are willing to pay more than those with a primary school educational level or below, while those with a middle or vocational school educational level show no difference from the reference level in terms of WTP. Respondents in the transportation, warehousing, or post and telecommunication industry are willing to pay more than those in the agriculture, forestry, animal husbandry, or fishery sector. Those in the finance, insurance, or real estate industry, who typically have higher income, also expressed higher WTP. Those who are in worse health status, exercise more frequently, receive more doses of vaccines, or wear face masks more often, are willing to pay more. Political identity also matters, and CPC members are willing to pay more. Those who perceive COVID-19 as posing greater mortality risk than influenza and those who perceive a higher level of risk in general are willing to pay more. Trust in the government is also positively associated with WTP.

<footnote>a Mean(SD)</footnote>
<table><caption>**Table 5**
Factors influencing WTP.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Mortality-risk-reduction</td><td>1.145<sup>***</sup><br>(0.028)</td><td>1.099<sup>***</sup><br>(0.022)</td><td>1.099<sup>***</sup><br>(0.022)</td><td>1.099<sup>***</sup><br>(0.022)</td><td>1.099<sup>***</sup><br>(0.022)</td><td>1.099<sup>***</sup><br>(0.022)</td></tr><tr><td>Gender (F = 1)</td><td>-0.071<sup>+</sup><br>(0.041)</td><td>-0.038<br>(0.041)</td><td>-0.004<br>(0.047)</td><td>-0.008<br>(0.049)</td><td>-0.001<br>(0.047)</td><td>-0.014<br>(0.044)</td></tr><tr><td>Age</td><td>0.008<br>(0.042)</td><td>-0.032<br>(0.053)</td><td>-0.032<br>(0.052)</td><td>-0.021<br>(0.050)</td><td>-0.009<br>(0.044)</td><td>-0.015<br>(0.040)</td></tr><tr><td>Ageüst</td><td>0.023<br>(0.023)</td><td>0.075<sup>**</sup><br>(0.026)</td><td>0.073<sup>**</sup><br>(0.028)</td><td>0.069<sup>*</sup><br>(0.028)</td><td>0.055<sup>**</sup><br>(0.021)</td><td>0.051<sup>*</sup><br>(0.020)</td></tr><tr><td>Marital_status (Married = 1)</td><td>0.485<sup>***</sup><br>(0.077)</td><td>0.303<sup>***</sup><br>(0.088)</td><td>0.262<sup>***</sup><br>(0.074)</td><td>0.231<sup>**</sup><br>(0.073)</td><td>0.198<sup>*</sup><br>(0.085)</td><td>0.198<sup>*</sup><br>(0.085)</td></tr><tr><td>Numer_of_Children</td><td>0.127<sup>*</sup><br>(0.064)</td><td>0.121<sup>*</sup><br>(0.059)</td><td>0.082<br>(0.051)</td><td>0.079<sup>+</sup><br>(0.047)</td><td>0.042<br>(0.048)</td><td>0.043 (0.043)</td></tr><tr><td>Education (Middle to vocational school)</td><td>0.445<br>(0.297)</td><td>0.373<br>(0.240)</td><td>0.356<br>(0.231)</td><td>0.280<br>(0.188)</td><td>0.260 (0.162)</td><td>0.260 (0.162)</td></tr><tr><td>Education (≥ College)</td><td>0.425<sup>+</sup><br>(0.251)</td><td>0.369<sup>+</sup><br>(0.210)</td><td>0.364<sup>+</sup><br>(0.196)</td><td>0.268<sup>+</sup><br>(0.142)</td><td>0.264<sup>*</sup><br>(0.119)</td><td>0.264<sup>*</sup><br>(0.119)</td></tr><tr><td>Income {(20,000, 50,000)}</td><td>0.033<br>(0.312)</td><td>0.042<br>(0.313)</td><td>0.011<br>(0.318)</td><td>0.085<br>(0.258)</td><td>0.094 (0.247)</td><td>0.094 (0.247)</td></tr><tr><td>Income {(50,000, 100,000)}</td><td>0.167<br>(0.289)</td><td>0.127<br>(0.305)</td><td>0.104<br>(0.312)</td><td>0.242<br>(0.261)</td><td>0.243 (0.248)</td><td>0.243 (0.248)</td></tr><tr><td>Income {(100,000, 200,000)}</td><td>0.384<br>(0.305)</td><td>0.306<br>(0.317)</td><td>0.282<br>(0.322)</td><td>0.435<br>(0.277)</td><td>0.433<sup>*</sup><br>(0.262)</td><td>0.433<sup>*</sup><br>(0.262)</td></tr><tr><td>Income {(200,000, 300,000)}</td><td>0.488<sup>*</sup><br>(0.296)</td><td>0.397<br>(0.301)</td><td>0.379<br>(0.319)</td><td>0.471<sup>*</sup><br>(0.272)</td><td>0.451<sup>*</sup><br>(0.264)</td><td>0.451<sup>*</sup><br>(0.264)</td></tr><tr><td>Income {(300,000, 500,000)}</td><td>0.649<sup>*</sup><br>(0.267)</td><td>0.525<sup>*</sup><br>(0.274)</td><td>0.553<sup>*</sup><br>(0.273)</td><td>0.567<sup>*</sup><br>(0.251)</td><td>0.539<sup>*</sup><br>(0.242)</td><td>0.539<sup>*</sup><br>(0.242)</td></tr><tr><td>Income {(500,000, 800,000)}</td><td>0.446<br>(0.506)</td><td>0.339<br>(0.525)</td><td>0.370<br>(0.500)</td><td>0.341<br>(0.458)</td><td>0.396 (0.435)</td><td>0.396 (0.435)</td></tr><tr><td>Income { > 800,000}</td><td>0.421<br>(0.386)</td><td>0.388<br>(0.346)</td><td>0.329<br>(0.351)</td><td>0.343<br>(0.301)</td><td>0.369 (0.323)</td><td>0.369 (0.323)</td></tr><tr><td>Income (Other answers)</td><td>-0.057<br>(0.510)</td><td>0.001<br>(0.584)</td><td>-0.085<br>(0.540)</td><td>0.157<br>(0.451)</td><td>0.083 (0.544)</td><td>0.083 (0.544)</td></tr><tr><td>Sector (Government agency or government- affiliated institutions)</td><td>0.097<br>(0.070)</td><td>0.126<br>(0.089)</td><td>0.152<sup>+</sup><br>(0.087)</td><td>0.123<br>(0.112)</td><td>0.132 (0.119)</td><td>0.132 (0.119)</td></tr><tr><td>Sector (Service industry)</td><td>-0.102<br>(0.130)</td><td>-0.067<br>(0.132)</td><td>-0.025<br>(0.139)</td><td>-0.008<br>(0.161)</td><td>0.007 (0.156)</td><td>0.007 (0.156)</td></tr><tr><td>Sector (Manufacturing industry, construction industry, or extractive industry)</td><td>0.122<br>(0.086)</td><td>0.138<sup>*</sup><br>(0.069)</td><td>0.133<sup>+</sup><br>(0.070)</td><td>0.125<br>(0.089)</td><td>0.126 (0.089)</td><td>0.126 (0.089)</td></tr><tr><td>Sector (Transportation, warehousing, or post and telecommunication industry)</td><td>0.469<sup>***</sup><br>(0.072)</td><td>0.468<sup>***</sup><br>(0.104)</td><td>0.464<sup>***</sup><br>(0.115)</td><td>0.494<sup>***</sup><br>(0.120)</td><td>0.515<sup>***</sup><br>(0.125)</td><td>0.515<sup>***</sup><br>(0.125)</td></tr><tr><td>Sector (Wholesael and retail industry or catering industry)</td><td>0.087<br>(0.074)</td><td>0.158<sup>*</sup><br>(0.073)</td><td>0.156<sup>*</sup><br>(0.071)</td><td>0.164<sup>*</sup><br>(0.071)</td><td>0.175<sup>*</sup><br>(0.075)</td><td>0.175<sup>*</sup><br>(0.075)</td></tr><tr><td>Sector (Finance, insurance or real estate industry)</td><td>0.063<br>(0.128)</td><td>0.089<br>(0.121)</td><td>0.092<br>(0.116)</td><td>0.191<sup>+</sup><br>(0.099)</td><td>0.211<sup>*</sup><br>(0.101)</td><td>0.211<sup>*</sup><br>(0.101)</td></tr><tr><td>Sector (Utilities sector)</td><td>0.343<sup>**</sup><br>(0.127)</td><td>0.383<sup>**</sup><br>(0.107)</td><td>0.397<sup>***</sup><br>(0.107)</td><td>0.328<sup>**</sup><br>(0.105)</td><td>0.323<sup>**</sup><br>(0.114)</td><td>0.323<sup>**</sup><br>(0.114)</td></tr><tr><td>Sector (Other sectors)</td><td>-0.262<sup>+</sup><br>(0.145)</td><td>-0.230<sup>+</sup><br>(0.134)</td><td>-0.184<br>(0.148)</td><td>-0.102<br>(0.165)</td><td>-0.106<br>(0.162)</td><td>-0.106<br>(0.162)</td></tr><tr><td>Vaccination_status</td><td>0.293<sup>**</sup><br>(0.058)</td><td>0.257<sup>***</sup><br>(0.053)</td><td>0.148<sup>**</sup><br>(0.047)</td><td>0.148<sup>**</sup><br>(0.047)</td><td>0.141<sup>**</sup><br>(0.047)</td><td>0.141<sup>**</sup><br>(0.047)</td></tr><tr><td>Wearing_mask</td><td>0.027<sup>**</sup><br>(0.009)</td><td>0.026<sup>**</sup><br>(0.009)</td><td>0.023<sup>**</sup><br>(0.008)</td><td>0.023<sup>**</sup><br>(0.008)</td><td>0.021<sup>**</sup><br>(0.008)</td><td>0.021<sup>**</sup><br>(0.008)</td></tr><tr><td>Health_status</td><td>0.177<sup>*</sup><br>(0.073)</td><td>0.146<sup>*</sup><br>(0.071)</td><td>0.120<sup>*</sup><br>(0.072)</td><td>0.135<sup>*</sup><br>(0.074)</td><td>0.135<sup>*</sup><br>(0.074)</td><td>0.135<sup>*</sup><br>(0.074)</td></tr><tr><td>Exercise</td><td>0.143<sup>***</sup><br>(0.012)</td><td>0.133<sup>***</sup><br>(0.015)</td><td>0.105<sup>***</sup><br>(0.017)</td><td>0.100<sup>***</sup><br>(0.018)</td><td>0.100<sup>***</sup><br>(0.018)</td><td>0.100<sup>***</sup><br>(0.018)</td></tr><tr><td>Covid_knowledge_1</td><td></td><td>0.463<sup>***</sup><br>(0.070)</td><td>0.308<sup>***</sup><br>(0.065)</td><td></td><td>0.298<sup>***</sup><br>(0.065)</td><td>0.298<sup>***</sup><br>(0.065)</td></tr><tr><td>Covid_knowledge_2</td><td></td><td>0.121<br>(0.074)</td><td>0.125<br>(0.076)</td><td></td><td>0.134<sup>*</sup><br>(0.077)</td><td>0.134<sup>*</sup><br>(0.077)</td></tr><tr><td>Covid_knowledge_3</td><td></td><td>0.004<br>(0.073)</td><td>0.034<br>(0.063)</td><td></td><td>0.057 (0.059)</td><td>0.057 (0.059)</td></tr><tr><td>Covid_knowledge_4</td><td></td><td>0.124<sup>***</sup><br>(0.037)</td><td>0.009<br>(0.062)</td><td></td><td>0.020 (0.065)</td><td>0.020 (0.065)</td></tr><tr><td>Perceived_risk</td><td></td><td></td><td>0.198<sup>***</sup><br>(0.019)</td><td></td><td>0.216<sup>***</sup><br>(0.024)</td><td>0.216<sup>***</sup><br>(0.024)</td></tr><tr><td>Riskpreference</td><td></td><td></td><td>0.056<sup>***</sup><br>(0.015)</td><td></td><td>0.061<sup>***</sup><br>(0.016)</td><td>0.061<sup>***</sup><br>(0.016)</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 5 (continued)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Trust</td><td></td><td></td><td></td><td></td><td>0.332<sup>***</sup><br>(0.043)</td><td>0.294<sup>***</sup><br>(0.042)</td></tr><tr><td>Being CPC member</td><td></td><td></td><td></td><td></td><td>0.147<sup>*</sup><br>(0.080)</td><td>0.142<sup>*</sup><br>(0.079)</td></tr><tr><td>Experience economic loss</td><td></td><td></td><td></td><td></td><td></td><td>-0.007<br>(0.038)</td></tr><tr><td>Experience covid infection</td><td></td><td></td><td></td><td></td><td></td><td>0.030 (0.075)</td></tr><tr><td>Experience community lockdown</td><td></td><td></td><td></td><td></td><td></td><td>0.120 (0.101)</td></tr><tr><td>Experience city lockdown</td><td></td><td></td><td></td><td></td><td></td><td>-0.215<sup>***</sup><br>(0.034)</td></tr><tr><td>Length of city lockdown</td><td></td><td></td><td></td><td></td><td></td><td>0.002<sup>*</sup><br>(0.001)</td></tr><tr><td>General covid experience</td><td></td><td></td><td></td><td></td><td></td><td>-0.050<sup>**</sup><br>(0.016)</td></tr><tr><td>Num.Obs.</td><td>10438</td><td>8598</td><td>8598</td><td>8598</td><td>8592</td><td>8592</td></tr><tr><td>R<sup>2</sup></td><td>0.073</td><td>0.074</td><td>0.099</td><td>0.116</td><td>0.167</td><td>0.172</td></tr><tr><td>R<sup>2</sup> Adj.</td><td>0.073</td><td>0.071</td><td>0.095</td><td>0.112</td><td>0.163</td><td>0.168</td></tr><tr><td>RMSE</td><td>1.77</td><td>1.76</td><td>1.73</td><td>1.72</td><td>1.67</td><td>1.66</td></tr><tr><td>Std.Errors</td><td>by: city</td><td>by: city</td><td>by: city</td><td>by: city</td><td>by: city</td><td>by: city</td></tr></tbody></table>

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

# 4. Discussions

In this study, we estimated both VSL across the seven cities in China and VSL for each city, and identified factors influencing VSL. Several interesting results are obtained and some of them need to be further discussed.

## 4.1. On the estimated VSL

We used a stated-preference approach to elicit respondents' willingness to pay for mortality risk reductions in the context of COVID-19. This approach is commonly used to estimate the value of non-market goods. It allows for tailoring scenarios in terms of the cause and size of the risks to be reduced. A major concern with this approach is its hypothetical nature, and respondents may not take it as seriously as in real settings or they may not accept the scenario (Cropper et al., 2011). This is an inherent issue in using stated-preference approaches. Scholars tried to use various approaches to examine whether respondents understood and accepted the choices provided, and used this as an indicator of the validity and reliability of the research design. In our study, we used probability comprehension test and internal consistency tests to screen respondents.

We used a purposive sampling approach and administered our questionnaire online. The representativeness of samples obtained with such an approach cannot compare with that obtained with probability sampling approaches. We tried to improve the representativeness of our sample with a few measures. We specified the requirement on the distribution of gender and age for our sample, but randomly reached out subjects in each age-gender group. We tried to have a large sample size and have the respondents' demographic characteristics as diversified as possible, as suggested by scholars on this topic (e.g., Guo & Hussey, 2004; Althubaiti, 2023). We also did weighted analysis on WTP based on the age and education proportions of the seven cities as reported in the Seventh National Census conducted in 2020. Besides, we tried to examine the extent to which our sample represents the population by comparing the demographic characteristics of our sample with those of the population obtained with census data. Among those characteristics which we have population-level data, our sample is representative in terms of age and gender, but overrepresents the educational level. Nevertheless, it's one of the limitations of our study that other characteristics of our sample may not be representative of the population.

As is typical in online survey studies, our samples tend to overrepresent individuals with a higher education level, which is generally associated with higher income and greater willingness to pay for mortality risk reductions. As a result, our study may have overestimated the value of statistical life (VSL). However, our analysis shows that the weighted and unweighted estimates for WTP (and thus VSL) are generally similar across all cities, except for Guangzhou, where the unweighted analysis seems to overestimate VSL compared to the weighted analysis.

Unlike most studies, we used more stringent consistency check to screen our sample in addition to the simple consistency check. Specifically, we required that WTP must be positive and approximately proportional (but slightly less than) the magnitude of risk reduction. Among previous studies conducted in China, only Hammitt et al. (2019) adopted a stringent consistency check. In their study, the authors found that VSL estimated from WTP which failed to pass the proportionality test were biased downward by a factor of two to three. However, in our study, we did not find such bias, even after performing sensitivity analysis on the proportionality setting (Fig. 5). Notably, VSL estimated based on WTP that passed the proportionality test are only slightly higher than those estimated based on WTP that passed the simple consistency check.

VSL estimated based on the overall sub-sample which passed either the simple or the stringent consistency check ranges from 8.0 million to 10.3 million Chinese Yuan. Comparing the VSL obtained in this study with those obtained in earlier studies, we found ours
<figure><img src="image_10.png" /><figcaption>Fig. 4. Regression coefficients and 95% confidence intervals.</figcaption></figure>

to be generally much higher (see Table 1). Given the fact that from 1998 to 2022, China's nominal GDP increased from 8520 billion to 121,021 billion Chinese Yuan,<fnref n="13" /> the increase in VSL is not exceptional. The VSL based on a survey conducted in Chongqing (a city geographically close to Chengdu) in 1998 is 34,458 US$ which is equivalent to 286,001 Chinese Yuan<fnref n="14" /> (Wang & Mullahy, 2006). A rough estimation based on our analysis shows that the ratio of economic size in 1998 and 2022 is 14, while the ratio of VSL in the same period ranges from 28 to 34.

<footnote n="13">¹³ Data are available in Supplemental Material S6.</footnote>

<footnote n="14">¹⁴ By assuming an exchange rate of 1 US$ = 8.3 Chinese Yuan in 1998.</footnote>
<figure><img src="image_11.png" /><figcaption>Fig. 5. Sensitivity analysis on the proportionality setting.</figcaption></figure>

## 4.2. On factors influencing VSL

Does the VSL-age relationship show certain pattern when mortality risks are unevenly distributed across age groups? We hypothesized that the distribution of mortality risks might matter and that people may be willing to pay more for reducing more unevenly distributed risks, reflecting a preference for equity (e.g., Fehr and Schmidt 1997). To answer this question, we need to accumulate evidence on the VSL-age relationship in such settings. In this study, our intention is to provide one preliminarily example. Against the background that COVID-19 poses greater risks to senior people, we found that VSL-age shows U-shaped pattern (Fig. 6).

We tentatively anazlyed the relationship between income elasticity of VSL and income level within the income range of middle-income countries. For the within-sample estimation, we first extracted the income coefficients from the regression model and then generated predicted WTP values based on these coefficients while holding other covariates at their mean values (Fig. 7). The results indicate that WTP increases as income increases. However, as income is measured as an ordinal variable, it is not possible to directly analyze the marginal contribution of income to WTP. To enable better comparison, we sequentially computed the average contrasts while holding other variables at their observed values and averaging unit-level estimates at different income levels. The results show that WTP does increase as income grows, but at a threshold of about 100,000 to 200,000 versus 50,000 to 100,000, the increase begins to slow down (Fig. 8). Based on the regression coefficients, we found that income elasticity decreases as income increases, which is consistent with previous findings (Kniesner, 2010). This finding is in line with theoretical predictions, which suggest that WTP is positively correlated with people's income.

For the between sub-samples estimation, we used the weighted average WTP and GDP per capita of each city (as city-average disposable income data are only available in a few cities) by assuming that the variation of WTP across cities are solely the result of differences in economic development in these cities. We estimated income elasticity by pair-wising cities, using the equation $\textit{elasticity} = (VSL_{city1}/VSL_{city2} - 1)/(Income_{city1}/Income_{city2} - 1)$. The average income elasticity estimated by using VSL and GDP per capita among all the cities is 0.05 with a range of -0.19-0.36. The average income elasticity estimated by using VSL and GDP per capita among the first-tier cities (Beijing, Shanghai and Guangzhou) is 0.18. The average income elasticity estimated by using VSL and GDP per capita among the second-tier cities (Changchun, Chengdu, Wuhan, and Xi'an) is 0.04. The average income elasticity by comparing the VSL and GDP between the first-tier cities and the second-tier cities is 0.03.

We also found that those who experienced city lockdowns or have a stronger feeling of being disturbed by governmental measures for coping with COVID-19 are willing to pay less. The plausible reasons are as such. First, lockdown may impact respondents' income and thus made them be willing to pay less. Second lockdown may make people underestimate the mortality risk associated with COVID-19, and thus made them be willing to pay less.

In previous studies, besides income and age, a number of other factors pertaining to the characteristics of individuals have been examined in terms of their effects on VSL. These factors include but not limited to gender, race, education, health status, family health
<figure><img src="image_12.png" /><figcaption>Fig. 6. The relationship between age and predicted WTP.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 7. Income and predicted WTP (logtransformed).</figcaption></figure>

history, health insurance, hospital admissions, and union affiliation (e.g., Krupnick et al., 2002; Viscusi & Aldy, 2003; Alberini et al., 2004; Cropper et al., 2011; Robinson & Hammitt, 2015; Hoffmann et al., 2017). The relationships between these factors and VSL are mostly inconclusive.

In our study, we identified a set of factors that statistically significantly influence VSL, including sector (field of work), political identity, health status, risk perception, behaviors (physical exercises, wearing face masks, getting vaccinated), knowledge, trust, and experiences. Particularly, we found that those in the agriculture, forestry, animal husbandry, or fishery sector and those in the finance, insurance, or real estate industry have similar WTP, while those in the transportation, warehousing, or post and telecommunication industry are willing to pay more. This may be because those working in the transportation, warehousing, or post and telecommunication industry have greater risk exposure and are more vulnerable to COVID control measures, while those in the finance, insurance, or real estate industry can work from home and are therefore less susceptible to these factors.
<figure><img src="image_14.png" /><figcaption>Fig. 8. Income and average contrasts of predicted WTP (logtransformed).</figcaption></figure>

**5. Conclusion**

We conducted a survey of the general population in seven Chinese cities in 2022 to estimate respondents' value of statistical life (VSL) based on their willingness to pay (WTP) for mortality risk reduction due to COVID-19. In addition, we investigated the factors that influence VSL. Our analysis of survey data from respondents who passed a simple consistency check yielded an overall estimated value of statistical life (VSL) ranging from 8.0 million to 9.7 million Chinese Yuan, which is equivalent to 1.2 million to 1.5 million US dollars.

The VSL estimated for the seven cities show a clear pattern of two groups. Respondents in the first-tier cities including Beijing (7.7–9.4 million), Shanghai (7.5–9.3 million) and Guangzhou (7.4–9.2 million) have higher VSL than those in the second-tier cities including Changchun (7.3–9.0 million), Chengdu (7.2–8.9 million), Wuhan (7.5–9.3 million) and Xi'an (7.4–9.1 million). The results are in alignment with the level of economic development. The VSL estimated in each of the cities can serve as the base value for the regions where they are located.

We also found a set of factors that significantly influence VSL, including income, education, sector (field of work), political identity, health status, risk perception, behaviors (physical exercise, wearing face masks, getting vaccinated), knowledge, trust in government, and experience. Specifically, the collective experience of city lockdown is negatively associated with respondents' WTP for mortality risk reduction due to COVID-19 and thus their VSL. The influence of age on VSL is overshadowed by those of income and education, although the VSL-age relationship shows a U-shaped pattern.

Finally, when more stringent consistency checks (positivity and proportionality) were applied, the estimated VSL (8.0 million to 9.7 million Chinese Yuan) was found to be comparable to that (8.3 million to 10.3 million Chinese Yuan) obtained when a simple consistency check (i.e., WTP for a larger mortality risk reduction is no less than WTP for a smaller mortality risk reduction) was applied. This is different from previous findings where the VSL obtained after a stringent consistency check was much higher (Hammitt et al., 2019).

Looking into the future, more work needs to be done. Although we tried to estimate VSL in a middle-income country with rigorous design, we did not use a probability sampling strategy. The educational level of our respondents is higher than the societal average. We may overestimate respondents' VSL. If more resources are secured, we would conduct a similar study with a probability sampling approach. Also, in many contexts like the COVID-19 one, mortality risk is just part of the risks. Morbidity risk and the impairment of life quality are all non-negligible. More valuation research is needed on this.

**CRediT authorship contribution statement**

**Jiakun Zheng:** Writing – review & editing, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.
**Jianhua Xu:** Writing – review & editing, Writing – original draft, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.
**Shiwei Fan:** Writing – review & editing, Visualization, Validation, Software, Methodology, Funding acquisition, Data curation. All authors made equal contribution to this study.
Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.

The first author is an Associate Editor for Risk Sciences and was not involved in the editorial review or the decision to publish this
article.

Acknowledgements

This study is supported by Beijing Social Science Research Foundation (18JDGLA009), the Bill & Melinda Gates Foundation (Project INV006261), China's National Social Science Foundation (23AZD061), and the Research Funds of Renmin University of China (Grant No. 22XNF006 and 22XNC001). Zheng gratefully acknowledges financial support from the French government under the "France 2030" investment plan managed by the French National Research Agency (reference: ANR-17-EURE-0020) and from Excellence Initiative of Aix-Marseille University-A*MIDEX. Zheng would also like to acknowledge the funding of the National Natural Science Foundation of China (Grant no. 72303232).

**Appendix A. Supporting information**

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.risk.2024.100007.

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