metadata: authors: 0: author: Bussolo,Maurizio
1: author: Sarma,Nayantara
2: author: Torre,Ivan
count: World
docna: 0: docna: Indirect Effects of COVID-19
            Nonpharmaceutical Interventions on Vaccine Acceptance
docty: Policy Research Working Paper
bdmdt: 2022-06-24T00:00:00Z
abstracts: cdata!: The information set from which
            individuals make their decision on vaccination includes
            signals from trusted agents, such as governments, community
            leaders, and the media. By implementing restrictions, or by
            relaxing them, governments can provide a signal about the
            underlying risk of the pandemic and indirectly affect
            vaccination take-up. Rather than focusing on measures
            specifically designed to increase vaccine acceptance, this
            paper studies how governments&#x2019; nonpharmaceutical
            policy responses to the pandemic can modify the degree of
            preventive health behavior, including vaccination. To do so,
            the paper uses repeated waves of a global survey on COVID-19
            beliefs, behaviors, and norms covering 67 countries from
            August 2020 to February 2021. Controlling for the usual
            determinants, the analysis explores how
            individuals&#x2019; willingness to get vaccinated is
            affected by changes in government restriction measures (as
            measured by the Oxford Stringency Index). This relationship
            is mediated by individual characteristics, social norms
            (social pressure to conform with what most people do), and
            trust in government institutions. The results point to a
            complex picture as the implementation of restrictions is
            associated with increased acceptance in some contexts and
            decreased acceptance in others. The stringency of government
            restrictions has significant positive correlations with
            vaccine acceptance in contexts of weak social norms of
            vaccine acceptance and lower trust in government. In
            countries or communities where social norms are tighter and
            trust in government health authorities is high, vaccine
            acceptance is high but less sensitive to changes in
            policies. These results suggest that the indirect effect of
            government policy stringency is stronger among individuals
            who report lower trust and weaker social norms of vaccine acceptance.
display_title: Indirect Effects of COVID-19
            Nonpharmaceutical Interventions on Vaccine Acceptance
closedt: 2022-06-24T00:00:00Z
listing_relative_url: /research/2022/06/33851599/indirect-effects-covid-19-nonpharmaceutical-interventions-vaccine-acceptance
topicv3: Social Analysis m1326036 744,Health Promotion and Disease Prevention
            m1326336 1458,Governance Indicators m1327488 834,Immunizations m1327505 1459,Public Health m1327519 635
publishtoextweb_dt: 2022-06-24T00:00:00Z
dois: 10.1596/1813-9450-10106
author_keys: 0: author_key: 669228
1: author_key: 1812473
2: author_key: 1521219
model_extractions: Policy Research Working Paper 10106 Indirect Effects of COVID-19 Nonpharmaceutical Interventions on Vaccine Acceptance _Maurizio Bussolo Nayantara Sarma Iván Torre_ South Asia Region & Europe and Central Asia Region June 2022
Policy Research Working Paper 10106

# **Abstract**

The information set from which individuals make their decision on vaccination includes signals from trusted agents, such as governments, community leaders, and the media. By implementing restrictions, or by relaxing them, governments can provide a signal about the underlying risk of the pandemic and indirectly affect vaccination take-up. Rather than focusing on measures specifically designed to increase vaccine acceptance, this paper studies how governments’ nonpharmaceutical policy responses to the pandemic can modify the degree of preventive health behavior, including vaccination. To do so, the paper uses repeated waves of a global survey on COVID-19 beliefs, behaviors, and norms covering 67 countries from August 2020 to February 2021. Controlling for the usual determinants, the analysis explores how individuals’ willingness to get vaccinated is affected by changes in government restriction measures (as measured by the Oxford Stringency Index). This relationship is mediated by individual characteristics, social norms (social pressure to conform with what most people do), and trust in government institutions. The results point to a complex picture as the implementation of restrictions is associated with increased acceptance in some contexts and decreased acceptance in others. The stringency of government restrictions has significant positive correlations with vaccine acceptance in contexts of weak social norms of vaccine acceptance and lower trust in government. In countries or communities where social norms are tighter and trust in government health authorities is high, vaccine acceptance is high but less sensitive to changes in policies. These results suggest that the indirect effect of government policy stringency is stronger among individuals who report lower trust and weaker social norms of vaccine acceptance.

This paper is a product of the Office of the Chief Economist, South Asia Region and the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, nsarma@worldbank.org, and itorre@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Indirect Effects of COVID-19 Nonpharmaceutical Interventions on Vaccine Acceptance**

Maurizio Bussolo, Nayantara Sarma and Iván Torre*

# **Declaration of Interest:** none

**Keywords:** COVID-19 **,** pandemic, vaccine hesitancy, vaccine acceptance, non-pharmacetutical interventions, trust, norms

**JEL:** I12,I18

*The authors are at the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. We thank Holly Krambeck, Gabriel Stefanini Vicente and the World Bank, the MIT, and Facebook-META teams of the development data partnership (https://datapartnership.org/) for making the data for this study available to us, and Adanna Chukwuma and Tania Dmytraczenko for useful comments.
# **1. Introduction**

How can countries vaccinate their populations against COVID-19? This question has become of paramount importance as governments across the world roll out vaccination campaigns to combat COVID-19. Vaccine hesitancy, or the “reluctance to get vaccinated despite availability” can undermine this effort.<sup>1</sup> Even with highly effective vaccines, the level of vaccine take-up in the population has to be very high for the pandemic to be contained and avoid a collapse of the health system. Understanding the drivers of vaccine acceptance, and the role that government action plays in it, is thus crucial to reduce the health and economic costs of the pandemic and accelerate its resolution.

The COVID-19 pandemic has resulted in 336 million cases and 5.56 million deaths (as of January 20, 2022). Economies across the world experienced sharp contractions linked to lockdowns to contain COVID-19 (World Bank GEP, 2021). Considering their economic toll, prolonged lockdowns are no longer considered a sustainable strategy to combat the virus. Governments are, instead, turning to the multiple and recently developed vaccines to provide a more effective solution. Ramped up production has eased supply constraints, particularly in some developed countries. However, vaccination programs are different from most other forms of health care, and demand-side factors play a substantial role in take-up. Household demand for preventive care is weaker than for curative health care and, often, the financial interests of the providers are also weaker (Gauri and Khaleghian, 2002).

The primary research aim of this paper is to examine the relationship between vaccine acceptance and governments’ non-pharmaceutical policies to curb the spread of the virus, such as lockdowns and other government restrictions. These policies provide information signals about the virus and the risks it poses. However, the interpretation of the signals is mediated by individual and social factors. Controlling for the usual determinants of vaccine acceptance – such as education, age, other individual characteristics, as well as country level prevalence and mortality rates – we focus on the role of social norms, trust in authorities, and individuals’ experience of the pandemic in mediating the link between the stringency of governments’ pandemic restrictions and vaccine acceptance.

We find that individual characteristics like higher education and age are, predictably, positively correlated with vaccine acceptance. Individual perception of risk based on personal knowledge of a COVID-positive case plays a significantly strong role in increasing vaccine acceptance, significantly more than the objective COVID-19 death rate prevailing in the country. Our main specification finds that a unit increase in the stringency of government policies is associated with a 0.4 percentage point increase in vaccine acceptance. In other terms, an increase from the 25<sup>th</sup> percentile to the 75<sup>th</sup> percentile of the sample distribution of policy stringency is associated with a 7.8 percentage point increase in vaccine acceptance, a magnitude larger than the difference in vaccine acceptance between individuals with primary education and those with tertiary education, and about the same size as the effect of knowing a positive case of COVID-19.

We also find that the efficacy of signals conveyed by the government’s non-pharmaceutical interventions is influenced by the prevailing social norms and the level of trust in the government. The correlations between policy

> 1 The term vaccine hesitancy abstracts from challenges in _accessing_ vaccines which is a serious concern in many countries. Our usage of the term is strictly a negation of vaccine acceptance while assuming availability and individual access to vaccines. Among international organizations, including the World Health Organization, the term acceptance is preferred as hesitancy can lead to blaming, so in the reminder of the paper we will use vaccine acceptance.

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stringency and vaccine acceptance are significantly stronger in societies with weaker social norms about vaccine acceptance, i.e., in societies where individuals do not feel social pressure to get vaccinated due to their perception that the majority of their peers (or other relevant reference group) will not accept a vaccine. These stronger correlations are also found where trust in government is low. Lockdowns and stringent restrictions plausibly convey a stronger signal of COVID-19 risk in these societies, rather than in societies with tighter social norms and with populations trusting their government more. Higher vaccine acceptance in these societies tends to pre-exist the implementation of stringent non-pharmaceutical interventions. These findings are in line with evidence from other studies (Blair et al., 2022; Lazarus et al., 2021; Jabar et al., 2021; Mannan and Farhana, 2021; Kerr et al., 2021).

Our paper contributes to three branches of the literature on vaccine uptake and adoption of other preventive health measures. Firstly, a large body of work exists on individual drivers or correlates of vaccine take-up. In developing countries, these studies mainly focus on child immunization rates and find that mothers’ characteristics such as education and household socio-economic status are significantly correlated to the probability of immunization (Devasenapathy et al., 2016). Education continues to play a role for adult vaccination, along with acceptance of vaccines for other diseases. Maurer et al. (2009) show that the intention to vaccinate against the novel H1N1 virus is strongly associated with uptake of seasonal influenza vaccinations in the US. The association between individual characteristics and vaccine take-up is often non-monotonic. For example, in a study conducted in Indonesia, illiterate women and women with secondary education are more likely to get their children vaccinated than women with primary education (Streatfield, Singarimbun, and Diamond, 1990). The authors argue that this is possibly due to higher social compliance with local authorities among women with no education, and greater awareness of the protective function of vaccines among women with higher education.

In addition to their socio-economic characteristics, individuals’ _beliefs and experiences_ are linked with their probability of being vaccinated. Perceptions of the risk of infection influence the decision to get vaccinated. Using data collected at the onset of the pandemic in the United States, Bundorf et al. (2020) find that individual beliefs about COVID-19 risks were related to actual preventive behavior. Those who considered themselves to be at high risk to infection significantly reduced activities which may expose them to the virus. Personal past experiences of disease positively affect the likelihood of getting vaccinated in the future. Jin and Koch (2018) show that this “learning by suffering” effect works both ways and find a negative impact on vaccination rate for individuals who got sick in the previous year despite having taken the shot.

The second strand of research that our paper contributes to deals with socio-cultural factors that influence individual health behavior. Social norms have increasingly been shown to play a role in vaccine acceptance and other preventive health measures (Moehring et al., 2021; Allen et al. 2021).<sup>2</sup> The direction of the effect is, however, dependent on the context. Social norms can have a conformity or peer effect such that individuals align themselves with others in adopting or rejecting preventive health measures (Goldberg et al., 2021). On the other hand, due to the positive externalities associated with vaccines, norms can generate a free-riding effect so that individuals benefit from others’ immunity without incurring the associated costs themselves. Using a study in Mozambique, Allen et al. (2021) show that the dominating effect depends on the level of local infection rates.

> 2 Norms can be descriptive (what people actually do) or injunctive (what people approve of) (Bicchieri, 2016 ; Benabou and Tirole, 2011).

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When COVID-19 infection rates are low, individuals tend to free-ride and reduce protective measures but as infection increases, the perceived risk pushes people to conform and take greater precautions by social distancing. The dominance of free-riding or pro-social behavior has also been explained by long-standing cultural values. Gelfand et al. (2021) suggest that countries with greater “cultural tightness”, i.e., stricter adherence to norms and punishments for deviance are better able to control the pandemic.

As our empirical analysis will show, the levels of vaccine acceptance in a society are not static. Since _individual_ uptake of vaccines depends on _external_ factors like access to information, perception of risk and social norms, there is a vital role for policy to “nudge” or influence individual behavior.<sup>3</sup> The third body of work that this paper contributes to is on effective policy action. The urgency and severity of the pandemic has spurred several studies focusing on informational interventions about the risks of exposure to COVID-19, effectiveness of preventive measures like masks (Abaluck et al., 2021), social distancing and vaccines, and updating people about prevailing social norms (Moehring et al., 2021). Interventions in the form of mandates or lockdowns also convey information about the threat of the virus.

Glaeser et al. (2020) find that when local governments lift mobility restrictions, individuals interpret this as a signal that the underlying risk of contracting COVID-19 is low and therefore indulge in more risky behavior – for instance, visiting an indoor restaurant. If the information signal is inaccurately interpreted – that is, if the level of virus circulation is not as low as individuals expect it to be – the lifting of restrictions can result in increased COVID-19 infections. Further, government mandates serve to reduce negative externalities arising from the relationship between subjective risk perceptions and preventive behaviors (Bundorf et al. 2021). To the point that demand for vaccination can also be related to perceived risks of infection (Betsch et al., 2015), if government action alters these risk perceptions, it has also the potential to affect the take up of COVID-19 vaccines – a topic which, so far, remains mostly unexplored. Recent evidence on the increase in interest in vaccination following the United States’ CDC recommendation for vaccinated users not to wear a face mask<sup>4</sup> suggests that government action has indeed a role to play.

Apart from government mandates and mobility restrictions, other policy interventions which have seen some success in increasing vaccine take-up include behavioral nudges such as reminders to get vaccinated (Dai, et al., 2021), messaging campaigns about its benefits and social acceptance (Argote at al., 2021) and tackling misinformation (Loomba et al., 2021). According to evidence in Sweden however, monetary incentives to get vaccinated have more bite than information campaigns (Campos-Mercade et al., 2021). If we consider that estimates of the value of a cure for COVID-19 range between 5-15 percent of total global wealth (Acharya, 2021), the use of monetary incentives or behavioral nudges may well be cost-effective (Costa-Font et al., 2021).

The rest of this paper is organized as follows. Section 2 describes the data we use to explore the relationship between vaccine acceptance, government stringency and other mediating factors. Section 3 provides some

> 3 'Joints for Jabs': As part of its strategy to vaccinate more of its population, Washington State will allow adults to claim a free marijuana joint when they receive a COVID-19 vaccination shot (NYTimes June7, 2021).

> 4 On the afternoon of May 13, 2021, the director of the US CDC announced the new face mask guidelines for vaccinated individuals, which recommended using face masks only in a limited set of circumstances. The website Vaccines.gov, which provides information on the vaccination campaign in the United States, saw its traffic increase to record levels in the hours following the announcement (https://www.cnn.com/2021/05/27/health/vaccination-interest-cdc-maskguidance/index.html).

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# Vaccine acceptance

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in the United States, on average individuals thought that around 55 people out of 100 in their community would accept a vaccine in November 2020. This figure increased to around 65 people out of 100 by March 2021. In Brazil the increase in believed vaccine acceptance moved from slightly below 70 people out of 100 in the community in November 2020 to around 75 people out of 100 in March 2021. In India, a small but consistent decrease in believed vaccine acceptance in the community was observed during the same period – from slightly above 70 people out of 100 to around 65 people out of 100. The fact that individual vaccine acceptance shows greater variation over time than social norms, which tend to be _stickier_ , suggests that factors other than social norms may be explaining the time variation in vaccine acceptance. Cross-country differences in vaccine acceptance, however, could potentially be explained by differences in norms.

The main time variant factor which this paper is interested in is government policy. When correlating the evolution of vaccine acceptance and government policy response – as measured by the Oxford Government Stringency Index, different patterns emerge. In countries like Brazil, the evolution of vaccine acceptance mirrors the evolution of government restrictions: the more stringent measures are, the higher the level of vaccine acceptance (Figure 3, panel a). In other countries, like Pakistan, vaccine acceptance and the stringency of government response appear to be uncorrelated over time (Figure 3, panel b). Lastly, in some other countries like Argentina (panel c) or Turkey (panel d), it is a particular government action -the start of the vaccination campaign- which appears to increase vaccine acceptance.

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Note: vertical dashed line marks the start of the vaccination campaign
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country and the timing of the vaccination campaign should nevertheless provide meaningful estimates of the association between policy and vaccine take-up.

Equation (1) is a reduced form equation derived from our analytical framework. The data at hand allows us to explore the role played by factors absent in a reduced form setting – namely, the role played by risk perceptions and _modifying factors_ such as norms and attitudes. To this end, our empirical analysis will also look into the interaction of the main independent variables -like government policies or individual characteristics- with risk perceptions, norms and attitudes.

# **5. Results**

# **5.a The role of government policies and individual characteristics**

In order to capture country level factors, our sample is restricted to the 18 countries where the survey was carried out repeatedly, at the pace of one country-level wave per month. The variables on trust and social norms were only included in the survey starting in October 2020, therefore we restrict our sample to the period October 2020 – March 2021. These variables are nevertheless missing for about 60% of individuals interviewed during the sample period. To assess the effect of the missingness in these variables, in Table 2 we present the estimation of the baseline equation excluding trust and social norms in both the whole sample (column 1) and in the sample of observations with non-missing trust and social norms variables (column 2). For all the variables, the magnitude of the coefficients and the statistical significance are very similar between both estimations, suggesting that the missingness of trust and social norms variables is orthogonal to the statistical relationship under analysis. In the next subsections, we will describe the results using the sample which only includes observations with non-missing information on individual trust and social norms.

# _Individual characteristics and vaccine acceptance_

The results in the baseline specification (Table 2, column 2) indicate that higher vaccine acceptance appears to be associated to being male, on average 8 percentage points more willing to take a vaccine if offered than women; being of older age, those 20 to 40 years old are on average 10 percentage points less willing to take a vaccine than those 80 years and older; and having tertiary education, around 3 percentage points more willing on average than having primary or secondary education. The correlates with age and gender match the mortality risk of COVID-19, a fact that suggests that risk perceptions appear to be a significant factor driving individual vaccine acceptance. In the case of education, the available evidence on socio-economic factors behind COVID-19 mortality risk suggests that low skill individuals tend to face a higher mortality due to the circumstances they live in and exposure to contagion through their occupations (Hawkins, Charles and Mehaffey, 2020). This positive correlation with education levels – higher vaccine acceptance for more educated people – does not seem to reflect risk perceptions, as more educated people tend to have lower COVID-19 related mortality but, rather, may be related to differences in information and knowledge about the pandemic and the vaccines. Lastly, individuals’ assessment of their own health does not appear to be correlated with increased or decreased acceptance.

# _Epidemiological situation_

Personally knowing someone who tested positive for COVID-19 is strongly associated with increased vaccine acceptance: those that know a positive case are on average 8 percentage points more willing to take a vaccine if 11
offered than those that do not know one. However, the country level death rate in the week before the individual was interviewed bears no correlation with vaccine acceptance. This suggests the importance of the information set the individual uses to assess the risk of COVID-19 and to decide about vaccination. Rather than the overall epidemiological situation, it is the immediate experience with the pandemic that is associated with increased willingness to get vaccinated.

# _Government policy response_

The stringency of government policy response to the pandemic appears to be positively associated with vaccine acceptance: the more stringent the measures are, the higher the willingness of individuals to take the vaccine. Each additional unit in the Oxford Stringency Index is associated to a 0.4 percentage point higher vaccine acceptance. Moving from percentile 25 in the distribution of the index within our sample (a value of 55.09) to percentile 75 (a value of 74.54) would be associated to a 7.8 percentage point increase in individual vaccine acceptance, a magnitude similar to that of personally knowing a positive COVID-19 case. Such a change in lockdown policies, however, would not be associated with an immediate increase in vaccine acceptance. The nature of the data at hand - including the exact day when the survey was carried out for each individual – allows us to assess the effect of actual _changes_ in the stringency index, which is also measured daily. Columns 3, 4 and 5 of Table 2 include as additional regressors the 1-day change, 7-day change and 14-day change of the stringency index respectively. The negative coefficients associated to the change variables indicate that when government policy changes, the effect on vaccine acceptance is not immediate. In particular, the coefficients associated to the 7-day and 14-day changes suggest that during that period the overall effect of an increase of one unit in the stringency index is 0.2 and 0.3 percentage points respectively – lower than the 0.4 percentage point result in the longer term. In any case, column 6 suggests that there are diminishing marginal effects of stringency on vaccine acceptance as its squared term is negative and significant. Further, prolonged and very stringent measures seem to have an adverse effect on vaccine acceptance. Moving from the 25<sup>th</sup> percentile in the distribution of days above a stringency score of 80 (equal to 0 days in our estimation sample) to the 75<sup>th</sup> percentile (equal to 80 days, incidentally) is associated with a 9.6 percentage point _decrease_ in individual vaccine acceptance.<sup>9</sup> This phenomenon, or diminishing effect of non-pharmaceutical interventions, is witnessed also with respect to actual spread of the disease as well and has been termed as _lockdown fatigue_ in other work (Goldstein, Levy Yeyati and Sartorio, 2021).

An important aspect of government policy response to the pandemic that is not captured by the stringency index -which is focused on non-pharmaceutical interventions- is the deployment of a vaccination campaign. The first country to launch a COVID-19 vaccination campaign was the United Kingdom on December 8, 2020. In the weeks that followed, several countries -including the 18 countries in our sample- started their vaccination campaigns. The different specifications of Table 2 all show that the presence of an active vaccination campaign is associated with an increase in vaccine acceptance of around 9 percentage points, a magnitude slightly higher than the effect of personally knowing a COVID-19 positive case or the effect of an increase in the stringency index from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution.

> 9 This is the product of the number of days implied by moving from the 25th percentile to the 75th percentile (80 days) times the coefficient associated to the effect of the number of days at high stringency on vaccine acceptance (-0.00123): 80 X - 0.00123 = -0.0984.

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# **5.b The role of risk perceptions**

Individuals form their risk perceptions about the pandemic using the information set available to them and their individual circumstances, like their age, gender or education. As our analytical framework indicates, these risk perceptions, mediated by other factors such as trust and social norms, drive vaccine behavior. While it is out of the scope of this paper to provide a detailed analysis of the formation of risk perceptions, the data allows to have a preliminary look at the relationship between vaccine acceptance and risk perceptions about COVID-19 infection. The survey we use includes a question on the likelihood that a person of the same age and in the same community as the respondent becomes sick from COVID-19. We create a dummy variable that indicates whether a respondent stated that this event was “very” or “extremely likely”. We call this variable the “perception of high risk of infection”. Columns 1 and 2 of Table 3 look at the correlations between this variable and individual characteristics and beliefs. Men, people living in rural areas, and younger individuals have all a perception of a lower risk of becoming sick from COVID-19. People in good health perceive their risk to be low as well. Interestingly, individual beliefs appear to be associated to risk perceptions in an unusual direction: individuals who have higher trust in government health authorities and those who believe that most individuals in their community will accept the vaccine also report _higher_ infection risk perceptions (Table 3, Column 2). While this correlational analysis does not allow to establish the exact causal nature of this relationship, it suggests that individuals who do not trust health authorities and who do not believe that the community in which they live will accept the vaccine may systematically underestimate the actual spread of the disease and the severity of the pandemic. Misinformation could be a potential explanation, as Roozenbeek et al. (2020) find that individuals who distrust scientists are more susceptible to misinformation about the pandemic.

When introduced as a separate, independent regressor in equation (1), this “perception of high risk of infection” variable is associated with a 9 percentage points higher probability of accepting the vaccine (Table 3, column 3). The coefficients associated to the stringency index and the personal knowledge of a positive COVID-19 case in that same specification remain significant but decrease slightly in magnitude. A similar pattern is observed if risk perceptions are interacted with the stringency index (Table 3, column 4).

These results confirm the relevance of risk perceptions as highlighted by our analytical framework. Government policies and individual experience of the pandemic have an effect on vaccine acceptance through risk perceptions. However, the fact that both variables still have a considerable explanatory power suggests that this particular measure of risk perception does not capture fully the risk perceptions that individuals have in mind. Other dimensions of risks beyond individual infection may be driving vaccine acceptance.

# **5.c Trust and social descriptive norms**

Information and individual circumstances form risk perceptions, which are in turn mediated by trust and social norms as individuals decide on accepting the vaccine or not. The different specifications of Table 4 explore the role that mediating factors such as social descriptive norms (the number of people in the community that the individual believes are going to get the vaccine) or trust in government health authorities play in individual vaccine acceptance. Column 1 presents a simple specification in which both variables are included as independent regressors. Both are strongly and positively correlated with vaccine acceptance. The more individuals believe vaccine acceptance to be the prevalent social norm in their community, the higher their own willingness to get the vaccine. For every additional percentage point of believed acceptance in the community, individual vaccine 13
acceptance increases by 0.6 percentage point. Moving from percentile 25 in the distribution of believed vaccine acceptance in the community (which represents a value of about 50 persons out of 100 accepting the vaccine in the community) to percentile 75 (a value of about 87 persons out of 100) is associated with a 22 percentage points increase in individual vaccine acceptance.

Similarly, trust in government health authorities is associated with, on average, 12 percentage points higher vaccine acceptance. It is noteworthy the fact that the effect of age on the degree of vaccine acceptance is diminished when social norms and trust are included as explanatory variables – the magnitude of the partial correlation coefficients associated to the different age group dummies is almost halved when comparing Table 2, column 2 to Table 4, column 1, and in some cases, they are no longer statistically significant. This suggests that a substantial part of the difference in vaccine acceptance across age groups can be attributed to differences in beliefs across age.

The conceptual framework we use suggests, however, that beliefs such as trust and social descriptive norms are not independent factors in the vaccine acceptance decision process, but they rather act as “filters” of the effects brought by differences in information or personal and social characteristics. Columns 2 and 5 in Table 4 present the results of an alternative specification in which trust in health authorities and beliefs on vaccine acceptance in the community are treated as interacting factors of government stringency. These interaction models do not show results that are qualitatively different from the specification in Column 1, except for the effect associated to trust in health authorities which is no longer statistically significant. Columns 3-4 and 6-7 present an alternative analysis by which the sample is split in two groups for each variable – individuals with low trust in government health authorities (column 3) or high trust in government health authorities (column 4); and individuals whose social context is one of loose social norms about vaccination (i.e. individuals believing that 50 or fewer people out of 100 in the community would take the vaccine, column 6) or those experiencing stringent social norms (i.e. believing that 50 or more out of 100 in the community would take the vaccine, column 7).

The results of the subsample analyses show that the effects of government stringency and the personal acquaintance with a positive COVID-19 case on vaccine acceptance are of lower magnitude among those who have high trust in government health authorities compared to those with low trust, while the average level of vaccine acceptance is substantially higher (columns 3-4). Individuals who do not trust health authorities appear to be more susceptible to change their minds with respect to vaccine acceptance when governments implement more strict non-pharmaceutical interventions or when they personally know someone who got infected. The age effect on vaccine acceptance -by which younger individuals are less willing to accept the vaccine than older onesand the education effect -by which more educated individuals are more willing to take the vaccine- are only statistically significant among those that distrust government health authorities. In this sense, trust in government health authorities seems to be a powerful mediating factor: changes in the information set of individuals, either triggered by government policies or by personal experiences, and differences in individual characteristics only have an effect on vaccine acceptance among individuals who do not trust health authorities. Individuals who trust them, instead, appear to have a uniformly higher willingness to take the vaccine, irrespective of individual circumstances. This result resonates with the findings of Blair et al. (2022), who find that while individuals who trust in government tend to comply more with public health measures, they are not at the same time more knowledgeable about COVID-19 – if anything, their knowledge about the disease may be even more limited than those who distrust government.

14
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estimation corresponds to the model in column 6 of Table 4. Panel d. presents the predicted probability for the two scenarios for the subsample of individuals who believe vaccine acceptance in their community is low (defined as believing that more than 50 people out of 100 in their community will accept the vaccine). The estimation corresponds to the model in column 7 of Table 4.

A graphic depiction of the mediating effect of trust and social norms in vaccine acceptance is presented in Figure 6. Panel “a” shows the predicted probability of accepting the vaccine for individuals with low trust in government health authorities under two scenarios – in the first one, government stringency is low (at the 25<sup>th</sup> percentile of the sample distribution) and the respondent doesn’t know personally any COVID-19 positive case; in the second scenario, government stringency is high (at the 75th percentile of the sample distribution) and the respondent knows personally a COVID-19 positive case. In the first scenario the probability of accepting the vaccine is 50.7%, while in the second scenario the probability is 67.1% - a difference of almost 17 percentage points. Panel “b” presents the same two scenarios for individuals that have high trust in government health authorities. In this case, the difference is smaller – about 12.5 percentage points, between 70.9% probability of accepting the vaccine in the first scenario to 83.4% in the second scenario.

Panels “c” and “d” repeat the same exercise, but this time distinguishing individuals between those who believe community acceptance of the vaccine is low (loose social norms) and those who believe community acceptance is high (stringent social norms). For the first group, the difference between the scenario of low stringency and no personal knowledge of a positive COVID-19 case and the scenario of high stringency and personal knowledge of a positive COVID-19 case is 20 percentage points – between a 36.3% probability of accepting the vaccine in the first scenario and 56.3% probability in the second scenario. For the group who believe community acceptance is high, the difference between both scenarios is smaller – about 10 percentage points, between 71.7% probability in the first scenario and 81.8% in the second scenario.

# **5.d Heterogenous effects across countries**

Government management of the pandemic and public reception to lockdown policies have varied across the globe. While some countries witnessed backlash or protests to lockdowns and mask-mandates, others have not. To examine heterogeneity of impact across countries, we estimate Equation 1 (using the specification of Table 2, Column 3) including an interaction of the stringency index with country fixed effects. Figure 7 plots the marginal effects of stringency across countries. The average effect of stringency across countries shows quite some variation, particularly in the case of Bangladesh (excluded from Figure 7 so as not to distort the scale) which is the one of the few countries, along with Argentina, the Philippines, and Nigeria with a significant and negative impact of stringency. In these four countries, the effect of government stringency on vaccine acceptance appears to go in the opposite direction as the rest of the world. The reasons for this are speculative: in these countries, individuals appear to behave as believing that non-pharmaceutical interventions (NPI) are a substitute rather than a complement of vaccination campaigns, and therefore may react accordingly – becoming more reluctant to get the vaccine when stricter NPIs are in place.

16
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© Low trust in government health authorities © High trust in government health authorities © Believes community acceptance is low © Believes community acceptance is high
not explain mask-wearing behavior once we control for individual risk perceptions, beliefs about mask efficacy, and social norms around mask-wearing.<sup>10</sup> Our results point to the importance of government policy stringency on vaccine acceptance, particularly in contexts where trust in government health authorities is low or where the prevailing social norm is for vaccine acceptance to be low. The implementation of stringent restrictions, beyond helping to curtail the spread of the disease by restricting mobility, may also contribute indirectly to reducing the circulation of the virus in another way – by increasing vaccine acceptance where it is low. The relationship between non-pharmaceutical measures and the acceptance of preventive health measures such as vaccination can be a useful lesson for other communicable diseases as well.

> 10 Results on mask-wearing and government stringency are available upon request.

19
# **7. Tables**

**Table 1** – Descriptive statistics

||mean|sd|min|max|observations|
|---|---|---|---|---|---|
|_Individual characteristics_||||||
|age 20-30 yrs.|0.248|0.432|0|1|232292|
|age 31-40 yrs.|0.251|0.434|0|1|232292|
|age 41-50 yrs.|0.205|0.404|0|1|232292|
|age 51-50yrs.|0.165|0.371|0|1|232292|
|age 61-70 yrs.|0.098|0.297|0|1|232292|
|age 71-80 yrs.|0.029|0.167|0|1|232292|
|age 80 above yrs.|0.004|0.065|0|1|232292|
|male|0.589|0.492|0|1|232292|
|urban|0.862|0.345|0|1|232292|
|primary edu. or less|0.045|0.208|0|1|232292|
|secondary edu.|0.257|0.437|0|1|232292|
|tertiary edu.|0.698|0.459|0|1|232292|
|ingood health|0.766|0.423|0|1|232292|
|_Experiences and subjective beliefs_||||||
|will accept vaccine|0.656|0.475|0|1|232292|
|Perception of high risk of infection|0.469|0.499|0|1|98277|
|know positive case|0.596|0.491|0|1|232292|
|trustsgovt. health authorities|0.384|0.486|0|1|85392|
|_Norms_||||||
|No. people out of 100 who will<br>vaccinate|63.2|27.8|0|100|178581|
|No.people out of 100 who wear mask|74.6|29.0|0|100|226156|
|_Country-level circumstances_||||||
|stringency index|64.0|11.78|29.6|88.0|232292|
|New deathsper million(7-dayavg.)|2.01|2.65|0|17.45|232292|

20
**Table 2** – Main specification

|Depe|Whole<br>sample<br>ndent varia|ble: Willin|Only<br>g to vaccina|with trust<br>te|and norm|s info||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Sti id|0003***|0004***|0004***|0004***|0005***|0012**|0004***|
|rngency nex|.<br>(0.001)|. <br>(0.001)|. <br>(0.001)|. <br>(0.001)|. <br>(0.001)|.<br>(0.004)|.<br>(0.001)|
|1-day change in stringency index (absolute)|||-0.004<br>(0.002)|||||
|7-day change in stringency index (absolute)||||-0.002*<br>(0.001)||||
|14-day change in stringency index (absolute)|||||-0.002**<br>(0.001)|||
|Stringency index^2||||||-0.000**||
|||||||(0.000)||
|Days above stringency score 80|||||||-0.001***<br>(0.000)|
|Vaccination campaign dummy|0.104***|0.092***|0.092***|0.093***|0.092***|0.095***|0.092***|
||(0.021)|(0.019)|(0.019)|(0.019)|(0.018)|(0.019)|(0.019)|
|Daily deaths per million (7 day avg)|0.007|0.001|0.001|0.001|0.001|0.002|0.000|
||(0.007)|(0.008)|(0.008)|(0.008)|(0.007)|(0.008)|(0.008)|
|Know positive case|0.086***|0.081***|0.081***|0.081***|0.080***|0.081***|0.080***|
||(0.010)|(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|
|_Individual attributes_||||||||
|male|0.072***|0.081***|0.081***|0.081***|0.081***|0.081***|0.081***|
||(0.014)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|urban|0.014|0.018|0.018|0.018|0.018|0.018|0.018|
||(0.010)|(0.013)|(0.012)|(0.012)|(0.013)|(0.013)|(0.012)|
|age 20-30|-0.096***|-0.105**|-0.105**|-0.105**|-0.105**|-0.105**|-0.105**|
||(0.027)|(0.046)|(0.046)|(0.045)|(0.046)|(0.045)|(0.046)|
|age 31-40|-0.103***|-0.113**|-0.113**|-0.113**|-0.113**|-0.114**|-0.113**|
||(0.027)|(0.045)|(0.045)|(0.045)|(0.046)|(0.045)|(0.045)|
|age 41-50|-0.093***|-0.099**|-0.099**|-0.099**|-0.098**|-0.099**|-0.099**|
||(0.027)|(0.045)|(0.045)|(0.045)|(0.045)|(0.045)|(0.045)|
|age 51-60|-0.074***|-0.079*|-0.079*|-0.079*|-0.079*|-0.079*|-0.079*|
||(0.023)|(0.044)|(0.044)|(0.044)|(0.044)|(0.044)|(0.044)|
|age 61-70|-0.030|-0.034|-0.034|-0.034|-0.034|-0.034|-0.034|
||(0.022)|(0.039)|(0.039)|(0.039)|(0.039)|(0.039)|(0.039)|
|age 71-80|0.005|0.022|0.022|0.022|0.022|0.022|0.022|
||(0.015)|(0.033)|(0.033)|(0.033)|(0.034)|(0.033)|(0.033)|
|Primary education or less|-0.050***|-0.037**|-0.037**|-0.037**|-0.038**|-0.037**|-0.037**|
||(0017)|(0016)|(0016)|(0016)|(0015)|(0016)|(0016)|
|Secondary education|.<br>-0.030***|.<br> -0.030***|.<br> -0.030***|.<br> -0.030***|.<br>-0.030***|.<br>-0.030**|.<br>* -0.030***|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|in good health|-0.003|0.000|0.000|0.000|0.001|0.000|0.001|
||(0.005)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|Constant|0.419***|0.391***|0.383***|0.353***|0.316***|0.132|0.464***|
||(0.045)|(0.071)|(0.074)|(0.084)|(0.091)|(0.142)|(0.078)|
|Observations|232292|61760|61760|61760|61760|61760|61760|
|R-squared|0.058|0.058|0.058|0.058|0.058|0.058|0.058|

Standard errors in parentheses clustered at country level. All estimations include country and month fixed effects.

* p<0.05  ** p<0.01  *** p<0.001

21
**Table 3** – The role of risk perceptions

||Percepti<br>risk of|on of high<br>infection<br>Dependen|Willing t<br>t variable|o vaccinate|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Stringency index|0.002*<br>|0.002*<br>|0.003**<br>|0.003**<br>|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Stringency index X Perception of high risk of||||-0.000|
|infection||||(0.001)|
|Perception of high risk of infection|||0.089*** <br>|0.103*<br>|
||||(0.010)|(0.058)|
|Vaccination campaign dummy|-0.026|-0.028|0.096***|0.096***|
||(0.018)|(0.018)|(0.021)|(0.021)|
|Daily deaths per million (7 day avg)|0.010**|0.010**|-0.000|-0.000|
||(0.004)|(0.004)|(0.006)|(0.006)|
|Know positive case|0.148***|0.148***|0.065***|0.065***|
||(0.014)|(0.013)|(0.014)|(0.014)|
|_Mediating factors_|||||
|No. people out of 100 who will vaccinate||0.001*<br>|||
|||(0.000)|||
|trusts govt. health authorities||0.048***<br>(0.013)|||
|_Individual attributes_|||||
|male|-0.051***|-0.051***|0.080***|0.080***|
||(0.014)|(0.014)|(0.019)|(0.019)|
|urban|0.046***|0.047***|0.009|0.009|
||(0.012)|(0.012)|(0.013)|(0.013)|
|age 20-30|-0.173***|-0.167***|-0.105*|-0.105*|
||(0.055)<br>|(0.055)<br>|(0.059)<br>|(0.059)<br>|
|age 31-40|-0.108*|-0.102*|-0.135**|-0.135**|
||(0.052)|(0.053)|(0.061)|(0.061)|
|age 41-50|-0.089|-0.085|-0.103|-0.103|
||(0.053)|(0.053)|(0.062)|(0.062)|
|age 51-60|-0.098*|-0.096*|-0.101|-0.101|
||(0.050)|(0.051)|(0.058)|(0.058)|
|age 61-70|-0.093|-0.092|-0.057|-0.056|
||(0.054)|(0.056)|(0.056)|(0.056)|
|age 71-80|-0.091|-0.090|-0.023|-0.023|
||(0.059)|(0.061)|(0.055)|(0.055)|
|primary edu or less=1|-0.044|-0.046*|-0.037*|-0.037*|
||(0.027)|(0.026)|(0.020)|(0.020)|
|secondary edu=1|-0.007|-0.007|-0.022*|-0.022*|
||(0.013)|(0.013)|(0.011)|(0.011)|
|in good health|-0.079***|-0.082***|0.000|0.000|
||(0.010)|(0.010)|(0.008)|(0.008)|
|Constant|0.071|0.052|0.447***|0.439***|
||(0.069)|(0.078)|(0.084)|(0.097)|
|Observations|15237|15237|15237|15237|
|R-squared|0.199|0.202|0.067|0.067|

Standard errors in parentheses clustered at country level. All estimations include country and month fixed effects.

* p<0.05  ** p<0.01  *** p<0.001

22
**Table 4** – Mediating effects: trust and descriptive norms

|Dependent va|riable: Will|ing to vacc|inate|||||
|---|---|---|---|---|---|---|---|
||||Low||Whole|Low|High|
||Whole|sample|trust|High trust|sample|belief|belief|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Stringency index|0.003***|0.003**|0.004**|0.003***|0.005**|0.005***|0.002**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Stringency index X Trust in govt health authorities||0.002<br>(0.001)||||||
|Stringency index X No. People out of 100 who will vaccinate|||||-0.000<br>(0.000)|||
|Vaccination campaign dummy|0.068***|0.087***|0.084***|0.088***|0.071***|0.103***|0.059***|
||(0.016)|(0.018)|(0.017)|(0.021)|(0.016)|(0.022)|(0.013)|
|Daily deaths per million (7 day avg)|-0.000|0.001|0.002|-0.000|-0.000|-0.001|0.002|
||(0.005)|(0.007)|(0.007)|(0.007)|(0.006)|(0.009)|(0.004)|
|know positive case|0.076***|0.081***|0.091***|0.063***|0.076***|0.101***|0.061***|
||(0.010)|(0.011)|(0.016)|(0.011)|(0.010)|(0.016)|(0.006)|
|_Mediating factors_||||||||
|No. people out of 100 who will vaccinate|0.006***||||0.008***|||
||(0.000)||||(0.002)|||
|trusts govt. health authorities|0.123***|0.041||||||
||(0.016)|(0.085)||||||
|_Individual attributes_||||||||
|male|0.067***|0.081***|0.082***|0.078***|0.066***|0.055***|0.079***|
||(0.012)|(0.014)|(0.017)|(0.011)|(0.012)|(0.018)|(0.014)|
|urban|0.012|0.021*|0.027*|0.009|0.009|0.021|0.003|
||(0.008)|(0.011)|(0.014)|(0.010)|(0.009)|(0.013)|(0.008)|
|age 20-30 yrs|-0.048|-0.095*|-0.110*|-0.066|-0.053|-0.145*|-0.023|
||(0.033)|(0.045)|(0.053)|(0.054)|(0.033)|(0.069)|(0.037)|
|age 31-40 yrs|-0.059*|-0.105**|-0.129**|-0.061|-0.063*|-0.163**|-0.031|
||(0.032)|(0.045)|(0.053)|(0.055)|(0.032)|(0.065)|(0.039)|
|age 41-50 yrs|-0.056*|-0.095**|-0.108*|-0.068|-0.057*|-0.153**|-0.027|
||(0.032)|(0.045)|(0.054)|(0.051)|(0.032)|(0.066)|(0.039)|
|age 51-50yrs|-0.051|-0.079*|-0.089|-0.057|-0.050|-0.149*|-0.018|
||(0.032)|(0.044)|(0.053)|(0.053)|(0.032)|(0.072)|(0.038)|
|age 61-70 yrs|-0.016|-0.034|-0.038|-0.023|-0.016|-0.135*|0.019|
||(0.031)|(0.040)|(0.044)|(0.054)|(0.031)|(0.069)|(0.034)|
|age 71-80 yrs|0.034|0.022|0.027|0.014|0.034|-0.071|0.057|
||(0.029)|(0.033)|(0.034)|(0.051)|(0.029)|(0.071)|(0.034)|
|primary edu or less|-0.046***|-0.046***|-0.061***|<br>-0.020|-0.040**|-0.037|-0.028**|
||(0.016)|(0.015)|(0.014)|(0.015)|(0.017)|(0.025)|(0.013)|
|secondary edu|-0.032***|-0.031***|-0.040***|<br>-0.015|-0.032***|-0.026**|-0.028***|
||(0.008)|(0.008)|(0.009)|(0.009)|(0.008)|(0.009)|(0.008)|
|in good health|-0.015**|-0.006|-0.015|0.015|-0.011|-0.021*|-0.000|
||(0.007)|(0.007)|(0.009)|(0.009)|(0.007)|(0.010)|(0.005)|
|Constant|0.039|0.390***|0.323***|0.505***|-0.080|0.186*|0.556***|
||(0.060)|(0.085)|(0.100)|(0.079)|(0.168)|(0.097)|(0.063)|
|Observations|61760|61760|38009|23751|61760|21941|39819|
|R-squared|0.179|0.086|0.065|0.051|0.165|0.069|0.047|

Standard errors in parentheses clustered at country level. All estimations include country and month fixed effects. * p<0.05 ** p<0.01 *** p<0.001 23