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COVID-19 Vaccine Acceptance and Hesitancy in Low and Middle Income Countries, and Implications for Messaging

March 2021

DOI:10.1101/2021.03.11.21253419

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Background As vaccination campaigns are deployed worldwide, addressing vaccine hesitancy is of critical importance to ensure sufficient immunization coverage. We analyzed COVID-19 vaccine acceptance across 15 samples covering ten low- and middle- income countries (LMICs) in Asia, Africa, and South America, and two higher income countries (Russia and the United States). Methods Standardized survey responses were collected from 45,928 individuals between June 2020 and January 2021. We estimate vaccine acceptance with robust standard errors clustered at the study level. We analyze stated reasons for vaccine acceptance and hesitancy, and the most trusted sources for advice on vaccination, and we disaggregate acceptance rates by gender, age, and education level. Findings We document willingness to take a COVID-19 vaccine across LMIC samples, ranging from 67% (Burkina Faso) to 97% (Nepal). Willingness was considerably higher in LMICs (80%) than in the United States (65%) and Russia (30%). Vaccine acceptance was primarily explained by an interest in personal protection against the disease (91%). Concern about side effects (40%) was the most common reason for reluctance. Health workers were considered the most trusted sources of information about COVID-19 vaccines. Interpretation Given high levels of stated willingness to accept a COVID-19 vaccine across LMIC samples, our study suggests that prioritizing efficient and equitable vaccine distribution to LMICs will yield high returns in promoting immunization on a global scale. Messaging and other community-level interventions in these contexts should be designed to help translate intentions into uptake, and emphasize safety and efficacy. Trusted health workers are ideally positioned to deliver these messages.

Reasons not to take the vaccine.

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Trusted actors and institutions, broken down by expressed willingness to take a COVID-19 vaccine.

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If a COVID-19 vaccine becomes available in [country], would you take it? Disaggre- gated by subgroups

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Reasons not to take the vaccine

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COVID-19 Vaccination Decision-making: most trusted source (continued)

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COVID-19 Vaccine Acceptance and Hesitancy in Low

and Middle Income Countries, and Implications for

Messaging

Julio S. Solís Arce, BA*1, Shana S. Warren, PhD∗2, Niccoló F. Meriggi, MSc∗3, Alexandra Scacco, PhD∗1, Nina

McMurry, PhD∗1, Maarten Voors, PhD∗4, Georgiy Syunyaev, MPhil∗1,5,24, Amyn Abdul Malik, PhD∗6, Samya

Aboutajdine, MA3, Alex Armand, PhD7, Saher Asad, PhD8, Britta Augsburg, PhD9, Antonella Bancalari, PhD10,

Martina Björkman Nyqvist, PhD11, Ekaterina Borisova, PhD5,12, Constantin Manuel Bosancianu, PhD1, Ali Cheema,

PhD8,13, Elliott Collins, PhD2, Ahsan Zia Farooqi, MA13 , Mattia Fracchia, MA7, Andrea Guariso, PhD14, Ali

Hasanain, PhD8, Anthony Kamwesigye, BSc2, Sarah Kreps, PhD15, Madison Levine, MSc4, Rebecca Littman,

PhD16, Melina Platas, PhD17 , Vasudha Ramakrishna, MSc18, Jacob N. Shapiro, PhD19 , Jakob Svensson, PhD20,

Corey Vernot, BS18, Pedro C. Vicente, PhD7, Laurin B Weissinger, DPhil21, Baobao Zhang, PhD14, Dean Karlan,

PhD†2,22 , Michael Callen, PhD†3,23 , Matthieu Teachout, PhD†3, Macartan Humphreys, PhD† 1,24, Saad B. Omer,

PhD‡6 , and Ahmed Mushﬁq Mobarak, PhD§25

1WZB Berlin Social Science Center

2Innovations for Poverty Action (IPA)

3International Growth Centre (IGC)

4Wageningen University & Research

5International Center for the Study of Institutions and Development (HSE University, Moscow, Russia)

6Yale Institute for Global Health

7Nova School of Business and Economics

8Lahore University of Management Sciences

9The Institute for Fiscal Studies

10University of St. Andrews & The Institute for Fiscal Studies

11Stockholm School of Economics and Misum

12Economics Department of Ghent University

13Institute of Development and Economic Alternatives

14Trinity College Dublin

15Cornell University

16University of Illinois Chicago

17NYU Abu Dhabi

18Yale Research Initiative on Innovation and Scale (Y-RISE)

19Princeton University

20Institute for International Economic Studies (IIES), Stockholm University

21Tufts University

22Kellogg School of Management at Northwestern University

23London School of Economics

24Columbia University

25Yale University

*First author

†Last author

‡Corresponding author. 1 Church Street, New Haven, CT 06510. Phone 203 432 3656. Email saad.omer@yale.edu

§Corresponding author. 165 Whitney Avenue, New Haven, CT 06520-8200. Phone 203 432 5787. Email

ahmed.mobarak@yale.edu

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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 13, 2021. ; https://doi.org/10.1101/2021.03.11.21253419doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Summary

Background

As vaccination campaigns are deployed worldwide, addressing vaccine hesitancy is of critical im-

portance to ensure sufﬁcient immunization coverage. We analyzed COVID-19 vaccine acceptance

across 15 samples covering ten low- and middle- income countries (LMICs) in Asia, Africa, and

South America, and two higher income countries (Russia and the United States).

Methods

Standardized survey responses were collected from ‘45,928 individuals between June 2020 and

January 2021. We estimate vaccine acceptance with robust standard errors clustered at the study

level. We analyze stated reasons for vaccine acceptance and hesitancy, and the most trusted sources

for advice on vaccination, and we disaggregate acceptance rates by gender, age, and education

level.

Findings

We document willingness to take a COVID-19 vaccine across LMIC samples, ranging from 67%

(Burkina Faso) to 97% (Nepal). Willingness was considerably higher in LMICs (80%) than in

the United States (65%) and Russia (30%). Vaccine acceptance was primarily explained by an

interest in personal protection against the disease (91%). Concern about side effects (40%) was

the most common reason for reluctance. Health workers were considered the most trusted sources

of information about COVID-19 vaccines.

Interpretation

Given high levels of stated willingness to accept a COVID-19 vaccine across LMIC samples, our

study suggests that prioritizing efﬁcient and equitable vaccine distribution to LMICs will yield

high returns in promoting immunization on a global scale. Messaging and other community-level

interventions in these contexts should be designed to help translate intentions into uptake, and

emphasize safety and efﬁcacy. Trusted health workers are ideally positioned to deliver these mes-

sages.

Funding

Beyond Conﬂict, Bill and Melinda Gates Foundation, Columbia University, Givewell.org, Ghent

University, HSE University Basic Research Program, International Growth Centre, Jameel Poverty

Action Lab Crime and Violence Initiative, London School of Economics and Political Science, Mu-

lago Foundation, NOVAFRICA at the Nova School of Business and Economics, NYU Abu Dhabi,

Oxford Policy Management, Social Science Research Council, Trinity College Dublin COVID19

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Response Funding, UK Aid, UKRI GCRF/Newton Fund, United Nations Ofﬁce for Project Ser-

vices, Weiss Family Fund, WZB Berlin Social Science Center, Yale Institute for Global Health,

Yale Macmillan Center, and anonymous donors to IPA and Y-RISE

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Introduction

A safe and effective vaccine against COVID-19 is a critical tool to control the pandemic. As of

March 2, 2021, there were 76 vaccines in clinical development and 16 had advanced to Stage 3

clinical trials.1Following clinical trials, several vaccines have been approved in multiple countries

and are being distributed across the globe. At present, global vaccine distribution remains highly

unequal, with much of the current supply directed toward high-income countries.2

While effective and equitable distribution of the vaccines is a key policy priority, ensuring the

population’s acceptance is equally important. Acceptance of vaccines and trust in the institutions

that administer them are likely key determinants of the success of any vaccination campaign.3Sev-

eral studies investigate high-income country residents’ willingness to take a potential COVID-19

vaccine.4,5Little is known about acceptance rates in low- and middle-income countries (LMICs),

however, where the majority of the world’s population resides.

Acceptance of childhood vaccination for common diseases —such as measles (MCV), Bacille

Calmette-Guérin (BCG) and diphtheria, tetanus, and pertussis (DTP)—is generally high in LMICs,

providing reasons for optimism about future uptake of COVID-19 vaccines. Table 1summarizes

general vaccine acceptance and uptake of common childhood vaccines for the countries included

in our study. Still, existing studies on COVID-19 vaccine acceptance document large variation,

both across and within countries, including in settings where overall vaccination rates are high.4

These studies highlight concerns about COVID-19 vaccine safety, and particularly concerns about

the speed of vaccine development, as reasons for hesitancy in higher income settings. Similar

concerns may apply in LMICs.

Additional reasons for hesitancy may feature more prominently in LMIC settings. Reported

COVID-19 mortality rates have consistently been lower in LMICs relative to higher income

countries.6If individuals in LMICs feel the risk of disease is less serious, they may be less

inclined to accept perceived risks associated with taking a recently-developed vaccine. Previous

studies have also highlighted factors such as concerns about healthcare quality,7negative historical

experiences,8weak support from traditional leaders,9and mistrust in government10 as barriers to

healthcare utilization in LMICs.

Understanding factors that may lead people in LMICs to reject COVID-19 vaccination is of global

concern, since a lag in vaccination in the developing world could facilitate the spread of new

variants of the virus to other countries.

In order to effectively promote the vaccine and devise messaging strategies, we need to know if

people are willing to take it, the reasons why they are willing or unwilling to do so, and the factors

inﬂuencing their decision-making. For this purpose, we developed and deployed a common set of

questions across 15 studies in ten LMICs in Africa (Burkina Faso, Mozambique, Nigeria, Rwanda,

Sierra Leone, Uganda), Asia (India, Nepal, Pakistan), and Latin America (Colombia). We compare

these ﬁndings in LMICs to those from two higher income countries (Russia and the United States).

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Methods

Survey questions and sample construction

Survey data were collected between June 2020 and January 2021. Our main outcome measure

is vaccine acceptance. Across studies, we asked respondents, “If a COVID-19 vaccine becomes

available in [your country], would you take it?”. If the respondent answered yes to this question,

we followed up with the question, “Why would you take it? [the COVID-19 vaccine]”. If the

respondent said they would not be willing to take the vaccine, we followed up with the question,

“Why would you not take it? [the COVID-19 vaccine]”. Finally, regardless of their expressed will-

ingness to take the vaccine, we asked about actors and institutions who would be inﬂuential in their

decision. The question was worded in the following way: “Which of the following people would

you trust MOST to help you decide whether you would get a COVID-19 vaccine, if one becomes

available?”. To examine heterogeneity across demographic strata, we collected information about

the gender, age, and education level of each respondent. Slight variations in questions wording and

answer options across studies are documented in Appendix B.

Studies vary in terms of geographic scope, sampling methodology, and survey modality. Seven

were national or nearly-national in scope. Among these, studies from Burkina Faso, Colombia,

Rwanda, and Sierra Leone (“Sierra Leone 1”) used nationally-representative samples of active

mobile phone numbers reached through Random Digit Dialing (RDD). Studies in the USA and

Russia were conducted online using quota samples obtained from private survey companies.

The remaining eight studies targeted sub-national populations. One study from Pakistan (“Pakistan

2”) used RDD to reach a representative sample of active mobile phone users in Punjab province.

Respondents in Mozambique, Nigeria, “Pakistan 1”, “Uganda 1”,“Uganda 2”, India, Nepal and

“Sierra Leone 2” were drawn from pre-existing studies to which COVID-19 vaccine questions

were subsequently added. For example, “Sierra Leone 2” has national coverage from a study on

access to electricity and “Uganda 1” sampled female caregivers of households in rural and semi-

rural villages as part of a large ongoing cluster-RCT implemented across 13 districts.

Table 2summarizes the geographic scope, sampling methodologies and survey modalities of all

15 studies. A detailed description of each study is included in Appendix D.

All LMIC surveys were conducted via telephone to minimize in-person contact and comply with

local government social distancing guidelines. Interviews were conducted by local staff in each

country in local language(s). Surveying by phone made rapid, large-scale data collection possible.

Surveys lasted approximately 15 to 40 minutes.

Taken together, we have data from 21,844 individuals from LMICs and 24,084 from the USA and

Russia, for a total of 45,928 respondents.

Statistical Analysis

Vaccine acceptance was deﬁned as the percentage of respondents who answered “yes” to the ques-

tion, “If a COVID-19 vaccine becomes available in [country], would you take it?”. This was

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calculated combining all other answer options (“No”, “Don’t Know” and “Refuse”) into a single

reference category. We estimated this outcome for each study with robust standard errors and

employed sampling weights where available.

In addition to study-level estimates, we combined data from all studies other than the USA and

Russia to calculate an aggregate estimate for all LMIC studies. For these analyses, each included

study received equal weight and standard errors were clustered at the study level. Averages in the

“All LMICs” group then reﬂect the expected share across studies. In this combined analysis we

also estimated the underlying heterogeneity of vaccine acceptance across studies using the between

studies variance estimator τ2from a random effects model.

We also conducted subgroup analyses by gender, age and education level and reported differences

between groups. For the “All LMICs” analyses we calculated the average of differences between

subgroups within studies with standard errors clustered at the study level.

We additionally examined reported reasons for COVID-19 vaccine acceptance and hesitancy, as

well as the types of actors respondents would trust when making the decision about whether to

take a COVID-19 vaccine. Among respondents who expressed willingness to take the vaccine, we

asked about several possible reasons why they would take it. For respondents unwilling to take the

vaccine, we asked about several possible reasons why they would not take it. Finally, we asked all

respondents, regardless of their answers to other questions, whom they would trust most to help

them decide whether to get a COVID-19 vaccine. We report estimates of agreement with reasons

for vaccine acceptance/hesitance and trust in actors for individual studies and for the “All LMICs”

group.

Role of the funding source

None of our funders played any role in the collection, analysis, interpretation, writing or decision

to submit this article for publication.

Replication code

Code and output of the analysis can be consulted here https://wzb-ipi.github.io/covid_vaccines/

replication.html.

Results

Our main results are shown in Figure 1and reproduced as Table 4in Appendix A. The ﬁrst column

provides overall acceptance rates in each study, while the remaining columns show acceptance

rates disaggregated by respondent characteristics. The “All LMICs” row reports averages for LMIC

countries only (and so excludes Russia and the US).

We document meaningful variation in vaccine acceptance across and within LMICs, but generally

high levels of acceptance in LMICs overall. The average acceptance across studies is 80.3% (95%

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CI 74.9–85.6), with a median of 78, a range of 30.1 percent points and an interquartile range of

9.7. Our estimate of τ2is 0.007 which implies a standard deviation over country averages of 0.084.

The acceptance rate in every LMIC sample is higher than in USA (64.6%, 61.8–67.3) and Russia

(30.4%, 29.1–31.7).

We ﬁnd limited evidence of variation across demographic subgroups in LMICs, as shown in Table

8. Women are generally less willing to accept the vaccine (average difference about 4.3 points, sig-

niﬁcant at p< .01). Younger respondents (deﬁned as aged <55 given younger-skewing populations

in LMICs) are marginally more willing to take the vaccine, but this difference is not statistically

signiﬁcant. Less educated people were on average more willing to take the vaccine in LMICs, but

this difference is not signiﬁcant.

To better understand the reasoning behind vaccine acceptance, we asked those who were willing

to take the vaccine why they would take it. We summarize in Table 3, with more details in Table 5.

The most common reason given for vaccine acceptance was personal protection against COVID-19

infection. The average across LMICs is 91% (86–96). In every individual study, it ranks as the

ﬁrst reason. In distant second place, LMIC respondents reported willingness to take the COVID-

19 vaccine in order to protect their families. The average across LMICs is 36% (28–43). In

comparison to self-protection, protecting the community did not feature prominently in the stated

reasons at all.

Self-protection also ranked as the most commonly expressed reason for taking the vaccine in Rus-

sia (76%, 74–78) and the USA (94%, 92–95).

This evidence contrasts with appeals to altruistic behavior and prosocial motivations in order to

promote vaccine acceptance.11 The risks and beneﬁts to personal well-being feature much more

prominently in people’s stated reasons for vaccine acceptance.

Figure 2summarizes the reasons given among respondents who said they were not willing to take

a Covid vaccine.

The most common reason expressed for reluctance to take the vaccine in LMIC studies was concern

about side effects. For studies Uganda 1 (85.1% 80.7–89.6), Sierra Leone 2 (57.9%, 50.1–65.7),

Sierra Leone 1 (53.5% 47.1–59.9) and Uganda 2 (47.3% 42.2–52.5), more than half of those re-

spondents unwilling to take the vaccine mentioned this reason. Respondents in Russia (36.8%,

35.2–38.4) and the USA (79.3%, 74.6–84), reported high levels of this same concern.

While serious adverse events that are life-threatening or require hospitalization are very rare, with

only .6% of respondents reporting at least one side effect in the Pﬁzer vaccine trial,12 one potential

explanation for the outsized concern about side effects could be the lack of widespread information

about features of the vaccine at the time of data collection. Media coverage of the few cases of

serious adverse events and spread of fake news may contribute as well.13

Concerns about side effects could also be due to a concern about mild side effects from experiences

with other vaccines. In the case of available COVID-19 vaccines, we now know that mild side

effects are common but transient. These include fatigue, muscle pain, joint pain and headache,

which were severe in fewer than 10% of people in the clinical trials of tens of thousands. Severe

fever occurred in fewer than 2% of them.14

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Allergic reactions from the COVID-19 vaccine seem to be extremely rare. Data from trials of the

Pﬁzer-BioNTech vaccines shows that anaphylaxis after reported administration occurs at a rate of

11.1 cases per million vaccine doses administered.15 Our data reﬂects this – no more than 6% of

respondents expressed concern about allergies in any of our LMIC studies.

Other concerns that make many respondents unwilling to take the vaccine could be countered

by accurately presenting the scientiﬁc data to the public. Studies Uganda 2 (31%, 25.9–36.2),

Mozambique (29.7%, 18.6–40.8) and Pakistan 1 (26%, 18–34) showed relatively high levels of

skepticism about vaccine effectiveness. This is also true for respondents in Russia (29.6%, 28.1–

31.1) and the USA (46.8%, 41–52.6). Recent clinical trials reveal very high rates of vaccine

efﬁcacy,16,17 so clearly communicating these results to the public is a high priority, given the

skepticism we observe in our data. In contrast, conspiracy theories were rarely mentioned by

respondents in any of our study samples, in spite of widespread popular discourse about anti-

vaxxer movements and theories in higher-income countries.18

Finally, respondents in some studies downplayed the seriousness of this disease, and listed this as

a reason not to be vaccinated. Studies USA (39.3% 33.5–45), Pakistan 1 (29.4%, 20.9–37.9) and

Nepal (20.4% 6.7–34.1) report high rates of lack of concern about getting seriously ill from the

disease.

The analysis above identiﬁes the nature of the information gaps that any vaccine messaging should

focus on, while in Figure 3we try to identify the actors who are best placed to deliver those

messages. We asked respondents about their most trusted source of information during the process

of deciding whether to take the vaccine, because these sources are vital to disease control strategies

during public health emergencies.19 Results from Figure 3are reproduced as Table 7in Appendix

We ﬁnd striking consistency across countries. In all but one study, respondents identiﬁed the health

system as the most trustworthy source to help them decide whether or not to take the COVID-19

vaccine (with the exception of Rwanda, where the government in general was identiﬁed as the

most trusted source, with the health system a close second). Family and friends were the next

most important reference points in most samples. Across samples, women were 3 percentage

points more likely to rely on family and friends than male respondents though this difference is not

signiﬁcant at conventional levels (Figure 4in Appendix D). By contrast, endorsements by religious

leaders or celebrity ﬁgures were not seen as important sources of inﬂuence in any sample other than

Nepal.

Discussion

To our knowledge, this is the ﬁrst study documenting rates of expressed COVID-19 vaccine accep-

tance and hesitancy in a large set of LMICs. Our ﬁndings show variable but broadly high levels of

prospective COVID-19 vaccine acceptance across LMICs using data from 45,928 respondents in

13 original household surveys from Africa (Burkina Faso, Mozambique, Nigeria, Rwanda, Sierra

Leone, Uganda), Asia (Bangladesh, India, Nepal, Pakistan), and Latin America (Colombia). Ac-

ceptance across LMIC averages 80.3, ranging between 66.5 and 96.6. We document considerably

lower levels of acceptance in Russia and the United States.

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Patterns of COVID-19 vaccine hesitancy are not well predicted by existing measures of concerns

about the safety of other vaccines (e.g., the Wellcome Global Monitor shown in Table 1). Com-

pared with other vaccines, COVID-19 vaccine acceptance is lower and more variable across LMIC

samples. This suggests that concerns may apply speciﬁcally to COVID-19 vaccines rather than to

vaccination more broadly.

Our study also documents reasons why respondents express intentions to take (or not take) a

COVID-19 vaccine. The main reason expressed for willingness to take such a vaccine was to

protect oneself. The most common reasons offered by those unwilling to take the vaccine were

concerns about safety (side effects) and efﬁcacy. Across all contexts, health care workers were the

most trusted source of information about vaccines.

Our study samples offer an important window into the motivations underlying COVID-19 vac-

cine acceptance in LMICs, but our data are not fully nationally representative. Random digit dial

samples and follow-up phone surveys, while necessary during a global pandemic, do not include

individuals who reside outside coverage areas, who do not own or cannot operate cell phones,

or who choose not to respond to telephone surveys. Care should also be taken in any attempt to

extrapolate to the population level from the samples representative of narrow subpopulations.

If intentions reported in our LMIC samples translated into actual vaccination uptake, the rates

would far exceed the range of what would be required for COVID-19 herd immunity (40-67% in

recent estimates).20,21 However, reported intent may not materialize into actual vaccine adoption.22

The high salience of COVID-19 due to extensive media coverage and government mitigation

efforts and excitement around vaccine release may have increased reported intention.23 Results

from the ﬁrst COVID-19 vaccine Phase 3 clinical trial were announced before some surveys were

ﬁelded; during others, subsequent approvals were granted. The fast-moving information environ-

ment may change people’s perceptions about vaccines by the time they are widely available in

LMICs.

Nonetheless, our ﬁndings provide some speciﬁc guidance on how to design messaging to boost

COVID-19 vaccine acceptance and uptake in LMICs. Our data have implications for both what

the content of the message should be, and who should deliver the message.

First, high levels of trust in the advice of health workers and governments on COVID-19 vaccine

decision-making suggest that social and behavioral change communication (SBCC) strategies that

engage local health workers may be particularly effective tools to encourage timely and complete

vaccine uptake, and to combat remaining vaccine hesitancy. The literature has explored messaging

strategies to promote welfare-improving behaviors, with considerable attention paid to celebrity

endorsements.24 Our data strongly support the view25 that those with the most relevant expertise -

as opposed to celebrities or general opinion leaders - are most trusted on this speciﬁc topic and are

therefore best positioned to deliver the message.

Second, the average COVID-19 vaccine acceptance rate across our LMIC samples is high, ap-

proximately 80.3%. Given such positive intentions, there may be high returns to investing in

straightforward “last-mile” nudges that help citizens convert intentions into actions. Reminder

messages from healthcare providers and messages alerting patients that vaccines have been re-

served for them at an upcoming appointment may provide a low-cost encouragement to initiate

and complete two-dose COVID-19 vaccinations, as was found in two recent large-scale studies in

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the United States.26,27 Similarly, childhood vaccination reminders plus cash incentives in Kenya

substantially increased full immunization,28 and cash and in-kind incentive programs in Nigeria

and India have also proven effective.29,30

Third, high coverage rates of existing vaccines, coupled with respondents’ reliance on friends and

family as information sources, suggest that the general pro-vaccination stance of many citizens

could be leveraged to convert intent to uptake. Social learning strategies and norm-setting are

powerful drivers of information diffusion and behavior change in many related sectors.31 Social

signalling of positive attitudes toward COVID-19 vaccines may also help shift social norms toward

even greater immunization acceptance and two-dose completion in the community at large.32

Finally, our ﬁndings offer guidance on the speciﬁc content of vaccine messaging that is likely to

be most persuasive. Messaging should highlight the high efﬁcacy rates of the COVID-19 vaccines

currently on the market in reducing or eliminating disease, hospitalizations and death. Alluding

to clinical data that addresses people’s concerns about potential side effects should be prioritized.

Messaging should also emphasize the direct protective beneﬁts of the vaccine to the adopter.

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Contributors

JSo, SW, NMe, AS, NMc, GS, MV and AM are co-ﬁrst authors. DK, MC, MT, MH, AMM and SO

are co-last authors. AMM and SO are also the corresponding authors. DK, AMM, MT, NMe, MC,

and MV conceived of the study and provided overall guidance. SAb and NMe led the literature

search, with input from AS, NMc, SW, AMM, AM and JSo. SW, NMe, AS, NMc, MV, GS, AA,

SA, BA, AB,EB, CMB, AC, EC, MF, AG, AK, SK, RL, MBN, MP, JSh, JSv, PV, LB, BZ, MC,

SAs, AC, AF, AH, MC, MT, and MH oversaw data collection as part of other research efforts. SW,

NMe, and MT coordinated the project across study samples. The following veriﬁed the underlying

data for individual study samples: EC (Burkina Faso, Colombia, Rwanda, Sierra Leone 1), BA

(India), AS and RL (Nigeria), AG and MBN (Uganda 1), CMB and MH (Uganda 2), NMe and

MV (Sierra Leone 2), GS (Russia), MF (Mozambique), AF and JSh (Pakistan 1), SAs (Pakistan

2), CV (Nepal), and NMc (USA). JSo, GS, MH and SA collated and processed all datasets used

for the analysis. NMe, MH, AMM, JSo, GS, SW, AS, EC, EB, MT, MV and NMc did the data

interpretation with guidance from SO and AM. JSo, GS, EC and MH veriﬁed ﬁnal datasets and

analysis. JSo and GS did the data analysis and produced output ﬁgures with input from MH,

AMM, DK, SW, EC, MV, NMe and MT. MH supervised the data analysis. JSo, SW, NMe, AMM,

AS, NMc and MV wrote the ﬁrst draft of the manuscript, with guidance from AM and SO. JSo,

SW, NMe, AS, NMc, MV, SAb, EB, MP, JSh, PV, BZ, MC, MT, MH, AMM and SO revised the

manuscript. All authors approved the ﬁnal version of the manuscript. All authors had full access to

all the data used in this study and had ﬁnal responsibility for the decision to submit for publication.

Declaration of interests

We declare no competing interests.

Data Sharing

Individual participant data (de-identiﬁed) that underlie the results reported in this article, analytic

code and replication ﬁles will be available immediately following publication to no end date for

anyone who wishes to access the data and use it for any purpose. A replication exercise is available

here https://wzb-ipi.github.io/covid_vaccines/replication.html.

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Research in context

Evidence before this study

COVID-19 vaccine acceptance has been widely studied in high-income countries. Much less evi-

dence exists for LMICs, and data that are available33,34 systematically exclude the estimated 66%

of individuals in LMICs who do not use the internet.35 Searches of PubMed and the Cochrane

Database of Systematic Reviews using the terms “vaccine hesitancy”, “low- and middle-income

countries”, “trust in vaccines”, “immunization campaign”, “vaccination incentives” and “vacci-

nation policy” to select studies investigating the determinants of COVID-19 vaccine uptake and

policy-led actions to increase it and restricted to studies published between Jan 1, 2020 and Jan

31, 2021 identiﬁed two studies addressing COVID-19 vaccine acceptance in LMICs, and no any

studies comparing uptake for the COVID-19 vaccine with general attitudes toward vaccines.

Added value of this study

This study documents COVID-19 vaccine acceptance across ten LMICs and identiﬁes key socio-

demographic predictors, combining analyses of data from 15 distinct studies that cover a total of

45,928 individuals. To date, no comparable quantitative mapping of COVID-19 vaccine acceptance

in LMICs has been released. By extending the analysis to attitudes toward vaccinations in general,

and by asking respondents to specify reasons for their acceptance or refusal, our study offers novel

insights that may help inform country-speciﬁc policies to smooth the path to vaccine acceptance

in LMICs.

Implications of all the available evidence

As mass immunization campaigns are deployed across the world, our analysis offers cause for

optimism regarding potential uptake of the COVID-19 vaccine in LMICs. Our ﬁndings suggest

that policies to encourage widespread uptake should focus on converting intentions to take the

vaccine into action. Our analysis of reasons for hesitancy and most trusted sources for advice

about vaccination suggests that communication campaigns focusing on vaccine safety and efﬁcacy

delivered through trusted health workers may be particularly effective in persuading those who are

still hesitant. While acceptance of COVID-19 vaccinations is high across our sample of LMICs,

acceptance of vaccines in general is even higher, highlighting opportunities to leverage existing

pro-vaccine attitudes and norms to encourage uptake and address remaining hesitancy. Social

signaling of vaccination status may also be effective in demonstrating local acceptance of safety

and efﬁcacy claims.

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Table 1: Vaccination beliefs and coverage for the countries in our sample

%Respondents agreeing Vaccines are... Vaccine coverage in 2019 (% of infants)

Effective Safe Important for

children to have

Tuberculosis (BCG) Diphtheria,

Tetanus

and

Pertussis

(DTP1)

Measles

(MCV1)

% of parents with

any child that was

ever vaccinated

Burkina Faso 87 72 95 98 95 88 97

Colombia 83 84 99 89 92 95 95

India 96 97 98 92 94 95 92

Mozambique 87 93 98 94 93 87 95

Nepal 89 93 99 96 96 92 95

Nigeria 82 92 96 67 65 54 95

Pakistan 91 92 95 88 86 75 94

Rwanda 99 97 99 98 99 96 100

Sierra Leone 95 95 99 86 95 93 97

Uganda 82 87 98 88 99 87 98

Russia 67 48 80 96 97 98 96

USA 85 73 87 . 97 90 95

Table 1 presents an overview of vaccination beliefs and incidence across countries in our sample. Columns 2-5 use data from the Wellcome Global Monitor 2018. Column 2 shows

the percentage of respondents who are parents and report having had any of their children ever vaccinated. Columns 3-5 show the percentage of all respondents that either strongly

agree or somewhat agree with the statement above each column. All percentages are obtained using national weights. Columns 6-8 use data from the World Health Organization on

vaccine incidence. Columns 6-8 report the percentage of infants per country receiving the vaccine indicated in each column.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Table 2: Summary of studies sampling

Study Geographic scope Sampling methodology Survey modality Weights

Burkina Faso National Random digit dialing (RDD) Phone Yes

Colombia National Random digit dialing (RDD) Phone Yes

India Subnational, Slums in 2 cities Representative sample of slum dwellers living in vicinity of a community

toilet and located in Uttar Pradesh

Phone Yes

Mozambique Subnational, 2 cities 1) Random sample in urban and periurban markets stratiﬁed by gender and

type of establishment in Maputo; 2) Random sample representative of

communities in the Cabo Delgado, stratiﬁed on urban, semiurban, and

rural areas

Phone No

Nepal Subnational, 2 districts Random sample of poor households from randomly selected villages in

Kanchanpur

Phone Yes

Nigeria Subnational, 1 state 1) Random sample of individuals in Kaduna; 2) Sample of phone numbers

from a phone list of Kaduna state residents

Phone No

Pakistan 1 Subnational, 2 districts Random sample of individuals in administrative police units in two

districts of Punjab

Phone Yes

Pakistan 2 Subnational, 1 province Random digit dialing (RDD) on a random sample of all numerically

possible mobile phone numbers in the region of Punjab

Phone No

Russia Subnational, 61 regions Sample recruited from the Russian online survey company OMI (Online

Market Intelligence). Sampling targeted at having a minimum of

respondents per region, as well as representation of age, gender and

education groups.

Online Yes

Rwanda National Random digit dialing (RDD) Phone Yes

Sierra Leone 1 National Random digit dialing (RDD) Phone Yes

Sierra Leone 2 National A random sample of households in 195 rural towns across all 14 districts

of Sierra Leone

Phone No

Uganda 1 Subnational, 13 districts Sample of women in households from semi-rural and rural villages across

13 districts in Uganda, selected according to the likelihood of having

children

Phone No

Uganda 2 Subnational, 1 district Random sample of households in Kampala Phone No

USA National Nation-wide sample of adult internet users recruited through the market

research ﬁrm Lucid

Online Yes

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Figure 1: Acceptance rates overall and broken down by respondent characteristics.

All

By gender

By education

By age

0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100

USA

(National, 1959)

Russia

(Subnational, 61 regions, 22125)

All LMICs

Uganda 2

(Subnational, 1 district, 1366)

Uganda 1

(Subnational, 13 districts, 3362)

Sierra Leone 2

(National, 2110)

Sierra Leone 1

(National, 1070)

Rwanda

(National, 1355)

Pakistan 2

(Subnational, 1 province, 1492)

Pakistan 1

(Subnational, 2 districts, 1633)

Nigeria

(Subnational, 1 state, 1868)

Nepal

(Subnational, 2 districts, 1389)

Mozambique

(Subnational, 2 cities, 862)

India

(Subnational, Slums in 2 cities, 3348)

Colombia

(National, 1012)

Burkina Faso

(National, 977)

estimate

Subgroups Female

Male

Up to Secondary

More than Secondary

<55

55+

All

If a COVID-19 vaccine becomes available in [country], would you take it?

Figure 1 presents average acceptance of the COVID-19 vaccine across studies and subgroups within studies. For each study, we summarize sampling

information in parentheses in the following way: First, we indicate whether the geographic coverage of the sample is national or subnational. If the

coverage is subnational we provide further details. Second, we list the number of observations included in the study. In the plot, points represent

the estimated percentage of individuals who would take the vaccine. “No”, “Don’t know” and “Refuse” are taken as a single reference category.

Bars around each point indicate a 95% conﬁdence interval for the estimate. An estimate of average acceptance for all studies in LMICs (excluding

USA and Russia) is also shown.

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Table 3: Reasons to take the vaccine

Protection

Study N Self Family Community

Burkina Faso 651 76 42 7

(73, 79) (38, 46) ( 5, 9)

Colombia 756 91 23 12

(88, 93) (20, 26) (10, 14)

Mozambique 768 83 32 4

(80, 86) (27, 38) ( 2, 5)

Nepal 1341 96 34 20

(95, 98) (32, 37) (17, 22)

Nigeria 1424 89 35 21

(88, 91) (33, 38) (19, 23)

Rwanda 1152 98 26 11

(97, 99) (23, 28) ( 9, 13)

Sierra Leone 1 836 94 37 21

(92, 96) (34, 40) (18, 23)

Sierra Leone 2 1855 91 62 21

(88, 93) (57, 66) (16, 27)

Uganda 1 2885 96 36 9

(95, 97) (34, 38) ( 8, 10)

Uganda 2 1045 96 28 11

(95, 97) (25, 31) ( 9, 12)

All LMICs . 91 36 14

(86, 96) (28, 43) ( 9, 18)

Russia 5887 76 69 41

(74, 78) (67, 71) (38, 43)

USA 1313 94 92 89

(92, 95) (90, 94) (87, 91)

Table 3 shows percentage of respondents mentioning reasons why they

would take the Covid-19 vaccine. The number of observations and per-

centage correponds only to people who would take the vaccine. Respon-

dents in all countries could give more than one reason. A 95% conﬁdence

interval is shown between parentheses. Studies India, Pakistan 1 and Pak-

istan 2 are not included because they either did not include the question or

were not properly harmonized with the other studies.

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Figure 2: Reasons not to take the vaccine.

Concerned

about side

eﬀects

Concerned

about getting

coronavirus

from the

vaccine

Not concerned

about getting

seriously ill

Doesn’t think

vaccines are

eﬀective

Doesn’t think

Coronavirus

outbreak is as

serious as

people say

Doesn’t like

needles

Allergic to

vaccines

Won’t have

time to get

vaccinated

Mentions a

conspiracy

theory

Other reasons

0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100

USA

Russia

All LMICs

Uganda 2

Uganda 1

Sierra Leone 2

Sierra Leone 1

Rwanda

Pakistan 1

Nigeria

Nepal

Mozambique

Colombia

Burkina Faso

estimate

Number of observations [50,500) 500+

Why would you not take the COVID-19 vaccine?

Figure 2 shows the percentage of respondents mentioning reasons why they would not take the COVID-19 vaccine. In the plot, points represent

the estimated percentage of individuals that would not take the vaccine or do not know if they would take the vaccine for each possible response

option. Bars around each point indicate a 95% conﬁdence interval for the estimate. An estimated average for all studies in LMICs is also shown.

Size of points illustrates the number of observations in each response option. Studies India and Pakistan 2 are not included because they either did

not include the question or were not properly harmonized with the other studies.

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Figure 3: Trusted actors and institutions, broken down by expressed willingness to take a

COVID-19 vaccine.

Colombia

Nigeria

Sierra Leone 1

Uganda 2

Russia

Burkina Faso

Nepal

Rwanda

Sierra Leone 2

All LMICs

USA

Health workers

Government or

MoH

Family or

Friends

Famous person,

religious leader

or traditional

healers

Newspapers,

radio or online

groups

Other

Don’t know or

Refuse

Health workers

Government or

MoH

Family or

Friends

Famous person,

religious leader

or traditional

healers

Newspapers,

radio or online

groups

Other

Don’t know or

Refuse

estimate

Answer No, Don’t know Yes Any

Which of the following people would you trust MOST to help you decide whether you would get a COVID-19 vaccine?

Figure 3 shows histograms of actors and institutions respondents say they would trust most to help them decide whether to take the COVID-19

vaccine. Respondents were only permitted to select one most trusted actor or institution. Studies India, Mozambique, Pakistan 1, Pakistan 2 and

Uganda 1 are not included because they either did not include the question or were not properly harmonized with the other studies.

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Supplementary Appendix

Appendix A: Tables from results

Table 4: If a COVID-19 vaccine becomes available in [country], would you take it? Disaggre-

gated by subgroups

Gender Education Age

Country Average acceptability Female Male > Secondary Up to Secondary <55 55+

Burkina Faso 66.5 62.1 68.4 60.8 70.1 66.0 61.0

(63.5, 69.5) (56.3, 67.9) (65.0, 71.9) (55.9, 65.8) (66.4, 73.8) (57.2, 74.8) (-6.5, 128.6)

Colombia 74.9 73.5 77.3 78.1 73.4 74.5 73.8

(72.2, 77.6) (70.1, 77.0) (73.0, 81.7) (73.6, 82.5) (70.1, 76.8) (70.9, 78.0) (65.0, 82.6)

India 84.6 80.8 85.6 89.4 85.8 84.6 85.1

(82.8, 86.4) (77.6, 84.1) (83.7, 87.5) (83.5, 95.4) (82.6, 88.9) (82.8, 86.4) (80.8, 89.4)

Mozambique 89.1 86.2 91.3 86.1 89.7 88.4 91.7

(86.5, 91.7) (82.5, 90.0) (88.4, 94.1) (81.8, 90.4) (86.5, 92.8) (85.5, 91.3) (88.1, 95.4)

Nepal 96.6 96.4 96.4 . . 96.8 93.8

(95.5, 97.6) (94.6, 98.2) (95.1, 97.7) . . (95.6, 98.0) (90.4, 97.2)

Nigeria 76.2 74.9 77.0 . . 76.3 74.7

(74.3, 78.2) (71.7, 78.1) (74.6, 79.4) . . (74.3, 78.3) (65.8, 83.6)

Pakistan 1 76.1 72.2 80.1 83.6 74.0 76.0 80.8

(70.0, 82.3) (65.6, 78.8) (73.8, 86.4) (76.6, 90.5) (67.4, 80.5) (69.9, 82.2) (64.9, 96.7)

Pakistan 2 66.5 . . 71.4 64.2 . .

(64.1, 68.9) . . (67.3, 75.5) (61.2, 67.1) . .

Rwanda 84.9 79.4 88.0 71.4 87.7 85.2 73.3

(82.9, 86.8) (75.8, 83.0) (85.8, 90.2) (65.5, 77.2) (85.8, 89.7) (83.3, 87.2) (59.2, 87.3)

Sierra Leone 1 78.0 74.1 80.1 74.4 80.2 78.2 74.0

(75.5, 80.5) (69.5, 78.7) (77.2, 83.1) (70.1, 78.7) (77.0, 83.3) (75.7, 80.8) (61.4, 86.6)

Sierra Leone 2 87.9 88.6 87.7 88.8 87.8 87.4 90.0

(86.2, 89.6) (85.7, 91.5) (85.9, 89.5) (85.0, 92.5) (86.0, 89.6) (85.5, 89.3) (87.4, 92.6)

Uganda 1 85.8 85.8 . 80.1 84.8 85.8 .

(84.4, 87.2) (84.4, 87.2) . (74.4, 85.9) (83.2, 86.5) (84.4, 87.2) .

Uganda 2 76.5 74.9 78.0 68.6 79.8 76.9 73.7

(74.3, 78.7) (71.5, 78.3) (75.2, 80.9) (64.3, 72.9) (77.3, 82.2) (74.5, 79.3) (67.3, 80.0)

All LMICs 80.3 79.1 82.7 77.5 79.8 81.3 79.3

(74.9, 85.6) (73.3, 84.9) (77.4, 88.0) (71.4, 83.6) (74.1, 85.4) (76.1, 86.5) (72.6, 86.0)

Russia 30.4 22.6 38.5 31.0 29.6 28.4 40.0

(29.1, 31.7) (20.9, 24.2) (36.5, 40.5) (29.6, 32.5) (27.3, 32.0) (27.1, 29.7) (35.9, 44.0)

USA 64.6 56.1 73.4 72.3 51.5 61.8 69.4

(61.8, 67.3) (52.1, 60.1) (69.8, 76.9) (69.5, 75.0) (46.0, 57.0) (58.4, 65.2) (64.8, 73.9)

Table 4 shows percentage of respondents willing to take the COVID-19 vaccine as plotted in Figure 1. A 95% conﬁdence interval is

shown between parentheses

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Table 5: Reasons to take the vaccine- All categories

Protection If recommended by

Study N Self Family Community Health workers Government Other

Burkina Faso 651 76 42 7 6 19 2

(73, 79) (38, 46) ( 5, 9) ( 4, 8) (16, 22) ( 1, 3)

Colombia 756 91 23 12 1 2 6

(88, 93) (20, 26) (10, 14) ( 0, 2) ( 1, 3) ( 4, 7)

Mozambique 768 83 32 4 . 7 3

(80, 86) (27, 38) ( 2, 5) . ( 5, 8) ( 2, 4)

Nepal 1341 96 34 20 2 3 7

(95, 98) (32, 37) (17, 22) ( 1, 2) ( 2, 4) ( 5, 9)

Nigeria 1424 89 35 21 . 6 4

(88, 91) (33, 38) (19, 23) . ( 4, 7) ( 3, 5)

Rwanda 1152 98 26 11 1 5 1

(97, 99) (23, 28) ( 9, 13) ( 0, 1) ( 4, 6) ( 1, 2)

Sierra Leone 1 836 94 37 21 12 23 7

(92, 96) (34, 40) (18, 23) (10, 14) (20, 25) ( 5, 9)

Sierra Leone 2 1855 91 62 21 59 . 16

(88, 93) (57, 66) (16, 27) (54, 63) . (11, 21)

Uganda 1 2885 96 36 9 . 10 6

(95, 97) (34, 38) ( 8, 10) . ( 9, 12) ( 5, 7)

Uganda 2 1045 96 28 11 1 15 2

(95, 97) (25, 31) ( 9, 12) ( 1, 2) (13, 17) ( 1, 3)

All LMICs . 91 36 14 12 10 5

(86, 96) (28, 43) ( 9, 18) (-8, 31) ( 4, 16) ( 2, 8)

Russia 5887 76 69 41 11 6 18

(74, 78) (67, 71) (38, 43) (10, 13) ( 5, 7) (16, 20)

USA 1313 94 92 89 . 67 .

(92, 95) (90, 94) (87, 91) . (64, 70) .

Table 5 shows percentage of respondents mentioning reasons why they would take the Covid-19 vaccine. The number of observations and percentage correponds

only to people who would take the vaccine. Respondents in all countries could give more than one reason. A 95% conﬁdence interval is shown between

parentheses

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Table 6: Reasons not to take the vaccine

Study N Concerned

about side

effects

Concerned

about getting

coronavirus

from the

vaccine

Not concerned

about getting

seriously ill

Doesn’t think

vaccines are

effective

Doesn’t think

Coronavirus

outbreak is as

serious as

people say

Doesn’t like

needles

Allergic to

vaccines

Won’t have time

to get

vaccinated

Mentions a

conspiracy

theory

Other reasons

Burkina Faso 325 40.9 8.0 7.4 19.5 13.5 3.5 1.5 0.9 17.9 8.7

(35.5, 46.3) ( 5.0, 11.0) ( 4.5, 10.2) (15.1, 23.8) ( 9.8, 17.2) ( 1.5, 5.6) ( 0.2, 2.8) (-0.1, 1.9) (13.7, 22.1) ( 5.6, 11.8)

Colombia 202 31.0 18.1 8.0 10.2 2.3 0.6 0.4 0.5 10.0 31.6

(24.4, 37.6) (12.7, 23.4) ( 3.9, 12.0) ( 5.9, 14.5) ( 0.3, 4.3) (-0.6, 1.8) (-0.4, 1.3) (-0.5, 1.5) ( 5.8, 14.2) (25.1, 38.2)

Mozambique 74 . . 2.7 29.7 . . . . . 21.6

. . (-0.7, 6.1) (18.6, 40.8) . . . . . (12.2, 31.0)

Nepal 48 9.3 7.9 20.4 15.2 15.7 4.4 1.8 . 2.8 12.1

( 0.3, 18.2) (-0.4, 16.3) ( 6.7, 34.1) ( 3.2, 27.2) ( 4.0, 27.3) (-1.9, 10.6) (-1.9, 5.5) . (-1.5, 7.2) ( 0.8, 23.5)

Nigeria 410 21.5 26.1 15.9 9.3 . . 0.2 . 4.9 26.8

(17.5, 25.5) (21.8, 30.4) (12.3, 19.4) ( 6.4, 12.1) . . (-0.2, 0.7) . ( 2.8, 7.0) (22.5, 31.1)

Pakistan 1 441 23.0 21.9 29.4 26.0 22.1 11.5 . . 13.2 19.6

(15.1, 30.8) (14.3, 29.4) (20.9, 37.9) (18.0, 34.0) (12.8, 31.3) ( 5.5, 17.4) . . ( 7.1, 19.4) (10.4, 28.8)

Rwanda 70 38.6 10.1 18.7 21.5 5.8 7.0 5.6 . 21.3 25.8

(26.9, 50.3) ( 2.8, 17.3) ( 9.3, 28.1) (11.6, 31.4) ( 0.1, 11.4) ( 0.9, 13.2) ( 0.1, 11.1) . (11.5, 31.1) (15.3, 36.3)

Sierra Leone 1 234 53.5 37.9 14.6 7.5 4.2 3.0 0.9 4.0 20.3 5.7

(47.1, 59.9) (31.6, 44.2) (10.1, 19.2) ( 4.2, 10.9) ( 1.6, 6.8) ( 0.8, 5.2) (-0.4, 2.2) ( 1.4, 6.5) (15.1, 25.5) ( 2.8, 8.7)

Sierra Leone 2 254 57.9 . . 17.3 . 5.1 . 0.0 3.5 24.8

(50.1, 65.7) . . (11.9, 22.7) . ( 2.5, 7.8) . ( 0.0, 0.0) ( 1.3, 5.7) (19.3, 30.3)

Uganda 1 289 85.1 . 3.8 24.2 1.7 1.7 . 1.0 . 8.0

(80.7, 89.6) . ( 1.7, 5.9) (19.2, 29.2) ( 0.2, 3.2) ( 0.2, 3.2) . (-0.1, 2.2) . ( 4.9, 11.0)

Uganda 2 319 47.3 10.7 5.0 31.0 4.1 1.6 0.3 . 10.3 6.0

(42.2, 52.5) ( 7.1, 14.2) ( 2.7, 7.3) (25.9, 36.2) ( 1.9, 6.2) ( 0.2, 2.9) (-0.3, 0.9) . ( 7.0, 13.7) ( 3.4, 8.5)

All LMICs . 40.8 17.6 12.6 19.2 8.7 4.3 1.5 1.3 11.6 17.3

(25.3, 56.3) ( 8.7, 26.5) ( 6.4, 18.8) (13.8, 24.7) ( 2.4, 14.9) ( 1.7, 6.8) (-0.2, 3.3) (-0.6, 3.2) ( 6.1, 17.0) (11.0, 23.7)

Russia 16238 36.8 13.9 5.4 29.6 6.4 3.7 10.2 1.0 21.4 5.1

(35.2, 38.4) (12.8, 15.1) ( 4.6, 6.1) (28.1, 31.1) ( 5.6, 7.3) ( 3.1, 4.3) ( 9.2, 11.2) ( 0.7, 1.4) (20.1, 22.8) ( 4.4, 5.8)

USA 462 79.3 . 39.3 46.8 . . . . 6.0 49.1

(74.6, 84.0) . (33.5, 45.0) (41.0, 52.6) . . . . ( 3.4, 8.7) (43.3, 54.9)

Table 6 shows percentage of respondents mentioning reasons why they would not take the Covid-19 vaccine. The number of observations and percentage correponds only to people who would NOT take the vaccine. Respondents in all countries

could give more than one reason. A 95% conﬁdence interval is shown between parentheses

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Table 7: COVID-19 Vaccination Decision-making: most trusted source

Study N Take

vaccine?

Health

workers

Government

or Ministry of

Health

Family or

friends

Famous

person,

religious

leader or

traditional

healers

Newspapers,

radio or

online groups

Other Don’t know

or Refuse

Burkina Faso 651 Yes 57.1 15.1 19.6 0.9 2.0 4.8 0.4

(53.3, 60.9) (12.4, 17.9) (16.5, 22.7) ( 0.2, 1.6) ( 0.9, 3.1) ( 3.2, 6.4) (-0.1, 0.9)

Burkina Faso 325 No 40.7 8.5 16.2 3.7 1.6 25.1 4.2

(35.3, 46.1) ( 5.5, 11.6) (12.1, 20.2) ( 1.6, 5.7) ( 0.2, 3.0) (20.3, 29.8) ( 2.0, 6.4)

Burkina Faso 976 All 51.6 12.9 18.4 1.8 1.9 11.6 1.7

(48.5, 54.8) (10.8, 15.0) (16.0, 20.9) ( 1.0, 2.7) ( 1.0, 2.7) ( 9.6, 13.6) ( 0.9, 2.5)

Colombia 756 Yes 41.4 12.7 36.9 0.9 1.7 . 6.3

(37.8, 45.0) (10.3, 15.2) (33.4, 40.4) ( 0.2, 1.5) ( 0.8, 2.7) . ( 4.6, 8.1)

Colombia 202 No 31.5 7.6 35.5 5.3 1.4 . 18.8

(24.9, 38.1) ( 3.8, 11.3) (28.8, 42.1) ( 2.2, 8.4) (-0.2, 3.0) . (13.2, 24.3)

Colombia 958 All 39.3 11.6 36.6 1.8 1.7 . 8.9

(36.2, 42.5) ( 9.6, 13.7) (33.5, 39.7) ( 1.0, 2.6) ( 0.9, 2.5) . ( 7.1, 10.7)

Nepal 1341 Yes 44.7 0.7 36.2 16.1 0.4 0.5 1.3

(40.9, 48.6) ( 0.3, 1.1) (33.5, 39.0) (13.1, 19.1) ( 0.0, 0.9) ( 0.1, 0.8) ( 0.7, 2.0)

Nepal 48 No 30.2 2.1 18.7 16.8 0.0 1.0 31.2

(14.6, 45.9) (-2.1, 6.2) ( 5.6, 31.7) ( 4.0, 29.6) ( 0.0, 0.0) (-1.1, 3.2) (13.6, 48.9)

Nepal 1389 All 44.2 0.8 35.6 16.1 0.4 0.5 2.4

(40.5, 47.9) ( 0.3, 1.2) (32.9, 38.3) (13.3, 18.9) ( 0.0, 0.8) ( 0.1, 0.8) ( 1.5, 3.3)

Nigeria 1424 Yes 63.8 21.6 6.3 5.1 . 2.6 0.6

(61.3, 66.3) (19.4, 23.7) ( 5.0, 7.5) ( 4.0, 6.3) . ( 1.8, 3.4) ( 0.2, 1.0)

Nigeria 410 No 37.6 5.6 13.9 17.8 . 8.5 16.6

(32.9, 42.3) ( 3.4, 7.8) (10.5, 17.3) (14.1, 21.5) . ( 5.8, 11.3) (13.0, 20.2)

Nigeria 1834 All 58.0 18.0 8.0 8.0 . 3.9 4.2

(55.7, 60.2) (16.2, 19.8) ( 6.7, 9.2) ( 6.7, 9.2) . ( 3.0, 4.8) ( 3.3, 5.1)

Rwanda 1152 Yes 23.8 27.4 15.1 1.0 0.7 32.0 0.1

(21.3, 26.2) (24.9, 30.0) (13.0, 17.2) ( 0.4, 1.5) ( 0.2, 1.2) (29.3, 34.7) (-0.1, 0.2)

Rwanda 70 No 10.1 15.6 12.8 2.9 0.0 53.2 5.5

( 2.8, 17.4) ( 6.9, 24.3) ( 4.8, 20.8) (-1.1, 6.9) ( 0.0, 0.0) (41.2, 65.1) ( 0.1, 11.0)

Rwanda 1222 All 23.0 26.7 15.0 1.1 0.6 33.2 0.4

(20.6, 25.3) (24.3, 29.2) (13.0, 17.0) ( 0.5, 1.7) ( 0.2, 1.1) (30.5, 35.8) ( 0.0, 0.8)

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Table 7: COVID-19 Vaccination Decision-making: most trusted source (continued)

Study N Take

vaccine?

Health

workers

Government

or Ministry of

Health

Family or

friends

Famous

person,

religious

leader or

traditional

healers

Newspapers,

radio or

online groups

Other Don’t know

or Refuse

Sierra Leone 1 836 Yes 47.6 36.9 7.3 3.8 0.5 3.1 0.8

(44.2, 51.0) (33.6, 40.2) ( 5.5, 9.1) ( 2.5, 5.1) ( 0.0, 1.0) ( 1.9, 4.2) ( 0.2, 1.4)

Sierra Leone 1 234 No 31.1 17.1 12.1 7.7 0.5 29.4 2.2

(25.1, 37.1) (12.2, 21.9) ( 7.9, 16.3) ( 4.3, 11.2) (-0.4, 1.3) (23.5, 35.3) ( 0.3, 4.1)

Sierra Leone 1 1070 All 44.0 32.5 8.4 4.7 0.5 8.9 1.1

(41.0, 46.9) (29.7, 35.4) ( 6.7, 10.0) ( 3.4, 6.0) ( 0.1, 0.9) ( 7.1, 10.6) ( 0.5, 1.8)

Sierra Leone 2 1855 Yes 94.1 . 3.0 0.9 0.1 1.9 0.0

(92.5, 95.7) . ( 2.0, 4.0) ( 0.3, 1.5) (-0.1, 0.2) ( 1.2, 2.7) ( 0.0, 0.0)

Sierra Leone 2 254 No 54.7 . 3.9 7.5 0.0 33.9 0.0

(46.5, 62.9) . ( 1.4, 6.5) ( 2.9, 12.0) ( 0.0, 0.0) (26.3, 41.4) ( 0.0, 0.0)

Sierra Leone 2 2109 All 89.3 . 3.1 1.7 0.0 5.8 0.0

(87.2, 91.5) . ( 2.2, 4.1) ( 0.8, 2.6) ( 0.0, 0.1) ( 4.4, 7.2) ( 0.0, 0.0)

Uganda 2 1045 Yes 38.3 36.5 9.8 7.0 5.0 3.5 0.0

(35.5, 41.1) (33.5, 39.4) ( 7.9, 11.6) ( 5.4, 8.6) ( 3.6, 6.3) ( 2.5, 4.6) ( 0.0, 0.0)

Uganda 2 319 No 24.5 19.1 8.5 7.8 7.5 32.0 0.6

(19.9, 29.0) (14.5, 23.7) ( 5.4, 11.5) ( 4.8, 10.9) ( 4.5, 10.5) (26.7, 37.3) (-0.2, 1.5)

Uganda 2 1364 All 35.0 32.4 9.5 7.2 5.6 10.2 0.1

(32.7, 37.4) (29.9, 35.0) ( 7.9, 11.1) ( 5.8, 8.6) ( 4.3, 6.8) ( 8.6, 11.8) (-0.1, 0.3)

All LMICs . Yes 51.3 21.6 16.8 4.5 1.5 6.9 1.2

(33.7, 68.9) ( 9.4, 33.8) ( 5.7, 27.9) ( 0.1, 8.8) (-0.1, 3.1) (-3.4, 17.2) (-0.6, 3.0)

All LMICs . No 32.5 10.8 15.2 8.7 1.6 26.1 9.9

(21.8, 43.3) ( 4.8, 16.8) ( 7.4, 23.0) ( 4.0, 13.4) (-0.9, 4.1) (10.2, 42.1) ( 0.6, 19.2)

All LMICs . All 48.1 19.3 16.8 5.3 1.5 10.6 2.4

(31.6, 64.5) ( 8.3, 30.3) ( 6.1, 27.5) ( 1.0, 9.6) (-0.2, 3.3) ( 0.7, 20.5) (-0.1, 4.9)

Russia 5887 Yes 47.1 24.4 16.5 2.0 4.1 5.8 .

(44.6, 49.7) (22.2, 26.7) (14.6, 18.5) ( 1.2, 2.8) ( 3.1, 5.1) ( 4.5, 7.0) .

Russia 16238 No 31.1 6.9 33.1 2.2 5.3 21.3 .

(29.6, 32.7) ( 6.1, 7.8) (31.5, 34.7) ( 1.7, 2.8) ( 4.5, 6.0) (20.0, 22.7) .

Russia 22125 All 36.0 12.3 28.1 2.2 4.9 16.6 .

(34.7, 37.3) (11.3, 13.2) (26.8, 29.3) ( 1.7, 2.6) ( 4.3, 5.5) (15.6, 17.6) .

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Table 7: COVID-19 Vaccination Decision-making: most trusted source (continued)

Study N Take

vaccine?

Health

workers

Government

or Ministry of

Health

Family or

friends

Famous

person,

religious

leader or

traditional

healers

Newspapers,

radio or

online groups

Other Don’t know

or Refuse

USA 1313 Yes 38.1 33.0 8.7 1.7 . 18.6 0.0

(34.8, 41.5) (29.8, 36.1) ( 6.7, 10.7) ( 0.7, 2.6) . (16.1, 21.1) ( 0.0, 0.0)

USA 462 No 25.3 21.3 18.7 4.2 . 30.3 0.2

(20.4, 30.3) (16.6, 26.0) (13.9, 23.4) ( 1.6, 6.9) . (25.0, 35.6) (-0.2, 0.7)

USA 1775 All 34.5 29.7 11.5 2.4 . 21.9 0.1

(31.7, 37.3) (27.0, 32.3) ( 9.5, 13.4) ( 1.4, 3.4) . (19.5, 24.2) (-0.1, 0.2)

Table 7 shows percentage of respondents that mention actors who they would trust the most to help them decide whether to get a COVID-19 vaccine. For all countries

the questions was asked regardless if respondent would take a vaccine, would not take it, does not know or does not respond. For India respondents were able to mention

more than one actor, for the rest of countries only one actor was allowed. While rows should sum to 100%, rounding makes number slightly above or below. A 95%

conﬁdence interval is shown between parentheses.

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Figure 4: Trusted actors and institutions, broken down by gender.

Colombia

Nigeria

Sierra Leone 1

Uganda 2

Russia

Burkina Faso

Nepal

Rwanda

Sierra Leone 2

All LMICs

USA

Health workers

Government or

MoH

Family or

Friends

Famous person,

religious leader

or traditional

healers

Newspapers,

radio or online

groups

Other

Don’t know or

Refuse

Health workers

Government or

MoH

Family or

Friends

Famous person,

religious leader

or traditional

healers

Newspapers,

radio or online

groups

Other

Don’t know or

Refuse

estimate

Answer Female Male All

If a COVID-19 vaccine becomes available in [country], would you take it?

Figure 4 shows histograms of actors and institutions that respondents say they would trust most to help them decide whether or not to take the

COVID-19 vaccine. Respondents were only permitted to select one most trusted actor or institution. Responses are broken down by acceptance of

the COVID-19 vaccine.

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Table 8: Differences in means

Estimate Std.error P.value Degrees of freedom Baseline category Variable

0.04 0.01 0.00 10 Male Gender

0.00 0.01 0.71 10 55+ Age

0.02 0.03 0.41 10 Up to secondary Education

Table 8 shows the results of subgroup mean differences. Subgroup differences were gener-

ated considering only LMICs. The differences in means for gender and age do not include

the Uganda 1 study, which only included female respondents under the age of 55.

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Table 9: Observations and missingness patterns.

Country N obs Take vaccine Gender Education Age

Burkina Faso 977 1.00 0.00 0.00 0.88

Colombia 1,012 0.95 0.00 0.00 0.32

India 3,348 1.00 0.00 0.78 0.00

Mozambique 862 0.98 0.00 0.04 0.00

Nepal 1,389 1.00 0.05 1.00 0.05

Nigeria 1,868 0.98 0.00 1.00 0.00

Pakistan 1 1,633 0.99 0.00 0.01 0.00

Pakistan 2 1,492 1.00 1.00 0.00 1.00

Russia 22,125 1.00 0.00 0.00 0.00

Rwanda 1,355 0.90 0.00 0.00 0.00

Sierra Leone 1 1,070 1.00 0.00 0.03 0.00

Sierra Leone 2 2,110 1.00 0.00 0.00 0.01

Uganda 1 3,362 0.94 0.00 0.19 0.05

Uganda 2 1,366 1.00 0.00 0.00 0.00

USA 1,959 0.91 0.00 0.00 0.00

Table 9 show the percentage of observations that are not missing values

for each variable included in Figure 1.

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Appendix B: Question wording and answer options per study

Table 10: Question wording and answer options- Vaccine acceptance

Study Question Fig. 1 Recoding Fig. 1

Burkina Faso If a COVID-19 vaccine became available in Burkina Faso, would you take

it?

Yes; No; Don’t know; Refuse

Colombia If a COVID-19 vaccine became available in Colombia, would you take it? Yes; No

India If a vaccine for coronavirus gets introduced, would you like to get it? Yes, only for free; Yes, even if I have to pay; No

Mozambique When a COVID-19 vaccine becomes available in the future, would you

take it?

Yes; No; Refuse

Nepal Should a vaccine against COVID become available in Nepal, would you

take it?

Yes; No

Nigeria If a COVID-19 vaccine became available in Niger, would you take it? Yes/Agree; No/Disagree

Pakistan 1 If a vaccine against the coronavirus becomes available, do you plan to get

vaccinated?

Yes; No; Don’t know; Refuse

Pakistan 2 If a vaccine against the coronavirus becomes available, do you plan to get

vaccinated?

Absolutely yes; Yes; Neutral; No; Absolutely no

Russia If a COVID-19 vaccine became available in Russia, would you take it? Yes, if a Russian vaccine will be available; Yes, if

an imported vaccine will be available; No; Not

sure

Rwanda If a COVID-19 vaccine became available in Rwanda, would you take it? Yes; No

Sierra Leone 1 If a COVID-19 vaccine became available in Sierra Leone, would you take

it?

Yes; No

Sierra Leone 2 Should a vaccine against COVID become available in Sierra Leone, would

you take it?

Yes; No

Uganda 1 When a COVID-19 vaccine becomes available in Uganda, would you take

it?

Yes; No

Uganda 2 If a COVID-19 vaccine becomes available in Uganda, would you take it? Yes; No; Don’t know; Refuse

USA If a COVID-19 vaccine becomes available in the United States, would you

take it?

Deﬁnitely yes; Probably yes; Probably not;

Deﬁnitely not, Refuse

Table 13 presents question wording and answer options from answers used in Figure 1 to get estimated vaccine acceptance. Answer options are

separated by a semicolon. In India options ’Yes, only for free’ and ’Yes, even if I have to pay’ are both recoded as ’Yes’. In Pakistan 2, ’Absolutely

yes’ is recoded as ’Yes’, ’Neutral’ is recoded as ’Don’t know’ and ’Absolutely no’ is recoded as ’No’. In Russia, ’Yes, if a Russian vaccine will be

available’ and ’Yes, if an imported vaccine will be available’ are both recoded as ’Yes’. In USA ’Deﬁnitely yes’ and ’Probably yes’ are recoded as

’Yes’, and ’Probably not’ and ’Deﬁnitely not’ are recoded as ’No’

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Table 11: Question wording and answer options- Reasons to take vaccine

Study Question Tab. 2 Protection: self Protection: family Protection: community

Burkina Faso Why would you

take it?

Protection: self

(general); Protection:

self, chronic condition

Protection: family Protection: community

Colombia Why would you

take it?

Protection: self

(general); Protection:

self, chronic condition

Protection: family Protection: community

Mozambique Why would you

take it?

I want to protect myself

from having COVID-19

in the future

I want to protect my

family/members of my

household against

having COVID-19 in the

future

I want to protect my

community against

having COVID-19 in the

future

Nepal Why would you

take it?

Protection: self

(general); Protection:

self, chronic condition/

vulnerable to covid

Protection: family Protection: community

Nigeria Why would you

take it?

I want to protect myself

from having COVID-19

in the future

I want to protect my

family/members of my

household against

having COVID-19 in the

future

I want to protect my

community against

having COVID-19 in the

future

Russia Why would you

take it?

Protection: self Protection: family Protection: community

Rwanda Why would you

take it?

Protection: self

(general); Protection:

self, chronic condition

Protection: family Protection: community

Sierra Leone 1 Why would you

take it?

Protection: self

(general); Protection:

self, chronic condition

Protection: family Protection: community

Sierra Leone 2 Why would you

take it?

I will take a vaccine to

protect myself from

having COVID-19 in the

future

I will take a vaccine to

protect my

family/members of my

household against

having COVID-19 in the

future

I will take a vaccine to

protect my community

against having

COVID-19 in the future

Uganda 1 Why would you

take it?

Protect myself from

having COVID-19

Protect my

family/members of my

household against

COVID-19

Protect my community

against COVID-19

Uganda 2 Why would you

take it?

Protection: self

(general); Protection:

self, chronic condition/

vulnerable to Covid

Protection: family Protection: community

USA Why would you

take it?

To protect myself from

COVID-19 infection

To protect my family

from COVID-19

infection

To protect my

community from

COVID-19 infection

Table 10 presents question wording and answer options used in Table 2 to get an estimated percentage of reasons to take the

COVID-19 vaccine. Columns ’Protection: self’, ’Protection: family’ and ’Protection: community’ show the answer options

that were recoded in each category. Answer options are separated by a semicolon.

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Table 12: Question wording and answer options- Reasons not to take the vaccine

Study Question

Fig. 2

Concerned about side

effects

Concerned about

getting COVID-19

from the vaccine

Not concerned about

getting seriously ill

Doesn’t think vaccines

are effective

Doesn’t think

COVID-19 outbreak is

as serious as people say

Doesn’t like needles Allergic to vaccines Won’t havetime to get

vaccinated

Mentions a conspiracy

theory

Other reasons

Burkina

Faso

Why would

you not take

it?

. Concerned about getting

coronavirus from the

vaccine

Not concerned about

getting seriously ill

Doesn’t think vaccines

work very well

Coronavirus outbreak is

not as serious as people

say

Doesn’t like needles Allergic to vaccines Won’t havetime to get

vaccinated

Conspiracy theory Other reason

Colombia Why would

you not take

it?

. Concerned about getting

coronavirus from the

vaccine

Not concerned about

getting seriously ill

Doesn’t think vaccines

work very well

Coronavirus outbreak is

not as serious as people

say

Doesn’t like needles Allergic to vaccines Won’t havetime to get

vaccinated

Conspiracy theory Other reason; Already

immune; Doesn’t have

symptoms

Mozambique Why would

you not take

it?

. . I am not concerned

about the risk associated

with me/my relatives

getting COVID-19

I don’t think vaccines

are effective

The coronavirus

outbreak is not as

serious as people say it

I don’t like needles . I won’t have time to go

get vaccinated

. Other

Nepal Why would

you not take

it?

I would be concerned

about the side effects

from the vaccine

I would be concerned

about getting infected

with coronavirus from

the vaccine

I’m not concerned about

getting seriously ill from

the virus

I don’t think vaccines

work very well

The coronavirus

outbreak is not as

serious as people say it

I don’t like needles I’m allergic to vaccines I won’t have time to go

get vaccinated

I think there is a

conspiracy theory with

vaccinations

Other

Nigeria Why would

you not take

it?

I would be concerned

about the side effects

from the vaccine

I would be concerned

about getting infected

with coronavirus from

the vaccine

I’m not concerned about

getting seriously ill from

the virus

I don’t think vaccines

work very well

The coronavirus

outbreak is not as

serious as people say it

I don’t like needles I’m allergic to vaccines I won’t have time to go

get vaccinated

The virus is a hoax /

does not exist; The

vaccine has microchips/

tracking devices

Other; Religious /

community leaders

advising me not to take

Pakistan 1 Why would

you not take

it?

I am concerned about

side effects from the

vaccine

I would be concerned

about getting infected

with coronavirus from

the vaccine

I don’t consider myself

or my family members

at risk of getting

seriously ill

I don’t think the vaccine

would work well

The coronavirus

infection is just like the

ﬂu and doesn’t warrant a

vaccine

I don’t like needles . . Vaccines are just

Western conspiracies to

stunt the growth of

Muslims

Muslims are prohibited

from taking a vaccine

before a disease is

contracted

Russia Why would

you not take

it?

Afraid of side effects Afraid of getting

infected with

coronavirus from the

vaccine

Not concerned with

getting seriously ill from

the virus

Don’t think vaccines are

effective

Coronavirus outbreak is

not as serious as people

say it is

Afraid of needles Can get allergic reaction Don’t have time to get

vaccinated

Hoax: Virus don’t exist;

Hoax: Virus was

designed so vaccines

won’t work; Proﬁt

motivation:

pharmaceutical

companies; Control:

contain things that

control our minds;

Global politics: China

can take advantage

Other; I already had

coronavirus and don’t

need a vaccine

Rwanda Why would

you not take

it?

. Concerned about getting

coronavirus from the

vaccine

Not concerned about

getting seriously ill

Doesn’t think vaccines

work very well

Coronavirus outbreak is

not as serious as people

say

Doesn’t like needles Allergic to vaccines Won’t havetime to get

vaccinated

Conspiracy theory Other reason; Already

immune; Doesn’t have

symptoms

Sierra

Leone 1

Why would

you not take

it?

. Concerned about getting

coronavirus from the

vaccine

Not concerned about

getting seriously ill

Doesn’t think vaccines

work very well

Coronavirus outbreak is

not as serious as people

say

Doesn’t like needles Allergic to vaccines Won’t havetime to get

vaccinated

Conspiracy theory Other reason

Sierra

Leone 2

Why would

you not take

it?

I will not take a vaccine

because I am concerned

about side effects

I will not take a vaccine

because I am not

concerned about the risk

associated with me/my

relatives getting

COVID-19ne is

I will not take a vaccine

because I am not

concerned about the risk

associated with me/my

relatives getting

COVID-19

I will not take a vaccine

because they are not

effective

. I will not take a vaccine

because I don’t like

needles

. I will not take a vaccine

because I don’t have

time

I will not take a vaccine

because I don’t think

COVID exists

I will not take a vaccine

because of other

reasons; I will not take a

vaccine because my

community objects it; I

will not take a vaccine

because I don’t have

symptoms; I will not

take a vaccine because I

am immune; I will not

take a vaccine because it

is provided by foreign

aid; I will not take a

vaccine because I don’t

know what a vaccine is

Uganda 1 Why would

you not take

it?

Concerned about the

side effects from the

vaccine/vaccines

I am not worried that my

relatives will get

COVID-19

I am not worried that my

relatives will get

COVID-19

I don’t think vaccines

are effective

Coronavirus is not as

serious as people say it

I don’t like needles . I won’t have time to go

get vaccinated

. Other; It will cost too

much

Uganda 2 Why would

you not take

it?

I would be concerned

about the side effects

from the vaccine

I would be concerned

about getting infected

with coronavirus from

the vaccine

I’m not concerned about

getting seriously ill from

the virus

I don’t think vaccines

work very well

The coronavirus

outbreak is not as

serious as people say it

I don’t like needles I’m allergic to vaccines I won’t have time to go

get vaccinated

I think there is a

conspiracy theory with

vaccinations

Other

USA Why would

you not take

it?

I am concerned about

possible side effects

. I am not concerned

about getting the virus

I don’t think vaccines

are effective

. . . . Mentions a conspiracy

theory (recoded from

responses in "Other"

category)

Cost or difﬁculty of

getting the vaccine

Table 11 presents question wording and answer options used in Figure 2 to get an estimated percentage of reasons not to take the COVID-19 vaccine. Columns 3-10 show the answer options that were recoded in each category. Answer options are separated by a semicolon.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Table 13: Question wording and answer options- Trusted actors and institutions

Study Question Fig. 3 Health workers Government or

Ministry of Health

Family or friends Famous person,

religious leader or

traditional healers

Newspapers, radio or

online groups

Other

Burkina Faso Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

Doctors or other staff at

a community health

clinic

Advice from Ministry of

Health

Family members;

Friends you see and talk

to; Friends you-’ve made

online

Famous person;

Religious leaders;

Traditional Healers

Traditional media

(newspaper,

radio);Online medical

discussion groups

Other/ Someone else

Colombia Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

Doctors or other staff at

a community health

clinic

Advice of the Instituto

Nacional de Salud

Family members;

Friends you see and talk

to; Friends you’ve made

online

Famous person;

Religious leaders;

Traditional Healers

Traditional media

(newspaper,

radio);Online medical

discussion groups

Other/ Someone else

Nepal Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

Doctors or other staff at

a community health

clinic

Advice of the national

health service

Family members;

Friends you see and talk

Famous person;

Religious leaders;

Traditional healers

Traditional media

(newspaper, radio);

Online medical

discussion groups

None of these/ Someone

else; Advice of the

WHO

Nigeria Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

Medical professionals

like doctors

NCDC; Government

ofﬁcials

Family members and

friends

Religious leaders . Some other sourcer;

Other community

leaders

Russia Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

Health workers Government; Health

Ministry

Family; Friends Famous people;

Religious leaders

Traditional media;

Online medical

discussion groups

Other

Rwanda Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

Doctors or other staff at

a community health

clinic

Advice of the Ministry

of Health

Family members;

Friends you see and talk

to; Friends you’ve made

online

Famous person;

Religious leaders;

Traditional healers

Traditional media

(newspaper, radio);

Online medical

discussion groups

None of these/ Someone

else; Myself

Sierra Leone 1 Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

Doctors or other staff at

a community health

clinic

Advice of the Ministry

of Health and Sanitation

Family members;

Friends you see and talk

to; Friends you’ve made

online

Famous person;

Religious leaders;

Traditional healers

Traditional media

(newspaper, radio);

Online medical

discussion groups

None of these/ Someone

else; I do trust

NOBODY

Sierra Leone 2 Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

A doctor, nurse or other

staff at a community

health clinic; A country

medical staff

. Family; Friends you see

and talk to; Friends

you’ve made online

A famous person; A

religious leader; A

traditional healer

Online medical

discussion groups

None of these/ Someone

else

Uganda 2 Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

Doctors or other staff at

a community health

clinic

Advice of the national

health service

Family; Friends you see

and talk to; Friends

you’ve made online

Famous person;

Religious leaders;

Traditional healers

Traditional media

(newspaper, radio);

Online medical

discussion groups

None of these; Someone

else

USA Which of the following people would you trust

MOST to help you decide whether you would get

a COVID-19 vaccine, if one becomes available?

Your doctor or

healthcare provider

Donald Trump; Anthony

Fauci; Your state’s

governor; Local public

health authority

Friends or family Your pastor, priest, or

other religious leader

. Other; Joe Biden

Table 12 presents question wording and answer options used in Figure 3 to get the percentage of respondents mentioning each actor or instituion that they would trust to decide whether to get the COVID-19 vaccine. Columns 3-8 show the

answer options that were recoded in each category. Answer options are separated by a semicolon.

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Appendix C: Additional contributors

We thank Madison Levine, Sellu Kallon, Vasudha Ramakrishna, Sarah Ryan for valuable intellec-

tual contributions and research assistance.

IPA would like to thank staff in Burkina Faso, Colombia, Rwanda, Sierra Leone and the United

States for their intellectual contributions, research assistance, and support throughout the RECOVR

survey: Achille Mignondo Tchibozo, Michael Rosenbaum, Hugo Salas, Filippo Cuccaro, Jean

Leodomir Habarimana Mfura, Doug Kirke-Smith, Savanna Henderson, Shahana Hirji, Kyle Hol-

loway, Margarita Cabra.

Ekaterina Borisova and Georgiy Syunyaev would like to thank staff at Online Market Intelligence

survey agency and Kirill Chmel and Vladimir Zabolotsky for their intellectual contributions and

research assistance.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Appendix D: Sample Descriptions

The case history data for all countries in our sample is extracted from the Johns Hopkins University

Center for Systems Science and Engineering (JHU CSSE) database.1

Burkina Faso, Research for Effective COVID-19 Responses (RECOVR) National RDD Sam-

ple, Innovations for Poverty Action (IPA)

COVID-19 Experience

• First conﬁrmed case: March 9, 2020

• Number of conﬁrmed cases 2,335 as of October 15, 2020

• Number of deaths: 65 as of October 15, 2020

Target Population: A random sample of all adults with mobile phone numbers in the country,

based on national communications authority number allocation plans.

Original Study Design: N/A

COVID-19 Survey Design: Numbers were called via random digit dialing (RDD), stratiﬁed by

mobile network operator market share for a two-round panel survey.

Sampling Frame: All mobile phone numbers in Burkina Faso.

Survey Dates: October 15 to December 4, 2020 (Round 1 June 6-15, 2020)

Sample size, tracking and attrition: Sample includes 977 respondents from the second round of a

panel. In the ﬁrst round conducted between June 6 to 15, 2020, 1,356 individual surveys were con-

tacted through Random Digit Dialing (RDD) from the sampling frame of all mobile phone numbers

in Burkina Faso. 2,313 working numbers yielded 1,383 eligible respondents for a completion rate

of 98% of eligible respondents.

Sampling Weights: Post-stratiﬁcation weights are computed to adjust for differential attrition be-

tween the ﬁrst and second rounds of the RDD panel, weighting on gender, region, and educational

attainment.

IRB Approval: This research was approved via IPA IRB Protocol 15608, and the Burkina Faso

Institutional Ethics Committee for Health Sciences Research, approval A13-2020.

Colombia, Research for Effective COVID-19 Responses (RECOVR) National RDD Sample,

Innovations for Poverty Action (IPA)

COVID-19 Experience

1Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time.

The Lancet infectious diseases, 20(5), 533-534.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

• First conﬁrmed case: March 6, 2020

• Number of conﬁrmed cases: 456,689 as of August 15, 2020

• Number of deaths: 14,810 as of August 15, 2020

Target Population: A random sample of all numerically possible mobile phone numbers in the

country, based on national communications authority number allocation plans.

Original Study Design: N/A

COVID-19 Survey Design:

Sampling Frame: Numbers were called via random digit dialing (RDD), stratiﬁed by mobile net-

work operator market share.

Survey Dates: August 15-25, 2020 (Round 1 May 8-15, 2020)

Sample size, tracking and attrition: Sample includes 1,012 respondents contacted in the second

round of a panel of 1,507.

Sampling Weights: Post-stratiﬁcation weights are computed to adjust for differential attrition be-

tween the ﬁrst and second rounds of the RDD panel, weighting on gender, region, and educational

attainment.

IRB Approval: This research was approved via IPA IRB Protocol 15582.

India, Coping with COVID-19 in Slums: Evidence from India Subnational sample, Nova

School of Business and Economics, The Institute for Fiscal Studies, University of St. Andrews

COVID-19 Experience

• First conﬁrmed case: January 30, 2020

• Number of conﬁrmed cases: 198,370 as of June 1, 2020

• Number of deaths: 5,608 as of June 1, 2020

Target Population: Random subset of slum populations in Lucknow and Kanpur, Uttar Pradesh,

India. Socio-economic variables are only collected for a representative sample of the population

relying on community toilets or open defecation to fulﬁl their sanitation needs.

Original Study Design: Randomized controlled trial, with complete census of households within

142 slums (September to December 2017), and a series of household and caretaker surveys, objec-

tive measurements, incentivized behavioural measurements, and a Structured Community Activity,

collected for a sub-set of 100 slums between April 2018 and September 2019.

Intervention: Catchment areas of CTs were randomly allocated to two interventions. The ﬁrst

intervention aimed at community toilet improvements by offering caretakers the choice of a grant

to be spent for improvements in the facility. Following the grant, caretakers were offered a large

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

ﬁnancial reward conditional on the cleanliness of the facility. The second intervention added to

this CT improvement awareness creation among potential users through face-to-face information

sessions, leaﬂets, monthly reminders using voice messages sent to mobile phones, and posters

hung in the CTs.

Sampling Frame: A two-step sampling was applied, ﬁrst, study households from the main study

sample were sampled, then households from the whole slum population were added.

Survey Dates: Baseline: June to July 2020, Follow-up 1: October toNovember 2020, Follow-up 2:

December 16, 2020 toJanuary 18, 2021.

Sample size, tracking and attrition: 3,991 households, with a mean of 28 households per cluster

(142). Non-response Baseline: 25%, Attrition rate Baseline to Follow-up (1 and 2): 13%, Ran-

domly selected replacement households for Follow-up (1 and 2): 1,277.

Sampling Weights: Included

IRB Approval: Approval was secured from London School of Economics (REC ref. 1132). The

pre-analysis plan was registered on the AEA RCT registry (RCT ID AEARCTR-0006564).

Mozambique Subnational sample, International Growth Center, Nova School of Business

and Economics

COVID-19 Experience

• First conﬁrmed case: March 22, 2020

• Number of conﬁrmed cases: 12,777 as of October 30, 2020

• Number of deaths: 91 as of October 30, 2020

Target Population: Microentrepreneurs in urban markets of Maputo and household heads from

the province of Cabo Delgado.

Original Study Design: Initial data were collected in-person in two different studies. For microen-

trepreneurs in Maputo, the data were collected between October 2013 and April 2014 (baseline),

and between July and November 2015 (endline).2For household heads in Cabo Delgado, the data

were collected in-person between August and September 2016 (baseline), and between August and

September 2017 (endline).3

Intervention: The ﬁrst study was dedicated to analyzing the impacts of interventions targeting

microentrepreneurs in urban markets on ﬁnancial inclusion and literacy. The second study focused

on the role of information to counteract the political resource curse after a substantial natural gas

discovery.

Sampling Frame: The ﬁrst initial sample was selected by in-ﬁeld random sampling in 23 urban and

periurban markets in Maputo and Matola. Stratiﬁcation was based on the gender of the respondent

2Original study: http://catiabatista.org/bsv_mm_urban.pdf

3Original study: https://www.aeaweb.org/articles?id=10.1257/aer.20190842

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

and on the type of establishment (stall vs. store). The second initial sample was selected to be

representative of 206 communities in the province of Cabo Delgado, randomly drawn from the list

of all 421 polling locations in the sampling frame, stratiﬁed on urban, semiurban, and rural areas.

This survey in this paper was done by phone.

Survey Dates: October 30 to November 21, 2020 (Maputo) and November 6 to November 30, 2020

(Pemba).

Sample size, tracking and attrition: 554 microentrepreneurs from Maputo and 308 households

from Cabo Delgado.

Sampling Weights: N/A

\emph{IRB Approval: The approval was secured from Universidade Nova de Lisboa on July 14,

2020.

Nepal, Western Terai Panel Survey (WTPS) Subnational sample, Yale University, Yale Re-

search Initiative on Innovation and Scale (Y-RISE)

COVID-19 Experience

• First conﬁrmed case: January 23, 2020

• Number of conﬁrmed cases: 233,452 as of December 1, 2020

• Number of deaths: 1,529 as of December 1, 2020

Target Population: Rural households in the districts of Kailali and Kanchanpur.

Original Study Design: Initial baseline data was collected in-person in July of 2019, and 5 rounds

of phone survey data were collected between August 12, 2019 and January 4, 2020.

Sampling Frame: The phone survey sample includes 2,636 rural households in the districts of

Kailali and Kanchanpur, which represent the set of households that responded to phone surveys

from an original sample of 2,935 households. This sample was constructed by randomly sampling

33 wards from 15 of the 20 sub-districts in Kailali and Kanchanpur and selecting a random 97

villages from within those wards. At the time of baseline data collection in July of 2019, 7 of these

97 villages were dropped from the sample due to ﬂooding. Households belong to the bottom half

of the wealth distribution in these villages, as estimated by a participatory wealth ranking exercise

with members of the village.

Survey Dates:December 1st - December 11, 2020

Sample size, tracking and attrition: 1,392 households

IRB Approval: This research was approved via Yale University IRB Protocol 2000025621.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Nigeria Subnational sample, WZB Berlin Social Science Center, University of Illinois

Chicago

COVID-19 Experience

• First conﬁrmed case: February 28, 2020

• Number of conﬁrmed cases: 65,693 as of November 18, 2020

• Number of deaths: 1,163 as of November 18, 2020

Target Population: Christian and Muslim men and women, age 18 and above, living in Kaduna

state, Nigeria.

Original Study Design: Initial data was collected from a subset of the sample in December 2019

(in person survey) and July - Aug 2020 (phone survey) as part of an experiment testing the effects

of a brief radio program on inter-religious animus. A random walk procedure and random sampling

were used within households to recruit a representative sample of adults in Kaduna town. The rest

of the sample was recruited for the study in Aug 2020 by purchasing phone lists for residents of

Kaduna State.

Intervention: The study examines the effects of a radio program and a TV drama on inter-religious

animus. The subset of the sample in the radio study was randomly assigned to listen to a brief

radio program on one of the following topics: (1) an inter-religious storyline, (2) an intra-religious

storyline, and (3) a message about maintaining safe health practices. All respondents in the sample

participated in a study examining the effect of viewing an inter-religious storyline unfolding over

a full season of a popular TV drama, Dadin Kowa. The season aired from Aug - Oct 2020. A

third of the sample were encouraged to watch Dadin Kowa, a third were encouraged to watch

the TV station Africa Magic Hausa at the same time Dadin Kowa aired, and a third were in the

treatment-as-usual group. All participants received a weekly incentivized SMS quiz from Aug -

Oct 2020.

COVID-19 Survey Design: This survey is not primarily about COVID-19, but was designed as an

endline survey to follow the TV drama intervention described above. The goal of this survey is to

measure a range of attitudinal outcomes related to Christian-Muslim relations (including prejudice,

intergroup threat perceptions, dehumanization, and support for the use of violence, among others).

We included nine of the standardized COVID-19 vaccine-related questions collected speciﬁcally

for this vaccine acceptance study in the ﬁnal module of the endline survey.

Sampling Frame: 950 respondents in the sample were recruited in person through a random sam-

pling procedure in the Kaduna metropolitan area (pre-COVID). The remaining 1,700 respondents

were recruited into the study over the phone from lists of phone numbers of Kaduna state residents

that were purchased from a private vendor.

Survey Dates: November 18 - December 18, 2020.

Sample size, tracking and attrition: All 1,834 individuals who completed the endline survey are

included.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Sampling Weights: N/A

IRB Approval: This study was reviewed by the IRB at the University of Pennsylvania (Protocol

834548), and it was determined on November 20, 2019 to meet the criteria for review exemption

(45 CFR 46.104, category #2).

Pakistan

COVID-19 Experience

• March 6: First conﬁrmed case: February 26, 2020

• Number of conﬁrmed cases: 271,887 as of July 24, 2020

• Number of deaths: 5,787 as of July 24, 2020

Pakistan, Economic Vulnerability Assessment (EVA) Subnational sample, Sheikhupura Po-

lice Study Sample, Institute of Development and Economic Alternatives, Lahore University

of Management Science, London School of Economics, Princeton University

Target Population: A representative sample of adults from 108 of 151 police beats in Sheikhupura

and Nankana districts of Punjab Province.

Original Study Design: N/A

COVID-19 Survey Design: The EVA survey involved calls to all households in the stratiﬁed

random sample for the policing study midline survey.

Sampling Frame: Households in Sheikhupura and Nankana districts.

Survey Dates: July 24 to September 9, 2020

Sample size, tracking and attrition: Sample includes 1,473 respondents.

Sampling Weights: Post-stratiﬁcation weights are computed to adjust for the sampling process,

which involved stratifying ﬁrst on 27 police stations, then within each police station on beats, then

PPS sampling within beats using Asiapop population data.

IRB Approval: This research was approved via Princeton University IRB Protocol 7250.

Pakistan, Economic Vulnerability Assessment (EVA) Subnational sample

Target Population: All possible mobile phone numbers (in the province of Punjab) generated

based on the local mobile phone number structure in Pakistan.

Original Study Design: N/A

COVID-19 Survey Design: The EVA survey involved making calls to individuals in Punjab based

on random digit dialing.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Sampling Frame: Individuals with mobile phones in Punjab.

Survey Dates: September 2 to October 13, 2020

Sample size, tracking and attrition: Sample includes 1,492 respondents.

Sampling Weights: N/A.

IRB Approval: This research was approved by Lahore University of Management Sciences IRB

Protocol LUMS-IRB/07012020SA.

Rwanda, Research for Effective COVID-19 Responses (RECOVR) National RDD Sample,

Innovations for Poverty Action (IPA)

COVID-19 Experience

• First conﬁrmed case: March 14, 2020

• Total cases: 5,017 as of October 22, 2020

• Total deaths: 34 as of October 22, 2020

Target Population: A random sample of all numerically possible mobile phone numbers in the

country, based on national communications authority number allocation plans.

Original Study Design: N/A

COVID-19 Survey Design:Phone survey

Sampling Frame: Numbers were called via random digit dialing (RDD), stratiﬁed by mobile net-

work operator market share.

Survey Dates: October 22 to November 5, 2020 (Round 1 June 4 -12, 2020)

Sample size, tracking and attrition:Sample includes 1,355 respondents contacted in the second

round of a panel of 1,480.

Sampling Weights: Post-stratiﬁcation weights are computed to adjust for differential attrition be-

tween the ﬁrst and second rounds of the RDD panel, weighting on gender, region, and educational

attainment.

IRB Approval: This research was approved via IPA IRB Protocol 15591, Rwanda National Institute

for Scientiﬁc Research permit No.0856/2020/10/NISR; and Rwanda National Ethics Committee

approval No.16/RNEC/2020.

Russian Federation, Research on COVID-19 in Russia’s Regions (RoCiRR) Subnational sam-

ple, International Center for the Study of Institutions and Development (HSE University,

Moscow, Russia) and Economics Department of Ghent University, WZB Berlin Social Sci-

ence Center, Columbia University

COVID-19 Experience

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

• First conﬁrmed case: January 31, 2020

• Number of conﬁrmed cases: 1,720,063 as of November 6, 2020

• Number of deaths: 29,654 as of November 6, 2020

Target Population: Adult internet users who reside in one of 61 federal subjects (federal cities,

oblasts, republics, krais and autonomous okrug) of Russia. The regions included in the study are

Republics: Bashkortostan, Karelia, Komi, Mariy El, Mordovia, Tatarstan, Udmurtia, Chuvashia.

Krais: Altai, Krasnodarsky, Krasnoyarsky, Permsky, Primorsky, Stavropolsky, Khabarovsky.

Oblasts: Arkhangelsk, Astrakhan, Belgorod, Bryansk, Vladimir, Volgograd, Vologda, Voronezh,

Ivanovo, Irkutsk, Kaliningrad, Kaluga, Kemerovo, Kirov, Kostroma, Kurgan, Kursk, Leningrad,

Lipetsk, Moscow, Murmansk, Nizhny Novgorod, Novgorod, Novosibirsk, Omsk, Orenburg, Orel,

Pskov, Penza, Rostov, Ryazan, Samara, Saratov, Sverdlovsk, Smolensk, Tambov, Tver, Tomsk, Tula,

Tyumen, Ulyanovsk, Chelyabinsk, Yaroslavl. Other: Moscow, Saint Petersburg, Khanty-Mansiysk

Autonomous Okrug – Ugra. The remaining 24 federal subjects were excluded from the study

due to inability to enroll sample size with desired characteristics (sample size, age, gender and

education group composition).

Original Study Design: N/A

COVID-19 Survey Design: The study was designed to measure the impact of pandemics on

Russians, mostly those who live in cities with more than 100,000 residents. It contains a number of

questions on the personal experience, norms and values, trust in government institutions, provision

of social services, and mass media use. Region and geolocality of every respondent are recorded.

Sampling Frame: In total 25,558 respondents received the module on vaccine acceptance. The

sample was enrolled from the pool of Russian online survey company OMI (Online Market In-

telligence). The sampling was speciﬁcally targeted at having a minimum of 150 respondents in

each of the 61 regions and including respondents from all the main age and gender groups within

each region. Respondents were also selected so that at least 40% of respondents did not have

higher education, in accordance with higher education rates in Russia. Out of 25,558 recruited

respondents, 22,125 completed the survey. Among 22,125 respondents who completed the survey,

20,821 were enrolled from the general pull of the survey company respondents, while the remain-

ing 1,304 respondents were enrolled among residents of cities with populations below 100,000 and

rural areas.

Survey Dates: November 6 - December 1, 2020

Sample size, tracking and attrition: 22,125 respondents who completed the survey with the vaccine

acceptance module included.

Sampling Weights: Post-stratiﬁcation weights are computed to match marginal population distri-

butions of age, gender and education with target proportions coming from the 2019 Yearbook and

2015 Microcensus released by Russian Federal Bureau of National Statistics (Rosstat).

IRB Approval: This study was approved via Columbia University IRB Protocol IRB-AAAT4453.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a

Sierra Leone

COVID-19 Experience

• First conﬁrmed case: March 20, 2020

• Total cases: 2,252 as of October 2, 2020 and 3,030 as of January 20, 2021

• Total deaths: 72 as of October 2, 2020 and 77 as of January 20, 2021

Sierra Leone, Research for Effective COVID-19 Responses (RECOVR) National RDD Sam-

ple, Innovations for Poverty Action (IPA) Target Population: A random sample of all numer-

ically possible

COVID-19 Vaccine Acceptance and Hesitancy in Low and Middle Income Countries, and Implications for Messaging

Abstract and Figures