metadata: authors: 0: author: Dayton,Julia M.
1: author: Edochie,Ifeanyi Nzegwu
2: author: Newhouse,David Locke
3: author: Cojocaru,Alexandru
4: author: Deudibe,Gildas Bopahbe
5: author: Kakietek,Jakub Jan
6: author: Kim,Yeon Soo
7: author: Montes,Jose
count: World
docna: 0: docna: COVID-19 Vaccine Hesitancy in 53 Developing
            Countries : Levels, Trends, and Reasons for Hesitancy
docty: Policy Research Working Paper
abstracts: cdata!: This paper presents new evidence on the
            levels and trends of vaccine hesitancy in developing
            countries based on harmonized high-frequency phone surveys
            from more than 120,000 respondents in 53 low- and
            middle-income countries. These countries represent a
            combined 30 percent of the population of low- and
            middle-income countries. On average across countries, one in
            five adults is hesitant about the COVID-19 vaccine, with the
            most cited reasons for hesitancy being concerns about the
            safety of the vaccine, followed by concerns about its
            efficacy. Between late 2020 and the first half of 2021,
            there tended to be little change in levels of hesitancy
            except in Iraq, Malawi, and Uzbekistan, where hesitancy
            increased. COVID-19 vaccine hesitancy is higher among
            female, young, less educated, and rural respondents, after
            controlling for selected observable characteristics. Country
            estimates of vaccine hesitancy from the high-frequency phone
            surveys are correlated with but lower than those from
            earlier studies, which often relied on less representative
            survey samples. The results suggest that vaccine hesitancy
            in developing countries, while less prevalent than
            previously thought, will be an important and enduring
            obstacle to recovery from the pandemic.
display_title: COVID-19 Vaccine Hesitancy in 53
            Developing Countries : Levels, Trends, and Reasons for Hesitancy
listing_relative_url: /research/2022/09/33906365/covid-19-vaccine-hesitancy-53-developing-countries-levels-trends-reasons-hesitancy
topicv3: Health Promotion and Disease Prevention
            m1326336 1458,Household Surveys m1504071 2761,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2022-09-26T00:00:00Z
dois: 10.1596/1813-9450-10191
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
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2: author_key: 450079
3: author_key: 648083
4: author_key: 1609808
5: author_key: 1357643
6: author_key: 1057128
7: author_key: 670552
model_extractions: Policy Research Working Paper 10191

# COVID-19 Vaccine Hesitancy in 53 Developing Countries

Levels, Trends, and Reasons for Hesitancy _Julia Dayton Eberwein Ifeanyi Edochie David Newhouse Alexandru Cojocaru Gildas Deudibe Jakub Kakietek Yeon Soo Kim Jose Montes_ Poverty and Equity Global Practice & Health, Nutrition and Population Global Practice September 2022
Policy Research Working Paper 10191

# **Abstract**

This paper presents new evidence on the levels and trends of vaccine hesitancy in developing countries based on harmonized high-frequency phone surveys from more than 120,000 respondents in 53 low- and middle-income countries. These countries represent a combined 30 percent of the population of low- and middle-income countries. On average across countries, one in five adults is hesitant about the COVID-19 vaccine, with the most cited reasons for hesitancy being concerns about the safety of the vaccine, followed by concerns about its efficacy. Between late 2020 and the first half of 2021, there tended to be little change in levels of hesitancy except in Iraq, Malawi, and Uzbekistan, where hesitancy increased. COVID-19 vaccine hesitancy is higher among female, young, less educated, and rural respondents, after controlling for selected observable characteristics. Country estimates of vaccine hesitancy from the high-frequency phone surveys are correlated with but lower than those from earlier studies, which often relied on less representative survey samples. The results suggest that vaccine hesitancy in developing countries, while less prevalent than previously thought, will be an important and enduring obstacle to recovery from the pandemic.

This paper is a product of the Poverty and Equity Global Practice and the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at jdayton@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **COVID-19 Vaccine Hesitancy in 53 Developing Countries: Levels, Trends, and Reasons for Hesitancy**

Julia Dayton Eberwein, Ifeanyi Edochie, David Newhouse, Alexandru Cojocaru, Gildas Deudibe, Jakub Kakietek, Yeon Soo Kim, and Jose Montes<sup>1</sup> JEL Codes: I12, I15, Keywords: vaccine hesitancy, COVID-19, high frequency phone surveys Topics: Coronavirus (COVID-19), vaccines, household surveys, health promotion and disease prevention

> 1 Affiliations: Dayton Eberwein, Kakietek: Health, Nutrition and Population Global Practice, The World Bank; Edochie, Cojocaru, Deudibe, Kim, Montes: Poverty and Equity Global Practice; Newhouse: Data, Analytics and Tools, Development Economics. Dayton Eberwein is the corresponding author: <u><mark>jdayton@worldbank.org.</mark></u> The authors are grateful to Renos Vakis and Sven Neelsen for useful comments on an earlier draft of the paper. All views and any remaining errors are those of the authors.
# **1. Introduction**

The world is entering the third year of the global COVID-19 pandemic, which has caused enormous devastation to both people’s health (450 million cases and 6 million deaths as of March 2022) and national economies, in the form of a global recession that has pushed millions into poverty (Dong, Du and Gardner 2020; World Bank 2022). With the continued emergence of new variants and limited treatments available, it is commonly accepted that widespread vaccination is the world’s best bet to contain the virus and it is also expected to play an important role in economic recovery, as widespread vaccinations would enable the reopening of the economy (IMF 2021; Hoogeveen and Lopez-Acevedo 2022).<sup>2</sup> As of early March 2022, over 60 percent of the world’s population had received at least one dose of a COVID-19 vaccine. However, there are stark disparities in vaccination rates across countries: only 14 percent of people in low-income countries had received at least one dose as of March 22, 2022, compared to 79 percent in high-income countries and 81 percent in upper-middle income countries (Ritchie et al. 2022) (Figure 1).

The lag in vaccine distribution in lower income countries put the focus mainly on supply-side constraints until recently. However, the latest industry estimates predict that total vaccine production will exceed global demand by mid-2022, more than two years after the start of the pandemic and about a year and half since a COVID-19 vaccine was first authorized for emergency use. In fact, there are multiple reports of a possible “supply glut” and doses going to waste (Sanjay and Bloomberg 2022). Moreover, low- and middle-income countries have also been catching up with high-income countries in terms of the rates of vaccine rollout (Glassman, Kenny and Young, 2022; see also Figure 2). Despite this progress, a significant share of the world’s population remains unvaccinated, and if COVID-19 becomes endemic, the necessity of periodic boosters will reinforce the importance of addressing barriers to vaccination both on the supply and demand sides. To this end, it is increasingly important to understand the extent to which individuals in low- and middle-income countries elect to be vaccinated, and if not, to understand the nature of the concerns of those who choose not to. This will help address underlying bottlenecks to achieving immunity among the wider population, especially given the fear that persistent pools of unvaccinated people around the world present a greater risk for the emergence of new SARS-CoV-2 variants (Mallapaty 2022).

> 2 Note that lagged increases in vaccination rates for other diseases were also found to be positively associated with increases in GDP growth rates across countries, and increasingly so over time (Masia et al. 2018), but for nonCOVID-19 vaccines the primary channel would be that of improved population health, rather than an enabling environment for economic activity.

2
_Figure 1: Vaccination rates by income group_ _Figure 2: Vaccine rollout rates by income group_ <!-- Start of picture text --> Share of people vaccinated against COVID-19, Mar 22, 2022 How many vaccine doses were administered in the previous 12 months?<br>Per 100 people in the population. The value shown for each date is the total number of vaccine doses administered<br>in the 12 months preceding that date. All doses, including boosters, are counted individually.<br>Share of people with a complete initial protocol Share of people only partly vaccinated<br>Upper middle income<br>Upper middle income 76% 4.8% 81% 100 High incomeWorldLower middle income<br>Low income<br>High income 74% 5.2% 79% 10<br>World 57% 6.9% 64%<br>1<br>Lower middle income 49% 10% 59%<br>.01<br>Low income 11% 14%<br>.001<br>0% 10% 20% 30% 40% 50% 60% 70% 80% Dec 15, 2020 Jun 4, 2021 Sep 12, 2021 Mar 22, 2022<br>Source: Official data collated by Our World in Data CC BY Source: Official data collated by Our World in Data CC BY<br>Note: Alternative definitions of a full vaccination, e.g. having been infected with SARS-CoV-2 and having 1 dose of a 2-dose protocol, are<br>ignored to maximize comparability between countries.<br><!-- End of picture text --> Data source: Ritchie et al. 2022 as of March 22, 2022.

Vaccine hesitancy refers to delay in acceptance or refusal of vaccination despite availability of vaccination services (MacDonald et al. 2015). It is not a new phenomenon, and some level of hesitancy exists for most vaccines, usually revolving around concerns about efficacy or safety. Even before the COVID-19 pandemic began, the World Health Organization (WHO) identified vaccine hesitancy as one of the top threats to global health (WHO 2019). The 2019 Wellcome Global Monitor from Gallup assessed general attitudes about the importance, safety, and effectiveness of vaccines across the globe prior to the onset of COVID19. Overall, on average, globally 7 percent of respondents either somewhat or strongly disagreed that vaccines are safe, and 5 percent either strongly or somewhat disagreed that vaccines are effective (Gallup 2019). Levels of vaccine hesitancy were lower in low- and middle-income countries compared with highincome countries. Across regions, countries in <mark>Eastern Europe and former Soviet states were among the least likely in the world to believe vaccines are safe and effective. A</mark> lthough these beliefs were expressed prior to the COVID-19 pandemic, they suggest that higher levels of COVID-19 hesitancy might be expected in Central and Eastern Europe as compared to other regions of the developing world. Furthermore, they suggest that concerns about safety are greater than concerns about efficacy.

Despite the importance of vaccinations against COVID-19, little is known about the prevalence of COVID19 vaccine hesitancy, and even less about the extent to which concerns about the COVID-19 vaccine are similar or different to longstanding concerns about other vaccines. Anecdotally, several factors likely contribute to COVID-19 vaccine hesitancy: for example, the accelerated speed of development of the COVID-19 vaccine, combined with its newness, has intensified concerns about unknown longer-term sideeffects. There are also reports of specific effects, such as of myocarditis in men under the age of 40 (Husby et al. 2022). Uncertainty about the duration of the vaccine’s effectiveness raises the possibility of needing annual vaccination. The availability of multiple types of vaccination, each with its own levels of efficacy and dosing schedule may also lead to confusion, and hence hesitancy. Relatedly, both the type of vaccine (e.g., mRNA) and country of origin of the vaccine have been identified as a factor in hesitancy. For example, a study from Brazil showed greater rejection among Brazilians for vaccines developed in China and the Russian Federation, as compared to vaccines from the United States or England, which could be related to local politics and foreign policy (Gramacho et al. 2021), as well concerns about data reporting related to vaccine development/approval (Bucci et al. 2021). Wong and colleagues (2021) reported greater hesitancy toward vaccines produced using the mRNA technology in Southeast Asia as compared with Europe and the Americas.

3
The multi-country studies that exist show relatively high levels of hesitancy to take the COVID-19 vaccine, but many of them focus on high-income countries. A systematic review and meta-analysis of 28 large (>1,000) nationally representative samples in 13 high-income countries collected between March and October 2020 found, across all 13 countries, that 72.9% of the sample intended to vaccinate (95% CI: 66.6% to 78.4%) (Robinson et al. 2021). The study also found that as the pandemic progressed (between March and October 2020), the percentage of people intending to vaccinate decreased and the percentage of people intending to refuse vaccination increased, although intentions varied between samples and countries. Another study of nationally representative surveys from mid-2020 in 19 countries, including 12 high-income countries (HICs), found an average across countries of vaccine acceptance rate across the 12 HICs of 66.6%, with wide variation ranging from 80% in the Republic of Korea to 55% in Russia (Lazarus et al. 2021). Both studies observed that being female, younger, of lower income or education level, and belonging to an ethnic minority group was consistently associated with being less likely to intend to vaccinate.

Less is understood about the levels and reasons of COVID-19 vaccine hesitancy in low- and middleincome countries. A few studies reported multi-country survey results from developing countries (de Figueiredo and Larson, 2021, Facebook 2021, Gallup 2021, Kanyanda et al. 2021, Lazarus et al. 2021, Solis Arce et al. 2021, and Wouters et al. 2021) or systematic reviews of individual country surveys (Sallam 2021), but few of the studies were based on nationally representative samples of respondents or included comparable results for many countries. Taken together, the available literature from low- and middleincome countries suggests that vaccine acceptance is higher in East and South Asia and Latin America and lower in the Middle East and Eastern and Central Asia. Results from Africa were mixed, as were results for different subpopulations. Some studies indicated women were more hesitant than men, younger respondents were more hesitant than older respondents, and those with less education were more hesitant than persons with more education. The predominant reason for hesitancy was concern over side effects from the vaccine. Given the limited availability of randomly selected samples that are representative at the national level, an important contribution of this analysis will be to validate these findings.

This paper extends the existing literature in the following four ways. First and most importantly it provides estimates of vaccine hesitancy for 53 developing countries, which are comparable across countries and largely based on household surveys that are more representative of the national population. In total, these 53 countries represent approximately 30 percent of the population of all low- and middle-income countries and 53.3 percent excluding India and China. Second, it investigates heterogeneity in hesitancy across demographic characteristics of the respondent such as urban or rural residency, gender, age and level of education. Better understanding this heterogeneity can inform the design of vaccination rollouts by helping to target specific population groups. Third, the study expands the evidence base on the reasons behind individuals’ reluctance to take the COVID-19 vaccine, even when it is available and free to them. Finally, the analysis enables a comparison between representative and previously published results from less representative sources. Taken together, the paper provides a rich set of results which can aid policy makers in the design of vaccination campaigns in developing countries.

The rest of the paper is structured as follows. Section 2 describes the data and methods, and section 3 provides the results, first for levels and correlates of vaccine hesitancy, followed by changes over time and concluding with reasons reported for being hesitant. Section 4 discusses the results, compares them with the existing literature and suggests some lessons including policy implications.

4
# **2. Data and Methods**

# _2a. Data_

This analysis describes the levels of vaccine hesitancy and its reasons in 53 developing countries between - October 2020 August 2021 using data from the World Bank’s COVID-19 high <u>frequency phone surveys</u> (HFPS) which were implemented to monitor the impact of COVID-19 on households around the world (World Bank 2020). Data are available for one survey round from 39 countries and two or more survey rounds for 14 countries. The countries in the pooled database represent five out of six regions defined by the World Bank: Latin America and the Caribbean (24 countries), Sub-Saharan Africa (14), East Asia and Pacific (7), Europe and Central Asia (6) and the Middle East and North Africa (2).<sup>3</sup> By income group, the sample includes 12 low-income countries, 13 lower-middle income countries, 23 upper middle-income countries and five high-income countries (Appendix Table 1 provides a list of countries with survey month, sample size, region and country income group).

A global core questionnaire served as the basis for each country’s survey but was then customized to fit the local context. As a result, the exact questions may vary across countries, but topics typically included knowledge and concerns about COVID-19, access to food, health care and education, employment and income loss, and safety nets and coping strategies. The survey data was then harmonized ex-post to enable cross-country comparability. The questionnaire was flexible and adapted over time to the pandemic context. The vaccine module, for example, was included more recently.

The sampling frame was drawn from pre-existing nationally representative household surveys in 19 countries, random digit dialing (RDD) in 29 countries, and a list of phone numbers typically obtained from mobile phone operators in 5 countries. The RDD design samples from all active mobile and landline phone numbers, such that RDD surveys are representative of the population aged 18 and over with an active phone number, conditional on response and survey completion. Sampling weights were then constructed to correct for selection bias due to the inability to contact households that did not participate in the survey either due to non-response or lack of access to a phone, with the goal of obtaining estimates that are as close to being nationally representative as possible. Information was collected from one respondent per household. In the case of countries where the sampling frame was derived from a previous survey, this was typically the household head. In other cases, such as when the sample utilized RDD, the respondent was more representative of individuals within the household. While the non-random selection of individuals in the former cases is not ideal, in an earlier analysis of labor market indicators derived from phone surveys, Kugler et al. (2021) find little evidence of differences in employment outcomes or trends over time being affected by the oversampling of household heads with respect to variables such as level of education, gender or location, the main source of bias being for age comparisons. Within the pooled data set, the household sampling weights are scaled such that countries are weighted equally, such that estimates of vaccine hesitancy reported in this study are averages of country averages.<sup>4</sup>

# _2b. Outcome measures_

<mark>The questions asked about vaccine hesitancy in the surveys varied</mark> depending on whether a vaccine was available in the country at the time of the survey. In surveys that took place before the vaccine rollout had

> 3 South Asia is not represented due to lack of available data. The Middle East and North Africa includes two countries—Iraq and Lebanon.

> 4 Using population weights would have resulted in the results being driven by a small number of large countries.

5
begun in that country, the question asked was “ _If an approved vaccine to prevent COVID-19 was to become available at no cost, would you agree to be vaccinated?_ ” In surveys that took place after the vaccine rollout had begun, the wording of the question was either “ _Are you planning to be vaccinated?_ ” or “ _When a vaccine to prevent COVID-19 is available to you, are you planning to be vaccinated?”_ In the sample, 47 countries offered three answer options (yes, not sure, and no), while the other six offered only two categories (yes and no). For this analysis, we combine the “no” and “not sure” answers to obtain the measure of vaccine hesitancy.

To obtain the respondent’s reason for vaccine hesitancy, survey respondents who answered “no” or “not sure” were asked “ _What is your (main) reason/concern for not wanting to be vaccinated / not being sure if you want to be vaccinated_ ?” The answer categories varied widely across surveys. To make these more comparable across countries, answers were remapped into the following nine most common categories (see Appendix Tables 2a and 2b for the original answer options in each survey and a mapping of how they were harmonized):

1. Safety, which includes concerns about side effects

2. Efficacy 3. Distrust of government, pharmaceutical industry, international community 4. Dislike of vaccines in general 5. Preference for natural immunity, which included perceiving self as low risk and already having had COVID-19 infection 6. Lack of knowledge or access, which included “I do not have enough information about the vaccine,” “too hard to get,” “health facility to far” and “I don’t have the time” 7. Religious reasons 8. Not eligible, which included “counter-indication,” “recent medical discharge, “have an underlying health condition and believe taking the vaccine will make it worse” 9. Other The surveys also differed across countries in terms of whether a single concern or multiple concerns were collected. To account for the difference in the number of response options, the results are presented separately.

# _2c. Contextual data_

Country-level contextual data were drawn from other sources. New COVID-19 cases per million, measured as a 7-day rolling average prior to the midpoint of the month household survey data was collected in each country, were obtained from Dong and colleagues (2022), and the Oxford Stringency Index was drawn from Hale et al. 2022. The latter is an index, ranging from 0–100, that indicates the degree to which various restrictions were put in place by governments to control the pandemic (for example, school closures and shelter-in-place requirements). Confidence in the press, the government, and the WHO was drawn from the World Values Survey Wave 7, fielded in 2017-2020, and aggregated up to the country level (Inglehart 2022). Excess deaths due to COVID-19 draw on WHO data (WHO 2022). Country geographic region and income group are based on the World Bank classifications.

# _2d. Analytical strategy_

Data from 53 countries was pooled into a single data set. First, we report point estimates and 95% confidence intervals for each country based on standard errors clustered at the state or province level within each country. We also report the simple average across all countries and stratified by: (i) World Bank region, (ii) World Bank country income group, (iii) urban vs. rural residence, (iv) gender of the 6
<!-- Start of picture text --> Sub-Saharan Africa<br>Middle East & North Africa<br>Latin America & Caribbean<br>Europe & Central Asia<br>East Asia & Pacific<br>Low income<br>Lower middle income<br>Upper middle income<br>High income<br>te) 25 50 75 100<br>Share of Vaccine Hesitant Households (%)<br><!-- End of picture text -->
<!-- Start of picture text --> Kazakhstan a<br>Bulgaria =<br>Congo, Dem.GeorgiaRep aSS ——<br>Haiti re<br>UzbekistanPhilippines FSS———————SS —_—<br>Jamaica EEE<br>raq ——————_—.<br>St. Lucia LSE<br>Thailand ———<br>Dominica ————=——<br>Croatia a<br>Gambia, The ee<br>Guatemala ee<br>Malawi reel<br>t ajikistanapelize EEee<br>Antigua and Barbudaay,olivia E EEE Countryi Ccolora SsSchemezs<br>yo wll insues ——— —— Latin America & Caribbean<br>“indonesiaMali aaeEE —— Europe & Central Asiar<br>ua),Guinea fC—_ — Sub-Saharan Africami<br>Ecuador a . .<br>eongons EE — East Asia & Pacific<br>icaraguaenya a=—_—=ra— . .<br>VietnamNigeria EEEer . Middle East & North Africa<br>aweHondurasaragua EE._—————a<br>Panama Ea<br>Congo,Lao PDRRep aa<br>Costa Rica =<br>ColombiaUganda _eded<br>Peru [ed<br>Argentine Si<br>El Salvadorruguay =Ld<br>Mexico =<br>Ethiopia(excludesDominican RepublicEritrea) ==<br>Chile =<br>Brazil =<br>0 25 50 75 100<br>Share of Vaccine Hesitant Households (%)<br><!-- End of picture text -->
<!-- Start of picture text --> kazakhstan | i<br>C<cri2 | ——<br>2k‘Philippinestuana,—— | _———a ee_——po_ _<br>21<br>i<br>nie Choice<br>——<br>cen,‘vi _ Wy ves<br>ci, _———<br>Antigua and -§‘i Barbuda) vi | | Not Surei<br> 0) )<br>‘i a<br>‘1c<br>|,_;_ cc C ountry'y ColorColor SchScheme<br>‘oni | a .<br>Nc}01\ <i, __ ~~— Sub-SaharanLatin America Africa& Caribbean.<br>Zimbabwe | i q_—__WJd__—<br>Parag — East Asia & Pacific<br>‘ii _<br>;;::, }_ _<br>Costa(0? |_ Europe& Central Asia<br>‘Use|,Rica | Middle. East & North Africa.<br>riPer |<br>1it)Uruguay | ccc<br> Saved‘a |<br>Dominican Rep<br>Ethiopialexcludes Eritrea)22|i ||_<br>Share of Households by Vaccine Choice<br><!-- End of picture text -->
<!-- Start of picture text --> Urban<br>Non-/Rural<br>Male<br>Female<br>Head<br>Non-Head<br>No education<br>Any Primary<br>Any Secondary<br>Any Tertiary<br>Under 35 yrs<br>Age 35 - 64 yrs<br>Age 65+ yrs<br>0 25 50 75 100<br>Share of Vaccine Hesitant Households (%)<br><!-- End of picture text -->
Differences across regions were statistically significant and, for some regions, quite large: those living in Europe and Central Asia are 36.3 percentage points more likely to be hesitant than those living in the reference region of Latin America and the Caribbean, those in the Middle East and North Africa are 23.1 percentage points more likely and those in East Asia and the Pacific, 21.1 percentage points more likely. Differences between Sub-Saharan Africa and Latin America and the Caribbean were not statistically significant, nor were differences by country income group. Over time, the probability of being hesitant to take the COVID-19 vaccine declined but not significantly.

_Table 1: Multivariate analysis of vaccine hesitancy in 53 developing countries_

|Dep. Var: Hesitancy|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Male|-0.028*|-0.027*|-0.028|-0.027*|-0.031*|-0.038**|
|Head of HH|(0.013)<br>0.012<br>(0.015)|(0.013)<br>0.009<br>(0.014)|(0.014)<br>0.019<br>(0.013)|(0.013)<br>0.000<br>(0.017)|(0.013)<br>0.011<br>(0.015)|(0.012)<br>0.007<br>(0.013)|
|Education of respondent (ref – Tertiary)|||||||
|No education|0.123***|0.125***|0.116***|0.134***|0.125**|0.143***|
||(0.033)|(0.035)|(0.032)|(0.037)|(0.040)|(0.036)|
|Any primary|0.061**|0.063**|0.060**|0.067**|0.052*|0.070**|
|<br>Any secondary|(0.018)<br>0.064***|(0.019)<br>0.065***|(0.018)<br>0.062***|(0.019)<br>0.065***|(0.020)<br>0.058***|(0.021)<br>0.062***|
||(0.011)|(0.011)|(0.011)|(0.012)|(0.011)|(0.011)|
|Age group (ref. -- 34 and younger)|||||||
|Working age (35-64)|-0.047**<br>(0.014)|-0.048***<br>(0.013)|-0.042**<br>(0.015)|-0.050***<br>(0.013)|-0.045**<br>(0.015)|-0.049***<br>(0.012)|
|Retirement age (65+)|-0.103***|-0.102***|-0.096***|-0.103***|-0.099***|-0.099***|
||(0.023)|(0.024)|(0.023)|(0.022)|(0.024)|(0.023)|
|Rural area|0.016|0.014|0.013|0.010|0.012|0.011|
||(0.019)|(0.018)|(0.018)|(0.015)|(0.015)|(0.011)|
|Region (ref – LAC)|||||||
|EAP|0.211**|0.218*|0.241**|0.127|0.299|0.249|
||(0.076)|(0.086)|(0.084)|(0.076)|(0.155)|(0.137)|
|ECA|0.363***|0.364***|0.347***|0.376***|0.369***|0.345***|
|MNA|(0.075)<br>0.231***|(0.076)<br>0.230***|(0.073)<br>0.262***|(0.076)<br>0.240***|(0.056)<br>0.288***|(0.068)<br>0.393***|
||(0.043)|(0.043)|(0.062)|(0.036)|(0.059)|(0.050)|
|SSA|0.082|0.089|0.078|-0.005|0.072|0.057|
||(0.075)|(0.079)|(0.074)|(0.101)|(0.086)|(0.103)|
|Income group (ref. – LIC)|||||||
|LMIC|-0.073|-0.071|-0.052|-0.090|-0.049|-0.053|
||(0.127)|(0.124)|(0.122)|(0.129)|(0.113)|(0.081)|
|UMIC|-0.052|-0.050|-0.071|-0.096|-0.026|-0.130|
||(0.154)|(0.154)|(0.146)|(0.154)|(0.135)|(0.107)|
|HIC|-0.161|-0.160|-0.183|-0.208|-0.175|-0.280*|
||(0.159)|(0.162)|(0.153)|(0.162)|(0.136)|(0.108)|
|Survey month (ref -- Nov 2020 - Jan|||||||
|<br>2021)|||||||
|March - May 2021|-0.073|-0.062|-0.102|-0.056|-0.113|-0.110|
||(0.095)|(0.108)|(0.104)|(0.101)|(0.112)|(0.095)|
|June - August 2021|-0.018|-0.012|-0.018|-0.028|-0.049|-0.026|
||(0.090)|(0.099)|(0.099)|(0.089)|(0.097)|(0.079)|
|New COVID-19 cases per million, terciles|||||||
|||-0.022||||0.023|
|Cases (middle tercile)||(0.065)||||(0.057)|
|||-0.009||||-0.011|
|Cases (top tercile)||(0.054)||||(0.049)|

11
|Dep. Var: Hesitancy|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Oxford stringency index terciles (ref. –<br>bottom tercile)|||||||
|Stringency (middle tercile)|||0.061|||0.148|
||||(0.073)|||(0.077)|
|Stringency (top tercile)|||-0.005|||0.028|
||||(0.060)|||(0.059)|
|WHO excess deaths due to COVID-19|||||||
|(ref.- bottom tercile)|||||||
|Excess deaths (middle tercile)||||-0.110*<br>(0.050)||-0.123<br>(0.080)|
|Excess deaths (top tercile)||||-0.059||0.045|
|||||(0.067)||(0.106)|
|Confidence in government index tercile|||||||
|(ref.- top tercile)|||||||
|Confidence in government|||||0.073<br>(0.054)|0.105<br>(0.064)|
|Confidence in government (top|||||0.016|0.055|
||||||(0.131)|(0.135)|
|Constant|0.282*|0.286*|0.265|0.395**|0.206|0.190|
||(0.136)|(0.140)|(0.141)|(0.143)|(0.149)|(0.145)|
|R-squared|0.090|0.090|0.095|0.097|0.102|0.123|
|N|65088|65088|65088|65088|65088|65088|

Notes: Weighted logit regressions. Marginal effects reported. Standard errors clustered at country level. *, **, *** indicates significance at the 95%, 99%, and 99.9% level.

In the extended regression model, we test two additional hypotheses. First, we expect that the severity of the pandemic, for which we use three different proxies, would be negatively correlated with hesitancy. Our first measure, in column (2) is the number of new cases of COVID-19 reported in the country. The higher the risk of infection and illness, as captured by the number of new cases, may make respondents more inclined to get vaccinated to protect against COVID-19 infections. At the same time, it should be recognized that testing capacity differed significantly across countries and over time, such that the measures of new COVID-19 cases per million is an imperfect proxy for pandemic severity. Nevertheless, this variable also captures the visibility of the pandemic to the overall population (even if true severity may be higher), and we would expect visibility / salience to be negatively correlated with hesitancy as well.

Our second proxy for pandemic severity, in column (3), is the degree of stringency in the government’s policy measures, as captured by the Oxford policy stringency index. We expect ex-ante that a more stringent the policy response, signaling higher severity of the pandemic and more restrictions on movement, would be positively associated with willingness to be vaccinated.

Our third proxy for pandemic severity, in column (4), is the number of excess deaths associated with the COVID-19 pandemic, as reported by the WHO. The excess mortality measure quantifies the direct and indirect impacts of COVID-19, measured in terms of the difference between the total number of deaths for a specific place and time period and the number of deaths that would have been expected in the absence of COVID-19. This difference is inclusive of both deaths directly attributable to COVID-19 as well as deaths indirectly associated with COVID-19 through its impact on health systems and society. As a prior, we would expect higher levels of excess COVID-19 deaths to be positively correlated with willingness to get vaccinated, although, at the same time, higher hesitancy can also bring about more excess deaths.

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In our data, none of the three proxies for pandemic severity and/or visibility is statistically associated with vaccine hesitancy with the exception of COVID-19 excess deaths, where in countries in the second tercile of the excess deaths distribution vaccine hesitancy is, on average, lower, relative to the bottom tercile baseline.

We also test whether trust in the government in general is associated with COVID-19 vaccine hesitancy, expecting a positive association due to widespread reporting of misinformation and antivax messages reported in the press, especially online. However, this association is not statistically significant in the regression model.

Finally, we also disaggregate the vaccine hesitant population into those who say that they would not get a COVID-19 vaccine and those who are not sure (see Figure 5) and consider whether the underlying characteristics of these two subgroups differ. In order to do so, we consider vaccine hesitancy as being a spectrum that spans from a rejection of the vaccine to the acceptance of the vaccine, with uncertainty being an intermediate category. This allows us to model the vaccine decision as an ordinal model, keeping the extended specification from table 1 (see Table 5b in the Appendix). When we examine the correlates of the “No” and the “Not sure” groups separately, we observe similar patterns, but they are much weaker in terms of magnitudes for the “Not sure” group. For instance, lower levels of education are associated with a higher probability of answering No, and this is true for the Not sure group, but the magnitude of the marginal effects is much smaller for the latter. Likewise, the probability of answering “Not sure” is also higher among the working age and older population, relative to those under the age of 35, but the strength of these negative correlations is considerably smaller. The ECA, MNA and EAP regions have a higher probability of both No and Not sure answers relative to the LAC region baseline.

# _3d. Changes in vaccine hesitancy over time_

Estimates of vaccine hesitancy were available for two or more survey rounds in 14 countries in the sample. Seven countries had estimates from two survey rounds (Burkina Faso, Republic of Congo, Ethiopia, Indonesia, Nigeria, Philippines, and Uganda) and seven countries (The Gambia, Georgia, Iraq, Kazakhstan, Malawi, Tajikistan, and Uzbekistan) had results from three or more survey rounds. All surveys were collected between October 2020 and August 2021, although the specific months of data collection varied across countries.

There are no clear patterns in terms of the changes in vaccine hesitancy in countries with multiple data points over the pandemic. Among the 14 countries with two or more surveys (collected between October 2020 and August 2021), the rates of COVID-19 vaccine hesitancy declined in half of the countries and increased in the other half, and changes in either direction were less than five percentage points in all but three countries (Figure 5 and see Appendix Table 6 for point estimates and confidence intervals). However, larger increases were observed in Iraq (30% increase), Malawi (36% increase), and Uzbekistan (18% increase). No patterns of change over time were observed across sub-populations either. It should be noted, however, that the period for which multiple rounds of the phone surveys are available is relatively short, and during this time very few countries experienced substantial increases in vaccination rates, such that individuals would have few in-person experiences from which to model behavior.

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averages mask significant variation across countries. Concerns about vaccine efficacy were greater or equal to concerns about safety in five Latin American countries (Argentina, Chile, Dominica, Dominican Republic, and Mexico). Respondents in the Democratic Republic of Congo and the Republic of Congo were hesitant because of dislike for vaccines in general (40% and 44% in March 2021 of hesitant respondents, respectively) and distrust for government, pharmaceutical industry, and the international community (40% and 22% in March 2021, respectively).

Supply-side concerns for not getting vaccinated such as “health facility too far or too hard to get to,” “there is shortage of vaccines in the country,” and “I don’t know how to access the vaccine” (see Appendix Table 2b for full list) only accounted for 6% on average across all surveys with the single answer option. However, over 10% of respondents in several Latin American countries reported the concerns “Health center too far or hard to reach” or “I don't have time to go to get vaccinated.” These included Antigua and Barbuda (22%), St. Lucia (18%), Jamaica (16%), Guyana (13%), Belize and Nicaragua (both 12%). The relatively incidence of supply-side concerns in the HFPS data may be influenced by the framing of the question, however, such as the access issues being queried in the context of other concerns, or because respondents were asked if they would get vaccinated “when the vaccine becomes available”, and access constraints may be equated in people’s minds with vaccines not being available presently, and thus vaccines becoming available signifying the alleviation of existing supply constraints. We are not able to test this formally, however.

Among the seven countries with multiple answer options, a similar pattern was observed: concerns about safety were the most common concern cited in five countries (Mali, Nigeria, Kenya, Sudan, Uganda). In The Gambia, dislike of vaccines in general was the most cited reason, followed by preference for natural immunity (which includes perceiving self as low risk and already having had COVID-19 illness). In Georgia, the only country in Europe and Central Asia with information on reasons for hesitancy, the most cited reason was “not eligible,” followed by lack of knowledge or access and preference for natural immunity.

Only one country, the Republic of Congo, reported reasons for two waves. Between December 2020 and March 2021, only small changes in reasons were seen: the share of respondents who cited distrust of the international community declined from 30% to 22%, and the share who cited concerns about safety and dislike of vaccines in general increased (from 27% to 31% and 42 to 44%, respectively).

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_Figure 8a: Reason for vaccine hesitancy, countries with single response_ <!-- Start of picture text --> Philippines<br>Laos<br>Mongolia<br>Malawi<br>Burkina Faso<br>Sierra Leone<br>Lebanon<br>Bolivia<br>Djibouti<br>Colombia<br>Ecuador<br>Ethiopia<br>Panama<br>Peru<br>Belice<br>Paraguay<br>El Salvador<br>Jamaica<br>Santa Lucia<br>Nicaragua<br>Uruguay<br>Guatemala<br>Dominican Republic<br>Indonesia<br>Honduras<br>Guyana<br>Costa Rica<br>Congo, Rep (Mar 2021)<br>Antigua & Barbuda<br>Argentina<br>Guinea<br>Congo, Rep (Dec 2020)<br>Malaysia<br>Mexico<br>Chile<br>Haiti<br>Dominica<br>DRC<br>Somolia<br>0 20 40 60 80 100<br>Share of vaccine hesitant households<br>Safety (includes side effects)<br>Efficacy<br>Distrust (of government/pharmaceutical industry/international community)<br>Dislike vaccines in general<br>Prefer natural immunity<br>Lack of knowledge or access<br>Religious<br>Not eligible<br>Other<br><!-- End of picture text --> Note: For the construction of harmonized response categories, see Appendix Table 2b.

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_Figure 8b: Reason for vaccine hesitancy, countries with multiple responses_ <!-- Start of picture text --> Kenya<br>Uganda<br>Sudan<br>Mali<br>Nigeria<br>Gambia<br>Georgia<br>0 20 40 60 80 100 120 140 160<br>Safety (includes side effects)<br>Efficacy<br>Distrust (of government/pharmaceutical industry/international community)<br>Dislike vaccines in general<br>Prefer natural immunity<br>Lack of knowledge/access<br>Religious<br>Not eligible<br><!-- End of picture text --> Notes: The X axis represents share of vaccine hesitant households. In some cases, the share is larger than 100% because multiple responses were allowed For the construction of harmonized response categories, see Appendix Table 2b.

# **4. Discussion**

# _4a. Principal findings_

Data from the HFPS were used to investigate COVID-19 vaccine hesitancy. On average across 53 countries, one in five adults are hesitant about getting a COVID-19 vaccine. The highest levels were observed in Eastern European and Central Asian countries and the lowest levels in Latin American and Caribbean countries. Among population groups, female respondents, younger adults, and those with less formal education reported higher levels of COVID-19 vaccine hesitancy than their respective counterparts. Between October 2020 and August 2021, there tended to be little change in levels of hesitancy except in Iraq, Malawi, and Uzbekistan, where hesitancy increased. The main self-reported reason for being hesitant was concerns over safety, especially worries about side effects, and to a lesser extent, concerns over vaccine efficacy and dislike of vaccines in general.

# _4b. Strengths and comparisons with other studies_

This study has several advantages over the existing literature. It is based on a very large sample of respondents from national surveys in 53 developing countries, a part of the world that is underrepresented in the literature. Over half of the surveys used random digit dialing, typically in upper middleincome countries in which a large share of the population uses mobile phones. In lower income contexts, most surveys, 35 percent overall, sampled based on previous face-to-face surveys which were in turn drawn from a census frame. In the remaining 10 percent of cases, sampling was carried out from lists provided by mobile phone operators. In each case, surveys drawn from pre-existing face-to-face surveys were reweighted using the baseline data to become more representative, while many random digit dialing surveys were also reweighted to make those samples more representative of the universe of phone numbers. While it is impossible to eliminate issues of representativeness in phone surveys, the high17
frequency phone surveys were typically carried out by National Statistics Offices and are more plausibly representative than convenience web surveys.

Overall, the estimates of COVID-19 vaccine hesitancy from the HFPSs were more conservative than other published estimates (Figures 9 and 10). There are various possible explanations for the differences. The first relates to the timing of data collection. Most of the existing literature is based on surveys carried out in mid to late 2020 (e.g., de Figueiredo and Larson 2021, Gallup 2021, Lazarus et al. 2021, Solis Arce et al. 2021, and Wouters et. A. 2021), whereas the HFPS data included in our sample were collected from end2020 through August 2021. It is plausible to believe COVID-19 vaccine hesitancy generally declined during this period as multiple vaccines became available and widespread vaccination was safely rolled out in high-income countries.

Another possible reason for the differences is the variation in how the vaccine hesitancy question was framed and the response options available. For example, the HFPS question in countries without general access to the vaccine at the time of data collection was <mark>“When a vaccine to protect you from COVID-19 is available to you, are you planning to be vaccinated?” whereas other studies included the phase “at no cost to you” or descriptors about the quality of the vaccine such as “COVID-19 vaccine proven safe and effective”) (Gallup 2021, Lazarus et al. 2021). There was even more variation across the studies in the answer options available to the respondents. One study relied on a 5-point Likert scale of agreement with the statement “I</mark> f a COVID-19 vaccine is proven safe and effective and is available, I will take it”: <mark>‘completely disagree’, ‘somewhat disagree’, ‘neutral/no opinion’, ‘somewhat agree’ and ‘completely agree” (Lazarus 2021). Others relied on a 4-point Likert scale to the question such as “y</mark> es, definitely,” “yes, probably,’ “no, probably not,” and “no, definitely not” (Africa CDC, Facebook 2021, de Figueiredo and Larson 2021, Wouters et al. 2021) <mark>, whereas the HFPS mostly relied on three answer options (yes, not sure, no). It is possible that “probably not” is not the same as “unsure,” although it is not known how this might bias the respondents’ answers and cross-study comparisons.</mark> Finally, variations in survey modality may have created biases. Among the published studies included for comparison, many studies were based on data from commercial online sample providers, often using quotas sampling to ensure an appropriate distribution in terms of gender, age, and region (Africa CDC, de Figueiredo and Larson, 2021, Facebook 2021, Gallup 2021, Lazarus et al. 2021, and Wouters et. A. 2021), while others were based on convenience samples (Anjorin et al. 2021, Asadi Faezi et al 2021, Bono 2021, Wong 2021), or a mix of methods (Solis-Arce et al. 2021 and Sallam 2021). There was also wide variety in the survey mode, including online, computer-assisted telephone, and face-to-face surveys.

<mark>It is not possible to know the extent to which each of the above-mentioned potential sources of bias affect the comparisons of the findings from the HFPS with the others. For example, it was possible to match the Facebook survey results for the month and year of the HFPS for 20 countries, thus taking away any differences in the timing of the survey. Nevertheless, the HFPS estimates were still lower than those reported by Facebook (Figure 9, Panel D), and this could be due to differences in question-and-answer wording and/or the sample frame.</mark> 18
<!-- Start of picture text --> Panel A: Comparison with Figueiredo and Larson, 2021 Panel B: Comparison with Wouters et al. 2021<br>ee:=&uv 60% =w 60%70% _ee<br>2 | | | | vwempal |_||———ee|| | | a |<br>c ra<br>5 40% 5 40% J<br>S i} ia<br>g° ; g3Nesta Leb§ °<br>220% @ GeGsnatia © 30% e<br>g 20% e lodonesia, 8 20%Vietnam<br>£ @ Vietna @ Nigeria @ Paraguay = 8 Argent *<br>S 10% Apatite 3 10% sa MEN oom.<br>i Mexico Fa e<br>“u£ 0% BrazilI @ Chile a 0% e e<br>0% 10% 20% 30% 40% 50% 60% 70% 0% 10% 20% 30% 40% 50% 60% 70%<br>Share definitely not or probably not vaccinate (Figueiredo and Larson) Share definitely not or probably not vaccinate (Wouters et al.)<br>Panel C: Comparison with Gallup Panel D: Comparison with Facebook<br>Georgia ongo,Dem<br>irea e an Rep. aarm<br>2 Uzbekistan Jamaica 2<br>2a 50% | Iraq Z 2a 50% |_| Z|<br>8 Thailand 3<br>= 40% raat © sox<br>g Guatey ala e g © Leb 4<br>3 ONE EAD ina Fas ai 8 30% uatemala © Belize<br>22$ 20% icaraguaMo ietnaaimbapwePD Costesaks a0 searsg o lquinednBO, RED: 2s$ 20% eebadsdonesia Nicaragué uo<br>= @ ElSalyadcrOTMEReHDAY ® 5 Bi dt@Be banama<br>3 10% Repubilig.. ® 10% tess te Wa 7<br>25 Ethiopia 3 razi| @ Chile &5 @ RiMexicoSalvador | . | Uruguay<br>a“ e e.B @ chile<br>0% ! 0% i<br>0% 10% 20% 30% 40% 50% 60% 70% 0% 10% 20% 30% 40% 50% 60% 70%<br>Gallup Facebook<br><!-- End of picture text -->
Panel A: COVID-19 Vaccine Hesitancy vs Importance of Vaccines for Children Panel B: COVID-19 Vaccine Hesitancy vs General Vaccine Safety Panel C: COVID-19 Vaccine vs Vaccine Effectiveness <!-- Start of picture text --> 0.8 A<br>@ Kazakhstan<br>> @ Georgia @ Bulgaria<br>§c 0.6 @ Haiti + @ BulgariaH + a<br>FA @ Philippines @ Kazakhstan<br>=o Uzbekistan<br>2 @ Croatia bd brakhstag<br>3 0.4 @hai ” 1 “<br>g @ Thailand © Croat @ Croatia<br>a F 4 Lebanon e » Georgia o Indonesia<br>ri e e<br>2 0.2 S | @®Philippings @ Georgia<br>fe) e Rbanor<br>1o} BeSliston<br>o@& ||e @e A es ee ee |<br>0 0.2 0.4 0.6 08 0 0.2 0.4 0.6 08 0 0.2 0.4 0.6 08<br>Do not agree vaccines are important for Do not agree vaccines are safe Do not agree vaccines are effective<br>children<br><!-- End of picture text -->
Figueiredo and Larson (2021) found a positive association between belief the government was handling the crisis well and willingness to be vaccinated in a sample of 32 countries with various income levels. Lazarus and colleagues reported the same, but only in bivariate odds ratios. Future analysis is needed to investigate this relationship, preferably using an individual level measure of trust in government rather than the national-level measure used in this analysis.

With respect to our results on changes over time, which showed that changes were minimal in all countries except Iraq, Malawi, and Uzbekistan, where we report relatively large increases. It is difficult to identify the factors that led to increased hesitancy in these cases. The larger body on changes over time in vaccine hesitancy during the same timeframe as the HFPS data were collected is mostly from highincome countries and shows mixed results: increased rates of vaccine hesitancy reported in Australia, Canada and the United States between April and December 2020 (To et al. 2021, Lavoie et al. 2022, Szilagyi et al. 2020), rates remained stable in Italy between June 2020 and January 2021 (Basio et al. 2022) and decreased in Greece between November 2020 and May 2021 (Sypsa et al. 2022).

The main reasons reported in the HFPS regarding COVID-19 vaccine hesitancy, which mainly revolve around safety and to a lesser degree efficacy, are consistent with the few studies that report reasons for COVID-19 vaccine hesitancy in low- and middle-income countries and with the main reasons for vaccine hesitancy historically. Solis Arce and colleagues (2021) also reported concerns about side effects to be the most frequently expressed reason in their sample of low- and middle-income countries. Facebook survey results for the same month were available for 21 countries, although reasons for not getting vaccinated were collected separately for respondents who would “definitely not” and “probably not” take the COVID19 vaccine and multiple reasons were collected. Nevertheless, they show that concerns about safety (worded as “concern about side effects” or “wait and see if it’s safe”) were the primary concern, followed in most countries by either concerns about efficacy or lack of trust in the government (Figure 11).

Concerns have been raised about supply constraints – both lack of vaccine supply and lack of infrastructure to deliver the vaccines -- being the greatest barrier to COVID-19 vaccination scale-up in developing countries. This study did not find this to be a major concern for respondents (6% average across all surveys with the single answer option). As we noted above, however, the relative incidence of supply-side concerns in the HFPS data may be influenced by the framing of the question, as respondents may have interpreted the question whether they would get vaccinated “when the vaccine becomes available,” as indicating a time when current access constraints get resolved. Furthermore, there was a set of small countries in Latin America where over 10% of respondents cited supply constraints as their main concern, specifically “health center too far or hard to reach” or “I don't have time to go to get vaccinated.” This finding reinforces the importance citizens place on having easy and quick access, especially in Caribbean countries that are highly dependent on tourism.

Counterindication – that is, believing one should not be vaccinated due to pre-existing health conditions – has been identified has a significant barrier to vaccination in Eastern Europe and Central Asia (World Bank, UNIFEF and JICA 2021). In Georgia, Tajikistan, Uzbekistan, and Kazakhstan, the most common reason for hesitancy was not being eligible (World Bank, UNIFEF and JICA 2021). Indeed, in many countries in that region, medical regulations recommend against vaccination for anyone with a preexisting medical condition. In Uzbekistan it was found that medical personnel interpreted the regulation even more strictly than it was written, regularly refusing to vaccinate persons with a pre-existing condition. For these countries, two policy responses may help to address this challenge: revising standard regulations (which 21
up until now have recommended against vaccination for people with a wide range pre-existing health conditions, including having had a recent surgery) about who is eligible to be vaccinated, and conducting awareness campaigns for both health care professionals and the public at large to stress the message that vaccination is recommended in nearly all cases, including among people with pre-existing health conditions (World Bank, UNIFEF and JICA 2021).

_Figure 11: Reason for “definitely not” (upper panel) or “probably not” (lower panel) taking the COVID-19 vaccine, Facebook data for month of HFPS)_ <!-- Start of picture text --> Respondent who will "definitely not" take the COVID-19 vaccine<br>Belize<br>Brazil<br>Colombia<br>Nigeria<br>Costa Rica<br>Lebanon<br>Panama<br>Honduras<br>Peru<br>El Salvador<br>Guyana<br>Respondent who will "probably not" take the COVID-19 vaccine<br>Panama<br>Costa Rica<br>Uruguay<br>Nicaragua<br>Nigeria<br>Iraq<br>Argentina<br>Guatemala<br>Honduras<br>Guyana<br>Indonesia<br>0 50 100 150 200 250 300<br>Safety Efficacy<br>Don't trust the government Dislike vaccines<br>Don't need a COVID-19 vaccine Other people need it more than me<br>Against my religious beliefs Concerned about the cost<br>Other<br><!-- End of picture text --> Notes: Y-axes indicate the share of survey respondents; multiple answers were allowed. Data source: Facebook (2021) for same month as reported in the HFPS. The month for each country is reported in Appendix Table 1.

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# _4c. Limitations of this study_

One potential limitation of this study is that it is based on phone survey data which exclude respondents who did not have access to a phone. This method of data collection was necessary to collect information quickly during the early months of the COVID-19 pandemic while respecting local movement restrictions and minimizing the risk of COVID-19 transmission. Given that having a phone may be non-random, this might have created a risk that the results are representative only of the population with access to a phone. However, in the countries in this sample, access to mobile phones was high, and in countries where the sample was based on a previous survey, sampling weights were used in the analysis to correct for the biases resulting from non-random access to phones. A recent analysis using the same phone survey samples from four African countries demonstrated that the weighting procedures successfully minimized the selection bias in the phone surveys for a wide range of indicators (Ambel, Mcgee, and Tsegay 2021). Nevertheless, the variation in hesitancy estimates from different data sources, such as the phone surveys vs Facebook, also points to the need for methodological research that would explore further the biases introduced by phone surveys, or by online surveys, vis-à-vis face-to-face surveys. These types of experiments would aid the interpretation of findings in environments where data collection modalities are constrained, as was the case with the COVID-19 pandemic, and would also help guide researchers on issues related to sample corrections.

A second limitation is that country surveys were collected at different times, and some surveys were collected before a vaccine was available in the country while others were collected after the vaccine was available. During data collection (and the pandemic more generally), influential events occurred, including adverse news about side effects of certain vaccines, which could have changed respondents’ intentions. Furthermore, there are multiple vaccine manufacturers and types, and the type of vaccine available or anticipated to become available in each country may influence levels of hesitancy.

# _4d. Conclusions and policy implications_

The main findings of this paper are that on average across countries, the level of COVID-19 vaccine hesitancy is approximately 20 percent and remained unchanged in most countries between late 2020 and the first half of 2021. These results suggest that COVID-19 vaccine hesitancy in developing countries, while less prevalent than previously thought, will be an important and enduring obstacle to recovery from the pandemic. The sizeable discrepancies with other studies of COVID-19 vaccine hesitancy suggest that measures of hesitancy depend greatly on the framing of the hesitancy questions and the nature of the sample. Knowing that people over the age of 65 years are the least likely to be hesitant provides an important opportunity to scale up vaccine rollout in this population group, which is at highest risk of severe disease and mortality from COVID-19.

Although supply constraints have long been thought to be the main barrier to vaccination rollout in developing countries, these constraints have abated over time, particularly in middle- and high-income countries. Our results showed only a very small proportion of respondents reported barriers in access or lack of supply as a reason for not getting vaccinated, albeit this may be due, in part, to the framing of the questions. The most cited reasons for hesitancy were concerns about the safety of the vaccine, followed by concerns about its efficacy. At the same time, in many low-income countries, access issues remain important – the estimates in this study showed that about 20 percent of the population was hesitant to get a COVID-19 vaccine, but with only 1 in 5 individuals in low-income countries vaccinated to date, there is still a large share of the population who would get vaccinated if vaccines were made available to them, and alleviating access constraints will remain a policy priority in such contexts.

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The estimates of COVID-19 vaccine hesitancy are higher than levels of hesitancy reported towards other vaccines, indicating the challenges in scaling up COVID-19 vaccination campaigns may be even greater than for other diseases. Given the high level, and the persistence of COVID-19 vaccine hesitancy, designing effective vaccination campaigns that address the key concerns underlying vaccine hesitancy will be very important. The results in this study aimed at helping guide policy makers in developing countries in their efforts to scale up national vaccination efforts. In particular, the findings point to the fact that it will be important to design vaccination campaigns that address concerns about safety, especially about sideeffects, and that effectively reach younger adults. This will likely call for multi-pronged strategies, given the heterogeneity in information sources across different population groups. For example, in Belize, the government has deployed several initiatives that were successful in increasing vaccine uptake, including door-to-door educational sessions in remote villages via mobile units that were deployed prior to the arrival of vaccination teams, as well as information-sharing via social media and using radio to reach those who lack internet access (Margolies et al., 2022). Providing accurate information about vaccines to the population is very important, but so are questions related to who provides this information, and how it is provided. A World Bank program aimed at supporting developing countries in understanding and reducing vaccine hesitancy using behavioral science has found that personalized messaging is more effective than generic messaging, while certain messengers, such as health care workers and friends/family members, can be more effective than others, pointing to the importance of identifying such messengers within the social networks in groups with high hesitancy rates (Bidani et al., 2022).

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