metadata: authors: 0: author: Hoy,Christopher Alexander
1: author: Rajee Kanagavel
2: author: Cameron,Corey Morales
count: Zambia
docna: 0: docna: Intra-Household Dynamics and Attitudes
            toward Vaccines : Experimental and Survey Evidence from Zambia
docty: Policy Research Working Paper
abstracts: cdata!: This paper explores how intra-household
            dynamics relate to attitudes toward vaccines in low-income
            countries, by drawing on three novel surveys from Zambia.
            The first is a nationally representative, in-person survey
            of over 10,000 households that asked all household members
            individually about their willingness to get a COVID-19
            vaccine. The second and third surveys involved conducting
            randomized experiments with over 2,000 participants using
            Facebook and SMS messages. They tested how the impact of
            information about the benefits from receiving a COVID-19
            vaccine on people&#x2019;s willingness to get vaccinated
            varied based on intra-household dynamics. These data sources
            showed that people&#x2019;s willingness (unwillingness)
            to get a COVID-19 vaccine was strongly associated with
            whether other household members were also willing
            (unwilling). Both experiments found that respondents who
            received information emphasizing either individual or
            household benefits from getting a COVID-19 vaccine were more
            willing to get vaccinated than those in the control group.
            This information was more potent among respondents who
            believed other members of their household would not get
            vaccinated but did not have a larger impact on respondents
            who were involved in household decision making. There was
            also some evidence of positive
            &#x201c;second-round&#x201d; effects whereby
            respondents who received the information treatments were
            more likely to encourage other household members to get a
            COVID-19 vaccine. An important implication that flows from
            this analysis is that although household members tend to
            have similar attitudes toward vaccines, communicating
            accurate information about the benefits of getting
            vaccinated can counter intra-household dynamics that
            undermine acceptance.
display_title: Intra-Household Dynamics and
            Attitudes toward Vaccines : Experimental and Survey Evidence
            from Zambia
listing_relative_url: /research/2022/08/33877026/intra-household-dynamics-attitudes-toward-vaccines-experimental-survey-evidence-zambia
topicv3: Health m1504661 3480,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2022-08-03T00:00:00Z
dois: 10.1596/1813-9450-10136
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1595364
1: author_key: NA
2: author_key: 2053506
model_extractions: Policy Research Working Paper 10136 Intra-Household Dynamics and Attitudes toward Vaccines Experimental and Survey Evidence from Zambia _Christopher Hoy Rajee Kanagavel Corey Cameron_ Poverty and Equity Global Practice August 2022
Policy Research Working Paper 10136

# **Abstract**

This paper explores how intra-household dynamics relate to attitudes toward vaccines in low-income countries, by drawing on three novel surveys from Zambia. The first is a nationally representative, in-person survey of over 10,000 households that asked all household members individually about their willingness to get a COVID-19 vaccine. The second and third surveys involved conducting randomized experiments with over 2,000 participants using Facebook and SMS messages. They tested how the impact of information about the benefits from receiving a COVID-19 vaccine on people’s willingness to get vaccinated varied based on intra-household dynamics. These data sources showed that people’s willingness (unwillingness) to get a COVID-19 vaccine was strongly associated with whether other household members were also willing (unwilling). Both experiments found that respondents who received information emphasizing either individual or household benefits from getting a COVID-19 vaccine were more willing to get vaccinated than those in the control group. This information was more potent among respondents who believed other members of their household would not get vaccinated but did not have a larger impact on respondents who were involved in household decision making. There was also some evidence of positive “second-round” effects whereby respondents who received the information treatments were more likely to encourage other household members to get a COVID-19 vaccine. An important implication that flows from this analysis is that although household members tend to have similar attitudes toward vaccines, communicating accurate information about the benefits of getting vaccinated can counter intra-household dynamics that undermine acceptance.

This paper is a product of the Poverty and Equity 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 choy@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
Intra-Household Dynamics and Attitudes towards Vaccines: Experimental and Survey Evidence from Zambia<sup>1</sup> Christopher Hoy<sup>*</sup> , Rajee Kanagavel<sup>**</sup> and Corey Cameron<sup>**</sup>

# **JEL-Classification:** I12, I15, I18, D90, D91.

# **Keywords:** COVID-19, Vaccines, Survey experiment, Low and Middle-income countries.

> 1 The survey experiment was pre-registered on the AEA RCT registry (AEARCTR-0009027). We wish to thank the Zambia Statistics Agency (ZAMSTATS) for access to data and acknowledge that the ZAMSTATS survey was supported by Arden Finn and Domingos Mazivila. We also wish to thank UNICEF and UNDP for access to the U-Report database and acknowledge that this work was supported by Edgar Lungu, Domingos Mazivila and Andre Lesa. We appreciate contributions made to the online survey experiment by Renos Vakis, Zeina Afif, Ellen Moscoe, Laura Zoratto, Mohamad Chatila, Daniel Pinzon Hernandez, Jungkyu Rhys Lim, Haena Kim and Michelle Dugas. We are grateful for comments by Leonardo Lucchetti and Rob Swinkels on an earlier version of the paper. This study was partially funded by the Alliance for Advancing Health Online. The findings, conclusions, and recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the collaborators or the organizations they work for.<sup>*</sup> World Bank and corresponding author (choy@worldbank.org),<sup>**</sup> World Bank.
# **1 Introduction**

The global rollout of COVID-19 vaccines has been substantially faster than any other vaccine rollout in history, however, progress has been very uneven. By mid-2022, low-income countries (LICs) still had relatively limited coverage of their populations and the take-up of COVID-19 vaccines had begun to slow down considerably (Figure 1). These trends are alarming as the possibility of many of these countries reaching herd immunity for COVID-19 seems far beyond reach (Kwok et al, 2020). This is at a time when many supply constraints for COVID-19 vaccines have been eased, which suggests limited demand is likely contributing to slower progress towards reaching the rest of the population. The drivers of people’s demand for COVID-19 vaccines have received relatively little attention in LICs as policy makers have tended to focus on procuring vaccines and making them as widely available as possible. There is a knowledge gap about the role intra-household dynamics may play in shaping people’s attitudes towards COVID-19 vaccines. This issue warrants close consideration as the rapid rollout of COVID-19 vaccines and near universal eligibility means that almost all members of the same household can get vaccinated at the same time, which is truly distinct from what has occurred with any other vaccine rollout in LICs.<sup>1</sup> We examine this issue of intra-household dynamics and attitudes towards vaccines by conducting novel randomized survey experiments and drawing on a unique nationally representative survey in Zambia. Specifically, we use individual level surveys for all household members in a nationally representative sample of over 10,000 households that were conducted in-person immediately prior to the widespread rollout of COVID-19 vaccines in mid-2021. Building on this initial survey, we conducted a randomized survey experiment through Facebook involving almost 3,000 participants in March and April 2022. This was followed by another randomized survey experiment conducted via UNICEF’s U-Report SMS messaging platform covering over 2,000 participants in August and September 2022. The randomized experiments tested how people’s self-reported willingness to receive the COVID-19 vaccine

> 1For instance, in most high-income countries (and some upper-middle-income countries), influenza vaccines are readily available for all household members, but this is not the case in LICs.

1
<!-- Start of picture text --> Coverage (%) =<br>Cowid-19<br>——wXn—reeeeEeE=>EEET0 1 2 ——— 3<br>Years from First Available Data<br><!-- End of picture text -->
showed respondents in the treatment groups were more likely to be willing to get vaccinated, regardless of whether they received information emphasizing the individual or household benefits of getting a COVID-19 vaccine. The treatment effects were more potent among respondents who believed other household members would not get vaccinated, while the treatments did not have a larger impact on respondents with a role in household decision making. There was also some evidence that there could be positive “second-round” treatment effects whereby treated respondents were also more likely to encourage other household members to get a COVID-19 vaccine.

A key implication that flows from this analysis is that household members tend to share similar attitudes towards vaccines; however, communicating accurate information about the benefits of getting vaccinated can counter intra-household dynamics that undermine acceptance. At first glance, the strong correlation between household members’ willingness to get vaccinated suggests there may be a need to reconsider efforts that are primarily targeted towards individuals getting vaccinated for COVID-19, such as providing vaccines at workplaces. Rather, it may be more appropriate to approach household members as a collective unit and undertake vaccination activities at the household level, such as door-to-door campaigns. However, the experimental results illustrate that there is still value in communicating the benefits of COVID-19 vaccinations to individuals in households that are not accepting of vaccines. In fact, this can lead both to the individual recipients of the information being more willing to get vaccinated and may create a virtuous cycle with others in the previously “hostile” household becoming more willing to get vaccinated. Collectively, the descriptive and causal findings show that there is considerable value in taking a multi-pronged approach (i.e., at both the individual and household level) to vaccine outreach. If one person in a household is provided timely and relevant information to ease their concerns about vaccination, they may be able to encourage their household members to get vaccinated and if a household is approached together, any concerns can be addressed in one interaction with the family unit.

This study makes two notable contributions to the existing literature. Firstly, to the best 3
of our knowledge this is one of the first studies to conduct a detailed analysis about how intrahousehold dynamics relate to attitudes towards vaccines. On topics other than vaccines there has been extensive work showing that intra-household dynamics shape attitudes (a notable example is pioneering work on social contagion theory (Christakis and Fowler, 2013)). There has also been some work on vaccines, prior to COVID-19, showing the existence of peer effects (e.g., see a review by Kontantinou et al., 2021). Previous studies that have focused on levels of acceptance of COVID-19 vaccines have relied on surveys with only one respondent per household, which limits the authors’ ability to dive deeply into intra-household dynamics. We overcome this hurdle by drawing on an in-person data set with individual level surveys covering all household members (aged 10 years and older) from a nationally representative sample of households in Zambia. The findings from the in-person survey informed the design of the randomized experiments that explicitly tested whether emphasizing benefits for individuals or their household had a greater impact on people’s willingness to get vaccinated. In addition, we examine heterogeneous treatment effects based on people’s beliefs about the attitudes of other household members towards vaccines and their role in household decision making. The small amount of prior experimental work has almost exclusively focused on individual benefits from vaccination and had not examined heterogeneous treatment effects along these dimensions.

Secondly, we contribute to the sparse literature that generates causal evidence about what underpins the acceptance of vaccines in LICs and LMICs, being among one of the first experiments to explicitly focus on COVID-19 vaccines. Seminal studies prior to the COVID19 pandemic have tended to focus on how mistrust towards vaccines was influenced by questionable activities being brought to light (Archibong and Annan, 2021; Martinez-Bravo and Stegmann, 2022). Since the outset of the pandemic there has been some experimental work in high-income countries about ways to boost COVID-19 vaccine acceptance (e.g., Kaplan and Milstein, 2021), but in LICs and LMICs existing publicly available studies have been almost exclusively descriptive (e.g., Kanyanda et al., 2021). An exception is a study by Hoy et al. (2021) in Papua New Guinea (PNG) that also uses social media to 4
conduct an online experiment. Our study extends beyond this previous paper in three ways. Firstly, our study is arguably far more generalizable to other LICs and LMICs, as rates of COVID-19 vaccine acceptance in Zambia are similar to the average for Sub-Saharan Africa (PNG has among the lowest COVID-19 vaccine take-up and highest levels of hesitancy in the world), and this experiment was conducted when vaccines were widely available (the PNG study took place before vaccines were widely available). Secondly, there are several design features of our experiment that allow for more credible heterogeneous treatment effect analysis, such as having a larger sample size, a reduced number of treatment groups, and preregistering the hypotheses. Finally, the treatments we provided were more targeted towards specific research questions, whereas the previous experiment was part of a “first generation” of surveys examining what type of information, if any, could boost the acceptance of COVID19 vaccines. A limitation of this type of research (including our study) is that respondents were asked to self-report their willingness to be vaccinated. While intention to vaccinate is an important issue to consider, it does not necessarily guarantee vaccine uptake as other barriers may block an individual from turning their intent into action.

The rest of the paper is structured as follows. Section 2 provides background about the related literature and the hypotheses that emerge from them, as well as details about the setting of the study. Section 3 outlines the methodology behind the in-person survey and shows the main results. Section 4 describes the methodology behind the online experiment and presents the key findings. Section 5 provides details about the methodology behind the SMS message experiment and presents the results. Section 6 discusses the findings across the two data sources and outlines the implications of this research.

5
# **2 Background**

## **2.1 Related Literature**

Our study is part of a growing literature that examines the drivers of people’s willingness to get a COVID-19 vaccine. This subsection provides a review of existing literature in two parts. Firstly, we discuss related research about attitudes towards vaccines in LICs and LMICs, and this is followed by an overview of studies related to intra-household dynamics and the acceptance of vaccines.

### **2.1.1 Attitudes towards vaccines in low- and middle-income countries**

Vaccination decisions are influenced by several factors, one of which is people’s beliefs and attitudes. The “increasing vaccination model” suggests that vaccine uptake is determined by three broad factors: individual cognitions, social processes, and practice issues (Brewer et al, 2017; MacDonald, 2015). Individual cognitions can range from individuals’ beliefs or attitudes towards vaccination, perceived efficacy, or safety concerns, to perceptions on types of vaccines. Factors of social processes can be interpersonal interactions with either friends or family about attitudes and perceptions of vaccination. Practice issues include behavior affecting vaccine accessibility, vaccination cost, and availability.

Studies prior to the COVID-19 pandemic suggest that vaccines in general are accepted by most people in LICs and LMICs. A 2018 Wellcome Global Monitor survey of 140,000 individuals in 140 countries regarding public attitudes towards health and science showed that over 90 percent of respondents in South Asia and East Africa described vaccines as safe. This is in contrast with results from Western Europe, where only 59 percent of the respondents believed vaccines to be safe. This is in line with another global study (Larson et al, 2016) on vaccine confidence conducted in 67 countries. The European region tended to be more negative when it comes to vaccine safety sentiment, with France being least confident (41 percent) in vaccine safety globally. The study also showed that LICs and LMICs, such as 6
Bangladesh, Ecuador, and the Islamic Republic of Iran, reported very high agreement that vaccines are important.

Despite this very positive outlook towards vaccines in general, existing cross-country research suggests slightly less accepting views towards COVID-19 vaccines. A multi-country study of 20 LICs and LMICs (sample of 10,477 participants) examining perceptions of COVID-19 vaccine effectiveness showed moderate to high levels of acceptance ranging from 69 percent in Indonesia to 94 percent in Bangladesh (Marzo et al., 2022). This betweencountry variation in attitudes towards COVID-19 vaccines is similar to a large-scale study focusing on 15 countries in Africa (Africa CDC, 2021) where the variation ranged from 59 percent in the Democratic Republic of Congo to 94 percent in Ethiopia. Results from a World Bank study on COVID-19 vaccines in six Sub-Saharan African countries by Kanyanda et al (2021) conducted between December 2020 and June 2021 showed acceptance rates ranging from 64.5 percent in Mali to 97.9 percent in Ethiopia. These studies also showed that trust in the effectiveness and safety of the vaccine was the most common reason for acceptance (Marzo et al., 2022; Arce et al., 2021).

Existing publicly available research on ways communication campaigns<sup>2</sup> can increase COVID-19 vaccine acceptance comes almost exclusively from high-income countries and has returned mixed results. Some experiments have successfully changed views, while others have not, or have only found certain treatments to be effective (Ashworth et al., 2021; Chang et al., 2021; Pink et al., 2021; Kaplan and Milstein, 2021; Loomba et al., 2021). One of the few published experimental papers on attitudes about COVID-19 vaccines in the United States found that norm-based appeals emphasizing vaccination uptake among others increase people’s propensity to state they will be vaccinated (Palm et al., 2021). To the best of our knowledge, the only publicly available experimental research on this in a low- or middleincome country comes from Papua New Guinea. This online experiment demonstrated that a message emphasizing the safety of the vaccine relative to getting COVID-19 increased

> 2Other approaches that have been shown to increase vaccine uptake, such as mandates and incentive schemes (Dubé et al., 2015; Eskola et al., 2014), are beyond the scope of this research.

7
willingness to get vaccinated by around 50 percent (Hoy et al, 2021).

# **2.1.2 Intra-household dynamics and the acceptance of vaccines**

Clustering of opinions and exchange of information is a common phenomenon in social networks, particularly households (Christakis and Fowler, 2013). The sources of such information can be external agents, such as health or government authorities, or internal agents, such as people in one’s social circle ranging from friends or co-workers to people living in the same household. The conditions under which intra-household decisions are made, such as gender roles in the household, status within the household and cultural norms, impact household outcomes (Ashraf, 2009). For example, studies on food security have shown that gendered aspects of intra-household decision making impact production diversity and dietary quality in Ethiopia (Sariyev et al., 2020) and Malawi (Jones et al., 2014).

Social networks and intra-household dynamics can play an essential role in influencing attitudes in vaccination uptake. A review of eleven studies on vaccine uptake and social networks found that individuals held more positive attitudes and had a higher probability to get vaccinated if their network consists of people, especially family and friends, with positive attitudes towards vaccines (Kontantinou et al., 2021). In a similar way, negative attitudes and lower acceptance were transmitted within households (ibid). A cross-cultural study by Taylor et al (2016) on household influence on flu vaccine decisions found that household members recommend one another to vaccinate, albeit at different levels, and that advice is congruous with an increased vaccination uptake.

Recent studies related to COVID-19 have also emphasized the role of social networks and households in impacting behavior. There is evidence that closely knit communities have reported relatively fewer COVID-19 cases and deaths because of the spread of information about preventive measures (Fraser et al., 2021; Makridis and Freeman, 2021; Pitas and Ehmer, 2020). In a study on COVID-19 vaccine uptake among Americans, Hao and Shao (2022) found that people are more likely to get vaccinated if a higher proportion of their family or close friends had already been vaccinated. An IMF study by Khan et al. (2021), 8
that used individual-level data from the COVID-19 Behavior Tracker, found that peers help in driving (and undermining) vaccine acceptance. They argue that people who receive warnings against the vaccine from family and friends are less willing to take it, and more likely to share negative information about vaccines with others.

# **2.2 Hypotheses**

Several hypotheses emerge from the existing literature about how intra-household dynamics are likely to relate to attitudes towards COVID-19 vaccines.

The first hypothesis that emerges from the existing literature that can be explored with descriptive data is as follows:

**– Hypothesis 1 People’s willingness to get vaccinated for COVID-19 will be closely correlated with whether other household members are willing to get vaccinated for COVID-19.**

This hypothesis is a natural extension of the widely accepted phenomenon whereby people’s decisions are influenced by the behavior of others around them, particularly in the same household. There is good reason to believe this is likely to apply to COVID-19 vaccines. Especially as information about the availability and reported benefits (or lack thereof) from COVID-19 vaccines is likely to be shared within households and, in some instances, access to vaccines may be provided to household members collectively (e.g., through door-to-door vaccination campaigns).

The second hypothesis that emerges from the very limited prior experimental work on boosting people’s willingness to get a COVID-19 vaccine is as follows:

**Hypothesis 2 - Respondents who receive information about the benefits of being vaccinated will be more (less) likely to state they are willing (unwilling) to get**

9
# **vaccinated.**

This hypothesis is underpinned by descriptive surveys in low- and middle-income countries that show people are more likely to be accepting of COVID-19 vaccines if they trust it has benefits. In addition, the nascent experimental literature on this topic (almost exclusively in high-income countries) suggests that in some instances information treatments can lead to people being more accepting of COVID-19 vaccines. Prior to field work commencing, when this hypothesis was pre-registered, we were agnostic on which specific types of information about the benefits from COVID-19 vaccines would lead to greater willingness to get vaccinated.

The third and fourth hypotheses build on the previous two hypotheses by inferring how the impact of information about the benefits of COVID-19 vaccines on people’s willingness to get vaccinated is likely to vary based on intra-household dynamics. These hypotheses are as follows:

**Hypothesis 3 - Respondents who receive an information treatment and have household members who are willing to be or have been vaccinated will be more (less) likely to state they are willing (unwilling) to get vaccinated.**

**Hypothesis 4 - Respondents who receive an information treatment and have a role in making household decisions will be more (less) likely to state they are willing (unwilling) to get vaccinated.**

Both hypotheses are founded on the premise that the elasticity of people’s willingness to get a COVID-19 vaccine is influenced by the attitude and behavior of others in their household. In the case of the third hypothesis, we suggest that the treatment will have a relatively smaller impact on people in households where others are unwilling to get vaccinated because their attitudes will be less malleable to outside sources of information that are 10
contrary to the prevailing view in the household. In the case of the fourth hypothesis, we postulate that people who have a role in decision making within the household will be more likely to respond to the treatment as they have greater autonomy over their own choices. Due to the novelty of this research, these hypotheses extend well beyond clear guidance that is available from prior research. As such they were somewhat speculative when these hypotheses were pre-registered prior to the online survey experiment commencing.

# **2.3 Setting**

Zambia is a large, landlocked, resource-rich country with sparsely populated land in the center of Southern Africa. More than 58% (2015) of Zambia’s 16.6 million people live on less than the international poverty line of USD1.90 per day (compared to 41% across SubSaharan Africa) and three-quarters of the poor live in rural areas (World Bank 2022). The Zambian Ministry of Health has implemented successful mass vaccination campaigns targeted at children (Carcelen et al, 2021). In 2019, 93 percent coverage was reported for the first dose of measles-rubella vaccine (MR). Despite this progress with MR vaccines, coverage of other vaccines, such as hepatitis B, is far more limited (ibid).

The first COVID-19 cases in Zambia were reported in the capital city of Lusaka in March 2020 when most countries started grappling with the virus. There have been 319,755 (as of May 1 2022) cumulative confirmed cases of COVID-19 in Zambia in the past two years, since the beginning of the pandemic (WHO, 2022). In 2021, the total number of confirmed cases and deaths increased almost tenfold from 20,997 cases and 390 deaths in January 2021 to 305,047 cases and 3,734 deaths by the end of December 2021 (Johns Hopkins University, 2022) (see Figure 2).

The Government of Zambia launched the national COVID-19 vaccination program in April 2021. Zambia received its vaccines mostly through vaccine initiatives such as the African Vaccine Acquisition Trust (AVAT) and COVID-19 Vaccine Global Access (COVAX), a partnership between CEPI, Gavi, UNICEF, and WHO. In June 2021, amid a third wave 11
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# **3 In-person Survey Methodology and Results**

## **3.1 Methodology**

The in-person survey was conducted by the Zambia Statistical Agency from February 2021 to April 2021 in collaboration with the World Bank and several UN agencies. Data was collected from over 10,000 households with respondents answering questions on household welfare and COVID-19 socio-economic impact at the individual and household level. The household level questionnaire was answered primarily by the household head, whereas the individual survey consisted of all people 10 years of age or older in households available at the time of the interview. The average proportion of eligible members in a household who participated in the survey (excluding children under 10 years of age) was 71.5 percent. Similar to what takes place for Demographic and Health Surveys, the individual survey modules were conducted in a way that meant people did not have to provide their answers in front of other household members. The in-person survey employed a two-stage stratified cluster sample design. Firstly, 419 Enumeration Areas (EAs) were selected with Probability Proportional to Size (PPS) of the stratum. Secondly, systematic sampling was employed to select 25 households from each EA (ZamStats, 2021). The sampling frame was based on the 2010 Census of Population and Housing. The sampling frame covered 10,490 households and produced results that were representative at the national, provincial, and urban/rural levels. The survey yielded a high response rate as 97 percent (10,213 households) of the planned households were reached.

Sample weights in the survey were constructed using the 2015 LCMS (CSO, 2016) methodology. To construct weights, the inverse of the probability of an EA being selected was multiplied by the inverse of the probability of a particular household in an EA being selected by 1 (i.e., the probability that an individual in that household is selected). These weights were then post-stratified to match population projections based on the 2010 Zambian census.

13
# **3.2 Findings**

## **3.2.1 Acceptance of COVID-19 vaccines**

In early 2021, around 95 percent of Zambians reported that they are aware of COVID-19. Nearly half of the survey respondents were also aware of the existence of a COVID-19 vaccine. Men tended to be slightly more aware than women (52 percent vs. 48 percent). Awareness of the COVID-19 vaccine was much higher among respondents in Lusaka (70 percent) compared to respondents living in urban areas excluding Lusaka (54 percent) and those in rural areas (30 percent). Nearly half of Zambians (48 percent) reported being willing to take a COVID19 vaccine if it became available. The remainder of the population were unsure whether they would get vaccinated (12 percent) or were not willing to get vaccinated (40 percent). Men tended to be slightly more willing to get vaccinated (50 percent vs 46 percent). Willingness to take the COVID-19 vaccine was higher among people in the rural areas (52 percent), compared to people living in urban regions (43 percent). There is no notable difference in acceptance of COVID-19 vaccines among people with different employment types or by age group.

Household heads were slightly more willing to get vaccinated compared to other members of the household. As seen in Figure 3, more than half of the household heads (51 percent) intended to get the COVID-19 vaccine, 40 percent did not intend to get vaccinated, and 9 percent were unsure. Spouses were the most likely to state they did not intend to get vaccinated (43 percent) and people with other positions in the household were more likely to say they were unsure (around 15 percent). These trends did not vary greatly based on the gender of the household head. In total, of the 10,213 households, 65.2 percent of the households had at least one member who is willing to get vaccinated against COVID-19.

The most common reasons for vaccine hesitancy were a lack of trust in the vaccine (40 percent) and fear of side effects (32 percent) . Around 10 percent of those who are unwilling to get the COVID-19 vaccine claimed COVID-19 vaccines to be experimental and 10 percent cited vaccines to be ineffective. There is no clear variation in reasons for hesitancy 14
mYes No mUnsure <!-- Start of picture text --> =<br>51<br>a<br>HH HEAD SPOUSE<br><!-- End of picture text --> OWN NIECE/NEPHEW SON/DAUGHTER <!-- Start of picture text --> a<br><!-- End of picture text --> GRAND OTHERS SON/DAUGHTER
<!-- Start of picture text --> HH members willing - I —e—<br>Rural - 1 -o-<br>HH Head - I ~_<br>Think COVID vaccine is .<br>effective‘ bs .1 e<br>Urban excl Lusaka - 1 —<br>Awareness of COVID .<br>contraction= I ad<br>Think COVID vaccine is treatable - —<br>Knowledge of COVID spread - —<br>Female - a<br>Age - °<br>HH size - e*<br>HH members aware of COVID - —— "<br>vaccine 1<br>0.0 0.1 0.2 0.3<br>Fstimates<br><!-- End of picture text -->
head was, while 13.6 percent were willing when the household head was not). This pattern is explored more rigorously in the following regression analysis whereby other background characteristics are also controlled for.

The regression analysis also shows that willingness of a household head to get vaccinated had a very strong association with the proportion of household members who would be willing to get a vaccine. After controlling for background characteristics, the proportion of household members who were willing to get vaccinated was around 35 percentage points higher when the household head was also willing to get vaccinated (see Figure 5 and Table A2.2 in the Appendix). The only other characteristic that had a very strong association was the proportion of household members who think that a COVID-19 vaccine would be effective (around 39 percentage points). All other factors had a negligible correlation.

# **3.3 Summary of Findings from the In-person Survey**

Three key findings about intra-household dynamics and attitudes towards COVID-19 vaccines emerge from this nationally representative, in-person survey that was conducted in Zambia prior to widespread access to COVID-19 vaccines. Firstly, there is considerable variation across households in terms of willingness to get vaccinated against COVID-19, with around half of individuals being willing. Secondly, the main reasons people were unwilling to get vaccinated seemed to somewhat stem from a lack of understanding about the benefits from getting a COVID-19 vaccine. Thirdly, consistent with Hypothesis 1, we illustrate that there is a strong correlation between the attitudes of individuals within households towards COVID-19 vaccines. Collectively, these results suggest that intra-household dynamics may play an important role in shaping people’s attitudes towards COVID-19 vaccines and that there could be greater acceptance of vaccines if people were made aware of the benefits.

17
<!-- Start of picture text --> Proportion of HH members who think I<br>COVID-19 vaccine is effective ~ T -<br>HH head willing to get _ I<br>COVID-19 vaccine .<br>Rural - + os<br>Proportion of literatemembersHH _ :I<br>Urban excl Lusaka - . oe<br>Proportion of HH members _ I °<br>aware of COVID-19 vaccine .<br>HH Head Age - i<br>HH Poor- ——<br>Female - =e<br>HH size - e-;-<br>Proportion of female HH 1<br>members - T<br>' ' ' ' '<br>0.0 0.1 0.2 0.3 04<br>Estimates<br><!-- End of picture text -->
# **4 Online Experiment Methodology and Results**

## **4.1 Methodology**

The design of the online experiment was based on prior surveys about COVID-19 vaccine acceptance conducted by the World Bank in 2021 (Kovacevic et al., 2022; Bidani et al., 2022). It took place via Facebook from March 4 to April 6, 2022. Participants were recruited using Facebook advertisements (see example in Appendix A3) and were taken to a chatbot in Facebook messenger to complete the survey (see example of how the questions appeared in Appendix A3). Advertisements to recruit people to this study reached approximately 825,000 Facebook users and 10,837 people clicked on an advertisement to begin the survey. The sample was drawn from all Facebook accounts in Zambia over the age of 18 and quotas were applied based on demographic characteristics (age, gender, and region) to ensure adequate representation in the sample. At the time of the online survey, it was estimated that almost one-third of adults in Zambia have a Facebook account (Napoleon Cat, 2022), but these individuals were more likely to be younger, male and in urban areas compared to the general population. To address this issue, survey data was weighted using inverse probability weights based on age, gender, and region calculated using the most recent Zambian census from 2010 (see Appendix A3 for details). As a robustness check, the main experimental findings are calculated without survey weights and the results (i.e., the sample average treatment effects) are qualitatively similar to those presented in the body of the paper.

The treatments in the online experiment were informed by the findings of the in-person survey, existing studies on vaccine acceptance, and were also tailored to suit the Zambian context based on consultation with local stakeholders. Two similar, but distinct treatments were included to explore whether any differences existed between messages emphasizing benefits at an individual versus household level from getting a vaccine. Efforts were made to ensure that the treatments were as comparable as possible and potential differences between treatment groups would be attributable to the content, as opposed to the format. The 19
resulting two treatments (individual benefit and household benefit) are in Table 1.

**Table 1: Design of randomized survey experiment**

|**Group**|**Question**|
|---|---|
|**Control**|Are you willing to get a COVID-19 vaccine?|
|**Individual Beneft**|The best choice you can make to protect yourself from|
||COVID-19 is to get fully vaccinated. This greatly reduces the|
||risk of severe illness, hospitalisation and dying from COVID-19.<br>Are you willing to get a COVID-19 vaccine?|
|**Household Beneft**|The best choice you can make to protect your household|
||and family from COVID-19 is to get fully vaccinated. This will|
||help to keep the people you love healthy, safe and strong.|
||Are you willing to get a COVID-19 vaccine?|

In total, 10,169 people completed the online survey, of which 2,937 people took part in the online experiment. As the experiment focused on ways to increase willingness to get vaccinated, only people who had not received a COVID-19 vaccine were eligible (i.e., all respondents to the Facebook advertisement who stated they were already vaccinated for COVID-19 did not participate in the experiment). Survey experiment participants were randomly allocated into three groups (the two treatment groups and the control group). Forty percent of participants were allocated to the control group and the remaining 60 percent were split between the two treatment groups. The sample sizes for the treatment and control groups were informed by best practice in the literature (Haaland et al., 2022). Randomization ensured there were very few statistically significant differences between treatment and control groups across demographic characteristics (see Table A4.1 in Appendix). As such, we present graphs showing treatment effects by comparing means across groups (with survey weights) in the body of this paper. In Appendix A4, we present tables showing the regression analysis of the treatment effects with controls added. Results from both methods are effectively the same.

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# **4.2 Findings**

## **4.2.1 Descriptive results**

Similar to the in-person survey, the descriptive findings of the online survey also reflect the prominent role the attitude of other household members has on an individual’s willingness to get vaccinated. Figure 6 shows the results of the equivalent regression analysis described in relation to the in-person survey in Section 3 (equivalent of Figure 4) but using data from respondents to the Facebook advertisements in the control group, who were not randomly allocated to receive a treatment (see Table A4.2 in Appendix for details). An individual was around 30 percentage points more willing to be vaccinated if others in their household were also willing. This factor has by far the strongest association with whether a respondent is willing to get a COVID-19 vaccine. All other factors had a negligible correlation. It is important to note this means that being involved in household decisions was not clearly associated with willingness to get a COVID-19 vaccine.

## **4.2.2 Main findings from the online experiment**

The overall results of the experiment illustrate that a message about the benefits of COVID19 vaccines can substantially increase willingness to get vaccinated and decrease unwillingness. Figure 7 shows that both treatments increased the share of respondents who were willing to get vaccinated by around 10 to 11 percentage points and decreased the share who were unwilling to get vaccinated by around 7 and 5 percentage points (individual benefit and household benefit respectively). Both of the treatment effects are statistically significant at conventional levels. After controlling for the characteristics of respondents, the treatment effects were still large and significant (see Table A4.3 in Appendix).

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<!-- Start of picture text --> HH members willing ! —o—<br>Sec edu or less | —e<br>Male i<br>18-29 years ao<br>Elsewhere —<br>Lusaka | —®— :<br>HH size ‘<br>HH had COVID-19 | —e<br>Vulnerable HH members _<br>HH head | —e—<br>Involved in HH decisions _—<br>0 4 2 3<br>Willngness to get vaccinated<br><!-- End of picture text -->
<!-- Start of picture text --> 65<br>no}<br>2<br>oO<br>&<br>re)<br>[)<br>o<br>><br>3 6<br>fo)<br>Be}<br>D<br>&<br>=<br>55<br>2<br>Cc<br>oO<br>ne}<br>c<br>fe}<br>Q<br>®<br>= $§<br>(}<br>o—<br>oO<br>&<br>yn<br>45<br><!-- End of picture text --> EE Control IE Individual benefit | MM Household benefit

<!-- Start of picture text --> 3 3<br>oO<br>&<br>3<br>[)<br>o<br>><br>©<br>fo)<br>2<br>am -25<br>&<br>=<br>Ke)<br>c<br>L<br>c<br>oO<br>ez 2<br>fe}<br>aq<br>n<br>o—<br>—<br>3°<br>oa<br>oO<br>£<br>op)<br>15<br>EE Control IE Individual benefit | MM Household benefit<br><!-- End of picture text -->
# **4.2.3 Heterogeneous treatment effects**

The heterogeneous treatment effects along the dimensions that were included in the preregistered hypotheses (discussed in Section 2) reveal important information about intrahousehold dynamics and attitudes towards vaccines (see regression results including interaction terms in Table A4.3 in Appendix). Firstly, Figure 8a shows that both treatments increased the share of respondents willing to get a COVID-19 vaccine regardless of whether other household members were willing. The order of magnitude of the treatment effects (in terms of percentage points) between these sub-groups were similar, but there were large differences in levels in the control group. Among respondents whose household members were willing to get vaccinated, over 70 percent of respondents in both treatment groups were willing, compared to just over 60 percent in the control group. This represents a treatment effect of more than 10 percentage points (i.e., more than 17 percent higher than the control group). Among respondents whose household members were unwilling to get vaccinated, over 30 percent of respondents in both treatment groups were willing, compared to less than 20 percent in the control group. This represents a treatment effect of more than 10 percentage points (i.e., more than 50 percent higher than the control group).

Secondly, Figure 8b shows that there were sizable differences in treatment effects, in terms of reducing unwillingness to get a vaccine between respondents based on whether other household members would be willing to get vaccinated. Both treatments dramatically reduced the share of respondents that were unwilling to get vaccinated among respondents whose other household members were not willing to get vaccinated. Less than 40 percent of respondents in both treatment groups were unwilling, compared to over 50 percent in the control group. This represents a treatment effect of around 15 percentage points (i.e., over 25 percent lower than the control group). Among respondents whose household members were willing to get vaccinated, there were negligible effects from the treatments.

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<!-- Start of picture text --> 7<br>no}<br>2<br>©<br>&<br>is)<br>3°<br>©<br>><br>©<br>Dn<br>26<br>D<br>&<br>3<br>a]<br>c<br>®<br>ne}<br><<br>8 5<br>7)<br>®a<br>e<br>°<br>®a<br>©<br>x<br>n<br>A<br>Not :involved Fin HH decisions_ Involved Fin HH decisions<br>Ml Control MMM Individual benefit MMM Household benefit<br><!-- End of picture text --> <!-- Start of picture text --> 3 3<br>©<br>&<br>[s)<br>is)<br>©<br>><br>2, 25<br>Be}<br>D<br>&<br>S<br>o 2<br>L<br>c<br>Oo<br>ne}<br><<br>fo}<br>a<br>BQa .15<br>eH°<br>2<br>©<br>on<br>dp)<br>1<br>Not involved in HH decisions Involved in HH decisions<br>Mi Control $MM Individual benefit MMM Household benefit<br><!-- End of picture text -->
Thirdly, Figure 9 shows that both treatments had similar effects on improving respondents’ willingness to get the COVID-19 vaccines regardless of their degree of involvement in household decision making. For example, among respondents who are involved in decision making, there was an almost 10 percentage point impact on people’s willingness to get vaccinated, while among respondents not involved in household decision making, there was a slightly larger than 10 percentage point impact on people’s willingness to get vaccinated. As such, the results from the online survey show that not only is involvement in decision making uncorrelated with willingness to get a vaccine, but there is also an absence of a notable difference in elasticity of preferences based on this dimension. In fact, there are only minor differences between the overall share of respondents willing to get vaccinated across this dimension.

# **4.2.4 Second round impact from treatments**

We illustrate that there were potentially “second round” effects from accurate information about the benefits of COVID-19 vaccines as treated respondents were more likely to encourage other household members to get vaccinated. As this is a “downstream” measure of the impact of information we combine treatments to maximize statistical power. We show that the treatments had a non-trivial impact on the likelihood respondents would encourage a household member to get vaccinated.

The overall effect was driven entirely by respondents who were in households where other members were unwilling to get vaccinated (see Figure 10a). Specifically, the share of respondents in the treatment groups that would encourage others to get vaccinated in their households was around 63 percent, while it was 55 percent in the control group (representing a treatment effect of 8 percentage points). The descriptive results of the online survey illustrate that respondents encouraging others in their household to get vaccinated would likely have additional benefits by creating a pro-vaccination social norm in the household. Among the unvaccinated in the control group, around one-third reported that a family member was the most likely person who could change their mind about getting the COVID-19 vaccine. As 27
was the case for the main outcome of interest, the treatments had similar effects on whether respondents would encourage others to get a COVID-19 vaccine regardless of their degree of involvement in household decision making (see Figure 10b).

# **4.3 Summary of Findings from the Online Experiment**

The online experiment provides several important findings about intra-household dynamics and people’s willingness to get vaccinated. Firstly, consistent with Hypothesis 1 and the results of the in-person survey, there is a strong association between the attitudes towards COVID-19 vaccines among members of the same household. Secondly, consistent with Hypothesis 2, accurate information about the benefits of getting vaccinated improved attitudes towards vaccines and this did not vary based on whether individual or household level benefits were emphasized. Thirdly, counter to Hypothesis 3, accurate information was far more effective at decreasing unwillingness to get vaccinated among those without other household members who were willing to get the COVID-19 vaccine. Fourthly, counter to Hypothesis 4, the treatments were not more effective at increasing willingness to get vaccinated among those who stated they were involved in household decisions. Finally, we present suggestive evidence that there may be second round effects from accurate information, whereby respondents who received a treatment were more likely to encourage their household members to get vaccinated (this was entirely driven by those without other household members who were already willing).

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# **5 SMS Message Experiment Methodology and Results**

## **5.1 Methodology**

The design of the SMS message experiment was identical to the online experiment, except it focused on whether it was possible to replicate the main results of the online experiment with a different set of respondents and consequently it included fewer questions.<sup>3</sup> In addition, it was necessary to have fewer questions because there is often high rates of attrition in SMS surveys when many questions are asked.<sup>4</sup> This SMS survey experiment was conducted using U-report. U-report is an open-source, mobile messaging program developed and managed by UNICEF Country Offices (UNICEF, 2022). People registered with U-report regularly conduct surveys, receive community announcements and seek advice via SMS messages. It has been in operation in Zambia since 2012 and is primarily targeted towards young adults. Respondents are required to be literate so that they can both read and respond to SMS messages written in English. It does not cover a nationally representative sample of the population as individuals registered on U-report users are more likely to be younger, male and in urban areas (see Appendix A5 for details). To address this issue, survey data was weighted using inverse probability weights based on age, gender, and region calculated using the most recent Zambian census from 2010 (see Appendix A5 for details). As a robustness check, the main experimental findings are calculated without survey weights and the results (i.e., the sample average treatment effects) are qualitatively similar to those presented in the body of the paper.

An SMS message was sent on August 26, 2022 inviting the entire universe of 249,432 individuals registered with U-report in Zambia to participate in the survey. In total, 13,770 U-report users responded to the SMS message that explicitly asked respondents to share their thoughts about COVID-19 vaccines (see Appendix A5 for details). These individuals

> 3For example, respondents were not asked about whether they were involved in household decisions as this was not strongly associated with people’s willingness to get vaccinated in the in-person and online surveys and there were no significant heterogeneous treatment effects on this dimension in the online experiment.

> 4To minimize the rate of attrition, the survey only involved six questions and respondents were informed at the start of the survey it should take less than two minutes to complete.

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were more likely to have joined U-report recently than individuals registered with U-report that did not respond (see Appendix A5 for details). Over 10,000 respondents claimed to have already received a COVID-19 vaccine and consequently were not eligible to participate in the survey experiment. Of the remaining unvaccinated participants, 2,331 provided at least one valid response to the questions sent via SMS message, of which 1,957 had information available on the U-report database from previous surveys about their age, gender and region. A further 238 did not provide valid answers to every question in the SMS survey, which meant that there was complete information for 1719 participants. We present the weighted results for the 1,957 respondents with information about their age, gender and region in the body of the paper and conduct robustness checks with the samples of 2,331 (that provided at least one valid response) and 1719 respondents (that provided valid responses to every question) (see Appendix 6 Tables A6.3 and A6.4). The results are qualitatively similar.

Survey experiment participants were randomly allocated into three equally sized groups (two treatment groups and the control group). The randomization was stratified by the characteristics of U-report users, when this information was available (e.g., age, gender, region, when they joined U-report and their last interaction with U-report). This ensured there were very few statistically significant differences between treatment and control groups across demographic characteristics (see Table A4.1 in Appendix). As such, we present graphs showing treatment effects by comparing means across groups (with survey weights) in the body of this paper. In Appendix A4, we present tables showing the regression analysis of the treatment effects with controls added. Results from both methods are effectively the same.

# **5.2 Findings**

## **5.2.1 Descriptive results**

Similar to the in-person and online surveys, the descriptive findings of the SMS survey illustrate the close relationship between the attitudes of other household members and an individual’s willingness to get vaccinated. Figure 11 shows the results of the equivalent 31
<!-- Start of picture text --> HH members willing : —e—<br>No tertiary education : —o—<br>Male Le<br>18-29 years ei<br>Lusaka 4<br>Respond to U-report last year _<br>Join U-report last 5 years i<br>0 2 4 6<br>Willngness to get vaccinated (regression coefficient)<br><!-- End of picture text -->
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## **5.2.4 Second round impact from treatments**

Similar to the online survey, we illustrate there were potentially “second round” effects from accurate information about the benefits of COVID-19 vaccines as treated respondents were more likely to encourage other household members to get vaccinated. As is the case above we combine treatments to maximize statistical power of this “downstream” measure of the impact of information. We show that the treatments had a non-trivial impact on the likelihood respondents would encourage a household member to get vaccinated. The overall positive treatment effect was driven entirely by respondents who were in households where other members were unwilling to get vaccinated (see Figure 14). In contrast, there was a negative treatment effect on respondents who were in households where other members were willing to get vaccinated (see Figure 14).

# **5.3 Summary of Findings from the SMS Message Experiment**

The SMS message experiment closely replicates the main findings from the online experiment. Firstly, consistent with Hypothesis 1 and the results of the in-person survey, there is a strong association between the attitudes towards COVID-19 vaccines among members of the same household. Secondly, consistent with Hypothesis 2, accurate information about the benefits of getting vaccinated improved attitudes towards vaccines and this did not vary greatly based on whether individual or household level benefits were emphasized. Thirdly, counter to Hypothesis 3, accurate information was far more effective at decreasing unwillingness to get vaccinated among those without other household members who were willing to get the COVID-19 vaccine. Finally, we present suggestive evidence that there may be second round effects from accurate information, whereby respondents who received a treatment were more likely to encourage their household members to get vaccinated (this was entirely driven by those without other household members who were already willing).

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<!-- Start of picture text --> 5 1<br>£<br>oo<br>o&<br>ce)Ss >3 9<br>Sa<br>ar<br>&a<br>S> 8<br>30<br>oO<br>ow<br>~ oO<br>QOD<br>sec<br>So<br>a2<br>85<br>eS<br><=<br>or<br>oO<br>aon<br>n 5<br>HH members not willing HH members willing<br>GH Control Ml Treatment<br><!-- End of picture text -->
# **6 Discussion and Conclusion**

Three key insights emerge from this study that have important implications for policy makers and future research. Firstly, there is clearly a strong correlation between people’s willingness (or lack thereof) to get vaccinated within the same household. This pattern is evident in all three surveys. As such, in many instances it would seem to be appropriate to consider the attitudes towards vaccines of household members as a collective unit as opposed to a group of individuals. Similar points have been made about other topics, such as political preferences whereby the correlation of attitudes within households tends to be extremely high (e.g., see Zuckerman and Kotler-Berkowitz, 1998).

Secondly, even though household members tend to share similar attitudes towards vaccines, communicating accurate information about the benefits of getting vaccinated can counter intra-household dynamics that undermine acceptance. The strong correlation about attitudes within households may at face value be a cause of concern for policy makers because household members that are unwilling to get vaccinated may not change their minds regardless of the content of communication campaigns. This partly underpinned our third hypothesis that suggested the elasticity of attitudes towards COVID-19 vaccines from the treatments would be lower among respondents in these “hostile” households. However, the opposite was the case. The experimental results show there is great value in communicating the benefits of COVID-19 vaccinations to members of households that were not accepting of vaccines. Respondents in these households were more likely to respond positively to the treatments than those in households who were more supportive of vaccination in the first place. In fact, the treatments led to not only the respondents being more willing to get vaccinated, but there is some evidence of a second-round effect whereby they were more likely to encourage others in their household to get vaccinated. In other words, communicating accurate information may create a virtuous cycle whereby both recipients of the information as well as others in previously “hostile” households become more willing to get vaccinated. Collectively, these first two insights suggest that there would be considerable value in 38
policy makers taking a multi-pronged approach (i.e., at both the individual and household level) to vaccine outreach. If one person in a household is provided timely and relevant information to ease their concerns about vaccination, they may be able to encourage their household members to get vaccinated. If a household is approached together, any concerns can be addressed in one interaction with the family unit.

The third insight from the study is that the strong correlation among household members’ attitudes towards vaccination does not appear to be purely a consequence of heads of households having a large degree of control over the attitudes of others in their household. If this was the case, then a way to increase willingness of the population at large to get vaccinated would be to exclusively focus on influencing the head of the household. Our fourth hypothesis was partly informed by this line of thinking as we speculated that respondents who have a role in household decisions would have greater autonomy over their vaccination choices and consequently more likely to be influenced by the treatment. However, the online experiment shows there is a lack of substantial differences between people’s acceptance of vaccines based on their role in household decision making. As such, there is clearly greater nuance in terms of how intra-household dynamics influence people’s attitudes towards vaccines. This evidence would suggest there is value in policy makers ensuring that communication campaigns reach a broad segment of society and are not too narrowly focused on household members that are thought to have the greatest influence.

Future research on this topic could take a few directions. Firstly, the quantitative evidence presented in this paper would be nicely complemented by qualitative interviews exploring further how intra-household dynamics relate to people attitudes towards vaccines. Secondly, there is considerable scope to conduct similar research to this study but in different locations, examining alternative types of vaccines and/or providing different treatments. Furthermore, the outcome measure was an individual’s self-reported willingness to get vaccinated, which may or may not translate to COVID-19 vaccine uptake. More research is needed to understand how intra-household norms influence the physical act of going to get vaccinated.

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