# Understanding hesitancy with revealed preferences across COVID-19 vaccine
types

Kristóf Kutasi  Rice University, Department of Economics, Houston TX,
77005-1827, USA  Júlia Koltai  Centre for Social Sciences, Computational
Social Science - Research Center for Educational and Network Studies,
Budapest, 1097, Hungary  Eötvös Loránd University, Faculty of Social Sciences,
Budapest, 1117, Hungary  Central European University, Department of Network
and Data Science, Vienna, 1100, Austria  Ágnes Szabó-Morvai  Eötvös Loránd
Research Network, Centre for Economic and Regional Studies, Health and
Population Lendület Research Group, Budapest, 1097, Hungary  Debrecen
University, Department of Economics, Debrecen, 4032, Hungary  Gergely Röst
University of Szeged, Bolyai Institute, Szeged, 6722, Hungary  Márton Karsai
Central European University, Department of Network and Data Science, Vienna,
1100, Austria  Alfréd Rényi Institute of Mathematics, Budapest, 1053, Hungary
Péter Biró  Eötvös Loránd Research Network, Centre for Economic and Regional
Studies, Mechanism Design Lendület Research Group, Budapest, 1097, Hungary
Corvinus University of Budapest, Department of Operations Research and
Actuarial Sciences, Budapest, 1093, Hungary  these authors jointly supervised
this work  Balázs Lengyel  Eötvös Loránd Research Network, Centre for Economic
and Regional Studies, Agglomeration and Social Networks Lendület Research
Group, Budapest, 1097, Hungary  Corvinus University of Budapest, Corvinus
Institute for Advanced Studies, Budapest, 1093, Hungary  these authors jointly
supervised this work  Corresponding author<EMAIL_ADDRESS>
###### Abstract

Many countries have secured larger quantities of COVID-19 vaccines than their
populace is willing to take. This abundance and variety of vaccines created a
historical moment to understand vaccine hesitancy better. Never before were
more types of vaccines available for an illness and the intensity of vaccine-
related public discourse is unprecedented. Yet, the heterogeneity of hesitancy
by vaccine types in certain segments of society has been neglected so far,
even though factual or believed vaccine characteristics and patient attributes
are known to influence acceptance. In this paper, we address this problem by
analysing acceptance and assessment of five vaccine types using information
collected with a nationally representative survey (N=1000) at the end of the
third wave of the COVID-19 pandemic in Hungary, where a unique portfolio of
vaccines were available to the public in large quantities. Our special case
enables us to quantify revealed preferences across vaccine types since one
could evaluate a vaccine unacceptable and even could reject an assigned
vaccine to wait for another type. We find that the source of information that
respondents trust characterizes their attitudes towards vaccine types
differently and leads to divergent vaccine hesitancy. Believers of conspiracy
theories were significantly more likely to evaluate the mRNA vaccines (Pfizer
and Moderna) unacceptable while those who follow the advice of politicians
evaluate vector-based (AstraZeneca and Sputnik) or whole-virus vaccines
(Sinopharm) acceptable with higher likelihood. We illustrate that the
rejection of non-desired and re-selection of preferred vaccines fragments the
population by the mRNA versus other type of vaccines while it generally
improves the assessment of the received vaccine. These results highlight that
greater variance of available vaccine types and individual free choice are
desirable conditions that can widen the acceptance of vaccines in societies.

##  1 Introduction

The fast development of vaccines against the SARS-COV-2 virus will go to
history books as an outstanding scientific achievement [1, 2]. Unprecedented
efforts and investments resulted vaccines of various types [3]: established
techniques yielded vector-based and whole-virus vaccines while cutting-edge
technologies were pioneered for mRNA vaccines. However, mostly due to
misinformation on vaccines, and uncertainties about their effects on health,
large populations remained hesitant against vaccination [4, 5, 6, 7],
threatening the efficiency of vaccination plans [8, 9, 10, 11, 12]. Previous
literature on vaccine hesitancy has considered the individual act of taking a
vaccine an uncertain decision because risks and benefits are difficult to
compare [13]. Hence, besides demographic and socio-economic characteristics
that correlate with hesitancy [14, 15, 16], personal beliefs are thought to be
critical as well[17, 18]. Consequently, the trusted source of information is
an important factor: those who trust public authorities and scientists are
generally less hesitant [4, 19, 20], while believers of conspiracy theories
are more hesitant about the vaccines[21, 22, 23].

Despite the intensive public discourse about the efficacy [24] and health
risks of specific vaccines [25], how the level of hesitancy differs across
vaccine types is unclear. It makes vaccine comparison difficult that most
countries apply only a few selected vaccines. Yet, individuals might not be
hesitant against all types of vaccines; instead, might be open for vaccines
that use certain technologies or come from certain countries [26]. However,
such insights were gained so far only by investigating hypothetical vaccines
[27]. Therefore, to increase vaccine acceptance, it is important to better
understand how individual characteristics, and especially how the trusted
source of information, can explain the hesitancy about particular vaccine
types.

In this paper, we compare the acceptance and assessment of five vaccine types
in Hungary. This country applied the greatest diversity of vaccines in the
third wave of the COVID-19 pandemiciiendnote: iVaccine diversity is simply
calculated by the number of available vaccine types from data at
https://ourworldindata.org/covid-vaccinations.. Besides the centralized
European Union (EU) purchase, which included the Pfizer-Biontech, Moderna, and
AstraZeneca vaccines, the Hungarian government imported Sputnik from Russia
and Sinopharm from China. (Although Janssen was also available in Hungary from
May 2021, only a small share of population received it. We excluded this
vaccine from the analysis.)iiiiendnote: iiOfficial names of the vaccines are
listed here. Pfizer-Biontech: Comirnaty/BNT162b2, COVID-19 mRNA vaccine.
Moderna: Spikevax/mRNA-1273. AstraZeneca: Vaxzevria, Covishield / AZD1222,
ChAdOx1 nCoV-19, ChAdOx1-S, AZD2816. Sputnik: Gam-COVID-Vac. Sinopharm:
Sinopharm: BIBP COVID-19 vaccine / Zhong’aikewei, Hayat-Vax. Janssen:
Ad26.COV2.S JNJ-78436735 Ad26COVS1 VAC31518 Although the application of
Sputnik and Sinopharm put Hungary among the quickest countries globally in the
early vaccination period and vaccination rate was still outstanding among
Central and Eastern European countries at the beginning of the fourth
waveiiiiiiendnote: iiiAccording to https://ourworldindata.org/covid-
vaccinations, vaccination per capita was much higher in May 2021, when our
data was collected, in Hungary than in other Central and Eastern European
countries where trust in public authorities is at a similar level., the
vaccination plan generated an intensive public debate. The government strongly
communicated the safety and efficacy of Sputnik and Sinopharm [28]; while mRNA
vaccines were rated high in society [29]. As hesitancy was high in the
beginning of the vaccination campaign [19] ($53\%$ in January 2021 and $20\%$
in May 2021 [29]) a rigid vaccine assignment could have potentially decreased
people’s willingness to vaccinate. Thus, individuals were allowed to reject
the assigned vaccine and wait for the preferred one.

This historically unique situation enables us to contribute to the ongoing
discussion in two ways. First, the globally outstanding diversity of available
vaccines in a single country allows us to compare hesitancy across vaccines by
technologies (mRNA, vector-based, and whole virus). We are able to identify
the individual characteristics that correlate with hesitancy of specific
vaccines, focusing on the role of trusted source of information. Second, the
special allocation mechanism (i.e. the possibility to reject a vaccine and
wait for another) allows us to analyze whether free choice across vaccine
types can mitigate hesitancy.

Our empirical analysis is based on a nationally representative survey (N=1000)
that we conducted at the end of the third wave of the pandemic when vaccines
were already available for everyoneivivendnote: ivNumber of cases can vary
among analyses. For a precise consideration of sample sizes, please see
Materials and Methods. We collected detailed demographic, socio-economic, and
health information of respondents. We asked respondents to identify sources
they accept vaccine-related advice from (these include medical doctors,
scientists, anti-vaccine propagators, politicians, family etc). They were also
requested to evaluate each vaccine and clarify whether their assessment has
changed over the third wave. Finally, a list of questions were used to map
their COVID-19 infection and vaccination history including the date of
potential positive tests, date and type of vaccines received and date and type
of vaccines rejected (18% of our vaccinated respondents rejected an assigned
vaccine and later took another type). This latter information provides new
insights into the nature of vaccine hesitancy through the analysis of revealed
vaccine preferences and allows for the comparison of vaccine assessments
between rejected and accepted vaccines. From now on, we use the word
$evaluate$ to refer on whether individuals considered the vaccine types
acceptable; $reject$ refers to the act when the individual was not willing to
take the assigned vaccine; while $accept$ is used to describe when a vaccine
is taken.

We find heterogeneous vaccine preferences. Most individuals prefer mRNA
vaccines but a non-negligible portion ($5.5\%$) of respondents who rejected a
vaccine preferred vector-based and whole virus vaccines against mRNA types.
The source of vaccine-related advice is an important factor of vaccine
hesitancy. We find evidence for divergent attitudes towards vaccine types that
can depend on the trusted source of advice. Believers of conspiracy theories
are significantly more likely to evaluate the mRNA vaccines unacceptable.
Those who follow the advice of national politicians are more likely to
evaluate less popular vector-based and whole virus vaccines acceptable, even
those that are not approved by the EU. We illustrate that the rejection of
non-desired and re-selection of preferred vaccines generally improve the
assessment of received vaccines. However, these revealed preferences fragments
the population into groups of individuals that either accepts the newest but
reject the more established vaccine technologies and vice versa.

The remaining of the article has the following structure. We first introduce
our data and illustrate patterns and dynamics of revealed preferences across
vaccine types. Then, we analyse determinants of individual hesitancy by
vaccine types using statistical techniques. Finally, we investigate vaccine
assessment across those who have accepted or rejected the assigned vaccine.
Our results call for diversified communication and vaccine allocation
strategies to convince the hesitant population. Government communication can
be effective in convincing the local population about less popular vaccines.
However, misinformation must be fought to mitigate hesitancy towards the newly
developed vaccines that might partly originate in uncertainties around new
technologies. Although our analysis cannot prove cause and effect, the
suggestive evidence can be used to develop a new hypothesis. In the
Discussion, we argue that a greater variance of available vaccine types and
individual free choice are desirable conditions that can widen the acceptance
of vaccines in societies.

##  2 Results

###  2.1 Vaccine assignment and revealed preferences

Let us give a general overview about the logistics of the COVID-19 vaccination
procedure in Hungary over the third wave of the COVID-19 pandemic, 2021
Spring. A vaccine seeking individual (patient) first had to register online at
a government portalvvendnote: vhttps://vakcinainfo.gov.hu/regisztracio-
oltasra. The general physician (GP) received the list of patients in its
region and contacted them in order of priority, based on chronic illness, age
and occupation. Upon contacting a patient, the GP offered a type of vaccine to
the patient. The offer followed the guidence of central vaccination plan and
depended on patient characteristics (as explained above) and the availability
of vaccines. However, the patient could either accept or reject the offered
vaccine. In case of a rejection, the GP had to reach out to the patient later,
when a different type of vaccine became available. This procedure
characterized early vaccination until the vast majority of priority groups
were vaccinated. Starting from April, when vaccines became increasingly
available, patients could register and select available vaccines through a
web-application (first AstraZeneca, then Sinopharm and Sputnik, and later
other vaccines as well).

The vaccine allocation procedure in Hungary is especially interesting as
patients could choose over the type of vaccination they would like to get. The
individual’s vaccine selection was in sharp contrast with the allocation
mechanism used in other EU countries or in the United States in 2021 Spring
where patients found out the assigned vaccine type only when they arrived to
the vaccination center. Besides giving floor for various individual
preferences, the Hungarian mechanism might have provided acceptable
alternatives for those who got scared from the assigned vaccine. For example,
the Sinopharm vaccine was assigned initially in Hungary to the older
generation, despite it was recommended to a younger population. Similarly,
AstraZeneca was assigned for the middle-aged generation, but many became
afraid when the connection between the vaccine and thrombosis cases were
discussed.

Table 1: Percentage of individuals in the sample by major socio-demographic characteristics and COVID vaccination history. Percentages in the last three columns add up to 100$\%$ by character groupings (except the rows on specific vaccine types where the last column is not relevant). Individuals are classified by type of vaccine taken in the last rows. | Evaluated all | Evaluated one vaccine | Vaccinated | Vaccinated after | Non-vaccinated
---|---|---|---|---|---
| vaccines | acceptable but | without rejection | rejection |
| unacceptable | another unacceptable |  |  |
| ($\%$) | ($\%$) | ($\%$) | ($\%$) | ($\%$)
Men | 1.1 | 7.9 | 30.7 | 4.6 | 11.7
Women | 1.2 | 10.3 | 32.2 | 6.2 | 14.5
University | 0.2 | 3.4 | 14.4 | 2.9 | 4.3
High-school | 1.7 | 11.7 | 32.7 | 6.2 | 14.7
Elementary | 0.4 | 3.1 | 15.9 | 1.7 | 7.2
Not Chronic Illness | 1.6 | 11.8 | 33.0 | 6.4 | 19.9
Chronic Illness | 0.7 | 6.4 | 29.9 | 4.4 | 6.3
Age 20-39 | 0.9 | 7.8 | 16.2 | 4.3 | 12.6
Age 40-59 | 0.8 | 5.9 | 20.6 | 3.4 | 9.2
Age 60-79 | 0.5 | 4.0 | 23.0 | 2.7 | 3.3
Age 80+ | 0.0 | 0.2 | 2.3 | 0.0 | 0.3
Pfizer | - | 5.0 | 20.3 | 5.8 | -
Moderna | - | 0.8 | 3.5 | 1.0 | -
AstraZeneca | - | 1.1 | 12.1 | 1.1 | -
Sputnik | - | 1.4 | 12.3 | 1.3 | -
Sinopharm | - | 1.5 | 14.7 | 1.6 | -

To gain insights into these revealed individual preferences on vaccine types,
we have conducted a nationally representative survey data collection on a
sample of $1,000$ people with CATI (computer assisted telephone interviewing)
[30, 31] between May 25 and 31, 2021. Until this date the majority of non-
hesitant population has already received at least one dose of vaccine [29].
The data is representative for the adult Hungarian population by gender, age,
education and domicile. For more detailed description of the data collection
and survey methods see the Methods Section.

Table 1 describes the vaccination related variables by socio-demographic
characteristics of the respondents. Evaluation and rejection of vaccines -
which information makes our dataset unique - are presented by sex, education
level, chronic illness, and age categories. Here we can distinguish those who
rate all vaccine types unacceptable ($2.3\%$) from those who evaluate at least
one vaccine unacceptable but evaluate another acceptable ($18.2\%$). Further,
we can compare those who accepted the first vaccine offered ($62.9\%$) with
those who rejected the first offer ($10.8\%$). $26.3\%$ of the sample has not
received any vaccine until the data collection. The ratio of non-vaccinated
individuals in the total population of the country was around $32\%$ in June
2021. This moderate bias might be due to the tendency that those who are more
concerned about the pandemic situation are more willing to take vaccines and
participate in related surveys, or, to conform to the public norm, some
unvaccinated might not have been admitted it. Apparently, women, the younger
cohorts, the lower educated and those with no chronic disease are more likely
to reject an offer or a vaccine in general.

Figure 1: Revealed vaccine preferences by rejection and re-selection. The
network of revealed vaccine preferences in Hungary. Vaccines are linked if
someone in our data has rejected one vaccine and later accepted another. The
direction of the link goes from rejected to accepted and the width of the
arrow corresponds to the percentage of individuals who acted accordingly among
those who rejected a vaccine.

The data allow us to investigate acceptance and revealed preferences by
vaccine types. Table 1 demonstrates that $5\%$ of those respondents who have
received Pfizer rejected at least one other vaccine; while these shares are
lower in the case of those who received other vaccine types. A similar pattern
characterizes our sample according to the rejection of vaccines: $20.3\%$ of
all respondents have accepted Pfizer without rejecting it while $5.8\%$ waited
for Pfizer after rejecting another. There are fewer individuals in the data
who accepted other assigned vaccines without rejection but except Moderna
($3.5$), which had the shortest supply in the country, AstraZeneca ($12.1\%$),
Sputnik ($12.3\%$), and Sinopharm ($14.7\%$) were accepted by large
populations without hesitancy. Although a smaller fraction of society than in
the case of Pfizer, but few individuals waited for these latter vaccines
specifically.

Figure 1 illustrates the network of revealed preferences across vaccine types
from our data. We observe a high ratio of individuals, among those who
rejected a vaccine, who received Pfizer but rejected Sinopharm ($23\%$),
Sputnik ($15\%$) or AstraZeneca ($9.7\%$). However, individuals also accepted
these latter vaccines after rejecting other types. We see a few individuals
who rejected the most accepted and then accepted a less popular vaccine. For
example, $2.2\%$ of the hesitant population have rejected Pfizer to receive
Sinopharm. The rules of network generation is described in the Methods
section.

Figure 2: Dynamics of vaccination by vaccine types and re-selection. A
Dynamics of vaccination by vaccine types. Dots represent number of vaccines
taken by weeks. Dark dots represent those vaccines that were taken after
rejecting another one. B The dynamics of vaccination by age and chronic
illness. The days are identified by first vaccine taken. Those who took a
vaccine after rejecting another are depicted with filled markers and those who
took the first assigned vaccine are depicted with hollow marker. Dashed lines
represent the local averages of vaccinated patients’ age (dark is used for
patients with chronic illness and bright is used for healthy).

Figure 2A demonstrates the dynamics of revealed preferences by vaccines types.
Although Pfizer was available early on, individual selection was only possible
later in spring when the prioritized groups has all been already vaccinated.
Sinopharm could be selected the same time as Pfizer and it was specifically
allocated to the older population. AstraZeneca, Moderna and Sputnik could be
chosen relatively early compared to the other vaccines. Figure 2B shows that
rejection and acceptance happened in two cohorts. Elder patients with chronic
illness rejected vaccines early so that they could get vaccinated with a more
preferred vaccine type in March. The younger individuals received their first
vaccine later and could also select a preferred vaccine. Supporting
Information 1 contains further description of the dynamics of vaccination and
rejection by age, chronic illness and vaccine types.

###  2.2 Trusted information sources characterize groups of vaccine
preferences

Let us turn now to the determinants of vaccine hesitancy. Individuals can be
influenced in a very complex manner by the public discourse on vaccines. The
competing messages of actors can be amplified by the communication channel
used (eg. social media) and the mechanisms are difficult to disentangle. To
better understand how external influence relates to hesitancy, the respondents
were asked to identify their trusted information sources on COVID-19
vaccination, as well as their socio-demographic, health-related information,
detailed history of vaccination and any infections. The following sources of
trusted information were listed: doctors, scientists, anti-vaccine
propagators, politicians, family, friends, journalists, celebrities. The
respondents also had to name their most frequently used information channels
on pandemic measures such as web news, social media, press, radio, TV, family,
friends. Major actors communicated differently about the vaccines. Doctors and
scientists argued for vaccinationviviendnote: viRecommendation for vaccination
strategy of the Hungarian Academy of Sciences:
https://mta.hu/english/recommendation-of-the-hungarian-academy-of-science-on-
the-management-of-the-covid-19-epidemic-in-the-short-and-long-term-110630,
whereas anti-vaccine propagators emphasized the non-effectiveness and the side
effects of the COVID-19 vaccinesviiviiendnote: viiA summary on anti-vax
movement in Hungary: https://www.iribeaconproject.org/our-work-analysis-and-
insights/2021-09-16/hungarys-anti-vax-movement-alive-and-kicking. Some medical
doctors warned against vaccines that are not approved by the
EUviiiviiiendnote: viiiScientists, doctors slam Hungarian government criticism
of western vaccines: https://www.reuters.com/article/us-health-coronavirus-
hungary-vaccines-idUSKBN2CE2C3. The government communication campaigned for
the non-EU-approved vaccines to improve their rate of acceptance by the
population ixixendnote: ixThe Hungarian prime minister is vaccinated with
Sinopharm: https://www.euronews.com/2021/02/28/hungary-s-pm-viktor-orban-
vaccinated-against-covid-with-chinese-sinopharm-vaccine but a heated debate
around vaccine types has fragmented the society xxendnote: xSurveys
https://hungarytoday.hu/hungarian-vaccination-politics-vaccine-preference-
coronavirus-survey-willingness/.

We apply a variable selection method to decrease the complexity of the problem
that both the source of information and the channel of communication (eg.
social media) can influence hesitancy simultaneously. The LASSO model (short
for least absolute shrinkage and selection operator) enables us to select the
most important variables that have high-enough predictive power (the process
is described in Methods). Estimation results are reported in Supporting
Information $2$, Table S1. We find that communication channels are not
important determinants of hesitancy, because all but one (web news) are
dropped from the model. We also see that both the demographic characteristics
and the sources of trusted information on COVID-19 vaccination are important
predictors of the outcome variables. We rely on this result when we select the
explanatory variables to the linear probability models.

Figure 3: Source of advice taken characterize hesitancy against vaccines
differently. Estimation results from linear probability models with robust
standard errors. The dependent variable equals 1 if the respondent evaluates a
vaccine type unacceptable and 0 otherwise. Markers denote point estimates and
95% confidence intervals are denoted by the bars. Non-significant coefficients
are in light blue ($p\geq 0.05$); significant coefficients are in dark-blue
($p<0.05$).

Next, we estimate linear probability models (LPM) to analyze the factors that
shape the evaluation of each vaccine individually. The dependent variables
take value of $1$ if the vaccine is evaluated "unacceptable" and value of $0$
if it is evaluated either as "acceptable", "fair", "good" or "best".
Independent variables include "Advice" indicators that take value 1 if the
individual follows the advice of the denoted source. "Age" is a numeric
variable and is measured in years, "Female" takes value 1 if the respondent is
woman and zero otherwise, "University" equals 1 if the respondent has a
university degree or equivalent, "Smoking" and "Chronic Illness" are binary
variables as well as "COVID Previously" and "Serious COVID previously". We
present the summary statistics in Supporting Information 3, which also
contains a correlation matrix and VIF statistics. The latter tests indicate no
issues of multicollinearity. The estimation specification can be found in
Methods.

Figure 3 illustrates point estimates and $95\%$ confidence intervals
calculated from heteroscedasticity-robust standard errors. (For detailed
results see Table S3 in Supplementary Information 4.) These results suggest
that the factors that affect the rejection decision differ among vaccine
types. The most remarkable differences are in the effect of the trusted
information sources. Those who trust the advice of anti-vaccine propagators
are 18.2 and 20.2 percent more likely to evaluate vaccines made with the
newest mRNA technologies (Pfizer and Moderna) unacceptable. On the contrary,
trusting doctors’ advice decreases the hesitancy probability by 5.7, 6.1, and
5.4 percent for Pfizer, Moderna, and Sinopharm. Trusting scientists reduce the
likelihood of evaluating a vaccine unacceptable by 3.5, 2.6 and 5.5 for
Pfizer, Moderna, and AstraZeneca. Those who take advice from politicians are
6.4, 7.6 and 8.4 percent more likely to evaluate AstraZeneca, Sputnik and
Sinopharm acceptable. Those who trust their family’s advice are more likely to
evaluate Pfizer, Moderna and Sinopharm acceptable by 4.3, 3.8 and 5.8 percent.
The coefficients for the advice of celebrities are either not or only slightly
significant. However, the point estimates are large and positive for all types
of vaccines.

Socio-demographic characteristics of respondents are found to correlate with
vaccine hesitancy in a plausible way. Age has a negative correlation implying
that vulnerable, older individuals are less likely to evaluate vaccines
unacceptable. Although the coefficient is small in Figure 3, the regression on
standardized variables reported in Table S3 (in Supporting Information 4)
reveals that age is an important factor of evaluating Pfizer, Moderna and
AstraZeneca acceptable. Females are more likely to evaluate AstraZeneca
unacceptable, probably due to reported cases of females’ blood
clotxixiendnote: xihttps://www.reuters.com/business/healthcare-
pharmaceuticals/uk-regulator-says-some-evidence-astrazeneca-clots-occur-more-
women-than-men-2021-05-06/. University degree, however, decreases hesitancy
against AstraZeneca.

We have carried out various robustness checks that are reported in Supporting
Information 4. First, our results hold if we use a logistic regression
specification instead of ordinary least squares. Second, our results remain
similar after the inclusion of subjective wealth as a control variable. One
limitation of our study follows from the fact that the survey was taken when
most registered persons have already been vaccinated. This means that
"spreading of the alternatives" [32] may have affected our results. That is,
after a difficult decision making, the valuation of the accepted alternative
increases, whereas, that of the rejected alternative decreases. Our results do
not change if we apply the models on a subsample of those who had stable
vaccine assessment over the period.

In sum, these results suggest diverse vaccine hesitancy across vaccine types.
Vaccines using the newest mRNA technologies (Pfizer and Moderna) are likely to
be evaluated unacceptable by those who follow anti-vaccine propagators. The
large coefficients imply the effectiveness of anti-vaccine propagators.
Vaccines using more established vector-based and whole virus technologies are
more likely to be evaluated acceptable by those who follow local politicians.
This suggests that public communication can make less popular vaccines
acceptable for many. We find that the advice of doctors and scientists can
mitigate hesitancy against all types of vaccines. Nevertheless, the
coefficients of advice taken from doctors, scientists, and politicians are
much smaller, half to one-third of the anti-vaccine voices. This difference
could explain the forms of communications: the anti-vaccine propagators
usually target fears and emotions, whereas doctors, scientists, and
politicians tend to use rational arguments.

###  2.3 Individual selection improves vaccine assessment

Figure 4: Average vaccine rating by assigned, re-selected and rejected
vaccines. A Ratings of those patients who accepted their first vaccine offer.
Individuals are grouped by columns of received vaccine. Rows correspond to
average rating of a certain vaccine. B Ratings of those patients who rejected
at least one assigned vaccine. Individuals are grouped by columns of received
vaccine. Rows correspond to average rating of a certain vaccine. C Ratings of
those patients who rejected at least one assigned vaccine. Individuals are
grouped by columns of rejected vaccine (marked by overline). Rows correspond
to average rating of a certain vaccine. The average rating in the first two
shaded columns are calculated from few observations only.

In this section, we analyze vaccine assessment of patients sorted by received
and rejected vaccines. This enables us to inform policy on how free choice of
vaccines can impact vaccine acceptance. We can also group vaccines based on
the tendency that individuals find both of them acceptable.

The nationally representative survey asked respondents to rate each of the
vaccines based on their personal opinion on a scale from $1$ to $5$. Figure 4
summarizes the vaccine evaluations in the form of a heat map. A heat map cell
corresponds to the average rating of a type of vaccine denoted by the row
among a group of patients who got and did not reject any vaccine (Figure 4A),
got and rejected any vaccine (Figure 4B), or were first offered and rejected
(Figure 4C) the same vaccine denoted by the column. Supporting Information 5
contains summary statistics of vaccine assessments depicted in Figure 4.

In Figure 4A, we consider patients who accepted their first vaccine offer.
Notice that Pfizer has the highest average ratings across all columns. This
implies that free vaccine choice does not necessarily mean that all patients
get the most preferred vaccine. Instead, individuals make an uncertain
decision considering acceptable alternatives and unknown waiting times between
the sequential vaccine offers. In the Supporting Information, Table S12
presents the distribution of the most preferred vaccine when the accepted
vaccine is not the most highly-rated vaccine. We infer that patients were
willing to accept a vaccine other than the preferred Pfizer or Moderna to
avoid the long anticipated waiting times. In Table S13, we show that patients
had to wait almost $2.76$ weeks on average to get their most preferred vaccine
if they rejected a less preferred vaccine. In Table S14, we also present
evidence that the anticipated waiting times closely follow the actual waiting
times on average. That is, if a patient accepts the first vaccine offer then
the following conditions must hold; the patient prefers the accepted vaccine
to the other vaccines or she has a belief that a more preferred vaccine, which
is Pfizer and Moderna with high likelihood, will not be offered anytime soon.
We provide an extended analysis in the Supporting Information $6$.

Figure 4B illustrates the ratings of those patients who rejected at least one
vaccine. We see that the diagonal values in Figure 4B are slightly higher than
the ones in Figure 4A. This implies that individuals who rejected any vaccines
before value their received vaccine slightly better than those who accepted
their first vaccine. Figure 4B also shows a high level of dislike towards
AstraZeneca, Sinopharm and somewhat Sputnik when it comes to rating vaccines
other than the accepted vaccine.

Comparing the mean values on Figure 4A and 4B, we can see fundamental
differences between the rejected none and rejected any groups. Based on the
values on the diagonal, those patients who rejected at least one vaccine rated
the obtained vaccine higher than those who accepted the first vaccine offer.
Looking at the off-diagonal, we see that the rejected any group tends to rate
the non-accepted vaccines to be worse. For instance, the mean ratings for
AstraZeneca at the off-diagonal are strictly smaller among the patients who
rejected a vaccine at least once than among those who accepted their first
offer. We also see those patients who received Sinopharm after rejection tend
to rate other vaccines worse than those who accepted Sinopharm in their first
offer.

Figure 4C highlights the benefits of free choice. We group patients based on
the first vaccine offer rejection denoted by the columns. We implicitly assume
here that the vaccine registration is not affected by the government’s
decision to allow free choice. Let us point out the number of observations is
small for the Pfizer and Moderna groups with only $6$ and $2$ observations,
respectively. In other words, patients rarely reject Pfizer or Moderna when
these vaccines are offered for the first time. Focusing on the AstraZeneca,
Sputnik and Sinopharm columns, we see that the values for the assigned
vaccines on the diagonal are strictly smaller than any of alternatives. We can
also see a strong preference towards Pfizer and Moderna from the AstraZeneca,
the Sputnik and the Sinopharm columns. Figure 4C suggests that free choice
significantly increased the efficiency of vaccine allocation because this
avoided unnecessary risks of final rejections by enabling re-selecting still
acceptable vaccines.

Assessment of vaccines might have dynamically changed as we learned their
effects on health antibody production. We report how assessment of vaccines
have changed over the third wave by vaccine types in Supporting Information 6.
Most of these stay well below 5 percent of the total sample. Only in case of
the AstraZeneca did the number of negative changes almost reach 10 percent of
the sample, which is most likely due to the strong negative influence of the
news about the side effects.

Finally, Supporting Information 7 contains a preference analysis of those
individuals who were not vaccinated but evaluated minimum 2 but maximum 3 of
the investigated vaccines acceptable. This enables us to eliminate those who
cannot accept a specific vaccine to look at co-acceptance among vaccines. We
find in Figure S6 that Pfizer is accepted by many who also accept Moderna
(this is the most common co-acceptance link), Sputnik and AstraZeneca, but
less so with Sinopharm. Yet, the Sputnik-Sinopharm co-acceptance link is the
most frequent one that does not include Pfizer. This suggests that a wide and
diverse vaccine portfolio including, both the newest technologies and the
older techniques can provide good second-best choices in a society of
divergent vaccine preferences.

In sum, we find that free choice across COVID-19 vaccine types allows
individuals to get an "acceptable" vaccine and reject the unacceptable
alternatives. Although mRNA vaccines are the most preferred ones in our
sample, some individuals have specifically chosen vector-based and whole virus
vaccines. Our results suggest that the society is fragmented into groups of
individuals that either prefers the newest vaccine technologies and refuse the
more established vaccine technologies or vice versa.

##  3 Discussion

The COVID-19 virus created an unprecedented challenge is human history. This
is the first global pandemic in the digital age, which concerned all countries
around the globe. Nations and international alliances, such as the European
Union, started financing the development of vaccines in a diverse way, since
the fast and reliable supply of effective vaccines seemed the only solution to
end the pandemic. A large number of vaccine developments have started
simultaneously, led by the producers of the main geopolitical powers (USA, EU,
China, Russia and India). So far, several of these vaccines have entered the
mass production phase but these use multiple technologies including
inactivated whole viruses (eg. Sinopharm), adeno-virus vectors (eg.
AstraZeneca, Janssen, Sputnik) and the mRNA technology (eg. Pfizer, Moderna).
Most governments decided to use multiple types of vaccines in order to
vaccinate the population as soon as possible.

There is a growing consensus that the vaccine diversity, generated by this
extraordinary competition between vaccine developers, has several values.
First of all, vast capacities have been developed with unprecedented speed
when the outcomes - the efficiency of the vaccine - were still very uncertain
[33, 34]. Many have also stressed the need for a production at full and
increasing capacities; while others highlight that lower efficacy vaccines are
better to use than wait for a more efficient one [35]. Furthermore, individual
vaccine preferences may differ by technology and country of origin that can be
better handled with a diverse vaccine portfolio [26]. This latter aspect
raises the question whether governments should give the freedom of choice to
the people over what type of vaccine they wish to get [36].

During the third wave of pandemic in Europe in the first half of 2021, which
was also the first period of vaccination under vaccine shortage, most European
countries gave no choice in between the available vaccines for the registered
people when getting their shots. However scientific studies argued the
potential benefits of free choice in the UK [37]. Survey tests in Germany also
suggested strong preferences in between the two most different vaccines
available there in April, Pfizer and AstraZeneca, providing experimental
evidence that free choice can mitigate vaccine hesitancy [38]. A recent survey
conducted in eight European countries shows the differences in vaccine
hesitancy and also the divergent preferences of the people over the available
vaccines [39]. Our paper adds to this line of research by analyzing a
representative survey in Hungary that not just ask the preferences of the
patients, but collects also information about their actual decisions over
accepting or rejecting offers for specific vaccines, thus providing their
revealed preferences over the available vaccines.

The available vaccines differ in many characteristics, such as technology,
country of origin, effectiveness, side effects, approval status by national
and international authorities, etc. These aspects are very hard to measure,
weight and compare. Even if experts are conducting the ranking based on
sophisticated techniques, the result can be different [40] depending on the
subjective valuations of the experts. It is then not a surprise that the
average patients can have very diverse preferences, that can significantly
vary across countries [39], and can also change over time [26], as more
scientific information becomes available on the effectiveness of the vaccines
in the long run and for new mutations, and more precise estimations on the
risky side-effects for different groups of populations. Our survey shows
surprisingly diverse preferences, where one of the most relevant aspect
identified was the technology, namely mRNA versus vector-vaccine and whole-
virus. The other major fragmentation in the preferences was in between over
the EU-approved Western vaccines (Pfizer, Moderna, AztraZeneca and Janssen)
and the Eastern vaccines (Sputnik and Sinopharm) that were additional vaccine
types to the common EU purchase.

Besides describing the surveyed preferences and actual choices over the
available vaccines, we aimed to understand the reasons behind the preferences.
A novel finding of our study, that was not considered in previous
international surveys [39], is the effect of information source that
respondents trust. We found that people trusting in politicians were more
likely to accept less popular vaccines, in our case vector-based (AstraZeneca
and Sputnik) or whole-virus vaccines (Sinopharm). Those open to conspiracy
theories were more likely to reject mRNA vaccines (Pfizer and Moderna). This
shows that the free choice policy in vaccine allocation should be combined
with the right communication strategy by the government and other decision
makers, where different populations should be targeted with different messages
when offered with a range of vaccines.

##  4 Methods

###  4.1 Data

The data used in this paper was collected with a CATI telephone survey on a
nationally representative sample of the Hungarian adult (18 years or older)
population between May 25-31, 2021. The sample size was $1,000$, which is
equal to the conventional sample size for nationally representative samples in
the country. Data was collected both over mobile and landline phones. This
campaign was a part of a larger data collection effort, which started in March
2020 and it is still ongoing. The Hungarian Data Provider Questionnaire [30,
31] continuously collects survey data through online questionnaires [41] and
is monthly asked on nationally representative phone surveys to follow the
behavioural changes of people in terms of their contact dynamics, mobility
patterns, self-protection habits, and ask people’s opinion related to the
COVID-19 pandemic. The sample was selected by a multi-step, proportionally
stratified, probabilistic sampling procedure. The sample is representative for
the Hungarian population aged 18 or older by gender, age, education and
domicile. Minor deviations of the data from population ratios were corrected
by iterative proportional weighting after the data collection. After data
collection, only the anonymised and hashed data was shared with people
involved in the project after signing non-disclosure agreements. The data
collection and processing have been evaluated and approved by the Research
Ethic Committee of the Medical Research Council of Hungary (ETT TUKEB
IV/3073-1/2021/EKU).

###  4.2 Availability of Data

For privacy protection we share only the relevant part of the data, which does
not allow the reidentification of participants. We exclude sensitive
information such as domicile and labels for categorical responses. The data is
available at https://doi.org/10.5281/zenodo.5575586.

###  4.3 Methods

In the LASSO model, the dependent variable takes values between 1 and 5, where
1 is "unacceptable" and 5 is "the best"). In models 1-5, we estimate
evaluation values by vaccine types. Then, in models 6 to 9, the ratings’
variance, mean, minimum, and maximum serve as dependent variables. In model
10, the dependent variable is binary taking the value of 1 if ever rejected a
vaccine offer and 0 otherwise. The dependent variable is 1 in model 11, if any
vaccine is unacceptable. The lasso coefficients
($\widehat{\beta}_{\lambda}^{L}$) minimize the following expression:
$\sum_{i=1}^{n}(y_{i}-\beta_{0}-\sum_{j=1}^{p}\beta_{j}x_{ij})^{2}+\lambda\sum_{j=1}^{p}\left|\beta_{j}\right|$
.

Linear probability models are used to predict rejection of vaccines. These
regressions are specified by the formula $P(Y=1|X)=\alpha+\beta X+\epsilon$,
where $Y$ equals 1 if the vaccine is rated unacceptable, $\alpha$ is the
intercept, $\beta$ denotes point estimates, $X$ stand for a vector of
independent variables, and $\epsilon$ is heteroscedasticity-robust standard
error.

In the heat-maps generation, we used the same vaccine ratings on a scale from
$1$ to $5$, as we mentioned above. We clustered the survey responses based on
the acceptance decision of the first vaccine offer. In case of rejection, we
grouped individuals based on the accepted vaccine and the first rejected
vaccine. Then, we used standard summary statistics tools to find the number of
observation, mean and the standard deviation for each of the groups. We report
the mean ratings in Figure 3 and the rest of the summary statistics in
Supporting Information Supporting Information 5: Summary statistics for
vaccine assessment matrices.

The link weights of the directed network presented in Figure 1 is generated
following the aggregation procedure $W_{ab}=\sum_{a}L_{ab}$, where $L_{ab}$
equals 1 if the individual has rejected vaccine $a$ but later received vaccine
$b$. The undirected co-occurrence network presented in Figure S6 follows the
rule of $W_{mn}=\sum_{m}L_{mn,m\neq n}$ where $L_{mn}$ equals 1 if the
respondent accepts both vaccines $m$ and $n$.

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###### Notes

  1. i Vaccine diversity is simply calculated by the number of available vaccine types from data at https://ourworldindata.org/covid-vaccinations.
  2. ii Official names of the vaccines are listed here. Pfizer-Biontech: Comirnaty/BNT162b2, COVID-19 mRNA vaccine. Moderna: Spikevax/mRNA-1273. AstraZeneca: Vaxzevria, Covishield / AZD1222, ChAdOx1 nCoV-19, ChAdOx1-S, AZD2816. Sputnik: Gam-COVID-Vac. Sinopharm: Sinopharm: BIBP COVID-19 vaccine / Zhong’aikewei, Hayat-Vax. Janssen: Ad26.COV2.S JNJ-78436735 Ad26COVS1 VAC31518
  3. iii According to https://ourworldindata.org/covid-vaccinations, vaccination per capita was much higher in May 2021, when our data was collected, in Hungary than in other Central and Eastern European countries where trust in public authorities is at a similar level.
  4. iv Number of cases can vary among analyses. For a precise consideration of sample sizes, please see Materials and Methods.
  5. v https://vakcinainfo.gov.hu/regisztracio-oltasra
  6. vi Recommendation for vaccination strategy of the Hungarian Academy of Sciences: https://mta.hu/english/recommendation-of-the-hungarian-academy-of-science-on-the-management-of-the-covid-19-epidemic-in-the-short-and-long-term-110630
  7. vii A summary on anti-vax movement in Hungary: https://www.iribeaconproject.org/our-work-analysis-and-insights/2021-09-16/hungarys-anti-vax-movement-alive-and-kicking
  8. viii Scientists, doctors slam Hungarian government criticism of western vaccines: https://www.reuters.com/article/us-health-coronavirus-hungary-vaccines-idUSKBN2CE2C3
  9. ix The Hungarian prime minister is vaccinated with Sinopharm: https://www.euronews.com/2021/02/28/hungary-s-pm-viktor-orban-vaccinated-against-covid-with-chinese-sinopharm-vaccine
  10. x Surveys https://hungarytoday.hu/hungarian-vaccination-politics-vaccine-preference-coronavirus-survey-willingness/
  11. xi https://www.reuters.com/business/healthcare-pharmaceuticals/uk-regulator-says-some-evidence-astrazeneca-clots-occur-more-women-than-men-2021-05-06/

## Acknowledgements

The authors acknowledge the help of Szabolcs Tóth-Zs in finalizing the
figures. Péter Biró gratefully acknowledges financial support from the
Hungarian Academy of Sciences, Momentum Grant (No. LP2021-2). Julia Koltai
acknowledges funding from the Premium Postdoctoral Grant of the Hungarian
Academy of Sciences. The work of Balazs Lengyel was financially supported by
Hungarian National Scientific Fund (OTKA K 138970). Agnes Szabo-Morvai was
financially supported by the National Research, Development and Innovation
Office (OTKA FK141322)

## Author contributions statement

J.K., A.Sz., G.R., M.K., P.B. and B.L. conceived the survey data collection,
K.K., A.Sz., P.B., B.L. designed the investigation, K.K., A.Sz., and B.L.
analyzed the data. All authors wrote and reviewed the manuscript.

##  5 Additional information

###  5.1 Accession codes

All codes to analyze data have been written in R (version 4.0.5) and are
available at https://doi.org/10.5281/zenodo.5575586.

###  5.2 Competing interests

All authors claim no conflicting interest.

## Supplementary Information

## Supporting Information 1: Detailed information on vaccination dynamics

Figure S1 illustrates the time horizon for the vaccination. We can see that
Pfizer was the only available vaccine initially, which was most likely offered
to medical workers. In the beginning of March, Sinopharm and AstraZeneca
became available for mostly chronic ill patients. We see a strict cutoff at
the age of $60$, where Astrazeneca was offered to patients below $60$ and the
Chinese Sinopharm to those who are above $60.$ Chronic ill patients who
rejected a vaccine get vaccinated in the end of March. In April, we see that
vaccines become widely available for patients under the age of $50$. In May,
those patients who have rejected a vaccine before and are not chronic ill get
finally vaccinated, mainly with Pfizer. Figure S1 suggests that chronic ill
and older patients who have not rejected any vaccines in the past tend to get
vaccinated sooner.

Figure S1: Vaccination date, age, vaccination type, chronic illness and
rejection status distribution

Figure S2 shows the distribution of different vaccine types conditionally on
chronic illness and rejection status. Out of the $996$ observations, most
patients received Pfizer ($n=260$), then Sinopharm ($n=162$), Sputnik
($n=136$), Astrazeneca ($n=122$), Moderna ($n=45$) and $n=261$ people decided
not to get vaccinated. We see that only a minority of patients rejected any
vaccines. We would like to highlight that Pfizer and Moderna had the highest
and AstraZeneca had the lowest ratio of patients who rejected a vaccine before
vaccination. We would also like to point out that Sputnik and Pfizer have the
two highest not chronic ill to chronic ill ratio of patients. More
importantly, Figure S2 suggests that chronic ill patients are less likely to
reject a vaccine than patients without any chronic illness.

Figure S2: Vaccine type distribution conditionally on chronic illness and
rejection status.

Figure S3 shows the mean and standard deviation of the age distribution
conditionally on vaccine type, chronic illness and rejection status. We see
that chronic patients tend to be older, as expected. Among patients without
chronic illness, Pfizer has the lowest average age, which we believe is a
consequence of $2$ reasons. First, younger medical workers and school teachers
could receive Pfizer sooner, as they were prioritized in the vaccine
allocation mechanism by the government. Second, younger patients with better
physical health felt less threatened by the pandemic and more inclined to wait
for their most preferred vaccine, which was Pfizer in most cases. On the other
hand, we see that patients without chronic illness, who received AstraZeneca
have the highest average age. A potential explanation could be that elderly
patient has a stronger time pressure and hence less likely to wait for
alternative vaccines. Figure S3 shows a lot of variation among vaccine types
and chronic illness status, which could be explained by prioritization of
patients with certain jobs and the amount of time pressure patients experience
to wait for an alternative vaccine.

Figure S3: Average age distribution conditionally on vaccine type, chronic
illness and rejection status. The black line shows $1$ standard deviation.

Figure S4 shows the average week of the first vaccination among vaccine types,
chronic illness and rejection status. We see that the average vaccination week
tends to increase among those who rejected a vaccine regardless of chronic
illness status and vaccination type. Let us point out the large variance in
the vaccination date among patients who received Pfizer. This indicates that
Pfizer was available for the longest time in Hungary. Also, the smaller values
for AstraZeneca suggests that patients were more likely to receive AstraZeneca
earlier, in February and early March. Figure S4 is in accordance with Figure
S1 as they both show that vaccines became available for the most patient
starting from the $14$th week and patients with Pfizer have the largest
variation in the time of vaccination.

Figure S4: Average vaccination week distribution in 2021 conditionally on
vaccine type, chronic illness and rejection status. The black line shows $1$
standard deviation.

## Supporting Information 2: Variable selection for the vaccine rejection
analysis

Table S1: Lasso models on vaccine rejection

| Pfizer | Moderna | AstraZeneca | Sputnik | Sinopharm | Variance | Mean | Min | Max | Any
---|---|---|---|---|---|---|---|---|---|---
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10)
Age | x | x | x |  |  |  | x | x |  | x
Female |  | x | x | x |  |  |  |  |  |
City Cat. by Size | x | x | x | x |  |  |  |  |  |
University |  | x |  | x |  | x |  | x |  |
Smoking |  | x | x | x | x |  |  |  |  | x
Chronic Illness |  |  | x | x |  |  |  | x |  |
Acute Illness |  |  |  | x |  | x |  |  |  |
Worked Last Week |  |  |  | x |  | x |  | x |  |
Wealth Pre COVID-19 |  | x |  |  |  |  |  |  |  |
Wealth Now |  | x | x | x | x | x | x | x |  | x
Adv. from Doct. | x | x | x | x |  | x | x |  | x | x
Adv. from Pol. |  |  |  | x |  | x |  |  |  | x
Adv. from Sci. | x | x | x | x |  | x |  | x | x | x
Adv. from Anti-vacc. | x | x | x | x |  | x |  | x |  | x
Prop. |  |  |  |  |  |  |  |  |  |
Adv. from Family |  |  |  |  |  |  |  | x |  |
Adv. from Friends |  |  |  | x |  |  |  |  |  |
Adv. from Celeb. |  |  |  |  |  | x |  |  |  |
Adv. from Journ. |  |  | x |  |  |  |  | x |  |
Online News |  |  | x | x | x | x |  | x |  | x

  * •

Note: Dependent variables: Pfizer and other vaccines: rating (1:
"unacceptable"; 5: "best"); Variance/Mean/Min/Max: variance/mean/min/max of
the ratings; UnacceptAny=1 if any vaccine rated as "unacceptable". Explanatory
variables excluded from each lasso model: SourceSocialNetwork; SourcePress;
SourceRadio; SourceTV; SourceFamily; SourceFriends; SourceOther (VaccineX:
Takes advice regarding COVID-19 vaccination from X; SourceX: Source of
information used to get to know about pandemic-related government measures)

## Supporting Information 3: Variable description: summary statistics,
correlation, VIF

Table S2: Summary statistics and VIF of variables in the linear probability model in Figure 2 Variable | Min | 1st Qu. | Median | Mean | 3rd Qu. | Max | VIF
---|---|---|---|---|---|---|---
Advice from Doctors | 0.000 | 0.000 | 1.000 | 0.729 | 1.000 | 1.000 | 1.121
Advice from Scientists | 0.000 | 0.000 | 1.000 | 0.595 | 1.000 | 1.000 | 1.168
Advice from Anti-vaccine Propagators | 0.000 | 0.000 | 0.000 | 0.042 | 0.000 | 1.000 | 1.092
Advice from Politicians | 0.000 | 0.000 | 0.000 | 0.065 | 0.000 | 1.000 | 1.236
Advice from Family | 0.000 | 0.000 | 0.000 | 0.222 | 0.000 | 1.000 | 1.536
Advice from Friends | 0.000 | 0.000 | 0.000 | 0.136 | 0.000 | 1.000 | 1.506
Advice from Journalists | 0.000 | 0.000 | 0.000 | 0.017 | 0.000 | 1.000 | 1.586
Advice from Celebrities | 0.000 | 0.000 | 0.000 | 0.015 | 0.000 | 1.000 | 1.583
Age | 18.00 | 34.00 | 48.00 | 48.86 | 64.00 | 85.00 | 1.542
Female | 0.00 | 0.00 | 1.00 | 0.53 | 1.00 | 1.00 | 1.075
University | 0.000 | .0.000 | 0.000 | 0.217 | 0.000 | 1.000 | 1.181
City Cat. by Size | 1.000 | 1.000 | 2.000 | 2.296 | 3.000 | 4.000 | 1.073
Wealth Pre COVID-19 | 1.000 | 5.000 | 5.000 | 5.406 | 6.000 | 10.000 | 1.141
Smoking | 1.000 | 1.000 | 2.000 | 2.208 | 4.000 | 4.000 | 1.114
Chronic Illness | 0.000 | 0.000 | 0.000 | 0.406 | 1.000 | 1.000 | 1.403
Covid-19 Previously | 0.000 | 0.000 | 0.000 | 0.095 | 0.000 | 1.000 | 1.157
Serious Covid-19 Previously | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | .00 | 1.123
Figure S5: Pearson correlation coefficients of variables in the linear
probability model in Figure 2

## Supporting Information 4: Alternative specifications of the vaccine
rejection analysis

Table S3: Linear probability models on evaluating a vaccine unacceptable. Results depicted in Figure 3 | Dependent variable: Evaluating a vaccine unacceptable
---|---
| Pfizer | Moderna | Astrazeneca | Sputnik | Sinopharm
| (1) | (2) | (3) | (4) | (5)
Advice from Doctors |  $-$0.057∗∗∗ |  $-$0.061∗∗∗ |  $-$0.029 |  $-$0.026 |  $-$0.054∗∗
| (0.018) | (0.019) | (0.023) | (0.019) | (0.025)
Advice from Scientists |  $-$0.035∗∗ |  $-$0.026∗ |  $-$0.054∗∗ |  $-$0.017 | 0.008
| (0.014) | (0.014) | (0.022) | (0.017) | (0.021)
Advice from Anti-Vaccine Propagators | 0.202∗∗∗ | 0.182∗∗∗ | 0.117∗ | 0.062 | 0.054
| (0.064) | (0.066) | (0.066) | (0.056) | (0.064)
Advice from Politicians |  $-$0.029 |  $-$0.035 |  $-$0.064∗∗ |  $-$0.076∗∗∗ |  $-$0.084∗∗∗
| (0.023) | (0.023) | (0.031) | (0.014) | (0.030)
Advice from Family |  $-$0.043∗∗∗ |  $-$0.038∗∗ | 0.028 |  $-$0.019 |  $-$0.058∗∗
| (0.017) | (0.019) | (0.032) | (0.026) | (0.028)
Advice from Friends |  $-$0.007 |  $-$0.007 |  $-$0.015 |  $-$0.025 |  $-$0.012
| (0.020) | (0.024) | (0.039) | (0.030) | (0.034)
Advice from Journalists |  $-$0.071 |  $-$0.032 | 0.023 |  $-$0.008 | 0.089
| (0.049) | (0.060) | (0.100) | (0.058) | (0.138)
Advice from Celebrities | 0.073 | 0.116 | 0.047 | 0.129 | 0.114
| (0.092) | (0.102) | (0.122) | (0.103) | (0.148)
Age |  $-$0.001∗∗ |  $-$0.001∗∗ |  $-$0.003∗∗∗ |  $-$0.0003 |  $-$0.0001
| (0.0004) | (0.0005) | (0.001) | (0.001) | (0.001)
Female | 0.006 | 0.001 | 0.045∗∗ |  $-$0.005 | 0.005
| (0.013) | (0.014) | (0.020) | (0.017) | (0.021)
University |  $-$0.021 |  $-$0.026∗ |  $-$0.054∗∗ |  $-$0.026 | 0.002
| (0.015) | (0.015) | (0.024) | (0.018) | (0.024)
City Category by Size | 0.0002 |  $-$0.005 |  $-$0.005 |  $-$0.004 | 0.010
| (0.005) | (0.006) | (0.009) | (0.008) | (0.010)
Smoking | 0.004 | 0.005 | 0.004 | 0.011 | 0.020∗∗
| (0.005) | (0.006) | (0.008) | (0.007) | (0.008)
Chronic Illness |  $-$0.004 |  $-$0.004 | 0.008 |  $-$0.011 | 0.002
| (0.016) | (0.015) | (0.023) | (0.021) | (0.025)
COVID-19 Previously | 0.007 | 0.002 |  $-$0.010 |  $-$0.034 |  $-$0.045
| (0.023) | (0.023) | (0.035) | (0.022) | (0.031)
Serious COVID-19 Previously | 0.151 | 0.149 | 0.031 |  $-$0.031 | 0.037
| (0.109) | (0.109) | (0.094) | (0.028) | (0.101)
Constant | 0.146∗∗∗ | 0.166∗∗∗ | 0.314∗∗∗ | 0.120∗∗ | 0.095∗
| (0.038) | (0.041) | (0.054) | (0.047) | (0.055)
Observations | 999 | 999 | 999 | 999 | 999
R2 | 0.095 | 0.080 | 0.070 | 0.026 | 0.028
Adjusted R2 | 0.080 | 0.065 | 0.055 | 0.010 | 0.012
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table S4: Linear probability models on evaluating a vaccine unacceptable with standardized variables | Dependent variable: Evaluating a vaccine unacceptable
---|---
| Pfizer | Moderna | Astrazeneca | Sputnik | Sinopharm
| (1) | (2) | (3) | (4) | (5)
Advice from Doctors |  $-$0.130∗∗∗ |  $-$0.130∗∗∗ |  $-$0.041 |  $-$0.046 |  $-$0.076∗∗
| (0.040) | (0.039) | (0.033) | (0.034) | (0.035)
Advice from Scientists |  $-$0.087∗∗ |  $-$0.062∗ |  $-$0.085∗∗ |  $-$0.034 | 0.013
| (0.034) | (0.034) | (0.034) | (0.034) | (0.033)
Advice from Anti-Vaccine Propagators | 0.207∗∗∗ | 0.174∗∗∗ | 0.076∗ | 0.049 | 0.035
| (0.066) | (0.063) | (0.043) | (0.045) | (0.041)
Advice from Politicians |  $-$0.036 |  $-$0.041 |  $-$0.051∗∗ |  $-$0.074∗∗∗ |  $-$0.066∗∗∗
| (0.029) | (0.027) | (0.024) | (0.014) | (0.023)
Advice from Family |  $-$0.092∗∗∗ |  $-$0.075∗∗ | 0.038 |  $-$0.031 |  $-$0.077∗∗
| (0.035) | (0.038) | (0.043) | (0.043) | (0.037)
Advice from Friends |  $-$0.011 |  $-$0.011 |  $-$0.016 |  $-$0.034 |  $-$0.013
| (0.035) | (0.039) | (0.043) | (0.041) | (0.037)
Advice from Journalists |  $-$0.047 |  $-$0.020 | 0.010 |  $-$0.004 | 0.037
| (0.032) | (0.037) | (0.041) | (0.030) | (0.057)
Advice from Celebrities | 0.045 | 0.067 | 0.018 | 0.062 | 0.044
| (0.057) | (0.059) | (0.047) | (0.050) | (0.057)
Age |  $-$0.089∗∗ |  $-$0.086∗∗ |  $-$0.200∗∗∗ |  $-$0.019 |  $-$0.003
| (0.041) | (0.041) | (0.039) | (0.042) | (0.040)
Female | 0.016 | 0.002 | 0.072∗∗ |  $-$0.010 | 0.008
| (0.033) | (0.033) | (0.032) | (0.034) | (0.033)
University |  $-$0.044 |  $-$0.051∗ |  $-$0.072∗∗ |  $-$0.042 | 0.003
| (0.031) | (0.029) | (0.032) | (0.029) | (0.032)
City Category by Size | 0.001 |  $-$0.027 |  $-$0.018 |  $-$0.017 | 0.033
| (0.028) | (0.029) | (0.029) | (0.033) | (0.034)
Smoking | 0.023 | 0.028 | 0.018 | 0.054 | 0.081∗∗
| (0.035) | (0.035) | (0.034) | (0.035) | (0.034)
Chronic Illness |  $-$0.009 |  $-$0.009 | 0.013 |  $-$0.022 | 0.004
| (0.039) | (0.036) | (0.036) | (0.040) | (0.040)
COVID-19 Previously | 0.010 | 0.003 |  $-$0.009 |  $-$0.040 |  $-$0.042
| (0.034) | (0.032) | (0.033) | (0.026) | (0.029)
Serious COVID-19 Previously | 0.076 | 0.071 | 0.010 |  $-$0.012 | 0.012
| (0.055) | (0.052) | (0.030) | (0.011) | (0.032)
Constant | 0.0003 | 0.0003 | 0.0001 | 0.0001 | 0.0003
| (0.030) | (0.031) | (0.031) | (0.031) | (0.031)
Observations | 999 | 999 | 999 | 999 | 999
R2 | 0.095 | 0.080 | 0.070 | 0.026 | 0.028
Adjusted R2 | 0.080 | 0.065 | 0.055 | 0.010 | 0.012
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table S5: Logistic regression on evaluating a vaccine unacceptable | Dependent variable: Evaluating a vaccine unacceptable
---|---
| Pfizer | Moderna | Astrazeneca | Sputnik | Sinopharm
| (1) | (2) | (3) | (4) | (5)
Advice from Doctors |  $-$1.262∗∗∗ |  $-$1.211∗∗∗ |  $-$0.298 |  $-$0.381 |  $-$0.510∗∗
| (0.396) | (0.358) | (0.229) | (0.266) | (0.217)
Advice from Scientists |  $-$1.085∗∗ |  $-$0.682∗ |  $-$0.573∗∗ |  $-$0.295 | 0.039
| (0.439) | (0.379) | (0.236) | (0.271) | (0.219)
Advice from Anti-Vaccine Propagators | 2.838∗∗∗ | 2.349∗∗∗ | 0.974∗∗ | 0.938 | 0.495
| (0.560) | (0.549) | (0.459) | (0.588) | (0.517)
Advice from Politicians |  $-$1.122 |  $-$1.228 |  $-$0.939 |  $-$3.267∗∗∗ |  $-$1.474∗
| (1.051) | (1.016) | (0.635) | (0.844) | (0.834)
Advice from Family |  $-$1.441∗∗ |  $-$1.041∗ | 0.138 |  $-$0.429 |  $-$0.722∗∗
| (0.629) | (0.554) | (0.278) | (0.441) | (0.363)
Advice from Friends |  $-$0.479 |  $-$0.343 |  $-$0.066 |  $-$0.461 |  $-$0.176
| (0.718) | (0.672) | (0.338) | (0.609) | (0.444)
Advice from Journalists |  $-$15.071∗∗∗ |  $-$0.544 | 0.380 | 0.790 | 1.221
| (1.224) | (1.644) | (1.211) | (0.901) | (1.515)
Advice from Celebrities | 3.350∗∗∗ | 2.947∗∗∗ | 0.910 | 2.621∗∗ | 1.347
| (0.886) | (0.905) | (1.341) | (1.018) | (1.390)
Age |  $-$0.029∗∗ |  $-$0.026∗∗ |  $-$0.037∗∗∗ |  $-$0.004 |  $-$0.001
| (0.014) | (0.013) | (0.008) | (0.009) | (0.007)
Female | 0.122 |  $-$0.006 | 0.467∗∗ |  $-$0.060 | 0.046
| (0.395) | (0.355) | (0.231) | (0.269) | (0.215)
University |  $-$0.857 |  $-$0.914∗ |  $-$0.525∗ |  $-$0.513 | 0.033
| (0.555) | (0.518) | (0.300) | (0.392) | (0.265)
City Category by Size |  $-$0.021 |  $-$0.179 |  $-$0.086 |  $-$0.070 | 0.105
| (0.165) | (0.163) | (0.104) | (0.134) | (0.104)
Smoking | 0.133 | 0.127 | 0.042 | 0.176∗ | 0.204∗∗
| (0.140) | (0.130) | (0.088) | (0.104) | (0.083)
Chronic Illness |  $-$0.080 |  $-$0.092 | 0.103 |  $-$0.203 | 0.017
| (0.481) | (0.409) | (0.276) | (0.338) | (0.262)
COVID-19 Previously | 0.097 | 0.005 |  $-$0.090 |  $-$0.811 |  $-$0.558
| (0.610) | (0.592) | (0.394) | (0.667) | (0.459)
Serious COVID-19 Previously | 1.941∗∗ | 1.727∗ | 0.321 |  $-$13.129∗∗∗ | 0.395
| (0.888) | (0.918) | (1.024) | (0.828) | (1.235)
Constant |  $-$0.840 |  $-$0.557 |  $-$0.097 |  $-$1.892∗∗ |  $-$2.295∗∗∗
| (0.933) | (0.850) | (0.550) | (0.741) | (0.582)
Observations | 999 | 999 | 999 | 999 | 999
Akaike Inf. Crit. | 292.334 | 340.300 | 654.138 | 499.692 | 701.087
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table S6: Linear probability models on evaluating a vaccine unacceptable with individual subjective wealth | Dependent variable: Evaluating a vaccine unacceptable
---|---
| Pfizer | Moderna | Astrazeneca | Sputnik | Sinopharm
| (1) | (2) | (3) | (4) | (5)
Advice from Doctors |  $-$0.058∗∗∗ |  $-$0.062∗∗∗ |  $-$0.029 |  $-$0.027 |  $-$0.054∗∗
| (0.018) | (0.019) | (0.023) | (0.019) | (0.025)
Advice from Scientists |  $-$0.034∗∗ |  $-$0.026∗ |  $-$0.054∗∗ |  $-$0.017 | 0.009
| (0.014) | (0.014) | (0.021) | (0.017) | (0.021)
Advice from Anti-Vaccine Propagators | 0.202∗∗∗ | 0.182∗∗∗ | 0.117∗ | 0.061 | 0.054
| (0.064) | (0.066) | (0.066) | (0.056) | (0.064)
Advice from Politicians |  $-$0.030 |  $-$0.037∗ |  $-$0.065∗∗ |  $-$0.079∗∗∗ |  $-$0.088∗∗∗
| (0.022) | (0.022) | (0.030) | (0.015) | (0.030)
Advice from Family |  $-$0.043∗∗∗ |  $-$0.037∗∗ | 0.029 |  $-$0.018 |  $-$0.057∗∗
| (0.017) | (0.019) | (0.032) | (0.026) | (0.028)
Advice from Friends |  $-$0.006 |  $-$0.006 |  $-$0.014 |  $-$0.024 |  $-$0.011
| (0.020) | (0.024) | (0.039) | (0.030) | (0.034)
Advice from Journalists |  $-$0.070 |  $-$0.030 | 0.024 |  $-$0.006 | 0.092
| (0.049) | (0.060) | (0.100) | (0.058) | (0.138)
Advice from Celebrities | 0.071 | 0.114 | 0.046 | 0.124 | 0.109
| (0.091) | (0.102) | (0.122) | (0.100) | (0.145)
Age |  $-$0.001∗∗ |  $-$0.001∗∗ |  $-$0.004∗∗∗ |  $-$0.0004 |  $-$0.0003
| (0.0005) | (0.0005) | (0.001) | (0.001) | (0.001)
Female | 0.006 | 0.0004 | 0.045∗∗ |  $-$0.005 | 0.004
| (0.013) | (0.014) | (0.020) | (0.017) | (0.021)
University |  $-$0.019 |  $-$0.023 |  $-$0.053∗∗ |  $-$0.021 | 0.008
| (0.014) | (0.015) | (0.024) | (0.018) | (0.025)
City Category by Size | 0.001 |  $-$0.005 |  $-$0.005 |  $-$0.003 | 0.011
| (0.005) | (0.006) | (0.009) | (0.008) | (0.010)
Wealth pre COVID-19 |  $-$0.004 |  $-$0.005 |  $-$0.003 |  $-$0.009∗ |  $-$0.011
| (0.004) | (0.004) | (0.006) | (0.005) | (0.007)
Smoking | 0.003 | 0.004 | 0.004 | 0.010 | 0.019∗∗
| (0.005) | (0.006) | (0.008) | (0.007) | (0.008)
Chronic Illness |  $-$0.005 |  $-$0.005 | 0.008 |  $-$0.013 |  $-$0.0002
| (0.016) | (0.015) | (0.023) | (0.021) | (0.025)
COVID-19 Previously | 0.008 | 0.003 |  $-$0.009 |  $-$0.032 |  $-$0.043
| (0.023) | (0.023) | (0.035) | (0.022) | (0.031)
Serious COVID-19 Previously | 0.148 | 0.146 | 0.029 |  $-$0.036 | 0.030
| (0.110) | (0.110) | (0.094) | (0.027) | (0.100)
Constant | 0.175∗∗∗ | 0.199∗∗∗ | 0.332∗∗∗ | 0.178∗∗∗ | 0.162∗∗
| (0.051) | (0.053) | (0.068) | (0.060) | (0.073)
Observations | 999 | 999 | 999 | 999 | 999
R2 | 0.096 | 0.082 | 0.070 | 0.029 | 0.031
Adjusted R2 | 0.081 | 0.066 | 0.054 | 0.013 | 0.014
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table S7: Linear probility models on evaluating a vaccine unacceptable among those individuals who had stable vaccine assessment over the period | Dependent variable: Evaluating a vaccine unacceptable
---|---
| Pfizer | Moderna | Astrazeneca | Sputnik | Sinopharm
| (1) | (2) | (3) | (4) | (5)
Advice from Doctors |  $-$0.052∗∗∗ |  $-$0.052∗∗∗ |  $-$0.018 |  $-$0.022 |  $-$0.038
| (0.018) | (0.018) | (0.023) | (0.020) | (0.024)
Advice from Scientists |  $-$0.034∗∗ |  $-$0.025∗ |  $-$0.042∗ |  $-$0.011 | 0.006
| (0.014) | (0.014) | (0.022) | (0.018) | (0.022)
Advice from Anti-Vaccine Propagators | 0.175∗∗∗ | 0.143∗∗ | 0.121∗ | 0.040 | 0.012
| (0.065) | (0.064) | (0.073) | (0.055) | (0.061)
Advice from Politicians |  $-$0.026 |  $-$0.031 |  $-$0.060∗∗ |  $-$0.076∗∗∗ |  $-$0.081∗∗
| (0.024) | (0.022) | (0.027) | (0.014) | (0.031)
Advice from Family |  $-$0.041∗∗ |  $-$0.033∗ | 0.029 |  $-$0.014 |  $-$0.055∗∗
| (0.017) | (0.019) | (0.031) | (0.027) | (0.028)
Advice from Friends |  $-$0.006 |  $-$0.005 |  $-$0.051 |  $-$0.023 |  $-$0.027
| (0.021) | (0.024) | (0.035) | (0.032) | (0.032)
Advice from Journalists |  $-$0.068 |  $-$0.027 | 0.090 |  $-$0.008 | 0.119
| (0.050) | (0.059) | (0.089) | (0.058) | (0.162)
Advice from Celebrities | 0.082 | 0.120 |  $-$0.102 | 0.131 | 0.122
| (0.104) | (0.103) | (0.064) | (0.102) | (0.158)
Age |  $-$0.001∗∗ |  $-$0.001∗ |  $-$0.003∗∗∗ |  $-$0.0002 |  $-$0.0001
| (0.0005) | (0.0005) | (0.001) | (0.001) | (0.001)
Female | 0.009 | 0.002 | 0.048∗∗ |  $-$0.006 | 0.013
| (0.013) | (0.014) | (0.020) | (0.017) | (0.021)
University |  $-$0.019 |  $-$0.022 |  $-$0.045∗ |  $-$0.017 | 0.004
| (0.015) | (0.015) | (0.025) | (0.019) | (0.025)
City Category by Size | 0.001 |  $-$0.005 |  $-$0.001 |  $-$0.005 | 0.008
| (0.005) | (0.006) | (0.009) | (0.008) | (0.010)
Smoking | 0.004 | 0.007 | 0.011 | 0.012∗ | 0.019∗∗
| (0.005) | (0.006) | (0.008) | (0.007) | (0.008)
Chronic Illness |  $-$0.004 |  $-$0.004 |  $-$0.0003 |  $-$0.005 |  $-$0.003
| (0.016) | (0.015) | (0.023) | (0.021) | (0.026)
COVID-19 Previously | 0.012 | 0.006 |  $-$0.029 |  $-$0.032 |  $-$0.040
| (0.024) | (0.023) | (0.033) | (0.024) | (0.033)
Serious COVID-19 Previously | 0.175 | 0.151 | 0.079 |  $-$0.031 | 0.039
| (0.120) | (0.111) | (0.101) | (0.028) | (0.101)
Constant | 0.136∗∗∗ | 0.143∗∗∗ | 0.263∗∗∗ | 0.103∗∗ | 0.093∗
| (0.037) | (0.039) | (0.052) | (0.046) | (0.055)
Observations | 962 | 983 | 898 | 951 | 946
R2 | 0.082 | 0.064 | 0.073 | 0.022 | 0.023
Adjusted R2 | 0.067 | 0.048 | 0.056 | 0.005 | 0.007
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01

## Supporting Information 5: Summary statistics for vaccine assessment
matrices

Table S8: Summary statistics for Figure 4A: Accepted first vaccine Accepted Vaccine | Rated Vaccine | Observations | Mean | Standard Deviation
---|---|---|---|---
Astrazeneca | Pfizer | 121 | 4.17 | 1.03
AstraZeneca | Moderna | 121 | 3.94 | 1.00
AstraZeneca | AstraZeneca | 121 | 3.84 | 1.00
AstraZeneca | Sputnik | 121 | 3.56 | 1.12
AstraZeneca | Sinopharm | 121 | 3.30 | 1.24
Moderna | Pfizer | 35 | 4.32 | 1.06
Moderna | Moderna | 35 | 4.28 | 0.92
Moderna | AstraZeneca | 35 | 3.19 | 1.04
Moderna | Sputnik | 35 | 3.59 | 1.18
Moderna | Sinopharm | 35 | 3.18 | 1.36
Pfizer | Pfizer | 202 | 4.58 | 0.70
Pfizer | Moderna | 202 | 4.05 | 1.00
Pfizer | AstraZeneca | 202 | 3.15 | 1.17
Pfizer | Sputnik | 202 | 3.46 | 1.19
Pfizer | Sinopharm | 202 | 3.07 | 1.27
Sinopharm | Pfizer | 146 | 3.99 | 1.17
Sinopharm | Moderna | 146 | 3.82 | 1.13
Sinopharm | AstraZeneca | 146 | 3.36 | 1.23
Sinopharm | Sputnik | 146 | 3.75 | 1.10
Sinopharm | Sinopharm | 146 | 3.90 | 1.15
Sputnik | Pfizer | 123 | 4.13 | 1.08
Sputnik | Moderna | 123 | 3.94 | 1.07
Sputnik | AstraZeneca | 123 | 3.14 | 1.09
Sputnik | Sputnik | 123 | 4.02 | 1.05
Sputnik | Sinopharm | 123 | 3.44 | 1.17

Table S9: Summary statistics for Figure 4B: Rejected at least $1$ vaccine; Grouped by accepted vaccine Accepted Vaccine | Rated Vaccine | Observations | Mean | Standard Deviation
---|---|---|---|---
AstraZeneca | Pfizer | 11 | 4.30 | 1.25
AstraZeneca | Moderna | 11 | 3.78 | 1.20
AstraZeneca | AstraZeneca | 11 | 3.91 | 0.83
AstraZeneca | Sputnik | 11 | 2.88 | 0.83
AstraZeneca | Sinopharm | 11 | 2.12 | 0.83
Moderna | Pfizer | 10 | 4.88 | 0.35
Moderna | Moderna | 10 | 5.00 | 0.00
Moderna | AstraZeneca | 10 | 3.12 | 0.99
Moderna | Sputnik | 10 | 3.75 | 1.04
Moderna | Sinopharm | 10 | 2.12 | 1.36
Pfizer | Pfizer | 58 | 4.77 | 0.54
Pfizer | Moderna | 58 | 4.36 | 0.86
Pfizer | AstraZeneca | 58 | 2.70 | 1.35
Pfizer | Sputnik | 58 | 2.92 | 1.25
Pfizer | Sinopharm | 58 | 2.73 | 1.39
Sinopharm | Pfizer | 16 | 3.78 | 1.20
Sinopharm | Moderna | 16 | 3.00 | 1.00
Sinopharm | AstraZeneca | 16 | 2.00 | 0.87
Sinopharm | Sputnik | 16 | 3.22 | 0.83
Sinopharm | Sinopharm | 16 | 4.18 | 0.87
Sputnik | Pfizer | 13 | 3.90 | 1.10
Sputnik | Moderna | 13 | 3.75 | 1.16
Sputnik | AstraZeneca | 13 | 2.50 | 1.27
Sputnik | Sputnik | 13 | 4.08 | 0.79
Sputnik | Sinopharm | 13 | 3.50 | 1.18

Table S10: Summary statistics for Figure 4C: Rejected at least $1$ vaccine; Grouped by assigned vaccine Assigned Vaccine | Rated Vaccine | Observations | Mean | Standard Deviation
---|---|---|---|---
AstraZeneca | Pfizer | 28 | 4.26 | 1.14
AstraZeneca | Moderna | 28 | 3.95 | 1.12
AstraZeneca | AstraZeneca | 28 | 2.20 | 1.08
AstraZeneca | Sputnik | 28 | 3.42 | 1.10
AstraZeneca | Sinopharm | 28 | 3.24 | 1.51
Moderna | Pfizer | 2 | 4.50 | 0.71
Moderna | Moderna | 2 | 2.00 | 0.00
Moderna | AstraZeneca | 2 | 2.50 | 0.71
Moderna | Sputnik | 2 | 2.50 | 0.71
Moderna | Sinopharm | 2 | 4.00 | 0.00
Pfizer | Pfizer | 6 | 3.25 | 1.71
Pfizer | Moderna | 6 | 3.25 | 1.71
Pfizer | AstraZeneca | 6 | 2.50 | 1.00
Pfizer | Sputnik | 6 | 4.00 | 0.82
Pfizer | Sinopharm | 6 | 4.00 | 1.22
Sinopharm | Pfizer | 44 | 4.76 | 0.43
Sinopharm | Moderna | 44 | 4.49 | 0.70
Sinopharm | AstraZeneca | 44 | 3.24 | 1.35
Sinopharm | Sputnik | 44 | 3.28 | 1.14
Sinopharm | Sinopharm | 44 | 2.62 | 1.10
Sputnik | Pfizer | 26 | 4.61 | 0.72
Sputnik | Moderna | 26 | 4.06 | 1.00
Sputnik | AstraZeneca | 26 | 2.78 | 1.31
Sputnik | Sputnik | 26 | 2.47 | 1.12
Sputnik | Sinopharm | 26 | 2.50 | 1.51

## Supporting Information 6: Dynamics of vaccine assessment

In the nationally representative survey, individuals are asked whether they
have changed their opinion about each of the available vaccine types over the
past few months. In Table S11, we show the number of observations who declared
a negative and positive change, respectively.

Table S11: Number of respondents who changed their attitudes towards vaccines | Negative change | Positive change
---|---|---
Pfizer | 19 | 18
Moderna | 5 | 11
AstraZeneca | 90 | 10
Sputnik | 17 | 31
Sinopharm | 34 | 19

In Table S12, we consider those individuals who accepted a vaccine that they
did not rate the highest among the available vaccines. Based on the accepted
vaccine type, we calculated the ratio of individuals who rate an other vaccine
the highest. For instance, among the $9$ observations who accepted Pfizer and
did not rate Pfizer as their most preferred vaccine there were $2$
individuals, who rate Moderna as the most preferred vaccine, and hence we
obtained $0.22.$ As individuals were allowed to give the highest rating to
multiple vaccines, we see that the sum of the "Best" ratings must be weakly
greater than $1$. Let us point out that Astrazeneca group has the most
observations, who rate another vaccine the highest, and then Sputnik and
Sinopharm follows. We can also see a strong preference toward Pfizer among
those who did not accept their most preferred vaccine, which suggests the
limited supply of Pfizer in the observed period in Hungary.

Table S12: Distribution of the most preferred vaccine types when the accepted vaccine is not the most preferred. Accepted vaccine | Observations | Pfizer | Moderna | Astrazeneca | Sputnik | Sinopharm
---|---|---|---|---|---|---
Pfizer | 9 | - | 0.22 | 0.11 | 0.78 | 0.44
Moderna | 3 | 1.00 | - | 0.00 | 0.33 | 0.00
Astrazeneca | 40 | 0.88 | 0.25 | - | 0.10 | 0.10
Sputnik | 29 | 0.93 | 0.48 | 0.07 | - | 0.10
Sinopharm | 29 | 0.79 | 0.41 | 0.17 | 0.17 | -

The representative survey asks individuals to estimate the number of weeks
that they would be willing to wait to get their most preferred vaccine. In
Table S13, we grouped individuals based on whether they rejected any vaccines
and they rate the accepted vaccine the highest. We see that those who accepted
a vaccine that they do not rate the highest estimated the expected number of
weeks until the best vaccine to be $2.34$ and $2.57$ weeks on average if not
rejected any and rejected at least $1$ vaccine, respectively. We see that
these estimations are relatively close to $2.76,$ which is the mean of the
actual number weeks that the those individuals waited, who rejected a vaccine
and ended up accepting a vaccine that they rate the highest.

Table S13: Expected and actual waiting time distribution based on rejecting any vaccines and rating the accepted vaccine the highest. Rejected any | Highest rating | Observations | Mean expected | Mean actual |  $90\%$ CI expected |  $90\%$ CI actual
---|---|---|---|---|---|---
|  |  | (weeks) | (weeks) | (weeks) | (weeks)
0 | 0 | 101 | 2.34 | - | (1.90,2.78) | (-,-)
0 | 1 | 526 | 0.29 | - | (0.21,0.36) | (-,-)
1 | 0 | 9 | 2.57 | 0.83 | (0.58,4.56) | (0.11,1.56)
1 | 1 | 99 | 0.25 | 2.76 | (0.11,0.39) | (2.20,3.32)

In Table S14, we grouped individuals, who accepted a vaccine that they do not
rate the highest and individuals who were first offered and rejected that
vaccine, and ended up getting a vaccine that they rate the highest. First,
notice that the waiting times are not homogeneous among the vaccine types. For
instance, the mean expected and the mean actual waiting times for Sinopharm
are $3.46$ and $3.78$ weeks, respectively, while for Pfizer we have only
$1.20$ and $1.00$. Let us also highlight the trend that AstraZeneca has a
higher acceptance to rejection ($\frac{40}{25}$) ratio than the one that
Sputnik or Sinopharm has ($\frac{29}{23}$ and $\frac{29}{41}$, respectively).
This could be explained by that the AstraZeneca group estimated the expected
waiting time to be longer than it actually was, while we see the opposite for
Sputnik and Sinopharm.

Table S14: Expected and actual waiting times for accepting and rejecting a vaccine that is not the most preferred. Vaccine type | Observations | Observations | Mean expected | Mean actual |  $90\%$ CI expected |  $90\%$ CI actual
---|---|---|---|---|---|---
| Accept | Reject | Accept | Reject | Accept | Reject
|  |  | (week) | (week) | (week) | (week)
AstraZeneca | 40 | 25 | 2.61 | 1.94 | (1.87,3.36) | (1.47,2.42)
Moderna | 3 | 1 | 0.00 | 2.00 | (-,-) | (-,-)
Pfizer | 9 | 5 | 1.20 | 1.00 | (0.09,2.31) | (-0.35,2.35)
Sinopharm | 29 | 41 | 3.46 | 3.78 | (2.37,4.55) | (2.60,4.96)
Sputnik | 29 | 23 | 1.61 | 2.58 | (1.04,2.19) | (1.37,3.80)

## Supporting Information 7: Co-acceptance of vaccines among non-vaccinated

Figure S6: Co-acceptance of vaccines. Edge labels reflect to the number of
individuals who accept both vaccines included. We only include those
individuals to create this network who accept 2 or 3 vaccines.