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Journal home page:
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How do country-
average health statistics impact citizens’
vaccination attitudes? Evidence from a cross-country
analysis
Nafisakhon SIDIKOVA
1
Westminster International University in Tashkent
ARTICLE INFO
ABSTRACT
Article history:
Received May 2025
Received in revised form
15 June 2025
Accepted 25 June 2025
Available online
15 July 2025
The COVID-19 pandemic has brought about widespread
disruptions across economies, political structures, and cultures.
Although vaccines were developed to curb the pandemic’s
impact, vaccine hesitancy remains prevalent in many countries.
This study investigates how macro-level health statistics and
socio-economic variables influence public attitudes towards
vaccination across 37 countries. Using pooled Ordinary Least
Squares (OLS) regression, the analysis finds a strong and
statistically significant positive relationship between the
number of COVID-19 cases per hundred and vaccination rates.
The results suggest that a higher number of confirmed cases
leads to increased public willingness to be vaccinated. This
finding
emphasizes
the
importance
of
transparent
communication and media coverage of infection data, along
with fostering public trust in government, to improve
vaccination uptake globally.
2181-
1415/©
2025 in Science LLC.
DOI:
https://doi.org/10.47689/2181-1415-vol6-iss6/S-pp125-141
This is an open access article under the Attribution 4.0 International
(CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/deed.ru)
Keywords:
COVID-19,
vaccine hesitancy,
public health,
macroeconomic indicators,
pooled OLS,
cross-country analysis,
pandemic response,
vaccination behavior,
trust in government.
1
Master of International Business Management, Westminster International University in Tashkent, Financial
Manager, Huawei Technologies Uzbekistan. E-mail:
n.ganikhonova09@gmail.com
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Mamlakatlar
bo‘yicha
o‘rtacha
sog‘liqni
saqlash
ko‘rsatkichlari fuqarolarning emlashga
munosabatiga
qanday ta’sir qiladi? Mamlakatlararo tahlil natijalari
asosidagi dalillar
ANNOTATSIYA
Kalit so‘zlar
:
COVID-19,
vaksina qabul qilishga
ikkilanish,
jamoat salomatligi,
makroiqtisodiy
ko‘rsatkichlar,
umumlashtirilgan OLS,
mamlakatlararo tahlil,
pandemiyaga qarshi
choralar,
emlash xulq-atvori,
hukumatga ishonch.
COVID-19 pandemiyasi iqtisodiyot, siyosiy tuzilmalar va
madaniyatlarda keng ko‘lamli izdan chiqishlarni keltirib chiqardi.
Pandemiya ta’sirini yumshatish uchun
vaksinalar ishlab chiqilgan
bo‘lsa
-
da, ko‘plab mamlakatlarda aholi orasida vaksinaga nisbatan
ikkilanish kuzatilmoqda. Ushbu tadqiqot 37 mamlakatda makro
darajadagi sog‘liqni saqlash ko‘rsatkichlari va ijtimoiy
-iqtisodiy
omillar aholining emlashga bo‘lgan munosabatiga qanday ta’sir
ko‘rsatishini o‘rganadi. Umumlashtirilgan eng kichik kvadratlar
(OLS) regressiya tahlili natijasida har yuz kishiga to‘g‘ri keladigan
COVID-
19 holatlari soni va emlash darajasi o‘rtasida kuchli va
statistik jihatdan ahamiyatli ij
obiy bog‘liqlik aniqlandi. Natijalar
shuni ko‘rsatadiki, tasdiqlangan kasallanish holatlari sonining
ko‘payishi aholining emlanishga bo‘lgan ishtiyoqini oshiradi.
Ushbu xulosa butun dunyoda emlash qamrovini kengaytirish
uchun kasallanish ma’lumotlarini sha
ffof tarzda yetkazish va
ommaviy axborot vositalarida yoritish, shuningdek, aholining
hukumatga bo‘lgan ishonchini mustahkamlash muhimligini
ta’kidlaydi.
Как средние показатели здоровья по странам влияют
на отношение граждан к вакцинации? Данные
межстранового анализа
АННОТАЦИЯ
Ключевые слова:
COVID-19,
нерешительность в
отношении вакцинации,
общественное
здравоохранение,
макроэкономические
показатели,
объединенный метод
наименьших квадратов,
межстрановой анализ,
реагирование на
пандемию,
поведение при
вакцинации,
доверие к правительству
Пандемия COVID
-
19 вызвала масштабные нарушения в
экономике, политических структурах и культурах.
Несмотря на разработку вакцин для сдерживания
последствий пандемии, во многих странах сохраняется
недоверие к вакцинации. Данное исследование изучает,
как макроуровневые статистические данные о здоровье и
социально
-
экономические
переменные
влияют
на
общественное отношение к вакцинации в 37 странах.
С использованием объединенной регрессии методом
наименьших квадратов (OLS) анализ выявил сильную и
статистически значимую положительную связь между
числом случаев COVID
-
19 на сто человек и показателями
вакцинации. Результаты показывают, что большее число
подтвержденных случаев приводит к повышению
готовности населения к вакцинации. Этот вывод
подчеркивает важность прозрачного информирования и
освещения данных о заболеваемости в СМИ, а также
формирования общественного доверия к правительству
для улучшения охвата вакцинацией во всем мире.
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INTRODUCTION
The COVID-19 pandemic has severely impacted economies, societies, and political
systems worldwide since its onset. Initial negligence of the virus’s potential
consequences in some nations placed many citizens, particularly the working class, at
significant risk. According to data from Johns Hopkins University, global COVID-related
mortality surged rapidly between February and April 2020, while the World Health
Organization (WHO) estimated an excess of 15 million deaths during 2020
–
2021.
In response to the rising death toll, governments
–
especially in developed
countries
–
implemented strict containment measures, often without fully considering
the economic costs. Lockdowns led to significant declines in income, particularly in
countries with large informal labor sectors. Recovery from these economic shocks
depends largely on halting the spread of the virus, for which vaccination remains the
most viable long-term solution.
Despite the availability of various effective vaccines, vaccine hesitancy continues to
obstruct immunization efforts globally. In many underdeveloped regions, the problem is
compounded by logistical challenges in vaccine supply. This research explores behavioral
and structural factors that influence public attitudes toward vaccination, using a cross-
country macro-level dataset. The study aims to identify the most significant variables
affecting vaccine uptake and provide data-driven recommendations to guide
policymaking and public health strategies.
ACKNOWLEDGEMENTS
I would like to thank my supervisor, Bilol Buzurukov, for his consistent support
and guidance during the project span. It was a great honour for me to be his supervisee.
I sincerely appreciate all the time he devoted to this research.
Furthermore, I would like to thank the WIUT academic support team for their
assistance in compiling sensible drafts of this paper.
Finally, I would like to thank my family for supporting me during the research
process.Abstract
Since the COVID-19 pandemic's outbreak, most nations have experienced
economic, political, and cultural disruptions. The creation of COVID-19 vaccinations was
intended to avert the crisis. Despite all the advantages, vaccine apprehension and
COVID-19 vaccine boycotts are still widespread in many countries. In order to develop
workable and generalizable policy suggestions, this study examined views towards a
wide range of social, economic, and healthcare macro-variables that were acquired via
the data portals of the World Bank Group and Our World in Data. The Pooled OLS was
predominantly used for the relationship estimate in the investigation. According to the
regression results, the number of immunizations has a positive correlation with the total
cases per hundred (tc_100). The effect of the letter is both substantial and large across all
estimations. It seems vaccination rates will rise by an average of more than 9 persons and
more than 15 vaccine doses per 100 people in a country with one more case of infection
per hundred. The analysis results paper suggests that continuous media coverage of new
coronavirus infection cases should be implemented. The proportion of people who are
willing to get immunised may rise by roughly 9% to 15% for every new infected patient
per hundred population.
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INTRODUCTION
The global pandemic of COVID-19 has imposed economic, political, and cultural
shocks on most countries since its outbreak. The initial negligence of the virus
consequences in some countries put the life of an average worker in great danger.
According to the data by Johns Hopkins University (2020), the per million daily
confirmed deaths because of coronavirus skyrocketed from 0.01 to 0.93 in all countries
between the 28
th
of February and the 15
th
of April in 2020. By the end of 2020, COVID-19
had taken over 1.8 million people and infected over 82 million. The World Health
Organization (WHO) estimates that the coronavirus pandemic has caused the deaths of
about 15 million people worldwide. This number is almost 14% more deaths than usual
in 2020 and 2021.
Figure 1.
Total Deaths per million
Source: Our World Data
As the public institutions in developed countries are heavily influenced by public
opinion, their governments had to propose packages of strict containment measures.
The negative economic externalities of lockdowns were not well-considered at the
beginning of the pandemic, as the well-being of the citizens was prioritized. However,
due to either slowdown or pause of economic activity in several industries, the average
income in the countries that enforced nationwide lockdowns dropped significantly.
According to the data by OECD (2020), the average constant GDP per capita in OECD
economies increased by 1.83% in the first quarter of 2020 and decreased by 9.42% in the
consecutive quarter. Overall, the world GDP growth rate in 2020 dropped to negative
3.1%. Before the COVID-19 pandemic, the last negative growth rates of the world GDP
were observed during the financial crisis in 2009 (The World Bank, 2022). The
lockdowns, in case they were imposed, should have affected incomes in developing
countries more significantly. This statement is based on the fact that usually shadow
economy usually significantly contributes to the income levels in emerging markets.
According to the ILO (2018), the share of informal employment in developing and
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
2.28
.20
3.1.
20
3.
3.
20
3.5.
20
3.7.
20
3.9.
20
3.
11
.2
0
3.13
.20
3.15
.20
3.17
.20
3.19
.20
3.21
.20
3.23
.20
3.25
.20
3.27
.20
3.29
.20
3.31
.20
4.2.
20
4.4.
20
4.6.
20
4.8.
20
4.10
.20
4.12
.20
4.14
.20
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emerging markets was almost 70% in 2016. Simultaneously, informal sector employees
are the most vulnerable to economic shocks, as their income highly relies on their output,
and they have restricted or no access to the social security schemes. Consequently, fast
post-pandemic economic recovery is crucial for lower- and middle-class employees in the
transition and developing economies.
Figure 2.
World GDP growth rate
Source: The World Bank
Obviously, in order for economies to start recovering from the negative
externalities of pandemics the spread of the disease should have been stopped and the
infected should have been cured. The most reliable long-term treatment for the
COVID-19 pandemic are still the vaccines such as Biotech Abdala, CanSino, Corbevax,
Medicago, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sputnik V, and Sputnik Light.
According to Watson et.al. (2022) 45% of deaths because of coronavirus could have been
averted if 20% of the population was fully vaccinated. Nevertheless, the data made public
by the World Health Organization (WHO) on 5 August 2021 shows that only 15.1% of the
world population had received all of their recommended vaccinations.
The development of vaccines against COVID-19 was meant to improve the
situation. Despite all the benefits, vaccine hesitancy and the boycotting of the COVID-19
vaccines are still common in many nations. On the other hand, official institutions are
struggling to supply enough amount of vaccine shots for underdeveloped economies. In
this paper try to investigate behavioural factors that may affect the general attitude
towards vaccination and identify their level of significance to the matter. Vaccination can
be made more popular by putting the right incentives for the population. However,
before policymakers can create the incentive schemes, the factors working for and
against vaccination should be properly identified.
LITERATURE REVIEW
The promotion process of vaccination uptake may be difficult, and the release of
the COVID-19 vaccine have been accompanied by a significant obstacle due to
uncertainties about vaccination side effects (Nawas et. al., 2022). Consequently, vaccine
-4,00
-3,00
-2,00
-1,00
0,00
1,00
2,00
3,00
4,00
5,00
6,00
7,00
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
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uptake might have suffered greatly when individuals failed to conduct simplistic
risk/benefit analyses, weighing potential health risks from vaccination much higher than
the benefits of not being infected.
Saleska and Choi (2021) tried to hypothesize the origins of vaccination biases
based on the theories of behavioural economics. They divided hesitancy biases into three
main categories as confirmation bias, negativity bias, and optimism bias. Based on the
systematic literature review, the authors concluded that an average citizen is notoriously
resistant to change when it comes to negative beliefs and myths about vaccines,
and a vaccination with regular COVID-like adverse effects may simply help to strengthen
these perceptions and misconceptions. The general suggestions provided in the paper are
the provision of well-grounded vaccination discussions by both practicing medical
doctors and local politicians.
Additional support to the impact of doctors’ recommendations was found in
Callow and Callow (2021). Employing the Theory of Planned Behaviour, the authors ran
hierarchical regression to analyse a convenience sample of 583 adults aged 60 and older.
Their results suggest a strong correlation between respondents’ attitudes towards
getting vaccinated and their perceived risk of getting infected, prejudice towards
vaccination, and political affiliation. However, because of the age limits and non-random
sample selection, the outcomes of this cannot be generalised in the global context.
In contrast, a comparative approach taken in Humer et. al. (2021) investigated the
topic of attitudes towards vaccination among adolescents in Austria and determined the
dependence of willingness to be vaccinated on education level, migration background,
and gender. The study was conducted using an online questionnaire, and it was
administered separately to younger peers and older students. In total, 2006 people
(1442 elementary students and 564 high school students) took part in the survey, and
the survey showed that more high school students were willing to receive the vaccine
compared to apprentices. Specifically, 53% of high school students and 28% of
elementary students indicated a clear willingness to get immunized, while 7% of high
school students and 22% of elementary pupils indicated a clear refusal to do so. Humer
et.al, (2021) concluded that greater efforts are required to boost teenagers with lower
levels of education, migrants, and women confidence and desire to receive vaccinations.
Duan et. al. (2022) studied the behaviour of patients with diabetes during
vaccination. In this study, a survey was conducted in the hospitals of the Changzhi
Medical College in 2021. The survey consisted of three main blocks, as patient health
status, vaccination awareness, and behaviour at the time of vaccination. The
questionnaire was completed by 645 people, of whom 162 were vaccinated and the
remaining 483 were not vaccinated. According to the study, one of the main reasons
respondents did not get vaccinated was the fact that 50.2% of all patients (324 out of
483 not vaccinated patients) were not provided recommendations by their doctor to do
so. Surprisingly, 79.7% (514/645) of their sample were aware of the extra risks of
COVID-19 infection imposed on patients with diabetes. Policy recommendations by the
authors include massive efforts on public promotion of vaccination benefits and
consequent increase of the prescription volume of vaccination to diabetic patients, if
their health conditions allow to do so.
Moreover, coronavirus is proven to be very contagious among pregnant women.
Firouzbakht et.al. (2022) study objective was to assess hesitancy about COVID-19
vaccination (HACV) in pregnant women using the Health Belief Model (HBM).
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Participants in this cross-sectional study were 352 pregnant women who were chosen
from a variety of medical facilities. To evaluate how the pre-identified factors might affect
HACV, logistic regression analysis was utilised. HACV prevalence was 42.61%. The three
variables
–
perceived advantages
*
, cues to action
*
, and experience of reproductive issues
*
,
significantly impacted HACV in the analysis. However, the perceived threat of vaccination
did not affect HACV that much. Overall, the analysis shows that HACV is very common in
future mothers.
Guo et. al. (2022) research paper seeks to identify correlations between county-
level COVID-19 immunisation rates, income per capita, and unemployment levels across
the United States. This study included 2,857 people from all counties in the US who
continuously reported their vaccination rates for six months (from January to July of
2021). Researchers used pooled ordinary least squares (OLS) with fixed effects to
investigate the economic repercussions of racial/ethnic disparities in county-level
COVID-19 vaccination rates over time. County-level COVID-19 immunization rates were
strongly correlated with both unemployment levels and per capita income.
At the broader scale, a multidisciplinary study agenda was developed through the
investigation of vaccine decision-making factors by fifteen social science, communication,
health, and medical specialists (Heidi et. al., 2013). The goal was to identify factors that
could affect vaccine decisions at all levels. Team members identified
factors/determinants of vaccine decision-making following a reverse brainstorming
session. The program participants divided their ideas into three main categories after
iteratively capturing them. These crucial variables/determinants were condensed into
research inquiries that might be applied to future investigation. The entire team came to
a consensus on the important variables/determinants that may support a research
design. The expert panel identified 61 elements that may have an impact on how
consumers, professionals, and policymakers behave in relation to vaccination. These
variables were further iteratively arranged into "clusters" that fell into one of three key
categories: communication and engagement, groups and social norms, and cognition and
decision-making. The three primary areas of impact on vaccination uptake led to the
development of research questions for additional study, as described below.
However vast, the majority of the research conducted in the field employs either
limited sampling or data gathering techniques. Convenient sampling and simple
questionnaires may be too biased to able to generalize their research outcomes for
policy-making purposes globally. My research tries to fill this gap and employs global
macro-level data on the COVID-19 pandemic and vaccination to identify unbiased
estimators for the factors affecting vaccine hesitancy among the general public.
Based on the literature analysed, the most frequent factors of positive vaccination
attitude are mortality rate, diabetes, pregnancy, age group, and income per capita.
The most prominent method for estimating their impact on the vaccination attitude was
pooled ordinary least squares (POLS). The following section includes a description of the
data that is going to be utilized in the current research and results of regression analysis.
*
Perceived advantages of vaccination.
*
Cues to action cues to action can also refer to advice from medical professionals or public health messages.
*
How many issues did women have throughout all pregnancies.
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DATA AND VARIABLES
In order to come up with viable and generalizable policy recommendations, this
paper employs a vast dataset constructed from a variety of social, economic, and
healthcare macro-variables obtained from the Our World in Data and the World Bank
Group’s data p
ortals. The former one contains daily data on the vast measures of the
COVID-19 pandemic dynamics to date. The latter database was used to derive most of the
macroeconomic variables. The overall number of countries in the data sample is 37. More
information about the origin of the variables employed in this text is provided in
Table 1 (Appendix).
Following the main goal of the research, the total number of people vaccinated
within one country is taken as the dependent variable. We impose a rational assumption
that the vaccination rates are lower in countries with higher average population
hesitancy towards vaccines. Within the text, the variable is denoted as
ppl_vac_100
and
stands for the number of people in one country who got at least one shot of the vaccine,
measured in per hundred terms. The higher the measure, the lower the average index of
vaccine hesitancy in a country. We also include variables related to the medical
conditions of patients, such as
𝑑𝑖𝑎𝑏𝑒𝑡𝑒𝑠_𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒
which means the prevalence of
diabetes in the population aged from 27 to 79. This particular variable is expected to
affect average vaccine hesitancy in a negative fashion and contribute to lowering the rate
of anti-vaccine attitudes. Moreover,
𝑡𝑑
100
and
𝑡𝑐
100
variables are included, which refer to
the total number of COVID-19-related deaths per hundred and confirmed cases per
hundred, respectively.
As the risk of infection and fatality goes up in any community, the population is
expected to have a more positive attitude towards vaccination. As Callow and Callow
(2021) state, coronavirus increases the risk of serious illness for older people.
Consequently, one of the other controls included in the empirical model is
𝑎𝑔𝑒_65_𝑜𝑙𝑑𝑒𝑟
that stands for the percentage share of the population aged 65 and over. Meanwhile, the
research acknowledges a negative image of some types of vaccines among the older
generation and seeks clarification of the effect this demographic variable has on the
vaccination hesitancy.
The WHO (2022) recommends that pregnant women be vaccinated to protect
themselves and their babies. Following this fact, I have included the variable
𝑟𝑒𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛_𝑟𝑎𝑡𝑒
, which can be linked to the number of pregnant females in the
country in the given period. Even though this is not a perfect measure of accounting for
the stock of pregnant women in a country, these variables are both assumed to affect
overall vaccine hesitancy negatively. In addition, I included
educ_comp
as an additional
control, which compares the number of mandatory schooling years between countries in
the sample. The hypothesis is that countries with higher average years of schooling tend
to have less hesitancy towards vaccination.
Since Guo et. al (2022) conclude that COVID-19 immunization rates at the county
level are strongly correlated with per capita income, we decided to add a natural log of
GDP per capita
as an independent variable. Also, gov trust (Government trust) is a crucial
component of social capital and a significant factor in promoting sustainable well-being,
which includes immunising people as well. In addition, Van Oost et. al (2022) looked at
the key reasons for vaccination and found that one of the main reasons for not getting
vaccinated is poor public relations.
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Descriptive Statistics
Variable
Obs
Mean
Std. Dev.
Min
Max
ppl vac 100
25197
21.442
17.865
0
72.681
ttl vac 100
25197
34.566
30.686
0
99.998
tc 100
25197
4.417
4.649
.001
45.851
td 100
25197
.088
.091
0
.593
reproduction rate
24385
.981
.294
-.02
3.94
ln GDP
24148
9.448
1.056
6.494
11.669
extreme poverty
17363
9.34
15.813
.1
77.6
educ comp
22116
9.889
2.989
0
17
gov trust
7065
45.893
15.113
17.146
84.633
diabetes prevalence
24703
7.641
3.356
.99
23.36
aged 65 older
24313
10.206
6.248
1.144
27.049
The table of descriptive statistics provided above represents a quick overview of the
variables of interest. As most of the variables come from the Our World in Data dataset, they
are presented in the daily format. The least represented variable in this dataset
–
trust in
government (
gov_trust
)
–
consists of more than 7,000 observations and constructed by
interpolation of yearly indexes into daily form for each corresponding country.
On the other hand, several variables display ambiguous behaviour. For instance, years
of compulsory education (
educ_comp
) has the maximum of 17 years. This fact leads to the
conclusion that some countries in the dataset may have mandatory university education.
However, the average for the variable can be viewed common, as it is around 9 years.
METHODOLOGY
The estimation of the relationship being investigated was done primarily using the
Pooled OLS technique in the literature (Callow and Callow (2021), Humer et. al. (2021),
Firouzbakht et.al. (2022) and Guo et. al. (2022)). However, the non-linear nature of
interactions between dependent and independent variables suggests employment of the
slightly more advanced estimation techniques such as Fixed effects and country-specific
dummy variables (Figure 3). Moreover, the OLS model does not account for unobserved
heterogeneity or time-invariant individual effects.
As our data is pooled from both cross-sectional and time-series observations, the
conventional way to proceed would be the identification of the best fir model for panel
data analysis. In the case of this research, we choose between Pooled OLS, Fixed Effect
and Random Effect estimators. The general state of the linear specification is provided
below and is consistent with the literature mentioned so far:
ppl_vac_100
=
𝜷
𝟎
+ 𝜷
𝟏
∗ 𝒕𝒄
𝟏𝟎𝟎
+ 𝜷
𝟐
∗ 𝒕𝒅
𝟏𝟎𝟎
+ 𝜷
𝟑
∗ 𝐫𝐞𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧
𝐫𝐚𝐭𝐞
+ 𝜷
𝟒
∗
𝒓𝒆𝒑𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏
𝒓𝒂𝒕𝒆
+ 𝜷
𝟓
∗ 𝒍𝒏
𝒈𝒅𝒑
+ 𝜷
𝟔
∗ 𝒆𝒙𝒕𝒓𝒆𝒎𝒆
𝒑𝒐𝒗𝒆𝒓𝒕𝒚
+ 𝜷
𝟕
∗ 𝒆𝒅𝒖𝒄
𝒄𝒐𝒎𝒑
+ 𝜷
𝟖
∗
𝒈𝒐𝒗
𝒕𝒓𝒖𝒔𝒕
+ 𝜷
𝟗
∗ 𝒅𝒊𝒂𝒃𝒆𝒕𝒆𝒔
𝒑𝒓𝒆𝒗𝒂𝒍𝒆𝒏𝒄𝒆
+ 𝜷
𝟏𝟎
∗ 𝒂𝒈𝒆𝒅_𝟔𝟓_𝒐𝒍𝒅𝒆𝒓
In addition to the controls presented in the formula, some estimation in the
Regression table below employ country dummies in order to grasp country specific
features not available to the author and minimize possible endogeneity and omitted
variable bias.
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(a)
(b)
(c)
(d)
Figure 3.
Scattergram of Dependent variable and some controls
For the same reason, the Between estimator model was included in the analysis.
The Between estimator model is a fixed effects regression model that is used to control
for unobserved heterogeneity or time-invariant individual effects. The Between
estimator model assumes that the unobserved heterogeneity or time-invariant individual
effects are fixed over time and that they are uncorrelated with the independent variables.
The Between estimator model is estimated by taking the difference between the average
value of the dependent variable and the average value of the independent variables for
each individual over time. The Between estimator model is commonly used in panel data
analysis when we have a large number of individuals and a small number of periods
(Greene, 2012).
The results of the Breusch-Pagan and Hausman tests provided in the Appendix
may advocate for the usage of the fixed effect model for our regression analysis. The
Fixed effect model includes individual-specific intercepts to control for unobserved
heterogeneity or time-invariant individual effects. The Fixed effect model assumes that
the unobserved heterogeneity or time-invariant individual effects are correlated with the
independent variables but uncorrelated with the errors (Wooldridge, 2010).
However, the same test does not reject the hypothesis of consistent estimates.
Thus, we can base our inference on the estimations by Pooled OLS regression. Moreover,
the Hausman test results reveal equal efficiency for both Fixed and Random effect
estimations. Consequently, the coefficients derived from Pooled OLS, Fixed, and Random
effect estimations are expected to be consistent in their correlation with the main
dependent variable.
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RESULTS AND DISCUSSIONS
The regression table presented below first uses per hundred quantities of
vaccinated people as a dependent variable and a link to the average level of vaccination
hesitancy among the population. The second variable used in the investigation of the
hesitancy rates is the number of total vaccination shots delivered per hundred
population in each country. The coefficients of the independent controls are derived
using Pooled OLS estimators with robust standard errors.
As expected, the number of total cases per hundred (
tc_100
) positively correlates
with the number of vaccinations. The effect is significant and huge across both regression
estimations. Based on the estimation, we can conclude that vaccination rates, in a country
with one additional COVID-19 case per hundred, will increase by more than 9 people and
more than 15 vaccine shots per hundred on average.
Dependent Variables
Number of people vaccinated
per 100
Number of total vaccinations per
100
tc_100
9.272***
15.25***
(0.922)
(1.482)
reproduction_rate
8.006**
6.927*
19.17***
17.29***
(3.457)
(4.157)
(5.328)
(6.650)
ln_gdp
-10.79
19.54
-16.84
32.99
(12.97)
(13.11)
(20.66)
(20.81)
educ_comp
-4.627***
-4.596***
-7.701***
-7.606***
(1.544)
(1.405)
(2.452)
(2.257)
gov_trust
0.982***
0.514*
1.548***
0.769*
(0.350)
(0.284)
(0.545)
(0.442)
diabetes_prevalence
1.406
0.846
2.580
1.650
(1.653)
(1.781)
(2.589)
(2.945)
aged_65_older
-0.663
-1.772*
-1.072
-2.882*
(1.045)
(1.022)
(1.664)
(1.635)
td_100
385.9***
631.2***
(29.07)
(50.53)
Constant
83.63
-180.8
121.5
-312.5*
(118.1)
(119.6)
(189.1)
(189.1)
Observations
6,532
6,532
6,532
6,532
Number of c_code
37
37
37
37
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
However, GDP per capita (
ln_gdp
) and the share of the population with diabetes
(
diabetes_prevalence
) are not consistent throughout estimations and provide no strong
evidence of correlation with both dependent variables. This fact may reflect a
homogeneous attitude towards vaccination in the nations with different levels of income.
This brings hope for the less developed countries as their chances of having successful
vaccination campaigns seem to be virtually the same as of those in the developed world.
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Despite the theory stated in the data section of this paper, we came up with a
consistent negative impact of the years of education on the rate of vaccination uptake.
According to the analysis, each additional year of compulsory education
(educ_comp)
can
decrease the number of vaccinated people by 4.6 on average and the number of uptakes
by 7.6-7.7 vaccine shots, on average. However, this finding is similar to the results by
Humer et. al. (2021), who found that higher average years of education slow down the
pace of vaccination.
In line with the number of positive coronavirus infection cases, the reproduction
rate of the country also seems to have a high positive correlation with the lower vaccine
hesitancy rates. The relationship preserves its significance at the minimum α = 0.1
through all estimations. This result supports the hypothesis of a higher frequency of
pregnant women in the country, adding to the vaccination rates. The slope for
coefficients varies between 7 and 19 additional vaccinations per hundred people with
1% increase in the reproduction rate. Moreover, it appears the level of public trust in the
local government plays an important role during the vaccination campaigns as the
variable
(gov_trust)
preserves statistical significance throughout all the regression runs.
In contrast to the hypothesis put forward in this paper initially, higher shares of
the older population (
aged_65_older
) do not contribute to the increase in the vaccination
rates. The opposite case is consistently true. At a minimum rate of significance of α = 0.1,
the variable presents a strong negative correlation with the number of people vaccinated
per hundred. The slope coefficient varies tremendously between -0.43 and -19.29. These
findings contradict results in Humer et. al. (2021) that higher age correlates with
willingness to get vaccinated.
RECOMMENDATIONS
The general recommendation that comes from the analysis presented in this paper
is massive media promotion that keeps publishing each new coronavirus infection case.
As the number of additional infections per hundred increases by one patient, the share of
people willing to get vaccinated may increase by approximately 9% to 15%. So, each new
case of infection should be broadly publicized and discussed with accessible means of
communication. Moreover, it seems that trying to be more transparent in terms of
government operations and overall image seems to affect the rates of vaccination
positively. Thus, being more transparent for the government may positively affect the
overall health of the population.
The findings of this study need to be considered in light of certain limitations,
such as the limited availability of daily measures of some macroeconomic variables.
This brings the problem of heterogeneity of variance of controls in the data.
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APPENDIX
Table 1.
Variable information
Variables codes
Definition of variable
Sources for data
ppl_vac_100
poeple_vaccinated per 100
Our World in Data
ttl_vac_100
total_vaccinations per 100
Our World in Data
tc_100
total_cases per 100
Our World in Data
td_100
total_deaths per 100
Our World in Data
reproduction_rate
Change of a generation into a new one
Our World in Data
ln_gdp
Natural log of gdp_per_capita
Our World in Data
extreme_poverty
Share of the population living in extreme poverty
Our World in Data
educ_comp
Number of compulsory education years
World bank
gov_trust
Trust to government
Our World in Data
diabetes_prevalence
Diabetes prevalence (% of population aged 20 to 79)
Our World in Data
aged_65_older
Share of the population that is 65 years and older
Our World in Data
total_vaccinations
Total COVID-19 Vaccinations
Our World in Data
total_deaths
Total Deaths because of COVID-19
Our World in Data
total_cases
Total Cases of COVID-19
Our World in Data
gdp_per_capita
GDP of country
Our World in Data
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Table 2.
Correlation matrix
V
ar
iab
les
(1
)
(2
)
(3
)
(4
)
(5
)
(6
)
(7
)
(8
)
(9
)
(1
0
)
(1
1
)
(1)
pp
l_v
ac
_10
0
1.000
(2)
ttl_v
ac
_10
0
0.983
1.000
(3)
tc
_100
0.466
0.495
1.000
(4)
td_
100
0.368
0.360
0.671
1.000
(5)
reprodu
cti
on_
r~
e
-0.08
8
-0.04
6
-0.20
0
-0.19
9
1.000
(6)
ln_
gdp
0.017
0.002
-0.13
1
-0.38
5
-0.07
1
1.000
(7)
ex
tre
m
e_p
overt
y
-0.11
3
-0.11
9
-0.03
8
0.156
0.033
-0.57
4
1.000
(8)
educ
_c
om
p
0.070
0.067
0.256
0.531
-0.08
1
-0.31
9
-0.12
9
1.000
(9)
gov_
tru
st
-0.08
2
-0.10
9
-0.43
2
-0.48
9
-0.04
4
0.582
-0.05
5
-0.34
2
1.000
(10)
di
abet
es
_p
rev~e
0.019
0.021
-0.07
6
0.150
-0.00
7
-0.32
5
0.025
0.568
-0.12
2
1.000
(11)
ag
ed_65_
older
0.026
0.033
0.157
-0.07
6
-0.07
5
0.607
-0.53
0
-0.31
2
0.113
-0.59
4
1.000
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Breusch-Pagan Lagrangian multiplier test for random effects
Hausman test for RE vs FE estimators
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