UIC Determinants of Public Trust in Governments During The COVID-19 Annotated Bibliography The goals of this assignment are the preparation of two written

UIC Determinants of Public Trust in Governments During The COVID-19 Annotated Bibliography The goals of this assignment are the preparation of two written components of the overall research effort: 1) an annotated bibliography; and, 2) an initial synthesis (“draft”) of the findings from the literature.

The format for submission is as follows:

1) Reference #1

a. Brief summary

b. How reference #1 will help YOUR effort

2) Reference #2

a. Brief summary

b. How reference #2 will help YOUR effort 8313
2020
May 2020
Global Evidence on the
Determinants of Public Trust
in Governments during the
COVID-19
Giray Gozgor
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CESifo Working Papers
ISSN 2364-1428 (electronic version)
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CESifo Working Paper No. 8313
Global Evidence on the Determinants of Public
Trust in Governments during the COVID-19
Abstract
Using the Worldwide COVID-19 Attitudes and Beliefs dataset covering 108,918 respondents
from 178 countries, the paper examines the determinants of public trust in governments during
the COVID-19. It is found that older and healthy people trust more to their governments.
Education is negatively related to trust in governments. The results are robust to consider different
measures of trust in government as well as including various controls, such as precautionary
behaviors, first-order beliefs, second-order beliefs, and the COVID-19 prevalence in the country.
The findings are also valid for countries at different stages of economic development as well to
varying levels of globalization, institutional quality, and freedom of the press.
JEL-Codes: I180, D810, C310, C830.
Keywords: the COVID-19 pandemic, trust in governments, global survey data, economic
development, institutional quality, freedom of the press.
Giray Gozgor
Istanbul Medeniyet University
Istanbul / Turkey
giray.gozgor@medeniyet.edu.tr
1. Introduction
Public trust is an essential tool for providing good governance in all political systems.
Governments, which have a higher level of public trust, can govern effectively (Cooper et al.,
2008). Trust in government is a reliable indicator of social capital, and it significantly enhances
economic performance via higher efficiency in the coordination, decisions, and regulations
(Kwon et al., 2013). Governments should provide security to citizens against unpredictable
events. During times of uncertainty, trust between government and individuals is crucial for the
social contract to hold between these actors. People can trust their government if they think that
the government has the capacity, expertise, and technical knowledge to make the best decisions
for the public interest and welfare. The extent of public trust in governments’ ability is also vital
to respond effectively to the COVID-19 pandemic in all around the world.
The current responses to the COVID-19 pandemic can offer new dimensions for people’s trust
in governments and informing the public. During the COVID-19 pandemic, there are wide
ranges of protective measures, such as general curfew or partial lockdown, closures of schools,
workplaces, public transportation, cancellation of public events, and restrictions on national
movements (Hale et al., 2020). There are also various stimulus packages to decrease the adverse
economic and social outcomes of the COVID-19 pandemic (Elgin et al., 2020). Obtaining
successful results of economic and social stimulus policies and providing full effect to slow down
the rate of outbreaks requires an unprecedented degree of social cohesion and public trust in
government during the COVID-19. Furthermore, governments need to be trusted in terms of
ending the pandemic and new measures on the COVID-19, such as testing procedures and
tracing programs, or possibly mass vaccination. To this end, this paper focuses on the individuallevel Worldwide COVID-19 Attitudes and Beliefs survey dataset covering 108,918 respondents
from 178 countries to find determinants of public trust in governments during the COVID-19
era.
According to the previous literature, there are various potential determinants of public trust in
governments. There is a wide variety of age, income, and level of education among countries, and
these demographic indicators can significantly determine trust in governments (Algan et al., 2016;
Christensen and Laegreid, 2005; Zhao and Hu, 2017). Gender, marital status, and number of
households (having a child or living with parents) can also affect public trust (Alesina et al., 2004;
Lu et al., 2020; Ulbig, 2007). For instance, Christensen and Laegreid (2005) and Zhao and Hu
(2017) show that older people have a higher level of trust in government than younger people
since older people can be defined as “collective-oriented.” Kim (2010) and Tolbert and
Mossberger (2006) observe that males have more trust in government in Japan and the United
States, respectively. According to Anderson (2010), individuals with a higher income have higher
confidence in government in the United States. However, Zhao and Hu (2017) find that a higher
salary is negatively related to trust in government in China. Overall, the effect of income on trust
in government is somehow mixed. 1 Furthermore, Gronlund and Setala (2007), Norris (1999), and
Zhao and Hu (2017) indicate that people with higher education have less trust in government
since they look more criticized towards governments. 2 Overall, individuals’ demographic groups
can be the main drivers of public trust in government.
Following the World Bank’s definition of the income levels of countries, we also analyze the effects of
demographic indicators on public trust in government for countries at different stages of economic development.
2 Note that Christensen and Laegreid (2005) find that people with a higher level of education have more trust in
government. Therefore, the impact of education on trust in government is also arguable in the empirical literature.
1
2
To motivate our findings on the health crisis during the COVID-19 pandemic era, we also
include the health conditions, behaviors, beliefs of individuals, and the COVID-19 prevalence in
countries. One of the novel findings in this paper is that people with better health conditions
trust more their governments all around the world.
Citizens’ perceptions of the issues of civil liberties and political rights are also an essential driver
of public trust. If there will be a higher level of civil liberties and political rights, people think that
they may affect government policies or even they can change the government. Therefore,
democracy is positively associated with public trust in the government in general. 3 Indeed, several
papers have found that higher levels of democracy and political rights (strong indicators of
institutional quality) lead to a higher level of trust in governments (Ariely, 2013; Ezrow and
Xezonakis, 2011; Levi and Stoker, 2000; Norris, 1999; Nye et al., 1997). To address these issues,
we divide the countries to re-estimate the regressions, according to their levels of civil liberties
and political rights following the dataset of Freedom House (2020a).
In addition to demographic indicators and institutional quality measures, several papers have
examined the role of media for trust in government. Most of these papers have found that
unofficial and official controls on media significantly affect trust in government (Chen and Sun,
2019). However, the impact of media on trust in government can be negative or positive.
Specifically, the media malaise hypothesis indicates that a free-press leads to less government trust
due to the negative news (Patterson, 2009). However, the mobilization hypothesis suggests that mass
media enhances the citizens’ political interest and participation; and therefore, media increases
public trust of the ruling party (Norris, 2000). Notably, the countries in whose media is not free,
the press uses for shaping political values propaganda and mobilizing voters or citizens
(Stockmann and Gallagher, 2011). To address these hypotheses and to follow previous findings,
we separate the countries to re-estimate the regressions, according to their levels of freedom of
the press following the dataset of Freedom House (2020b).
Globalization may also affect trust in the government. According to Algan et al. (2016) and Bergh
and Bjørnskov (2011), public trust in government has a positive influence on the size of the
welfare state. At this stage, globalization can also positively affect the size of the welfare state,
according to the compensation hypothesis introduced by Rodrik (1998). Globalization can also
capture the effects of cultural variables, economic policy variables, and technology (such as
internet use) (see, e.g., Potrafke, 2015). Therefore, globalization is one of the leading indicators
for shaping public trust in government. To address the impact of globalization on confidence in
governments, we divide the countries to re-estimate the regressions, according to their levels of
globalization following the dataset of Gygli et al. (2019).
In this paper, we aim to provide the determinants of public trust in governments during the
COVID-19 pandemic. For this purpose, we use the individual-level survey data in the Worldwide
COVID-19 Attitudes and Beliefs dataset covering 108,918 respondents from 178 countries. To
the best of our knowledge, this is the first paper in the literature to determine the drivers of
public trust governments during the COVID-19 era. We observe that older and healthy people
trust more to their governments. However, education is negatively related to trust in
governments. These findings are robust to consider different measures of trust in government as
well as including various controls for the COVID-19 related policies. These findings are also
valid for countries at different stages of economic development as well to varying levels of
globalization, civil liberties-political rights, and freedom of the press. We suggest that the drivers
of public trust in governments can be crucial for providing full effect to slow down the rate of
Democracy also promotes economic performance (Acemoglu et al., 2019; Gründler and Krieger, 2016). Hence
there is an indirect positive impact of economic growth on trust in government.
3
3
outbreaks, ending the pandemic, taking new measures, such as testing procedures and tracing
programs, or potentially, mass vaccination.
The rest of the paper is organized as follows. Section 2 describes the data and the empirical
models. Section 3 provides empirical findings. Section 4 implements additional robustness
checks. Section 5 concludes with potential implications.
2. Data and Empirical Models
2.1. Data Sample and Measuring Global COVID-19 Attitudes and Beliefs
In this paper, we use the individual-level survey data covering 108,918 respondents from 178
countries. The original dataset has been provided by Fetzer et al. (2020). The data of Fetzer et al.
(2020) have been generated by the snowball sampling method, which includes the survey
instruments that were translated into 69 languages. The surveys capture the responses to various
questions for the period from March 20, 2020, to April 16, 2020. 4 One hundred seventy-eight
countries of respondents in the survey sample are provided in the Data Appendix.
In Table 1, we report a summary of the descriptive statistics, including the sources of data. In this
paper, inspiring from the definition of Levi and Stoker (2000), we use the two measures of trust
in government as the dependent variables (Fetzer et al., 2020): Truthfulness of Government
Communication (TGC) and Trust in Government (TIG). Both measures are defined as an index from 1
to 5. The former measure captures the responses to the question, “How much do you trust your
country’s government to take care of its citizens?” and the latter captures the answers to the question,
“How factually truthful do you think your country’s government has been about the coronavirus outbreak?”A
higher level of the indices indicates a higher trust in governments and a higher truthfulness of
governments, respectively. According to Table 1, the mean of the index from responses is around
3, which indicates that respondents are “Neither trust nor distrust” and “Neither truthful nor untruthful”
to their governments, respectively.
[Insert Table 1 around here]
Our first set of controls represents the personal information and demographic variables (Fetzer
et al., 2020). First, we consider age in years in 2020 (based on year of birth), with the mean value
of 39, where the minimum age is 18 and a maximum of 110. 5 Second is the education, and people
in the sample have spent 16 years on their education on average. The third variable is log income,
which is measured as a log of monthly household income (before tax in domestic currency).
Fourth is the dummy variable for marital status (married/co-living=1 and single and divorced=0).
Here, 56.3 % of the total people in the sample are married and living together. Fifth is the number
of household members, and there are three people on average in the households in the sample. Sixth
indicator is the dummy variable for gender (male=1 and female and other=0), and 43.2% of
individuals in the sample identify themselves as male. Finally, we control for a health condition, is
the index from 1 to 4, and it captures responses to “How healthy are you?”. A higher level of the
index of health condition indicates a higher level of health. The average of the index from
responses is around 3, which means that respondents have “good health conditions” in general.
The second set of controls represents the captures of the personal responses to the new type of
the COVID-19 pandemic (Fetzer et al., 2020). First, we consider precautionary behaviors, which
describe the behavior of people in the sample for the past week. Specifically, on average, 80.5%
of individuals “stayed at home.” 90.9% of people “did not attend social gatherings,” and 75.8% of them
“kept a distance of at least two meters.” Besides, 93% of people declare that “If I had exhibited symptoms
4
5
Visit http://www.covid19-survey.org for more information and the detailed descriptions of variables.
Note that the minimum age is 18; and therefore, all individuals in the sample are also voters.
4
of sickness, I would have immediately informed the people around me.” Finally, 91.9 % of the total people in
the sample “washed their hands more frequently than the month before.”
Besides, we control for first-order beliefs and second-order beliefs to capture how scientific statements
can affect people’s behavior during the COVID-19 pandemic and their thoughts on the society
for the related announcements. For instance, on average, 97.6% of people in the sample believe
that “people in their country should cancel their participation at social gatherings.” They think that 68 of 100
people in their country are in line with this statement. Also, 96.7% of people state that “people in
their country should not shake other people’s hands.” They estimate that 76 of 100 people in their country
shares this view. Besides, 81.6% of people in the sample think that “all shops in your country other
than supermarkets, pharmacies, post offices, and gas stations should be closed.” They indicate that 59 of 100
people in their country are in line with this view. Finally, 70.5 % of the total people in the sample
indicate, “there should be a general curfew in the country.” However, they believe that 50 of 100 people
in their country shares this view.
Finally, we include the indicators of the COVID-19 prevalence in countries on date respondents
participated. Specifically, we include i) confirmed cases, ii) confirmed deaths, and iii) patients recovered. We
use the data provided by John Hopkins University, which is introduced by Dong et al. (2020).
Furthermore, we include the Stringency Index for controlling government policies relating to the
COVID-19. The Stringency Index measures the government policies, such as closures of schools,
workplaces, public transportation, cancellation of public events, public information, and
restrictions on national movements. The related data are introduced by Oxford University, and
the measure is described in detail by Hale et al. (2020). We suggest that the personal responses,
the first-order beliefs, the second-order beliefs, the COVID-19 prevalence, and government
policies relating to the COVID-19 can significantly change the effects of demographic and
personal information on the measures of public trust in governments in all around the world.
In Table 2, we provide the correlation matrix. It is observed that the correlation between two
measures of trust in government is 0.75. Trust in government is negatively correlated with
education and income. Besides, the relationship between indicators of trust in government, and
age, being married, the number of household members, identifying the gender as male, and health
conditions are favorable.
[Insert Table 2 around here]
2.2. Empirical Models
In this paper, we estimate the following empirical models via the Ordinary Least Squares (OLS)
with the robust standard errors:
_ , = 0 + 1 1 , + 2 2 , + 3 1 + 4 2 + 1 ,
(1)
_ , = 0 + 1 1 , + 2 2 , + 3 1 + 4 2 + 2 ,
(2)
In Eq. (1) and Eq. (2), _ , and _ , are the indices of trust and truthfulness
of government in country i. 1 , is the “vector of controls” (capturing age, education, gender,
health condition, income, marital status, and members of the household), which represents the
personal information. 2 , is the “vector of additional controls” (including precautionary
behaviors, first-order beliefs, second-order beliefs, and the COVID-19 measures in the country),
which captures the personal responses to the global pandemic and the COVID-19 prevalence in
5
countries. Note that D1 and D2 capture the country-specific effect and time-trend, respectively.
represents the error-term in the estimations.
For showing the robustness of the empirical findings, we re-estimated the Eq. (1) and Eq. (2) for
the countries at different stages of economic development as well as for countries at the different
levels of globalization, institutional quality, and freedom of the press.
3. Empirical Findings
3.1. All Countries
The results for all countries are reported in Table 3. Columns from 1 to 4 reports the findings for
TGC. In Columns from 5 to 8, the results for TIG are reported. Columns 2 and 6 also consist of
the country-effects, and Columns 3 and 7 include the time-trend. The results in Columns 4 and 8
include both the country-effects and the time-trend.
All results indicate that age, being married, the number of households, and health conditions
increase TGC and TIG. Education is negatively related to both TGC and TIG. Log income is
negatively to TGC and TIG. Identifying as male is positively associated with both TGC and TIG.
However, these results are not robust to consider different measures of government in trust since
their coefficients are statistically insignificant when TIG is considered.
[Insert Table 3 around here]
The positive impact of age on trust in government is similar to previous findings of Christensen
and Laegreid (2005) and Zhao and Hu (2017). Education is negatively related to trust in
governments….
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