Libraries

library(foreign)
library(dplyr)
library(ggplot2)
library(tidyr)
library(stringr)
library(RColorBrewer)
library(knitr)
library(sjPlot)
library(rcompanion)
library(sjstats)
library(pwr)
library(rstatix)
library(psych)
library(viridis)
library(car)
library(DescTools)
library(corrplot)
library(gridExtra)

Cleaning the dataset

cz <-read.spss("C:/Users/user/Downloads/ESS10-subset.sav", use.value.labels = T, to.data.frame = T)

cz$respc19 <- as.character(cz$respc19)
cz$respc19[cz$respc19 == "Yes, I tested positive for COVID-19"] <- "Yes"
cz$respc19[cz$respc19 == "Yes, I think I had COVID-19 but was not tested/did not test positive "] <- "Yes"
cz$respc19[cz$respc19 == "No, I have not had COVID-19"] <- "No"
cz$respc19 <- as.factor(cz$respc19)

cz$nwspol <- as.numeric(as.character(cz$nwspol))

cz$panfolru <- as.character(cz$panfolru)
cz$panfolru[cz$panfolru == "Much more important to follow government rules"] <- "0"
cz$panfolru[cz$panfolru == "Much more important to make your own decisions"] <- "10"
cz$panfolru <- as.numeric(cz$panfolru)

cz$panmonpb <- as.character(cz$panmonpb)
cz$panmonpb[cz$panmonpb == "Much more important to monitor and track the public"] <- "0"
cz$panmonpb[cz$panmonpb == "Much more important to maintain public privacy"] <- "10"
cz$panmonpb <- as.numeric(cz$panmonpb)

cz$gvhanc19 <- cz$gvhanc19 %>% str_replace_all("Extremely dissatisfied", "0")
cz$gvhanc19 <- cz$gvhanc19 %>% str_replace_all("Extremely satisfied", "10")
cz$gvhanc19<-as.numeric(cz$gvhanc19)

Introduction and theoretical framework

In this project, I would like to explore how citizens’ expectations of government actions (in particular, in my project, expectations of monitoring citizens’ actions), conformity and viewing political news affect satisfaction with government actions in connection with Covid-19.

Research question: What are the predictors of satisfaction with government’s handling of COVID-19 in Czechia?

Monitoring the public and satisfaction with government’s handling of COVID-19

During the COVID-19, there was a great public discussion about citizens’ privacy. According to Ioannou, Tussyadiah (2021), citizens who show greater trust in the government tend to be more flexible about restrictions on their privacy for public safety purposes. They can see such measures as a necessary evil in the fight against the pandemic and trust the Government to take appropriate measures. Another study shows that citizens are not ready to share all personal information in full and prefer to be able to manage the data provided, regardless of their trust in the government.(Simko, Chang, Jiang, Calo, Roesner, Kohno, 2022). In my opinion, the relationship between these variables exists. I assume that the more a citizen trusts the government, the more they will agree to provide personal data for the purpose of monitoring covid 19.

Hypothesis: those who preferred monitoring the lives of citizens were more satisfied with the actions of the government

Conformity (governmental rules or people’s own decisions) and satisfaction with government’s handling of COVID-19

Hypothesis: those who turned out to be more conformist and believed that it was always more important to follow the decisions of the government were more satisfied with its activities during covid-19

News about politics and current affairs and satisfaction with government’s handling of COVID-19

One of the articles I found looked at the relationship between the frequency of news consumption and political trust. As a result, they concluded that in free countries, people who follow the news are more likely to have less political trust, while in non-free societies the relationship is reversed (Jiang & Zhang, 2021). Another article states that the higher people’s satisfaction with the government’s actions during the pandemic, the higher their trust in it (Wu et al., 2021). I am interested in the relationship between news consumption and satisfaction with government actions during pandemic, however, I didn’t find articles with this pure relationship. Therefore, based on the results of studies mentioned above, my hypothesis is that with an increase in the time of news consumption, satisfaction with government’s handling of COVID-19 decreases. I think so because, according to Freedom House data, which was used in the mentioned article to determine the level of freedom, the Czech Republic belongs to the category of free societies. Because of this, the level of trust should be lower and since less satisfaction with government actions leads to less trust, I assume that reading the news will also lead to less satisfaction with actions.

Hypothesis: With an increase in the time of news consumption, satisfaction with government’s handling of COVID-19 decreases

COVID-19 status and satisfaction with government’s handling of COVID-19

I believe that the fact of COVID-19 disease can also become a predictor, because according to Chen et. al. (2021), this is one of the important factors influencing the opinion about government policy during the pandemic in connection with the concept of sick role and worries about the general level of morbidity, worries about loved ones who may to get started. For these reasons, I decided to make the respc19 variable an additional predictor.

Hypothesis: People who had COVID-19 are less likely to be satisfied with government’s handling of COVID-19 decreases

Describing variables

First of all, let’s look closer at the variables that we have selected for analysis.

Variable = c("panmonpb", "gvhanc19", "nwspol", "panfolru", "respc19") 
Meaning = c("More important for governments to monitor and track the public or to maintain public privacy when fighting a pandemic", "How satisfied with government's handling of COVID-19", "News about politics and current affairs, watching, reading or listening, in minutes", "More important to follow government rules or to make own decisions when fighting a pandemic", "Whether the respondent was ill with COVID-19")
Type <- c("Quasi-interval", "Quasi-interval", "Ratio", "Quasi-interval", "Binary")
Class <- c(class(cz$panmonpb), class(cz$gvhanc19), class(cz$nwspol), class(cz$panfolru), class(cz$respc19))
df1 <- data.frame(Variable, Meaning, Type, Class, stringsAsFactors = FALSE)
print(df1)
##   Variable
## 1 panmonpb
## 2 gvhanc19
## 3   nwspol
## 4 panfolru
## 5  respc19
##                                                                                                                 Meaning
## 1 More important for governments to monitor and track the public or to maintain public privacy when fighting a pandemic
## 2                                                                  How satisfied with government's handling of COVID-19
## 3                                   News about politics and current affairs, watching, reading or listening, in minutes
## 4                           More important to follow government rules or to make own decisions when fighting a pandemic
## 5                                                                          Whether the respondent was ill with COVID-19
##             Type   Class
## 1 Quasi-interval numeric
## 2 Quasi-interval numeric
## 3          Ratio numeric
## 4 Quasi-interval numeric
## 5         Binary  factor

In total, I selected 5 variables for analysis, since the ESS data almost does not contain continuous variables, I selected quasi-interval variables that have 10 categories and can be used for analysis as interval variables. As a categorical predictor, I will use the variable “respc19” and “panfolru” as a continuous predictor.

News about politics and current affairs

polnews <- c(mean(cz$nwspol, na.rm = T), DescTools::Mode(cz$nwspol, na.rm = T), median(cz$nwspol, na.rm = T), sd(cz$nwspol, na.rm = T), min(cz$nwspol, na.rm = T), max(cz$nwspol, na.rm = T),skew(cz$nwspol, na.rm = T), Kurt(cz$nwspol,na.rm = T), parameters::standard_error(cz$nwspol, na.rm = T))
names(polnews) <- c("mean", "mode", "median",  "sd",  "min",  "max",  "skew",  "kurtosis",  "st.error")
print(polnews)
##       mean       mode     median         sd        min        max       skew 
##  65.654617  60.000000  45.000000  81.436812   0.000000 960.000000   4.037808 
##   kurtosis   st.error 
##  23.899553   1.699553

From the table we see that the mean is 65.65, median is 45, but a maximum value is 960. Also the data is skewed positively (4 > 0.5) and the distribution is sharp (kurtosis > 1). So the data is not normal, on average people spend 66 minutes a day on news, however, we have outliers with larger news consumption.

ggplot(subset(cz, !is.na(cz$nwspol)), aes(x= nwspol)) +
geom_histogram(fill="#FEB24C", color="#FEB24C", alpha=0.5, binwidth = 15)+
theme_minimal() +
labs(y = "Number of people", x='Minutes')+
  ggtitle("Time spent on news about politics and current affairs")+
theme(plot.title = element_text(size=18, face='bold', hjust=0.4))+
scale_x_continuous(breaks = c(0,100,200,300,400,500,600,700,800,900))+
geom_vline(aes(xintercept=mean(nwspol, na.rm=T), color="mean"), linetype="solid", linewidth = 1)+ geom_vline(aes(xintercept=median(nwspol, na.rm=T), color="median"), linetype="dashed", linewidth = 1)+ geom_vline(aes(xintercept=Mode(nwspol), color="mode"), linetype="dotted")+ 
  scale_color_manual(name = "Measurement", values = c(median = "#E31A1C", mean = "#800026", mode = "#4b4b4b"))

Again we see that the data is skewed to the right, however, the number of people that spend more than 400 minutes on news is really small. Most people spend not more than 70 minutes. Also, compared to people who spend several hours on the news, many more people spend almost no time on it (more than 200 respondents at 0 point).

Other continuous variables

For “panmonpb” 0 is “Much more important to monitor and track the public”, 10 is “Much more important to maintain public privacy”. For “gvhanc19” 0 is “Extremely dissatisfied with the government’s handling of the coronavirus pandemic”, 10 is “Extremely satisfied”. For “panfolru” 0 is “Much more important to follow government rules”, 10 is “Much more important to make your own decisions”.

cz %>%
  summarise_at(vars(c(gvhanc19, panmonpb, panfolru)), list(Mean= ~mean(., na.rm = T), Median=~median(., na.rm = T), Mode = ~DescTools::Mode(., na.rm = T), SD=~sd(., na.rm = T), Min=~min(., na.rm = T), Max=~max(., na.rm = T)))%>%
  pivot_longer(cols = gvhanc19_Mean:panfolru_Max, names_to = c("Variable", "Statistics"), names_sep = "_", values_to = "Values")%>%
  pivot_wider(names_from = Variable, values_from = Values)
## # A tibble: 6 × 4
##   Statistics gvhanc19 panmonpb panfolru
##   <chr>         <dbl>    <dbl>    <dbl>
## 1 Mean           5.23     7.01     5.75
## 2 Median         5        7        6   
## 3 Mode           5       10        5   
## 4 SD             2.51     2.46     2.81
## 5 Min            0        0        0   
## 6 Max           10       10       10

Mode and median for satisfaction with the government’s handling of the pandemic is 5, mean is 5.23, standard deviation is 2.5. We can see that people occupy a middle position, that is, they are not fully satisfied with how the government is coping, but it cannot be said that they are not satisfied with it at all. For importance for governments to monitor and track the public the mean and median is 7, and the mide is 10. So people mostly think that it is much more important to maintain public privacy for governments than monitor the public. For following government rules or making own decisions the mean and median is around 6, mode is 5. On average people think that it is important to make their own decisions when fighting a pandemic, but following government rules is also important because answers lay almost in the middle.

media <- ggplot(subset(cz, is.na(cz$panmonpb) == F), aes(x = as.factor(panmonpb))) +
geom_bar(fill='#FEB24C',color = "#FC4E2A", alpha=0.7) +
theme_minimal() +
labs(x = "Monitoring the public or maintaining public privacy", y = "Number of people")

restricting <- ggplot(subset(cz, is.na(cz$gvhanc19) == F), aes(x = as.factor(gvhanc19))) +
geom_bar(fill='#FED976',color = "#FD8D3C", alpha=0.7) +
theme_minimal() +
labs(x = "How satisfied with government's handling of COVID-19", y = "Number of people")

decisions <- ggplot(subset(cz, is.na(cz$panfolru) == F), aes(x = as.factor(panfolru))) +
geom_bar(fill='#BD0026',color = "#800026", alpha=0.7) +
theme_minimal() +
labs(x = "Follow government rules or to make own decisions", y = "Number of people")

grid.arrange(media,restricting,decisions, nrow = 2)

From plots we see that more people think that it is important to maintain public privacy for governments, also they are more satisfied with the government’s handling of the pandemic, but extreme values (9 and 10) we see rarely. For following government rules or making own decisions as I earlier discussed people mostly answer in the middle, however, more people think that it is important to make their own decisions when fighting a pandemic.

Categorical variable

Respc19. This categorical variable shows whether the respondent was ill with COVID-19 or not. It has 2 calegories: Yes and No. It the graph and table below we can observe frequency of answers

cz1 <- cz %>%
  select(respc19, medcrgvc, panresmo, nwspol, panfolru, gvhanc19)

cz1 <- na.omit(cz1)

ggplot(cz1)+
  geom_bar(aes(x=respc19, fill=respc19))+
  scale_fill_brewer(palette = "YlOrRd")+
  xlab("covid-19")+
  ylab("Number of people")+theme_minimal()

respc19_1 <- data.frame(cz1 %>% 
                          group_by(respc19) %>% 
                          summarise(n=n()))

print(respc19_1)
##   respc19    n
## 1      No 1448
## 2     Yes  652

As we can see, more than half of the respondents did not have COVID-19.

Here I want to examine whether the experience of COVID-19 has influenced satisfaction with the actions of the government. I assume that citizens who were ill and who not, may have a different opinions about the anti-coronavirus policy

ggplot(cz1)+
  geom_boxplot(aes(y=gvhanc19, x=respc19, fill=respc19),alpha=0.5)+
  scale_fill_brewer(palette = "YlOrRd")+
  theme_minimal()+
  labs(y="satisfaction with the Government's actions \n in connection with covid-19",y=NULL,
       x="the respondent has covid-19", 
       title = "Comparing the level of satisfaction with government \n 
       actions due to whether the respondent had covid-19")+
  theme(legend.position="none",  plot.title = element_text(face = "bold",hjust = 0.5))

The graph shows that the number of people who were sick with covid-19 and those who were not sick is about the same. At the same time, those who have not been sick with coronavirus are more satisfied with the government’s policy to combat coronavirus.

Correlation matrix

Let’s look at the correlations between outcome variable (gvhanc19 - satisfaction with the government’s handling of the pandemic) and other variables.

cor_mat <- cor(cz[c("gvhanc19", "nwspol", "panmonpb", "panfolru")], use="pairwise.complete.obs", method="spearman")

corrplot(cor_mat, method="color", addCoef.col = "black", tl.col="black", diag=FALSE, type="lower", number.digits = 2, col = COL1('YlOrRd', 10))

Correlation between the outcome and “panfolru” is medium and negative (-0.31). For “panmonpb” and the outcome the correlation is small and negative (-0.13), and for news consumption and outcome variable is really small and positive (0.05). So I can say that variable about following government rules or making your own decisions (panfolru) have decent association with satisfaction with the government’s handling of the pandemic and the more people believe that decisions should be made on their own, the less satisfied they are. As for correlations between other variables, the only medium correlation is between “panfolru” and “panmonpb” and it is positive (0.3). In general, this is quite logical since people who prefer to make decisions themselves probably also want the government not to monitor them but to preserve public privacy in the fight against the pandemic.

My hypothesis about the connection between conformity and satisfaction with the actions of the state has been confirmed: there is a medium effect between them. The hypothesis about the connection between the opinion about the importance of monitoring the public and satisfaction with the actions of the state was not confirmed: the effect was too weak to say that they correlate

I created a scatterplot in order to determine a linear relationship between variables

ggplot(cz1)+
  geom_point(aes(x=panfolru, y=gvhanc19), color="#FEB24C", alpha=0.04, size=9)+
  labs(title="Relationship between importance of following the rules and level of trust", x="Importance of following the rules", y="Satisfaction with government’s handling of covid")+
  theme_minimal()+
  theme(plot.title=element_text(size=15, face='bold', hjust=0.5))

In the graph we see that relationship is not quite linear.

Linear regression models

Building of the models

So, with the variables of choice (gvhanc19, panfolru and respc19) I build two regression models. First, I build a model (m1) with one continuous predictor (panfolru). Then, I build a model (m2) with two predictors: continuous (panfolru) and categorical (respc19).

ss = cz %>% select(gvhanc19, panfolru, respc19)
ss <- drop_na(ss)

m1<-lm(gvhanc19 ~ panfolru, data = ss)
m2<-lm(gvhanc19 ~ panfolru+respc19, data = ss)

Comparison

In order to decide, which model has the best fit, we must 1) look at their determination coefficient (R^2), 2) compare them using the anova() function.

  1. The determination coefficient of m1 is 0.114, while the determination coefficient of m2 is a little higher at 0.118.
print(summary(m1)$r.squared)
## [1] 0.1138955
summary(m2)$r.squared
## [1] 0.1178138
  1. The p-value is less than 0.5, so the second model is significantly better than the first one.
anova(m1,m2)
## Analysis of Variance Table
## 
## Model 1: gvhanc19 ~ panfolru
## Model 2: gvhanc19 ~ panfolru + respc19
##   Res.Df   RSS Df Sum of Sq      F   Pr(>F)   
## 1   2362 12839                                
## 2   2361 12783  1    56.776 10.487 0.001219 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Interpretation

To interpret the model, we shall look to the determination coefficient once more. It is 0.118 for this model, which means that predictors explain about 12% of the variance of gvhanc19.

Well, this isn’t a really good model. However, it checks out with some theoretical points. While this model isn’t perfect, it’s great that it can explain at least some of the variance that is going on with gvhanc19.

I assume that the conformity of citizens explains satisfaction with the actions of the government only by such a small fraction, because there are still many factors that may be more influential. Such factors may include: retirement age, because during the pandemic the government was generous to them and allocated bonuses, which could improve their opinion of the government; entrepreneurial activity, because the government also conducted an active business assistance program during the pandemic; working status, because the Czech government reacted rather late and poorly to layoffs and “endless” vacations, which could make its actions less attractive to employees; political orientation, because stereotypes about the weakness and chaotic nature of populist leaders who mainly decided the fate of the Czech Republic are quite common; family status, because there are large and single-parent families in the Czech Republic they are most at risk of poverty due to weak government support; access to information, because In the Czech Republic, there were problems informing citizens about the course of covid-19 and its consequences, which also led to distrust (Greer et al, 2021; Klimovsky, 2021)

Let’s look closer at some of the coefficients of this model’s equation. To do this, it’s nice to look at a pretty table. So let’s build it!

sjPlot::tab_model(m2)
  gvhanc19
Predictors Estimates CI p
(Intercept) 7.04 6.82 – 7.26 <0.001
panfolru -0.29 -0.33 – -0.26 <0.001
respc19 [Yes] -0.34 -0.54 – -0.13 0.001
Observations 2364
R2 / R2 adjusted 0.118 / 0.117

p-value is less than 0.05. Cool!

The coefficients I am interested in are estimates of the intercept, panfolru and respc19[yes].

The intercept estimate is 7.04. This means that when the panfolru is equal to 0 (when the person thinks it’s the most important to follow government rules) and when the respc19 is “No” (when the respondent didn’t have COVID-19), the gvhanc19 is equal to 7.04 (so the person is leaning towards being satisfied with the governmental policies during COVID-19).

The panfolru estimate is -0.29. This means that when the panfolru variable is increased by 1, gvhanc19 is decreased by 0.29 (so when the person thinks it’s more important to think for themselves, they are a little less likely to be satisfied with the governmental policies for COVID-19).

The respc19[yes] estimate is equal to -0.34. This means that when the person has had COVID-19 (or thought they had COVID-19), they are less likely to be satisfied with the governmental policies for COVID-19.

Conclusion

To summarize, let’s look at how things stand with my hypotheses

  1. My hypothesis about the consumption of political news and satisfaction with how the government is handling the pandemic has not been confirmed. From the correlation matrix, we can see that the relationship is very weak and positive (0.05).

  2. The hypothesis about the connection between the opinion about the importance of monitoring the public and satisfaction with the actions of the state was not confirmed: the effect was too weak (-0,13)

  3. My hypothesis about the connection between conformity and satisfaction with the actions of the state has been confirmed: there is a medium effect between them. But regression analysis showed that this relationship explains the variation of outcome by only 12%, even with the added predictor in the form of a variable saying whether the respondent was sick with covid-19 or not, which made the model more significant. I can relate this to the influence of many other factors Thus, I can conclude that of the predictors I have considered, only the conformity of the population can explain satisfaction with the actions of the Government of the Czech Republic - and explain only partially

References

Chen, C. W. S., Lee, S., Dong, M. C., & Taniguchi, M. (2021). What factors drive the satisfaction of citizens with governments’ responses to COVID-19? International Journal of Infectious Diseases, 102, 327–331. doi:10.1016/j.ijid.2020.10.050

Freedom House (2020) Freedom in Czechia https://freedomhouse.org/country/czechia/freedom-world/2020

Greer, S.L. et al. (2021) Coronavirus politics: The comparative politics and policy of covid-19. University of Michigan Press.

Idnes.cz (2020) Vláda omezila volný pohyb do 1. dubna, brzdí s EET, odpouští i odvody. https://www.idnes.cz/zpravy/domaci/jednani-vlady-koronavirus-pomoc-podnikatelum-zakaz-pohybu-pendleri.A200323_051446_domaci_kop

Ioannou, A., & Tussyadiah, I. (2021). Privacy and surveillance attitudes during health crises: Acceptance of surveillance and privacy protection behaviours. Technology in Society, 67, 101774. https://doi.org/10.1016/j.techsoc.2021.101774

Jiang, A., & Zhang, T. H. (2021). Political freedom, news consumption, and patterns of political trust: evidence from East and Southeast Asia, 2001-2016. Political Science, 73(3), 250-269.

Klimovsky, D., Nemec, J. and Bouckaert, G. (2021) ‘The COVID-19 pandemic in the Czech Republic and Slovakia’, Scientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration, 29(1). doi:10.46585/sp29011320.

Simko, L., Chang, J., Jiang, M., Calo, R., Roesner, F., & Kohno, T. (2022). COVID-19 Contact Tracing and Privacy: A Longitudinal Study of Public opinion. Digital Threats, 3(3), 1–36. https://doi.org/10.1145/3480464

Wu, C., Shi, Z., Wilkes, R., Wu, J., Gong, Z., He, N., … & Nicola Giordano, G. (2021). Chinese citizen satisfaction with government performance during COVID-19. Journal of Contemporary China, 30(132), 930-944.