About the Data

The data was taken by European Social Survey (ESS), an academically-driven multi-country survey covering over 20 nations. Its three aims are, firstly - to monitor and interpret changing public attitudes and values within Europe and to investigate how they interact with Europe’s changing institutions, secondly - to advance and consolidate improved methods of cross-national survey measurement in Europe and beyond, and thirdly - to develop a series of European social indicators, including attitudinal indicators.

Loading library

# Data Wrangling
library(haven)
library(dplyr)
library(tidyr)
library(labelled)
library(tidyverse)

# Plotting
library(ggplot2)
library(plotly)
library(glue)
library(GGally)

Reading data

Here we have selected data from ESS round 2 regarding country, gender, age, education, social trust, political trust, subjective well-being, economic morality, and objective well-being. Each variable will be elaborated on each dedicated section.

Full data can be found here https://ess-search.nsd.no/en/study/5296236e-b5ee-40dc-a554-81ea09211d1d

raw_survey_selected <- read_sav("ESS2e03_6/ESS2e03_6.sav")
head(raw_survey_selected)

Dropping Missing Values

There are multiple missing values declared for each question:

  1. Refuse: 77/7
  2. Don’t know: 88/8
  3. No answer: 99/9
  4. Blanks/NA
  5. Others: 55/5
  6. Not applicable: 66/6

I will be dropping datas with no:

  • gender
  • age
  • education level and years
  • subjective well being score

I will be replacing the rest of the missing value with 0.

cln_data <- raw_survey_selected %>%
    filter(gndr != 9, agea != 99, across(c(!edulvla, !eduyrs, !swb), ~!.x %in%
        c(77, 88, 99, 55, 66))) %>%
    mutate(across(c(ppltrst, pplfair, pplhlp, trstlgl, trstplc, trstplt, trstep,
        trstun, trstprt, trstprl), ~if_else(.x %in% c(77, 88, 99), 0, .x)),
        across(c(ctzhlpo, scbevts, ctzchtx, tstrprh, tstfnch, tstpboh, pyavtxw,
            slcnflw, flinsrw, pbofvrw, mnyacth, olwmsop, ignrlaw, bsnprft, frmwktg,
            cmprcti, frdbnft, kptchng, payavtx, slcnsfl, musdocm, flinsr, pbofvr,
            flgvbnf, gdsprt, clmrlx, actvgrs, lfintr, frshrst), ~if_else(.x %in%
            c(7, 8, 9), 0, .x))) %>%
    drop_na()

Unlabelling gender

cln_data$gndrcd <- labelled(cln_data$gndr, c(Male = 1, Female = 2)) %>%
    to_factor()

Social Trust

At the nation level, trust is understood as generalized or social trust, which is trust in those one does not know, i.e. ‘trust in strangers’. Trust, it is said, contributes to economic growth and efficiency in market economics, to the provision of public goods, to social integration, co-operation and harmony, to personal life satisfaction, to democratic stability and development, and even to good health and longevity.

Wrangling social trust data

# Overall social trust score
cln_data$social_trust <- cln_data$ppltrst+cln_data$pplfair+cln_data$ppltrst

Social trust by genders

# Subsetting data
st_gend <- cln_data %>% 
            select(gndrcd, social_trust, ppltrst, pplfair, pplhlp) %>% 
            group_by(gndrcd) %>% 
            summarise(avg_st=mean(social_trust),
                      avg_tr=mean(ppltrst),
                      avg_fr=mean(pplfair),
                      avg_hp=mean(pplhlp))

stg_dim <- st_gend %>% pivot_longer(cols = c(avg_tr, avg_fr, avg_hp))

# Plotting data
ggplot(st_gend, aes(x=gndrcd, y=avg_st))+geom_col(aes(fill=gndrcd))+theme_minimal()+
  labs(title = "Social Trust by Genders", x = NULL, y = "Avg Social Trust", fill=NULL)+
  theme(legend.position = "none", plot.title = element_text(hjust = 0.5), panel.grid.minor = element_blank())+
  coord_cartesian(ylim = c(15,16))

ggplot(stg_dim)+
  geom_bar(aes(x=name, y=value, fill=gndrcd), position = "dodge", stat = "identity")+theme_minimal()+
  labs(title = "Social Trust Dimension by Genders", x = NULL, y = "Avg Score", fill=NULL)+
  theme(legend.position = "none", plot.title = element_text(hjust = 0.5), panel.grid.minor = element_blank())+
  coord_cartesian(ylim = c(4,6))+scale_x_discrete(labels = c("People are trustworthy", "People are fair", "People are helpful"))

We can see that there is no significant differences regarding average social trust score in Male and Female participant. Although on average woman social trust score are lower than men, they tend to trust that people are generally more trustworthy and fair than men. Unlike woman, men generally thinks that people tries to be helpful.

Social trust by education level

# Subsetting data
st_edu <- cln_data %>% 
            select(edulvla, social_trust, ppltrst, pplfair, pplhlp) %>% 
            group_by(edulvla) %>% 
            summarise(avg_st=mean(social_trust))

st_edyr <- cln_data %>% 
            select(eduyrs, social_trust, ppltrst, pplfair, pplhlp) %>% 
            group_by(eduyrs) %>% 
            summarise(avg_st=mean(social_trust))

# Plotting data
ggplot(st_edu, aes(x=edulvla, y=avg_st))+geom_col(aes(fill=avg_st))+theme_minimal()+
  labs(title = "Social Trust by Education Level", x = NULL, y = "Avg Social Trust", fill=NULL)+
  theme(legend.position = "none", plot.title = element_text(hjust = 0.5), panel.grid.minor = element_blank())+
  coord_cartesian(ylim = c(12,19))

ggplot(st_edyr, aes(x=eduyrs, y=avg_st))+geom_line(size=0.75)+geom_point(size=2)+theme_minimal()+
  labs(title = "Social Trust by Education Years", x = "Year", y = "Avg Social Trust", fill=NULL)+
  theme(legend.position = "none", plot.title = element_text(hjust = 0.5), panel.grid.minor = element_blank())

We can see that there is a notable differences regarding average social trust score by education level. We can see the higher your education level is and the more year you have spent in education, the higher your average social trust score. Empirical studies demonstrate that social trust is highest among people who are more educated, with high incomes, and who are satisfied with life. Conversely, social trust is found to be lowest among people who are unemployed, poor in health and live in areas with high degrees of income inequality. Education will teach people to trust in low risk and well-functioning societies where trust is rewarding. However, in a high-risk environment, education is likely to teach people to be more aware of social and institutional risks and therefore not to trust. Since the survey was done in Europe, it can be inferred that the environment was low risk and well-functioning since education teaches people that trust is rewarding.

Political Trust

Political trust is one of a family of terms referring to citizens’ feelings about their government. The foundation of trust is that someone judges a target to be trustworthy, that he or she will act with integrity and competence and with one’s interests paramount. In politics, those interests may include the “goals of good policy, peace and sound economic stewardship”, in addition to the citizen’s own welfare.

Wrangling political trust data

# Overall social trust score
cln_data$pol_trust <- cln_data$trstlgl+cln_data$trstplc+cln_data$trstplt+cln_data$trstep+cln_data$trstun+cln_data$trstprt+cln_data$trstprl

Political trust by country

# Subsetting data
pt_ctr <- cln_data %>% 
            select(cntry, pol_trust) %>% 
            group_by(cntry) %>% 
            summarise(avg_pt=mean(pol_trust)) 
pt_ctr$lb_ctr <- pt_ctr$cntry %>% lab_to_char()
pt_ctr <- pt_ctr %>% mutate(label = glue("{lb_ctr} ({cntry}): {round(avg_pt, 2)}"))

# Plotting data
plt_ctr <-  ggplot(pt_ctr, aes(x=reorder(cntry, avg_pt), y=avg_pt, group=cntry, text=label))+geom_col(aes(fill=avg_pt))+theme_minimal()+
            scale_fill_gradient(low="#de4c09", high="#95e82e")+
            labs(title = "Political Trust by Country", x = NULL, y = "Avg Political Trust", fill=NULL)+
            theme(legend.position = "none", plot.title = element_text(hjust = 0.5), panel.grid.minor = element_blank())
ggplotly(plt_ctr, tooltip = "text")

There is possibility that certain demographic groups have different baseline levels of trust due to systematically different experiences. Yet demographics have weak associations with trust that vary with time and context. It is argued that social trust and political trust goes hand in hand.

Well-being

  1. Subjective well-being: often defined as happiness, examines people’s subjective evaluations of their own lives.
  2. Objective well-being: investigates the objective dimensions of a good life.

So how happy are people in EU in general? To be very specific, how happy do people think they are.

# Subsetting data
plt_swb <-  cln_data %>% 
            select(swb) %>% 
            group_by(swb) %>% 
            summarise(cnt=n())

# Plotting data
ggplot(plt_swb, aes(x=as.factor(swb), y=cnt))+geom_col(aes(fill=cnt))+theme_minimal()+
            labs(title = "Subjective Well Being", x = NULL, y = "Count", fill=NULL)+
            theme(legend.position = "none", plot.title = element_text(hjust = 0.5), panel.grid.minor = element_blank())

From the graph above we can say that EU people think they are happy, as the answer falls more in 6-10 from the likert scale (falls in the upper half).

Objectively how happy are they?

# Overall objective well-being
cln_data$owb <- cln_data$gdsprt+cln_data$clmrlx+cln_data$actvgrs+cln_data$lfintr+cln_data$frshrst

# Subsetting data
plt_owb <-  cln_data %>% 
            select(owb) %>% 
            group_by(owb) %>% 
            summarise(cnt=n())

# Plotting data
ggplot(plt_owb, aes(x=as.factor(owb), y=cnt))+geom_col(aes(fill=cnt))+theme_minimal()+
            labs(title = "Objective Well Being", x = NULL, y = "Count", fill=NULL)+
            theme(legend.position = "none", plot.title = element_text(hjust = 0.5), panel.grid.minor = element_blank())

From the graph above we can say that objectively, most EU people falls under the lower half of objective well being. But this observations is so different with how people views their life. This just shows that people are still able to view their life favorably (and view themselves happy with their life) although the quality of their lives objectively might not be deemed a “good life”.

Economic Morality

Economic morality can be interpreted as a mental condition that underlies a person to behave economically. It is reflected in the attitude and actions of obeying the institutions and fulfilling obligations in the economy (imperative); cares about the existence of others and can weigh the impact of actions on others (tolerance); respect equality by considering the conditions of the surrounding community (equality) and respect equal rights as economic actors and upholding honesty, ethics of social life, pro-social and prioritizing cooperation in economic behavior (commitment). In principle, moral economic behavior refers to ones’ attitudes and actions in relation to others.

Wrangling economic morality data

# Reversing some of the scale
rev_scl <- cln_data %>% select(ctzhlpo, ctzchtx, olwmsop, cmprcti) %>% likert_reverse(top = 5, bottom = 1)

# Overall social trust score
cln_data$ecm <- rev_scl$ctzhlpo + cln_data$scbevts + rev_scl$ctzchtx + cln_data$tstrprh + cln_data$tstfnch + cln_data$tstpboh + cln_data$pyavtxw + cln_data$slcnflw + cln_data$flinsrw + cln_data$pbofvrw + cln_data$mnyacth + rev_scl$olwmsop + cln_data$ignrlaw + cln_data$bsnprft + cln_data$frmwktg + rev_scl$cmprcti + cln_data$frdbnft + cln_data$kptchng + cln_data$payavtx + cln_data$slcnsfl + cln_data$musdocm + cln_data$flinsr + cln_data$pbofvr + cln_data$flgvbnf

Economic morality by genders

# Subsetting data
em_gend <- cln_data %>% 
            select(gndrcd, ecm) %>% 
            group_by(gndrcd) %>% 
            summarise(avg_cm=mean(ecm))

em_gend

Economic morality by education

# Subsetting data
em_edu <- cln_data %>% 
            select(edulvla, ecm) %>% 
            group_by(edulvla) %>% 
            summarise(avg_cm=mean(ecm))

em_edu

We can see that economic morality is a variable that has little to no variables regardless the age or the education. There might be other psychological factor that contributes to economic morality.

Reference

  1. Citrin, J., & Stoker, L. (2018). Political Trust in a cynical age. Annual Review of Political Science, 21(1), 49–70. https://doi.org/10.1146/annurev-polisci-050316-092550
  2. Delhey, J., & Newton, K. (2003). Who trusts?: The Origins of Social Trust in seven societies. European Societies, 5(2), 93–137. https://doi.org/10.1080/1461669032000072256
  3. Torpe, L., & Lolle, H. (2010). Identifying Social Trust in cross-country analysis: Do we really measure the same? Social Indicators Research, 103(3), 481–500. https://doi.org/10.1007/s11205-010-9713-5
  4. Voukelatou, V., Gabrielli, L., Miliou, I., Cresci, S., Sharma, R., Tesconi, M., & Pappalardo, L. (2020). Measuring objective and subjective well-being: Dimensions and data sources. International Journal of Data Science and Analytics, 11(4), 279–309. https://doi.org/10.1007/s41060-020-00224-2
  5. Wahyono, H., Narmaditya, B. S., Wibowo, A., & Kustiandi, J. (2021). Irrationality and economic morality of smes’ behavior during the COVID-19 pandemic: Lesson from Indonesia. Heliyon, 7(7). https://doi.org/10.1016/j.heliyon.2021.e07400
  6. Zanin, L. (2016). Education and life satisfaction in relation to the probability of Social Trust: A conceptual framework and empirical analysis. Social Indicators Research, 132(2), 925–947. https://doi.org/10.1007/s11205-016-1322-5