The recent growth in support of far-right and anti-establishment ideology has prompted a great debate in the political research world regarding the exact causes that motivate the public to entertain such ideas.
Thus, this paper aims to investigate two research questions: a) how does people’s happiness level affect their immigration sentiment? and b) how does people’s happiness level affect their trust of politicians?
I will use the European Social Survey Wave 8 (2016) to test my two hypotheses:
H1: Happier people are more likely to be sympathetic towards immigrants and have a positive immigration sentiment.
H2: Happier people are more likely to have a high level of trust of politicians.
Variable Description:
The variables are taken from the dataset produced by the ESS Wave 8 (2016), which conducted a cross-national survey on 23 countries asking citizens about a range of social questions and issues. The dataset is of such a magnitude that it allows for a sample size of over 10,000 data points, which is beneficial as the parameters of β cannot be considered significant unless the sample size is of that dimension.
The independent variable in this study is happiness, which is measured in the dataset through the question: “taking all things together, how happy would you say you are?” The scale ranges from 0 (extremely unhappy) to 10 (extremely happy) (ESS 2016), visualized in Figure 1.
Using the command sum, I conclude that the mean level of happiness is 7.44 with a median of 8. The fact that mean<median indicates a skew to the left, indicating that the majority of people lean toward identifying as happier than not. A box plot of the variable (figure 2) indicates outliers with the values that fall outside of the lower whisker. I will take this into consideration and control for outliers in regression, to eliminate potential bias in the model.
The first dependent continuous variable measured is immigration sentiment. In efforts to create one general variable that denotes the general immigration sentiment, I combined the following variables:
a)Imbegco: immigration is “bad/good for the country’s economy”?
b)Imueclt: immigration undermines/enriches a “country’s cultural life”?
c)Imwbcnt: immigrants make the country better/worse place to live?
These three variables are highly correlated, thus can be combined into one variable “Immigration Sentiment” which ranges responses from 0=extremely anti- immigrant to 30= extremely pro-immigrant. The distribution of the variable can be observed in Figure 2, and upon summarizing in STATA, the mean (average immigration sentiment) is at 15.3 with a median of 16. This indicates an almost normal distribution, with most data concentrated around a medium level of immigration sentiment.
The second dependent variable measured is the level of trust in politicians. I use the variable trstplt from the data set, which ranks how much the respondent personally trusts politicians from from 0 to 10 (0=No trust, 10=Complete Trust). I created a new variable named “Level of Trust in Politicians” which organizes the values of trstplt into three categories: 1. Low level of trust (0-3), 2. Medium level of trust (4-6) and 3. High level of trust in politicians (7-10). Figure 3 shows the variable in categorical form; the majority of values are grouped in the categories of low and medium trust level, and a description of the variable in STATA reveals the mean is 1.67, indicating that the average of values lies between the first and second categories (low and medium trust level).
I am also controlling for multiple variables to ensure that any effect observed on the dependent variables is as a result of a change in the independent variable, rather than an external unmeasured variable. I am controlling for age, education level, ethnicity, and employment within the last 5 years. All of these variables could influence both immigration sentiment and trust in politicians, so by controlling for them I hope to isolate the effect to the independent variable being studied. Other control variables that could strengthen the model include rural/urban upbringing, etc., however the dataset does not include these variables in its survey.
Happiness and Immigration: OLS Regression
To test the relationship between happiness and immigration sentiment, I will use the Ordinary Least Square method of regression, due to the dependent variable being continuous. The OLS method generates an estimation of the parameters β0 and β1, which represent the y-intercept and the slope of regression respectively.
The OLS method also seeks to minimize the sum of squared errors in the data. The regression equation for this variable will be as followed: Immigration
Sentiment (Y) = β0 + β1 (Level of Happiness) + β2 (Employment for past 5 years) +
β3 (Age) + β4 (Education Level) + β5 (Ethnicity) + error.
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