Table of Contents
The interest in life satisfaction or happiness, which measured as subjective well-being in general, has been a prolonged topic and has increased rapidly over the last twenty decades in economics (Frey & Stuzer, 2002; Dolan, et al., 2008). In the early nineteen seventies, the development of the economics of happiness literature started with the pioneering work of such researcher as Richard Easterlin. Daniel Kahneman stated that economists traditionally thought the individual utility can not be measured and ‘hence proxied by income can be measured’ (Brereton, et al., 2008). Using personal income at an individual level and national income – gross national product (GNP) and gross domestic product (GDP) – at the macro level (Brereton, et al., 2011).
By employing happiness data from surveys is the method to be the empirical approximations of individual utility (Brereton, et al., 2008). Economists have began to use the subjective well-being (SWB) scores from self-reported happiness and life satisfaction data as a direct proxy for quality of life (Moro, et al., 2008). In terms of subject matter (questions on life satisfaction and happiness are frequently employed) and range of scale (three-points to ten-point scales have been employed in the literature), different surveys have different specific question which asked various throughout the literature (Brereton, et al., 2008). For example, the literature by Brereton, et al. (2008) has examined the role of socio-economic and socio-demographoc variables on individual well-being.
The field includes the characteristics of the individuals and their socio-demographic characteristics, which will influence their happiness, such as their age, gender, health, martial status (Brereton, et al., 2008). At the mirco-economic characteristics, such as income, household tenure and employment status, with employment having a profound negative influence on well-being (Brereton, et al., 2008). Similar for macro-economic characteristics, the impact focusing on ‘the national inflation and unemployment rates and also the type of governance present in the person’s area’ (Clark & Oswald, 1994; Frey & Stuzer, 2002; Brereton, et al., 2008).
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Economic and Unemployment rate in Ireland
Ireland’s financial crisis has severely affected its economy, exacerbating the domestic economic problems associated with the collapse of Irish property bubble (Bergin, et al., 2011). After 24 consecutive years of annual growth from 1984 to 2007, Ireland first experienced a short-term technical recession from the season two to the season three of 2007, followed by a recession from the season two to the season three of 2009 (Index Mundi, 2017).
After a year of stagnant economic activity in 2010, Irish real GDP grew by 2.2% in 2011 and 0.2% in 2012, mainly due to improvements in the export sector, while private consumption remained subdued (Index Mundi, 2017). Irish economy grew 4.8 percent in 2014 (Index Mundi, 2017). In 2018, the Irish economy has been very strong again.
When it comes to the unemployment, Irish seasonally adjusted unemployment rate decreased from 5.6% last month to 5.4% in March 2019, which was the lowest unemployment rate since February 2018 (CSO, 2019). As the number of unemployed persons declined by 3400 to 131300 (CSO, 2019). From 1983 to 2019, the average unemployment rate in Ireland was 10.79%, reaching an all-time high of 17.30% in December 1985 and an all-time low of 3.90% in November 2000 (CSO, 2019).
The seasonally adjusted number of males unemployed in March 2019 was 70500, down from 72200 in February 2019 (CSO, 2019). In March 2019 the seasonally adjusted number of females unemployed was 60800, a decrease of 1700 when compared to February 2019 (CSO, 2019). The ESS data I used was in 2016. In December 2016 the seasonally adjusted umployment rate was 7.4%, down from 9% in January 2016 (CSO, 2019).
Unemployment and Unhappiness
Clark and Oswald (1994) revealed that ‘there is a relationship between the rate of joblessness in a region and the average loss of well-being from being unemployed’. They used the mental well-being scores from a form named as the General Health Questionaire to evaluate that unemployed people were relatively happy or unhappy in the 1990s (Clark & Oswald, 1994). This survey has twelve questions related to the mental condition of people and participant’s answers to these questions are coded on a four-point scale running from ‘disagree strongly’ to ‘agree strongly’ (Clark & Oswald, 1994). ‘The data provide a mental stress or less accurately, ‘unhappiness’ level for each individual in the sample’ (Clark & Oswald, 1994).
As many literature showed that unemployment not only means the fall in income but also has the negative impacts on the mental status of the individual (Darity & Arthur, 1996; Brereton, et al., 2009; Clark & Oswald, 1994). At the same time, being employed, self-employed, retired, or in full-time education is associated with well-being (Di Tella & Oswald, 2001; Blanchflower & Oswald, 2004a).
The relationship between unemployment and well-being can be concluded as the functionalistic approach in which five psychological functions – ‘time structure, social contacts, participation in collective purposes, status and identity and regular activity’ – will be provided in the employment situation (Jahoda, 1982; Warr, 1987). As a result of not controlling of the life situation, unemployment will have the negative effect for individual (Brereton, et al., 2009). Di Tella and Oswald (2001) and Clark (2003) drew a conclusion that higher income is correlated with other variables which reduce well-being, such as hours of work.
At the regional level, high unemployment rates also have the negative effects that affect individual in the society, such as ‘crime, public finances and the increase in income inequality’ (Torres, 2016). The fear of crime will decrease the happiness of individual. The factors related to people’s individual workplaces, such as changes in working hours, salaries and probablity of job loss will be affected by high unemployment rates (Torres, 2016). Hence, the employed have the similar mental stress feeling the rish that they might lose their job if unemployment grows up (Torres, 2016). General negative externalities and decreased economic security appear when unemployment grows up, so that worker’s well-being reduces (Luechinger, et al., 2010).
Clark and Oswald (1994) mentioned in their survey that the umployment rate in Britain was much higher among young people than among the elders and the distress from unemployed was at the greatest for those who are highly educated. The reason might be that the young people have lower levels of stress than have the lower level of stress than the old or the young people think it is common without a job and they accept it more easily (Clark & Oswald, 1994). Furthermore, unemployment rates affect suicide rate (Torres, 2016).
Data Analysis
To evaluate the impact of different variables, The ESS survey data in Ireland can be used to analyse the comparison and correlation. The variables included in the analysis can be divided into two groups: variables of interest and control variables (Torres, 2016).
Variables of Interest
The indicator of well-being is the variable life satisfaction, which is based on the answers to the following question (which was preceded by a range of questions regarding various aspects of the respondent’s life) (Brereton, et al., 2009). This variables “All things considered, how satisfied are you with your life as a whole nowadays? Please answer using this card, where 0 means extremely dissatisfied and 10 means extremely satisfied” (European Social Survey, 2016).
Self-reported life-satisfaction data and happiness data interchangeably are treated some studies (Brereton, et al., 2009). The happiness question is the similar format. “Taking all things together, how happy would you say you are? Please use this card” (European Social Survey, 2016).
According to the overall distribution of this variable in the ESS sample, around 74% of the respondents say that his/her life satisfaction is greater or equal than 7. The happiness distribution shows that over 75% of the respondents feel they are happier or equal than 7. Figure 2 shows that most of the people in this sample are satisfied with life and feel happy during their daily life.
Main Activity for last 7 days: this variable refers to the question “which of these descriptions applies to what you have been doing for the last 7 days?” and the answers mainly have “paid work”, “education”, “unemployed”, “sick or disabled”, “retired”, “community or military service”, “ housework” or other.
Respondents in paid work occupied the largest proportion in main activity for last 7 days. Over 40% of Employed feel more satisfied than 7 and the highest point of people feel satisfied with 9 are nearly 60%. Most of the respondent of retired feel satisfied in the middle level, equally satisfied with their life. On the other hand, the respondent of unemployed and looking for a job are more than the respondent of unemployed without looking for a job. Both of unemployed feel dissatisfied with their life and it shows that unemployment has a negative relationship with life satisfaction.
Control Variables
Gender: this variable refers to whether the respondent is “male” or the respondent is “female”. In this sample there are 1352 males and 1404 females (European Social Survey, 2016).
Age of respondent calculated: this variable comes from the year of birth and the answer is the born year of respondents so that the interviewer can calculate the age and adds this number (European Social Survey, 2016).
Highest level of education: this variable refers to the different educational level listed in the question. The question is “What is the highest level of education you have successfully completed?” and the answers have 10 levels which varied from primary school to the PH.D degree (European Social Survey, 2016).
Legal martial status: this variable asked the question which are “This question is about your legal martial status not about who you may or may not be living with. Which one of the descriptions on this card describes your legal marital status now?” There are six possible answers, “married”, “registered”, “separated”, “divorced”, “widowed”, “never married” (European Social Survey, 2016).
Number of people living in the household: this question is “Including yourself, how many people – including children – live here regularly as members of this household?” The respondents could answer any number (European Social Survey, 2016).
Children living at home: this variable refers to whether the respondent lives with children or not (European Social Survey, 2016).
I set up a research hypothesis that the relationship between these control variables and life satisfaction is negative. I will test the null hypothesis that there is no relationship between these variables and life satisfaction.
Age and Children living in home are the variables which have the negative relationship with life satisfaction. Legal marital status, Highest education level and Number of people in household are the variables which have the positive relationship with life satisfaction. SPSS calculates the first correlation coefficient to be -.033, this shows that there is a moderate relationship between satisfaction with age, which is opposite to correlation coefficient of the year of birth (.030).
The correlation coefficient for the marital status and education level are to be .098 and .094 separately, this shows that there is a moderate to strong relationship between satisfaction and them. Gender’s correlation coefficient only to be .004 and it is too weak to prove the correlation. The chi-square of 0 suggests no relationship exists between the gender and life satisfaction.
The model 3 analyse the relationship between past unemployment experience. There are three variables defined from the time of unemployment, “Ever unemployed and seeking work for a period more than three months”, “Any period of unemployed and work seeking lasted 12 months or more” and “Any period of unemployed and work seeking within last 5 years” (European Social Survey, 2016).
According to the Figure 4, past unemployment affects negatively the life satisfaction of the employed and the unemployed. People feel happier when they did not experience any unemployment than the unemployed. The unemployed that have been unemployed within last 12 months and the unemployed that have been unemployed within last 5 years are better-off than the unemployed that have experienced the periods of unemployment more than three months.
However, the impact of past unemployment that lasted 12 months or more on current life satisfaction and the impact of past unemployment that lasted 5 years are similar, but both of which are higher for those who have not been unemployed 12 months or more.
Conclusion
There has been an important decrease of the unemployment rate in Ireland and it shows the down trend in the future. It is interesting to research the relationship between unemployment with the life satisfaction. As many literatures previously talked about the employment and unemployment between the life satisfaction throughout the years and all agree on the same point that the unemployed are less happy than the employed (Brereton, et al., 2009; Clark & Oswald, 1994; Darity & Arthur, 1996).
The past unemployment experience has a negative effect on the well-being of unemployed and on the well-being of the unemployed, but the impact is smaller for the ones who experience many periods of unemployment. There are several variables that have impact on life satisfaction and happiness. The age variable and the children living in home variable have a negative effect on the life satisfaction. The marital status, education level, number of household variables have a positive effect on the life satisfaction.
