The Relationship Between Income, Education And Financial Satisfaction In The UK

Education and Financial Satisfaction

All over the globe, it is important for every individual belonging to different economic, social and financial environments to be more responsible so that the financial satisfaction and protection is secure for the future. The economic situation is difficult in most of the countries and thus the job market is extremely instable (Graafland and Lous 2017). Thus, it is important for the families to have a planning for their immediate future as well as the long-term future since all the life events have become unexpected.

Nowadays, the life expectancy and the quality of life for the individuals have become higher. This has resulted in higher expenses in healthcare for them as well as for their families. With the advancement in time, the expenses for the education have also increased. Thus, planning of finances is important by the parents for the education of their children. Hence, it can be said that as the expectancy in life increases, there is development in the quality of life (Headey and Muffels 2016). As the economy of the countries develop, the responsibilities of the individuals also increases and this leads to financial satisfaction over the years. This also indicates that as the scale of income increases, the financial satisfaction of the individuals increases as they can make smoother future planning (Ludeke and Larsen 2017).

The aim of this research is thus to find out the effect income and education has on the financial satisfaction of the people in the United Kingdom.

In this report, the conceptual framework of the study will be discussed to assess the relationship the independent variables Income and Education on the dependent variable Financial Satisfaction. Following the conceptual framework, the research methodology will be discussed followed by the discussion of the results of data analysis.

Researches have shown that the financial security of a person increases with the increase in the highest level of education attained by a person (Xiao and Porto 2017). Better skills and experiences are earned by an individual with the help of education and this leads to getting better paid jobs (Xiao and O’Neill 2016). The future planning of a family with higher income must be smooth and thus, there will be financial satisfaction in the family. A higher college degree will help a person to get the best jobs that is available in the market in the current economic condition (Luo, Stiffler and Will 2017). Thus, a person with higher educational qualification or with a professional degree is expected to have a decent living standard and also a successful life. Though recently, it has been observed that the number of people attending college is too many and thus, it is a matter of concern that whether all the college graduates are getting jobs based on the condition of the economy as well as of the market (Solis and Durband 2015). This can have a negative effect on the financial satisfaction of a person. If a person is unable to have a job which is suitable to this or her level, the financial satisfaction of that person will be less.

Income and Financial Satisfaction

One of the most researched topics is financial satisfaction of the people. There has been observation of various concepts and results from various studies based on the relationship between income of the people and their financial satisfaction. It has been found out by Mulligan (2013) that there is a positive relationship between income and financial satisfaction. The effects have found to differ in the multivariate context moreover. The effect of income and financial satisfaction has been found to be positive among males but not so strong with females. Further, this difference in financial satisfaction has also been observed across religion. The effect has been found to be strong in lesser religious people and weak in highly religious people. According to Powdthavee and Wooden (2015), when there is not satisfactory income for a family, it is a misery for them. Thus, there should be a positive relationship between income and Financial Satisfaction. 

Quantitative analysis techniques have been used to carry out this research. Based on the results obtained from the hypotheses testing, a deductive analysis has also been performed. The data used for this analysis has been collected from World Values Survey (WVS) Wave 5.

For the purpose of this research data has been collected from the WVS sources, who conducted the survey by designing a questionnaire which was distributed to the citizens of the United Kingdom selected randomly. WVS conducted the survey in the year 2005 (Worldvaluessurvey.org, 2005). There is a disadvantage to this data collection method. The survey has been conducted generally and not with respect to any particular study. Thus, questions may arise about the validity of the data.

Independent variables are the variables which are used to predict other variables and themselves cannot be affected by other variables (Gupta 2017). In this study, the independent variables that has been considered are Income and Education of the respondents.

Dependent variables are the variables, measurement of which are performed on the basis of the independent variables. Thus, if there are changes in the independent variables, it is expected that there should be changes in the dependent variable as well (Holcomb 2016). In this study, the dependent variable that has been considered is Financial satisfaction.

The data that has been collected from the data sources of WVS has been analyzed with the help of the statistical software SPSS. The analyses are shown and discussed further in this report.

Before starting the analysis of the data, it is necessary to find out whether the data is suitable for the analysis. For that, the data has to satisfy certain assumptions. These are Normality, linearity, multicollinearity and homoscedasticity (Ott and Longnecker 2015). The residuals of the dependent variable must be normal and homoscedastic and the independent variables must be free from multicollinearity. There should be a linearity between the variables. Satisfying all these assumptions, further analysis will be performed on the data.

From the normal Q-Q plot obtained as a result of the normality test of the variables, it can be seen that the residuals of the dependent variable financial satisfaction follow a linear trend. This indicates that the residuals follow the assumption of normality.

Methodology

The presence of inter-relations between the independent variables are determined with the help of multicollinearity. The VIF indicates the presence of multicollinearity between the independent variables. A VIF value higher than 5 indicates the presence of multicollinearity. In this case, the VIF value is less than 5 as can be seen from table 1 (Fahrmeir 2013). Thus, the problem of multicollinearity does not exist. Hence, the independent variables are not inter-related and will be of help to the analysis.

If the relationship between the variables have found to be linear, then the linearity assumption will be satisfied. Further, if the residuals of the dependent variable are found to be scattered in the de-trended normal Q-Q plot, then the homoscedasticity assumption of the variable is satisfied (Hinton 2014). It can be seen from figure 3 that the residuals in the de-trended normal Q-Q plot are quite scattered and hence, the homoscedasticity assumption is satisfied. 

The number or percentage of occurrences of an event in a variable is established with the help of the descriptive statistics measures. Further the shape of the data can also be understood with the help of these measures (Anderson et al. 2013).

With the help of the frequency distribution table, the number of responses and their percentages for each category of a variable are illustrated (Sullivan 2013).

It can be seen from the frequency table as well as from the pie chart that most of the percentage of the respondents do not have financial satisfaction. Very little percentage of people have shown satisfaction with their finances. 

It can be seen from table 3 and figure 5 that most of the respondents (45.07%) have completed technical or vocational type secondary school. 18.44 percent of the people have completed their university level education with a degree. 

It can be seen from figure 6 and table 4 that maximum number of respondents selecyted belong to the lower income group. It can be said from here hypothetically that the respondents do not have a very high degree of education and thus their income group is also low.  

The average of the values of a variable in a dataset is known as the mean of the variable, median indicates the value above which 50% of the values of the variable lie. Standard deviation is the average of the deviation of all the values from the mean value (Holcomb 2016).

The standard deviation of the values has been found to be less and thus, it can be said that the values are mostly close to the average value. The presence of outliers in the variables can be identified when there are huge differences in the mean and the median values. That is not the case for any of the variables here except for highest education level. Thus, it can be said there can be some outliers present for this variable.

The association between two variables are denoted with the help of correlational analysis. If the degree of association is found to be positive, it will indicate that there is positive correlation between the variables and a negative sign will indicate a negative correlation between the variables. A positive correlation indicates increase in one variable when the other variable increases and a negative correlation indicates decrease in one variable when the other variable increases (Chatterjee and Simonoff 2013).

World Value Survey

From the table of correlations. It can be seen that Financial satisfaction has a negative correlation with education level (- 0.084) and a positive correlation with income (0.204). Moreover, it can be seen that the correlation between education and financial satisfaction is very weak. Further, it can be seen that the significance values are less than 0.05. Thus, it can be said that the correlations are significant.

The nature of fit of the model is tested with the help of regression. The beta value and the R² value indicates the type of fit in the model. It can be seen that the significance is less than 0.05, which indicates that the model is significant (Chatterjee and Simonoff 2013). The R-Square value indicates that 4.3 percent of the variability in the financial satisfaction can be explained by income and education. The B value for education is negative which contradicts the hypothesis 1 and The B value for income is positive which supports the hypothesis 2.

Discussion and Conclusion

The data from the WVS survey has been used for the purpose of the analysis and to meet the research aim of this report. The aim of this research was to establish the relationship between education and income on financial satisfaction. The statistical assumptions of normality, homoscedasticity, multicollinearity and linearity has been satisfied by the data and thus further correlation and regression analysis has been performed. The fitted model has been found to be significant and thus is considered to be a good fit. Income has been found to be impacting financial satisfaction positively and education has been found to be negatively impacting the financial satisfaction of the people in UK. 

References

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Chatterjee, S. and Simonoff, J. (2013). Handbook of Regression Analysis. Chicester: Wiley.

Fahrmeir, L. (2013). Regression. Berlin: Springer.

Graafland, J. and Lous, B. (2017). Economic Freedom, Income Inequality and Life Satisfaction in OECD Countries. Journal of Happiness Studies.

Gupta, S.C., 2017. Fundamentals of statistics. Himalaya Publishing House.

Headey, B. and Muffels, R. (2016). Towards a Theory of Medium Term Life Satisfaction: Similar Results for Australia, Britain and Germany. Social Indicators Research, 134(1), pp.359-384.

Hinton, P.R., 2014. Statistics explained. Routledge.

Holcomb, Z.C., 2016. Fundamentals of descriptive statistics. Routledge.

Ludeke, S. and Larsen, E. (2017). Problems with the Big Five assessment in the World Values Survey. Personality and Individual Differences, 112, pp.103-105.

Luo, M.N., Stiffler, D. and Will, J., 2017. The Long-Term Outcomes of Graduates’ Satisfaction: Do Public and Private College Education Make a Difference?. Journal of Global Education and Research, 1(1), p.2.

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Ott, R.L. and Longnecker, M.T., 2015. An introduction to statistical methods and data analysis. Nelson Education.

Powdthavee, N. and Wooden, M. (2015). Life satisfaction and sexual minorities: Evidence from Australia and the United Kingdom. Journal of Economic Behavior & Organization, 116, pp.107-126.

Solis, O. and Durband, D.B., 2015. Financial support and its impact on undergraduate student financial satisfaction. College Student Journal, 49(1), pp.93-105.

Sullivan, M. (2013). Fundamentals of statistics. Upper Saddle River, N.J.: Pearson.

Worldvaluessurvey.org. (2018). WVS Database. [online] Available at: https://www.worldvaluessurvey.org/WVSContents.jsp [Accessed 03 Aug. 2018].

Xiao, J.J. and O’Neill, B., 2016. Consumer financial education and financial capability. International Journal of Consumer Studies, 40(6), pp.712-721.

Xiao, J.J. and Porto, N., 2017. Financial education and financial satisfaction: Financial literacy, behavior, and capability as mediators. International Journal of Bank Marketing, 35(5), pp.805-817.

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