Relationship Between Age, Education and Income - PDF

Running Head: RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
Relationship between Age, Education and Income
Name of the Student
Name of the University
Author Note

1RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
Table of Contents
1.0 Introduction..........................................................................................................................2
2.0 Conceptual Framework........................................................................................................3
3.0 Methodology........................................................................................................................4
4.0 Data Analysis and Results....................................................................................................6
4.1 Description.......................................................................................................................6
4.2 Assumptions...................................................................................................................11
4.3 Correlation......................................................................................................................11
4.4 Regression......................................................................................................................12
5.0 Discussion and Conclusion................................................................................................13
References................................................................................................................................15
Appendix..................................................................................................................................17

2RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
1.0 Introduction
A central role is played by education in the labour market of the modern day in UK. A
lot of studies have been made over the years on this topic on UK and it has been established
that individuals with a better background of education are earning higher wages and lesser
amount of unemployed individuals are found among the highly qualified individuals (Stone et
al. 2018). The work performed by the individuals with a higher educational background are
most often more prestigious than the jobs performed by the individuals with lesser
educational background. Though, due to lack of evidence, it has never been possible to study
whether the people with higher educational background earn more or the people who earned
more could only opt for higher education (Livingstone 2018). According to various studies,
the difference between the education groups have shown significant difference in the income
status of the individuals. Thus, the income groups had a very high correlation with education.
On the other hand, inequality has been observed in terms of income as people grow
old. This inequality in come is lower in the households with younger earning members and
higher in households with older earning members (Van Deursen, van Dijk and Peter 2017).
One important question that comes into mind for this observation is that whether the
difference in the ability to work of luck is the factor behind this inequality. Another factor
that can be responsible is that there are a lot of opportunities for a young individual than an
older individual (Willis 2017). If a younger individual is offered less salary to work, he might
look for other options which is not available for an older individual. Thus rises the inequality.
Thus, according to the discussed literature, it can be seen that there is a positive
relationship between the level of education and income and a negative relationship between
age of an individual and income (Losonc 2016). As the education level increases, the level of
income also increases and the younger people are earning more than the older people. The

3RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
main aim of this research is to establish the relationship between the age of an individual and
the level of education of an individual on the income of the individual.
Aim of the Research: The relationship of age and education with the level of income.
The first section represents the introduction and the background of the topic followed
by the conceptual framework, research methodology, data analysis, discussions and
conclusions in the following sections.
2.0 Conceptual Framework
It is often a very common verse that “Education is the key to success”. The mind of an
individual is broadened with the help of education. Education also helps an individual to face
new challenges and accept the failures. Confidence building is also one of the key helps of
education (Kawachi and Subramanian 2014). Education also opens the door to newer and
better job opportunities to individuals. Though there are some exceptions to this theory. For
example, Steve Jobs, the co-founder of Apple Inc., did not have any college graduation
degree. Again, Bill Gates, one of the richest persons in the world have no university degree
and yet they are earning millions. These little exceptions do not prove the theory wrong
(Pickett and Wilkinson 2015).

4RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
On the other hand, the cost of education especially in colleges have grown a lot and
up to 500% since 1985 (Fall et al. 2015). The tuition fees in the private schools have also
shown considerable increase. This has given rise to the debts in education. Thus, considering
the current status of the world, a college degree is not enough to secure a position for a good
job.
In this regard, the following research hypothesis can be framed:
Hypothesis 1: Education has a positive relationship with Income.
As the age of a person increases, so does his experience of life and work. This is
supposed to increase the demand of an experienced candidate in the job market.
Unfortunately, this is not the case in the real world. As the age of an individual increases, his
opportunities to shift to a new job decreases as no company would be interested to hire an
older employee who is almost at the age of retirement by offering him or her a severance
package (Steptoe, Deaton and Stone 2015). It would rather be more economical for a
company to hire a younger employee and offer him the desired package. Thus, the scope for
hike or increasing income decreases as people grow old. Also, the capability to work under
pressure is more in younger individuals than older individuals (Bradshaw 2016). This also
one of the reasons why younger people are preferred over older people. Thus, rises the
income inequality (Van Deursen, van Dijk and Peter 2015). Hence, in the context of the
above discussion, the following hypothesis can be framed:
Hypothesis 2: Age has a negative relationship with Income.
3.0 Methodology
The research conducted here is a quantitative research. The data that has been
considered here has been collected from the world value survey wave 5. The approach of this
research will be deductive and exploratory method. The necessary relationships between age

5RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
and education with income will be established with the help of these deductive and
exploratory approaches. These approaches are further discussed in the results section.
The data on age, education level and income of the individuals have been collected
from a secondary source. Thus, there was no need to develop a questionnaire and distribute it
to collect data on these attributes. The data has been collected on 1041 individuals of the
United Kingdom. The downloaded dataset contained information of a lot of individuals
residing in the United Kingdom. Among all the individuals, 1041 individuals have been
selected randomly for the purpose of the study.
From the data, it can be clearly understood that each of the individuals were asked
about their age and the highest degree of education attained by them. For the variable income,
the respondents were not asked directly the amount of income. Instead, they were asked to
mark the appropriate range within which their income lies.
The main aim of this research is to establish a relationship between age, education
level and income of the respondents. Moreover, the effect of age and level of education on
the income of the respondents has to be established. Thus, age of the respondents and the
highest level of education attained by the respondents have been considered as the predictor
variables or independent variables and the income of the respondents have been considered as
the response variable or the dependent variable.
This research is performed using secondary data. There are some strengths and
weaknesses of using secondary data in a research. There are a lot of secondary data available
freely. Thus, data can be selected according to the purpose of the research accordingly. The
past data can be researched with the help of the official statistics. There are certain limitations
as well to the data. The data collected might not be authentic and some parts of the data might
be missing. No information about the collector of the data can also be obtained and thus

6RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
biasness about the data cannot be verified. Thus, the data might not be able to represent the
population completely.
4.0 Data Analysis and Results
The data will be analysed using appropriate statistical techniques to establish the
relationship between age and education on income of people. A regression analysis will be
the best method to establish this relationship. Thus, to do the tests, it is assumed that the data
is normally distributed.
4.1 Description
The data considered for the analysis will follow a normal distribution if the data is
symmetric and mesokurtic. From the results of the skewness and kurtosis (Given in table 1,
appendix), it can be observed that both the skewness and kurtosis values are negative for the
highest education level. This indicates that the data is negatively skewed and platykurtic.
From the results of the skewness and kurtosis (Given in table 1, appendix), it can be
observed that the skewness is positive and kurtosis value is negative for age of the
respondents. This indicates that the data is positively skewed and platykurtic.
From the results of the skewness and kurtosis (Given in table 1, appendix), it can be
observed that the skewness is positive and kurtosis value is negative for income scale of the
respondents. This indicates that the data is positively skewed and platykurtic.
It can also be seen that the VIF values for each of the independent variables are less
than 5. This indicates that there is no existence of multicollinearity in the dataset. It can also
be seen from figure 4.1 that the residuals are almost linear. Thus, it can be said that the data is
homoscedastic.
Table 4.1: Coefficientsa

7RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
Model Sig.
Collinearity Statistics
Tolerance VIF
1 (Constant) .000
Highest educational level attained V238 .000 .970 1.031
Age V237 .003 .970 1.031
Figure 4.1: Normal Q-Q Plot testing Homoscedasticity
To analyse the data according to the research question, at first descriptive analysis has
to be performed on the data. It can be seen that data has been obtained on 1041 individuals.
Among the 1041 individuals, 1025 individuals shared information about their highest level of

8RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
education attained and the remaining 16 values were found to be missing. On the other hand,
age and the scale of income has been obtained for each of the 1041 individuals.
From the table of descriptive statistics given in table 4.2, it can be seen that average
level of education has been obtained as 6.05. This indicates that the average level of highest
education obtained among the selected respondents are incomplete secondary school. It can
also be seen from table 4.3 that 462 people out of the 1025 responses have completed their
vocational type secondary school which is 45.1 percent of the selected respondents.
The average age of the respondents as obtained from the data is 45.69 years with a
standard deviation of 18.54 years which is quite high. Thus, the ages of the respondents are
scattered and are not close to the average age of the respondents. 25 percent of the
respondents are below 30.5 years old and 25 percent of the respondents are above 60 years
old.
Table 4.2: Descriptive Statistics
Highest educational level
attained V238 Age V237 Scale of incomes V253
N Valid 1025 1041 1041
Missing 16 0 0
Mean 6.05 45.69 2.45
Median 5.00 43.00 2.00
Std. Deviation 1.977 18.543 1.156
Percentiles 25 5.00 30.50 2.00
50 5.00 43.00 2.00
75 8.00 60.00 3.00
Table 4.3: Highest educational level attained V238

9RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
Frequency Percent Valid Percent
Cumulative
Percent
Valid No formal education 31 3.0 3.0 3.0
Incomplete primary school 3 .3 .3 3.3
Complete primary school 24 2.3 2.3 5.7
Incomplete secondary school:
technical/ vocational type
69 6.6 6.7 12.4
Complete secondary school:
technical/ vocational type
462 44.4 45.1 57.5
Incomplete secondary school:
university-preparatory type
22 2.1 2.1 59.6
Complete secondary school:
university-preparatory type
124 11.9 12.1 71.7
Some university-level
education, without degree
101 9.7 9.9 81.6
University - level education,
with degree
189 18.2 18.4 100.0
Total 1025 98.5 100.0
Missing Missing; Not asked by the
interviewer
8 .8
No answer 4 .4
Don´t know 4 .4
Total 16 1.5
Total 1041 100.0

10RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
Figure 4.2: Histogram showing the highest level of education attained by the people
Figure 4.3: Boxplot showing the distribution of age among the individuals

11RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
4.2 Assumptions
To test the hypothesis stated in the section 2 of the report, correlation and regression
analysis has to be conducted. There are certain assumptions to the regression analysis. A
regression analysis assumes that the data considered for the analysis has to be normally
distributed. If the condition of normality is not satisfied, regression analysis cannot be
conducted.
Table 4.4 gives the results of the normality test of the data. From the significance
values obtained as a result of the normality tests, it can be seen that the p-value is less than
the level of significance (0.05). This indicates that the null hypothesis is rejected and the data
does not come from a normal distribution. This is applicable for all the independent and the
dependent variables that are a part of the study.
Table 4.4: Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Highest educational level
attained V238
.277 1025 .000 .871 1025 .000
Age V237 .071 1025 .000 .966 1025 .000
Scale of incomes V253 .268 1025 .000 .874 1025 .000
4.3 Correlation
The correlation shows the relationship between the variables. It can be seen from the
correlation table given in table 4.5 that the Peasrson Correlation value for income and highest
level of education is -0.270, which is negative. This indicates that there is a negative
relationship between education level and income. The higher the highest degree of education,
the lesser will be the income of the individual. Again, the Peasrson Correlation value for

12RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
income and age is 0.131, which is positive. This indicates that there is a positive relationship
between age and income. With the increase in the age of the people, the income of the
individuals also increases. It can also be seen that the values of the correlation are less than
0.3. This indicates that the relationship that has been established between the variables are
weak.
Table 4.5: Correlations
Highest
educational level
attained V238 Age V237
Scale of incomes
V253
Highest educational level
attained V238
Pearson Correlation 1 -.173** -.270**
Sig. (2-tailed) .000 .000
N 1025 1025 1025
Age V237 Pearson Correlation -.173** 1 .131**
Sig. (2-tailed) .000 .000
N 1025 1041 1041
Scale of incomes V253 Pearson Correlation -.270** .131** 1
Sig. (2-tailed) .000 .000
N 1025 1041 1041
4.4 Regression
The regression expresses the extent of the relationship between the independent and
the dependent variable. The extent to which the independent variables can explain the
dependent variable is established with the help of regression. Here, the independent variables
are age and highest level of education and the dependent variable is income. From table 4.6,
it can be seen that the value of R Square is 0.081. This indicates that 8.1 percent of the
variability in the average income of the individuals can be explained by age and education.
From the ANOVA table in table 4.7, it can be seen that the model developed is significant.

13RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
With the increase in the level of education, the income decreases by 0.150 and with the
increase in age, the income increases by 0.008.
Table 4.6: Regression Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change
1 .284a .081 .079 1.114 .081 44.996
Table 4.7: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 111.693 2 55.847 44.996 .000b
Residual 1268.447 1022 1.241
Total 1380.140 1024
Table 4.8: Regression Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
tB Std. Error Beta
1 (Constant) 3.102 .154 20.089
Highest educational level
attained V238
-.150 .018 -.255 -8.369
Age V237 .006 .002 .090 2.949
5.0 Discussion and Conclusion
It can be seen from the analysis conducted so far that the relationship between highest
degree of education and income is negative. Thus, from here it can be said that that
hypothesis 1 is rejected. There has not been enough evidence to support the hypothesis from

14RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
this dataset. This result also contradicts the literature that stated that more qualified people
earn more as their work is more prestigious than the ones who are less qualified. It can also
be seen from the analysis that the relationship between age and income is positive. Thus,
from here it can be said that that hypothesis 2 is rejected. There has not been enough evidence
to support the second hypothesis from this dataset. This result also contradicts the literature
that stated that due to the decrease in the capability to work harder the demand for the aged
employees decreases gradually and thus their income decreases.
Thus, it can be said the analysis conducted that age has a positive relationship with
income and education has negative relationship with income. All the results so obtained from
the analysis has contradicted with the literature stated earlier. This might be due to the reason
that the normality assumptions have not been satisfied by the data. Thus, the results obtained
from the regression and correlation analysis is not appropriate and violating all the past
studies.

15RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
References
Bradshaw, J. ed., 2016. The Well-being of Children in the UK. Policy Press.
Fall, C.H., Sachdev, H.S., Osmond, C., Restrepo-Mendez, M.C., Victora, C., Martorell, R.,
Stein, A.D., Sinha, S., Tandon, N., Adair, L. and Bas, I., 2015. Association between maternal
age at childbirth and child and adult outcomes in the offspring: a prospective study in five
low-income and middle-income countries (COHORTS collaboration). The Lancet Global
Health, 3(7), pp.e366-e377.
Kawachi, I. and Subramanian, S.V., 2014. Income inequality. Social epidemiology, 126.
Livingstone, D.W., 2018. The Education-Jobs Gap: Underemployment or Economic
Democracy?. Routledge.
Losonc, A., 2016. Global inequality: A new approach for the age of
globalization. Panoeconomicus, 63(4), p.493.
Pickett, K.E. and Wilkinson, R.G., 2015. Income inequality and health: a causal
review. Social science & medicine, 128, pp.316-326.
Steptoe, A., Deaton, A. and Stone, A.A., 2015. Subjective wellbeing, health, and ageing. The
Lancet, 385(9968), pp.640-648.
Stone, A.A., Schneider, S., Krueger, A., Schwartz, J.E. and Deaton, A., 2018. Experiential
wellbeing data from the American Time Use Survey: Comparisons with other methods and
analytic illustrations with age and income. Social Indicators Research, 136(1), pp.359-378.
Van Deursen, A.J., van Dijk, J.A. and Peter, M., 2015. Increasing inequalities in what we do
online: A longitudinal cross sectional analysis of Internet activities among the Dutch
population (2010 to 2013) over gender, age, education, and income. Telematics and
informatics, 32(2), pp.259-272.

16RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
Willis, P., 2017. Learning to labour: How working class kids get working class jobs.
Routledge.

17RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
Appendix
Descriptives
Statistic Std. Error
Highest educational level
attained V238
Mean 6.05 .062
95% Confidence Interval for
Mean
Lower Bound 5.93
Upper Bound 6.17
5% Trimmed Mean 6.13
Median 5.00
Variance 3.909
Std. Deviation 1.977
Minimum 1
Maximum 9
Range 8
Interquartile Range 3
Skewness -.041 .076
Kurtosis -.393 .153
Age V237 Mean 45.80 .578
95% Confidence Interval for
Mean
Lower Bound 44.67
Upper Bound 46.94
5% Trimmed Mean 45.32

18RELATIONSHIP BETWEEN AGE, EDUCATION AND INCOME
Median 43.00
Variance 342.810
Std. Deviation 18.515
Minimum 15
Maximum 94
Range 79
Interquartile Range 29
Skewness .327 .076
Kurtosis -.862 .153
Scale of incomes V253 Mean 2.45 .036
95% Confidence Interval for
Mean
Lower Bound 2.38
Upper Bound 2.53
5% Trimmed Mean 2.39
Median 2.00
Variance 1.348
Std. Deviation 1.161
Minimum 1
Maximum 5
Range 4
Interquartile Range 1