1. 1
The Relationship between Individual Earnings and
Education
By
Mark Clerkin
Student Number: 14396171
Advanced Economic Analysis: ECON20170
Abstract: This paper intends to observe the relationship between education and
individual earnings. This relationship is examined using data from the 2004 ‘Health and
Retirement Study’ in the United States. This rich collection of data is analysed through
regression models, scatter graphs, bar charts, and pie charts. The findings from these
calculations and methodologies observe that a relationship between the two variables does
exist, although exogenous factors also contribute to the overall result.

2. 2
1: Introduction
In 1758 Benjamin Franklin became a pioneer in the realms of economics and finance when
he authored one of the first and most respected books on financial advice called ‘The Way to
Wealth’. An interesting excerpt from this revolutionary text is “An investment in education
pays the best interest”, drawing attention to the paramount link between education and
individual wealth (Kozuskanich 2015, 4). Although many things have changed in the realms
of economics and society since Franklin wrote this text, one thing which remains is this
pivotal relationship. The quilt and ink have transformed into computers and datasets, with
analysis of labour markets and the various contributing factors that affect them being put
under the scope on a daily basis. One such component is the aforementioned role of
education. With the ongoing dissolving of borders and easier access to higher education since
the 19th Century, there has been a global influx in education participation with people striving
to improve their standard of living (Newman 2014, 355). This global upsurge in human
capital investment is evident when one examines the influx in third level students across the
globe (OECD 2013, 43). Furthermore, this increase in people attaining a higher education can
be credited to government policy when one factors in fiscal spending in education and
government programmes (Newman 2014, 347). If one takes Ireland for example, expenditure
on educational institutions has increased substantially (OECD 2013, 214). However, the
question still exists as to the importance of education and its effect on earnings.
Since the birth of labour economics itself studies have taken place to unearth the truth as to
whether more education will lead to higher earnings. Are higher incomes a result of
education or do individuals with a larger earnings capacity decided to acquire more
education? Economists affirm that increased investment in human capital helps maximise
productivity and subsequently leads to higher wages (Borjas 2008, 244). This argument is
usually supported by the theory that education is a signalling device and has great influence
on one’s productive efficiency and therefore one’s wage (Spence 1973, 356). Contrary to this
are those who believe that the productive efficiency that leads to higher wages can emerge
from factors other than education including family, personal history, and other innate
qualities (Cahuc 2004, 89-90). Despite the rich amount of research undergone in this field,
there still remains an ambiguity as to the correlation between education and earnings. Are
people wealthier due to their educational background? Do certain qualifications lead to
greater earnings? Do external factors including race and gender contribute? If so, is there a
wage gap? This paper intends to shed more light on all of these queries. Although it doesn’t
plan to derive an overall solution to the arguments surrounding these issues, this paper
intends to cast a cold eye on the relationship between education and earnings and unearth
answers to vital questions that have plagued labour economics since its inception. In the quest
to dissect these issues this paper will proceed in the following manner. Section 2 shall contain
a brief synopsis of the relevant literature, models, and economic theories surrounding this
topic. Section 3 shall provide an independent data analysis undergone as part of this study.
The paper will conclude in Section 4 with a short review of its findings and its significance in
this realm of labour economics.

3. 3
2: Literature Review
2.1: Wage-Schooling Locus
One key model relevant to the correlation between education and earnings is the wage
schooling locus. The wage-schooling locus, figure (a) in the below diagram, is an economic
model formulated to give a workers earnings for a specific level of schooling. It constructs a
stopping rule that indicates when it is optimal to leave school, as well as the returns at that
level. Market forces determine the salary associated with each equilibrium point where
supply and demand meet. The slope of the locus, (Δw/ Δs), indicates how much an
individual’s earnings will change if they remain an additional year in education. The wage-
schooling locus possess three key properties including:
1. An upwards slope: The longer the time spent in education means the financial gain of
employment will be larger. Employers have to compensate staff for the foregone
earnings incurred whilst in education.
2. The slope exhibits how an individual’s earnings will increase with an extra year of
schooling: The slope of the wage-schooling locus will be similar to the rate of return
to schooling.
3. The function is concave: The increase in earnings from an extra year of education
begins to experience diminishing marginal returns, with each additional year in
education generating a smaller amount of knowledge and earnings than the one prior.
0 13 14 1812
30,000
20,000
23,000
25,000
Years of
Schooling
DollarsFigure (a)

4. 4
The only costs associated with this model are the foregone earnings from not entering the
labour force. The percentage change in an individual’s earnings from an auxiliary year of
schooling is referred to the marginal rate of return to schooling (Borjas 2008, 251). A
noteworthy aspect of the marginal rate of return to schooling, noted as ‘MRR’, is that it
experiences diminishing marginal returns due to the concavity of the wage-schooling locus
(Borjas 2008, 251). As an individual spends more years in education, the wage increase
associated with it will eventually decrease. The optimal stopping rule, in other words the year
to leave education for the labour force, associated with this model can be obtained where the
marginal rate of returns curve and a constant discount rate labelled ‘r’ intersect. This
intersection is evident in the economic model referred to as the schooling decision, which is
figure (b) in the above diagram. The formula for the schooling decision is outlined in the
following equation:
Level at which to stop schooling: (Marginal rate of return to schooling = discount rate r)
One must factor into account the trade-off associated with leaving education when deciding
to maximise the present value of their earnings. Therefore if the marginal rate of returns to
schooling is greater than the discount rate an individual will not enter the labour force and
continue to consume education, thus driving down the marginal returns until it is equal to the
discount rate (Borjas 2008, 252). Once the marginal returns to schooling and the discount rate
meet in equilibrium, the individual may enter the labour force because they have reached
their stopping rule. Another important component of this model is that the decision to leave is
also vulnerable to external factors including chance encounters, the economic climate, and
uncertainty (Altonji 1993, 49). One important factor omitted in this model is the ability
differential that contaminates the correlation between education and earnings (Borjas 2008,
256). Although the classical model does not integrate the concept of individuals possessing
different wage-schooling loci, many labour economists have attempted to remedy the issue
Figure (b)

5. 5
by adjusting for the omission (Ashenfelter 1999, 90). The higher one’s ability, whether it be
down to family background or the secondary school curriculum, the higher their discount rate
will be (Altonji 1993, 71). Overall the wage-schooling locus can be regarded as a useful aid
in obtaining the amount of schooling that maximises the present value of one’s earnings,
which is of paramount importance in studying the relationship between earnings and
education (Borjas 2008, 253).
2.2: Age-Earnings Profile
An alternative approach in estimating the wage path over an individual’s life cycle is the age-
earnings profile associated with one’s respected option in regards to schooling. A major
component of labour economics and human capital studies, the age-earnings profile delves
into the projected earnings for various different people who choose different schooling paths
(Borjas 2008, 247). In the most common earnings stream example the model examines two
options for a high school graduate to participate in third level education or to enter the labour
force. As outlined in the below diagram, one can easily dissect the difference in one’s
earnings between going to college and going directly into the labour force. If the graduate
decides not to invest in further education, they initially experience greater earnings than if
they were to go to college. However, further down the line their incremental earnings decline,
become stagnant, and are eventually outweighed by the wage if they had consumed further
education. Their earnings when they decide to participate in third level education are initially
negative and lower than if they entered the labour force due to the opportunity cost of
foregone earnings associated with going to college, as well as the direct costs of tuition fees.
However upon entering the labour force after graduating from college with further education
attained, their earnings begin to climb up and overtake the earnings of a high school graduate.
Hamermesh and Rees affirm this by outlining that people who have attained more education
will possess age-earnings profiles with later peaks and steeper curves than that of their lesser
educated counterparts (Hamermesh, Rees and Rees 1984, 84). Through analysing how the
wage path of a college graduate outweighs that of a high school graduate, this economic
model presents a credible theory of how further investment in education may lead to higher
earnings.
EarningsStream:
College Graduate
EarningsStream:
HighSchool
Graduate
Foregone
Earnings
DirectCosts
Gross Benefits
Figure (c)

6. 6
Similar to the wage-schooling locus, this model of basic human capital investment entails
workers acquiring the educational level that maximises the present value of earnings (Borjas
2008, 246). To determine whether one should go to college or directly enter the labour force
can be unearthed through the following basic human capital formula:
∑
𝐸(𝑡) 𝑈𝑛𝑖𝑣 − 𝐸(𝑡) 𝑆𝑒𝑐
(1 + 𝑟) 𝑡
𝑇
𝑡=5
> 𝐶 + ∑
𝐸(𝑡) 𝑆𝑒𝑐
(1 + 𝑟) 𝑡
4
𝑡=1
This formula is significant in examining the economic trade-off involved in one’s schooling
decision (Borjas 2008, 248). The function on the left represents the present value of the
earnings stream if a worker decides to consume third level education, while the function on
the right represents the present value of the earnings associated with directly entering the
labour force after secondary level education. The constant C represents the direct costs of
third level education including books and tuition fees. If the present value of the earnings of
third level education outweigh the opposing function, then the individual should invest in
further educational attainment rather than working. One key variable in this formula is the
discount rate labelled ‘r’. The higher one’s discount rate is, the less likely they will be to
invest in education as their current earnings are of more value than future earnings (Borjas
2008, 249). A benefit of this model is that given one’s discount rate, earnings in time, and
direct costs of third level education; they can derive whether they should invest in further
education, thus avoiding any uncertainty of whether they will benefit from college (Borjas
2008, 249). Despite the simplicity of this model and its mathematics, it is a key component in
analysing the relationship between education and earnings and which wage path is optimal.
2.3: Mincer Earnings Equation
Often referred to as one of the most widely used models in empirical economics, this
earnings function model estimates the rate of return to educational investments. Explaining
earnings as a function of schooling and experience, Jacob Mincer outlined this model through
the following formula:
Representing how the sum of years spent in education and the quadratic function of potential
experience, one can derive the logarithm of earnings. With the variable ‘lny0’ representing
earnings with no education, ‘S’ standing for years spent in schooling, and ‘X’ being the years
of potential labour market experience; this classic model has revolutionised labour economics
and the realm of human capital, being credited as a “good sample of mainstream human
capital theory”(Granovetter 1977, 608). One striking component of this equation if the
inclusion of potential experience in the labour market, which is in place instead of direct
information on experience which is difficult to quantify (Ashenfelter and Layard 1986, 1081).
It represents the amount of years one could have worked, taking into account that one began
school at six years of age and invested ‘S’ years attaining a certain level of education.
Despite various enhancements and additional components being attached to this equation, it
is Jacob Mincer’s original model that is utilized as the primary tool in analysing estimate

7. 7
earnings regression. One key factor of Mincer’s equation which differentiates it from the
work of other labour economists is that the disturbance term represents the individual effects
omitted in other studies (Walker, Harmon and Westergaard-Nielsen 2001, 1-20) (). An
implication of this however would be the effect these factors have on the schooling decision
and present value of the associated earnings, such as ability bias (Borjas 2008, 257).
Although this equation is acclaimed for how it represents the rewards for schooling and
experience, it does possess its flaws. These critiques include that the decline in return to
schooling is concentrated on solely college education, that the rate of return is sensitive to the
assumption made about the length of working life, and that both the schooling coefficient and
the internal rate of return give misleading information about the value of adult education
(Björklund and Kjellström 2002, 195). Despite these critiques, the model is credited as a
great contribution to economic research, holding respect in the field of labour economics with
Mincer laureled as the father of it (Björklund and Kjellström 2002, 209).
2.4: Instrumental Variables & Compulsory Schooling Laws and Month of Birth
Joshua Angrist and Alan Krueger put the concept of returns to education under the scope of
analysis in this paper, examining a sample from data compiled in the US census through an
instrumental variables (IV) estimate. This method of measurement, commonly calculated in
order to determine causal relationships when other measures are infeasible or unable to cover
the aggregate landscape of a sample, in regards to this study results in a payoff greater than
that of an ordinary-least squares (OLS) estimation (Angrist and Krueger 1991, 981). The
paper establishes that particular quarters of birth are linked to various levels of educational
attainment due to two contributing variables; policy on the starting age for schooling and the
compulsory school attendance laws (Angrist and Krueger 1991, 979). Government policy
such as compulsory schooling laws are implemented in order to allow the juxtaposition
among workers to be equal. Upon implementation, these laws require individuals to continue
education until a particular age, which is 16 in this case study. This paper analyses the
relationship between the aforementioned legislation and educational attainment by comparing
people born in different seasons of the year. Children born in the first quarter of the year enter
education a year later than children born in the final quadrant of the year due to missing the
deadline with school starting age policy (Borjas 2008, 259). With this and a compulsory
schooling age of 16 taken into account, Angrist and Krueger affirm that the child born in the
earlier quadrant will attain a shorter time in school due to reaching the legal dropout age
earlier than their younger counterparts born in later quadrants (Angrist and Krueger 1991,
980). The paper juxtaposes two individuals born at different sides of the calendar who begin
education in 1960. The student born in January starts schooling at 6.5 years old, whereas the
pupil born in December enter education at 6.1 years old.
One interesting theory dissected in this paper is that children from lower socio-economic
background possess greater returns to schooling from an additional year of education (Kling,
2001). Angrist and Krueger make the conclusive note that individuals who spend longer time
in education under compulsory student laws earn higher earnings, and that such legislation is
effective as an incentive for greater attendance (Angrist and Krueger 1991, 1010). Not only
can this variation be observed as an estimate of the returns to schooling, it can be used as a

8. 8
methodology to nudge individuals along a shared wage-schooling locus (Borjas 2008, 259).
Assuming there is no ability difference between the two individuals studied, the only reason
for a variation in wages will be more education attained, which will therefore estimate the
real returns to schooling. With ability bias accounted for Angrist and Krueger calculate a rate
of return to schooling through instrumental variables of 7.5%, observed as of minimal
difference to the alternative ordinary-least squared estimation (Angrist and Krueger 1991,
981).
This examination has received prestige and recognition in the realms of labour economics
and econometric study, but has also received criticism based on the measurement from the
instrumental variables estimate. One argument against the employment of an instrumental
variable estimate is the fact that they explain very little of the endogenous variables
variation’s discrepancies involved regardless of a link held by instruments and the error of an
equation (Bound, Jaeger and Baker 1995, 443). Furthermore, in finite samples the instrument
variables estimate sample are biased in the same direction as ordinary-least squared
estimation (Bound, Jaeger and Baker 1995, 443). In other words the magnitude of bias of
instrumental variables measure approaches the basis of OLS as the correlation between the
instruments and explanatory components reach 0. In the instance that the paper suffers from
finite sample bias, it can be argued that the instruments measurement is more difficult to
derive than previously imagined. A remedy in overcoming the hurdle of problems with the IV
measurement would be computing the correlation and F statistics to determine the quality of
it (Bound, Jaeger and Baker 1995, 443). Overall, the case study on compulsory schooling
laws and monthly births makes the argument that there is a link between education and
earnings; although there exists criticism in some academic circles regarding to the estimation
of the returns to school.
3: Data Analysis
3.1: Dataset
The data analysed in this paper have been extracted from the 2004 ‘Health and Retirement
Study’. A longitudinal data set rich with trinkets of information for economists, historians
and scientists; the study focuses on elderly Americans. The questions cover several topics
including health, education, and income. Two key variables within this study that this paper
delves further into are education and individual earnings. All calculations and investigations
of this study assume that it is a simply random sample, meaning the interviewer has no
discretion over whom they survey and that selecting the sample involves the planned use of
chance (Freedman et al 2007, 341). By putting these factors under the scope of an economic
analyst this paper hopes to draw some correlations between the two variables, and determine
how strong this correlation is. Another aspect of the data this paper delves further into is the
share of aggregate earnings held by each stage of educational qualification. Furthermore, this
paper also examines various auxiliary components including race, gender, and educational

9. 9
qualification in order to examine whether a wage gap exists between these auxiliary
variables.
3.2: Calculations & Methodologies
As part of the data analysis this paper has constructed 7different calculations on the
aforementioned variables. All calculations for this data analysis were constructed on
Microsoft excel, a resource which was an aide in filtering out unnecessary aspects of data.
For example, throughout this data analysis the amount of people in the sample was filtered
down to people working, so that the paper’s statistics would not fall victim to irrelevant
factors of the sample that would contaminate the accuracy of the graphs. This is of substantial
importance to undergo when examining variables such as earnings, as many people within a
sample may not be in the labour force or not earning for various reasons. The calculations in
this paper have undergone this filtering and should therefore be more precise for the paper’s
intended examination. This paper has created 7 calculations on the aforementioned topics
discussed in 3.1. Firstly, a scatter graph was created to exhibit the relationship between
education attained and individual earnings. As well as that, this paper compiled graphs on the
relationship between education and individual earnings between different subsets of the
sample. For example, one graph examines the relationship when divided between men and
women in order to find if there is a gender based wage gap. Another graph analyses the
relationship between the two key variables with a division on the basis of race being
implemented in order to derive whether a wage gap in relation to this existed. Another scatter
graph showing the relationship between individual earnings and education was constructed
with a division amongst the five different levels of educational qualification being created
with the aim of determining whether there is much difference between the earnings attained
from one level and another. Finally, two pie charts were created with the intention of
portraying what share of total earnings is held by each stage of education, as well as what
proportion of the earning population do these stages represent. This final graph helps provide
a concrete image of the significance of progression in education, whilst also highlighting the
issue of income inequality. The calculations conducted include the equation of a line,
correlation, regression lines, and the mean; all of which assist in portraying the strength of the
relationship between the respected variables.
3.3: The relationshipbetweenYears of Education and Individual Earnings
The primary and most important relationship conducted in this paper is the relationship
between the amount of years spent in education and individual earnings. There exists a
positive correlation between the two variables, implying that more years spent in education
may lead to higher future earnings. The below graph (figure d) outlines this correlation, and
is a visual aid in determining the strength of the relationship. Although the relationship is
positive, it is quite weak with a correlation of 0.0855. One critique of the significance of this
relationship is the existence of outliers who possess a large amount of earnings with little
educational background, for figures who dropped out of college and became entrepreneurs.
However this argument doesn’t appear to be of great significance to this sample. There does
not exist one person in the sample who has attained a maximum amount of 14 years

10. 10
education and earns over 200000 dollars a year in individual earnings, thus highlighting the
significance of further education. However, this relationship is between earnings and years
spent in education, not taking into account the various stages of education obtained. This
means that somebody may have spent several years in education but may only possess a
High-School qualification, perhaps due to external factors such as failing final exams or
illness. Also, despite the positive correlation between these two variables it is noteworthy that
correlation does not mean causation. The regression line only provides a description of the
data that one sees and is not responsible for predicting the outcomes of exogenous
intervention (Friedman et al 2007, 206).
3.4: The Relationshipbetween years of education and earnings per educational
qualification
Similar to the previous examination this scatter graph analyses the relationship between years
spent in education and individual earnings. However, in this case the several different levels
of educational qualification are divided in order to demonstrate the significance of different
levels on individual earnings, relative to other qualifications. When one examines the orange
circles representing a college qualification or higher in the below graph, it is evident that the
people in the sample that invested in this level of human capital benefited with the greatest
levels of individual earnings. In fact, they earn a substantial deal more than the third most
credible qualification of high school. Sample members who have a level of some college
degree have the second highest individual earnings, however still quite low compared to the
level mentioned prior. High school graduates appear to receive quite low individual earnings,
with none of them in the sample receiving more than 150000 a year. Sample members who
achieved the level LT high-school are quite dispersed with many receiving below 100000
y = 4012x - 22846
R² = 0.0855
-100000
0
100000
200000
300000
400000
500000
0 2 4 6 8 10 12 14 16 18
IndividualEarnings
Years spent in Education
The relationship between individual earnings and
years spentin education
Figure (d)

11. 11
besides one outlier who earns 200000 in individual earnings. The lowest earnings seem to be
received by those individuals in the sample who have achieved no more than a high school
qualification. Based on the first graph below (figure e) it can be observed that one should
undergo further education in order to obtain grater individual earnings compared to people
entering the work force at an early age, as it implies that greater consumption of education
increases future earnings. The second graph (figure f) emphasises this point by examining the
average and maximum earnings attained at each stage of education. As evident in the graph,
the maximum earnings, and after high school the average earnings, grow significantly with
further education. However, as mentioned before with regards to the mincer equation,
exogenous variables should also be taken into account as there are more factors at play than
educational attainment. Despite the relevance of external factors this relationship still holds a
great deal of significance.
0
100000
200000
300000
400000
500000
0 2 4 6 8 10 12 14 16 18
IndividualEarnings
Years Spent in Education
The Relationship between years of education and earnings per
educational qualification
LT High-School High-School GED Some College College and above
Figure (e)

12. 12
3.5: The relationshipbetweenyears spent ineducation and individual earnings
amongst three race groups
This alternative perspective on the relationship between years of education attained and
individual earnings divided on the basis of race is constructed in order to determine whether a
wage gap exists due to the exogenous variable of ethnicity. Throughout the 20th Century the
wage discrimination against certain ethnic groups was a regular occurrence, until the eventual
implantation of employment equality and affirmative action’s programmes in western society
(Borjas 2008, 390). According to the below graphs, there appears to be a substantial wage
gap between Caucasians and the other race groups. However, this could easily be down to the
fact that there is more Caucasians within the sample population. A noteworthy element of the
graph is that the relationship between the two key variables with respect to each different race
group possesses a strikingly similar correlation. Although this graph is two dimensional, it
does give insight to a third factor, participation rates in education per race. As evident in the
first graph (figure g), as the level of education progresses from LT High-School to College
and above the level of African Americans and other races declines while the proportion of
Caucasians rises. This indicates that not many African American and other ethnicities are
gaining access to education when juxtaposed with their Caucasian counterparts. However,
this could be purely down to there being more Caucasians in the sample.
When the average individual earnings per educational stage for each race is dissected, one
can deduct several fascinating insights into this earnings gap. Firstly, the mean earnings of
Caucasians remains constant due to the number of white people in each educational stage
progresses along with the earnings increase. This is evident when one examines the graph
below (figure h) and the relatively steady mean wage of Caucasians. However, this differs for
the other two races. The number of other races is smaller as the stages progress; initially
Figure (d)
13,681.32
24,516.14
20,868.53
30,055.50
47,566.23
100,000
109,500
115,000
190,000
450,000
0.00
50,000.00
100,000.00
150,000.00
200,000.00
250,000.00
300,000.00
350,000.00
400,000.00
450,000.00
500,000.00
L T H I G H -
S C H O O L
H I G H -SC HO OL G ED S O ME C O L L EGE C O L L EGE AN D
AB O V E
INDIVIDUALEARNINGS
STAGE OF EDUCATION
MEAN AND MAXIMUM EARNINGS PER
STAGE OF EDUCATION
Mean Earnings per Stage Maximum Earnings per Stage
Figure (f)

13. 13
higher earnings due to a smaller amount to divide by, but eventually falls with the low
amounts of students of these particular races under other. Likewise with African Americans,
the average wage faces a decrease as the stages of education increase because of the fall in
African American students in these levels. In summation, this relationship conveys how more
Caucasians partake in higher stages of educational attainment and as a result receive higher
earnings as their African American and other race counterparts.
0
100000
200000
300000
400000
500000
0 1 2 3 4 5 6
IndividualEarnings
Stages of Education
The relationship between earnings and levels of education
between 3 race groups
White/Caucasian African American Other
LT High-School High School GED Some College
College and
Above
White/Caucasian 32578.2 28587.71 34474.68 29972.72 29295.03
African American 34491.67 31952.6 25342.59 23528.57 22788.65
Other 32333.33 45000 30583.33 32848.03 11833.33
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
MeanIndividualEarnings
Stage of Education
Mean Earnings of Three Race Types per Education Stage
White/Caucasian African American Other
Figure (g)
Figure (h)

14. 14
3.6: The relationshipbetweenyears of education and individual earnings
between men and women
This paper also intends to put under scope the issue of a wage gap between men and women
with the hope of determining whether one exists. A topic of heated debate in recent times, the
wage gap between men and women appears to be of paramount importance amongst social
activists and feminists. However, does one actually exist? The answer is yes. According to
the table and graph (figure i) below, there does exist a wage gap between male and female
earnings. Correlation is not the key factor to be examined here and therefore a linear
regression model is unnecessary. What is of notable importance is the fact that men appear to
earn more on average as both sexes climb up the years of education ladder. For example, if
one analyses each gender’s average earnings at the first stage ‘LT High-School’, men earn
more than double than women with an approximate average wage gap of $10,139.09.
Furthermore, when examining people who have attained a college degree or above, men earn
on average $12723.91 more than their female counterparts. Although the wage gap is quite
small in some stages, it is an issue that must be addressed in order to ensure equality for all in
society.
Male Male Male Female Female Female
Mean
Earnings
Total
Counted
Max
Earnings
Mean
Earnings
Total
Counted
Max
Earnings
LT High-
School
17,765.12 87 100,000 7,626.03 59 35,000
High-
School
29,695.45 23 100,000 18,818.9 21 109,500
GED 24,357.02 131 104,000 17,484.19 135 115,000
Some
College
32,596.94 96 190,000 28,293.19 138 160,000
College
and above
52,630.68 184 450,000 39,906.77 122 240,000
Figure (i)

15. 15
3.7: Share of total earnings attained by each level of education
Another fascinating aspect of the data which helps portray a vivid imagine of the
relationship between education and individual earnings is the share of aggregate
earnings held by each educational stage. As evident in the first graph below (figure k),
the share of earnings held per stage grows incrementally. Individuals who have only
obtained a High-School classification represent a mere 4% of total earnings, while
people who have attained a college degree or above hold 48%. Furthermore, the
second graph (figure l) contains the share of the total sample population earning
represented by each stage of education attained. This helps convey how much wealth
is held by the various factions of society. For example 48% of the wealth is held by
individuals in the highest quintile of a college degree or above, which represent 31%
of the earning population. Conversely the lowest quintile stage of education in terms
of earnings attained, High-School, represents 15% of the earnings population but only
4% of earnings. Individuals with some college degree represent 24% of the earning
population, only 7% less than people with a college degree or more. However, they
only possess 23% of total earnings, less than half of the amount held by the quintile
above them. Overall, this variation in the number of people in each stage as a
proportion of the earning population and the amount of total earnings they hold may
imply that there is a certain degree of income inequality in this sample. Furthermore, it
conveys that the earnings may be greater as one consumes more education.
17,765.12
29,695.45
24,357.02
32,596.94
52,630.68
7,626.03
18,818.90 17,484.19
28,293.19
39,906.77
0.00
10,000.00
20,000.00
30,000.00
40,000.00
50,000.00
60,000.00
LT High-School High-School GED Some College College and above
MeanEarnings
Stage of Education
Mean Earnings of Men and Women at Progressive Stages of
Education
Male Female
Figure (j)

16. 16
LT High-School
7% High-School
4%
GED
18%
Some College
23%
College andabove
48%
Total Individual Earnings per Education Stage attained as a
share ofAgrregate Earnings
LT High-School High-School GED Some College College and above
145
43
265
233
305
Population of each Stage of Education as a share ofAggregate
Sample Population Earning
LT High-School High-School GED Some College College and above
Total Sample Population Earnings =287
Figure (k)
Figure (l)

17. 17
4: Conclusion
The intention of this paper was to examine the correlation between education and individual
earnings. The general results from the paper’s study were as follows. There exists a positive
correlation between years of education and individual earnings. However the strength of this
positive correlation is quite weak. This positive correlation was reflected when the
relationship was divided amongst the five different levels of educational qualification which
indicated that the greatest earnings were received by those in the sample who had a
qualification at college level or above. As well as that, the paper also examined the
relationship between the two major variables whilst comparing it between various subsets of
the sample on the basis of gender and race. This paper has also largely amalgamated the
existing theories and relevant literature and economic models on the paramount importance
of education in the labour market. The significance of education in the realms of human
capital and wage differentials was expressed by highlighting the work of Jacob Mincer and
his contribution to labour economics, research in the field of returns to schooling by Joshua
Angrist and Alan Krueger, along with classic human capital models including the wage-
schooling locus and age-earnings profile.
The study undergone in this paper has aimed to shed light on the topic of human capital and
the relationship between education and earnings. However, thanks to the rich amount of work
by respected economists who have allowed this paper to dig up gems from the well of
archived empirical evidence, this paper was not delving into the unknown and therefore able
to build on the prior research completed. It was shown graphically and through reviewing
economic models the relevance of exogenous factors of the labour market in the attainment of
individual earnings. However, while these external factors are always of importance and
should be noted, this paper finds no reasons as to why one should doubt the credibility of the
effect years of education has on individual earnings and the research completed by this paper.
Although work may already exist on a certain topic it is always good to cast a cold eye and
seek further one’s understanding of a particular topic or relationship. Through analysis of
detailed and credible data one can deduce that there are several contributing factors towards
individual earnings including the secondary school level curriculum, the state of the
economy, and family background (Altonji 1993, 71). However, the inclusion of all these
external components are beyond the scope of this essay and is something to be considered for
further investigation. Conclusively, from the research and evidence compiled in this paper, it
is safe to say that Benjamin Franklin is as right today as he was in 1758 in highlighting the
relationship between education and earnings when he said “An investment in education pays
the best interest” (Kozuskanich 2015, 4).

18. 18
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