Investigating the Link Between Education Level and Wage Growth

This report investigates the relationship between education levels and wages, focusing on the economic principles that link the two. It begins by establishing a background on the importance of education as an investment in human capital, influencing job types and future earnings. The report references the human capital theory and Mincer’s wage determination model, highlighting the positive correlation between education and salaries. Empirical data is analyzed using regression equations to validate the hypothesis that higher education leads to increased wages. The findings indicate a positive correlation, supported by statistical analysis of wage and education data. The report also touches upon wage inequality and the role of education in achieving a society with equal opportunities, ultimately concluding that increased education levels generally result in higher wages, while acknowledging the need for further research with different datasets to solidify the findings. The study uses qualitative research methods, reviewing existing literature to establish the relationship between education level and wages, and applies linear and multiple regression analysis to model the relationship between education and income.

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Author’s Name
University/ Institution
Professor’s Name
Date of Submission
The Higher the Education Level, the Higher the Wages
Introduction and Background Information
The educational system receives a significant amount of resources of all kinds from
the public sector, families and students themselves. In return for this significant amount of
resources, it is expected that the sacrifice taken on in the present allow us to obtain a number
of benefits in the future, especially for those who have received educational training. We can
say therefore that from an economic perspective, that education is one of the fundamental
instruments to achieving adequate standards of living and a society with equal opportunities.
The level of education has influence in determining the type of jobs and wages to be
obtained in the future. This increase in educational training allows the student to be a more
efficient worker, being more attractive for employers, and, consequently, it increases the
possibility of higher earnings.
There have been several contradicting beliefs on how the level of education influences
the incomes of individuals. there have been varied opinions on the subject with those
insinuating a measurable relationship between the two variables of level of education and
wages while others suggesting that there is no direct correlation between the two (whether
positive or negative ). We, therefore, aim to clarify this aspect through data analysis to
explicitly verify if there is a correlation or not and if there is then what form is it.

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In recent decades especially in advanced countries, the value of education has risen
effectively putting a higher wage premium on education. this because higher education is
often associated with a higher understanding and mastery of the subject as well as higher skill
level and cognitive abilities (Goldin & Katz, 2009). Education is a right that enables people
to develop themselves, improve productivity and therefore living conditions as well (Martí
Linares, 2015). Since the development of human capital theory, the study of effects of
education in the field of economics has been of great interest .the level of education
influences the type of jobs and therefore wages. For instance, in certain fields, the amount of
specialized education directly translates to higher earnings eg. The health sector where
specialists almost earn double what the general practitioners earn. (Hacgeland & Jakob, 2009)
Table 1: Mean Earnings by Highest Degree Earned, $: 2009 (SAUS, table 232)
The table Cleary shows a direct positive correlation between wages and time spent in
education. Education is a key aspect of the economic growth of any country. International
evidence shows that there is a direct, permanent and positive relationship between education
and salaries. This is to mean that holding all factors constant the higher the worker's
education the higher the wages or earnings. The theory of human capital (mincer, 1974)

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presents a dynamic model for wage determination, it focuses on the various life stages of with
respect to income. In this type of model, Mincer’s proposal is to complete the basic model
incorporating variables measuring time in weeks worked per year and post-schooling
education investment.
A world report and US news conducted surveys and reported that holders of
bachelors, masters, doctoral and professional degrees earned about $2.27, .67, 3.35 and 3.65
million dollars in their lifetime. The report also showed that degree holders earned slightly
more than college or diploma degrees (US news and world report, 2011). Differences in
incomes reflect the financial incentives for an individual to invest in further education. For
instance, a graduate with a higher level of education faces a lower risk of unemployment and
has greater opportunities for further training and higher income, which result in enhanced
skills and higher productivity.
The goal of this paper is to validate empirically that increasing education leads to an increase
in salary by estimating wage equations using data obtained from the survey.
Education and wages
According to international evidence education has a positive, permanent and direct
relationship with salaries; this means that, ceteris paribus, the higher the educational level of
workers, the higher the earnings. Education is essential to the economic growth of a country,
key to achieving a society with equal opportunities and allowing people to achieve an
adequate living level. Thus, wage inequality is caused in part by the different educational
levels of workers. When we talk about the relationship between education and the level of
wages, an increase in the level of education, assuming, all else being equal, will produce an
increase in salary.

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In this case, we consider education as an investment by the individual. It involves an
initial cost which is expected to be recovered in the future. Therefore, there are three
different classes of investment for workers: education, migration and the search for new jobs;
see chapter 9 of Ehrenberg and Smith (2012). The three investments have an initial cost and
the three are based on the expectation of higher future incomes that allow individuals to
recoup the initial costs. To reflect the similarity of these three investments economists refer
to as investments in human capital, a term that conceptualizes workers as embodying a set of
skills that can be “rented out” to employers.
This investment in the skills and knowledge of workers takes place in three stages: the first is
childhood where individual decisions are determined by others, parents and compulsory
education rules; the second stage is adolescence where knowledge and skills are acquired as
students; and the last stage is incorporation into the labour market. A challenge that confronts
any behavioral theory is explaining why people in the same environment have different
choices
Research Topic
Leading Research Question
What is the relationship between higher education level and wages?
Focus
Education and wage are both interdependent on each other. These two elements are
major players in the development process of any nation. Two elements belong to two
different categories- one leads to progress in economy while other leads to decline in
economy.
Definition of terms
Wages: A wage is monetary compensation (or remuneration, personnel expenses, labour)
paid by an employer to an employee in exchange for work done

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Education: the procedure of offering or receiving systematic instructions particularly at a
learning institution such as school or even universities.
Rationale
The study will be providing primary information that relates to the effects of both
higher level of education and wages on each other as well as the economy. The subject is of
much significance to the larger audience in understanding the importance of education. In
addition, the research will attempt to demonstrate how the lack of high levels of education
affects wages. Through the proposition of alternatives that can be utilized in ensuring that the
issues are solved this will contribute highly towards creating knowledge that is useful in the
sector for future study. Through the delivery of the specific information this will be useful in
forming ground for research and act as a major recommendation to facilitating the change of
states perception of education and unemployment. The information will be useful in the
creation of conducive environment for ensuring that the ability to access to education is
increased and hence lower the rising risks of unemployment that negative impacts both the
society and the economy. The research is useful since it will increase my knowledge in
regarding to the contributions of education towards employment and the economy.
Methods
The study will utilize specifically a qualitative research in gathering information. The
acquired existing literature will then be reviewed in relation to the relationship between
high education level and wages. The secondary sources that will be utilized for the study
will be journals, books, websites and articles to provide an effective basis in
understanding the research subject and establish the relationship between unemployment
and education level. Qualitative research was chosen based on its general ability to
gather maximum information. In addition, the existing literature is essential in offering

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justification to the study’s hypothesis. In addition the qualitative approach is suitable
because it requires less time to gather information, more flexible and less costly. This
therefore implies that adequate information will be acquired in order to offer support
to the study based on the literature offered in the studies by different authors. Through
maximization of information and efficiency it will therefore be easier for the research
objective to be achieved in a reliable and accurate manner.
Therefore under this method, the natural log of income is not separable from education hours
and other variables such as gender and experience. This can be shown in the equation
Log (w) = α0+α1L+α2X+α3X2+ε
Where α0,...,α3 are regression parameters, w is the worker's wage and L are the level of
education
However, this proposal does not separate between the cause and effect of education level on
increased wages. This may be because of growth of productivity from education. In these
terms, we observe that Mincer’s equation is consistent with the human capital theory
Looking back at our data, of the 100 entries of wages per hour and education level we obtain
a mean of 22.3081 for wages and average education years of 13.76. The wages have a
standard deviation of 13.951154 while years of education have a standard deviation of
2.7133743.
wage
mean
reduce
mean ds wage ds educe
wage
min
wage
max educ min
educ
max
22.3081 13.76 13.951154 2.7133743 4.33 76.39 6 21

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The wage standard deviation is very high (13.951154) about the same value as the mean, this
shows high variance/dispersion in the wage entries among different levels of education hours.
This is further enhanced by the huge margin between the minimum wage value (4.33) and the
max (76.39) bringing a very high range of 72.06. The standard deviation for the education
hours is relatively lower at 2.71 while the min and max are 6 and 21 respectively. This show
a minimal variance but still a high range of 15.
The regression equation did not provide a good fit as majority of the points were not along
the line. For a person with 12 years of education, the wage would be
• Linear regression: Y = a + bX + u
• Multiple regression: Y = a + b1X1 + b2X2 + b3X3 + ... + btXt + u
• Where:
• Y = the variable that you are trying to predict (dependent variable).
• X = the variable that you are using to predict Y (independent variable).
• a = the intercept.
• b = the slope.
• u = the regression residual.
Y=0.083x + 11.968Y (12) =0.083(12) +11.968= 12.964
For a person with 14 years of education, predicted wages would be

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Y (14) =0.083(14) +11.968=13.13
The difference in hourly rate is 13.13-12.964=0.166
Wage equation
The wage equation here will be applied to test whether, after controlling for education and
tenure, higher work experience leads to higher hourly wages.
0 10 20 30 40 50 60 70 80 90
0
5
10
15
20
25
f(x) = 0.0803348215314874 x + 11.9678827677934
R² = 0.170611590067957
educ
educ
Linear (educ)
Axis Title
wag
Figure 1: graph showing relationship between wages and education level
Conclusion
. Before we can come to a concrete conclusion I would recommend another population
analysis of the same with a different set of data. The methodology, however, is efferent and
simple for any researcher. From the regression analysis and other analysis conducted, the
positive coefficients are obtained and based on the findings therefore, it can be asserted the
higher the level of education, the higher the wage.

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References
Ann P. Bartel and George J. Borjas., (1981). Wage Growth and Job Turnover: An Empirical
Analysis. [pdf]. Available at:
Arrow, K. J. (1973). Higher education as a filter. Journal of public economics, 2(3), 193-216.
Arrow, K. J. (2013): “Higher education as a filter”, Journal of Public economics, 2, págs.
193-216.
-Barro, R.J. (2011): Human Capital and Growth, American Economic Review, 91, 12-
Becker, G. (2014): Human Capital. Nueva York, Columbia University Press.
Cabrales, A., (2013): “PIAAC, el examen de PISA para adultos”. Nada es gratis, [online] 13
de Octubre. Available at:
Card (Eds.) Handbook of Labor Economics, volume 3, chapter 30. Elsevier.
Card D. (2012). “The Causal Effect of Education on Earnings”, En Ashenfelter, O., D.
Day, J. C., & Newburger, E. C. (2002). The Big Payoff: Educational Attainment and
Synthetic Estimates of Work-Life Earnings. Special Studies. Current Population Reports.

Surname 10
Diamond, Arthur M., Jr. "Zvi Griliches's Contributions to the Economics of Technology and
Growth." Economics of Innovation and New Technology 13, no.4 (June 2014):
Duncan, G. & Hoffman, S.D. (2011): The Incidence and Wage Effects of Overeducation,
Economics of Education Review, 1, 75-86.
Ehrenberg, R.G. and Smith, R.S and (2012): Modern Labor Economics: Theory and Public
Policy, 11th edition. Prentice Hall.
Freire, M.J y Teijeiro, M., (2010). Las ecuaciones de Mincer y las tasas de rendimiento de la
educación en Galicia. [pdf] Galicia. Available at:
<http://2010.economicsofeducation.com/user/pdfsesiones/095.pdf>
Goldin, C. D., & Katz, L. F. (2009). The future of inequality: The other reason education
matters so much.
Gregorio, J. D., & Lee, J. W. (2002). Education and income inequality: new evidence from
cross‐country data. Review of income and wealth, 48(3), 395-416.
Griliches, Z. (2017): “Estimating the return to schooling: some econometric problems”,
Econométrica, 45, págs. 1-22.
Groot, W. & Maassen van den Brink, H. (2015): Overeducation in the Labor Market: A
Meta-analysis. Economics of Education Review, 19, 145-158.
Hacgeland, T., & Jakob Klette, T. (1999). Do higher wages reflect higher productivity?
Education, gender and experience premiums in a matched plant-worker data set. In The

Surname 11
creation and analysis of employer-employee matched data (pp. 231-259). Emerald Group
Publishing Limited.
Hacgeland, T., & Jakob Klette, T. (1999). Do higher wages reflect higher productivity?
Education, gender and experience premiums in a matched plant-worker data set. In The
creation and analysis of employer-employee matched data (pp. 231-259). Emerald Group
Publishing Limited.
Hartog, J., & Oosterbeek, H. (1998). Health, wealth and happiness: Why pursue a higher
education?. Economics of Education Review, 17(3), 245-256.
Heckman James J. (energy 2013) «Sample selection bias as a specification
error»Econometrica. Journal of the Econometric Society (47): pp.153-161.
Lipsey, R. E., & Sjöholm, F. (2004). Foreign direct investment, education, and wages in
Indonesian manufacturing. Journal of Development Economics, 73(1), 415-422.
Martí Linares, R. M. (2015). An empirical examination of the relationship between wages
and education.
Mincer, J. (1974). Schooling, experience and earnings, National Bureau of Economic
Research (NBER), Nueva York, Estados Unidos.
Mincer, J. (1974). Schooling, Experience, and Earnings. Human Behavior & Social
Institutions No. 2.
Mincer, J. (1974). Schooling, Experience, and Earnings. Human Behavior & Social
Institutions No. 2.
Neumark, D.: “Biases in Twin Estimates of the Return to Schooling: a Note on Recent
Research”, en Economics of Education Review, 18 (2019), pp. 149-157.
OECD, (2014). Elementos principales de la evaluación (PIAAC). Available at:

Surname 12
PIAAC, (2013). Programa internacional para la evaluación de las competencias de la
población adulta. [pdf] Madrid. Available at:
Schultz, T. W. (1960). Capital formation by education. Journal of political economy, 68(6),
571-583.
Schultz, T.W. (1962): Investment in Human Capital, American Economic Review, 51, 1-17.
Schultz, T.W. (2010): Capital Formation by Education, Journal of Political Economy,
Solomon W. Polachek., (2017). Earnings Over the Lifecycle: The Mincer Earnings Function
and Its Applications. [pdf] Germany.
Wooldridge, J. M. (2010): Introducción a la Econometría: Un Enfoque Moderno, 4st ed.
Madrid: Ed. CENGAGE Learning.