Education and Wage Rate Analysis

This assignment examines the connection between years of education and hourly wage rates. Students are tasked with conducting a linear regression analysis to explore this relationship, identifying potential trends and patterns in the data. The analysis should also critically evaluate the limitations of using only years of education as a predictor of wages, considering factors like educational specialization and experience. Finally, students are expected to propose policy recommendations based on their findings that could bridge the gap between education and wage outcomes.

Running head: ECONOMICS AND QUANTITATIVE ANALYSIS
Economics and quantitative analysis
Name of the university
Name of the student
Author Note

1ECONOMICS AND QUANTITATIVE ANALYSIS
Purpose:
The chief purpose of this report is to establish a relation between education and wage.
This report will state that how education influences wages of workers. Moreover, this report
will try to find out a positive or negative relation between these two variables. To understand
the relation between these two variables, a linear regression line has drawn.
Figure 1: Simple linear regression between education and wage
Background:
People try to get higher level of education to obtain higher wage rate per hour. It is
believed that higher level of education makes skilled worker (Yirmiyahu, Rubin and Malul
2017). Moreover, a skilled worker gets higher amount of wages compare to an unskilled or
semi-skilled worker. Economists also consider this relation as interesting. The government of
a country can impose any policy to promote higher education. On the other side, skilled
workers work efficiently as they get higher wages compare to others. This further helps a
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f(x) = 2.1237563837879 x − 6.91478784092146
Education
Wage

2ECONOMICS AND QUANTITATIVE ANALYSIS
country to increase its gross domestic product (GDP). People with higher level of education
generally enjoy a good standard of living. Hence, for developing and developed countries,
education is an important factor (Verger, Altinyelken and Novelli 2018). It further helps a
country to develop. Therefore, education is an important factor for a country’s economy.
Hence, economists try to establish a relation between education and wage rate of a country.
This further helps to implement a theory. Moreover the government of a country can apply
different policies, based on the relation.
Method:
Here, education is considered as an independent variable and wage is considered as a
dependent variable. To obtain higher level of education, a person has spent many years. In
this report, education is measured by this number of years. Moreover, wage is measured as
the earning per hour of a worker. To understand an association between education and wage,
a statistical tool will be used. The simple linear regression analysis is used to analyse this
relation (Fox 2015). Furthermore, a scatter plot will help to draw a simple linear regression
analysis. To analyse further, a trending line will help to plot this regression line by covering
maximum scattered points. By analysing this regression line, one can understand that whether
education has any impact on wage or not. It will also describe that whether both variables
have positive or negative relations.
Results:
This section will summarise various outcomes of both education and wage by doing
statistical analysis.
In this section of the report, descriptive analysis related to education is done.

3ECONOMICS AND QUANTITATIVE ANALYSIS
Education:
Mean: 13.76
Standard deviation: 2.727044
Minimum: 6
Maximum: 21
Descriptive analysis of wage is shown below.
Wage:
Mean: 22.3081
Standard deviation: 14.02144
Minimum: 4.33
Maximum: 76.39
In figure 1, a scatter diagram shows the relation between education and wage.
Education is taken as independent variable. This scatter diagram is roughly showing a
positive relation between education and wage. However, it does not follow any exact relation
between number of years of education and wage per hour (Fox 2015). At a same education
level, some persons are getting high amount wage per hours and some are getting
comparatively low wages per hour. Hence, wage discrimination is sharply found among
different workers with same years of educational experience.
The estimated regression equation is y= 2.123x – 6.914. Here, y is the independent
variable, that is, education. On the other side, x represents wage, that is, dependent variable.

4ECONOMICS AND QUANTITATIVE ANALYSIS
Slope coefficient measures the steepness of a regression line between education and
wage. Here, slope is 2.123. As the equation gives a positive value of the slope, it indicates a
positive relation between these two variables (Mooi, Sarstedt and Mooi-Reci 2018). When
education increases by 1 unit, wage increases by 2.123.
In this equation, P value is 0.0000. P-value of 5% or less that indicates statistically
significant. Here, P value is less than 0.5. This will reject the null hypothesis that the
coefficient has no effect (Walsh et al. 2014). Hence, this shows a statically significant
association.
The regression equation does not provide a good fit. Best-fitted line represents the
best approximation of all given data. Here, a trend line has drawn. There are many points,
which are above the line and below the line. Here, the value of r-square is 0.1706. This
indicates that wage explains an estimated 17% of the variation in education (D'Agostino
2017). This value is very low.
Using the equation y= 2.123x – 6.914, predicted wage rate will be calculated. Here, x
is the year of education of a worker and y is the predicted wage rate of that worer. When a
person has 12 years of education, the predicted wage will be 18.562. On the other side, when
a person has 14 years of education, the predicted wage rate will be 22.808. Hence, as a person
has two years of extra education, he can earn extra 4.246 wage per hour.
Description:
The result does not provide any sharp relation between education and wage. However,
this research has done based on only 101 data. As the sample size is small, the outcome
cannot be predicted exact outcome. Hence, the linear regression is showing a rough image of
the whole data set.

5ECONOMICS AND QUANTITATIVE ANALYSIS
The outcome of this analysis is not consistent. People believe that higher studies will
help them to attain higher amount of wage. This is one of the chief reasons behind higher
education. However, in this report the outcome does not present this concept accurately (Fox
2015). This wrong outcome further negatively affects higher education. There are various
points, which are situated far from the regression line. Moreover, at the same level of
education, some people are earning higher amount of wage per hour.
This will not help the government of a country to implement a particular policy.
Hence, this report will note properly help any researcher to further. However, number of
education does not indicate any particular stream of education. Different people chose
different streams of education. Hence, with same educational year with different educational
stream affect wage rate.
Recommendation:
To implement a proper recommendation, a proper association between education and
wage is needed. The government or private sector will offer higher wage for those people,
who have higher years of educational experience. Wage discrimination of workers with same
educational level is not wanted. Educated people with more years of experience want to earn
more compare to other people, who have low level of education. This will further help those
workers to work efficiently. If two persons with same educational level will earn different
amount of wages, then it will adversely affect the efficiency level of them.
Furthermore, both private and government sectors will increase their hourly wage
rate to attract more workers. On the other side, government should promote higher level of
education so that people can earn higher amount of wage. This will further help a country to
operate efficiently. Education helps a country to grow and develop further. Hence, perfect
policy related to education is important.

6ECONOMICS AND QUANTITATIVE ANALYSIS
Moreover, only number of years of education is mentioned. However, various streams
of education are not mentioned. Engineers or doctors earn more wages compare to other
people. Hence, those data do not provide any detail information related education. This will
further mislead researches. Hence, with number of education, types of jobs should be
mentioned.

7ECONOMICS AND QUANTITATIVE ANALYSIS
References:
D'Agostino, R., 2017. Goodness-of-fit-techniques. Routledge.
Fox, J., 2015. Applied regression analysis and generalized linear models. Sage Publications.
Mooi, E., Sarstedt, M. and Mooi-Reci, I., 2018. Regression Analysis. In Market
Research (pp. 215-263). Springer, Singapore.
Verger, A., Altinyelken, H.K. and Novelli, M. eds., 2018. Global education policy and
international development: New agendas, issues and policies. Bloomsbury Publishing.
Walsh, M., Srinathan, S.K., McAuley, D.F., Mrkobrada, M., Levine, O., Ribic, C., Molnar,
A.O., Dattani, N.D., Burke, A., Guyatt, G. and Thabane, L., 2014. The statistical significance
of randomized controlled trial results is frequently fragile: a case for a Fragility
Index. Journal of clinical epidemiology, 67(6), pp.622-628.
Yirmiyahu, A., Rubin, O.D. and Malul, M., 2017. Does greater accessibility to higher
education reduce wage inequality? The case of the Arab minority in Israel. Studies in Higher
Education, 42(6), pp.1071-1090.

8ECONOMICS AND QUANTITATIVE ANALYSIS
Appendix:
educ wage
Mean 13.76 Mean 22.3081
Standard Error 0.2727044 Standard Error 1.402143746
Median 13 Median 19.39
Mode 12 Mode 38.45
Standard Deviation 2.7270438 Standard Deviation 14.02143746
Sample Variance 7.4367677 Sample Variance 196.6007085
Kurtosis 1.3173332 Kurtosis 2.606500644
Skewness 0.4408791 Skewness 1.485828052
Range 15 Range 72.06
Minimum 6 Minimum 4.33
Maximum 21 Maximum 76.39
Sum 1376 Sum 2230.81
Count 100 Count 100
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.413051559
R Square 0.17061159
Adjusted R Square0.162148443
Standard Error 12.83441505
Observations 100
ANOVA
df SS MS F Significance F
Regression 1 3320.6936 3320.693589 20.15935553 1.94674E-05
Residual 98 16142.777 164.7222097
Total 99 19463.47
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -6.9148 6.6339 -1.0423 0.2998 -20.0795 6.2500 -20.0795 6.2500
educ 2.1238 0.4730 4.4899 0.0000 1.1851 3.0624 1.1851 3.0624