Education and Wage Differentials Analysis

This assignment presents a dataset exploring the connection between education and wages. It includes numerical data on both variables, along with instructions to perform descriptive statistical analysis (mean, standard deviation, minimum/maximum). The analysis should highlight key relationships within the data. Additionally, the assignment encourages referencing relevant academic works in economics and education for further context.

Running head: RELATIONSHIP BETWEEN EDUCATION AND WAGES
RELATIONSHIP BETWEEN EDUCATION AND WAGES
Name of the Student
Name of the University
Author Note

1RELATIONSHIP BETWEEN EDUCATION AND WAGES
Executive Summary
It has been believed that the education level of a person often reflects upon the wages they
earn. This report aims to analyze the results of a sample data of 100 observations, which
provided education level and their corresponding wages per hour. Regression Analysis was
used to make a comment upon the two variables. Education was taken as the independent
variable whereas wages was taken as the dependent variable. Microsoft Excel Data Analysis
tool was used to conduct the Regression Analysis. The result obtained showed little or no
relationship between the two variables. The findings and recommendations have been made
at the end of the report.

2RELATIONSHIP BETWEEN EDUCATION AND WAGES
Table of Contents
Purpose.......................................................................................................................................3
Background................................................................................................................................3
Method.......................................................................................................................................4
Results........................................................................................................................................4
Descriptive Analysis: Table 1................................................................................................4
Scatter Diagram......................................................................................................................5
Regression Line......................................................................................................................5
Analysis of the Regression Equation.....................................................................................6
Association between Education and Wages...........................................................................6
Fit of Regression Equation.....................................................................................................6
Calculation.............................................................................................................................7
Discussion..................................................................................................................................8
Recommendations......................................................................................................................8
Excel Data................................................................................................................................10
References................................................................................................................................14

3RELATIONSHIP BETWEEN EDUCATION AND WAGES
Purpose
Education can be described as an experience that has a formative effect on the
character, physical ability and the mind of a person. Thus, as education tends to increase and
develop the intellectual ability of a person it is believed that through gaining knowledge one
can increase their earning capacity. The primary purpose of this report is to find out the
relationship between the earning capacity of a person taken to be the wages of the person and
the educational level and qualification (Psacharopoulos 2014). The main question behind the
studies is to find out the influence of education on wages. A regression analysis will be
performed in which the education will be taken as the independent variable and the wages
earned will be taken as the dependent variable. The results shall be interpreted and discussed.
Background
As stated earlier, education tends to enrich the lifestyle of a person and increases the
capability. It is often believed that skilled and educated labor is more productive than the
unskilled one. Hence, policy makers often tend to emphasize on the importance of education
to increase labor productivity. It is often considered that a labor with higher education will
produce higher and thus have a higher wage (Reardon 2013).
However, previous research on the same topic in the African region and surroundings
state that every worker will receive the same pay. A similar research conducted in the other
parts of the world also state that not much relationship has been found between the two given
factors. People with higher education have been getting similar wages like those with lower
wages (Siegel 2016). People who have a higher educational are often employed in areas with
low wages in an area o establishment.

4RELATIONSHIP BETWEEN EDUCATION AND WAGES
Method
In order to examine the relationship between the education of a person and his wages
the approach that will be used is the Regression Analysis. The education will be taken as the
`X` variable which is the independent variable and the wages will be taken to be the `Y`
variable which is the dependent variable (Jaggia et al., 2016). The education is taken to be the
independent variable because educational level has an impact on the wages and income
received. Therefore, the variables have been taken accordingly.
Regression Analysis can be described as a set of statistical processes, which can be
used to estimate the relationship among a variety of variables. There are several techniques,
which may be used to model and analyze the various variables wherein the focus lies on the
dependent variable and the independent variable. Specifically, the regression analysis helps to
analyze and observe the change in the dependent variable based on the changes in the
independent variable. The independent variable is believed to remain fixed (Draper and
Smith 2014). The analysis is primarily used for prediction and forecasting the future or even a
relationship between two variables.
Hence, in this report, the education has taken to be the independent variable and the
wages are taken to be the dependent variable. The Regression Analysis will be conducted
using Microsoft Excel Data Analysis tools after which the results shall be interpreted.
Results
Descriptive Analysis: Table 1
Descriptive
analysis
Educatio
n Wages
Standard
Deviation
2.727043
8
14.02143
7
Mean 13.76 22.3081
Maximum 21 76.39
Minimum 6 4.33

5RELATIONSHIP BETWEEN EDUCATION AND WAGES
Scatter Diagram
4 6 8 10 12 14 16 18 20 22
0
10
20
30
40
50
60
70
80
90
Scatter Diagram
Education
Wages
Figure 1: Scatter diagram of X and Y variables.
The scatter diagram given above describes the relationship between the education and
the wages received. It can be observed that the relationship between the two variables is
extremely weak and that education has not impact on the wages received by a person
(Chatterjee and Hadi 2015). The R square is almost zero, which reflects that the explanatory
power of the Regression is zero.
In simple terms, it can be stated that the scatter diagram reflects no relationship
between the dependent variable that is the wages and the independent variable that is the
education.
Regression Line
Y=0.0803X+11.967

6RELATIONSHIP BETWEEN EDUCATION AND WAGES
Analysis of the Regression Equation
From the data analysis, it can be seen that the resultant Regression equation is given
as above. In the given regression equation, the line has been given in the form of y=mx+c,
where m is the slope of the line. In the given equation, it can be observed that the slope of the
given regression line and analysis is 0.0803 which is equivalent to zero. According to
Darlington and Hayes (2016), when the slop of a line is equal to or approximately 0 then the
line is a vertical line and there stands no relationship between the two given variables.
In the given scenario, according to the result, there lies no relationship between the
education of a person and the wages received.
Association between Education and Wages
In the given scenario, as per the calculation, the value of Multiple R is 0.41305, which
is not even one percent. The value of Adjusted R square is 0.1 per cent .This proves that it is
not even one percent. Adjusted R square explains the explanatory power of the regression,
which is not even one percent with respect to the given dependent variable, which is wages.
The chosen variable is therefore not a good choice.
Hence, it can be stated that according to the given criteria and results that were
obtained after the regression analysis had been performed, there lies no relationship between
the dependent and the independent variable. Hence, it can be stated that education
qualification of a person does not reflect the person`s wage income.
Fit of Regression Equation
The regression equation is Y=0.0803X+11.967
It did not provide a good fit as the slop of the line is extremely role thus defining and
stating the fact that there is no relationship between the two variables

7RELATIONSHIP BETWEEN EDUCATION AND WAGES
Calculation
For a person with 14 years of education
Therefore, as x is education
X= 14
Wages(y) = 0.0803X+11.967
= 0.0803(14) +11.967
=1.1242+11.967
=13.0912
Hence, according to the regression equation obtained the predicted wages of a person
with 14 years of education is 13.0912/hr.
For a person with 12 years of education
X= 12
Wages(y) = 0.0803X+11.967
= 0.0803(12) +11.967
=0.9636+11.967
=12.9306
Hence, according to the regression equation obtained the predicted wages of a person
with 12 years of education is 12.9306/hr.

8RELATIONSHIP BETWEEN EDUCATION AND WAGES
The difference between their hourly wage is approximately 0.1606 wages is not even a unit.
Hence, this reflects the results that the educational level of a person has no effect on one`s
wages
Discussion
Therefore, from the given analysis it can be stated that there lies no relationship
between the educational level of the person and the wages the person receives. The regression
line obtained from the study showed an extremely low slope and this reflects that there lies no
relationship between the two variables or the sample size is too small to come to a
conclusion.
Other studies made by various authors reflect a similar finding. According to Patrick
et al. (2014), similar results were observed with a sample taken from workers in Ghana. The
research is often conducted by the authors because the logic suggests that people with more
education should be earning higher, but practical studies tell a different story.
These findings imply that the government should adopt policies with the help of
which the highly educated people get jobs according to their criteria. In the present scenario,
the people with different educational backgrounds are performing the same job because of the
dearth of employment. This should not be the case.
Recommendations
The following recommendations can be made:
 The government must try to increase the enrolment in schooling. These programs
need to be effectively monitored.

9RELATIONSHIP BETWEEN EDUCATION AND WAGES
 The investments to education and other relevant learning activities should be
subsidized by the government in order to make sure that the low-wagers can improve
their standard of living from their income.
 Women must be encouraged to pursue higher education.
 The workers should be provided with on-the job training to improve their skills.

10RELATIONSHIP BETWEEN EDUCATION AND WAGES
Excel Data
SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.413051559
R Square 0.17061159
Adjusted R Square 0.162148443
Standard Error 2.496178555
Observations 100
ANOVA
df SS MS F
Significance
F
Regression 1 125.6110771 125.6110771
20.1593555
3 1.94674E-05
Residual 98 610.6289229 6.230907377
Total 99 736.24
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Lower
95.0% Upper 95.0%
Intercept 11.96788277 0.470769474 25.42196007 6.17064E-45 11.03365609 12.90210944
11.0336560
9 12.90210944
X Variable 1 0.080334822 0.017892273 4.489917096 1.94674E-05 0.044828189 0.115841454
0.04482818
9 0.115841454
RESIDUAL OUTPUT
Observation Predicted Y Residuals Standard Residuals
1 13.10542384 -1.105423841 -0.445100148
2 13.59064616 2.409353837 0.970129022
3 12.93431067 -6.934310671 -2.79210796
4 13.58261268 -1.582612681 -0.637240769
5 13.99633701 -0.996337011 -0.401176214
6 15.05675666 2.943243344 1.18510023
7 13.51272139 -0.512721386 -0.20644784
8 14.98043858 -2.980438575 -1.200076931
9 13.11265397 -1.112653975 -0.448011369
10 15.67613813 0.32386187 0.130403345
11 13.89993523 2.100064774 0.845593434
12 14.13692295 -2.136922949 -0.860434419
13 13.97625331 -1.976253306 -0.795740608
14 13.31991781 2.680082186 1.079138094
15 13.51272139 2.487278614 1.00150552
16 12.61538143 0.384618571 0.154867098
17 13.07248656 -1.072486564 -0.431837916
X
V
ar
i
a
b
le
1
Li
n
e
F
i
t
P
l
o
t

11RELATIONSHIP BETWEEN EDUCATION AND WAGES
18 12.8515658 -0.851565805 -0.342883925
19 13.27573366 2.724266338 1.096928893
20 12.8298754 1.170124597 0.47115198
21 12.48041893 5.519581071 2.222465501
22 13.54485531 -1.544855314 -0.622037723
23 15.86813835 0.131861647 0.05309424
24 13.72560866 -0.725608663 -0.292167141
25 13.5745792 -0.574579198 -0.231354958
26 13.41390956 -1.413909555 -0.569312266
27 12.54629348 3.453706517 1.390638798
28 12.7415071 -0.741507099 -0.298568664
29 14.74987764 -0.749877637 -0.301939071
30 13.13273768 -1.13273768 -0.456098096
31 13.70552496 -1.705524958 -0.686731535
32 13.01223545 2.987764552 1.203026744
33 13.9095754 2.090424596 0.841711805
34 15.4423638 2.557636201 1.029835082
35 13.25323991 -1.253239912 -0.504618455
36 15.05756 2.942439996 1.184776761
37 12.87566625 0.124333749 0.050063123
38 14.83021246 -2.830212459 -1.139588217
39 13.13273768 -0.13273768 -0.053446976
40 13.33357473 -1.333574734 -0.536965361
41 13.66696424 -0.666964243 -0.2685539
42 15.57250621 0.42749379 0.172130853
43 14.05257139 3.947428613 1.589436553
44 13.02026893 -1.02026893 -0.410812428
45 13.89993523 -0.899935226 -0.362359927
46 14.16905688 -2.169056878 -0.873373182
47 12.93190063 -0.931900626 -0.375230831
48 12.89574996 0.104250044 0.041976397
49 12.77123098 -0.771230983 -0.310537019
50 15.4423638 0.557636201 0.224532841
51 17.76243344 -5.762433445 -2.320250282
52 12.69089616 5.309103838 2.137716608
53 15.05675666 0.943243344 0.379797989
54 13.75934929 2.240650712 0.902200519
55 14.37792741 1.622072586 0.653129344
56 16.30997987 -0.309979872 -0.124813743
57 12.69089616 -0.690896162 -0.278190113
58 12.66117228 -0.661172278 -0.266221758
59 14.28393567 -1.283935673 -0.516978137
60 13.65491402 0.34508598 0.138949256
61 12.8314821 0.168517901 0.067853922
62 13.69347473 -1.693474734 -0.681879499
63 14.35864706 -1.358647057 -0.547060759

12RELATIONSHIP BETWEEN EDUCATION AND WAGES
64 13.97625331 -1.976253306 -0.795740608
65 16.21598813 4.78401187 1.926287739
66 12.57039393 -1.570393929 -0.632320875
67 12.77926447 1.220735535 0.491530531
68 12.48202563 -0.482025626 -0.194088158
69 14.06462161 -2.06462161 -0.831322204
70 12.73106357 -0.731063572 -0.294363566
71 12.97206804 1.027931963 0.413897956
72 13.04838612 -1.048386117 -0.422133845
73 13.01062875 -0.010628751 -0.004279679
74 12.57039393 0.429606071 0.172981366
75 13.27332362 -7.273323618 -2.928611903
76 18.10465978 -4.104659785 -1.652745861
77 13.94652942 2.053470578 0.826832229
78 14.51690665 1.483093345 0.597169197
79 13.81558366 -1.815583663 -0.731046796
80 12.31573255 -4.315732545 -1.737734544
81 13.27332362 -1.273323618 -0.512705181
82 15.75406291 0.245937093 0.099026846
83 14.86395308 1.136046916 0.457430563
84 13.17290509 4.827094909 1.943635173
85 12.73106357 -0.731063572 -0.294363566
86 13.40266268 -1.40266268 -0.5647837
87 13.53842853 0.461571471 0.18585227
88 14.83985264 -0.839852638 -0.338167605
89 12.49005911 -2.490059108 -1.002625089
90 13.21548255 -1.215482546 -0.489415409
91 13.82201045 -0.822010449 -0.330983428
92 16.08825576 4.911744236 1.977719319
93 13.53842853 0.461571471 0.18585227
94 13.37374214 -1.373742145 -0.553138813
95 13.47416067 -1.474160672 -0.593572446
96 12.77123098 0.228769017 0.092114101
97 12.54147339 -0.541473394 -0.218024868
98 13.85655442 7.143445578 2.876316364
99 14.28393567 6.716064327 2.704230825
100 12.65072875 1.349271249 0.54328558
Descriptive
analysis
Educatio
n Wages
Standard
Deviation
2.727043
8
14.02143
7
Mean 13.76 22.3081
Maximum 21 76.39

13RELATIONSHIP BETWEEN EDUCATION AND WAGES
Minimum 6 4.33
4 6 8 10 12 14 16 18 20 22
0
10
20
30
40
50
60
70
80
90
Scatter Diagram
Education
Wages

14RELATIONSHIP BETWEEN EDUCATION AND WAGES
References
Anderson, D., Sweeney, D. and Williams, T., 2014. Modern business statistics with Microsoft
Excel. Nelson Education.
Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D. and Cochran, J.J.,
2014. Essentials of statistics for business and economics. Cengage Learning.
Chatterjee, S. and Hadi, A.S., 2015. Regression analysis by example. John Wiley & Sons.
Darlington, R.B. and Hayes, A.F., 2016. Regression analysis and linear models: Concepts,
applications, and implementation. Guilford Publications.
Draper, N.R. and Smith, H., 2014. Applied regression analysis. John Wiley & Sons.
Jaggia, S., Kelly, A., Beg, A.B.M., Leighton, C., Olaru, D., Salzman, S. and
Sriananthakumar, S., 2016. Essentials of business statistics: communicating with numbers.
McGraw-Hill Education.
Patrick, E., Hagan, E., Ahouandjinou, E. and AttahObeng, P. 2014. Relationship between
Education and Wage differentials in Ghana: A Case Study of Accra - a Suburb of greater
Accra Region,International Journal of Academic Research in Business and Social Sciences
January 2014, Vol. 4, No. 1 ISSN: 2222-6990
Psacharopoulos, G. ed., 2014. Economics of education: Research and studies. Elsevier.
Reardon, S.F., 2013. The widening income achievement gap. Educational Leadership, 70(8),
pp.10-16.
Siegel, A., 2016. Practical business statistics. Academic Press.