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The Effect of Education and Experience on Wages: The Case Study of Saudi Arabia

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American Journal of Industrial and Business Management, 2018, 8, 129-142
http://www.scirp.org/journal/ajibm
ISSN Online: 2164-5175
ISSN Print: 2164-5167
DOI:
10.4236/ajibm.2018.81008 Jan. 22, 2018 129 American Journal of Industrial and Business Management
The Effect of Education and Experience on
Wages: The Case Study of Saudi Arabia
Hemaid Alsulami
Faculty of Engineering, King Abdulaziz University, Jeddah, KSA
Abstract
Investment in human capital is a major factor for the economic growth of the
country. Working salaries are main living aims for each individual,
and no
one is interesting less than having a continuous increasing in the monthly i
n-
come of his own. In Saudi Arabia, a variation in the individual income is a n
o-
ticeable issue to be considered, an
d this research is looking closely at several
scenarios that were conducted from the effect of several factors; education
major, educational degree, experience and working sector. After these scen
a-
rios are specified, prioritization matrix technique is used
to find the scenario
that results in the highest income for the Saudi males and females
monthly
income separately. As a result, a male who has doctoral degree in science m
a-
jor and has been working in a private sector from 10 to 14 years or more than
20 years is the highest monthly income among males
in Saudi Arabia. While a
female who has doctoral degree in science major and has been working in a
public sector from 15 to 20 years or more earns
the highest income among
females. The results show
also the most two factors that significantly affect the
Saudi salaries for both males and females are
education qualification and the
sector either public or private.
Keywords
Education Major, Educational Degree, Experience, Working Sector, Income,
Saudi Individual
1. Introduction
Various researches across several years have demonstrated the importance of
investing in the human capital as it is one of the major factors in the economic
growth for a country. Increasing the educational level and abilities of an indi-
vidual are the main source of increasing productivity and economy within a so-
How to cite this paper:
Alsulami, H.
(201
8) The Effect of Education and Expe-
rience on Wages: The Case Study of Saudi
Arabia
.
American Journal of Industrial and
Business Management
,
8
, 129-142.
https://doi.org/10.4236/ajibm.2018.81008
Received:
December 29, 2017
Accepted:
January 19, 2018
Published:
January 22, 2018
Copyright © 201
8 by author and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons
Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
H. Alsulami
DOI:
10.4236/ajibm.2018.81008 130 American Journal of Industrial and Business Management
ciety [1]. Education has been always one of the prominent interests inherent
with the development of the modern State of Saudi Arabia. This is demonstrated
by the launch of Saudi vision 2030 that includes a primary pillar in improving
the educational level in Saudi Arabia by 2030. Saudi vision for 2030 stated the
goal of “Education that contributes to economic growth” which supported the
importance of investing in education to build a productive society [2].
There are two reasons to believe in the importance of education: first, is the
great demand for education, particularly public education, in all developed and
developing countries, secondly is due to the clear and strong relationship be-
tween education and income at the individual and national level [3]. 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 [4]. The
average monthly income per Saudi individual is approximately about SR 6.5 k
according to General Authority for Statistics [5]. There are many factors playing
key roles in the variation of individual’s income/salary. The focus here is on
studying the educational level, experience level and job sector that lead to ac-
quire the highest Saudi individual’s salary. The goal of this study is to illustrate
the combination of factors that will lead to acquire the highest possible income
among Saudi individuals’ income. In addition, the study aims to find the highest
income among Saudi males and females based on the effect of academic degree,
academic major, experience and work sector.
An American study demonstrated that most of the individuals with a bache-
lor’s degree earn an average of $2.27 million over their lifetime, while others
with master’s, doctoral and professional degrees earn about $2.67 million, $3.25
million, and $3.65 million, respectively. Furthermore, the study illustrated that
an individual with a bachelors degree in majors like engineering, management,
and science or technology earn more than others with literature majors like
community service [6]. Similarly, the Ministry of Education in New Zealand
stated that the income for those with a higher education degree is higher by 24%
on average, compared to those with a non-higher education degree [7]. Conse-
quently, this can clarify the mutual relationship between the educational level
and the income of an individual. Unfortunately, no data or statistics were availa-
ble that show which factors affected the Saudi individual incomes with regard of
the educational level, experience and work sectors. Thus, one of the purposes of
this research is to survey and record the required data of the Saudi community
and share the analyzed information accordingly.
2. Literature Review
This literature review will focus on education as a major factor affecting income
at several locations. For instance, education plays an important role in a variety
of domains, including economic growth, wages, income inequality and society.
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10.4236/ajibm.2018.81008 131 American Journal of Industrial and Business Management
The Effect of Education Level on Economic Growth
Based on the existing literature, there is a significant evidence of the relation-
ship between the human capital and the economic growth. While a few experi-
mental studies focus on the impact of education levels on economic growth. Two
different studies will be presented below about the impact of the educational le-
vels on the economic growth.
Denise Hawkes and Mehmet Ugur from the University of Greenwich in Lon-
don, England conducted a research in 2012 to find empirical evidence for the
impact of education and skills development on economic growth in low-income
countries (LICs). Hawkes and Ugur found that there is a stable positive relation
between the education and skills development and the economic growth in LICs.
They conducted their study using a Meta-analysis after identified and synthesis
thirty-three different experimental papers. The selection of papers had a limited
effect of bias due to the selection process that used PIOS framework (Population,
Independent variable, Outcome, Study design) to eliminate 3842 unique studies
to 33 empirical papers and 6 theoretical papers. In addition, they conducted the
Meta-analysis using STATA software to avoid any errors or failures. The wide
range of education and skills measures provided a limited number of observa-
tions for each type of education and this was the largest problem faced by
Hawkes and Ugur. Conforming to the two researchers from the University of
Greenwich, the limited number of observation may affect the importance of the
results that indicated to prove the impact of education on promoting the eco-
nomic growth in LICs [8].
Likewise, Gangadhar Dahal from the University of Warsaw in Poland studied
the instrumental role of education between 1995 to 2013 on reducing the pover-
ty, protecting the environment, improving the sustainability and the most im-
portant aspect is the role of education in enhancing the productivity and eco-
nomic growth in developing countries such as Nepal. According to Dehal, Nepal
suffers from low-skilled human resources due to poor education quality and lack
of professional training and thus became a major constraint for Nepal’s eco-
nomic development. The objectives behind Dehal’s research are to find the im-
pact of primary, secondary and tertiary/high education on Nepal’s economic
growth, also to check if there is a long relationship between the education and
the economic expansion. Ordinary Least Square (OLS) diagnostic and Johansson
cointegration techniques are what Dehal used to achieve the aims of his study.
The results of OLS diagnostic indicate that there is a significant impact of pri-
mary, secondary and tertiary education on the economic evolution in Nepal.
While the results of Johansson cointegration test prove a long relationship and
high contribution between the education levels and the economic growth of de-
veloping countries like Nepal. Finally, the researcher recommended the govern-
ment of developing countries to invest in education and give it the top priority
and time since education is playing a crucial role in the development of their
economy [9].
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10.4236/ajibm.2018.81008 132 American Journal of Industrial and Business Management
The previous two papers were focusing on the role of education in economic
growth. Both papers proved this result using different methods and techniques
at different locations.
The Effect of Education and Experience on Wages
Through years, many research proved that work experience has a significant
role on the worker wages. This part of the literature review will illustrate two
papers discussed the impact of education and experience improvements on the
wages increase.
Thailand as an emerging country has faced a significant gap between the ris-
ing level of educational attainment and the growing demand for skilled labor
along with the dramatic difference between the urban and rural labor markets.
This has been resulting in an income inequality when the wage distribution lacks
uniformity across the region. Wannakairoj from the University of Washington,
examine and evaluate the relationship between the education and work expe-
rience on wages in urban and rural areas in Thailand, by applying an empirical
model on a raw data collected by the national statistical office of Thailand from
19,099 Thai individuals in the third quarter of 2012. The empirical model de-
veloped using the ordinary least square (OLS) regression model of Mincerian
wage equation [10]. More on this topic, Shahab, Sanaullah, Ubaid Ali and Mu-
hammad Kaleem noticed the same relation between education and experience
on the wages at Khyber Pakhtunkhwa, Pakistan. They used cross-sectional sur-
vey and questionnaire design, multi-stage sampling procedures and studied
three hypotheses using simple multiple regression to examine the effect of edu-
cation, experience and skills on the workers’ wages [11]. This study was com-
prehensive regarding to the work components which are education, experience
and skill, and studied each factor in a separate way to observe its effect.
The results obtained from the OLS regression model done by Wannakairoj
demonstrated that there is a strong relationship between the education, expe-
rience and wage with the difference in the urban and rural areas. The results also
illustrated a significant positive relationship between an additional year of edu-
cation and wage while the coefficient of an additional year of experience was rel-
atively small. On the other hand, Shahab and his colleagues concluded that the
coefficient of education shows that one-year increase in education can increase
the income by PKR. 2370.3. Moreover, a one-year increase in experience can in-
crease earnings by PKR. 1107.91. In addition, the coefficient of education shows
that if the value of skill dummy is changed from 0 to 1 it can increase earnings
by PKR. 1169.27 [11].
People usually get promoted in their work through more than one year of ex-
perience, while an additional year of education can make a difference in defining
the initial job position. This indicates that an additional year of education is as-
sociated with the percentage increase in wage level [10]. The research took into
consideration the differences in the labor force between the urban and rural
areas. In addition, t-test was conducted to support the results of the OLS regres-
sion model. However, there was some limitation in the study. According to
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10.4236/ajibm.2018.81008 133 American Journal of Industrial and Business Management
Wannakairoj [10], some assumptions have been set which limited the experi-
ment. First, the study assumed monthly wages for the labor in the rural market
while this is not the case, they receive their wages seasonally. Second, the expe-
riment didnt consider the quality of the education although it is an important
factor. Wannakairoj illustrated that the wage gap will narrow in Thailand and
the income inequity will be reduced when the modern sector starts absorbing
labors from the rural market. Also, the writer suggested for further studies to be
conducted regarding this topic especially in Thailand and further factors should
be taken into consideration.
From the previously discussed papers, the impact of education and experience
has been clear by two different studies conducted at a different location by sev-
eral methods.
Education and Income Inequality
At many nations, individuals suffer from income inequality as an issue speci-
fies their life’s path. For instance, researchers studied this problem and tried
their best to detect the factors causing this problem and discover solutions to
reduce the impact of these factors. This part will illustrate several researches
which studied the role of education in income inequality problem.
For decades, the United States individuals suffered from income inequality as
Scott and Jessica mentioned in their article [12]. As these two examine several
types of secondary data sources (e.g. previous statistics and research), they aimed
to list the factors that affect the income and cause inequality among American
individuals. Pranob Kumar Mishra noticed the same issue at Maharashtra state
in India. The workers at the unorganized sectors are facing high-income inequa-
lity and most of them are low paid. Pranob chose the education level as a factor
to explain the inequality. Gathering primary data by conducting questioners
through personal interviews and filed surveys in the labor market at Maharash-
tra state is the method Pranob used to conduct his study and he used SPSS, Excel
and statistical graphs to analyze the findings [13]. In addition to that, Kevin
Sylwester from Southern Minois University at the United States conducted a re-
search to determine which factor will have the most significant effect in reducing
the income inequality. Kevin focused in his study on two factors, increasing in-
vestments in public education and increasing human capital of the country using
Gini coefficient, which was found in a previous study [14].
The results from Scott and Jessica’s study demonstrated a strong relationship
between education and individual’s income. In fact, people with high education
level are more skilled and has less chance of being unemployed. Also, the more
educated the person is the more he knows how to manage his income, save
money, increase wealth and reduce the chance of having debt or loans. In fact,
having a strong relation between education and income does not mean that
education is the only factor affecting income inequality. For instance, natural
ability of the individual, assertive mating, inheritance and health are all main
factors that play important role in income inequality [12]. The findings of Pra-
nob are supporting Scott and Jessica’s in considering education as a main factor
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10.4236/ajibm.2018.81008 134 American Journal of Industrial and Business Management
of income inequality. For instance, he found that higher educated workers have a
higher chance of getting long period contracts (e.g. Months, years) and though
higher income. Also, lower educated workers do not have the ability to make
future investments since they are low paid, does not have social security and
work in conditions that are hazardous or unhealthy. In fact, these low educated
workers are not aware of the minimum wage act, and they are paid less than the
minimum without their knowledge [13]. The result of Kevin’s study is to deter-
mine if expenditures in public education are more beneficial than increasing
human capital of the country showed that increasing public education expendi-
tures is more effective than increasing human capital in reducing income in-
equality. Kevin recommended governments to invest more in education as one
factor to overcome the inequality problem. One issue in Kevin’s study is that it
did not consist which level of the public education (primary, secondary or high
education) will be more effective to increase its expenditures [14].
The previous part discussed the education as the main factor causing income
inequality. The studies persuaded increasing the level of education as one factor
to reduce income inequality in societies.
The Benefits of Higher Education for Individuals and Society
Figures can illustrate the importance of education easier than words. This is
what Sandy and Kathleen adopted in their report. They presented detailed evi-
dence and pointed well-integrated picture of both the private and public benefits
of higher education by integrating government statistics with some academic re-
search from the American society. Sandy and Kathleen examined the benefits of
investing in higher education to individuals and society [15]. All statistics dem-
onstrated a correlation between the higher level of education and higher income
for both men and women. Moreover, a student in the higher education obtains a
variety of personal and financial benefits. Looking to the societal benefits of in-
vesting in higher education, people with high level of education faces a lower
risk of unemployment and they are less likely to depend on social safety-net
programs. In addition, high level of education associated with great levels of civ-
ic participation such as volunteer work and blood donation, along with the low
smoking rates among colleges graduates which in return indicate to positive
perceptions of personal health. The final answer is that education does pay both
individuals and society benefits from the investments in higher education [15].
The Impact Grades on Salary
After discussing the importance of education in many aspects, one factor is
believed to have a significant impact on salary, success and job performance and
is related to education, which is the obtained grades. Philip, Roth, Richard and
Clarke from Clemson University conducted a Meta-analysis to discover the rela-
tion between grades and salary as the dependent variable. They used the Hunt-
er-Schmidt approach to analyzing the data. The reliability of grades was ex-
amined and estimated by Reilly and Warech (1993), as their correction was used
in this study analysis. Based on cumulating statistics describing the correlation
between grades and salary, two relations were found, a relation between grades
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with starting salary and another between grades with current salary. The Modest
correlation was found with the starting salary and moderate range correlation
with the current salary. The size of the grade-salary growth correlation was neg-
ligible. By these results, it was concluding and suggested that grades “may be” a
meaningful predictor of current salary and a “somewhat” meaningful predictor
of a starting salary. This paper tried to correct some previews studies with some
correction criteria but it was depending on some previews literatures that have
small sample size [16].
To sum up, this literature review illustrated the importance and impact of
various levels of education on individual’s income at different nations. For in-
stance, education has a strong relationship with the countries’ economic growth,
since higher educated individuals are paid higher wages and countries who ex-
penses on education will have more skilled individuals and this will reduce in-
come inequality among them. In fact, statistics demonstrated a correlation be-
tween the higher level of education and higher benefits for both individuals and
society regardless the obtained grades of education.
3. Methodology
The most suitable method that will simplify the process in achieving the research
objectives is the prioritization matrix. Prioritization matrix is a useful technique
to rank different topics generated using weighted criteria that are important to
the research. It is helpful in making a priority of the available choices and pro-
vides sort diverse set of items into an order of importance [17]. The following
points represent the typically used weights:
Equally important and its weight equal to 1;
Significantly more important and its weight equal to 5;
Extremely more important and its weight equal to 10;
Significantly less important and its weight equal to 1/5;
Extremely less important and its weight equal to 1/10.
The decisions of importance will be decided based on the salary average for
each factor. The studied factors with their categories will be listed in the follow-
ing points:
Academic degree:
- Doctoral Degree;
- Master Degree;
- Bachelor Degree;
- Diploma;
- High School or less.
Education major:
- Science;
- Literature.
Job sector;
- Public;
- Private;
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10.4236/ajibm.2018.81008 136 American Journal of Industrial and Business Management
- Other.
Experience years:
- More than 20 years;
- 15 - 20 years;
- 10 - 14 years;
- 5 - 9 years;
- Less than 5 years.
The process of constructing the prioritization matrix involves four steps [18]:
1) Define factors and their categories that represent the area of study;
2) Create the matrix for each factor by listing its categories along on columns
and rows;
3) Compare between every two categories using the weights;
4) Prioritize the categories based on their total scores.
Data collection
The used method of choosing the sample is stratified random sampling, where
random Saudi individuals are chosen then they are sub-grouped by their gender.
The method used for data collection was a survey. The survey included eight
multiple choice questions as listed in the bellow with their explanation:
1) What is your gender? Multiple choice question with two choices:
Male or Female.
2) What is your nationality? Multiple choice question with two choices:
Saudi or non-Saudi.
3) What is your living region?
Multiple choice question with two choices:
Large region (including Riyadh-Makkah-Eastern
Region-Madinah-Asir) or small region
(AlQassim-Tabuk-Hail-Jazan-Najran-AlBahah-
AlJawf-Northern Borders).
4) What is your academic degree?
Multiple choice question with five choices:
High School or less, Diploma, Bachelor
Degree, Master Degree or Doctoral Degree.
5) What is your educational major? Multiple choice question with two choices:
Science major or Literature.
6) What is your job sector?
Multiple choice question with three choices:
employer at public job, employer at privet
job or other (
i.e.
having his/her own business).
7) How many years of
experience do you have?
Multiple choice question with five choices:
Has more than 20 years, between 15 - 20 years,
10 - 14 years, 5 - 9 years or less than 5 years of experience.
8) What is your monthly salary?
Multiple choice question with six choices:
More than 28,000 SAR, between 20,000 - 28,000 SAR,
between 15,000 - 19,999 SAR, between 10,000 - 14,999 SAR,
5000 - 9999 SAR or Less than 5000 SAR.
The total responses to the survey were 2470 response. The survey targeted
Saudi employees with 18 years old. Therefore, 53 participant’s responses were
eliminated since it did not match with participants constraints. As the research is
concerning of male and female employees’ respondents separately, the first
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question helped in separating the two genders. The total female responses were
33% and the male responses were 67% as shown in Table 1 which indicates also
the majority of participants were from the large regions of Saudi Arabia.
Data analysis
The raw data was analyzed using Prioritization matrix and the weights were
given based on the salary average for each category. The study was conducted on
Saudi males and females in order to determine which top three cases from each
gender have the highest salary in Saudi Arabia. They are 150 cases and each case
has one category from each factor. Table 2 listed the average salary for each cat-
egory under each factor for males and females.
Table
1.
Participants
sociodemographic.
Region
Small regions including
(AlQassim, Tabuk, Hail, Jazan,
Najran-AlBahah, Al Jawf
and Northern Borders)
Large regions including
(Riyadh, Makkah, Eastern
Region, Madinah and Asir)
Gender Male Female Male Female
Participants 22% 10% 45% 23%
Table
2.
Salary
average
for
each
category
.
Factor: Academic Degree
Category Average Male Salary Average Female Salary
Doctoral Degree 26,308 21,553
Master Degree 19,765 13,038
Bachelor Degree 12,538 11,511
Diploma 9889 13,250
High School or less 11,306 6333
Factor: Education Major
Category Average Male Salary Average Female Salary
Science 15,625 12,266
Literature 12,854 11,479
Factor: Job Sector
Category Average Male Salary Average Female Salary
Public sector 14,410 13,611
Private sector 17,155 8924
Other 10,676 6095
Factor: Experience
Category Average Male Salary Average Female Salary
Less than 5 years 11,267 6568
5 - 9 years 13,278 10,260
10 - 14 years 16,250 10,216
15 - 20 years 14,846 13,095
More than 20 years 18,382 15,452
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The following tables (Tables 3-10) will illustrate the Prioritization matrix for
each factor and the weights was assigned to each category based on the compar-
ison of the categories’ monthly average salary. Tables 3-6 belong to males’ data
while Tables 7-10 belong to females’ data.
Based on the conducted matrices the categories’ priority percent under each
factor are as the following:
Table
3.
Prioritization
matrix of academic degree factor for males.
Academic
Degree
Doctoral
Master
Bachelor
Diploma
High
School
or
less
Row
Total
Decimal
Value
Doctoral
5.00 10.00 10.00 10.00 35.00 0.51
Master
0.20
5.00 10.00 5.00 20.20 0.30
Bachelor
0.10 0.20
5.00 1.00 6.30 0.09
Diploma
0.10 0.10 0.20
0.20 0.60 0.01
High
School
or
less
0.10 0.20 1.00 5.00
6.30 0.09
Total 68.40
Table
4.
Prioritization
matrix
of
education
major
factor
for
males
.
Education
Major
Science
Literature
Row
Total
Decimal
Value
Science
5.00 5.00 0.96
Literature
0.20
0.20 0.04
Total 5.20
Table
5.
Prioritization
matrix
of
job
sector
factor
for
males
.
Job
Sector
Public
Private
Other
Row
Total
Decimal
Value
Public
0.20 5.00 5.20 0.25
Private
5.00
10.00 15.00 0.73
Other
0.20 0.10
0.30 0.01
Total 20.50
Table
6.
Prioritization
matrix
of
years
of
experience
factor
for
males
.
Experience/Years
<
5
5 - 9 10 - 14 15 - 20 >20
Row
Total
Decimal
Value
<
5
0.20 0.10 0.20 0.10 0.60 0.01
5 - 9 5.00
0.20 1.00 0.20 6.40 0.12
10 - 14 10.00 5.00
5.00 1.00 21.00 0.38
15 - 20 5.00 1.00 0.20
0.20 6.40 0.12
>20
10.00 5.00 1.00 5.00
21.00 0.38
Total: 55.40
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Table
7.
Prioritization
matrix
of
academic
degree
factor
for
females
.
Academic
Degree
Doctoral
Master
Bachelor
Diploma
High
School
or
less
Row
Total
Decimal
Value
Doctoral
5.00 5.00 5.00 10.00 25.00 0.53
Master
0.20
1.00 1.00 5.00 7.20 0.15
Bachelor
0.20 1.00
1.00 5.00 7.20 0.15
Diploma
0.20 1.00 1.00
5.00 7.20 0.15
High
School
or
less
0.10 0.20 0.20 0.20
0.70 0.01
Total 47.30
Table
8.
Prioritization
matrix
of
education
major
factor
for
females
.
Education
Major
Science
Literature
Row
Total
Decimal
Value
Science
5.00 5.00 0.96
Literature
0.20
0.20 0.04
Total 5.20
Table
9.
Prioritization
matrix
of
job
sector
factor
for
females
.
Job
Sector
Public
Private
Other
Row
Total
Decimal
Value
Public
5.00 5.00 10.00 0.81
Private
0.20
1.00 1.20 0.10
Other
0.20 1.00
1.20 0.10
Total 12.40
Table
10.
Prioritization
matrix
of
years
of
experience
factor
for
females
.
Experience/Years <5 5 - 9 10 - 14 15 - 20 >20
Row
Total
Decimal
Value
<5
0.20 0.20 0.10 0.10 0.60 0.01
5 - 9 5.00
1.00 0.20 0.20 6.40 0.12
10 - 14 5.00 1.00
0.20 0.20 6.40 0.12
15 - 20 10.00 5.00 5.00
1.00 21.00 0.38
>20
10.00 5.00 5.00 1.00
21.00 0.38
Total: 55.40
Academic degree:
- Doctoral Degree with 51% priority for males and 53% priority for females;
- Master Degree with 30% priority for males and 15% priority for females;
- Bachelor Degree with 9% priority for males and 15% priority for females;
- Diploma with 1% priority for males and 15% priority for females;
- High School or less with 9% priority for males and 1% priority for females.
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Education major:
- Science with 96% priority for males and females;
- Literature with 4% priority for males and females.
Job sector:
- Public with 25% priority for males and 81% priority for females;
- Private with 73% priority for males and 10% priority for females;
- Other with 1% priority for males and 10% priority for females.
Experience years:
- More than 20 years with 38% priority for males and females;
- 15 - 20 years with 12% priority and 38% priority for females;
- 10 - 14 years with 38% priority and 12% priority for females;
- 5 - 9 years with 12% priority for males and females;
- Less than 5 years with 1% priority for males and females.
After that, the score for each case has been calculated by multiplying the priority
percentages of the categories. The following tables (Table 11 and Table 12) illu-
strate the cases with the highest weight for males and females respectively.
According to Table 10, the Saudi males who have the highest income are:
Table
11.
Cases
weights
for
males
.
Cases Score % (Male)
Doctoral_Science_Public_10 - 14 4.73%
Doctoral_Science_Public_>20 4.73%
Doctoral_Science_Private_5 - 9 4.16%
Doctoral_Science_Private_10 - 14 13.65%
Doctoral_Science_Private_15 - 20 4.16%
Doctoral_Science_Private_>20 13.65%
Master_Science_Private_10 - 14 7.88%
Master_Science_Private_>20 7.88%
Table
12.
Cases
weights
for
females
.
Cases Score % (Female)
Doctoral_Science_Public_5 - 9 4.73%
Doctoral_Science_Public_10 - 14 4.73%
Doctoral_Science_Public_15 - 20 15.54%
Doctoral_Science_Public_>20 15.54%
Master_Science_Public_15 - 20 4.47%
Master_Science_Public_>20 4.47%
Bachelor_Science_Public_>20 4.47%
Diploma_Science_Public_15 - 20 4.47%
Diploma_Science_Public_>20 4.47%
H. Alsulami
DOI:
10.4236/ajibm.2018.81008 141 American Journal of Industrial and Business Management
1) A male who has doctoral degree in science major and has been working in a
private sector from 10 to 14 years or more than 20 years.
2) A male who has master degree in science major and has been working in a
private sector from 10 to 14 years or more than 20 years.
3) A male who has doctoral degree in science major and has been working in a
public sector from 10 to 14 years or more than 20 years.
The Saudi females who have the highest income are:
1) A female who has doctoral degree in science major and has been working in
a public sector from 15 to 20 years or more.
2) A female who has doctoral degree in science major and has been working in
a public sector from 5 to 14 years.
3) A female who has master degree in science major and has been working in
a public sector from 15 to 20 years or more. Also, a female who has bachelor in
science major and has been working in a public sector for more than 20 years.
Finally, a female who has diploma degree in science major and has been working
in a public sector from 15 to 20 years or more.
Therefore, the results show that the most two factors that significantly affect
the Saudi Salaries for both male and male is education qualification and the sec-
tor either public or private sector. Hence, Saudis who want to increase their in-
come should carefully look at these two factors and do extra work to improve
education qualification or may shift from public to private sector.
4. Conclusion and Recommendation
In an attempt to research about the explanation of the differences in people in-
come around the world, one interesting issue is what can cause this variation in
the individual income. Saudi individual both males or females who might be of
the same age and work in the same field can gain different amounts of monthly
salary due to different factors that can cause this variation. This paper looked
closely at several scenarios regarding the factors that caused the variation in the
salary of Saudi individuals and specified which factors resulted in the highest
salary. As a future recommendation, this research can be made in more specific
details according to specifying several factors, study each factor and find if they
have significant impact on the individual salary using statistical analysis. More-
over, it is recommended to increase the number of sample size, also, study dif-
ferent factors, and find which factor is highly increasing the individual salary ei-
ther for male or female. Furthermore, in Saudi Arabia, there are several regions
such as the western, eastern and middle region which can be studied separately,
and compare between them to find out if factors that cause the salary to increase,
differ from region to another.
Acknowledgements
The author would like to acknowledge the contributions made by the following
individuals: Dania Al-Fozan, Heba Gogandy Khadija Mughrbil and Roaa Felim-
ban. They have provided valuable inputs while being students at the Industrial
H. Alsulami
DOI:
10.4236/ajibm.2018.81008 142 American Journal of Industrial and Business Management
Engineering Seminar Course.
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Economic Growth and Education in Saudi Arabia: An Application of Simultaneous Equations Method
  • A B M Almaliki
  • B Obeid
Almaliki, A.B.M. and Obeid, B. (2010) Ahmad Ben Suleiman. 2010. Economic Growth and Education in Saudi Arabia: An Application of Simultaneous Equations Method, Alriyad, Saudi Arabia Kingdom.