Economic and Socio-Demographic Factors of Labor Mobility
in the Service Sector
Dina Anggraini, Yunisvita
and Imelda
Faculty of Economics, Universitas Sriwijaya, Palembang, Indonesia
yunisvita, imeldazainal
@unsri.ac.id
Keywords: Mobility Labor, Wage, Experience Works, Education, Age, Binomial Logistic
Abstract: This study discuss about the mobility of labor between services and non-services sectors in Palembang city.
Labor mobility is divided into moving sector of employment and not moving sector of employment. Data
used in this study is secondary data obtained from the Central Statistics Agency of Palembang, as well as
primary data obtained through a questionnaire of 100 respondents. Respondents were obtained using non-
probability sampling technique with purposive sampling method. The analysis technique used is a binary
logistic regression. The results showed that wage and work experience significantly influencing the
possibility of labor mobility. Wage variable positively effect on the probability of labor mobility between
sectors, while work experience has a negative effect on it.
1 INTRODUCTION
The city of Palembang is one of the major cities on
the island of Sumatra which has the potential to
experience a shift in the structure of the economy
which certainly changes the sector in employment.
Palembang initially relied heavily on the industrial
sector as a sector that was able to generate the
largest share in Gross Regional Domestic Product
(GRDP), but due to the current contribution
experienced a shift in the leading sectors, namely the
industrial sector which experienced a decline and
vice versa the service sector increased more
(Yunisvita, 2011) .
According to Clark (1941), Kuznets (1957), and
Fuchs (1980) in Ahmed and Ahsan (2011) observed
that the shift in the economic structure from
agriculture to industry then from industry to services
occurred during economic development. The service
sector is considered to be an indicator that shows
progress in the economy.
The service sector is divided into several sub-
sectors, including large and small traders (including
hotels and restaurants), transportation, government,
finance, professional services and personal services
such as education, health, and real estate services.
Meanwhile, the manufacturing sector runs its
business by converting raw materials into semi-
finished products or finished products. There are
several manufacturing sectors, including producers
of transportation equipment, food and soft drinks,
machinery, medicines, home furnishings, textiles,
and so on.
Table 1: Sectoral Gross Regional Domestic Product
Contribution in Palembang City 2010-2016 (%)
Source: Palembang Central Bureau. The Gross Regional
Domestic Product of Palembang City According to the
2012- 2016 Economics Sector, processed in 2018
The service sector has the largest contribution to
the economy in the city of Palembang. The
contribution of Gross Regional Domestic Product
from the services sector during 2010-2016
experienced an average contribution of 61.58% with
the largest contribution in 2016 reaching 63.13%.
Years
Agriculture
Industry
Service
2010
0.55
39.55
59.90
2011
0.54
39.07
60.39
2012
0.52
38.38
61.10
2013
0.53
37.45
62.02
2014
0.55
37.06
62.39
2015
0.54
37.03
62.14
2016
0.53
36.34
62.13
378
Anggraini, D., Yunisvita, . and Imelda, .
Economic and Socio-Demographic Factors of Labor Mobility in the Service Sector.
DOI: 10.5220/0008440403780386
In Proceedings of the 4th Sriwijaya Economics, Accounting, and Business Conference (SEABC 2018), pages 378-386
ISBN: 978-989-758-387-2
Copyright
c
2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reser ved
While the industrial sector contributes an average of
only 37.38% and the largest contribution occurred in
2010, which was 39.55%. Furthermore, the
agricultural sector has the smallest average
contribution of only 0.54%. This shows that the
service sector dominates the GRDP of Palembang
City.
Economic structure transformation is not only
defined as sectoral domination in the formation of
Gross Regional Domestic Product (GRDP) but also
sectoral domination of employment absorption.
Thus, when the industrial share falls, the absorption
of labor in the sector will also go down. This causes
the service sector to become more dominant. The
phenomenon of labor reduction in certain sectors as
a result of workers preferring to work in other
sectors (Herdianti, Burhan, and Pratomo, 2015).
The provision of employment opportunities
usually follows the economic developments that
occur. If in the early years of economic development
more people worked in the agricultural sector, then
in line with economic development there was a
transformation of employment to more complex
jobs, namely industry and eventually to the service
stage (Tjiptoherijanto, 1997 in Utomo, 2014).
Kusreni's research (2009) explains that
employment opportunities in Indonesian cities are
generally dominated by the service sector that is
public service, therefore the service sector has
experienced a rapid increase in labor for urban areas.
While the agricultural sector experienced a decline
while the industrial sector was relatively constant.
Alexandi and Marshafeni (2013) said the
increase in labor in the service sector was influenced
by the minimum wage policy determined in an area.
This means that a high minimum wage causes the
absorption of labor in the service sector to also
increase.
Research conducted by Herdianti, Burhan, and
Pratomo (2015) states that an increase in the number
of workers in the industrial sector and services is a
result of a decrease in the workforce in the
agricultural sector. This decrease will increase the
community's per capita income.
According to Soekartawi (1995) in Utomo
(2014) an increase in the number of people in rural
areas causes difficulties in obtaining productive
employment in the agricultural sector. Because the
work in the agricultural sector is not too much and
only relies on a small number of workers, thus
encouraging workers who are not accommodated
will move to the non-agricultural sector.
Table 2: Population aged 15 years and over According to
the job sector of Palembang City in 2013-2015 (people)
Job Sector
Years
2013
2014
2015
Agriculture
6583
14 290
33 997
Industry
65 026
81 339
72 291
Service
528 791
565 563
565 863
Amount
600 408
661 192
672 151
Source: Palembang in Figure 2013-2015,
Palembang Central Bureau, processed in 2018
From Table 2 it can be seen that during 2013-
2015 the number of workers in the agricultural
sector continued to increase. This increase is more
than double every year. But the increase in the
number of workers is not much greater when
compared to the service sector.
The service sector which includes trade, hotels
and restaurants, community services, other services,
has the most number of workers from 2013 to 2015.
The biggest increase in labor is in 2014, which is
around 36,772 people more than in 2013. Then the
number increased in 2015 even though the increase
was not too large.
In the industrial sector the number of workers in
2015 decreased by 0.89 lower than the number of
years before. But when compared with the number
of workers in 2013, the number is 1.11 times more.
The number of workers in the industrial sector
fluctuates and tends to decline, while the number of
labor services in the city of Palembang continues to
grow. This shows that workers in the industrial
sector perform labor mobility between sectors.
Worker mobility occurs as a result of a decrease in
employment in the industrial sector and an increase
in employment in the service sector.
According to Sulistyono (2011), the mobility of
labor from certain sectors is influenced by: (1) the
level of education, where the higher the level of
education of a person, the easier it is for someone to
move labor. (2) the age factor makes someone
choose the right job. Old age makes people choose
jobs that are easy, and do not require a lot of energy.
(3) the level of wages is the main factor that
determines a person to work in a particular sector. If
the sector of work that has been undertaken has a
lower wage rate than other occupational sectors, the
greater the motivation of someone to move jobs.
The difference in wage systems is one of the
factors that determine the mobility of workers
between sectors. Permata, Yanfitri, and Prasmuko
(2010) argue that the difference in wage systems is
Economic and Socio-Demographic Factors of Labor Mobility in the Service Sector
379
one of the factors that directly affect the mobility of
workers both across companies in the same industry
or across different industries.
Akkemik (2005) that workers are more likely to
move to other sectors that provide wage increases,
meaning that workers prefer sectors with better
productivity than other economic sectors. Ahmed
and Ahsan (2011) argue that the higher the skills,
education, work experience, and work needed, the
higher the wages paid. Miskiyah, et al (2017),
Euwals (2001), and McLaughlin and Bils (2001)
state that if wages received in jobs are now very low,
workers tend to look for jobs that offer higher
wages.
Previous work experience also affected labor to
move to other sectors. Permata, Yanfitri, and
Prasmuko (2010), show that workers who have had
work experience in the formal sector have a
tendency to move 45% more than workers who do
not have work experience in the formal sector. Even
in the industrial sector, workers with formal work
experience have a tendency to move 66.4% higher.
Miskiyah et al. (2017) shows that individuals
who have had longer work experience, the
opportunity to change jobs is increasing. Experience
controlling all types of jobs and also sources for
these workers to earn better income from previous
jobs.
Education can affect the workforce in finding
and carrying out work. Workers who have a higher
level of education will make it easier to move jobs.
Sulistyono (2011) states that education has a positive
effect on worker mobility. This means that the
higher the education that is owned by workers, then
encourages workers to do mobility.
According to Souza-Pouza and Henneberger
(2004) workers with low education tend to do fewer
jobs. This can be explained in terms of the theory of
human resources that the higher a person's education
shows the higher investment in education as well as
the wide scope of expertise that can be offered to
company owners.
Age can be a factor that influences labor
mobility. Markey and Parks (1989), Souza-Pouza
and Henneberger (2004), age is the most prominent
determinant factor in determining the transfer of
positions of voluntary work. The more age a person
tends to experience a decrease in the transfer of
positions. Most workers move jobs under the age of
45
McLaughlin and Bils (2001) also stated that the
increasing age of workers tended to have fewer jobs.
The rate of job turnover has declined sharply with
the increase in the age of workers also stated by
Goldberg and Aaronson (1999) that the increasing
age of workers will reduce the chance of moving
jobs between industrial sectors as a whole. Magnani
(2001) also argues that younger female workers tend
to change jobs more.
2 LITERATURE REVIEW
2.1 Fisher Development Model
Fisher (1939) proposed a theory of shifting patterns
of economic structure that focused on changes in
production and the use of factors of production with
the development of an economy. Fisher's hypothesis
is famous for the Three Stages of Economics
Development or three stages in economic
development consisting of pre-industrial (pre-
industrial), industrial (industrial), and post-industrial
(post-indutrial) and divides the economy into three
sectors namely the sector primary, secondary and
tertiary sectors. In the final stages of economic
development, consumer demand for services will
increase.
Fisher's results were supported by Clark's (1949)
statistical study in Herdianti, Burhan and Pratomo
(2015), that consumer demand for manufactured
goods will stagnate and consumer demand will shift
to the service sector as well as labor. The movement
of labor from one sector to another is also caused by
differences in productivity of each sector. Both
studies are often known as the Fisher-Clark Model
of Development.
2.2 Job mobility
Job mobility Worker mobility is the change of work
to a different job, type of work to another type of
work, or employment status to a different job status
(Alatas and Trisilo, 1990). Work mobility can be
seen from two sides. First, the view of the status of
job mobility which includes workers who have
moved jobs and workers who have not / have never
changed jobs. Second, work mobility is seen from
shifting types of work from one sector to another.
The transition from one job to another has an
important role in the economy, because it involves
improving the welfare of workers. In addition to job
shifting, there is a match between the company and
the workforce, where companies want a quality
workforce while workers expect higher wages. In
making a decision to move a job or keep working
long time is usually influenced by several factors,
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380
but usually the most dominant factor is the desire to
get more income. (Miskiyah, et al, 2017).
According to Tarmizi (2014) the mobility of
workers between types of work or occupation
depends on: a) incentives to move to other jobs with
higher skills, b) disintegration out of work with
lower skills, c) retraining, and d) facilities for energy
counseling services work. While labor mobility
between companies is affected by: a) recruitment
and dismissal, b) overpayment, c) reservation wages,
d) requirements for the bidding of permanent
employment contracts and e) retirement claims.
2.3 Migration Theory
The definition of migration in the broadest sense is a
place where geographical mobility resides which
includes all population movements that cross certain
boundaries in a certain period (Mantra, 1992).
Migration is the transfer of a worker from one
economic region to another. Migration can affect the
supply of labor in the long run and can also be seen
as an investment decision because a migrant hopes
to obtain a higher income stream in the future
(Santoso, 2012).
2.3.1 Theory Harris-Todaro
Todaro said the migration from the traditional sector
in rural areas to the modern sector in urban areas
was determined by two factors, First, the level of
real wage differences between the agricultural sector
(rural) and the industrial (urban) sector. Second,
there are opportunities to get jobs in urban areas.
Migration will occur if there is an expected rate
difference between the agricultural sector in rural
areas and the industrial sector in urban areas. If the
expected expected rate is higher in the rural
agricultural sector, there will be no migration from
urban to rural areas. (Sjafrizal, 2012).
Based on Haris – Todaro's theory, Palembang
City is an urban area whose agricultural sector is not
very dominant. Characteristic of urban areas is the
dominant industrial sector and services sector
(Sjafrizal, 2012), therefore it is assumed that
migration in the city of Palembang is from the non-
service sector to the service sector where the non-
service sector represents the sector in rural areas and
the service sector represents the urban sector (
Sjafrizal, 2012).
3 RESEARCH METHODS
This study analyzes the factors that influence
opportunities for mobility of workers from the non-
service sector to the service sector. The object that
will be examined in this study are workers who work
in the service sector in the city of Palembang. The
variables to be examined include the dependent
variable namely worker mobility and independent
variables namely wages, work experience, level of
education and age.
The population used in this study were all
residents of Palembang City who worked in the
service sector. Based on BPS data from Palembang
City in 2015, the number of workers in the service
sector is 565,863 people.
The size of the sample to be taken is determined
using the Slovin formula (Bungin, 2011) so that the
number of samples obtained is 100 people. The
sampling technique uses purposive sampling
technique. Respondents who will be given a
questionnaire must have the following
characteristics: 1) Workers in the service sector 2)
and / Have worked in the non-service sector.
The data analysis method used is the Binary
Logistic Regression Model. Variable regression
analysis of worker mobility between sectors is based
on two categories: (1) move; and (0) do not move
According to Hosmer, Lemeshow, and Studirvant
(2013), and Agresti (2007), the binary logit equation
model is:
From the equation, the model in this study is as
follows:
Remarks: MP = Job Mobility (1 = Move job sector;
0 = Not move the job sector)
P = Probability of respondents who perform work
mobility between sectors; 1-P = Probability of
respondents who do not perform work mobility
between sectors; WAGE = Respondent's salary /
month (in rupiah); EXP = Respondent's work
experience (in years); EDU = Formal education of
respondents (in years); AGE = Age of respondent (in
years)
Economic and Socio-Demographic Factors of Labor Mobility in the Service Sector
381
According to Yamin and Kurniawan (2014) the
results of the logistic regression equation cannot be
directly interpreted from the coefficient value as in
ordinary linear regression. Interpretation can be done
by looking at the value of Exp (B) or the exponent
value of the regression equation coefficient that is
formed.
Nachrowi and Usman (2002) describe the
interpretation of coefficients in logistic models
carried out in the form of odds ratios (risk
comparison) or in adjusted probability (probability
occurs). Odds are defined as p / (1-p) (risk), p
represents the probability of success (occurrence of
events y = 1) and 1 - p states the probability of
failure (occurrence of event y = 0).
Odds Ratio, Ψ is a comparison of the Odds value
(risk) in two individuals, for example individuals A
and B.
Odds Ratio is written as:
Based on bivariate data (X, Y); X is a continuous
independent variable and Y is the response variable
one-zero, then the interpretation of the coefficients
in the logistic model is any rise in unit C unit on the
independent variables will result in the risk of y = 1
exp (C) times greater.
3.1 Goodness of Fit Test
To assess the hypothesized model fit by using: 1)
Hosmer and Lemeshow Test that is, if the
probability values> 5%, it means the binomial
logistic regression model is feasible for further
analysis. 2) to see the value of logistical survival (-
2LL). If there is a reduction in the value of the initial
value -LL (block 0) with the next step -LL value
then this indicates that the hypothesized model is fit
to the data Sujarweni (2014).
3.2 Operational Definition of Variables
and Indicators
Table 3: Operational Definition of Variables and
Indicators
Variable
Definition
Unit
Scale
Labor
Mobility
transfer of
jobs from
Certain
sectors to
the same or
different
sectors
1 =
Move
the
employ
ment
sector;
0 = No
change
sector of
employ
ment
Nominal
Wage
The
consideratio
n received
in the form
of money or
goods
Rupiah
Nominal
Work
Experi-
ence
Length of
employment
to be taken
years
ratio
Education
The
duration of
formal
education
completed
years
ratio
Age
Worker's
Age
years
Nominal
4 RESULTS AND DISCUSSION
4.1 Cross tabulation of Job Mobility
and Economics Variables
Cross tabulation of worker mobility and economic
variables, including wages and work experience.
Table 4 shows that service sector worker
respondents who have a wage of Rp.2,000,000 to
less than Rp.3,000,000 reach a balanced amount,
which is 15% between those who do not move or
those who move jobs. At the same wage range this
amount is the biggest for workers who do not change
jobs.
As many as 9% of respondents who received a
wage of Rp.3,000,000 to less than Rp.4,000,000 did
not carry out mobility and as much as twice that
amount did work mobility. This is also the largest
number of all respondents who conduct mobility.
= Ψ
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Service sector workers who have a wage of
Rp.4,000,000 up to less than Rp.5,000,000 are only
6% who do mobility. The comparison between those
who do mobility and not mobility is 1: 2.
Table 4: Cross tabulation of Wage and Job Mobility
Wage (million)
job Mobility
Total
Not move the
job sector
Move job
sector
<2
6
5
11
2 - <3
15
15
30
3 - <4
9
18
27
4 - <5
3
6
9
> 5
7
16
23
Total
40
60
100
Source: Field Survey, 2018
Table 5: Cross tabulation of Work Experience and Job
Mobility
Work Experience
job Mobility
Total
not move
the job
sector
Move
job
sector
<5
14
46
60
5 - <10
8
8
16
10 - <15
2
4
6
15 - <20
2
2
4
> 20
14
0
14
Total
40
60
100
Source: Field Survey, 2018
Table 5 shows that service sector workers who
have less than 5 years of work experience are the
most engaged in labor mobility, which is 46%. All
respondents who had more than 20 years of
experience did not carry out mobility as much as
14%.
The same amount is also found in the work
experience of less than 5 years. This amount
includes the most for respondents who do not
perform worker mobility. Furthermore, as many as
4% of service sector workers who have 10 years of
work experience and less than 15 years of age have
moved jobs and half of that number is settled in their
jobs.
In the range of work experience of 5 years to less
than 10 years, the number of respondents who did
work mobility or who did not do mobility were 8%.
This is also found in different experience ranges of
15 years to less than 20 years. But the number is
four times less than the range of 5 years to less than
10 years
4.2 Cross Tabulation of Job Mobility
and social variables demograpic
Cross tabulation of worker mobility and social
demographic variables, including education and age.
Table 6 shows that respondents with high school
education are the most dominant in carrying out
labor mobility, which is 35%. Then 20% is also the
most dominant in deciding to stay on the job.
Respondents who do mobility are almost twice as
many as those who do not mobility. As many as 6%
of respondents with D3 education were the least
engaged in labor mobility and as much as 9% at the
same level of education also became the least settled
in their jobs.
Table 7 shows that service sector workers who
have an age range of 25 <30 years are the most
mobility, namely 27%. This number is three times
more than the number of workers from the three
oldest age ranges, only 9%. Whereas 19% of
workers who are in the age range <40 years are the
most who do not change jobs. This is because the
older age makes it difficult for workers to get new
jobs so they prefer to stay in old jobs. In addition to
age, older workers have limitations in terms of
physical strength and productivity.
Table 6: Cross tabulation of Education and Job Mobility
Education
Job Mobility
Total
Not move
the job
sector
Move
job
sector
Senior High
School
20
35
55
Diploma
9
6
15
S1
11
19
30
Total
40
60
100
Source: Field Survey, 2018
Table 7: Cross tabulation of Age and Job Mobility
Age
Job Mobility
Total
not move
the job
sector
Move
job
sector
<25
7
24
31
25 - <30
7
27
34
30 - <35
4
3
7
35 - <40
3
3
6
> 40
19
3
22
Total
40
60
100
Economic and Socio-Demographic Factors of Labor Mobility in the Service Sector
383
4.3 Binomial Logistic Result
Based on the hypothesis, with α = 0.05 and degrees
of freedom (df) = k = 8, a value of χ² (p) is obtained
from the chi-square distribution table of 5875. with a
significance value of 0.66. This means that a binary
logistic regression model that is feasible to use for
the next analysis and the null hypothesis which
states that no independent variable has a significant
effect on the dependent variable can be rejected.
Furthermore, by using the log likelihood value it
can be seen that there is a reduction in the value of -
LL at step 0 that is equal to 134.602 with a value of -
2LL in step 1 which is equal to 95.525. So that it can
be stated that the model hypothesized is fit with the
data. it can be concluded that together
(simultaneously), 4 independent variables
significantly affect the mobility of workers between
sectors.
Table 8 The Summary Model shows that the data
obtained has a Nagelkerke's R Square value of
0.437. This shows that the ability of independent
variables, namely wages, work experience,
education and age in explaining the dependent
variable, namely the probability or opportunity of
worker mobility between sectors in the city of
Palembang is 43.7 percent, while 56.3 percent is
explained by other factors outside the model.
Table 8: Model Summary
-2 log
likelihood
Cox & Snell
R Square
Nagelkerke R
Square
95.525
0.323
.437
Source: Field Survey, 2018
By using binary logistic regression, the test
results are shown in Table 9 below.
Table 9: Binomial Logistic Results
Variables
B
Sig
odds Ratio
Wage
0,000
0,029
1,000
Exp
-0.135
0.092
0.874
Edu
-0.184
0.184
.832
Age
-0.059
.219
.942
Constant
4.245
.070
69.777
Source: Field Survey, 2018
Based on the table, the regression equation
model that will be formed is as follows:
Zi = 4,245 + 0,000 Wage - 0,135 Exp - 0,184 Edu -
0,059 Age.
The wage variable has a coefficient of 0,000 with
a value of Odds Ratio 1,000. This shows that the
wage variable has a positive influence on the
probability or opportunity for worker mobility
between sectors. When wages rise, the probability or
opportunity for workers to move the job sector is
1,000 (one) times higher than workers who do not
change jobs. For example, the current wage offered
is 1 million, which moves the employment sector by
50%, then if wages rise to 2 million, mobility
workers increase 1-fold to 100%.
This is in accordance with the Haris-Todaro
theory that the level of difference in real wages
between the traditional sector which is assumed to
be the non-service sector and the modern sector is
assumed to be the service sector will cause high
opportunities for workers to carry out mobility.
Workers who do mobility are attracted to high
wages because high wages can meet all their living
needs to achieve prosperity.
As expressed by Akkemik (2005) that workers
will move when new jobs have a high level of
productivity so that the wages they produce are also
high. Permata, Yanfitri, Prasmuko (2010) also
argued that the same level of wages in each sector of
employment resulted in the movement of labor.
Sulistyono (2011) also got the same result that high
wages in the non-agricultural sector will increase
worker mobility. Maulida's opinion (2013) a wage
increase of only Rp1 will cause in-migration to
increase by 0.32%.
Work experience variables have a negative and
insignificant effect on the probability or opportunity
of worker mobility between sectors in the City of
Palembang. This can be seen from the coefficient of
-0.135 and a significance level of 0.092 greater than
the significance level of 5% or 0.05. But at a
significance level of 10% or 0.1 experiencing
variables have a negative and significant effect. The
Odds Ratio value of the experience variable is 0.847.
Odds Ratio values show that the more work
experience a worker has, the probability of moving a
job sector is 0.832 times lower than not moving the
job sector.
The results of the study are different from
Miskiyah. et al (2017) that experience variables are
the most influential on worker mobility. Workers
who have longer work experience, the opportunity to
change jobs is increasing. Nasa (2017) also got the
same results in his research on the decision to
migrate. Work experience variables have a positive
and significant influence. This means that the higher
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the worker's experience, the higher the decision to
migrate (settle) in Palembang City.
Educational variables have a coefficient value of
-0.184. and Odds Ratio value of 0.832. The results
show that the higher the education that is owned by
workers, the opportunity or opportunity to move the
job sector is 0.832 times lower than not moving the
work sector. But the influence of education variables
is not significant.
Education variables are not significant because
most respondents who have high school, D3, and S1
levels have the same opportunity to move the job
sector. The level of education can be accepted in any
sector. Even some respondents with secondary
school education are the most likely to move
because there are insiders who are known by some
respondents. In addition there are other factors,
namely their desire to change jobs or settle down.
Workers who have a higher level of education are
usually reluctant to move because they feel
comfortable with the current work environment.
Furthermore, the age variable of the age variable
has an Odds Ratio value of 0.942 with a coefficient
of -0.059. This means that the more the age of the
worker, the probability or probability of the worker
doing mobility becomes 0.942 lower. But education
variables also have no significant effect on the
mobility of workers between sectors in the city of
Palembang.
Based on the facts obtained in the field, the
reason for the age variable is insignificant, namely
that there are some workers whose ages are old but
are forced to change jobs because they are laid off
and have to find another job. The average worker
who is old and still working is the head of the family
and the backbone of the family.
5 CONCLUSION
The average wage of workers in the service sector is
Rp. 3,411,000. The average wage, which is only
Rp.2,294,000, can be classified as wages for workers
in the service sector. Experience and work is 7.18
years and things that are relevant to affordable work
in the service sector. The average level of education
in the service sector is secondary school, which can
be used in this sector, especially what is needed. The
average time given by workers in the service sector
is 30.8 years. This shows that workers in the service
sector are still relatively productive.
Variables that affect mobility between worker
sectors are wage variables and work experience.
Positive influential wage variables that provide high
yields are offered in sectors that generate a lot of
work in the Palembang city sector. Negative variable
work experience. If the work experience is longer,
the lower the chance to change jobs. education and
stock variables. While the level of education and the
influence of age are not significant.
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