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Volume 20, Number 3
International Journal of Administrative Science & Organization, September 2013
Bisnis & Birokrasi, Jurnal Ilmu Administrasi dan Organisasi
ISSN 0854 - 3844, Accredited by DIKTI Kemendiknas RI No : 64a/DIKTI/Kep/2010
The Effect of Educational, Health, Infrastructure Expenses
on the Workforce Employment and Poverty
NIKEN SULISTYOWATI
Faculty of Economy, Universitas Jayabaya, Jakarta
[email protected]
Abstract. The research aims to analyze the influence and impact of educational, health, and infrastructure expenses on the
employment of workforce (from agricultural, industrial, services sectors) and poverty in Central Java. The model was built
by using an econometric approach in the form of a system of simultaneous equation model consisting of six blocks (human
capital, inputs, outputs, revenues, expenditures and social welfare) with 33 equations (24 structural equations and 9 identity
equation). The method of estimation model uses Two Stage Least Squares (2SLS). The simulation results show that the policies
of increasing expenses on education, health, and infrastructure with the same value result in the increased employment of
workforce in all sectors and reduced poverty. Among these policies, the increased health expenses have the most significant
influence on the increase of workforce employment in services sector and reduce poverty. While the policy of increasing
expenses on infrastructure has the greatest effect on the increase of workforce employment in industrial and agricultural sectors.
Keywords: education, health, infrastructure, workforce
Abstrak. Penelitian ini bertujuan untuk menganalisis pengaruh dan dampak pengeluaran pendidikan, kesehatan dan
infrastruktur terhadap penyerapan tenaga kerja sektoral (pertanian, industri, jasa) dan kemiskinan di Jawa Tengah. Model
dibangun dengan menggunakan pendekatan ekonometrika dalam bentuk sistem persamaan simultan (simultaneous equation
model), terdiri dari 6 blok (human capital, input, output, penerimaan, pengeluaran dan kesejahteraan masyarakat) dengan
33 persamaan (24 persamaan struktural dan 9 persamaan identitas). Metode pendugaan model menggunakan Two Stage
Least Squares (2SLS). Hasil simulasi menunjukkan bahwa kebijakan peningkatan pengeluaran pendidikan, kesehatan dan
infrasruktur dengan nilai yang sama menyebabkan peningkatan penyerapan tenaga kerja semua sektor dan mengurangi
kemiskinan. Di antara ketiga kebijakan tersebut, peningkatan pengeluaran kesehatan paling besar pengaruhnya dalam
meningkatkan penyerapan tenaga kerja jasa dan mengurangi kemiskinan. Sedangkan kebijakan peningkatan pengeluaran
infrasruktur paling besar pengaruhnya dalam meningkatkan penyerapan tenaga kerja industri dan penyerapan tenaga kerja
pertanian.
Kata kunci: infrastruktur, kesehatan, pendidikan, tenaga kerja
INTRODUCTION
The recent concept of development is improving,
marked by the inclusion of public welfare aspect, in
addition to economic aspect (the growth of GDP per
capita). Public welfare cannot be separated from the
fulfillment of human basic needs, i.e. the increase of the
quality of education and health or the quality of Human
Resources. Poverty is one of the low qualities of human
resources, hence reducing the number of poor people is a
must for the goals of today’s development.
Underdevelopment and poverty exist partly because
people do not have modern knowledge, skills and attitude.
Poor education and health can cause people unable to
work optimally and affect the level of income (Schultz,
1961). Poor people have low income, thus cannot meet
their basic needs properly, and later results in a declining
work productivity and income. If this takes place in some
generations, it is called the vicious circle of poverty, as
illustrated in figure 1 below.
Poverty is negatively and significantly correlated to the
family’s decision to send the children to school. The need
of schooling is an effective way to reduce the phenomenon
Poor Health and
Education
Low savings and
consumption rate
(poverty)
Low productivity
Low level of income
Figure 1. The Vicious Circle of Poverty
Source: Kasliwal.1995; Capello, 2007
of child labor in Ghana (Canagarajah and Coulombe,
1997). Such is also common in Indonesia, where poverty
is the main reason for the kids to get out of school.
According Suryahadi et al. (2005) approximately 95% of
children in Indonesia are out of the basic education. The
contributing factor is the lack of a fee by 50-80%, helping
the elderly by 8-17% and the rest is for other reasons.
There is a tendency that the need to work to help parents
is due more to the severity of poverty.
Poverty cannot be separated from the high number
of unemployed population. The increase of population
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is not balanced with the provision of jobs, causing
unemployment rate to rise. On a national scale, Nanga
(2006) states that unemployment has an indirect effect in
increasing poverty. Unemployment positively gives an
impact to the increase of income inequality that enhances
poverty. Meanwhile, according to Hartati (2012)
unemployment has a direct effect in multiplying poverty.
Central Java was a notably strategic province, since it
lies between two major provinces, namely West Java and
East Java. From an economic standpoint it is supposed
to be beneficial for business development. Nevertheless,
ironically, the favorable position is not supported by
better public welfare. One of the fundamental problems
of economic development in Central Java is a high rate
of poverty. Compared with national poverty data, the
percentage of poverty in Central Java is still relatively
high. According to the data of Central Bureau of Statistics
(BPS), the poverty in Central Java in 2011 was amounted
to 5,256 thousand people or 16.21%, that was above the
national poverty rate of 12.49% (BPS, 2012). The BPS
data in 2008 showed that poverty is most prevalent in the
population with educational level of Junior High School
(SMP) or below, i.e. amounting to 92.8%. Meanwhile,
residents who have Senior High School (SMA) education
or above have less contribution to poverty, amounting
to 7.19% (BPS, 2009). This situation suggests that the
poverty rate can be due to a low level of public education.
Today, the standard of Human Development
Index (HDI) becomes very important to measure the
achievement of development goals. Local government
through its budget policy (particularly education, health,
and infrastructure expenses) is expected to help people
in improving the quality of human resources (human
capital), increase productivity and ultimately reduce
poverty (Sulistyowati, 2011). According to Duff (1997),
the government has a role and duty to achieve justice
(equity objective) which includes poverty eradication,
reduction of income inequality, welfare, equality of
access, compliance with clothing, food, housing, health,
and education. Through this role, the government is
expected to help the poor get out of the trap of the vicious
circle of poverty. Based on the background, the researcher
is interested in analyzing the influence and impact of
educational, health, and infrastructure expenses on the
employment of workforce and poverty in Central Java.
The purpose of the study is to analyze factors affecting
employment (in agricultural, industrial, and services
sectors) and poverty and the effect of increased education,
health, and infrastructure expenses on the employment of
workforce and poverty in Central Java.
According to Saariluoma (2005), human resources
is one of the major variables in economic growth, in
addition to several other variables such as technology,
social and economic conditions of the people, security,
and natural resources. In Endogenous growth models,
human capital formation is included in the production
function. Economy will achieve a high growth due to
the increase of investment in human capital. This theory
describes the influence of educational level on the growth.
Volume 20, Number 3
The results of the empirical study states that there is a
significant influence of human capital on the production.
Human capital is believed to be an important factor in the
process of economic growth.
Endogenous growth theorists argue on the assumption
of constant returns to capital, and perceive knowledge
as a capital. Compared with other forms of capital,
theoretically it is uncommon to assume that knowledge
has a diminishing returns unit. Even, the continuously
increasing knowledge and technological innovation thus
far make some economists argue that there is increasing
knowledge returns. Then the endogenous growth model
with the assumption of constant returns to capital becomes
a more promising description in long-term economic
growth. Similarly, human factors can develop, following
the development of science and technology. Advances
in science and technology become an important growth
factor (Mankiw, 2003).
According to Mankiw (2003), the differences in
income per capita are due to: differences in factors of
production, such as the quantity of physical capital and
human capital, and differences in the usage efficiency
of production factors. There is a positive correlation
between capital accumulation factors (including HR) and
the efficiency of production. Countries with a high level
of physical and human capital are likely to use the factors
of production efficiently. One hypothesis is that efficient
economy may encourage capital accumulation. Resources
and incentives to stay in school will accumulate a larger
human capital, so that the economy can function properly.
Endogenous growth theory was pioneered by Romer
(1986) who argue that economic growth is influenced by
the level of human capital through technological advances,
with the aggregate production function as follows: Y = F
(A, K, L, H), where: A is technological advances, K is
physical capital, H is human resources (accumulation of
education and training), and L is workforce. This is also
confirmed by Lucas (1988) who argues that in addition
to physical capital, human capital accumulation is crucial
for economic growth. On the other hand, Solow (1998)
argues that capital only includes supplies plant and
economic equipment; thus it is reasonable to assume
diminishing returns. Investment in physical capital and
workforce cannot be fully implemented independently
(internalized) by investor. The existence of externalities
can create increasing returns to scale, thus repairing the
assumption of constant returns to scale used by the neoclassical model.
RESEARCH METHODS
This research uses positivistic paradigm by employing
quantitative approach. The data used is pooled data, i.e. a
combination of time series data in 2004-2007 and cross
section (35 districts/municipalities in Central Java). The
models were created by using econometric approach
in the form of a system of simultaneous equations,
consisting of 33 equations (24 structural equations and 9
identity equations). The models are divided into 6 blocks,
SULISTYOWATI, THE EFFECT OF EDUCATIONAL, HEALTH, INFRASTRUCTURE
namely: 1) human capital block, 2) input block (physical
capital and sector workforce employment), 3) output
block (GDP), 4) government revenue block, 5) regional
expenses block (educational, health and infrastructure
expenses), and 6) poverty block.
The parameter estimation method uses a two stage
least square (2SLS). The advantage of using 2SLS is
among others offering consistent and efficient model
(Koutsoyiannis, 1977). The estimation is performed
by using the program of Statistical Analysis System/
Econometric Time Series (SAS/ETS) version 9.2 and the
procedure of linear systems (SYSLIN). To test whether
the explanatory variables jointly explain the diversity
of the endogenous variables in each equation, the F
statistical test is used, with a significance level ( ) of 1%.
Meanwhile, to test whether each explanatory variable is
individually influential or not to the endogenous variables
in each equation, the statistical test t is used, with a
significance level ( ) of 10%. The validation of estimation
models uses the proportion of bias (UM) and Theil’s
Inequality coeficient (U Theil) with SIMNLIN procedures
(Pindyck and Rubinfeld, 1991). This research employs a
historical simulation (ex-post simulation) that consists of
a range of possibilities for the following policies: (1) an
increase in educational expenses by 20 billion rupiahs, (2)
an increase in health expenses by 20 billion rupiahs, and
(3) an increase in infrastructure expenses by 20 billion
rupiahs.
The research hypothesis states that government
policies for education, health and infrastructure are
expected to increase sector workforce employment and
reduce poverty in Central Java. From the hypothesis an
outline of research model framework is made as described
in figure 2 below.
Figure 2 illustrates that the increase on education
and health expenses can improve the quality of
human resources (human capital). On the other hand,
infrastructure expenses can increase physical capital.
Endogenous growth theory states that the increase on
workforce, physical capital, and the quality of human
resources (human capital) can increase the regional output
(GDP). The increase in GDP can increase the workforce
employment and may ultimately reduce unemployment
and poverty. Besides, the increase in GDP can also boost
government revenue. Increased government revenue leads
to an increase in government expenses for education,
health and infrastructure sectors. Increased government
expenses budget will be a stimulus for various sectors to
increase GDP, and so on; all variables mutually interact
with each other in a system of simultaneous equations.
Health
expenses
Human
Capital
GDP
Phisical
Capital
Sector
workforce
employment
Educational
expenses
Infrastructur
e expenses
Government
revenue
Pemerintah
unemployment
Poverty
Figure 2. The Framework of Research Model
123
RESULT AND DISCUSSION
Results of equation parameter estimation of agricultural
workforce employment have a coefficient of determination
(R2) of 79.59%. Agricultural workforce employment
is significantly influenced by the explanatory variables
simultaneously (F statistic) with a value of 85.80. The
results showed that agricultural workforce employment is
significantly influenced by District/municipal Minimum
Wage, agricultural GDP, workforce, district/municipal
dummy and previous year’s agricultural workforce
employment, with the sign in accordance with the
hypothesis. Further, the equation parameter estimation
of agricultural workforce employment can be seen in the
following Table 1.
The minimum wage of district/municipality (UMK)
has a negative sign towards the agricultural workforce
employment. Seen from the elasticity, the effect of UMK
changes on the increase of the agricultural workforce
employment in short term was unresponsive (inelastic),
but in the long term it was responsive (elastic). In the
short term any increase in UMK by 10% led to reduced
demand for farm labor at 9.47%. While in the long term
any increase in UMK by 10% led to the reduction of
agricultural sector employment by 16.65%. This can
happen due to the low quality of agricultural human
resources, thus very susceptible to the termination of
employment (PHK) as a result of an increase in UMK, as
proposed by Ramos (1970).
The increase in agricultural GDP has a significant
and positive effect in improving agricultural workforce
employment with a relatively small magnitude, both in
the short and long terms. In the short term, any increase
in agricultural GDP by 10% will increase agricultural
employment by 1.75%. This is similar to the research
conducted by Nanga (2006).
Increased workforce also has positive and significant
effect in increasing agricultural employment. In the short
and long term, agricultural workforce employment has an
inelastic response towards the total work force. This result
is in line with research conducted by Rindayati (2007). In
the short term, any increase in the work force by 10%
will increase the agricultural workforce employment by
3.41 %.
The minimum wage of district/municipality (UMK)
has a negative sign towards the agricultural workforce
employment. Seen from the elasticity, the effect of UMK
changes on the increase of the agricultural workforce
employment in short term was unresponsive (inelastic),
but in the long term it was responsive (elastic). In the
short term any increase in UMK by 10% led to reduced
demand for farm labor at 9.47%. While in the long term
any increase in UMK by 10% led to the reduction of
agricultural sector employment by 16.65%. This can
happen due to the low quality of agricultural human
resources, thus very susceptible to the termination of
employment (PHK) as a result of an increase in UMK, as
proposed by Ramos (1970).
The increase in agricultural GDP has a significant
and positive effect in improving agricultural workforce
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International Journal of Administrative Science & Organization, September 2013
Bisnis & Birokrasi, Jurnal Ilmu Administrasi dan Organisasi
Table 1. The Result of Equation Parameter Estimation of
Agricultural Workforce Employment
Variable
Estimation
parameter
Prob >[t]
Intersep
171732.1
0.0251
Minimum
Wage of
district /
municipal
-599595
0.0427
Short term
Elasticity
Long term
-0.947
-1.665
elasticity
Agricultural 35.92333
GDP
0.0071
0.175
0.308
Workforce
0.119270
0.0009
0.341
0.600
District /
municipal
Dummy
-49767.9
0.0015
Time trend
2510.102
0.5336
Agricultural
employment
lag
0.431454
0.0001
Fhit = 85.80 Prob.F = 0.0001
Dw = 1.610385
Table 2. The Estimation Result of Equation Parameter on
Industrial Workforce Employment
Variable
R = 0.79592
employment with a relatively small magnitude, both in
the short and long terms. In the short term, any increase
in agricultural GDP by 10% will increase agricultural
employment by 1.75%. This is similar to the research
conducted by Nanga (2006).
Increased workforce also has positive and significant
effect in increasing agricultural employment. In the short
and long term, agricultural workforce employment has an
inelastic response towards the total work force. This result
is in line with research conducted by Rindayati (2007). In
the short term, any increase in the work force by 10%
will increase the agricultural workforce employment by
3.41 %.
District/municipal dummy has a negative and significant
sign towards agricultural workforce employment. This
indicates that agricultural employment are more prevalent
in the district (which is identical to the agricultural area)
compared to the municipality. Considering the low quality
of workforce in agricultural sector, the government has
an obligation to improve the quality of farmers through a
range of counseling and training on technologies that can
improve the productivity of farmers.
The estimation result of equation parameter on industrial
workforce employment has a coefficient of determination
(R2) of 66.418%. Industrial employment is significantly
affected by the explanatory variables, jointly indicated by
the F statistic with the value of 52.61. The results showed
that the industrial workforce employment is significantly
influenced by industrial GDP, workforce, time trend, and
previous year’s industrial workforce employment, with
a sign in accordance with the hypothesis. The complete
result of equation parameter estimation on industrial
workforce employment can be seen in table 2.
As is the case with the agricultural sector, the increase
in industrial GDP has a positive effect in increasing the
industrial workforce employment. In the short and longterms, the industrial employment has an inelastic response
to industrial GDP. Every 10% increase of industrial GDP
Estimation
parameter
Prob >[t]
Short term
Elasticity
Long term
elasticity
Intersep
-4443.41
0.7341
Industrial
GDP
11.38713
0.0001
0.158
0.266
Workforce
0.095784
0.0001
0.433
0.728
District /
municipal
Dummy
-14761.8
0.1755
Time trend
4356.659
0.1097
Industrial
employment
lag
0.405330
0.0001
Fhit = 52.61
2
Volume 20, Number 3
Prob.F = 0.0001
Dw = 1.408899
R2 = 0.66418
will increase industrial employment by 1.58%, lower than
the agricultural sector.
The increase in workforce will increase the industrial
employment that is inelastic both in the short and long
terms. In the short-term, the increase in workforce by
10% will increase the industrial employment by 4.33%.
Compared to agriculture, industrial employment is
more responsive to the increase in workforce. It can be
interpreted that the increase in a more educated workforce
will better encourage the increase in industrial than
agricultural employment.
The estimation result of equation parameter on service
workforce employment has a coefficient of determination
(R2) of 86.7%. the service employment is significantly
affected by the explanatory variables, jointly indicated
by the F statistic with the value of 174.34. The results
showed that the service workforce employment is
significantly affected by the services GDP, workforce,
district/municipal dummy, and previous year’s service
employment with a sign in accordance with the results
of the hypothesis. The complete detail on the estimation
result of equation parameter on service workforce
employment can be seen in the following table 3.
Table 3. The Estimation Result of Equation Parameter on Service
Workforce Employment
Variable
Estimation
parameter
Prob >[t]
Short term
Elasticity
Long term
elasticity
Intersep
-52304.5
0.0001
Service
GDP
9.256525
0.0001
0.086
0.098
Workforce
0.350446
0.0001
1.028
1.171
District /
municipal
Dummy
65901.58
0.0001
Time trend
517.2909
0.8217
Service
employment
lag
0.121977
0.0054
Fhit = 174.34 Prob.F = 0.0001
Dw = 0.967161
R2 = 0.86762
SULISTYOWATI, THE EFFECT OF EDUCATIONAL, HEALTH, INFRASTRUCTURE
Similar to agricultural and industrial sectors, the
increase in services GDP has a significant and positive
influence to the increase in service workforce employment.
In the short and long-term, services employment has an
inelastic response to changes in services GDP. In the short
term, the increase in services GDP by 10% can improve
service workforce employment by 0.86%, lower than the
agricultural and industrial sectors.
Increased workforce has a significant and positive
effect on services workforce employment. In contrast
to the agricultural and industrial sectors, services
employment in the short and long term has an elastic
response to changes in workforce. In the short term, any
increase in workforce by 10% will increase the services
employment by 10.28%, while in the long term any
increase in workforce by 10% will increase services
employment by 11.71%.
District/municipal dummy has a positive and significant
sign towards the services workforce employment. This
suggests that services employment are widely available
in urban than rural areas. This condition is different from
the agricultural and industrial sectors that employ more
workforce from rural areas.
The estimation result of poverty equation parameter
indicates the coefficient of determination (R2) of 88.8%.
Poverty is significantly influenced by the explanatory
variables, jointly indicated by the F statistic with the
value of 128.5. The estimation result of the parameter
indicates that poverty is significantly influenced by
household expenditure per capita, income distribution,
poverty line, population number, the share of agricultural
workforce, district/municipal dummy, time trend, and
previous year’s poverty with the sign consistent with the
hypothesis. Further details on the estimation result of
poverty equation parameter can be seen in table 4 below.
In terms of elasticity magnitude, the increase in
the household expenditure per capita has the greatest
Table 4. The Estimation Result of Poverty Equation Parameter
Variable
Intersep
Estimation
parameter
Prob >[t]
Short term
Elasticity
Long term
elasticity
-25106.1
0.7262
Household
-3.433E8
expenditure
per capita
0.0001
-1.595
-2.361
Gini Index
342415.2
0.0716
0.422
0.625
Poverty line
2499804
0.0001
1.147
1.699
Population
number
0.173885
0.0001
0.826
1.223
The share of 877.5121
agricultural
workforce
0.0094
0.158
0.233
District /
municipal
dummy
109214.9
0.0001
Time trend
-19791.3
0.0001
Poverty Lag
0.324639
0.0001
Fhit = 128.50 Prob.F = 0.0001
Dw = 1.37782
R2 = 0.88773
125
magnitude in reducing poverty. Poverty has an elastic
response to the household expenditure per capita, both
in short and long terms. In the short term, any increase
in household expenditure by 10% will reduce poverty by
15.95%. This may imply that the increase in the household
expenditure per capita will lead to reduced poverty. It is
quite logical considering the household expenditure is a
function of household income.
Income inequality (Gini index) has a significant
and positive effect on poverty, with a relatively small
magnitude (inelastic). In the short term, any reduction
in the Gini index by 10% will reduce poverty by 4.22%.
These result is in line with research conducted by Nanga
(2006).
Increased poverty line was responsive in increasing
poverty, both in short and long terms. In the short-term,
any increase in the poverty line of 10% will increase
poverty by 11.47%. In the long term, any increase in the
poverty line by 10% will increase poverty by 16.99%.
The poverty line that is too low will make the poverty
number seem small, although in reality it is not. It can be
used by the rulers (government) to claim their success in
reducing poverty.
The increase of population number has a significant and
positive effect on the increasing poverty in Central Java.
Although in the short term poverty is not responsive to the
population number, in the long run it will be responsive.
In the short term, any increase in the population number of
ten 10% will increase poverty by 8.26%. In the long term,
any increase in the population number of ten 10% will
increase poverty by 12.23%. These conditions suggests
that the increase in the population number in Central
Java is still a burden for the development. Considering
its important role for the success of regional/central
development, the improvement of human resources then
must get serious treatment from both regional/central
governments.
Based on the elasticity, the share of agricultural
workforce has a positive effect in increasing the number
of poor people, despite having a relatively small
magnitude. In the short term, any increase in the share of
agricultural workforce of 10% will increase poverty by
1.63%. Compared to the industrial and services sectors,
the agricultural sector has the lowest level of workforce
productivity. The low productivity of agricultural
workforce has an impact on the low income and welfare
of farmers. Such result supports the research by Pradhan
et al. (2000), affirming that agriculture is a sector that has
the highest poverty rate than other sectors.
The first simulation, the increase in educational
expenses of twenty billion rupiahs will result in the
following effects (table 5).
Table 5 shows that an increase in educational expenses
does not directly influence the increase of GDP, all sectors
employment, and the reduction of unemployment and
poverty. Increased workforce employm
ent is mostly enjoyed respectively by industrial sectors
of 2.56%, services sector of 0.85% and agricultural sector
of 0.67%. Overall, the increase in educational expenses
by 20 billion rupiahs, led to the increase of workforce
employment from 452,779 people to 458,268 people (a
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International Journal of Administrative Science & Organization, September 2013
Bisnis & Birokrasi, Jurnal Ilmu Administrasi dan Organisasi
Table 5. The Simulation Result of the Increased Government
Expenses for Education by 20 Billion Rupiahs towards
Workforce Employment and Poverty
Volume 20, Number 3
1.21% increase). The result suggests that an increase in
educational expenses was more lucrative for industrial
and services sectors in urban areas than agricultural sector
in rural areas. If the government aims to reduce poverty,
the regional government should increase the budget
allocation for education in rural areas.
The improved economic performance due to an
increase in educational expenses resulted in the increase
of public welfare. It is characterized by the declining
unemployment number, from 32,662 people into 27,173
people, reduced by 16.81% and the number of poor people
fell from 102,503 people into 81,716 people, reduced by
20.28%. The results of this study suggest the importance
of government intervention in improving public welfare
through allocating the effective educational budget in
order to improve the quality of life and well-being of
society.
The second simulation, the increase in government
expenses for Health of twenty billion rupiahs will result
in the following (table 6).
In Table 6 it appears that the increase in health expenses
has no direct effect in increasing the GDP and all sectors
employment, as well as reducing unemployment and
poverty. The increase in GDP caused the company to
increase its production capacity and increase the number
of new jobs to boost workforce employment. This policy
is most beneficial for the industrial sector since when
viewed from the percentage of changes, it causes the
greatest industrial workforce employment, absorbing
113,944 people than previously only 117,656 people,
up by 3.26 %. The service sector followed, then, rising
1.09 %, shadowed by the agricultural sector that rose by
0.82%. Overall, the increase of health expenses by twenty
billion rupiahs led to the increased workforce employment
from 452,779 people to 459,718 people, or up by 1.53%.
In line with the first simulation, the increase in health
expenses is also much more profitable for industrial and
services sectors (non-agricultural sector) as compared to
agricultural sector. The result also supports the notion
that compared with the non-agricultural sectors, the
agricultural sector was the least benefited from the
success of development.
The improved economic performance resulted in the
increased well-being of society, as can be seen from several
indicators, i.e. the declining unemployment number from
32,663 unemployed people into 25,724 people, reduced
by 21.24% and a decrease in the number of the poor from
102,503 people into 74,554 people, reduced by 27.27%.
The third simulation, the increase in expenses for
infrastructure of twenty billion rupiahs will result in the
following effects (table 7).
Table 7 states that an increase in infrastructure expenses
was indirectly influential in increasing the output (GDP).
The increase in output causes increased employment,
thus resulted in increased workforce absorption. The
result suggests that the industrial sector was the most
benefited in workforce employment, from 113,944
people to 118,111 people, or up by 3.66%; followed
by the agricultural sector, experiencing an increase of
1.57%, shadowed by services sector, with an increase
of 0.76%. In terms of workforce employment, different
from the educational and health expenses, the increased
Table 6. The Simulation Result of the Increased Government
Expenses for Health by 20 Billion Rupiahs towards Workforce
Employment and Poverty
Table 7. The Simulation Result of the Increased Government
Expenses for Infrastructure by 20 Billion Rupiahs towards
Workforce Employment and Poverty
Endogenous
Variable (unit)
Basic
value
Simulation
Value
Change
(%)
GDP (billion IDR/year)
4248.1
4556
7.25
Agricultural workforce
(people)
170549
171690
0.67
Industrial workforce
(people)
113944
116865
2.56
Services workforce (people)
168286
169713
0.85
Total workforce employment 452779
(people)
458268
1.21
Unemployment (people)
32662.5
27173.2
-16.81
Poverty (people)
102503
81716.6
-20.28
Endogenous
Variable (unit)
Basic
value
Simulation
Value
Change
(%)
Endogenous
Variable (unit)
Basic
value
Simulation
Value
Change
(%)
GDP (billion IDR/year)
4248.1
4639.8
9.22
GDP (billion IDR/year)
4248.1
4631.6
9.03
Agricultural workforce
(people)
170549
171947
0.82
Agricultural workforce
(people)
170549
173229
1.57
Industrial workforce
(people)
113944
117656
3.26
Industrial workforce
(people)
113944
118111
3.66
Services workforce (people)
168286
170115
1.09
Services workforce (people)
168286
169558
0.76
Total workforce employment 452779
(people)
459718
1.53
Total workforce employment 452779
(people)
460898
1.79
Unemployment (people)
32662.5
25724
-21.24
Unemployment (people)
32662.5
24544.1
-24.86
Poverty (people)
102503
74554.3
-27.27
Poverty (people)
102503
80848.2
-21.13
SULISTYOWATI, THE EFFECT OF EDUCATIONAL, HEALTH, INFRASTRUCTURE
infrastructure expenses was most profitable respectively
for industrial, agricultural and, services sectors.
The improved economic performance due to increased
infrastructure expenses was implicated to increased
public welfare. It can be seen from several indicators, i.e.
reduced unemployment from 32,663 people into 24,544
people, declined by 24.86% and declined poverty from
102,503 people into 80,848 people, reduced by 21.13%.
The aforementioned three policy simulation results
conclude that similar absolute value, and the increase in
health expenses give the best result in increasing GDP,
and services workforce employment, as well as reducing
poverty. While the increase in infrastructure expenses gives
the best result in improving the industrial and agricultural
workforce employment and reduce unemployment. The
required economic growth is one, capable not only to
increase output, but at the same time can also reduce
unemployment and poverty. The results of this study
indicate that the policy of increasing educational, health
and infrastructure expenses, in addition to increasing the
output can also increase all sector workforce employment
and reduce poverty in Central Java.
CONCLUSION
Agricultural workforce employment is significantly
influenced by District/municipal Minimum Wage,
agricultural GDP, workforce, district/municipal dummy
and previous year’s agricultural employment. The
increase in agricultural GDP by 10% will increase
agricultural workforce employment by 1.75 %.
Industrial workforce employment is significantly
affected by industrial GDP, workforce, and previous year’s
industrial employment. The increase in industrial GDP by
10% will increase industrial workforce employment by
1.58%, lower than the agricultural sector.
Services workforce employment is significantly
affected by services GDP, workforce, district/municipal
dummy, and previous year’s services employment. The
increase in services GDP by 10% will improve services
workforce employment by 0.86%, lower than the
agricultural and industrial sectors.
Poverty is significantly influenced by household
expenditure per capita, income distribution, poverty line,
population number, the share of agricultural workforce,
district/municipal dummy, time trend and previous year’s
poverty. Increased household expenditure per capita
of 10% will reduce poverty by 15.95%. The simulation
results of educational, health and infrastructure expense
policies with similar nominal value showed an increase in
GDP, and all sector workforce employment, as well as a
reduction of poverty.
The increased health expenses has the most significant
contribution to the increased GDP, services workforce
employment and the reduced poverty. While the policy
of increasing infrastructure expenses most significantly
improves industrial and agricultural workforce
employment, and reduces unemployment.
Policies to increase educational and health expenses
give the greatest effect on the workforce employment,
127
most widely enjoyed by respectively sectors of industry,
services and agriculture. While the policy of increasing
infrastructure expenses most significantly affects the
workforce employment in respectively sectors of industry,
agriculture and services.
Poverty needs to be reduced by a variety of programs
that can improve people’s productivity and income.
The implication of policy for addressing poverty is to
improve the quality of human resources by opening
access to education and training as much as possible
for the community, so that they are able to manage the
existing natural resources, increase their productivity,
improve their economic welfare and expand employment
opportunities, as well as control the population number.
For subsequent research, it is recommended to
conduct treatment differentiation (disaggregation) on
districts and municipalities to determine the effect of
policies for the two areas, and to include all aspects of
Human Development Index as part of the assessment
of development performance, in addition to forecasting
through historical simulation.
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