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INTERNATIONAL
JOURNAL OF SOCIAL SERVICE AND RESEARCH
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ANALYSIS OF FACTORS
AFFECTING POVERTY LEVEL IN JAVA ISLAND
Varenia Deby Alsya, Nunuk
Triwahyuningtyas, Sri Murtatik
Faculty of Economics
and Business, Veterans National Development University, Jakarta, Indonesia
Email: [email protected],
[email protected], [email protected]
Abstract
Java Island is an area
that has the highest poverty level in Indonesia for every year. Java Island is
a centralization area of economic development, but there is also a poverty
problem centered on Java Island. Factors that can affect poverty include total
population, income per capita and labor force participation rate. The research
was conducted to find out how the influence of total population, income per
capita and labor force participation rate on poverty levels in Java Island. The
data used in this study are 6 provinces in Java for the period 2013 � 2019, so
the panel data regression model is the method used in this study, with the
selected model being is Fixed Effect Model (FEM). The results of this study
represent that the total population does not effect on poverty levels in Java
Island, income per capita has effects on poverty levels in Java Island and the
labor force participation rate has effects on poverty levels in Java Island.
Keywords: poverty
level; total population; income per capita; labor force participation rate
Received 21
September 2021, Revised 30 September 2021, Accepted 10 October 2021
INTRODUCTION
The most basic economic problem is poverty, where poverty
can cause a decrease in welfare and hinder economic development in an area. In
Indonesia, the number of poor people in 2019 was around 24.79 million people or
9.22% of the total population in Indonesia. One of the areas with a fairly high
poverty rate is Java Island. On the island of Java, there are 2.56 million poor
people with a percentage of 8.29%, meaning that Java Island has half the
population of poor people in Indonesia (Statistik, 2019).
In general, the centralization of development in Java is
considered to be one of the factors that trigger the high poverty rate every year.
Economic development that does not move to other areas results in an increase
in the number of residents every year. Income per capita is more less than the
total population, accordingly the Java Island have the highest poverty rate in
Indonesia (Ilham,2015).
Graph 1
Poverty Level of Java
Island in 2013-2019 (%)
Based on the table above, the provinces that have a high
poverty rate are East Java, Central Java, Yogyakarta Special Region, and West
Java. Meanwhile, the poverty level of DKI Jakarta and Banten provinces is
relatively low. Based on graph 1, the poverty rate in all provinces of Java
Island increased in 2015 due to an increase in inflation which made it
difficult for people to meet their daily needs due to high prices of basic
necessities (Giovanni, 2018). In
the following years the poverty rate in Java Island has decreased, however, the
highest poverty rate in Indonesia is still owned by the Java Island region.
The population density on the island of Java reached 1,184
people/km2 in 2019, the island is home to 60% of Indonesia's population. The
population on the island of Java always increases every year because Java is
the center of economic development. However, population growth that continues
to increase can result in a decrease in resource capacity, hampering economic
development and causing other socio-economic problems (Shalihah, 2021).
Graph 2
Total Population of
Java Island 2013-2019
Based on graph 2, during the 2013-2019 period, the
population in all provinces on the island of Java always increases every year,
but the poverty rate in all provinces of Java has actually decreased from
2017-2019. Although poverty in Java has decreased, if the population continues
to increase, it will hamper efforts to reduce poverty. The article according to
BPS in recent years there has been a slowdown in poverty reduction in Java,
namely the decline is around 0.5% while the increase in population is faster,
namely 1.5% every year. As a result, the distribution of social assistance and
the distribution of prosperous rice (rastra) for the poor is hampered and
uneven because the population is always increasing, therefore the number of
poor people in Java is still high every year (Mustami, 2017).
Per capita income is an indicator of the development and
level of welfare of a region, the higher the per capita income, the more
prosperous the region. The basic concept of poverty is related to the level of
income and minimum needs. If income cannot meet the minimum needs, then a
person can be said to be poor (Munawaroh & Puruwita, 2012).
Graph 3
Per Capita Income of
Java Island in 2013-2019
Based on graph 3, per capita income in Java has always
increased from 2013-2019. Although per capita income always increases, its
contribution to poverty can be said to be not optimal because the decline in
the poverty rate is quite slow, namely the average decline is only 0.5% per
year. In addition, the increase in per capita income that occurred in Java was
influenced by the large population, not by the increase in wages/salaries
received by the community, although as a center of the economy, but the Java
Island has 4 provinces that have the lowest Provincial Minimum Wage (PMW) in
Indonesia. Indonesia and the figure is around 1 million rupiah, namely
Yogyakarta, East Java, West Java, and Central Java (Deny, 2018). In
addition, there is still income inequality that occurs on the island of Java,
for example in DKI Jakarta Province and other provinces on the island of Java.
DKI Jakarta's per capita income in 2019 was 165.872 million Rupiah while other
provinces such as Yogyakarta were only 25.776 million Rupiah. According to BPS,
all provinces in Java have income inequality above 0.35%, which means that
inequality in Java is at a moderate level, indicating that Java is the center
of the economy, but the achievement of welfare is not evenly distributed (Sovita, 2016).
The Labor Force Participation Rate (LFPR) describes the
working age population who have productivity or are economically active in
working in daily activities. The increase in the workforce if it is not
accompanied by the provision of employment opportunities causes low job
opportunities which lead to an increase in the number of poor people because
the opportunities to earn income are getting smaller (Lail, 2018).
Graph 4
Java Island Labor
Force Participation Rate in 2013-2019
Based on Figure 4, LFPR in 6 Java Island Provinces
experienced fluctuating development with an increasing trend. In 2014 the
average LFPR decreased, but the phenomenon that occurred, namely the poverty
rate on the island of Java also experienced a decline. The same phenomenon
occurred in 2019, when the LFPR in Yogyakarta decreased, but the poverty rate
in Yogyakarta also decreased. The cause of the decline in LFPR in Yogyakarta is
the reduced number of job opportunities (Kota, 2020).
Based on the problems that have been described in the
background above, there are differences in the results of previous studies and
the dissimilarity between theory and data, making the authors decided to
conducting a study entitled "Analysis of Factors Affecting Poverty Levels
in Java".
METHOD
1. Population
and Sample
The population in this study
is the total population, per capita income and Labor Force Participation Rate (LFPR)
in Java, which consists of 6 provinces, namely East Java, Central Java,
Yogyakarta, West Java, Banten and DKI Jakarta. This study investigate data for
the 2013-2019 period with a population of 6 provinces, and the number of
samples in this study is 42 data. The saturated sample technique was chosen as
the sampling technique in the study, namely the sampling technique when all
members of the population were used as samples.
2. Data
collection technique
In this study, the type of
data used is secondary data, where secondary data is obtained indirectly,
namely from intermediary media such as historical reports and archives, both
published and unpublished. The data source is obtained from the official
website of the Central Statistics Agency. Collecting data through documentation
and literature study.
3. Data
analysis technique
This study using panel data
regression analysis techniques, which combines the reression time series data
collected from time to time and cross section data from the number of
individuals at one time (Widarjono & Pengantar, 2016).
The time series data is for the period 2013-2019, and the cross section data is
6 provinces on the island of Java. The test is carried out through the use of
the panel data equation function, then the regression equation that has been
determined is:
Y_it= β0+ β1 X1_it+
β2 X2_it+β3 X3_it 〖+ε〗_it
where:
Y �������� :
Poverty Level
i ��������� :
Province in Java Island
t ��������� :
Period 2013 to 2019
0 �������� :
Constant
1,β2,β3
: Coefficient
X1 ������ :
Total Population
X2 ������ :
Income Per capita
X3 ������ :
Labor Force Participation Rate
E �������� :
Confounding variable
RESULTS
AND DISCUSSION
1. Classic
Assumption Test
a.
Normality test
This test aims to see whether the independent
and dependent variables in the regression model are normally distributed or
not. If the Jarque-Bera Probability value is more than 0.05, then the
conclusion that can be drawn is that the data is normally distributed. If the
probability value is less than 0.05, then the data is not normally distributed (Ghozali, 2013).
Table 1
Normality Test Results
|
Jarque-Bera
|
1.62225
|
|
Probability
|
0.922090
|
According to these results, the Jarque-Bera
probability value is 0.922090 more than 0.05, so the conclusion that can be
drawn is that the data is normally distributed and it means that there is no
problem of normality and the classical assumptions about normality have been
met.
b.
Autocorelation Test
This test is carried out to detect whether or
not there is a relationship between the nuisance error in period t and the
error in the previous period (t-1). To find out the problem with
autocorrelation, you can use the Durbin Watson test. By looking at the
Durbin-Watson value, it is one way to find the autocorrelation problem. D-W
value less than -2 indicates positive autocorrelation. The D-W value is between
-2 and +2, so there is no autocorrelation problem. D-W value more than +2
indicates a negative autocorrelation (Santoso, 2012).
Table 2
Autocorrelation Test Results
|
Durbin-Watson stat
|
1.637740
|
The DW value in the table above is 1.637740
where the number lies between -2 and +2, so it can be concluded that there is
no autocorrelation problem in the model above.
c.
Multicollinearity Test
This test is used in order to see in the
regression model, whether there is a correlation or relationship between
independent variables. A good regression model does not have multicollinearity
problems among the independent variables. If the correlation coefficient
between independent variables less than 0.8, the model is free from
multicollinearity problems. However, there is a problem if the correlation
coefficient between independent variables is more than 0.8 (Ghozali, 2013).
Table 3
Multicollinearity Test Results
|
Correlation
|
|
X1
|
X2
|
X3
|
|
LN Total Population
|
1.00000
|
-0.170307
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-0.323355
|
|
LN Income Per
Capita
|
-0.170307
|
1.00000
|
-0.234688
|
|
TPAK
|
-0.323355
|
-0.234688
|
1.00000
|
According to the table above, the correlation
coefficient between the variables LN Total Population, LN Income Per Capita and
LFPR less than 0.8, so the conclusion that can be drawn is that the model is
free from multicollinearity problems.
d.
Heteroscedasticity Test
This test has the aim of testing whether the
regression model has a difference in variance from the residuals of one
observation to another observation. If the magnitude of the probability value
is less than 0.05, then a problem occurs. Then if the magnitude of the probability
value is greater than 0.05, then it is free from heteroscedasticity (Ghozali, 2013).
Table 4
Table of Heteroscedasticity Test Results
|
Dependent
Variabel : RESABS
Method
: Panel Least Square
|
|
Variable
|
Prob
|
|
C
|
0.9427
|
|
LN
Total Population
|
0.6512
|
|
LN
Income Per Capita
|
0.2211
|
|
TPAK
|
0.3084
|
Based on the table above, each variable
obtained a probability value more than 0.05, so it can be concluded that the
data above does not have heteroscedasticity problems.
2. Model
Determination Technique
a.
Hausman test
To get the right model between Fixed Effect
Model (FEM) or Random Effect Model (REM). The criteria for determining the Hausman
test are:
H0 : The probability value is < 0.05 then
the selected model is Fixed Effect Model
H1� : The probability value is > 0.05, then
the selected model is Random Effect Model
Table 5
Hausman test results
|
Test Summary
|
Prob
|
|
Cross-Section
Random
|
0.0000
|
Based on the table above, the probability
value achieved is 0.0000, where the value is smaller than 0.05. So in this
Hausman test, the Fixed Effect Model (FEM) was chosen as the model. After
testing to determine the best regression model, the results of the Chow test
and Hausman test show that the model chosen in this study is the fixed effect
model. The regression results obtained from the selected model are:
Table 6
Fixed Effect Model
|
Method : Panel Least Square
|
|
Variable
|
Coefficient
|
Std.Error
|
t-statistic
|
Prob
|
|
C
|
61.85020
|
64.98808
|
0.951716
|
0.3482
|
|
LN Total Population
|
9.143122
|
5.179349
|
1.765303
|
0.0868
|
|
LN Income Per Capita
|
-10.83735
|
1.478688
|
-7.329031
|
0.0000
|
|
LFPR
|
-0.241386
|
0.044729
|
-5.396616
|
0.0000
|
Based on the regression equation above, it is
obtained:
1)
The coefficient value is 61.85020, meaning
that if the variables of population, per capita income and Labor Force
Participation Rate (LFPR) are considered constant, resulting in a poverty rate
of 61.85020.
2)
The regression coefficient value for the
population variable is 9.143122, the coefficient is positive, meaning that
there is a unidirectional relationship, each increase in population is 1%, thus
increasing the poverty rate by 9.143122%.
3)
The value of the coefficient of income per
capita is -10,83735, the coefficient is negative, meaning that there is an
inverse relationship, every increase in income per capita is 1%, so it will
reduce the poverty rate -10.83735%.
4)
The LFPR coefficient value is -0.241386, the
coefficient is negative, meaning that there is an opposite relationship, every
1% increase in LFPR, so it will reduce the poverty rate -0.241386%.
3. Hypothesis
testing
a.
Partial Test (t-test)
This test aims to determine the magnitude of
the partial effect of one independent variable on the dependent variable. In
the statistical t-test, decisions are taken by comparing the t-count value with
the t-table value, and also paying attention to the probability value. If t
count more than t table or probability less than (0.05) then the independent
variable affects the dependent variable. If t count less than t table or
probability value more than (0.05), then the independent variable has no effect
on the independent variable. For t table achieved is 2.02439.
Table 7
t test results
|
Variable
|
t-Statistic
|
Prob.
|
|
C
|
0.951716
|
0.3482
|
|
LN
Total Population
|
1.765303
|
0.0868
|
|
LN
Income Per Capita
|
-7.329031
|
0.0000
|
|
LFPR
|
-5.396616
|
0.0000
|
Based on the regression results above, the
population has a t-count value (1.765303) < t table (2.02439). The
probability value obtained on the population variable is (0.0868 > 0.05), so
statistically the population variable (LN Total Population) does not partially
affect the poverty level variable. So with this it can be concluded that H0 is
accepted, which means that the population variable has no effect on the poverty
level, therefore H1 is rejected.
Per capita income has a t count (-7.329031)
> t table (2.02439). The probability value obtained on the income per capita
variable (0.0000 < 0.05). So statistically the income per capita variable
(LN Income Per Capita) has a partial effect on the poverty level variable. So
with this it can be concluded that H0 is rejected, meaning that the income per
capita variable has an influence on the poverty level variable, therefore H2 is
accepted.
The Labor Force Participation Rate (LFPR) has
a t-count value (-5.396616) > t table (2.02439). The probability value
obtained in the LFPR variable (0.0000 < 0.05). Then the Labor Force
Participation Rate variable (LFPR) partially affects the poverty level
variable. So with this it can be concluded that H0 is rejected, meaning that
the LFPR variable has an influence on the poverty level variable, therefore H3
is accepted.
b.
Simultaneous Test (F Test)
The principle describes whether or not there
is a simultaneous influence of the independent variable on the dependent
variable. To understand the results, you can look at the significance value and
compare the calculated F value with the F table, with the F table in this study
being 2.85.
Table 8
Simultaneous Significance Test Results (Test F)
|
F-statistic
|
450.8357
|
|
Prob(F-statistic)
|
0.000000
|
The F table value is obtained by the formula
F(k-1,n-k), so that the F table value is 2.85. Thus, the value of F count >
F table (450.8357>2.85) and the probability value of 0.0000 <0.05, so it
can be concluded that the population, per capita income and LFPR have a
simultaneous or joint influence on the poverty level.
c.
Coefficient of Determination Test (Adjusted
R2)
This test is to measure how far the ability of
the independent variable in explaining the dependent variable (goodness of fit
test).
Table 9
Coefficient of Determination Test Results (Adjusted R2)
|
Adjusted
R-Square
(R2)
|
0.988735
|
Based on the output above, the adjusted R2
value is 0.988735, which means that in this study the independent variable was
able to describe its effect on the dependent variable of 98.87%. The rest,
which is 1.13%, is influenced by other independent variables that are not
included in this study.
4. Discussion
a.
The Effect of Population on Poverty Levels in
Java.
According to the regression estimation results
of the Fixed Effect Model, the value of t count (1.765303) < t table
(2.02439) with a probability value of (0.0868 > 0.05). So it is hereby
concluded that H0 is accepted, which means that the population variable has no
effect on the poverty level in Java for the 2013-2019 period, therefore
Hypothesis 1 is declared rejected. Because the population is always increasing,
while poverty tends to decrease. Population growth in Java can be controlled
through the family planning program so that the increase in population does not
increase sharply. There is a Family Planning Village program that has been
carried out by the government since 2016 to suppress the population growth on
the island of Java. There is a special village fund budget to be allocated to
the family planning program, namely to support the use of contraceptives, the
priority of this program is carried out in suburban or remote areas in order to
improve the quality of life of the community by regulating the ideal number of
children in one family and creating quality small families. According to
SP2020, the population on the island of Java is dominated by Generation X and
Millennials, which are people of the working age group, these two generations
belong to the productive age so that they have the potential to grow the
economy quickly and can encourage development. In addition, the quality of
human resources is reflected in the Human Development Index (HDI), which always
increases, such as in 2019 the HDI in Java, which is 74.41%, which is higher
than the National HDI, which is 71.92%, because since 2014 until now it has
been continuously carried out. improving education and improving health
services which include the provision of Smart Indonesia Cards (SIC), Healthy Indonesia
Cards (HIC) and National Health Insurance (NHI) so that the existing population
does not affect poverty in Java.
According to Adam Smith, an increase in
population can grow an economy if the population has knowledge and is able to
expand the market for goods and services so that the production process and
economic activity are higher. In addition, a large population is able to
encourage specialization and technological progress. The process of
specialization in the field of work can increase worker productivity and
increase labor income, in this cycle the economy will continue to experience
development.
The results of this study are supported by (Mahsunah, 2013), the
population does not affect poverty, because of the success of the family
planning program since the results of SP2000 so that the shape of the
population pyramid resembles an inverted barrel, which means that the
population distribution is more dominant by the working age population. The
number of residents indicates the number of workers who can participate in
economic activities. For the productive age population, the opportunity to
improve the standard of living is still wide open.
b.
The Effect of Per Capita Income on Poverty
Level
Based on the results of the regression
estimation using the Fixed Effect model, the t value is -7.329031 > t table
2.02439 with a probability value of 0.0000 < 0.05. So in this study,
statistically the income per capita variable (LN Income Per Capita) has a
significant negative effect on the poverty level in Java for the 2013-2019
period, meaning that if per capita income increases, the poverty rate will
decrease. So with this it can be concluded that H0 is rejected, therefore
Hypothesis 2 is declared accepted. Per capita income in Java continues to
increase, per capita income is very dependent on the potential of natural
resources and human resources as well as existing production factors. The large
population and rapid infrastructure development make Java Island a centralized
area of economic growth. Poverty that occurs in Java will
decrease when there is economic growth. When per capita income increases, the
purchasing power of the people in fulfilling their daily needs will be better,
thus welfare can increase.
Strengthened by the theory put forward by (Todaro, 1999),
income per capita is a reflection of the ability of people's income in an area
to meet basic needs or minimum needs. The low standard of living is closely
related to the low per capita income. The low per capita income is the result
of the low productivity of the labor force. In meeting the minimum needs of the
community, it can indicate the welfare obtained from the aspect of equitable
distribution of community income in the region. If the per capita income is
high, the purchasing power of a person will increase, so that in this case
poverty can be reduced.
The results of this study are also supported
by (Azizah et al., 2018), per
capita income has a negative effect on poverty in East Java. Increasing per
capita income can reduce poverty. Per capita income can be a parameter of
community welfare in an area. High per capita income can indicate that the more
prosperous the people in the area are.
c.
Effect of Labor Force Participation Rate on
Poverty Level
Based on the results of the regression
estimation of the Fixed Effect model in table 20, the t-count value is
-5.396616 > t-table 2.02439 with a probability value of 0.0000 <0.05. So
that in this study, statistically LFPR has a negative effect on the poverty
level in Java for the period 2013-2019, so it can be concluded that H0 is
rejected, therefore Hypothesis 3 is declared accepted. When LFPR increases, the
poverty rate will decrease. The increase in LFPR is influenced by the large
number of the workforce involved in economic activities. On the island of Java,
LFPR continued to experience a fairly good increase, precisely from 2017 to
2019, due to the increasing job opportunities provided by the service sector.
The more the workforce that participates in the workforce and has work
productivity, the faster economic growth will be. Likewise, per capita income
will increase along with the increase in labor force participation, so that the
poverty rate can decrease. However, this expansion of job opportunities must
continue to be carried out, both in urban and remote areas, because even though
the poverty rate has decreased, the poverty rate in Java Island is still quite
high every year when compared to other islands in Indonesia.
The results of this study are reinforced by
the theory pioneered by (Todaro, 1999),
which states that the most important mechanism in reducing poverty is to
overcome unemployment and employment problems. High labor force participation
has a major contribution to economic growth, where high labor force
participation can encourage the economy so as to reduce poverty.
This result is also supported by research
conducted by (Ahmaddien, 2019), one
of the causes of high poverty is the low LFPR. The poverty rate is not only
related to unemployment, but a low LFPR indicates the low productivity of the
workforce which affects the income received. Poor people who have a fixed
income but fall into the poverty criteria because the income they receive is
very small due to low work productivity, so LFPR is very relevant in
influencing poverty levels.
CONCLUSION
Based on the results of this study, can be concluded that
the influence of the three independent variables on poverty in Java is as
follows, the total population has no effect on the level of poverty. The
population on the island of Java can already be controlled through the family
planning program and the population is dominated by the working age population
followed by improvements in the quality of human development, so this large
number of people can encourage the economy, so it does not affect poverty in
Java. Income per capita affects the level of poverty, with an increase in
income per capita, the ability of the community to purchase minimum needs will
increase, thus the poverty rate in Java will decrease along with the increase
in people's welfare. The labor force participation rate (TPAK) affects the
poverty level, with an increase in the LFPR, the number of working age
population who have productivity and are involved in the labor market will also
increase, this has a good impact on improving people's welfare because people
are involved in economic activities will have income to meet basic needs so
that the poverty rate will decrease along with the increase in labor force
participation in boosting the economy.
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