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INTERNATIONAL
JOURNAL OF SOCIAL SERVICE AND RESEARCH
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DETERMINATION OF
ECONOMIC GROWTH IN JAVA ISLAND
Ita Dwi Febrianti, Sugianto,
Renea Shinta Aminda
Faculty of Economics
and Business, Veterans National Development University, Jakarta, Indonesia
Email: [email protected],
[email protected], [email protected]
Abstract
High economic growth
and continuing to grow and sustainably become a component that determines the
development of economic development. The level of economic growth is
inseparable from the use of production factors such as natural resources,
labor, and capital goods as determinants of economic growth. This research is
motivated by the fact that the GRDP in Java Island has a high value compared to
other islands in Indonesia. This study aims to analyze the determinants of
economic growth in Java. The variables used in this study are GRDP at constant
prices, investment realization, open unemployment rate, and inflation rate.
This study uses multiple linear regression, with a panel model which is a
combination of time series data for 6 years from 2014-2019 as well as cross-section
data for 6 provinces in Java and using the selected Fixed Effect Model (FEM),
the data is processed using the program EViews-10. The findings in this study
prove that the investment variable has a positive effect on economic growth.
While the unemployment variable hurts economic growth, and the inflation
variable hurts economic growth.
Keywords: Economic
Growth, Investment, Unemployment, Inflation
Received 15
September 2021, Revised 25 September 2021, Accepted 1 October 2021
INTRODUCTION
High economic growth and
continuing to grow, and sustainably become a component that determines the
development of economic development because economic growth is the economic
condition of a country leading to a position of superior and sustainable
economic development within a certain period, which can increase the prosperity
and welfare of the state and society. Problems of economic development and
problems of economic growth cannot be separated from each other, economic
growth can accelerate the course of economic development, and economic
development can encourage economic growth. The parameter for measuring economic
growth is Gross Domestic Product (GDP) data, and the parameter for measuring
regional economic growth is Gross Regional Domestic Product (GRDP) data (Hashim, 2016).
The economic growth of a
country is strongly supported by the contribution of economic growth given by each
region, reflected by the value of GRDP which describes the capability in
realizing income and remuneration for production factors produced by a region,
so it affects the Gross Domestic Product (GDP), therefore the contribution each
region to economic growth is not the same, this is due to differences in the
characteristics of each region such as natural, social, and economic
conditions. Economic growth is determined by three factors (1) supply factors,
consisting of natural resources, human resources, capital stock,
entrepreneurship, and science and technology, (2) demand factors, and (3)
non-economic factors. Therefore, the GRDP value of each region is not the same (Hashim, 2016).
Graph 1
Regional Contribution to
Indonesia's Gross Domestic Product in 2014-2019
The data presented in Figure
1 explains the contribution of the region in the last six years spatially,
Indonesia's largest economic growth was contributed by Java, Sumatra, and
Kalimantan islands, it can be seen that there are differences in the
contribution of each region. The highest contribution to GDP in Indonesia was
given by Java in the last six years, in 2019 the contribution reached up to 59
percent, this is because economic activity is centered on the island of Java (Statistik, 2020).
Graph 2
PDRB by Field of Business at
2010 Constant Prices in Java
Gross Regional Domestic
Product has benefits as a parameter of a region's economic growth and can see
the shape of the regional economy. Economic growth based on GRDP in Java has
increased over the last six years, this is because the island of Java is supported
by industry and agriculture, so that structurally Java has a major role,
especially in the business field as a supporter of economic growth. The island
of Java consists of six provinces that have different economic capacities
according to the conditions of each region so that the GRDP value of each
region is different. From 2014 to 2019 DKI Jakarta Province was ranked at the
top among the other six provinces with a GRDP of 1,838,500,708 rupiahs in 2019,
while the Province of the Special Region of Yogyakarta was ranked last during
the period 2014 to 2019, the GRDP obtained by the Special Region of Yogyakarta
in 2019 amounted to 98,026,563 rupiahs.
To increase the value of
Gross Regional Domestic Product (GDP) required components that can increase the
economy, in Keynes' view in the book The General Theory in 1936 explained the
theory of the role of aggregate expenditure in increasing the economy. The
components in aggregate expenditure include household consumption, private and
government investment, government consumption expenditure, exports, and
imports. The increasing aggregate expenditure will affect the increase in
economic activity, national income, and the level of employment opportunities
so that GDP will increase (Sukirno, 2013).
Investment is an element that
influences aggregate expenditure. Investment is investment expenditure and the
purchase of capital goods for production equipment to increase the production
of goods and services in the economy (Sukirno, 2013).
Graph 3
Investment Realization in
Java in 2014-2019
Java Island as an area that
excels in investment, Java Island has an attraction for investors so that the
realization of the largest investment is in Java Island. According to the data
in Figure 3, it can be seen that the investment realization combined with the
Domestic Investment and Foreign Investment data tends to fluctuate, fluctuating
investment is due to uneven infrastructure development so that the impact on
investment realization varies between regions or provinces. However, the
decline in the level of investment was followed by an increase in economic
growth as seen in the value of GRDP on the island of Java, this is contrary to
Keynes's theory which says that increased investment will affect increasing
economic activity, national income, increasing Gross Domestic Product (GDP).
The highest realization of investment in Java was achieved by West Java
Province, in 2019 investment in West Java reached 137 trillion, while
investment in Yogyakarta Province was very low in 2019 only at 26 trillion.
With high and increasing investment in an area, it can promote economic growth
and have an impact on people's welfare, employment, reduce unemployment, and
increase income which can affect public consumption, while low levels of
investment can hamper economic growth.
The main economic problems
experienced by every society are unemployment and inflation because they have a
very bad impact on the economy, society, and politics. Unemployment is the
condition of not having a job experienced by a group of workers, and they have
tried to get a job, but have not got it (Sukirno, 2013).
Graph 4
National Open Unemployment
Rate and Java Island 2014-2019
The data presented in Figure
4 shows that unemployment is increasing followed by high economic growth in
Java, this is in contrast to Keynes's theory in 1936 which analyzed that when
aggregate expenditure increased, employment opportunities would increase and
affect the decreasing unemployment. Unemployment conditions in Indonesia and
Java Island based on the Open Unemployment Rate (OUR) data show that Banten,
DKI Jakarta, and West Java provinces have unemployment rates above the national
unemployment rate, while Banten Province is the province with the highest
unemployment rate, in 2019 the unemployment rate reached 8,11 percent, based on
research conducted by (Mahroji & Nurkhasanah, 2019),
Banten Province has not yet reached full employment, causing Banten Province to
become the province with the highest unemployment rate, while the lowest
unemployment rate in Java is occupied by the Special Region of Yogyakarta. ,
the unemployment rate in 2019 was 3.14 percent.
The next economic problem is
inflation, inflation is the condition of an increase in the overall price of
goods and services. Inflation is a barrier to economic activity. Therefore,
inflation results in economic growth, this is reinforced by previous research
by (SUDANA, KUSMAWAN, & RAHMANTARI, 2020)
which explains that inflation has a significant effect on economic growth.
Keynes's analysis in 1936 revealed that inflation and unemployment as a
reflection of the gap in aggregate demand. The aggregate demand gap is the
difference between aggregate demand and potential output (Soelistyo, 2017).
Graph 5. Inflation Rate in
Java Island 2014-2019
Based on the data presented
in Figure 5, inflation fluctuates, but the rising inflation rate is followed by
increased economic growth in Java. In the data in Figure 5, inflation in Java
for the period 2015 to 2019 was relatively stable at the level of 2 to 3
percent, but in 2014 inflation in Java was mostly above 7 percent, this was due
to an increase in fuel prices, rising fuel prices. affect the price of goods
and services to increase related to fuel, so that it will exacerbate
inflationary pressures (Kartini, 2019).
Several previous studies
regarding economic growth are the research conducted by (Yuliana, Bashir, & Rohima, 2019)
entitled The Effect of Investment Toward Economic Growth in The Local Economy,
in this study stated that investment has a positive and significant effect on
economic growth, meanwhile, inflation provides a positive but not significant
effect on economic growth in South Sumatra Province. Meanwhile, in the research
conducted by (Wepukhulu & Otieno, 2019)
with the title Effect Of Macroeconomic Factors On Economic Growth In Kenya in
this study the unemployment variable is negative and has a significant
relationship, and the inflation variable has a negative and significant linear
association. In contrast to the results of research conducted by (Reniati, Kamarudin, Wardhani, & Akbar, 2020)
with the title The Effect of Unemployment and Investment Levels on Economic
Growth in the Province of Bangka Belitung Islands, 2015-2019, it is stated that
unemployment has no significant effect, and investment has a positive and
significant effect.
Based on the background that
has been described, the researchers are interested in carrying out further
research with the title "Determination of Economic Growth in Java".
This study aims to analyze the determinants of economic
growth in Java. The findings in this study prove that the investment variable
has a positive effect on economic growth. While the unemployment variable hurts
economic growth, and the inflation variable hurts economic growth.METHOD
1. Types
of Research
This study uses a
quantitative method, focusing on the study of the effect of investment,
unemployment, and inflation on economic growth in Java for the period
2014-2019, using independent variables consisting of investment, unemployment
and inflation, and economic growth as the dependent variable.
2. Data
collection
This study uses secondary
data obtained from data published by the Central Statistics Agency, the
Investment Coordinating Board (ICB), and the One-Stop Integrated Service and
Investment Service (OSIRIS) in the Province of Java Island with various
publications years. The technique used is purposive sampling or sampling
carried out with certain aims and objectives. The sample in this study includes
Gross Regional Domestic Product Ln at Constant Prices, Investment Ln,
Unemployment Rate, and Inflation Rate in Java Island in 2014-2019, so the total
sample is 36 observers.
3. Data
analysis technique
The analysis technique uses a
panel data approach, or uses a mixture of information from two cross-sectional
approaches and time series, using panel data will result in an increased total
observation, because it is viewed from two individual dimensions and time so
that panel data is estimated to be more accurate than other models (Firdaus, 2020).
Panel data is a technique
used for multiple linear regression analysis and uses the EViews 10 program to
process the data. Panel data consists of time series data from 2014 to 2019 or
for 6 (six) years with all variables from 6 (six) provinces in Java, the total
observations are 36 data and using the multiple linear regression formula with
the natural logarithm model, this is done to equalize the units of the
variables used.
The equations in the panel
data estimation carried out are:
LnYit = β0 + β1LnX1it + β2X2it
+ β3Xit + eit
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Information
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: Ln Gross
Regional Domestic Product
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: Ln Investment
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: Open
Unemployment Rate
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: Inflation
Rate
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: Intercept
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: Regression
Coefficient
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: Component
error at time t for unit cross-section i
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: 1-6 provincial
cross-section data
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: 1-6 time-series
data from 2014 to 2019
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RESULTS
AND DISCUSSION
1. Classic
assumption test
This test is needed to
achieve a BLUE estimator (Best Linear Unbiased Estimator) (Rosadi, 2016).
According to (Winarno, 2017) in
this test, which is generally used are normality, autocorrelation,
multicollinearity, and heteroscedasticity tests.
The normality test is carried
out because of the assumption that the data follows a normal distribution so
that the normality test aims to test the normality of the data. One of the most
widely used normality tests is the Jarque Bera (JB) test. The residuals are
normally distributed, if the p-value is < the level of significance then the
hypothesis is rejected, whereas if the p-value is > the level of
significance then the hypothesis is normally distributed (Rosadi, 2016). The
probability value of JB 0.568720 is greater than the alpha level of 0.05,
therefore the residuals are normally distributed and the classical assumptions
regarding normality have been met.
Table 1
Normality Test
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Jarque-Bera
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1.128735
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Probability
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0.56872
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Source: Results of Data
Processing Using Eviews 10
The autocorrelation test was
carried out to find out whether or not there was a correlation between errors
in the current year and the previous year. How to find out the problem of
autocorrelation can be seen with the Durbin-Watson test. If the value of DW
<-2, then there is a positive autocorrelation, if -22<DW<+2, then
there is no autocorrelation problem, and if DW> +, then there is a negative
autocorrelation. In this study, it was obtained that the Durbin Watson value
was between -2 to +2, namely the Durbin Watson value of 0.860588, therefore the
model did not have autocorrelation problems.
Table 2
Autocorrelation Test
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Durbin-Watson stat
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0.860588
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Source: Results of Data
Processing Using Eviews 10
The multicollinearity test
was carried out to look at the regression model whether there was a correlation
or relationship between independents. Free from multicollinearity if the
correlation coefficient between variables < 0.8. In this study, the
correlation coefficient value is smaller than 0.8 and the conclusion is that
the model does not have a multicollinearity problem.
Table 3
Multicollinearity Test
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Correlation
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Ln Investment
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Unemployment
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Inflation
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Ln Investment
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1.000000
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0.550101
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-0.20231
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Unemployment
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0.550101
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1.000000
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0.170816
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Inflation
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-0.202314
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0.170816
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1.000000
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Source: Results of Data
Processing Using Eviews 10
The heteroscedasticity test
has a purpose to measure the variation of error is fixed or variable. If the
p-value is smaller than 0.05, then heteroscedasticity occurs and vice versa.
And in this study, the probability value (p-value) in each independent variable
is greater than the alpha level of 0.05, therefore the data is free from
heteroscedasticity problems.
Table 4
Heteroscedasticity Test
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Variable
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Coefficient
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Std. Error
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t-Statistik
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Prob.
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C
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13.46213
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0.653285
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1.79862
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0.0833
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Ln Investment
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-0.394971
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0.020083
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-1.718323
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0.0972
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Unemployment
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-0.109917
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0.010323
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-0.94889
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0.3511
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Inflation
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-0.001071
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0.00362
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-0.028334
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0.9776
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Source: Results of Data Processing Using Eviews 10
2. Model
Determination Technique
Hausman test
Hausman test is used to get
the best model between the random effect model or the fixed effect model, if
the probability value is < 0.05 then the best model is the fixed effect
model, while if the probability value is > 0.05 then the best model is the
random effect model.
Table 5
Hausman test
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Cross-section
random
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0.0000
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Source: Results of Data
Processing Using EViews 10
The test results on the
Hausman test found a probability value of 0.0000 < (0.05), so the best model
is the Fixed Effect Model (FEM). After testing for the determination of the
model through the Chow test and the Hausman test, the fixed effect model was
obtained as the best model.
3. Research
result
To know the results of this
study can be seen from the output of multiple linear regression. The results of
multiple linear regression analysis using the fixed-effect model are as
follows:
Table 6
Multiple Linear Regression
Equation Fixed Effect Model
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Dependent Variabel: LNPDRBADHK
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Method: Panel Least Squares
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Variable
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Coefficient
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Std. Error
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T- Statistics
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Prob.
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C
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16.30233
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1.387803
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11.74687
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0.0000
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Ln Investment
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0.138817
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0.042620
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3.257091
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0.0030
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Unemployment
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-0.048225
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0.021478
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-2.24526
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0.0331
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Inflation
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-0.015654
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0.007007
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-2.23391
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0.0340
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Source: Results
of Data Processing Using EViews 10
LnPDRBit=16.30233+0.138817
Ln Investmentit�0.048225 Unemployedit�0.015654 Inflationit+eit
(2)
Based on the regression
equation above, it is obtained:
a. The constant of 16.30233, shows that if the
investment, unemployment, and inflation variables are 0, then the economic
growth is 16.30233.
b. The regression coefficient for the
investment variable is 0.138817, meaning that for every 1% increase in
investment, economic growth will increase by 0.138817%.
c. The regression coefficient for the
unemployment variable is -0.048225, meaning that an increase in the
unemployment rate of 1% will cause economic growth to decrease by 0.048225%.
d. The regression coefficient for the
inflation variable is -0.015654, meaning that an increase in the inflation rate
of 1% will cause economic growth to decrease by 0.015654%.
4. Hypothesis
testing
a.
t-test
The t statistic test aims to partially test
the hypothesis. The consideration is based on the probability value or the
comparison of the value of t statistics with the value of t table, if the
probability value is < 0.05, or t count > t table or �t count <- t table
there is a strong influence between the independent variable and the dependent
variable, and vice versa.
Table 7
t-test results
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Variable
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T-Statistics
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Prob.
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C
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11.74687
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0.0000
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Ln Investment
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3.257091
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0.0030
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Unemployment
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-2.245261
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0.0331
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Inflation
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-2.233911
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0.0340
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Source: Results of Data
Processing Using EViews 10
Obtained the value of t-statistic.
In this study, the value of df = n-k produced
is df = 36-4 = 32, with a significance value of 0.05 so that the value of t table
is 2.03693, then:
1)
The investment variable, based on the test
results, the value of t count > t table (3.257091) > (2.03693), the
probability value of 0.0030 is smaller than the alpha level of 0.05 (0.0030
< 0.05), so that H0 is statistically rejected, the investment variable (Ln
Investment) has a significant effect on economic growth.
2)
Unemployment variable based on the test
results obtained the value �t count < -t table (-2.245261) < (-2.03693),
the probability value is 0.0331 smaller than the alpha level 0.05 (0.0331 <
0.05) so that H0 is rejected statistically the unemployment variable has a
significant effect on economic growth.
3)
Inflation variable, based on the test results
obtained value �t count < -t table (-2.233911) < (-2.03693), probability
value 0.0340 smaller than alpha level 0.05 (0.0340 <0.05), so H0 is rejected
statistically inflation variable has a significant effect on economic growth.
b.
F test
This test intends to prove the regression
coefficient together. Criteria if F count > F table or prob < 0.05 H0 is
rejected and H1 is accepted or there is a significant effect and vice versa.
Table 8
F test
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F-statistics
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1243.842
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Prob (F-statistics)
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0.000000
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Source: Results of Data
Processing Using EViews 10
This test intends to prove the regression coefficient
together. The F table value is obtained (df1 = 3, df2 = 32), with a
significance value of 0.05, then the F table value is 2.90. Thus, the value of
F count > F table (1243,842 > 2.90) and the probability value of 0.000000
< alpha level of 0.05, so that the independent variables including
investment, unemployment, and inflation simultaneously affect the dependent
variable, namely economic growth.
c.
Coefficient of Determination Test (Adjusted
R2)
This test is to measure the contribution of
the independent variable which can be explained by the dependent variable. The
adjusted-R2 value is close to 1 (one) then the fit or suitability of the
regression model is getting more correct.
Table 9.
Coefficient of Determination
Test
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R-squared
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0.997294
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Adjusted
R-squared
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0.996492
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Source: Results of Data
Processing Using EViews 10
The adjusted R-squared value is 0.996492,
meaning that the independent variables including investment, unemployment, and
inflation can explain their effect on the dependent variable, namely economic
growth 99.64%, while the remaining 0.36% is influenced by other variables
outside of this study.
5. Discussion
a.
The Effect of Investment on Economic Growth
Based on the regression results, investment
has a positive effect on economic growth in Java Island for the 2014-2019
period, if investment increases by one percent, then economic growth will
increase by 0.138817%, or there is a unidirectional relationship, meaning that
if investment increases, it will increase economic growth. Java Island is the
region with the highest investment compared to other regions, although the
realization of investment in Java fluctuates but tends to increase. Increasing
investment can have an impact on increasing economic growth in Java, this is
because investment can increase economic production by increasing the capital
stock. Investment spending can increase the capability in creating goods and
services that can grow consumer purchasing power and household consumption so
that these expenditures can increase the demand for community needs which will
affect the increase in regional and state revenues so that economic growth will
grow. The results of this study are reinforced by the theory expressed by
Harrod Domar which proves that investment that takes place in a certain year
will cause an increase in the capacity of capital goods to create goods and
services in the following year so that the economy increases. The results of this
study are also in line with previous research, namely (Meilaniwati & Tannia, 2021)
which showed that PMDN and PMA had a positive effect this was because PMA and
PMDN were sources of capital used to improve and assist the process of
infrastructure and project development to improve economic growth, further
research conducted by (Jamil, 2020)
which states that investment in Java tends to be stable both period to period
and regionally, in terms of infrastructure Java Island is better.
b.
The Effect of Unemployment on Economic Growth
Based on the regression results, the
unemployment variable hurts economic growth in Java for the 2014-2019 period,
if unemployment increases by one percent, then economic growth will decrease by
0.048225% with the assumption that other variables remain. High unemployment
can cause economic growth to slow down or decline in Java, based on data the
unemployment rate in Java is relatively high, high unemployment means that the
absorption of labor in employment is small, resulting in a decrease in
community productivity so that people's income will also decrease, with This decrease
in income hurts the demand for goods and services produced due to people's
purchasing power, and a decrease in the factors of production of goods and
services so that the demand for labor decreases and causes unemployment to
increase, resulting in reduced output and economic growth will decline. . This
decrease in purchasing power is because high unemployment causes the real
national income received by the community to be lower than potential income, it
can be concluded that high unemployment can reduce economic activity, resulting
in slowing or declining economic growth. The results of this study are
reinforced by the theory of Okun's Law, namely the theoretical concept that
explains the relationship between unemployment and GDP, which was put forward
by Arthur Okun who suggested that there was a negative relationship between
unemployment and GDP. The results of this study are also in line with previous
research by (Febryani, 2017)
which showed that unemployment hurt economic growth, the negative relationship
between economic growth and unemployment was caused by an unproductive
population or unemployment causing an increase in the burden of dependence,
this was caused by a population who was not productive. work to bear
unemployment or residents who have no income as long as they have not found
work.
c.
The Effect of Inflation on Economic Growth
Based on the partial regression results, the
inflation variable has an effect on economic growth in Java for the 2014-2019
period, if inflation increases by one percent, then economic growth will
decrease by 0.015654% assuming other variables remain. High inflation can
reduce economic growth, this is due to prolonged changes in price levels which
can hurt economic activity and make the economy sluggish. Prolonged price
increases will cause productivity to be very unprofitable so that investment
will decrease, and with rising inflation, it will also hurt trade, where
domestic products are unable to compete in the International Market because
their capacity decreases, so that exports decline and imports increase. High
inflation rates can cause people's welfare to decline because the income earned
is reduced, so people with fixed incomes cannot buy goods and services and
cause purchasing power to decrease. Therefore, the resulting output decreases
so that economic growth decreases. The results of this study are also in line
with previous research, namely (Fernanda & Asnawi, 2018)
which proves that inflation hurts economic growth, this is because the increase
in prices is not proportional to the increase in income, so that this increase
in prices causes a decrease in consumption levels and lowers income nationally.
CONCLUSION
Based on the results of research on the effect of
investment, unemployment, and inflation on economic growth in Java in 2014-2019
which have been discussed and calculated statistically to test the hypothesis
that has been carried out. Based on multiple linear regression analysis with
panel data approach, the output variable of investment affects economic growth
in Java, this is due to an increase in investment, the capital stock can
increase the production of goods and services in the economy which can grow
people's purchasing power so that economic growth will grow. Unemployment
variables affect economic growth in Java, this is due to a reduction in the
unemployment rate, so productivity and economic activity can increase so that
economic growth increases. The inflation variable affects economic growth in
Java, this is due to an increase in inflation so it can have a bad influence
and make overall economic activity sluggish, resulting in decreased economic
growth.
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