INTRODUCTION
Indonesia is one
of the countries that is still undergoing a process of economic development and
aims to achieve a society's welfare. In achieving a welfare, one of which is
needed for job opportunities that support and equal distribution of income in
society, in Indonesia there is a gap between job opportunities, namely an
increase in the number of job opportunities that is not balanced with an
increase in the workforce which increases faster, this will have an impact on
the creation of unemployment (Nurcholis, 2014).
Unemployment is an economic
problem that can affect survival directly. Losing a job will lower the standard
of living for the community. Unemployment can have a
negative impact on the person himself as well as on society or the surrounding
environment. Economic uncertainty is a business cycle driver, and its leading
features make it a significant advanced indicator for assessing the impact of
socioeconomic factors on suicide prevention (Claveria, 2022). This is due to
reduced job opportunities which can be caused by a slow economy, reduced
individual potential, loss of work skills, decreased income taxes and low
levels of community welfare (Podi et al., 2020). Unemployment has become an
economic problem in many countries and not only in Indonesia. Due
to unemployment, the level of productivity and income of the community is
reduced, resulting in poverty and social problems (Hartanto & Masjkuri, 2017).
Economic growth is an increase in the ability of the
economy to produce goods and services over a certain period of time. Economic growth is an increase in the ability of an economy
to produce goods and services. In other words, economic growth refers to
changes that are quantitative in nature and are usually measured using data on
gross domestic product (GDP) or output income per capita. The
economy of a country can be said to be growing if the economic activities of
its people directly affect the increase in the production of goods and
services. By knowing the level of economic growth, it is possible for the
government to make plans regarding state revenues and future development.
The problem of
unemployment can be influenced by several indicators, including economic
growth, minimum wages, and the human development index. If a country's economic
growth increases, it is expected to affect a decrease in the unemployment rate.
Efforts to increase economic growth are one of the important indicators in
overcoming the problem of unemployment (Baba, 2021). Okun
's law states that when there is an increase in economic growth it will have a
negative effect on the unemployment rate.
Economic growth is usually
measured using data on gross domestic product (GDP) or per capita output
income. Gross Domestic Product (GDP) data based on current prices and constant
prices is one of the important indicators to determine the economic condition
of a country in a certain period. Moreover, countries in the global north and
south have been developing service employment, much of it is poor wages, for
several decades in order to attain more economic stability and secure a dynamic
labor market (Antipova,
2021).
Based on the
unemployment rate in 2020
reached 7.70%,
this figure increased rapidly from previous years. This is due to the COVID-19 pandemic. One of
the main causes of the rising unemployment rate during this pandemic is layoffs.
Indonesia's economic growth in 2020 contracted minus 2.07 percent with the deepest
growth contraction in the transportation and warehousing sector of 15.04
percent. Ba and
the Central Statistics Agency (BPS) stated that Indonesia's 2020 economic growth
was minus 2.07 percent. The realization of this Gross Domestic Product (GDP)
decreased compared to 2019 which grew 5.02 percent, as well as the worst since
the 1998 crisis which grew minus 13.16 percent. According
to BPS calculations, the Indonesian economy experienced a growth contraction in
2020, mainly due to the Covid-19
pandemic that had occurred since early 2020.
The Minimum Wage
is also one of the indicators to overcome the unemployment rate (Panjawa & Soebagiyo, 2014). If the minimum
wage in an area is low, then the population has a low standard of living and a
low level of consumption (Gorry, 2013). On the other
hand, districts/cities with high regional minimum wages have a high standard of
living and consumption levels.
In addition to
economic growth and the minimum wage, the human development index is also an
important indicator in overcoming the problem of unemployment. The
human development index is an acceptable measure to describe the quality of
human life in a certain period. Increased human development through the
development of human capital which is reflected in the level of education and
health can increase human productivity so
that it will increase the demand for labor and decrease
the unemployment rate (Kurnia & Septiani, 2021).
Table 1 above
shows the human development index in Indonesia for the
last 20 (twenty) years. The Central Statistics Agency (BPS) recorded a slowdown
in the growth of the Human Development Index (HDI) in 2020 compared to previous
years. This condition is caused by the COVID-19 pandemic that hit Indonesia.
Indonesia's HDI in 2020 was recorded at 71.94 , or an increase of 0.02 points
compared to the previous year's achievement. The slowdown in HDI growth in 2020
was strongly influenced by the decline in the adjusted average per capita
expenditure. Based on the explanation
of economic growth, minimum wage and human development index, a problem arises
that must be investigated regarding economic growth, minimum wage and human
development index in Indonesia.
Amrullah et al. (2019) analyze the determinants of the open unemployment rate in java
in 2007-2016. This study uses the variables of minimum wage, grdp and
inflation rate on open unemployment. In this study, the panel data regression model was used
using the fixed effect model (FEM) Approach. The results of this study simultaneously
show that the independent variables of GRDP, Provincial Minimum Wage, and
Inflation have a significant effect on the dependent variable of the open
unemployment rate. The results of the partial test analysis show that GRDP has
a significant effect while the Provincial Minimum Wage and Inflation have an
insignificant effect on the open unemployment rate for the period 2007-2016.
Mahihody et al. (2018)
analyze the effect of wages and Human Development Index (HDI) on unemployment
in Manado. This
study uses a variable wage and human development index on unemployment The
analytical method used is multiple regression analysis. Based on the results of
the study, the minimum wage level in Manado City has a significant negative
effect on unemployment and the human development index has a significant
negative effect on unemployment in Manado City.
Baba (2021)
analyze economic determinants of unemployment in Malaysia,
the research
variables Unemployment, GRDP, Investment, Inflation, Population, used VECM
approach. The results show that there is short-term causality between variables
as well as long-term. GDP has a significant negative impact while investment
has a significant positive impact on unemployment.
Tsaurai (2020)
analyze Macroeconomic
Determinants of Unemployment in Africa, the variables
used Information and communication technology, Unemployment Human resources and
infrastructure. method used Panel data analysis (fixed effects, random effects,
pooled ordinary least squares, dynamic generalized methods of moments). Random effects and OLS show that economic
development has a significant positive effect on population unemployment and
open trade has a significant positive impact on unemployment. Information and
communication technology and human resources have a significant negative effect
on unemployment. Fixed effects and pooled OLS method show that economic growth
has a significant negative effect on unemployment. Meanwhile, this research
took place in Indonesia and at a time when the COVID-19 pandemic condition
occurred, using the VECM approach.
METHOD
A. Data types and sources
The type of research used in this study
is quantitative data. The
secondary data used in this study is time series data or annual data from 2001-2020. Sources of data in
this study were taken from data published by the Central Statistics Agency and Bank Indonesia. This data was
taken from the Central Statistics Agency due to the completeness of the data
published by BPS and Bank
Indonesia.
1)
Operational Definition
a.
Unemployment:
the number of people of working age who are not working or have not
worked in the study period. Percentage of Unemployment Rate in
Indonesia in 2001-2020.
b. Minimum Wage: the
lowest monthly wage set annually as a safety net in an area. Minimum Wage Data
in Indonesia for 2001-2020 in rupiah.
c. Economic growth: an
increase in the value and amount of production of goods or services in
Indonesia within a certain period of time. proxied with Indonesia's GRDP in the
period 2001 � 2020.
d. Human Development Index: Human
development achievement is based on a number of basic components of quality of
life. As a measure of the quality of life in Indonesia. with 3 main indicators,
namely health indicators, education levels and economic indicators in 2001 -
2022.
B. Data Collection
Data was collected by observing the data,
literature, reports, journals, and other sources that support and have a
relationship with this research. The data for this study were obtained from
data published by the Central Statistics Agency (BPS) and Bank Indonesia.
C. Data
Analysis
The analysis used in this research is
Vector Error Correction Model (VECM) analysis. Vector
Error Correction Model (VECM) is a method derived
from VAR.
1) Data
Stationarity Test
The
test method used to test the stationarity of
the data is the ADF (Augmented
Dickey-Fuller) test. using a significance level of 5 percent. If the
t-ADF value is less than the critical value, it can be concluded that the data
used is stationary (does not contain unit roots). The unit root test is carried
out at the level up to the second difference. Stationary data tend to be close
to the average value, fluctuating around the mean value. Non-stationary data
can result in quasi-regression, which is a regression that describes the
relationship between two or more variables that looks statistically significant
when in fact it is not.
2) Optimal
Lag Determination
Problems
that arise if the lag length is too small will make the model unusable because
it cannot explain the relationship. Vice versa if the length of the lag used is
too large, the degrees of freedom will be greater so that it is no longer
effective. Determining the optimal lag is important because in the VAR method,
the optimal lag of the endogenous variable is the independent variable used in
the model. Long test lag will be very helpful in eliminating the problem of
auto correlation in the VAR system which is used as a VAR stability analysis.
So that by using the optimal lag, it is expected that auto problems are
expected the
correlation will no longer appear.
3) Cointegration
Test
The
purpose of the cointegration test in this study is to determine whether the
group of non-stationary variables at these levels meets the requirements of the
integration process, that is, if all variables are stationary to the same
degree. This test is intended to determine
whether there is a long-term effect on the variables studied. If cointegration
is proven to exist, then the VECM step can be continued. However, if it cannot
be proven, then VECM cannot be continued.
4) Granger
Causality Test
Causality test is conducted to determine
whether an endogenous variable can be considered as an exogenous variable. This
stems from a lack of understanding of the influence between variables. If there
are two variables y and z, then whether y causes z or z causes y or applies
both or there is no relationship between the two variables.
5) Vector Error Correction Model
(VECM)
VECM data is used in a non-structural VAR
model if the time series data is not stationary at the levels but is stationary
at the differential data and is cointegration
so that it shows a theoretical relationship between variables. In VECM, there
is a speed of adjustment from short to long term.
VECM is a limited form of VAR due to the
non-stationary form of data but cointegration.
VECM is often referred to as a VAR design for non-stationary series that has a
cointegration relationship.
According
to Syahfitri , the general VECM model is:
Yt : vector containing the
variables analyzed in the study, 
:
vector intercept, 
:
regression coefficient vector t : time trend, 
:
coefficient speed of adjustment,
:
cointegration vector, 
:
variable in-level,
:
regression coefficient matrix,
:
VECM order of VAR,��� 
:
lag,
:
error term
6) Impulse Response Function
(IRF)
Impulse Response Function (IRF) analysis
is a method used to determine the response of an endogenous variable to shocks
of certain variables. The Impulse Response Function (IRF) is also used to see
the shock of another variable and how long the effect lasts. If a variable
cannot be affected by shocks, then the specific shock cannot be known but the
shock in general.
7) Variance Decomposition (VD)
Variance Decomposition is a method to
describe the dynamic system contained in VAR. It is used to compile an estimate
of the error variance of a variable, namely how big the difference between the
variance before and after the shock, both from the shock that comes from
oneself or from other variables.
Forecast
error variance decomposition (FEVD) describes the innovation of a variable
against the components of other variables in the VAR. The information conveyed
in the FEVD is the proportion of sequential movements caused by the shock
itself and other variables.
D. Operational
Definition
�
Unemployment
: the number of people of working age who are not working or�� have not��
worked in the study period. Percentage of Unemployment Rate in Indonesia
in 2001-2020
�
Minimum Wage:
the lowest monthly wage set annually as a safety net in an area. Minimum Wage
Data in Indonesia for 2001-2020 in rupiah
�
Economic growth:
an increase in the value and amount of production of goods or services in
Indonesia within a certain period of time. proxied with Indonesia's GRDP in the
period 2001 � 2020
�
Human Development Index : Human
development achievement is based on a number of basic components of quality of
life. As a measure of the quality of life in Indonesia. with 3 main indicators,
namely health indicators, education levels and economic indicators. 2001 -
2022. in percent
RESULTS
AND DISCUSSION
A. Stationary Test
Table 1
ADF
Stationary Test At Level
|
Variable
|
ADF statistics
|
|
t-Statistics
|
critical value
|
Prob.
|
|
Unemployment Rate
|
-0.944
|
-3.029
|
0.750
|
|
Economic growth
|
-0.835
|
-3.029
|
0.785
|
|
Minimum wage
|
-1.232
|
-3.040
|
0.636
|
|
HDI
|
-2.401
|
-3.029
|
0.154
|
Source: data processed
Based
on Table 1, the stationary test at the level level, it can be seen that the ADF
t-statistic value on the variables of unemployment, economic growth, minimum
wage and human development index is not stationary. This is because the
critical value is greater than the ADF t-statistic value. Therefore, it is
necessary to process 1 st difference to find out all the variables
are stationary or not. The following are the results of the stationarity test
at the first difference level :
Table 2
ADF
Stationary Test at Level 1st difference
|
Variable
|
ADF
statistics
|
|
t-Statistics
|
critical value
|
Prob.
|
|
Unemployment Rate
|
-3.100
|
-3.040
|
0.044
|
|
Economic growth
|
-1.519
|
-3.040
|
0.501
|
|
Minimum wage
|
-6.070
|
-3.040
|
0.000
|
|
HDI
|
-4.471
|
-3.040
|
0.002
|
Source: data processed
Based
on Table 2, the results of data processing for 1st difference , it
can be seen that the variables of the unemployment rate, minimum wage, and the
human development index are stationary at level 1 st difference
where the ADF t-statistic value is greater than the critical value . On the other hand, the
economic growth variable is not stationary where the critical value of -3.040
is still higher than the ADF t-statistic which is -1.519. Therefore, it is
necessary to carry out a 2 nd difference process to see whether all variables
are stationary or not. The following is a table of ADF stationary test results
at level 2 nd differences:
Table
3
ADF
Stationary Test at Level 2nd difference
|
Variable
|
ADF
statistics
|
|
t-Statistics
|
critical
value
|
Prob.
|
|
Unemployment Rate
|
-6.222
|
-3.052
|
0.000
|
|
Economic growth
|
-3.117
|
-3.052
|
0.044
|
|
Minimum wage
|
-9.573
|
-3.052
|
0.000
|
|
HDI
|
-7.596
|
-3.052
|
0.000
|
Source: data processed
Based
on Table 3, the results of the above data processing, it can be seen that all
data are stationary based on the results of the 2 nd difference unit
root, this is because the ADF t-statistic value is greater than the critical value. The probability of ADF
t-statistics of the unemployment rate variable is -6.222 which is greater than
the critical value of -3.052. The economic growth variable has stationary data
at the second difference, where the probability of the ADF t-statistic of the
economic growth variable is -3.117 which is greater than the critical value of
-3.052.
The
minimum wage variable has stationary data at the 2 nd difference
level , because the probability of the ADF t-statistic is -9.573 greater than
the critical value , which is -3.052. The Human Development Index (HDI)
variable also has stationary data at the 2nd difference level, because the
probability of the ADF t-statistic is -7.596 which is greater than
the critical value , which is -3.052. It can be concluded that the variable
data on the unemployment rate, minimum wage, economic growth and also the human
development index are stationary at the second difference level.
B. Optimal Lag Test
Table
4
Optimal
Lag Test Results
Source:
data processed
Table
4 shows the amount of lag in this study is based on the smallest or minimum
value. Apart from being seen from the smallest or minimum value, it can also be
seen from the number of stars in the lag. In the table it can be seen that the
optimal lag length lies in lag 1. Thus, the recommended optimal lag is lag 1.
C. Cointegration Test
Table
5
Johansen
Cointegration Test Results
Source:
data processed
Table
5 shows the trace statistic at
none , at most 1, at most 2 and at most 3 is greater than the critical value
with a significance level of 5 percent. Based on the results of the
cointegration test, a sign (*) was found on none , at most 1, at most 2 and at
most 3, then the equation must be solved using the VECM method. Thus, among the
variables of unemployment rate, economic growth, minimum wage and HDI, there is
stability and movement in the long term. Meanwhile, in the short run, all
variables adjust to each other to achieve balance in the long run.
D. Granger Causality Test
Table
6
Granger
Causality Test Result
![]()
Source:
data processed
Based
on Table
6, the probability value, if the probability value is below
0.05, then the variables have an influence. On the other hand, if the
probability value is above 0.05, then the variables have no influence on each
other. One of them is the variable of economic growth which has a statistically
significant effect on the unemployment rate, while unemployment does not have a
statistically significant effect on economic growth.
E.
Vector Error Correction Model (VECM) Test
Table 7
Long-Term and Short-Term VECM Estimates
|
Variable
|
Coefficient
|
t-stats
|
t-table
|
|
Long-term
|
|
Economic
growth
|
59.34795
|
6.14707
|
1.74588
|
|
Minimum
wage
|
-593.6873
|
-3.98820
|
|
HDI
|
-40.01235
|
-7.67578
|
|
Short-term
|
|
CointEq1
|
0.00506
|
-1.61863
|
1.74588
|
|
Economic
growth
|
0.497935
|
2.21716
|
|
Minimum
wage
|
-6.735205
|
-2.81136
|
|
HDI
|
-0.342571
|
-2.52685
|
Source: data processed
Based
on table 7, In the long term economic growth has a positive and
significant effect on the unemployment rate. It is known from the t-statistical
value which is greater than the t-table value. Each minimum wage variable shows
negative and insignificant results as seen from the t-statistic value which is
smaller than the t-table value. In the short-term VECM estimation results,
economic growth shows positive and significant results with a t-statistic value
of 2.21716 which is greater than the t-table value of 1.74588. Meanwhile, the
minimum wage and HDI variables showed negative results and did not have a
significant effect on the unemployment rate variable because the t-statistic
value was smaller than the t-table, namely 1.74588.
Based
on the table In the long term economic growth has a positive and
significant effect on the unemployment rate. Each minimum wage variable shows
negative and insignificant
. In the short-term VECM estimation results, economic
growth shows positive and significant. Meanwhile, the minimum wage and HDI
variables showed negative results and did not have a significant effect on the
unemployment rate
F.
Impulse
Response Function (IRF)
Table
8
Impulse
Response Function (IRF) Value
�Respon of
D(Unemployment):
�Period.����
D(Unemployment)����� D(GDP).����� D(LogMW)���� ����D(HDI)
Source: data processed
Table
8 �shows the unemployment rate
variable responds to the shock given the variables of economic growth, minimum
wage, and human development index are changing every period . Both respond
positively and negatively to the unemployment rate variable
Response of D(Unemployment)
to D(Unemployment
Source:
data processed
Figure
1. Test of Impulse Response to
Unemployment
From
the Figure 1, it can be seen that the tendency of the unemployment variable
above the horizontal line which indicates that this variable has a positive
impact. This is because the unemployment rate affects itself so that it can
control its own impact.
��������������������������������������� Respon
of D(Unemployment) to D(GDP)
Source: data processed
Figure
2. Impulse Response Test of
Economic Growth on Unemployment
Figure
2 shows the trend of the variable economic growth below and above the
horizontal line, which means that the variable economic growth has a negative
impact as well as a positive impact according to each period. The shock given by -0.149458 in the 10th
period means that if there is an increase in economic growth it will reduce the
unemployment rate.
������������������������������� ����Respon of D(Unemployment) to D(LogMinimumWage)
Source:
data processed
Figure 3. Minimum Wage Impulse Response
Test on Unemployment
Figure
3 shows the trend of the variable economic growth below and above the
horizontal line, which means that the variable economic growth has a negative
impact as well as a positive impact according to each period. The shock given
was -0.111105 in the 10th period.
Response of D(Unemployment) to D(HDI)
Source: data processed
Figure
4.� HDI Response Test on the Unemployment
Rate
Figure
4 shows the tendency of the variable economic growth below and above the
horizontal line, which means that the variable economic growth has a negative
impact as well as a positive impact according to each period.
Table
9
Variance
Decomposition (VD) Results
�Variance Decompositon of D(Unemployment) :
� Period.���������������������� SE.����� D(Unemployment��� D(GDP )�����������
D(LogMW)������������ D(HDI
Source: data processed
Table
9 explains the results of the variance decomposition test where in the first
period the unemployment rate is influenced by the unemployment rate itself.
However, as the period increases, other variables begin to influence, although
the magnitude is not as large as the influence of the unemployment rate itself.
The minimum wage has the second largest influence after the unemployment rate
variable, where the effect at the beginning of the period is 4.66 and continues
to increase for 3 periods, and after that it decreases until the end of the
period the effect is 15.6 on the unemployment rate. The smallest effect is
given by the human development index variable on the unemployment rate of 3.26
percent at the end of the period, as for the economic growth variable seen from
the variance decomposition test , which is in the 3rd place, its effect on the
unemployment rate is 14.2 percent at the end of the period.
Based on the results of the above calculations, the
following is a further discussion which is explained as follows:
1) The Effect of Economic Growth
on Unemployment
Based on the short-term and long-term test results, the
economic growth variable shows positive and significant results on the
unemployment rate. This condition is not in accordance with the theory of
Okun's Law and the hypothesis of this study which states that when there is an
increase in economic growth, it will have a negative effect on the unemployment
rate. However, this condition is in accordance with the results of research
from Anggoro (2015) which
states that positive economic growth is due to economic growth not being
accompanied by an increase in production capacity, so unemployment continues to
increase in line with economic growth.
2) Effect of Minimum Wage on
Unemployment
Based on the short-term and long-term test results, the
minimum wage variable shows negative results and does not have a significant
effect on the unemployment rate. This means that if wages rise, the
unemployment rate will decrease. If wages are set at too low it will result in
high levels of unemployment.
This condition is in accordance with Keynes's theory
which states that if the wage rate increases, it will affect the decrease in
the unemployment rate. When wages increase, income also increases. The impact
that occurs if income increases is that purchasing power will also increase and
public spending will increase, so the production capacity will be increased in
accordance with the demand for goods and services, so that the company will
increase its workforce to meet community demand and the use of full employment
will increase.
The results of this study also show the relationship
between the minimum wage and the unemployment rate which is not significant.
According to research conducted by Amrullah
et al. (2019) regarding
the determinants of the Open Unemployment Rate in Java in 2007-2016, it is
stated that whatever the minimum wage increases, it will not affect the
unemployment rate. An increase in the minimum wage is not always accompanied by
a decrease in the unemployment rate. This means that the increase in the
minimum wage that occurs does not absorb the existing workforce so that the
unemployment rate does not decrease.
3) The Effect of the Human
Development Index on Unemployment
Based on the short-term and long-term test results, the
human development index variable shows negative results and does not have a
significant effect on the unemployment rate (Sanitra, 2021). That is, if the human development index increases, the unemployment
rate will decrease. On the other hand, if the human development index
decreases, the unemployment rate will increase. HDI includes three dimensions,
namely a long and healthy life, knowledge, and a decent life.
The first dimension, a long and healthy life as measured
by a higher life expectancy at birth, indicates that public health is
classified as good, and in the long term it will increase work productivity.
When work productivity increases, income will increase, so this will have an
impact on decreasing the unemployment rate. For the dimension of knowledge, it
is measured by the expectation of long schooling and high average length of
school, it will increase the quality of self in the community. When the quality
of human resources increases, they are quickly absorbed in the world of work
because they have expertise. This has an impact on job absorption and reduces
the unemployment rate.
The third dimension is a decent life as measured by the
average amount of per capita expenditure. If the people of an area have a high
average per capita expenditure, this illustrates the high purchasing power of
the people. This indicates a high community income and a low unemployment rate.
If these three dimensions increase every year, then human development is
considered successful. Thus the government has succeeded in increasing human
development and making people quickly absorbed into the world of work. This is
in accordance with the research conducted by Mahihody
et al. (2018) with
the title of the research entitled The Effect of Wages and Human Development
Index (IPM) on Unemployment in Manado City.
The results of this study also show the relationship
between the minimum wage and the unemployment rate which is not significant.
This means that the human development index does not have a significant effect
on the unemployment rate. According to research by Latifah (2017), it is said that the occurrence of unemployment is not
only caused by the quality of human resources, the number of college graduates
who are still unemployed because the existing job opportunities are not in
accordance with the interests of increasing educated unemployment.
CONCLUSION
Based on testing with the VECM test, it shows
that economic growth positively affects the unemployment rate in the short and
long term in the period 2001-2020. Economic growth has a positive and
significant effect on the unemployment rate in the long term. The minimum wage
does not have a significant effect on the unemployment rate in the long run.
The human development index does not have a significant effect on the
unemployment rate in the long run. In the short-term VECM estimation test
results, only economic growth has a positive and significant effect on the unemployment
rate. Meanwhile, the minimum wage and the human development index have no
significant effect on the unemployment rate.
this research provides good and appropriate information for academics and
the government to be able to reduce the problem of unemployment in Indonesia by
using an economic and social approach.The
weakness of this research is that the data was also taken during covid 19, so
it does not provide a real picture of unemployment in indonesia
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EXAMINING
THE EFFECTS OF ECONOMIC GROWTH ON UNEMPLOYMENT IN INDONESIA