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INKUBIS:
Jurnal Ekonomi dan Bisnis |
Vol. 6 No. 1
Juli-Desember 2023 |
Does Economic Growth Interact With The Basic Sector, Human Development, and Provincial
Minimum Wage on Poverty in Sumatra Island Provinces?
Arienal Martha Zulha1, Vivi
Silvia2, Muhammad Abrar3
Fakultas Ekonomi dan Bisnis, Universitas Syiah Kuala, Banda
Aceh, Indonesia
Email:
1arienalm@mhs.usk.ac.id, 2vivisilvia@usk.ac.id, 3muhammadabrar@usk.ac.id
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KEYWORD |
ABSTRACT |
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Poverty, Economic
Growth, Base Sector, Human Development Index, Regional Minimum Wage, ARDL,
Location Qoutient. |
A high level of poverty in an area can have a significant impact on
the economic performance of a region. This study will analyze the influence
of the base sector, human development, and provincial minimum wage on poverty
through the role of GDP growth in Sumatra. The ARDL and Location Quotient
(LQ) panel data regression methods were used to analyze panel data consisting
of 10 provinces in Sumatra for the period 2011 to 2022. The findings found
that there is one sector that is dominant in all provinces on the island of
Sumatra, namely the Agriculture, Fisheries, and Forestry sectors. The direct
influence between the variables of the base sector, human development, and
regional minimum wage on poverty and economic growth shows that in the long
run, human development and the minimum wage have a negative effect on poverty
and economic growth in a positive direction, while the base sector has no
effect on poverty and economic growth. Economic growth plays a mediating role
in mediating the three variables. The government, in reducing poverty and
encouraging inclusive economic growth, is advised to allocate greater
resources and investment to the agriculture-based sectors, and the government
can also focus policies on increasing human development and minimum wage,
considering that these variables have proven to have a positive influence on
economic growth directly. Vivi Silvia Articles with
open access under license
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INTRODUCTION
Poverty is the main focus in
the development agenda of developing countries, a challenge that must be
overcome comprehensively. This is a common concern for all developing
countries, which place poverty alleviation as one of the top priorities in
efforts to achieve development goals (Mukthar et al., 2019). The issue of
poverty is creeping to the surface as the number of poor people soars sharply
as a result of the ongoing economic crisis. The problem of poverty in Indonesia
has now reached an urgent national level, forcing the government to actively
seek solutions in an effort to overcome this challenge
and lift the welfare of the people of Indonesia out of the trap of poverty (Sabyan & Widyanti, 2022).
Provinces
on the island of Sumatra and other densely populated areas face high poverty,
measured by the number of individuals below the poverty line and the percentage
of the poor population, although Sumatra is famous for its natural resources,
such as its mining products and coconut plantations, poverty is still rampant
in many regions. Factors such as lack of access to quality education, lack of
formal employment, and limited infrastructure further complicate this situation
(Darsana & AA Gede, 2019). To support the above narrative, the following
poverty data is attached:
Source: Central
Statistics Agency, (2023)
Figure 1. Percentage of Poor People in the Province on
the Island of Sumatra in 2018 2022 (Percent)
The percentage of poor people in Sumatra between
2018-2022 fluctuated in each Province, influenced by government policies,
global and local economic conditions, and natural factors such as disasters or
climate change. For example, Aceh's decline in 2022 may be due to post-disaster
recovery or social programs. South Sumatra was stable until 2021 but declined
in 2022 due to changes in social and economic policies.
Every country has goals to achieve in its economic
development. One of the main goals that continues to occupy a central position
among the macroeconomic goals of developing countries is to achieve economic
growth. Economic growth refers to the increase in a country's productive
capacity and changes in the growth rate of Gross Domestic Product (GDP)
(Picardo, 2020). The economic development of a region cannot be separated from
economic growth; this is because the two things are mutually sustainable with
each other where good economic development will encourage an increase in
economic growth, and if economic growth goes well, it will facilitate the
process of economic development (Muna, 2023). The following is data on economic
growth on the island of Sumatra in recent years:
Source: Central
Statistics Agency, (2023)
Figure 1.2 Economic Growth in Provinces on the Island
of Sumatra in 2018 2022 (Percent)
Economic growth on the island of Sumatra in 2018-2022
fluctuated. In 2018, the majority of provinces showed positive growth, and
South Sumatra was the highest (6.04%). In 2020, the majority experienced a
significant decrease due to the pandemic; the Riau Islands recorded negative
growth (-3.80%). In 2021-2022, the majority recovered with positive growth,
marking a post-pandemic economic recovery. Factors such as government policies,
investment, and economic sectors play a role in fluctuations.
The development of certain economic sectors can be
seen from the base sector of a region, which will help encourage the economic
growth rate of the region so that the sectoral approach becomes a strategy for
encouraging the economy and the potential of a region. The development of the
base sector must be optimized because this sector can be the driving force of
the economy in each region (Solikin et al., 2018). This is in line with what
was conveyed (Aulianur et al., 2023) that the basic model of the basic economy
can determine a brief overview of the economy of a region because it can be
used to predict the effect of economic growth from outside the region on the
economy of a region. The development of the base sector is important for the
region as one of the right development patterns to be applied in the region.
The Human Development Index (HDI) was formulated by
the United Nations in 1990 as a composite index to evaluate long-term
achievements in three main aspects of human development: longevity, educational
attainment and access to knowledge, and decent living standards (Prasetyo,
2023). The HDI measurement components are education, health, and expenditure,
which are very important to improve because they will have an impact on
productivity and income increase, which will result in poverty rates tending to
decrease (Prasetyo & Fitanto, 2023). HDI is a benchmark for the development
of a country in terms of health, education, and living standards. The higher
the HDI of a country, the better the welfare and quality of life of its people.
Therefore, the higher the HDI in Indonesia, the less likely it is that people
will live in poverty (Aisyah et al., 2023).
Wages are one of the other factors that can affect
poverty levels. The increase in the provincial minimum wage is the main driver
in increasing the number of workers and affects the level of labor absorption
in various industrial sectors (Pratama & Silvia, 2020). Along with the
increase in wages that must be borne by companies, the spirit of setting the
provincial minimum wage has proven to be in line with the goal of improving the
economic conditions of workers, especially in the Sumatra region. The positive
impact of this policy is the creation of a more stable and equitable economic
environment, which in turn will spur sustainable economic growth and improve
the welfare of the working community as a whole (Mankiw, 2014).
The Base Sector, Human Development Index (HDI), and
Provincial Minimum Wage (UMR) have an important role in economic growth and
poverty alleviation. However, research that integrates these three factors is
still rare. Through this study, there is an opportunity to explore more
thoroughly the complex relationship between the phenomenon of poverty, the
dynamics of economic growth, and key variables such as the base sector, the
human development index, and the provincial minimum wage in the Sumatra Island region.
The purpose of this study is to analyze the base sectors on the island of
Sumatra, human development, and the provincial minimum wage, as well as the
long-term and short-term effects on the poverty rate through the economic
growth of the island of Sumatra.
RESEARCH
METHODS
The scope of this study includes an analysis of the
role of economic growth in mediating the influence of the base sector, human
development index, and provincial minimum wage on poverty rates in 10 Sumatra
Island Provinces. The data collected in this study is secondary data obtained
from the report of the National Central Statistics Agency (BPS). By its nature,
quantitative data is data in the form of numbers that can be measured with a
certain measure and have a certain value (Silvia, 2020). The quantitative data
used is a section combined panel data consisting of 10 provinces on the island
of Sumatra and the Times Series with the observation year 2011 - 2022. The data used in this study are the percentage of the poor
population, the percentage of regional economic growth rate, the 2010 ADHK PRDB
by business field, the human development index, and the provincial minimum
wage.
This study uses two methods. The
location quotient (LQ) analysis approach is used to identify the economic
sectors that are the base sectors. This analysis is used to determine the state
of the base and non-base economy. The goal is to find out the advantages of
compatibility in each sector of the city, measuring from the base or non-base
version. After finding the LQ value in an economic base sector, it will be
analyzed using descriptive and inferential analysis methods. The goal is to
find out the advantages of compatibility in each sector of the city, measuring
from the base or non-base version. The base sector is a sector with an LQ value
of > 1. At the same time, non-base is a sector with an LQ value of < 1.
The LQ calculation is formulated as follows (Tarigan, 2015):
.. (3.1)
Information:
Vij:
GDP Value of Sector i in Province j
Vj:
Total GDP in Province j
PDRBiR : GDP Value of Sector I in Sumatra
PDRBR : Total GDP of Sumatra
After the LQ value in an economic base sector is found,
it will be analyzed using descriptive and inferential analysis methods, and inferential analysis will be used in the form of panel data
regression analysis and path analysis. The ARDL method is a dynamic model in
econometrics that describes the flow of time-independent variables in relation
to values in the past. A combination of autoregressive (AR) and distributed lag
(DL) methods, ARDL allows the use of past data from dependent variables to see
future values. Thus, this model can distinguish short-term and long-term
responses from the variables studied (Jumhur, 2020).
Equation 1:
Equation
2:
Then it
can be explained the variables (poverty), (economic growth), (human
development), (Regional Minimum Wage), (Leading Sectors), (Short-term dynamics
of the model), (Long-term relationship of the model), (Difference (change)
between two values of a variable), (Normally distributed error).
Path
Analysis (Jalur Analisis)
This analysis is used to analyze
the influence or direct relationship between independent variables and
dependent variables, as well as indirect influences or relationships through
mediation or intervening variables. Path analysis is first carried out to form
a path that can be seen from the square root formed from the value of the
determination coefficient (R-squared). After these stages are carried
out, each variable formed in the path analysis must have a significant direct
influence on the independent variable (Silvia et al., 2023).
Figure 3.
Path Analysis
Sobel Test
(Uji Sobel)
As for
finding out whether the relationship through a mediating variable is
significantly able to act as a mediator in the relationship, the Sobel test is
used. The formula used in this test is:
Where:
A =
Regression coefficient of independent variables to mediating variables.
b =
Regression coefficient of the mediating variable against the independent
variable.
A =
Standard error of estimation of the influence of independent variables on
mediation.
SEb =
Standard error of estimation of the influence of mediating variables on
independent variables.
If the result of the calculated z-value is
greater than the z-table at a significance level of up to 5 percent, then the
mediating variable can significantly mediate the independent variable against
the dependent variable.
RESULTS
AND DISCUSSION
4.1 Descriptive Statistics
This analysis is an initial study that aims to identify
relevant research variables to understand the phenomenon being studied. The
data that has been collected will be processed and analyzed to obtain
descriptive statistics that will provide a summary of the characteristics of
each variable. These descriptive statistics will provide an overview of the
distribution of data and its characteristics. Then it can be seen as follows:
Table 4. 1 Descriptive statistics
|
LS(Index) |
HDI(%) |
LOGMWR(%) |
Growth(%) |
Poverty(%) |
|
|
Minimum |
0.278356 |
64.20000 |
13.61094 |
-0.020000 |
14.64000 |
|
Mean |
1.877789 |
70.13108 |
14.45863 |
4.514250 |
16.26000 |
|
Maximum |
2.506469 |
76.46000 |
15.04330 |
7.860000 |
18.05000 |
|
Std. Dev. |
0.418985 |
2.475598 |
0.361387 |
1.731557 |
1.116408 |
|
Observation |
120 |
120 |
120 |
120 |
120 |
Source:
Data Processed (2024).
Some descriptive statistics show the data characteristics
of the explanatory variables of the base sector, human development, and
regional minimum wage, as well as the endogenous variables of economic growth
and poverty. This combined panel of data covering 120 observations has average,
minimum, maximum, and standard deviation values that describe the distribution
and dissemination of data. The base sector has an average value of 1,877 with a
standard deviation of 0.418, average human development of 70.13 with a standard
deviation of 2,475, average regional minimum wage of 14,458 with a standard
deviation of 0.361, average economic growth of 4,514 with a standard deviation
of 1,731, and average poverty of 16,260 with a standard deviation of 1,116. The
data showed that the level of deviation for all variables was relatively low,
indicating an even distribution of the data.
4.2
Data Identification Analysis
4.2.1
Stationary Test
The stationary test using the Augmented Dickey-Fuller (ADF)
method aims to determine whether a time series is stationary or not. The ADF
method uses regression equations in the form of Dickey-Fuller Augmented to test
the existence of unit roots. This ADF test involves testing a hypothesis to
make a decision whether or not a variable can be considered stationary. The
results of the stationary test in this study are shown in Table 4.1 below:
Table 4.2 Variable Stationary Test
|
Variable |
Level First Different Second Different |
||||||
|
Augmented
Dickey-Fuller T-statistic (5%) |
Prob. |
Augmented Dickey-Fuller
T-statistic (5%) |
Prob. |
Augmented
Dickey-Fuller T-statistic (5%) |
Prob. |
||
|
LS |
27.2687 |
0.1279 |
34.3890 |
0.0167 |
48.6503 |
0.0003 |
|
|
HDI |
16.9048 |
0.6591 |
34.3255 |
0.0121 |
63.3589 |
0.0000 |
|
|
LOGMWR |
80.6401 |
0.0000 |
9.67419 |
0.9737 |
66.6209 |
0.0000 |
|
|
GR |
27.7225 |
0.1161 |
63.4145 |
0.0000 |
67.7574 |
0.0000 |
|
|
POV |
18.3384 |
0.5651 |
42.0429 |
0.0027 |
55.4527 |
0.0000 |
|
Source: Data Processed (2024)
Based on the information from
Table 4.2, it can be stated that the results of the stationary test for the
level-level variable show that only the regional minimum wage and the rest of
the variables are stationary because the ADF Probability value is above the 5%
significance level. As a next step, a First Difference level test was carried
out, but the result was only a non-stationary minimum wage with the probability
of ADF being above the 5% significance level. At the Second Difference level,
and the results showed that all the variables tested had reached a significance
level below 5%.
4.2.2 Determination of Optimal Lag
Determining
the optimal lag involves using informational criteria, such as the Akaike
Information Criterion (AIC) or the Schwarz Bayesian Information Criterion
(BIC), to select the most appropriate amount of lag for the ARDL model. The
process of determining the optimal lag begins by testing different ARDL models
with different lags. Each model is then evaluated using informational criteria
to measure how well the model understands the data by taking into account its
complexity.
Table 4.3 Optimum Lag of the Three Models
|
Type |
LogL |
AIC* |
BIC* |
HQ* |
ARDL Lag |
|
1 |
-109.09160 |
3.241832 |
4.622572 |
3.800643 |
ARDL (1, 1, 1, 1) |
|
2 |
-85.813159 |
2.776263 |
4.157003 |
3.335074 |
ARDL (1, 1, 1, 1) |
|
3 |
-87.315218 |
2.826304 |
4.233096 |
3.395658 |
ARDL (1, 1, 1, 1) |
Source: Data Processed (2024)
Table 4.3 shows that the
determination of the optimal lag on the ARDL panel is found in the most optimal
model (1,1,1,1) based on the statistical criteria used. The three models in
this study chose the first lag as the optimal one, which is expected to provide
better results in analyzing the long-term and short-term relationships between
the variables in this study.
4.2.3 Cointegration Test
The analysis test used aims to
identify long-term relationships between two or more time variables. This
method is useful for overcoming the problem of nonstationarity in economic data
and allows the evaluation of the causal relationship between the research
variables. The test criteria used a significant level of 5%, where the
hypothesis is acceptable if the probability value is less than 0.05. The
following are the results of the cointegration test in this study:
Table 4.4 Variable
Cointegration Test
|
Pedroni
Cointegration Test |
Statistics |
Weighted
Statistic |
|
V-Statistic
Panel |
-0.684310(0.7531) |
-1.069822 |
|
rho-Statistic
Panel |
2.250091(0.9878) |
2.396694 |
|
Panel
PP-Statistic |
-1.279371(0.1004) |
-1.302613 |
|
Panel
ADF-Statistic |
0.023376(0.5093) |
-0.186160 |
|
Group
rho-Statistic |
3.763226(0.9999) |
|
|
Group
PP-Statistic |
-2.690236(0.0036) |
|
|
Group
ADF-Statistic |
0.751242(0.7737) |
|
|
KAO
Cointegration Test |
Prob. |
|
|
ADF |
0.0126 |
Source: Data Processed (2024)
The results of the ARDL panel
cointegration test showed that most of Pedronion's statistics do not show any cointegration
due to the high p-value, except for Group PP-Statistic, with a p-value of
0.0036. The KAO test also showed a cointegration with a p-value of 0.0126.
Although most of Pedroni's statistics do not support the existence of
cointegration, the results of the Group PP-Statistic and the KAO Test show that
there is a cointegration in the data panel, which shows that the variables of
the base sector, human development, regional minimum wage, economic growth, and
poverty are well correlated and well cointegrated from the short to the long
term.
4.3 Research Results
Determining the base sector is
an important step in the economic planning and development of a country or
region. This process involves identifying economic sectors that have the
potential to be the main drivers of economic growth. The base sector that has
the most in a region will be used in the next processing stage; therefore,
before moving to the data processing stage, the researcher will first look for
the base sector based on the region as follows:
Table 4.5 Determination of
Base Sectors by Sumatra Province
|
GDP Sector |
Selected Range |
||
|
8 - 10 |
5 - 7 |
1 - 4 |
|
|
A. Agriculture, Forestry, and Fisheries |
********* |
- |
- |
|
B. Mining and quarrying |
- |
***** |
|
|
C. Processing Industry |
- |
- |
**** |
|
D. Electricity and Gas Procurement |
- |
- |
- |
|
e. Water Procurement, Waste Management, Waste and
Recycling |
- |
***** |
- |
|
F. Construction |
- |
- |
*** |
|
g. Wholesale and retail trade; Car and Motorcycle
Repair |
- |
***** |
- |
|
H. Transportation and Warehousing |
- |
***** |
- |
|
I. Provision of Accommodation and Food and Beverage |
- |
- |
- |
|
J. Information and Communication |
- |
- |
* |
|
K. Financial Services and Insurance |
- |
- |
- |
|
L. Real Estate |
- |
***** |
|
|
M, N. Corporate Services |
- |
- |
* |
|
O. Government, Defense, and Compulsory Social Security
Administration |
- |
- |
**** |
|
P. Educational Services |
- |
- |
*** |
|
Q. Health Services and Social Activities |
- |
- |
**** |
|
R, S, T, U. Other Services |
- |
- |
- |
Source: Processed Data, (2024).
Description: the sign (*) represents the selected
Province
Based on Table 4.5, it seems
that the agricultural sector dominates as the base sector in Sumatra Province
compared to other sectors, so this study decided to use the base sector index
value in the agriculture, forestry, and fisheries sectors as an explanatory variable
for poverty through economic growth mediating variables.
The analysis of this study
interprets the relationship between the explanatory variable and the affected
variable by using three ARDL panel regression equation models to obtain
accurate parameter coefficients. The ARDL Panel approach evaluates the influence
of base sectors, human development, and the regional minimum wage on poverty,
with economic growth as a mediating variable, both in the long and short term.
The following table provides a breakdown of the characteristics and relevant
data for each variable, aiming to provide a comprehensive understanding of the
contribution and interaction of the variables in the model.
Table 4.6 Results of Data
Regression of ARDL Panel Poverty Function
|
Variable |
Prob. Value |
|
|
Short Run Long Run |
||
|
Coefficient T-Statistic Prob.
Coefficient T-Statistic Prob. |
||
|
Cointq LS HDI LOGMWR |
-0.5015 -5.0191
1.9870 1.5567
1.3004 1.9910 -0.4104 -0.0991 |
0.000*** 0.125 0.460 0.7075 0.482 0.051* -4.546 -6.4093 0.000*** 0.921 -2.645 -6.1472 0.000*** |
Source: Data Processed (2024)
Note: significant level ***(1%), **(5%),*(10%).
Based on Table 4.6, the
cointegration variable (Cointq) shows the existence of a significant error
correction mechanism with a coefficient of -0.5015, T-Statistic -5.0191, and
Prob. Value 0.000, which means that the deviation from the long-term balance will
be adjusted by 50.15 percent. The base sector did not affect poverty in both
the short term (Prob. 0.125) and the long term (Prob. 0.482), with coefficients
of 1.9870 and 0.460, respectively. Human development has a significant impact
on reducing poverty both in the short term (coefficient -1,300, Prob. 0.051)
and long-term (coefficient -4,546, Prob. 0.000). The minimum wage does not
affect short-term poverty (Prob. 0.921, coefficient -0.410), but has a
significant long-term impact on reducing poverty (Prob. 0.000, coefficient
-2.645).
Table 4.7 Results of Data
Regression of ARDL Panel Economic Growth Function
|
Variable |
Prob. Value |
|
|
Short Run
Long Run |
||
|
Coefficient T-Statistic Prob.
Coefficient T-Statistic Prob. |
||
|
Cointq LS HDI LOGMWR |
-1.2060 -17.536 -2.0739 -0.8901 5.0149 5.1647 4.4173 1.1567 |
0.000***
0.377
2.9257 52.275 0.000*** 0.000***
1.6755 -17.925 0.000*** 0.252
0.7913 18.250 0.000*** |
Source:
Data Processed (2024).
Note: significant level
***(1%), **(5%),*(10%).
Table 4.7 shows a strong
long-term relationship between the variables studied and economic growth. The
negative coefficient of cointegration indicates a rapid error correction
mechanism, corrects deviations from long-term equilibrium, and indicates the stability
of the model in the long term. In the short term, the base sector has no effect
on economic growth but has a positive effect in the long term. Human
development has a positive effect on economic growth both in the short and long
term. The minimum wage has no effect in the short term but has a positive
effect in the long term. The study used three ARDL panel regression models to
analyze the relationship of explanatory variables with poverty, with particular
attention to how economic growth mediates these relationships.
Table 4.8 Data Regression of
ARDL Panel Mediation Variables on Poverty
|
Variable |
Prob. Value |
|
|
Short Run Long Run |
||
|
Coefficient
T-Statistic Prob. Coefficient
T-Statistic Prob. |
||
|
Cointq GR |
-0.3564 -5.4536 -0.2572 5.2524 |
0.000*** 0.000*** -0.7222 -7.0548 0.000*** |
Source:
Data Processed (2024)
Note: significant level ***(1%), **(5%),*(10%).
The results of cointegration
in Table 4.8 show that economic growth and poverty have a significant long-term
relationship with a cointegration value of -0.3564, t-statistic -5.4536, and
probability of 0.000. This association indicates that any deviation from the
long-term balance will be corrected at a rate of 35.64% per period, reducing
poverty significantly. A very small probability confirms that this result is
statistically significant.
4.3.1 Sobel Test
The results of the soil test
in this study can be described into three models shown in Table 4.8, and
details about mediation are found in the role of economic growth in mediating
the variables of the base sector, human development, and minimum wage against
poverty on the island of Sumatra.
Table 4.9 Variables of
Economic Growth Mediation Against Poverty
|
Mediation |
Prob. Value |
|
|
Short Run Long Run |
||
|
S-Statistic Prob.
S-Statistic Prob. |
||
|
LS เ GR เ POV HDI เ GR เ POV MWR เ GR เ POV |
-0.8776 3.6822 1.1297 |
0.190 5.2267 0.000*** 0.000
-5.0416
0.000*** 0.129 5.0486 0.000*** |
Source: Data Processed (2024)
Note: significant level ***(1%), **(5%),*(10%).
Mediation analysis shows that
in the short term, economic growth cannot mediate the relationship between the
base sector and poverty (p = 0.190 > 10%), but in the long term, it is able
(p = 0.000 < 0.01). For human development variables, economic growth can
mediate the relationship with poverty in both the short and long term (p =
0.000 < 1%). As for the minimum wage variable, economic growth cannot
mediate in the short term (p = 0.129 > 10%), but it can in the long term (p
= 0.000 < 0.01). This shows that the role of economic growth as a mediator
varies depending on the time period and variables analyzed.
4.4 Discussion and Implications of
Research Results
The results of the analysis of
the first equation model show that the base sector, human development, and
minimum wage have a significant influence on economic growth in Sumatra. Base
sectors such as agriculture provide a long-term positive impact on the economy
through increased productivity, economic diversification, and increased human
capacity. However, the impact may not be immediately visible in the short term
due to structural adjustments and large initial investments. The results of
Mhaka & Runganga's (2023) research show that agricultural production has a
positive impact on economic growth in the long term but not in the short term.
The agricultural sector plays a crucial role in long-term economic development,
making a significant contribution to economic growth as the economy develops
(Awam et al., 2015). On the island of Sumatra, the agricultural sector plays an
important role in long-term economic growth through food security, labor
absorption, and export income (Akadiri et al., 2022). Although agriculture
creates the foundation for sustainable economic development by increasing rural
household incomes and boosting the agricultural processing industry, challenges
such as weather uncertainty, commodity price fluctuations, and technological
limitations often hinder its effects in the short term (Wang et al., 2017).
Research also shows that human
development has a positive influence on economic growth both in the short and
long term. Investments in education, health, and human skills increase labor
productivity and purchasing power, which in turn drives consumption and
economic growth. In the long run, better education and health produce a more
skilled and innovative workforce, as well as extend productive lifespans. The
results of the research by Rahman et al. (2020) show that human development has
a significant positive effect on economic growth in the long and short term,
with a significance level of 1%. These findings support the Cobb-Douglas
contribution theory, which considers the role of human capital in economic
growth, as well as the neoclassical growth theory, which emphasizes the
importance of human capital in production (Khatoon et al., 2021). Higher
education, better health, and increased incomes contribute to improved living
standards, improved intellectual abilities, and labor productivity, ultimately strengthening
overall economic growth (Gulcemal, 2020).
The provincial minimum wage
has a strong influence on economic growth in the long term, although the impact
is not significant in the short term. Increases in the minimum wage increase
workers' purchasing power, encourage domestic consumption and spur companies to
invest in technology or training to increase productivity. In the long term, an
increase in the minimum wage could stimulate production and investment,
contributing to stronger economic growth. The findings of this study are in
line with Rizal & Mustapita (2024), who stated that an increase in the
minimum wage can increase overall consumer purchasing power. The short-term
impact may not be immediately apparent as companies and workers need time to
adapt to the new policies. Companies may incur additional costs without
changing the price of their products or operations, while workers may not feel
the full increase in purchasing power. However, in the long term, an increase
in the minimum wage can boost domestic consumption and stimulate production and
investment (Bicerli & Kocaman, 2019; Screwdriver, 2015).
The second model equation, the
latest research, evaluates the influence of the base sector, human development,
and the regional minimum wage on poverty. The results show that the base sector
has no significant effect on poverty in the long or short term, with
probability values of 0.482 and 0.125, which are lower than the significance
level of 10%. Structural and economic factors also explain that the
agricultural sector is ineffective in reducing poverty due to dependence on
weather factors and fluctuations in market prices. Agricultural workers
generally face low wages and unstable working conditions, which hinder their
chances of getting out of poverty (Siburian, 2021; Adeneye & Aremu, 2020).
The findings of this study are
in line with previous studies such as Garidzirai & Meyer (2020) which found
that the agricultural sector has no effect on poverty, because in the short
term, farmers' incomes are often not stable or high enough to bring about
significant changes in living standards, especially due to volatile and often
low commodity prices.
This study shows that human
development is closely related to poverty levels, with positive effects in the
short term and negative effects in the long term. Short-term analysis
highlights that investments in human capital, such as education, health, and per
capita expenditure, take time to have a significant productive impact.
Sustainable investment in these sectors creates a strong foundation for
inclusive and sustainable economic growth (Dahliah, 2023; Ali et al., 2019).
This study concludes that human capital has a significant effect in the long
and short term. Improving access to and quality of education, health, and
income, as well as the Human Development Index (HDI), form a solid foundation
for improving human capacity and quality of life. Better education provides the
skills and knowledge to enter the labor market better, while better access to
healthcare improves the physical and mental well-being of individuals.
Increasing incomes help reduce economic inequality and increase access to
economic opportunities (Syofya 2018; Adeniyi & Ameru 2020).
The regional minimum wage has
a long-term negative effect on poverty (prob. 0.000 < 1%) but has no effect
in the short term (prob. 0.921 > 10%). David Card and Alan B. Krueger note
that the minimum wage can reduce poverty by increasing the income of low-wage
workers, thereby increasing their purchasing power to meet basic needs such as
food, housing, health, and education. This increase in purchasing power not
only improves the well-being of individuals and their families but also
encourages domestic consumption, which contributes to economic growth. The
results of this study are in line with previous research (Tanjung et al.,
2024), which stated that the minimum wage has a negative effect on poverty
reduction. Increasing the minimum wage can scientifically reduce poverty by
increasing the direct income of low-wage workers, who are often below the
poverty line (Sotomayor, 2021).
This analysis shows that
economic growth has not been able to mediate the influence of the base sector
on poverty rates with sufficient significance, with a probability value of
0.190 > 10% in the long term and 0.000 < 0.05 in the short term. The agricultural
sector, with its low productivity and informal working conditions, does not
exert a significant influence in reducing poverty through economic growth. In
contrast, human development, reflected in the Human Development Index (HDI),
plays an important role in reducing poverty through increased productivity and
better employment. In addition, the regional minimum wage shows a more
significant long-term influence on poverty through economic growth, although
its impact in the short term is still limited.
CONCLUSION
The dominant agriculture,
fisheries, and forestry sectors on the island of Sumatra are the main focus in the analysis of poverty and economic growth,
while sectors such as mining, water supply, waste management, transportation,
warehousing, and real estate also have a significant role. The variables of the
base sector, human development, and regional minimum wage show different
influences on poverty and economic growth in the long and short term. In the
long term, human development and the regional minimum wage have a negative
impact on poverty, while the base sector is insignificant. In the short term,
only human development affects poverty, while the base sector and minimum wage
have no effect. The influence on economic growth shows that in the long term,
all three variables have a positive effect, but only human development has an
effect in the short term, with the base sector and the minimum wage not
significant. Economic growth plays a mediator in long-term poverty reduction
through human development, while it is unable to mediate the influence of the
base sector and the minimum wage in the short term. The
government needs to focus on economic diversification and increasing
productivity in the agricultural sector through technology investment, access
to capital, and skills training. This will strengthen the contribution of the
agricultural sector to long-term economic growth. In addition, investment in
human development by improving access and quality of education and health
services and incentives for private investment can support an increase in the
Human Development Index (HDI), creating a strong foundation for inclusive
economic growth. Prudent regional minimum wage arrangements and investments in
transportation infrastructure and digital connectivity are also needed to
improve market access and competitiveness of Sumatran products in national and
international markets. Furthermore, the development of sustainable economic
policies that take into account environmental, social,
and economic aspects will create an environment that supports inclusive and
sustainable economic growth in Sumatra.
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