Basic Infrastructure and Economic Growth in Sumatra Province,

Indonesia

HERU WAHYUDI

Economic Development, Faculty of Economics and Business,

University of Lampung,

Jln. H. Komarudin, Rajabasa Raya, Rajabasa, Bandar Lampung,

INDONESIA

ADINDA PUTRI MULYYA

Economic Development, Faculty of Economics and Business,

University of Lampung,

Landani House, Rajabasa, Rajabasa, Bandar Lampung,

INDONESIA

FAKHRI RIZAL HUSAIN

Economic Development, Faculty of Economics and Business,

University of Lampung,

Puri Sejahtera Blok F No. 3, Hajimena, Natar, Lampung Selatan,

INDONESIA

WIDIA ANGGI PALUPI

Economic Development, Faculty of Economics and Business,

University of Lampung,

Wisma Cantik Manis, Rajabasa, Rajabasa, Bandar Lampung,

INDONESIA

Abstract: - This study aims to examine the impact of basic infrastructures, such as roads, electricity use,

irrigation, and educational facilities, on economic growth in the Sumatra region. The Badan Pusat Statistik

(BPS) of the Republic of Indonesia, PLN, the Ministry of Education and Culture, the Ministry of Public Works

and Public Housing of the Republic of Indonesia, and Bank Indonesia provided data for this research. The FEM

(fixed effect model) technique was used to analyse panel data from 2015 to 2019 in 10 provinces in Sumatra.

Economic growth is a temporary variable; the independent variables are roads, electricity, irrigation, and

educational infrastructure. According to the findings, the variable length of provincial highways in excellent

and moderate condition, electricity consumption, irrigation, and educational infrastructure in the form of school

buildings had a positive and significant influence on economic growth in Sumatra provinces from 2015 to

2019. As a result, the government must provide facilities and infrastructure to boost economic growth.

Key-Words: - Economic Growth, Roads, Electricity, Irrigation, Educational, Infrastructure, Sumatra

Received: April 17, 2022. Revised: January 11, 2023. Accepted: February 7, 2023. Published: March 7, 2023.

1 Introduction

The role of government is relatively significant in a

socialist economic sy0.e government provides what is

called public goods. One of the government's roles in

increasing economic growth is to provide facilities and

infrastructure. The authors in [1], define infrastructure

as physical facilities developed or required by public

agents for government functions to facilitate social and

economic goals.

Infrastructure has a significant role in

accelerating economic development in general.

According to Todaro and Smith, [2] the available

infrastructure in a nation is a substantial and

decisive element in the speed and scope of

economic growth.

Infrastructure is required to boost

competitiveness, promote more investment,

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manufacturing, and trade activities, and increase

economic growth and equitable development to

reduce poverty and unemployment rates. In

addition, the existence of infrastructure is also

essential so that the process of developing human

resources in an area can run well. The development

process accompanied by rapid technological

developments necessitates a truly appropriate

approach to infrastructure development programs.

Several provinces in Indonesia have experienced

immediate structure improvements, while some

have yet to be realized.

World Bank, [3], divides infrastructure into

three categories: economic infrastructure

(electricity, telecommunications, water, sanitation,

gas), public works (roads, dams, bridges, canals,

irrigation, and drainage), and transportation

(railways, bus terminals, ports, airports), social

infrastructure (education, health, housing, and

recreation), and administrative infrastructure (law

enforcement).

Sumatra Island, with an area of about

443,065.8 km2, is one of Indonesia's biggest islands

and the island with the second highest economic

development after Java Island. Because Java Island

has a complete infrastructure, it is faster than

Sumatra Island, and its economic growth is higher

on average.

Factors, including infrastructure development,

shape economic growth on the island of Sumatra.

The country's overall Gross Domestic Product

(GDP) determines its economic growth. "GDP is

the total final output of goods and services

produced by a country's economy in that territory,

both by citizens and non-citizens", [2]. Because of

rising demands and the need for a global supplier

of products and services to meet those demands,

the importance of developing transportation

infrastructure has rarely been more excellent today,

[4]. In his book, the author in [5],

systematically states that long-term economic

growth consists of two main aspects: an increase in

GDP and population growth. The following table

provides thorough information on the economic

growth rates of Sumatra's provinces.

Table 1. Average Economic Growth Rate of Provinces in Sumatra 2015-2019 (percent)

Province 2015 2016 2017 2018 2019 Average

Aceh -0,73 3,29 4,18 4,61 4,15 3,1

Sumatera Utara

5,1

5,18

5,12

5,18

5,22

5,16

Sumatra Barat

5,53

5,27

5,29

5,14

5,05

5,26

Riau 0,22 2,18 2,68 2,34 2,84 2,05

Jambi 4,21 4,37 4,64 5,26 5,33 4,76

Sumatera

Selatan 4,42 5,04 5,51 6,04 5,71 5,34

Bengkulu 5,13 5,28 4,98 4,99 4,95 5,07

Lampung 5,13 5,14 5,16 5,25 5,62 5,26

Bangka

Belitung

4,08 4,1 4,47 4,45 3,32 4,08

Kep.Riau

6,02

4,98

4,56

5,21

4,55

4,46

Source: BPS (Data Processed)

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Three provinces have an average economic

growth below the average growth rate in Sumatra:

Aceh, Riau, and Bangka Belitung. Meanwhile, the

other seven provinces' average economic growth

rate exceeds Sumatra's average economic growth

rate. South Sumatra had the most incredible and

significant economic growth at 5.34%, while Riau

Province had the lowest at 2.05%.

Economic growth in Sumatra before the

development of basic infrastructure, for example,

roads. Provincial roads play an essential role in the

transportation sector, particularly important for the

distribution of products and services and the

movement of people across areas. Road

construction by adding or repairing roads is needed

to support the pace of economic growth. If many

roads have damaged conditions, it will be difficult

for the community to get raw materials or goods

and services from other areas. The number of

damaged roads in Sumatra and access have

hampered the growth of the region, which will

hinder the existing economic growth.

Likewise, with electric electrification, the lack

of access to isolated areas away from crowds

makes it difficult for electricity to enter. The main

obstacles are roads, access roads that are damaged,

and even still in ground-level conditions in several

provinces in Sumatra, making it difficult for PLN

to increase the electricity electrification ratio. In

2018, data on the electrification ratio of electricity

in Indonesia reached 98.3 percent, meaning that

there are still 1.7 percent left, almost 5 million

people in Indonesia who have not received

electricity or lighting services. Electricity is

mandatory for households and production

companies. Suppose there is a need for the

electrification of electricity. In that case, it will

hamper daily life and processing production at

factories, reducing the production of goods and

services and causing economic growth in an area to

weaken. Based on the results of the Growth

Diagnostic study conducted by Bank Indonesia in

2015 in 24 of the 34 provinces, the availability of

electricity became the most binding constraint in

almost all the provinces that were an object of the

study. These results indicate that the need for

electrical energy is very urgent. The other main

obstacle of which is the problem of road quality.

According to the Sumatra Island Infrastructure

Development Master Plan, the main inhibiting

factors for economic growth in Sumatra are

electricity and road quality.

According to [6], road infrastructure positively

and significantly influences per capita income,

indicating that road infrastructure may affect per

capita [7]; the road infrastructure variable

positively and substantially influences economic

growth in Jambi Province. On the other hand, the

author in [8], on the other hand, concluded that

road infrastructure had no considerable effect on

GRDP.

Electricity is one of the essential things that

must exist for life today. According to research

performed by [9], a lack of electrical capacity is a

crucial impediment to the development of

businesses in Nigeria. Vital energy in developing

modern human existence is electrical infrastructure,

the use of electricity in urban and rural regions for

various purposes. The energy demand is expanding

in tandem with the social progress of civilization.

According to authors [8], electrical infrastructure

has a significant influence. However, according to

[10], electricity infrastructure has a negative impact

econ economic growth in Sibolga's development

The view is that infrastructure development in

agriculture impacts economic growth significantly.

The government has made different attempts to

offer high-quality agricultural infrastructure,

including repairing or expanding the capacity of

damaged infrastructure and new construction.

Infrastructure in agriculture is a physical building

to support agricultural development. The

supporting facilities include providing irrigation

water (dams and pump wells), irrigation and

drainage channels, and agricultural roads.

Infrastructure development in agriculture is a

crucial driver of regional economic prosperity.

Infrastructure development in agriculture has a

significant impact on economic growth. Irrigation

is one of the agriculture sector's supporting

infrastructures. According to the authors in [11],

the irrigation variable has a positive and

considerable influence on the expansion of

Sumatra's agricultural industry. According to

authors in [12], roads, irrigation, and network

expenditures significantly affect the economic

growth of cities in North Sulawesi. On the other

hand, the author in [13] claimed that irrigation

network expenditures have no significant influence

on economic growth. It can be seen in Figure 1. A

good and wide irrigation area will lead to increased

rice production. In South Sumatra Province, an

irrigated area of 239,577 ha can increase rice

production by 2,603,396.24 tons. However, in

contrast to Riau Province in 2019, Riau's irrigation

area in 2019 was 437,779 ha, more significant than

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South Sumatra. Still, Riau Province was only able

to produce a rice production of 230,873 tons. The

lowest irrigated area is in the Riau Archipelago

Province, with the most rice. An irrigated area of

196 ha only produced 1,150.8 tons of rice in 2019.

The area of irrigation will affect the rice production

of each region. Rice production will affect each

part's income in the agricultural sector. This will

affect the economic growth rate in each area.

Fig. 1: Comparison of the area of irrigation and rice production in the provinces of Sumatra in 2019

Source: Badan Pusat Statistik

In the regulation of the Minister of Public

Works and Public Housing of the Republic of

Indonesia, Number 14/PRT/M/2015, concerning

criteria and determination of the status of irrigation

areas, the authors in [14] explains that irrigation is

an effort to provide, regulate, and discharge

irrigation water for supporting agriculture, with

types including surface irrigation, swamp

irrigation, and underground water irrigation. It is

often said that an Irrigation Area (DI) is a unit of

land that receives water from an irrigation network.

Irrigation networks are made up of canals,

buildings, and other facilities essential for

supplying, distributing, managing, using, and

disposing of irrigation water.

According to research by Schultz in [15], the

development of human-centered education directly

contributes to the economic growth of a country by

boosting the workforce's skills and production

capacity. Human capital theory shows that formal

education is the main factor in creating a

productive society. Education requires investment.

Thus, the government must be able to provide

adequate educational buildings and systems. The

average educational infrastructure in the form of

school buildings consists of high school buildings,

vocational high schools, and madrasas, equivalent

from public and private from the year to most

prominent is located in North Sumatra Province

with as many as 1,617 units.

This improvement in school infrastructure

demonstrates the government's attempts to promote

education through developing educational

infrastructure such as school building amenities.

Educational infrastructure is essential in developing

productive personnel with appropriate competence,

knowledge, and abilities. Employees with proper

education and quality are the decisive variables for

boosting production capacity and stimulating

economic growth, [16]. According to authors in

[17], education infrastructure had a positive and

significant impact on economic growth in Central

Java. The authors in [18] analyzed the effect of

infrastructure development on economic growth in

Bengkulu Province. The results show that the

number of schools has a positive and significant

impact on economic growth in the Province.

However, according to the research in [19],

education at the tertiary level has a negative and

significant influence on economic growth in

Sidoarjo Regency. Thus, the education

infrastructure must be continuously improved so

that competent human resources will increase.

The novelty of this research is to use

infrastructure in electricity and irrigation. Both of

these infrastructures have a vital role in economic

activity. Electricity plays an essential role in the

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industrial world, especially now that the industrial

world uses many electric-powered machines in

production activities. Irrigation channels also play a

crucial role in the economy because Indonesia is an

agricultural country where the farm sector

contributes to national income. The existence of

irrigation channels can supply plants' water needs,

ensure water availability in the dry season, reduce

soil temperature, and reduce soil damage which

succeeds in agricultural activities.

2 Methodology and Variables

2.1 Methodology

This study is both descriptive and quantitative.

Secondary data is utilised, mainly gathered and

released by entities such as the Republic of

Indonesia's Central Statistics Agency (BPS), PLN,

the Ministry of Education and Culture, the

Republic of Indonesia's Ministry of Public Works

and Housing, and Bank Indonesia. Economic

development is the study's dependent variable,

whereas the independent variables include road

length, electrical energy sold, clean water supplied,

and the education index. The scope of this study

covers ten provinces on the island of Sumatra for

five observation periods, namely from 2015-2019.

Panel Data Regression Analysis was employed as

the analytical approach in this study. This approach

is used because panel data is a combination of two

forms of data: time series and cross-section, and it

may supply more data, resulting in a higher degree

of freedom, and by utilising panel data, one can

solve the difficulty of removing missing variables,

[20]. The analysis tool in this study uses EViews 10.

The regression model in this study is as follows:

(1)

Where the is Economic growth (percent),

Is the length of provincial roads according

to conditions in the Provinces of Sumatra (Km),

is the ratio of electrification and energy

consumed per capita (kWh/capita), Is

Irrigated area (Ha), Is high school and

equivalent vocational school units in the Provinces

of Sumatra (units), is 1, 2, . . .n, indicates the

number of individual crosses (cross-sections), t is

1, 2, . . .t, shows the time series dimension (time

series), β0 is the intercept, β1, β2, β3,β4,β5 is

regression coefficient, and  is the error term.

2.2 Variables

2.2.1 Economic Growth

Growth is measured by using rate data rate growth

in every Province in percentage the year. Research

this using consistent data from 2010 in form

percent in provinces in Sumatra in 2015-2019.

(2)

2.2.2 Provincial Road According to Condition

A provincial road is a collector in a system network

connecting the primary road capital province with

the capital district/city or between capital

districts/cities and roads strategic province. Study

this use condition road fine and on the way to the

area. Data used sums long road provinces in

condition good with long road provinces in

medium circumstances.

2.2.3 Consumed Electrical Electrification

This research uses installed kWh data sold per capita

from 2014-2019. Sold energy to the customer is energy

(kWh) sold to TT customers (voltage high), TM (voltage

medium), and TR (voltage low) is appropriate with the

number of kWh generated accounts (TUL III-09).

Electricity consumption per capita (Kwh/Capita) is the

ratio of the total sale of power electricity to the total

number of residents.

2.2.4 Coverage Area Irrigation

According to the Ministry of PUPR, Irrigation

Areas (DI) are land units that receive water from an

irrigation network. Irrigation networks consist of

canals, buildings, and complementary buildings,

constituting a single unit required to supply,

distribute, administer, use, and dispose of irrigation

water. Research this using local data irrigation with

Ha unit.

2.2.5 Infrastructure Education Total Building

School

According to Minister of Education and Culture

No. 6 of 2019 concerning Guidelines Organization

and Work Procedure, Elementary and Secondary

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Education Unit State Education units at the

secondary education level and Education Units at

an education special are below authority and

responsibility to the service area hosting province

affairs education.

3 Result and Discussion

3.1 Result

3.1.1 Selection of a Regression Model

There are three methods of model estimation in

panel data regression, namely the common effects

model (CEM), the fixed effects model (FEM), and

the random effects model (REM). To find out the

method used in panel data regression was

determined through several tests, including the

Chow test, the Lagrange multiplier (LM) test, and

the Hausman test.

3.1.1.1 Chow test

Chow's test was performed to choose the method

best between Common Effect Model (CEM) and

the Fixed Effect Model (FEM), using Redundant

Fixed Effect-Likelihood Ratio. The Conclusion

from results testing done with look mark

probability (P-value). If the P-value < than level

real (α), then the Fixed Effect model is more

appropriate; otherwise, if the P-value > level real

(α), then more models Common Effect Models

appropriate

Table 2. Chow test results

Effect Test

Statistics

Prob.

Decision

Cross-section F

11.393608

(9,36)

0.0000

FEM

Chi-square cross-sections

67.382901

0.0000

Table 3. LM Test Results

Test Hypothesis

Decision

Cross-section

time

Both

Breusch-Pagan

18.43922

0.000512

18.43973

BRAKE

(0.0000)

(0.9819)

(0.0000)

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Table 4. Hausman Test Results

Test Summary

Chi-Sq. Statistics

Chi-Sq. df

Prob.

Decision

Random cross-sections

14.693455

0.0054

FEM

As can be seen in Table 2, obtained mark

probability (p-value) of 0.0000. Less chow of 5%

rate trust (α), and the calculated χ value is

67.382901 enormous from χ table 12.59. So the

results testing the more fixed effect model (FEM)

method are reasonable compared to the standard

effect model (CEM) method for analysing data in a

study.

3.1.1.2 Lagrange Multiplier Test (LM)

Lagrange Multiplier (LM) test was performed to

choose the method best between Common Effect

Model (CEM) and Random Effect Model (REM),

and the Conclusion from the results testing was

done with look mark probability (P-value).Based

on Table 3, the Breusch-Pagan value of 18.43922 is

more significant than the χ table of 7.81, so the

more random effect model (REM) method is

suitable compared to the standard effect model

(CEM) method for analysing the research data.

3.1.1.3 Hausman test

The Hausman test was used to determine which

method was better between the Random Effect

Model (REM) and the Fixed Effect Model (FEM);

if the p-value < alpha (0.05), then the Fixed Effect

Model method is selected, whereas if the p-value >

alpha (0,05) then the Random Effect Model method

is determined.

Based on Table 4, the value of the Chi-Square

Statistic (χ- count) is 14.693455 larger than the

Chi-Squares table (χ- table) of 2.58. Weight is also

obtained with a probability of 0.0054 < 0.05, so

results testing this is a more Fixed Effect Model

(FEM) method suitable for analysing and studying

this. Based on the table above, it can be concluded

that the best model for estimating the research data

is the Fixed Effect Model (FEM).

3.1.2 Classic Assumption Test

Classical assumptions must be met for the available

OLS estimator to be the best. Because it is essential

in regression analysis, the conditions that need to

be completed are: unbiased, linear, and having

variance (BLUE = Best Linear Unbiased

Estimator). According to authors [20], normality,

multicollinearity, heteroscedasticity, and

autocorrelation tests are used to determine whether

or not the estimated model deviates from classical

assumptions.

3.1.2.1 Normality test

The normality test works to test whether, in a

regression model, the confounders have a normal

distribution. From the data from the initial test

results that are not normally distributed, a

probability value of 0.00000 is obtained, which is

smaller than 0.05. For the data to be normally

distributed, the data is transformed into a

logarithmic form or by using winsorizing. So, from

this logarithmic transformation, the residuals are

normally distributed with a probability of

0.840260.

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-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

Series: Standardized Residuals

Sample 2015 2019

Observations 28

Mean 1.55e-17

Median 0.009625

Maximum 0.372635

Minimum -0.398565

Std. Dev. 0.199826

Skewness -0.017333

Kurtosis 2.454877

Jarque-Bera 0.348088

Probability 0.840260

Fig. 2: Normality Test Results

Table 5. Multicollinearity Test Results

JALAN

LISTRIK

IRIGASI

PEND

JALAN

1.000000

-0.196978

0.585288

0.789942

LISTRIK

-0.196978

1.000000

-0.131391

-0.159956

IRIGASI

0.585288

-0.131391

1.000000

0.306944

PEND

0.789942

-0.159956

0.306944

1.000000

Table 6. Heteroscedasticity Test Results

Variable

Coefficient

Std. Error

t-Statistic

Prob

1.284676

0.601584

2.135488

0.0396

JALAN

-0.000359

0.000369

-0.973484

0.3368

LISTRIK

-0.000282

9.57E-05

-1.947400

0.0856

IRIGASI

-1.43E-06

1.12E-06

-1.278148

0.2094

PEND

-0.000225

0.000391

-0.575576

0.5685

3.1.2.2 Deteksi Multikolinieritas

A multicollinearity test was performed to examine

whether or not the independent variables in the

equation model had a strong link. A decent

regression model has no connection between the

independent variables. The results of

multicollinearity testing with a correlation

coefficient matrix are shown below.:

Based on Table 5, the results of the

multicollinearity test are obtained where the

correlation coefficient between the four

independent variables in this study shows a

coefficient number that is less than 0.90, so it can

be concluded that there is no big multicollinearity

problem from the data.

3.1.2.3 Heteroscedasticity Test

The heteroscedasticity test is a test to see whether

or not there is the same variance from one residual

to another residual. Heteroscedasticity testing in

this study used the Glejser test. If the probability

value of each independent variable is > 0.05, the

model is free of heteroscedasticity.

From the regression above, it can be concluded

that the selected FEM model has a probability >

0.05, meaning there is no heteroscedasticity

problem.

3.1.2.4 Autocorrelation Test

Autocorrelation is the correlation between one

observation and members of another word at

various periods. This study performs the

autocorrelation test using the Durbin-Watson test

method. The results show that Durbin-Watson

(FEM) is Durbin-Watson Stat 1.740166 with dL and

dU values in the Durbin-Watson table, where n = 50

and k = 4, so the importance of dL = 1.3779 and dU

= 1.7214. As a result, it is possible to conclude that

there is no autocorrelation.

3.1.3 Regression Estimation Results

According to the testing result, the LM and

Hausman test results belong to the Fixed Effect

Model (FEM), so the Fixed Effect Model is the best

model to interpret the data in this study (FEM). The

following are the regression findings.

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Table 7. Regression Model Selection Test Results

Variable

Coefficient

Std. Error

t-Statistic

Prob.

1.688354

1.105343

1.527449

0.1354

JALAN

0.001467

0.000678

2.164988

0.0371

LISTRIK

0.000268

0.000176

2.522341

0.0367

IRIGASI

5.81E-06

2.06E-06

2.828089

0.0076

PEND

0.000667

0.000719

1.927922

0.0496

Effects Specification

Cross-section fixed (dummy variables)

R-squared

0.756257

Mean dependent var

4.493000

Adjusted R-squared

0.668239

S.D. dependent var

1.260324

S.E. of regression

0.725930

Akaike info criterion

2.428770

Sum squared resid

18.97108

Schwarz criterion

2.964136

Log-likelihood

-46.71924

Hannan-Quinn criteria.

2.632640

F-statistic

8.592064

Durbin-Watson stat

1.740166

Prob(F-statistic)

0.000000

Table 8. Individual Parameter Significance Test Results (t-Test)

Variable

t-statistic

t-table

Prob.

Conclusion

Description

Jalan

2,164988

1,67

0,0371

Tolak H0

Significant

Listrik

2,522341

1,67

0,0367

Tolak H0

Significant

Irigasi

2,828089

1,67

0,0076

Tolak H0

Significant

Pend

1,927922

1,67

0,0496

Tolak H0

Significant

Based on the table above, the following regression equation was obtained:

(2)

3.1.4 Statistical Test

3.1.4.1 Individual Parameter Significance Test

(t-Test)

The t-test is used to determine if there is a

significant relationship between the independent

variable and the dependent variable.According to

the table above, road length, electricity, irrigation,

and educational infrastructure partially positively

and significantly influenced economic growth in 10

Sumatran provinces from 2015 to 2019.

3.1.4.2 Simultaneous Significance Test (F Test)

A simultaneous significance test (F test) was

conducted to determine whether all independent

variables had a concurrent or joint effect on the

dependent variable.

Table 9. Simultaneous Significance Test Results

(Test F)

F-table

F-Statistik

45(n-k-1)

0,05

2,58

8,592064

WSEAS TRANSACTIONS on SYSTEMS

DOI: 10.37394/23202.2023.22.9

Heru Wahyudi, Adinda Putri Mulyya,

Fakhri Rizal Husain, Widia Anggi Palupi

E-ISSN: 2224-2678

Volume 22, 2023

According to Table 9, the results of the F-

statistics simultaneous test are 8.592064 and an F-

table of 2.58. As a result, all independent variables

consisting of basic infrastructure, such as the length

of roads in excellent and medium condition,

electricity, irrigation, and the number of high

school and vocational high school buildings,

together have a significant impact on economic

growth in 10 provinces on the island of Sumatra in

2015-2019.

3.1.4.3 Coefficient of Determination (R2)

Based on the results of the analysis, the coefficient

of determination (R2) is 0.756257, which means

that variations in economic growth can be

explained by variations in the number of local road

infrastructure according to excellent and moderate

conditions, electricity, irrigation area, and the

number of educational buildings for SMA and

SMK equivalents. by 75%, The remaining 25% is

impacted by a variable that is not accounted for in

the model.

3.2 Discussion

3.2.1 The Influence of Roads on Economic

Growth in Provinces throughout Sumatra

in 2015-2019

The growth of basic infrastructure in the form of

provincial road lengths based on excellent and

moderate conditions has a positive and significant

impact on economic growth in provinces

throughout Sumatra. The results of this study are

research conducted by authors [7] on road

infrastructure variables that positively and

significantly influence economic growth in Jambi

Province. This is due to the Harrord-Domar theory,

which states that the more capital, or capital, in this

case, road infrastructure, The more output will

increase. This increase in production will affect

economic growth. For example, if a farmer wants

to improve rural production by buying a new

tractor but there are no excellent road facilities to

transport the additional agrarian output to the

market, the investment from this farmer will not

increase food production much in that area.

In other research conducted by authors [21],

partial variables in road infrastructure have a

positive and significant impact on the economic

growth rate. This is because an increase in the

economic growth rate in the Province of Bali

accompanies every increase in road infrastructure.

According to authors [22], the road variable

positively and significantly affects economic

growth. The results of this study are consistent with

the hypotheses suspected and with the theory that

claims that roads have a substantial impact on

economic growth because the Solow theory implies

that there are only multiple forms of capital. In

addition, research with results similar to this study,

namely [23], demonstrates that roads significantly

impact economic growth in a given location.

Roads, according to Law No. 38 of 2004, are

land transportation infrastructure that includes all

parts of the road, including the development of its

accessories and equipment intended for traffic, that

are on the surface of the ground and water and

above the surface of the water, except for railroads,

lorry roads, and a cableway. Roads are

infrastructure that various regions must own; good

road conditions can facilitate access to trade

between provinces. However, poor and unpaved

road conditions can hamper these activities. There

are still many roads that still need to be asphalted

or are still dirt in several areas in the Province of

Sumatra, which has made economic growth in the

regions in Sumatra not increase significantly.

According to Regulation of the Minister of

Public Works No. 13 of 2011, Chapter I, Article 1,

paragraph 12, road maintenance is an activity for

handling roads in the form of prevention,

maintenance, and repairs required to maintain road

conditions so that they continue to function

optimally serving traffic and the specified plan age

can be achieved. Road maintenance is urgently

needed to improve existing provincial roads to

facilitate access between regions.

The role of allocation, essentially the function

of the government as a provider of public goods

and services such as road construction, school

building, lighting facilities, telephones, and so on,

is one of the functions of the government to boost

economic growth. The government must now offer

road access to current community activities. The

availability of road infrastructure significantly

impacts the distribution of production components

or the products and services generated by industry.

3.2.2 The Effect of Electricity Consumption on

Economic Growth in Provinces

throughout Sumatra in 2015-2019

The results of this study indicate that the

electrification of electricity consumption has a

positive and significant impact on economic

growth. This is in line with existing theories and

hypotheses. The results of this study are also in line

with research conducted by Prasetyo and Firdaus

[24]. The use of electrical energy has a positive and

WSEAS TRANSACTIONS on SYSTEMS

DOI: 10.37394/23202.2023.22.9

Heru Wahyudi, Adinda Putri Mulyya,

Fakhri Rizal Husain, Widia Anggi Palupi

E-ISSN: 2224-2678

Volume 22, 2023

significant impact on Indonesia's economic growth.

Electrical energy production activities have an

essential role; for this reason, to increase economic

growth, electrical energy is needed.

Another research that was carried out by [25]

found that electricity has a positive and significant

influence on economic growth in West Java; the

considerable influence of electrical infrastructure

on economic growth demonstrates that the usage of

electricity, particularly in the industrial sector, is

critical in generating economic growth since it is

required as the primary component in supporting

manufacturing process activities.

Furthermore, research by Azuwulandari et al.

[26] said that electrical energy positively and

significantly influences economic growth. More

electricity distributed and installed in Bengkulu

Province will encourage economic growth.

Electricity infrastructure development is a form of

fiscal policy from the government to increase

development and economic growth. Subsequent

research, namely [27], stated that electricity

consumption has a more significant and immediate

effect in the short term. Unfortunately, in the long

run, the influence of electricity consumption on

economic development is insignificant.

According to Yoo, [28], electricity

consumption has a causal relationship (two-way)

with economic growth. This indicates that a high

level of electricity consumption will encourage

economic growth and vice versa. High economic

growth is needed to increase the level of electricity

consumption.

The development of electricity infrastructure is

necessary for the continuity of community

activities and the production of goods. The

availability of energy supplies, especially adequate

and affordable electricity, is crucial in the

development of the industrial sector because one of

the critical aspects in the business of the

manufacturing industry sector is the guarantee of

the availability of electrical energy, [29]. Electricity

is one of the concentrations for the government,

especially local governments, because many areas

still do not have electricity. Electricity can affect

economic growth in the short term; therefore, if the

government wants to increase economic growth,

the government is expected to be able to meet the

supply of electricity needs.

3.2.3 The Influence of Irrigation on Economic

Growth in Provinces throughout Sumatra

in 2015-2019

According to the results of this study, irrigation has

a positive and significant influence on economic

growth. This is in line with existing theories and

hypotheses. The results of this research are also

consistent with the findings of [11], which found

that irrigation has a positive and significant impact

on the growth of the agricultural sector on the

island of Sumatra. Irrigation will create change and

increase productivity, boosting the farming

industry. The island of Sumatra has good

agricultural potential; the agricultural sector plays a

vital role in the economic development of the

Sumatra region, which is 22.27 percent. The

leading commodity in Sumatra is rice.

According to a study by Rarun [12], spending

on roads, irrigation, and networks significantly

impacts the economic growth of cities in North

Sulawesi. Capital expenditures on roads, irrigation,

and networks include costs for the procurement,

addition, replacement, improvement, and

maintenance of irrigation roads and networks, as

well as expenses for planning, supervision, and

management of irrigation roads and networks that

add capacity until the irrigation roads and networks

are ready for use. Spending on infrastructure is

expected to increase economic growth.

The effect of irrigation on economic growth in

provinces throughout Sumatra has a negligible

effect because many other factors have a significant

influence. Irrigation will have more impact on

agricultural products, which will later increase the

GRDP of the farming sector and further increase

the region's economic growth rate. As with

research conducted by [30], the effect of irrigation

on yields can increase the productivity of food

crops, especially rice. Agricultural productivity per

hectare is higher, thus providing more income to

farmers and increasing employment in agriculture.

3.2.4 The Influence of Education Infrastructure

on Economic Growth in Provinces

throughout Sumatra in 2015-2019

Based on the results of this study, educational

infrastructure, such as the number of high school

buildings and comparable vocational high schools,

has a positive and significant impact on economic

growth. This is in line with existing theories and

hypotheses. The results of this research are also in

line with the results by Imp and Resmi [17], which

found that educational infrastructure had a positive

and significant effect on economic growth in

Central Java from 2011 - 2015. This research uses

the number of school units in provinces throughout

Sumatra. School infrastructure is essential for the

WSEAS TRANSACTIONS on SYSTEMS

DOI: 10.37394/23202.2023.22.9

Heru Wahyudi, Adinda Putri Mulyya,

Fakhri Rizal Husain, Widia Anggi Palupi

E-ISSN: 2224-2678

Volume 22, 2023

sustainability of quality human resources. More

educational facilities accessible will be able to

increase the quality of the human resources

themselves, resulting in trained human resources

capable of propelling the economy forward.

According to the Solow-Swan hypothesis,

economic development depends on the availability

of production inputs such as population, labor,

capital accumulation, and technical innovation.

Solow-Swan proposes that the labor component can

drive economic development in this approach. The

workforce here includes not only the amount but

also the quality of the labor. Human resources are a

significant asset for increasing productivity. The

greater a person's degree of education, the higher

his output productivity, which can boost regional

economic growth. The government's role, in this

case, must provide the facilities and infrastructure

needed during the teaching process. The

government should focus on remote areas where

there are no educational facilities.

4 Conclusion

Based on the data analysis and discussion above,

the following conclusions can be drawn: (1) Road

infrastructure positively and significantly impacted

economic growth in provinces throughout Sumatra

in 2015-2019. This signifies that the provincial

road infrastructure is in good shape and contributed

to Sumatra's economic growth between 2015 and

2019. The existence of good road infrastructure

will make it easier for the community to distribute

goods and services between regions; good road

conditions will also make it easier for PLN to add

electricity to remote areas, (2) Consumption of

electrified electrical energy has had a positive and

significant impact on economic growth in Sumatra

Provinces from 2015 to 2019. This signifies that

the electrification of electricity contributes to

economic growth in Sumatra's regions. Many

provinces still need more electricity, making some

areas depend on other sites. For example, in West

Sumatra Province, which supplies electricity to the

surrounding regions, (3) The size of irrigated areas

positively and significantly impacted economic

growth in the Provinces of Sumatra for the 2015–

2019 period. Irrigation has a positive effect, which

indicates that it contributes to economic growth. In

this case, if the irrigation area is expanded, it will

increase the production of agricultural products,

resulting in a large number of farm products that

can be sold, which will affect economic growth,

and (4) Educational infrastructure in the form of

high school and vocational high school buildings,

both public and private, has had a positive and

significant impact on the level of economic growth

in the provinces on the island of Sumatra in 2015-

2019. Schools have no direct effect on a region's

economic progress.

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DOI: 10.37394/23202.2023.22.9

Heru Wahyudi, Adinda Putri Mulyya,

Fakhri Rizal Husain, Widia Anggi Palupi

E-ISSN: 2224-2678

Volume 22, 2023

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

Heru Wahyudi created a research framework and

team leader. Adinda Putri Mulya wrote the

research. Fakhri Rizal Husain collected and

managed the research data. Widia Anggi Palupi

compiles articles, adapts to the format, and

compiles a bibliography.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

The research in this manuscript is supported by

Lembaga Penelitian dan Pengabdian kepada

Masyarakat (LPPM) Universitas Lampung.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

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DOI: 10.37394/23202.2023.22.9

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Fakhri Rizal Husain, Widia Anggi Palupi

E-ISSN: 2224-2678

Volume 22, 2023

Conflict of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.