The Influence of Infrastructure on The Economic Growth of

Regencies/Cities in The Province of South Sumatera

Dede Mardhian, Syamsurijal A. Kadir, and Muhammad Subardin

Faculty of Economics, Universitas Sriwijaya, Palembang, Indonesia

Keywords: Length of Road Infrastructure, Electric Power, Clean Water, Economic Growth.

Abstract: This study aims to determine the effect of infrastructure which consists of the length of the road, electric

power and clean water for the economic growth of the regencies/city in South Sumatra Province. The data

used in this study is panel data consisting of 15 regencies/cities from 2008-2015. Data were obtained from

the publication of the regencies/city Statistics Center in South Sumatra Province. The model used in this study

is the Fixed Effect model. The estimation results show that the variables of road length, electricity, and clean

water together and partially have a significant effect on economic growth variable at a significant level of 5

percent. More than 99 percent of the variation in economic growth variables can be explained by variations

in road length, electricity, and clean water variables.

1 INTRODUCTION

One of the government's objectives listed in the

opening of the 1945 Constitution is to promote the

general welfare and standard of living of its people.

In an effort to achieve prosperity, the government is

trying to accelerate economic growth through various

policies and development, including building

infrastructures such as roads and bridges, power

plants and clean water treatment facilities. With the

increase in infrastructure, it is expected that the ability

of a country or region to produce goods and services

will increase. The increase in goods and services that

can be produced by a country or region is reflected by

changes in Gross National/Regional Domestic

Products which are calculated based on constant

prices for a given base year. The development of

Gross Regional Domestic Product (GRDP) for

Regencies/Cities in South Sumatra Province in the

period of 2008 - 2015 can be seen in Figure 1 below.

Figure 1: PDRB City Regencies in South Sumatra Province at Constant Prices of 2010 during the period 2008-2015 (in

billion rupiah).

100000

Gross Regional Domestic Product (PDRB) Based at

Constant Prices of Regency / City in South Sumatra

Province

2008 2009 2010 2011

2012 2013 2014 2015

484

Mardhian, D., A. Kadir, S. and Subardin, M.

The Inﬂuence of Infrastructure on The Economic Growth of Regencies/Cities In The Province of South Sumatera.

DOI: 10.5220/0008441604840491

In Proceedings of the 4th Sriwijaya Economics, Accounting, and Business Conference (SEABC 2018), pages 484-491

ISBN: 978-989-758-387-2

Figure 1 shows that the GRDP of Regency/City in

South Sumatra Province has always experienced an

increase during 2008-2015. In that period, the city of

Palembang became the largest contribution to the

GRDP of South Sumatra Province, followed by Musi

Banyuasin Regency in the second place. While

regencies/cities that contribute the least in the GRDP

of South Sumatra Province are the City of Pagaralam.

According to Todaro (2009: 170) there are three

most important components in increasing economic

growth including: (1) capital accumulation, which

includes all new investments in land, physical

equipment and natural resources through various

improvements in health, education and quality of

resources; (2) Growth of population and labor force

as well; (3) Technological progress.

The capital in question is one of them comes from

the infrastructure sector or physical investment. The

existence of infrastructure will encourage an increase

in productivity for production factors. Wahyuni,

(2009: 4) said that infrastructure development such as

roads and bridges, electricity generation, and clean

water are very important roles in efforts to improve

the economy and living standards of people in a

region. Therefore, based on the explanation above,

one of the factors influencing economic growth in

South Sumatra Province is infrastructure.

Road infrastructure is a means of connecting from

one region to another and has an important role in

accelerating the flow of goods and services

distribution. Research by Iskarno, Kuncara, and

Irianto (2014) and Suminar, Hanim, and Prianto

(2016) stated that road length infrastructure had a

positive and significant impact on economic growth,

but different results were revealed in the research of

Sumadiasa, Tisnawati, and Wirathi (2015), road

infrastructure has an influence positive but not

significant to economic growth.

Variable electric power is a very important energy

for the household and industrial life. Electricity is not

only used by the household but can also be used by

the private sector as a factor of production. Putri's

research (2014) revealed that electricity has a positive

and significant influence on economic growth.

However, according to Chaerunnisa's research

(2014), electricity has a negative and insignificant

effect on economic growth. Furthermore, clean water

as a variable that can affect economic growth has

been investigated by Warsilan and Noor (2015). In

this study revealed that the variable clean water has a

positive and significant influence on economic

growth.

Basically, infrastructure is one of the keys to

supporting economic growth, effective infrastructure

development is expected to accelerate economic

growth and increase community prosperity. Based on

the description above, this study will discuss how

infrastructure influences such as road length,

electricity and clean water to the economic growth of

Regencies/Cities in South Sumatra Province.

2 LITERATURE REVIEW

2.1 Endogenous Growth Theory

This theory assumes that public and private sector

investment in human resources results in an external

economy and increased productivity which reverses

the tendency for natural decline. This theory explains

the existence of increasing scale of returns and long-

term growth patterns that vary between countries.

Because technology still plays an important role in

these models, exogenous change is no longer needed

to explain long-term growth (Todaro, 2009: 183).

According to Mankiw (2010;223) endogenous

growth theory rejects Solow's basic assumption of

changes in exogenous technology (which comes from

outside). Start with a simple production function: Y =

AK, where Y is output, K is a capital stock, and A is a

constant that measures the amount of output produced

by each unit of capital (note that this production

function does not have a declining return on capital).

An additional unit of capital produces an additional

unit of output regardless of the available capital. The

absence of this declining return on capital is a key

difference between this endogenous growth model

and the Solow model. Capital accumulation can be

described by the equation: ΔK= sY-δK. This equation

states that changes in capital stock (ΔK) are equal to

investment (sY) minus depreciation (δK). We

combine this equation with the production function

above and get it:

ΔY/Y = ΔK/K = sA – δ

(1)

The above equation shows what determines the

rate of growth of output ΔY/Y. As long as sA>δ,

economic income grows forever, even without the

assumption of exogenous technological progress. In

the Solow model, savings encourages temporary

growth, but increasingly declining capital returns

ultimately drive the economy closer to a steady state

where growth depends only on exogenous

technological progress. Conversely, in the

endogenous growth model, savings and investment

can drive sustainable growth.

The Inﬂuence of Infrastructure on The Economic Growth of Regencies/Cities In The Province of South Sumatera

485

2.2 Infrastructure

According to the Grand Indonesian Dictionary

(2008), infrastructure can be interpreted as public

facilities and infrastructure. Public facilities are

known as infrastructures such as hospitals, roads,

bridges, sanitation, telephone, and other facilities. In

the World Bank Report infrastructure is divided into

3 groups, namely; (1) Economic infrastructure, is a

physical asset that provides services and is used in

production and final consumption including public

utilities (telecommunications, drinking water,

sanitation and gas), public works (dams, irrigation

and drainage channels) and the transportation sector

(roads, trains, port transport and airfields). (2) Social

infrastructure is an asset that supports the health and

expertise of the community including education

(schools, and libraries), health (hospitals, health

centers) and for recreation (land, museums, etc.). (3)

Administrative / agency infrastructure, including law

enforcement, administrative control and coordination

and culture (The World Bank, 1994: 13). While

Todaro (2009: 170) explained that economic

infrastructure is the amount of physical and financial

capital that takes the form of highways, railway

facilities, water transportation facilities, air force

facilities, and means of transportation and

communication, plus various other facilities such as

water supply, financial institutions, electricity, and

public services such as health and education.

2.3 Previous Research

Many studies have been conducted to see the

influence of infrastructure on economic growth.

Winanda (2016) in his study discussing electricity

infrastructure, clean water infrastructure and Length

of Road infrastructure that is linked to economic

growth, concluded that the variables of electricity

infrastructure and clean water infrastructure have a

positive and significant influence on economic

growth, while the Length of Road infrastructure

variable has a negative relationship and significant to

economic growth in Bandar Lampung City. While the

research by Prasetyo and Firdaus (2009) on the

relationship of electricity infrastructure, road length

and clean water to regional economic growth showed

slightly different results. The study shows that

electricity infrastructure, road length and clean water

have a positive influence on the economy in

Indonesia.

Sumadiasa, Tisnawati and Wirathi (2015)

conducted a study using variable lengths of road

infrastructure, electricity and PMA to see its effect on

GRDP growth. The study concluded that the road

length variable had a positive but not significant

effect on GRDP growth, while the variables of

electric power and foreign investment (PMA) had a

positive and significant influence on the growth of

GRDP in Bali Province.

According to Pranessy, Nurazi and Anitasari

(2010) in their study of the influence of infrastructure

development on economic growth, based on the

results of their study indicate that electrical energy,

the number of health centers and the number of

schools have a positive and significant effect on

economic growth in Bengkulu Province.

Morimoto and Hope (2001) examine the impact

of electricity supply on economic growth. The results

of the study explain that flows and changes in

electricity supply have a significant impact on

changes in real GDP in Sri Lanka, any increase in 1

MWh of electricity supply will increase GDP

between 88,000 to 137,000 Sri Lankan Rupees.

Furthermore, Worku (2010) and Peter, Rita and Edith

(2015) analyzed the relationship between road

infrastructure and economic growth, the results of

these studies indicate that road infrastructure has a

positive impact on economic growth.

3 METHODOLOGY

This study is a causality study that analyzes the

effect of infrastructure which consists of the length of

the road, the amount of electricity sales and the

amount of clean water channeled to the economic

growth of the Regency / City in South Sumatra

Province for the period 2008-2015. The data used is a

panel data consisting of 8 years time series data and

15 cross-section data in South Sumatra Province.

The data analysis method used in this study is a

multiple regression analysis technique with a panel

data regression model to measure the influence of

infrastructure which consists of the length of the road,

the amount of electricity sales and the amount of

clean water distributed to the economic growth of

regencies / cities in South Sumatra Province.

This test is done with the following equation

model:

= ɑ

+ ß

(2)

Where:

= Log PDRB (in miryar rupiah)

= Constants

- ß

= The regression coefficients of each

independent variable

= Log Road Length by Regency / City in

South Sumatra (in kilometres)

SEABC 2018 - 4th Sriwijaya Economics, Accounting, and Business Conference

486

= Log of electricity sold by regencies / cities

in South Sumatra (in GWh)

= Log The amount of water distributed

according to regencies / cities in South

Sumatra (in Dam

)

i = Regency / city

t = Year

= Standard error

4 RESULTS AND DISCUSSION

4.1 Selection of Final Estimation Model

To determine the best model to be chosen in this

study, two tests were tested, namely Chow Test and

Hausman Test. Here are the results and explanations

of the Chow Test and the Hausman Test:

Table 1: Regression Results with Chow Test

Effects Test

Statistic

d.f.

Prob.

Cross-section F

210.554563

(14,102)

0.0000

Cross-section Chi-square

407.741597

0.0000

Source: Chow Test Regression Results, Data processed in 2018

The Chow test (Table 1) is used to determine

whether the model is either an ordinary OLS model,

a fixed effect model or random effect. Based on the

Chow Test results show that the Chi-square Cross-

Section value is 407.74 with a probability of <0.05

which indicates that Ho is rejected because the

probability value of this test is smaller than the five

percent error level with a 95 percent confidence level

which means the Fixed Effect model is accepted.

The Hausman test (table 4.2) is then carried out to

determine whether a suitable model is a random

model or a fixed effect model. Based on the results of

the Hausman test the results are as follows:

Table 2: Regression Results with Hausman

Test Summary

Chi-Sq. Statistic

Chi-Sq. d.f.

Prob.

Cross-section random

42.805346

0.0000

Source : Hausman Test Regression Results, Data processed in 2018

Table 2 explains that the value of the random

chi-square cross section is 42.81 with a probability of

<0.05, indicating that Ho is rejected because the

probability value of this test is smaller than the five

percent error level with a 95 percent confidence level,

in other words the Fixed Effect model cannot be

rejected. The choice of the best model chosen for this

study is based on the regression results of the Chow

Test and the Hausman Test using the Fixed Effect

Model method

4.2 Fixed Effect Model

The final model used in this study is the Fixed

Effect model as indicated by the Chow Test and

Hausman Test. Following is the panel data regression

calculation with the Fixed Effect method:

The Inﬂuence of Infrastructure on The Economic Growth of Regencies/Cities In The Province of South Sumatera

487

Table 3: Regression Estimation Results with Fixed Effect Method

Dependent Variable: LOG(PDRB?)

Method: Pooled Least Squares

Date: 09/14/18 Time: 09:58

Sample: 2008 2015

Included observations: 8

Cross-sections included: 15

Total pool (balanced) observations: 120

Variable

Coefficient

Std. Error

t-Statistic

Prob.

6.413240

0.344284

18.62776

0.0000

LOG(PJ?)

0.269324

0.050877

5.293609

0.0000

LOG(L?)

0.059076

0.017128

3.449013

0.0008

LOG(A?)

0.056606

0.016967

3.336202

0.0012

Fixed Effects (Cross)

_OKU--C

-0.117824

_OKI--C

0.428600

_ME--C

0.922610

_LHT--C

0.059228

_MURA--C

-3.78E-05

_MUBA--C

1.340158

_BA--C

0.464593

_OKUS--C

-0.593517

_OKUT--C

-0.079937

_OI--C

-0.521910

_EL--C

-0.913989

_PLG--C

1.775525

_PRA--C

-0.564022

_PGA--C

-1.281303

_LLG--C

-0.918175

Effects Specification

Cross-section fixed (dummy variables)

R-squared

0.991720

Mean dependent var

8.980140

Adjusted R-squared

0.990340

S.D. dependent var

1.015699

S.E. of regression

0.099831

Akaike info criterion

-1.633201

Sum squared resid

1.016549

Schwarz criterion

-1.215077

Log likelihood

115.9921

Hannan-Quinn criter.

-1.463399

F-statistic

718.6030

Durbin-Watson stat

0.473119

Prob(F-statistic)

0.000000

Source :Fixed Effect Method Result, Data processed in 2018

By using a confidence level of 95 percent ( =

0.05) with df1 (nominator) = 3 and df2 (denominator)

= 116, the F-table value is 2.68. Whereas in this study

the F-count value is 718.6030 which means that the

significance level is much smaller than 0.05 (F-count

= 718.6030> F-table = 2.68). The results of this test

indicate that the variable length of road infrastructure,

electricity infrastructure and clean water

infrastructure for 15 regencies/cities in South

Sumatra Province together significantly affect

economic growth at a 95 percent confidence level.

Furthermore, based on the estimation results obtained

the coefficient of determination (R2-adjusted) of 0.99

which means that the variation in the variable Gross

Regional Domestic Product is determined by the

variation in the variable length of the road, the

variable electric power and the variable clean water

by 99 percent, while the remaining one percent is

determined by other variables outside the model.

SEABC 2018 - 4th Sriwijaya Economics, Accounting, and Business Conference

488

To see which variables partially affect economic

growth, 95 percent confidence level ( = 0.05) is also

used. Based on the estimation results with the above

Fixed Effect method, with degree of freedom (n-k) =

120-4 = 116 obtained t-table of 1.98, which means

that there is a positive and significant relationship

between the variable Length of Road (PJ), variable

Electricity ( L) and the variable Clean Water (A) with

the variable Economic Growth, which is indicated by

the coefficient of the variable Length of the Road of

0.27 (t-count = 5.29), then the variable coefficient of

Electricity is 0.06 (t-count = 3.45) and the variable

coefficient of Clean Water equal to 0.06 (t-count =

3.33) with each probability far smaller than one

percent.

5 DISCUSSION

The estimation results are carried out using the

fixed effect method theoretically in accordance with

expectations, all coefficients for each variable show a

positive sign. This sign can be interpreted that the

Length of Road infrastructure has a positive effect on

the Economic Growth of Regency / City in South

Sumatra Province with a variable coefficient value of

0.27. This means that every time there is a one-

percent increase in road length, the GRDP will

increase by 0.27 percent.

Although the road infrastructure in the Regency /

City of South Sumatra Province there are still some

roads that are not good and there are still many roads

that are in damaged condition, besides the mode of

transportation in the province of South Sumatra not

only uses land transportation but also uses river and

rail transport but can still encourage mobility of

economic activity. The results of this study are

different from the research conducted by Sumadiasa,

Tisnawati, and Wirathi (2015), where road

infrastructure has a positive but not significant effect

on the growth of Bali Province GRDP in 1993-2014.

Estimation results conducted using fixed-effect

method theoretically correspond to expectations, all

coefficients for each variable show a positive sign.

This sign can be interpreted that the Length of Road

infrastructure has a positive effect on the Economic

Growth of Regency / City in South Sumatra Province

with a variable coefficient value of 0.27. This means

that every time there is a one-percent increase in road

length, the GRDP will increase by 0.27 percent.

Furthermore, it can also be seen that the variable

electric power has a positive coefficient that is equal

to 0.06, which means that every electrical energy sold

increases by one percent, it will increase the GDP by

0.06 percent. Therefore statistically the electricity

variables significantly affect economic growth, so

that from the estimation results of the model it can be

concluded that the electricity infrastructure in the

regencies/cities in the province of South Sumatra

between 2008 and 2015 had a positive and significant

influence on economic growth. The results of this

study are in line with the results of research conducted

by Pranessy, Nurazi, and Anitasari (2010) and

Winanda (2016).

Then, the results of panel data regression using the

fixed effect method also shows that the variable clean

water has a positive and significant effect on the

economic growth of the regencies/city in South

Sumatra Province with a variable coefficient value of

0.06, which means that every one percent increase of

the variable of clean water will increase GDP growth

by 0.06 percent. The results of this study are in line

with the results of research conducted by Prasetyo

and Firdaus (2009) which examined the influence of

infrastructure on regional economic growth in

Indonesia, which shows that clean water

infrastructure has a positive and significant influence

on the economy in Indonesia.

Based on the results of the estimation of the Fixed

Effect model, the intercept value of the Regency/city

in South Sumatra Province is known as follows at

Table 4.

The Inﬂuence of Infrastructure on The Economic Growth of Regencies/Cities In The Province of South Sumatera

489

Table 4: Regression Intercept of Regency / City in South Sumatra Province

Kab/Kota

Effect

C + C Wilayah

Peringkat

_OKU—C

-0.117824

6.41324

6.29542

_OKI—C

0.428600

6.41324

6.84184

_ME—C

0.922610

6.41324

7.33585

_LHT—C

0.059228

6.41324

6.47247

_MURA--C

-3.78E-05

6.41324

6.4132

_MUBA--C

1.340158

6.41324

7.7534

_BA—C

0.464593

6.41324

6.87783

_OKUS--C

-0.593517

6.41324

5.81972

_OKUT--C

-0.079937

6.41324

6.3333

_OI--C

-0.521910

6.41324

5.89133

_EL--C

-0.913989

6.41324

5.49925

_PLG--C

1.775525

6.41324

8.18877

_PRA--C

-0.564022

6.41324

5.84922

_PGA--C

-1.281303

6.41324

5.13194

_LLG--C

-0.918175

6.41324

5.49507

Table 4 shows the differences in economic growth

rates for each regencies/cities in the province of South

Sumatra. The calculation results show that

Palembang City ranks first with an intercept of 8.19,

while the last rank is the City of Pagaralam with an

intercept of 5.13. Based on the calculation results

above indicate that during the period 2008-2015 the

economic growth between regencies/cities in the

province of South Sumatra varied greatly.

6 CONCLUSION

The suitable estimation model in this study is the

fixed effect model. Estimation results show that the

variable length of the road, variable electric power

and variable clean water together and partially have a

positive and significant influence on the economic

growth of the Regency / City in South Sumatra

Province. The estimation results also show that there

are differences in growth between regencies/cities in

the province of South Sumatra.

Based on the results of this study that the

economic growth of an area cannot be separated from

the availability of infrastructure, the local government

in implementing development is expected to improve

the quality and quantity of road infrastructure,

provision of electricity and drinking water

infrastructure and other infrastructure in efforts to

achieve economic growth between regencies / cities

in the province of South Sumatra.

REFERENCE

BPS. Sumsel Dalam Angka Berbagai Edisi. Badan Pusat

Statistik.

Chaerunnisa, Desty Nurhidayanti. 2014. Pengaruh

Infrastruktur Terhadap Pertumbuhan Ekonomi di Kota

Sukabumi tahun 1990-2012. (Skripsi). Bogor: Institut

Pertanian Bogor.

Iskarno, Puput Evira dkk. 2014. Pengaruh Tingkat

Pendidikan dan Infrastruktur Terhadap Pertumbuhan

Ekonomi di Indonesia Tahun 2008-2012. Jurnal

Ekonomi. Hal.1-17..

Mankiw, Gregory N. 2010. Macroeconomics. Worth

Morimoto, Risako and Chris Hope. 2001. The Impact Of

Electricity Supply On Economic Growth In Sri Lanka.

The Judge Institute Of Management Studies. p.1-17.

Peter, Siyan, Ereminkhale Rita and Makwe Edith. 2015.

The Impact of Road Transportation Infrastructure on

Economic Growth in Nigeria. International Journal of

Management and Commerce Innovations. Vol.3. p.

673-680.

Pranessy, Lise, Ridwan Nurazi dan Merri Anitasari. 2010.

Pengaruh Pembangunan Infrastruktur Terhadap

Pertumbuhan Ekonomi Provinsi Bengkulu. Jurnal

Ekonomi Dan Perencanaan Pembangunan (JEPP).

Vol.4 No.3. Hal.51-62.

Prasetyo, Rindang Bangun dan Muhammad Firdaus. 2009.

Pengaruh Infrastruktur Pada Pertumbuhan Ekonomi

Wilayah Di Indonesia. Jurnal Ekonomi dan Kebijakan

Pembangunan. Vol.2 No.2. Hal.222-236.

Putri, Phany Ineke. 2014. Pengaruh Investasi, Tenaga

Kerja, Belanja Modal dan Infrastruktur Terhadap

Pertumbuhan Ekonomi Pulau Jawa. Journal of

Economics and Policy. Vol.7 No.2. Hal.109-120.

Sumadiasa, I Ketut dkk. 2016. Analisis Pengaruh

Pembangunan Infrastruktur Jalan, Listrik Dan PMA

Terhadap Pertumbuhan PDRB Provinsi Bali Tahun

SEABC 2018 - 4th Sriwijaya Economics, Accounting, and Business Conference

490

1993-2014. E-Jurnal EP Unud. Vol.5 No.7 Hal.925-

947.

Suminar, Hasti Aringga, Anifatul Hanim dan Fajar Wahyu

Prianto. 2016. Pengaruh Pembangunan Infrastruktur

Terhadap Pendapatan Regional Kabupaten Jember.

Artikel Ilmiah Mahasiswa 2016. Hal.1-5.

Todaro, Michael P. 2009. Pembangunan Ekonomi. Jakarta:

Erlangga.

Wahyuni, Krismanti Tri. 2009. Analisis Pengaruh

Infrastruktur Ekonomi Dan Sosial Terhadap

Produktivitas Ekonomi Di Indonesia. (Skripsi). Bogor:

Fakultas Ekonomi dan Manajemen Institut Pertanian

Bogor.

Warsilan dan Akhmad Noor. 2015. Peranan Infrastruktur

Terhadap Pertumbuhan Ekonomi dan Implikasi pada

Kebijakan Pembangunan di Kota Samarinda.

MIMBAR. Vol.31 No.2. Hal.359-366.

Winanda, Ade Ayu. 2016. Analisis Pengaruh Infrastruktur

Terhadap Pertumbuhan Ekonomi Di Kota Bandar

Lampung. (Skripsi). Bandar Lampung: Fakultas

Ekonomi dan Bisnis Universitas Lampung Bandar

Lampung.

World Bank. 1994. World Development Report:

Infrastructure for Development. Oxford University

Press, New York.

Worku, Ibrahim. 2010. Road Sector Development And

Economic Growth In Ethiopia. Ethiopian Journal of

Economics. Vol.XIX No.2. p.101-146.

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