Available via license: CC BY-SA 4.0
Content may be subject to copyright.
976
______________________________________________________________
DOI: https://doi.org/10.33258/birci.v4i1.1699
The Effect of Islamic Financial Inclusion on Economic Growth:
A Case Study of Islamic Banking in Indonesia
Fauziyah Adzimatinur1, Vigory Gloriman Manalu2
1,2Universitas Universitas Kuningan, Indonesia
f.adzimatinur@uniku.ac.id, vigoryglo@uniku.ac.id
I. Introduction
Indonesia is one of the nations with the world's largest Muslim population. It has
dual banking system. In this system, Indonesia uses conventional and Islamic banking
systems. Islamic banking in Indonesia is growing quite rapidly although it is not yet
comparable to conventional banks. The first Islamic bank established is Bank Muamalat in
1991. Currently, there are 14 Islamic Commercial Banks (BUS) and 20 conventional banks
that have Islamic Business Unit (UUS) with 2.433 total offices. Comparison of the
development of Islamic banking and conventional banking can be seen in Table 1.
Table 1. The Development of Sharia and Conventional Banking in Indonesia in 2020
Islamic Banks
Conventional Banks
Assets
IDR 576.813 Billion
IDR 9.053.446 Billion
Number of Offices
2.433
30.755
Third Party Funds
IDR 464.193 Billion
IDR 6.634.998 Billion
Total Financing / Credit
IDR 89.539 Billion
IDR 5.516.904 Billion
Source: (Statistik Perbankan Syariah, 2020) and (Statistik Perbankan Indonesia, 2020)
In 2018, Indonesia's total Islamic financial assets reached USD 86 billion, an
increase of USD 4 billion from the previous year. The increase in total assets places
Indonesia in the 7th position with the largest total Islamic financial assets in the world.
Indonesia's Islamic financial assets were able to grow 14.01% (yoy) to IDR 1,468.07
trillion from the previous year which amounted to IDR 1,287.65 trillion. Islamic banking
with a share of 36.67% of total Islamic financial assets was able to grow positively at a rate
of 9.93% (yoy) (Laporan Perkembangan Keuangan Syariah Indonesia, 2019).
Abstract
This research aims to examine the effects of financial inclusion in
Islamic banking on economic development in Indonesia. The economic
growth indicator is represented by the Industry Production Index (IPI)
while the financial inclusion indicator is represented by the amount of
Third Party Funds, the amount of financing, the number of Third
Party Funds accounts, and the number of financing accounts. The
data used is time series from January 2011 to February 2020. The
Vector Error Correction Model (VECM) is used to analyze the data.
The results show that in the long run, inflation has a positive effect,
while in the short term, inflation has a positive effect on lag one and
has a negative effect on lag 2. While the financial inclusion indicator
shows that the financial inclusion of Islamic banking in Indonesia has
a positive effect on economic growth.
Keywords
financial inclusion; economic
growth; Islamic banking
Budapest International Research and Critics Institute-Journal (BIRCI-Journal)
Volume 4, No 1, February 2021, Page: 976-985
e-ISSN: 2615-3076 (Online), p-ISSN: 2615-1715 (Print)
www.bircu-journal.com/index.php/birci
email: birci.journal@gmail.com
977
The banking sector has an intermediary function to channel capital or savings to
business actors so that it plays an important role in moving the real sector. The
intermediation function plays a role in accelerating the flow of investment which will have
a positive impact on economic growth. The easier the access for each level of society to
banking services, the more optimal the implementation of the bank intermediation function
will be. Expanding access to financial services for the poor and micro and small businesses
has become the focus of the Indonesian government as stated in the national financial
inclusion strategy designed since 2010. In 2017, 48.9 percent of adults in Indonesia had
access to formal financial institutions, according to the (Global Financial Inclusion Index,
2020). From this results, 37% of poor people already have access to formal financial
institutions, and 57% of rich people already have access to formal financial institutions. In
addition, digital payment activities have increased, including making or receiving
payments online. Digital payment activities increased to 72% in 2017 compared to 2014 of
62%. These data indicate that the sharia banking intermediation function to move the
economy is very large. With such great growth, more and more people will be served. The
more widespread the reach of Islamic banking shows that the role of Islamic banking is
getting bigger for economic growth.
The inclusive financial system is not only about how to provide credit for the poor
and micro and small businesses but has a more holistic goal of reducing poverty,
distributing income to achieve higher quality and sustainable economic growth without
sacrificing and even supporting financial system stability. The determining factors for the
success of financial inclusion are banking penetration or bank customer size, availability of
banking services, use of banking services, availability of insurance products and
availability of pensions that are protected from inflation (Farooq, 2013). Financial
inclusion will make a meaningful contribution to inclusive and sustainable economic
development at local and national levels. (Bank Indonesia, n.d.).
The inclusion and literacy of Islamic finance in Indonesia is growing. The third
National Financial Literacy and Inclusion Survey (SNLIK) conducted by the Financial
Services Authority (OJK) in 2019 found that the index of financial literacy reached 38.03%
and the index of financial inclusion reached 76.19%. This statistic is an improvement
relative to the 2016 OJK survey findings, including the 29.7 percent financial literacy
index and the 67.8 percent financial inclusion index. Thus, there has been a rise of 8.33
percent in public financial awareness (literacy) and 8.39 percent in access to financial
goods and services (financial inclusion) over the last three years. As many as 12,773
respondents in 34 provinces and 67 cities/districts were included in the 2019 OJK SNLIK
study, considering gender and urban/rural area strata. (Survey Nasional Literasi dan Inklusi
Keuangan, 2019).
As seen by the substantial increase in funding and financing in 2010-2014, Islamic
banking has tremendous potential to introduce financial inclusion, and the findings of the
financial ratio study also indicate that Islamic banking's efficiency and financial position
are good. (Nengsih, 2015). The vision of the 2017-2019 Sharia Financial Development
Roadmap for Indonesia is to realize a rising and prosperous, inclusive sharia financial
services industry that contributes to the stabilization of the national economy and financial
system to the realization of Indonesia as the world's financial center for sharia. (Roadmap
Pengembangan Keuangan Syariah Indonesia 2017-2019, 2017).
Several studies have been carried out to analyze the influence of financial inclusion
on economic development. Inclusion in finance will lead significantly to economic
development. (Iramayasari & Adry, 2020), (Suidarma, 2019), (Kim et al., 2018),
978
(Onaolapo, 2015), (Erlando et al., 2020). Previous researches used annual data of financial
inclusion and GDP as the proxy for economic growth.
The goal of this study is to examine the role of Islamic financial inclusion in
Indonesia's increased economic development. In this study, economic growth was
represented by the Industrial Production Index (IPI) variable, while the financial inclusion
variable was represented by the number of third party funds (Dana Pihak Ketiga/DPK)
accounts, the number of accounts for financing, financing and third party funds, and
macroeconomic variable represented by inflation. The method used in this study is
VAR/VECM to see the short and long term impact and the contribution of each variable to
the economic growth.
II. Review of Literatures
Since the 2008 recession, the term financial inclusion became a movement, primarily
based on the effect of the crisis on groups at the bottom of the pyramid (low and irregular
wages, people living in rural areas, people with disabilities, employees that do not have
legal identification papers, and disadvantaged communities) who are usually unbanked,
which is perceived to be quite high outside of developed countries. At the 2020 G20
summit, world leaders endorsed the Global Partnership for Financial Inclusion (GPFI) G20
High Level Policy Guidance on Digital Financial Inclusion for Youth, Women and SMEs
and also welcomed the 2020 G20 Financial Inclusion Action Plan, which will direct the
work of the GPFI over the next three years. (GPFI, 2020).
According to the World Bank, financial inclusion ensures that individuals and
organizations have access to valuable and accessible financial goods and services, such as
purchases, transfers, deposits, credit and insurance, that will fulfill their needs. A first step
towards greater financial inclusion is access to trading accounts. Transaction accounts are
also a gateway to other banking institutions and will guarantee that financial services are
open to individuals around the globe.
Bank Indonesia also notes that the strategy of financial inclusion is primarily a
method of deepening of financial resources targeted at the society at the bottom of the
pyramid to make use of structured financial goods and services such as secure money
saving, transfers, savings and loans and insurance. This is achieved not only by providing
the object in a fitting way, but also in tandem with different factors.
A research conducted by (Iramayasari & Adry, 2020) examines the effect of financial
inclusion from the amount of ATMs inclusions and the amount of bank branches
inclusions on financial stability and economic growth in ASEAN countries. This study
uses panel data from 2004 – 2017 consisting of 6 countries in ASEAN. The results show
that financial inclusion has a significant effect on economic growth in ASEAN, and The
amount of inclusion ATMs has a significant effect but has a negative relationship with
economic growth in ASEAN; The amount of inclusion bank branches has a significant
influence on economic growth in ASEAN.
The nexus between financial inclusion and economic growth in ASEAN was
examined by (Suidarma, 2019). The goal of this study is to examine the effect and long-
term relationship of financial inclusion on ASEAN economic growth via Gross Domestic
Product (GDP) through the instrument of the number of Automated Teller Machine
(ATM)s and commercial bank branches. The statistics used are secondary data in the
context of an annual panel of ASEAN countries for the period 2008-2015 in order to see
the post-global crisis effect that has arisen. In order to see the long-term relationship and
the GDP reaction when shocks arise in the variable financial inclusion, the approach used
979
is the Panel Vector Error Correction Model (VECM). The findings of the estimate indicate
that financial inclusion has added significantly to the economic development of ASEAN by
the number of ATMs and the number of branches of commercial banks.
Kim (2018) examines the effect of financial inclusion on economic growth in the
Islamic Conference Organization (OIC) countries. The research was conducted on 55 OIC
countries using dynamic panel data analysis as well as VAR, IRF, and Granger Causality
Test. The results show that financial inclusion has a positive effect on economic growth.
The results of the IRF show that financial inclusion has a positive influence on future
economic growth and the results of the Granger Causality Test show that financial
inclusion and economic growth influence each other (Kim et al., 2018).
Onaolapo AR (2015) examines the effect of financial inclusion on economic
growth in Nigeria. The research was conducted for the period 1982-2012 using the
Ordinary Least Square (OLS). The results show that there is a significant influence
between financial inclusion and poverty reduction as well as a positive and significant
effect on economic growth (Onaolapo, 2015).
Research Framework
III. Research Methods
3.1 Types and Sources of Data
This study analyzes the effect of financial inclusion on economic growth. The
indicator of financial inclusion uses sharia banking data which represents indicators of
access, use and quality of banking services, namely the number of accounts, financing and
Third Party Funds. Meanwhile, the macroeconomic indicators use the inflation variable
and the economic growth indicators use the variable Industry Production Index (IPI). The
data is monthly data from January 2011 to February 2020. Data is taken from Islamic
Banking Statistics published by the Financial Services Authority (OJK) and Bank
Indonesia. The analytical method used is the vector autoregressive correction model (VECM).
3.2 Method
(Juanda, 2009) suggests that in the terminology of the econometric model, there are
terms of the dependent variable and the independent variable. Dependent variables are
variables that are influenced by other variables, namely independent variables or determining
variables. The relationship between dependent and independent variables is as follows:
Financial Inclusion
Inflation
Financing accounts
Third Party Fund Account
Financing
Third Party Fund
Economic Growth
(IPI)
Macroeconomics
Figure 1. Research Framework
980
Subscript i denotes the number of observations from 1 to N for population data, or up
to n for sample data. Yi is the ith observation for the dependent variable, Xki is the ith
observation for the independent variable Xk, the coefficient β1 is the regression model
intercept, β2, ..., βn is the coefficient for each variable.
Juanda and Junaidi (2012) state that Structural or theoretical models are mainly
econometric models of the time series, namely models based on current economic theory.
To evaluate current hypotheses, estimating this model will provide numerical information
as well as results. Economic theory, however, has always not been able to establish the
relevant model parameters because the actual economic phenomena are so complicated.
Model Vector Autoregressive (VAR) can be used to overcome this in time series
data(timeseries)for the VAR model is built with an approach that minimizes the theory in
order to capture the phenomenon.
Often the relationship between variables in a dynamic system cannot be explained by
only a single static equation model, but must be with several equations that are dynamic
and influence each other. The VAR model can examine the dynamic relationship between
these variables.
A VAR model consists of m variables, each variable is represented by a linear
function with a(laglag) p of the variable itself and of the m-1 of the other variables. For
example, there are two variables x and y with the order p, so the VAR model is expressed
as follows:
βxyp is the coefficient of y in the equation x in the interval (lag) p, vty and vtx is the error
term.
VAR models exist in many ways, one of which is the restricted VAR, also known
as the Vector Error Correction Model (VECM). Restriction is provided since the
information is not stationary but co-integrated, which implies that a long-term relationship
occurs. The VECM specification limits the convergence of the long-term relationship of
endogenous variables into their co-integration relationship, but also enables short-term
dynamics to occur. Due to the gradual correction by short-term changes to the variance
from the long-term balance formula, the term Vector Error Correction Model (VECM) is
used. (Juanda and Junaidi, 2012). The VECM is expressed in the following equation:
Yt = α0 + α1xt is the long-run cointegration relationship between the two variables.
λy and λx areparameters error-correction that measure how y and x react to deviations from
long-run equilibrium. In general, the VECM model specifications can be stated as follows:
981
Where:
yt = vector containing the variables analyzed in the study
µ0x =vector intercept
µ1x = regression coefficient vector
t = time trend
Πx = αx β 'where b' contains long-term cointegration equations
yt-1 = variable in-level
Γk = regression coefficient matrix
k-1 = VECM order of VAR
εt = error term
The VECM models used in this study are:
Description:
IPI : Industrial Production Index
Inflation : Inflation (%)
RekDPK : Number of Third Party Fund Account
RekPemb : Number of Financing Account
LnDPK : Third Party Funds (Billion Rupiah)
LnPemb : Financing (Billions of Rupiah)
IV. Results and Discussion
4.1 Stationarity Test
The Augmented Dickey Fuller (ADF) test uses the Stationary Test. Using the
Schwarz Knowledge Criteria for automated lag length selection with a cumulative lag of 9,
the test was conducted at the level up to the first difference. The variables in this study
indicate that the data is stationary in the first difference. Therefore, cointegration testing
will be carried out, if there is cointegration then the method will be used Vector Error
Correction Model (VECM), but if there is no cointegration then VAR will be used first
difference.
4.2 VAR Stability Test
(Firdaus, 2011) states that by calculating the roots of the polynomial equation,
referred to as the roots of characteristic polynomials, the VAR stability test is performed. If
the values of all the roots of the polynomial function have an absolute value <1 or are in a
unit circle, In order for the resulting Impulse Response Function (IRF) and Forecast Error
Variance Decomposition (FEVD) to be considered valid, the VAR model is considered
stable. The modulus value of <1 is seen in the VAR stability table in the appendix. This
shows that the VAR model used is considered stable, so that the IRF and FEVD are
considered valid.
982
4.3 Optimal Lag Test
The optimal lag test is used to determine how long the influence of a variable is on
other variables. In addition, optimal lag testing is used to avoid autocorrelation in the
model (Firdaus, 2011). The test results show that the optimum lag is 2.
4.4 Cointegration Test
The cointegration test aims to determine whether the non-stationary variables are
cointegrated or not (Firdaus, 2011). This study uses the Johansen Cointegration Test. The
results show that there are two cointegration ranks at the 5% level, this means that
multivariate long-term linear equations are contained in the model.
4.5 The Influence of Islamic Banking Financial Inclusion on Economic Growth
The factors that influence financing can be seen from the VECM estimation results.
The VECM estimation shows the effect of the studied variables in the short and long term.
The determination of the significance of the variables based on the 5% level.
Table 2. The VECM estimation results
Short-term
Variable
Coefficient
IPI(-1)
0.376171
IPI (-2)
-0.033372
INFL (-1)
-58.30257
INFL (-2)
33.76398
LN_DPK (-1)
1011,977
LN_DPK (-2)
2596,810
LN_PEMB (-1)
106.6592
LN_PEMB ( -2)
-284.3799
LN_REKDPK (-1)
515.4382
LN_REKDPK (-2)
936.5485
LN_REKPEMB (-1)
1194.471
LN_REKPEMB (-2)
-255.3980
Long Term
variable
coefficient
C
-18581.97
infl
62.26665
LN_DPK
3291,024
LN_PEMB
14.06793
LN_REKDPK
210.7260
LN_REKPEMB
-455.6884
The estimation results shows the effect of each variable on economic growth in the
short and long term. In the short term, the lags used are 1 and 2. All variables both at lag 1
and lag 2 have a significant effect on economic growth. Inflation (INFL) in lag 1 has a
negative effect on economic growth, while lag 2 has a positive effect. Inflation can have a
positive effect on economic growth when existing inflation can stimulate producers to
increase production due to increased price. Meanwhile, inflation can have a negative effect
on economic growth when rising prices will only reduce people's welfare.
983
Third Party Funds (LN_DPK) in lag 1 and 2 have a positive effect on economic
growth. Third Party Funds (DPK) in Islamic banking will be channeled through financing
that can stimulate development in the real sector.
Financing (LN_PEMB) at lag 1 has a positive effect and lag 2 has a negative effect
on economic growth. The amount of financing is closely related to the real sector, so that it
directly affects economic growth.
The number of DPK accounts (LN_REKDPK) has a positive effect on economic
growth both at lag 1 and 2. The number of financing accounts (LN_REKPEMB) has a
positive effect on economic growth at lag 1 and has a negative effect on economic growth
at lag 2. This shows that more people have Accounts can increase economic growth
because more people have easy access to finance so that it can trigger a development in the
real sector.
In the long run, all variables have a significant effect on economic growth. The
positive and negative coefficient values show the relationship of each variable to economic
growth. The INFL, LN_DPK, LN_PEMB, and LN_REKDPK variables have a positive
effect on economic growth, while the LN_REKPEMB variable in the long run has a
negative effect on economic growth.
The conclusions of this report are consistent with previous studies demonstrating that
financial inclusion has a positive effect on economic development. (Iramayasari & Adry,
2020), (Suidarma, 2019), (Kim et al., 2018), (Onaolapo, 2015), (Erlando et al., 2020). The
factors of financial inclusion, such as the number of ATMs, bank branches and the number
of deposits, have a positive influence on economic development, representing GDP per
capita (Kim et al., 2018). The financial inclusion variable in this study has a positive effect
on economic growth except for the variable of number of financing account
4.6 Contribution of Each Variables in Explaining Economic Growth
Forecast Error Variance Decomposition (FEVD) Analysis is a VAR model aimed at
predicting the percentage contribution of each variable variance due to changes in those
variables in the VAR method. To explain the relative value of each component in the VAR
system due to shock, FEVD analysis is used. (Juanda & Junaidi, 2012).
984
The FEVD estimation results show that the variable that most influences economic
growth is Third Party Funds. The findings of the FEVD illustrate the complex contribution
of the variables analyzed to the diversity of economic development (IPI). The diversity of
the IPI is primarily affected by the IPI itself, followed by the TPF and the amount of
accounts receivable.
V. Conclusion
The Islamic banking financial inclusion variables used in this study indicate a
significant influence on economic growth. Meanwhile, the Islamic financial inclusion
variable that has the greatest influence on economic growth is Third Party Funds. This
shows that the development of Islamic banking in Indonesia needs to be encouraged so that
it can boost Indonesia's economic growth. Islamic banking has a high concern for the
growth of the real sector which has a major influence on economic growth.
Based on the results obtained from this study, there are several suggestions that we
provide, including: The Authority should have friendly policies to speed up its
development in the country in order to further encourage the contribution of Islamic
banking to economic growth. (Rama, 2013).
Islamic financial literacy needs to be improved. This is the role of various parties,
both government and bank and non-bank financial institutions. This is because high
financial literacy can increase financial inclusion so that it will have an impact on
increasing the amount of Third Party Funds or TPF accounts, as well as other financial
inclusion variables which will ultimately have an impact on economic growth.
To see in more detail about the effect of financial inclusion, the financial inclusion
index variable issued by the World Bank can be included. In addition, it is necessary to add
several other macroeconomic variables such as population and unemployment rate. In
addition, the Impulse Response Function (IRF) analysis can be used to determine the
response of each variable to shocks.
References
Bank Indonesia. (n.d.). Keuangan Inklusif. Retrieved February 1, 2021, from
https://www.bi.go.id/id/fungsi-utama/stabilitas-sistem-keuangan/keuangan-
inklusif/Default.aspx
Erlando, A., Riyanto, F. D., & Masakazu, S. (2020). Financial inclusion, economic growth,
and poverty alleviation: evidence from eastern Indonesia. Heliyon, 6(10), e05235.
https://doi.org/10.1016/j.heliyon.2020.e05235
Farooq, U. (2013). Seeking Financial inclusion Through Islamic Finance: Opportunity and
Challenges. Rizvi Institute of Management Studies and Research, 4.
Firdaus, M. (2011). Aplikasi Ekonometrika untuk Data Panel dan Time Series (Edisi Revi).
IPB Press.
Global Financial Inclusion Index. (2020).
http://www.worldbank.org/en/programs/globalfindex
GPFI. (2020). G20 leaders highlight during the Riyadh Summit the importance of restoring
strong and inclusive growth amid the COVID-19 pandemic.
https://www.gpfi.org/news/g20-leaders-highlight-during-riyadh-summit-importance-
restoring-strong-and-inclusive-growth-amid
985
Iramayasari, & Adry, M. R. (2020). Pengaruh Inklusi Keuangan Terhadap Stabilitas Sistem
Keuangan dan Pertumbuhan Ekonomi di ASEAN. Jurnal Kajian Ekonomi Dan
Pembangunan, 2(1), 161–176.
Juanda, B. (2009). Ekonometrika: Permodelan dan Pendugaan. IPB Press.
Juanda, B., & Junaidi. (2012). Ekonometrika Deret Waktu. IPB Press.
Kim, D. W., Yu, J. S., & Hassan, M. K. (2018). Financial inclusion and economic growth
in OIC countries. Research in International Business and Finance, 43, 1–14.
https://doi.org/10.1016/j.ribaf.2017.07.178
Laporan Perkembangan Keuangan Syariah Indonesia. (2019).
https://www.ojk.go.id/id/kanal/syariah/data-dan-statistik/laporan-perkembangan-
keuangan-syariah-indonesia/Pages/-Laporan-Perkembangan-Keuangan-Syariah-
Indonesia-2019.aspx
Nengsih, N. (2015). Peran Perbankan Syariah Dalam Mengimplementasikan Keuangan
Inklusif di Indonesia. Etikonomi, 14(2), 105–120.
https://doi.org/10.15408/etk.v14i2.2272
Onaolapo, A. R. (2015). Effects of financial inclusion on the economic growth of Nigeria
(1982-2012). International Journal of Business and Management Review, 3(8), 11–
28.
Rama, A. (2013). Perbankan Syariah dan Pertumbuhan Ekonomi di Indonesia. Signifikan,
2(1), 33–56.
Roadmap Pengembangan Keuangan Syariah Indonesia 2017-2019. (2017).
https://www.ojk.go.id/id/kanal/syariah/berita-dan-
kegiatan/publikasi/Pages/Roadmap-Pengembangan-Keuangan-Syariah-Indonesia-
2017-2019.aspx
Statistik Perbankan Indonesia. (2020). https://www.ojk.go.id/id/kanal/perbankan/data-dan-
statistik/statistik-perbankan-indonesia/default.aspx
Statistik Perbankan Syariah. (2020). https://www.ojk.go.id/id/kanal/syariah/data-dan-
statistik/statistik-perbankan-syariah/default.aspx
Suidarma, I. M. (2019). The Nexus Between Financial Inclusion and Economic Growth in
ASEAN. Jejak, 12(2), 267–281. https://doi.org/10.15294/jejak.v12i2.18747
Survey Nasional Literasi dan Inklusi Keuangan. (2019). https://www.ojk.go.id/id/berita-
dan-kegiatan/publikasi/Pages/Survei-Nasional-Literasi-dan-Inklusi-Keuangan-
2019.aspx#:~:text=Survei Nasional Literasi dan Inklusi Keuangan (SNLIK) ketiga
yang dilakukan,inklusi keuangan 76%2C19%25.
