The Effect of Islamic Banking on the Welfare of Indonesian Society
Muhammad Amin1*, Abdul Muta’ali2, Muhammad Cholil Nafis1
1 School of Strategic and Global Studies, Universitas Indonesia, Jakarta, INDONESIA
2 Faculty of Humanities, Universitas Indonesia, Depok, INDONESIA
*Corresponding Author: [email protected], [email protected] Accepted: 15 July 2020 | Published: 31 July 2020
_________________________________________________________________________________________
Abstract: Islamic Banking in Indonesia is continuously showing rapid development. As of February 2020, the number of Islamic banking in Indonesia has reached 14 Islamic Commercial Banks (BUS), 20 Islamic Business Units (UUS), and 164 Islamic Rural Credit Banks (BPRS). However, the question is how far Islamic banking capable of contributing to society’s welfare? Therefore, the purpose of this study is to find out how the role of Islamic banking is on society’s welfare in Indonesia. The method used in this research is panel data regression using the fixed effect model. The results show that Islamic banking variables have a positive and significant effect on society’s welfare. In other words, the results of the study indicate that the existence of Islamic banking in Indonesia has a positive impact on society’s welfare.
Keywords: Islamic banking, welfare of society, panel data regression
_________________________________________________________________________
1. Introduction
The development of financial sector plays an important role in the welfare of a country's society as a driving force of economic growth in the real sector. Indonesia applies a dual banking system, namely conventional banking and Islamic banking. Both have the same function as financial intermediary institutions to facilitate economic activities. However, what makes the two systems different is their core-banking. Conventional banking implements an interest while Islamic banking operates a profit-sharing system with such contracts as Murabaha, Mudharabah, and others.
Islamic Banking in Indonesia has experienced rapid development since its inception. The first Islamic Commercial Bank in Indonesia, Bank Muamalat Indonesia (BMI), was established on November 1, 1991, and officially operated on May 1, 1992. Since then, Islamic banking has grown steadily. According to the data from The Financial Services Authority (OJK), up to February 2020, the number of Islamic banking in Indonesia has reached 14 Islamic Commercial Banks (BUS), 20 Islamic Business Units (UUS), and 164 Islamic Rural Credit Banks (BPRS).
Table 1: The Development of Islamic Banking Main Indicators
Indicator Year
2014 2015 2016 2017 2018 2019
Islamic Commercial Bank (unit) 12 12 13 13 14 14
Islamic Business Unit (unit) 22 22 21 21 20 20
Number of Offices (unit) 2163 1990 1869 1825 1875 1885
Number of Employees (billion
people) 45818 55816 55597 55746 56694 54460
Source: Financial Services Authority Republic of Indonesia (2020)
Table 1 shows that Islamic banking experienced an increase in indicators from 2014 to 2019.
The number of Islamic commercial banks increased in 2018 with the conversion of the East Nusa Tenggara regional development bank (BPD) from an Islamic business unit to an Islamic commercial bank. However, the number of office units and the number of employees in Islamic commercial banks and Islamic business units decreased and increased in 2014-2019.
The decrease in the number of offices occurred from 2014 to 2017, while the increase in the number of offices occurred in 2018-2019.
2. Literature Review
Islamic banking is a financial institution that operates its activities based on Islamic principles. The development of Islamic banking in Indonesia is expected to increase the welfare of society. Some researchers have conducted research related to the influence of Islamic banking on the economy. Rabaa & Younes (2016) analyze the impact of financial liberalization and performance of Islamic banks on economic growth in the Arab Gulf States using panel data from 2001 to 2012. The results found that there is a positive impact of Islamic banking on economic growth. Next, Furqani & Mulyani (2009) found that investment in Islamic banking in the short and long term had a positive effect on economic growth.
Meanwhile, Al-Oqool et al., (2014) found a long-term causality relationship of Islamic banking to Gross Regional Domestic Product Tunisia. The research uses the Vector Error Correction Model (VECM) analysis with panel data from 1980 to 2012. As a result, they found a positive relationship between Islamic banking financing and the welfare of the Jordanian society.
Nurzaman (2011), on the other hand, analyzes the role of zakat in poverty alleviation in Jakarta by using the dependent variable, the Human Development Index (HDI). The results show that zakat does not significantly influence human development but can shift the pattern of society from the recipient of zakat to the giver of zakat. Meanwhile, Hayati (2014) conducted a study to measure the influence of Islamic banking on the welfare of society which is proxied by HDI. The results show that the variable of Islamic banking financing has a positive and significant effect on the welfare of Indonesian society in 2010-2012. However, different research results were shown by Nurdany (2016) which found that Islamic banking assets have a negative and significant effect on the welfare of Indonesian society in 2012- 2014 as measured by HDI.
2.1 Problem Statement
Regarding previous researches, it was found that Islamic banking generally has a positive effect on the economy. However, it is necessary to conduct further research on the influence of Islamic banking on the welfare of society. This research is intended to bridge the gap that occurred in previous research that mostly showed the influence of Islamic banking on
economic growth. This study also uses the latest panel data with a more extended period than the previous studies (from 2014 to 2019), so that the research results are expected to show the real situation.
3. Methodology
This research uses secondary data with the type of panel data from 33 provinces in Indonesia from 2014 to 2019. The Human Development Index (HDI) from 33 provinces in Indonesia set as the dependent variable, which was obtained from the publication of the Indonesian Central Bureau of Statistics (BPS), while the independent variables are Islamic banking assets and financing in 2014-2019 which was obtained from the publication of Financial Services Authority (OJK).
Table 2: Research Variables and Data References
Research Variable Reference
Human Development Index Central Bureau of Statistics
Islamic Banking Asset Financial Services Authority
Islamic Banking Financing Financial Services Authority Source: Computed by author
The variables along with their operational definitions used in this study are as follow:
Table 3: Definition and Operational Variables
Variable Explanation Definition Measurement
HDI Human Development
Index
An index that represents the level of social welfare in each province of Indonesia
Point ASSET Islamic Banking Asset Total assets owned by Islamic Commercial Banks
and Islamic Business Units
Billion Rupiah
FIN Islamic Banking
Financing
Total financing channeled by Islamic banking Billion Rupiah Source: Computed by author
3.1 Data Analysis
This study uses panel data regression analysis. The three models used for panel data estimation are the common effect model (CEM), the fixed-effect model (FEM), and the random effect model (REM). Next, to determine the best model for this research, we conduct the Chow and Hausman test. According to Baltagi (2006), the Chow test is conducted to determine the best model between the common effect model and the fixed-effect model, while the Hausman test is conducted to determine the best model between the fixed-effect model and the random effect model.
According to Nachrowi & Usman (2006), the panel data regression model can be written as follow:
Yit = 0 + 1X1it + 2X2it + 3X3it + uit (1) Where: i = 1,2,3,….., N (Number of observations)
t = 1,2,3,..., N (Period of research)
Regarding the model above, the form of the panel data regression model equation in this study is as follows:
HDIit= 1ASSETit + 2FINit + it (2)
Where: HDIit : Human Development Index ASSETit : Islamic Banking Asset FINit : Islamic Banking Financing
: Intercept
: Slope
it : Error term
i : 33 Province in Indonesia
t : Period of research from 2014 to 2019
This model was selected to find out how much the independent variables affect the dependent variable. Before analyzing the regression coefficients, it is necessary to test the classic assumptions of the regression model to ensure that the model used fulfills the criteria of BLUE (Best Linear Unlimited Estimator). This is intended to determine whether the variables used are the proper model in explaining the effect of the independent variables on the dependent variable. The classic assumption test in this study was conducted with a multicollinearity and heteroscedasticity test.
The multicollinearity test aims to see whether there is a correlation between the independent variables in the regression model. If we find a high correlation between the independent variables, then the relationship between the independent variable and the dependent variable is disturbed. We examine multicollinearity by looking at the correlation coefficient between independent variables. According to Gujarati & Porter (2017), if the correlation between independent variables is < 0.8 then we can conclude that the model is free from multicollinearity symptoms, and vice versa. Next, we take a heteroscedasticity test through the Glejser test. This test is carried out to find out whether there is an inequality of variance of errors for all observations of the independent variables in the regression model. If the probability of the significance test is greater than 0.05 (α = 5%) than we can conclude that the regression model is free from heteroscedasticity symptoms.
4. Result and Discussion
Islamic Banking has a great opportunity to improve the welfare of Indonesian society. This is because the majority of Indonesia's population is Muslim. To find out the effect of Islamic banking on society’s welfare, a test with several stages is conducted in table 4.
Table 4: The Output of Chow and Hausman Test The Output of Chow Test
Effects Test Statistic d.f. Prob.
Cross-section F 86.622861 (32,163) 0.0000
Cross-section Chi-square 572.356464 32 0.0000
The Output of Hausman Test
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 8.480395 2 0.0144
Source: Data processing
The determination of the best regression model begins with a Chow test based on decision making if the probability is greater than 0.05 (α = 5%) or if the result is not significant, the regression model using the common effect method is better. Meanwhile, if the probability is smaller than 0.05 (α = 5%) or if the results are significant, the regression model using fixed effects is better. Chow test result shows that the probability of the fixed effect significance test is 0.0000 which is less than 0.05 (α = 5%), so based on the decision-making conditions we conclude that the regression model using the fixed effect method is better. Furthermore, the Hausman test result shows that the probability of the random effect significance test is 0.0144 which is smaller than 0.05 (α = 5%), based on that the better regression method is a fixed-effect model.
Table 5: The Output of Multicollinearity Test
ASSET FIN
ASSET 1.000000 0.469663
FIN 0.469663 1.000000
Source: Computed by author
After taking Chow and Hausman test to determine the best model, we take the classical assumption test which began with a multicollinearity test to determine the correlation between independent variables. The results of the multicollinearity test in table 5 show that the correlation between independent variables is < 0.8, it is logical to conclude that the estimation of panel data regression used in this study is free from multicollinearity symptoms.
Table 6: The Output of Glejser Test
Variable Coefficient Std. Error t-Statistic Prob.
ASSET 0.047812 0.043510 1.098881 0.2734
FIN 0.000309 0.002253 0.137361 0.8909
C 0.013817 0.171902 1.243832 0.2153
Source: Data Processing
The next classic assumption test is the heteroscedasticity test using the Glejser test. We take this test to find out whether there is an inequality of variance of errors for all observations of the independent variables in the regression model. The Glejser test results in table 6 show that the probability of the significance test is greater than 0.05 (α = 5%). So, we can conclude that the regression model in this study is free from heteroscedasticity symptoms.
Table 7: Regression Output Using CEM, FEM, and REM Methods
Variable CEM FEM REM
ASSET 0.010638 0.019341*** 0.018681**
(0.851238) (2.66077) (2.471078)
FIN 0.011368 0.020681** 0.015633**
(0.887331) (2.350434) (1.972667)
C 4.06645*** 3.924561*** 3.969064***
(237.0533) (142.1357) (161.7273)
*** indicate significance at 1 %
** indicate significance at 5 % Source: Data processing
After taking the Chow and Hausman test and determining the best model, we decide that the best regression model for analyzing the influence of Islamic banking on the welfare of the
Indonesian society is the fixed effect model. The model has fulfilled the criteria of BLUE after testing the classical assumptions namely multicollinearity and heteroscedasticity. From the fixed effect model, the equation model can be written as follow:
HDIit = 3.924561 + 0.019341ASSETit + 0.020681FINit + it (3)
Regarding the results of data processing in table 7, we can see the effect of the independent variables on the dependent variable using a significance level of 5%. The Islamic banking asset variable has a coefficient of a positive sign with a t-statistic value of 2.66077 and a probability of 0.0086. Meanwhile, the Islamic banking financing variable has a coefficient of a positive sign with a t-statistic value of 2.350434 and a probability of 0.0199. Thus, it is logical to conclude that Islamic banking assets and financing have a positive and significant effect on the welfare of Indonesian society. Besides, the results of the study show that the regression constant value of 3.924561 it means that the average value of HDI before starting of the Islamic banking assets and financing is relatively low.
Statistics F test result shows that the statistical probability F is 0.0000, which means simultaneously the independent variables in this study, namely Islamic banking assets and financing, have a significant effect on the welfare of Indonesian society. The regression results show that the coefficient of determination R2 in the regression model using the fixed- effect model is 0.9642 (96%). This shows that the independent variable in the model can explain the dependent variable by 96 percent and the rest is explained by other variables outside the research model.
The economic interpretation from the results of data processing is conducted to explain Islamic banking variables that affect the welfare of society and why these variables affect or not, how much the effect is, as well as comparison with previous research findings. Based on the results of the study, we found that the variables of Islamic banking assets have a positive and significant effect on the Human Development Index. The regression coefficient of 0.019341 shows that if Islamic banking assets increase by 1 billion rupiahs, the people's welfare increases by 0.019341 points.
The results of this study are different from previous studies. Nurdani (2016) states that Islamic banking assets have a negative effect on society's welfare because the income of Islamic banks is mostly allocated into assets, thereby reducing the amount of financing disbursed. However, the results of the study are supported by Boukhatem & Mousa (2018) which states that Islamic banking assets can improve the welfare of society because it is supported by clear regulations so that there is no error in the placement of Islamic banking revenue. Besides, Harahap (2016) states that Islamic banking assets have a relationship with society’s welfare. It proved in some Muslim majorities countries such as Malaysia, Saudi Arabia, Sudan, and others.
The results show that the Islamic banking financing variable has a positive and significant effect on the Human Development Index. The regression coefficient of 0.020681 means that if Islamic banking financing increases by 1 billion rupiahs, the Human Development Index increases by 0.020681 points and vice versa. Financing distributed to the community by Islamic banking is capable of increasing the production of goods, processing raw materials, trade volume, and the implementation of other economic activities by partners who apply for financing. The increase in Islamic banking financing can also increase the exchange of goods and services in society so that it can improve the welfare of society in the economic term.
Some research results that support the positive impact of Islamic banking financing on public welfare are found by El-Galfy & Khiyar (2012) which uses a literature study to explain that the role of Islamic banking financing has a positive effect on society's welfare in developed and developing countries. According to them, macroeconomic stabilization and government policies should pay attention to the effect of Islamic banking on economic growth.
Furthermore, Abduh & Omar (2012) found that Islamic banking business activities including financing distributed to partners affect the welfare of society. The study uses the extended Error Correction Model (ECM) method with the Autoregressive Distributed Lag (ADRL).
Research results support the improvement of regulations to develop the growth of Islamic banking.
However, the results of this study differ from Hachicha & Ammar (2015) and Afandi &
Amin (2019) which show that Islamic banking financing has a negative effect on society's welfare. That is because Islamic banking financing tends to be done with consumptive Murabaha contracts and the lack of productive use of Mudharabah contracts. The results of Afandi & Amin's research (2019) show that productive financing in Indonesia was decreasing gradually. It is provable by the financial services authority data which shows that the Murabaha contract dominates Islamic banking financing by more than 50 percent.
Nonetheless, Leon and Weill (2018) highlight that the growth of Islamic banking has a positive impact on access to finance when conventional banking growth is low.
5. Conclusion
Regarding the analysis research data that has been done, it is logical to conclude that the variable assets along with financing of Islamic banking in Indonesia have a positive and significant effect on the welfare of Indonesian society. This means that the existence of Islamic banking can contribute positively and significantly to improve the welfare of Indonesian society. This positive contribution occurred because the financing distributed can increase the activities of goods production, processing of raw materials, trade volumes, and the implementation of other economic activities by partners who propose financing. The results of this study can be taken into consideration for the Indonesian government to improve the development of Islamic banking.
References
Abduh, M., & Omar, M. A. (2012). Islamic Banking and Economic Growth: the Indonesian Experience. International Journal of Islamic and Middle Eastern Finance and Management, 5(1), 35–47. https://doi.org/10.1108/17538391211216811.
Afandi, M. A., & Amin, M. (2019). Islamic Bank Financing and Its Effect on Economic Growth: A Cross Province Analysis. Signifikan: Jurnal Ilmu Ekonomi, 8(2), 243-250.
http://dx.doi.org/10.15408/sjie.v8i2.10977.
Al-Oqool, M. A., Okab, R., & Bashayreh, M. (2014). Financial Islamic Banking Development and Economic Growth: A Case Study of Jordan. International Journal of Economics and Finance. 6(3), 72-79. http://dx.doi.org/10.5539/ijef.v6n3p72.
Baltagi, B. H. (2006). Panel Data Econometrics: Theoretical Contributions and Empirical Applications. Amsterdam: Elsevier.
Boukhatem, J., & Moussa, F. B. (2018). The Effect of Islamic Banks on GDP Growth: Some Evidence from Selected MENA Countries. Borsa Istanbul Review, 18(3), 231–247.
https://doi.org/10.1016/j.bir.2017.11.004.
El-Galfy, A., & Khiyar, K. A. (2012). Islamic Banking and Economic Growth: A review.
Journal of Applied Business Research, 28(5), 943–956. https://doi.org/
10.19030/jabr.v28i5.7236.
Furqani, H. & Mulyaniy, R. (2009). Islamic Banking and Economic Growth: Empirical Evidence from Malaysia. Journal of Economic Cooperation and Development, 30(2).
59-74.
Gujarati, D. N., & Porter, D. C. (2017). Basic Econometrics. USA: The McGraw-Hill.
Hachicha, N., & Amar, A. B. (2015). Does Islamic Bank Financing Contribute to Economic Growth? The Malaysian Case. International Journal of Islamic and Middle Eastern Finance and Management, 8(3), 349–368. https://doi.org/10.1108/IMEFM-07-2014- 0063.
Harahap, I. (2016). Peranan Perbankan Syariah dalam Upaya Peningkatan Kesejahteraan Masyarakat. At-Tijarah 2(1), 112-126.
Hayati, S. R. (2014). Peran Perbankan Syariah terhadap Pertumbuhan Ekonomi Indonesia (The Role of Islamic Banking on The Indonesian Economic Growth). Indo-Islamika, 4(1), 41–66.
Léon, F., & Weill, L. (2018). Islamic Banking Development and Access to Credit. Pacific- Basin Finance Journal, 52, 54-69. https://doi.org/10.1016/j.pacfin.2017.04.010.
Nachrowi, N. D., & Usman, H. (2006). Pendekatan Populer dan Praktis Ekonometrika untuk Analisis Ekonomi dan Keuangan. Jakarta: Lembaga Penerbit Fakultas Ekonomi Universitas Indonesia.
Nurdany, A. (2016). Pengaruh Pembiayaan, Aset, dan FDR Perbankan Syariah terhadap Kesejahteraan Masyarakat di Indonesia. Jurnal Ekonomi & Keuangan Islam, 2(2), 1-9.
https://doi.org/10.20885/jeki.vol2.iss2.art1.
Nurzaman, M. S. (2011). Zakat and Human development: An Empirical analysis on poverty allevation in Jakarta, Indonesia. In 8th International Conference on Islamic and Finance.
Rabaa, B. & Younes, B. (2016). The Impact of The Islamic Banks Performance on Economic Growth: Using Panel Data. International Journal of Economics and Finance Studies 8(1), 101-111.
