Al-Iqtishad: Jurnal Ilmu Ekonomi Syariah (Journal of Islamic Economics) Volume 11 (1), January 2019 P-ISSN: 2087-135X; E-ISSN: 2407-8654 Page 135 - 152 1Department of Economics Universitas Islam Indonesia 2Department of Economics International Islamic University Malaysia and Department of Economics Universitas Gadjah Mada E-mail: 1prastowo@uii.ac.id or prastowo.putriani@gmail.com Income Inequality and Regional Index of Financial Inclusion For Islamic Bank in Indonesia Prastowo1, Diyah Putriani2 Abstract. This research is proposed to measure financial inclusion index in 2 dimensions (2D-FII) in Indonesia Islamic banks. This research contributes to the measurement 2D-FII at the regional level in Indonesia. The analysis of cross- section data from 33 provinces in Indonesia between 2014 and 2015 shows that the value of FII in Islamic banking in Indonesia is still low. Previous works show one of the determinants on increasing inequality in any country is limited access to the financial sector, especially for low-income household (Akimov, Wijeweera, and Dollery 2006; Kenourgios and Samitas 2007; Levine 2003; D. Park and Shin 2015). Thus, the low level of FII in Indonesia perhaps is caused by inequality of income. Therefore, this research recommends policymaker to have more concern on poverty alleviation program and open new Islamic banks branches at the regional level. Keywords: 2 Dimension – Financial Inclusion Index (2D-FII), income inequality, Poverty alleviation, Islamic banks Abstrak. Penelitian ini bertujuan untuk mengukur indeks inklusi keuangan pada 2 dimensi (2D-FII) pada bank-bank syariah di Indonesia. Secara umum, penelitian ini berkontribusi pada pengukuran 2D-FII di tingkat regional Indonesia. Analisis terhadap data dari 33 provinsi di Indonesia antara tahun 2014-2015 menunjukkan bahwa nilai FII pada perbankan syariah di Indonesia masih rendah. Kajian-kajian terdahulu menunjukkan bahwa salah satu penentu peningkatan ketimpangan di berbagai negara adalah akibat akses terhadap sector keuangan yang masih terbatas, khususnya pada rumah tangga dengan pendapatan rendah (Akimov, Wijeweera, and Dollery 2006; Kenourgios and Samitas 2007; Levine 2003; D. Park and Shin 2015). Dengan begitu, rendahnya tingkat FII di Indonesia bisa jadi disebabkan oleh ketimpangan pendapatan. Oleh karena itu, penelitian merekomendasikan pengambil kebijakan untuk lebih fokus pada program pengentasan kemiskinan dan pembukaan cabang-cabang bank syariah di tingkat regional. Kata kunci: 2 Dimension – Financial Inclusion Index (2D-FII), ketimpangan pendapatan, pengentasan kemiskinan, Bank Syariah 136 http://journal.uinjkt.ac.id/index.php/iqtishad Al-Iqtishad: Jurnal Ilmu Ekonomi Syariah (Journal of Islamic Economics) Vol. 11 (1), January 2019 Introduction Organisation for Economic Cooperation and Development (OECD) reports that there is an evidence of rising gap between the rich and the poor in the last three decades in the OECD member countries which show that the richest 10 percent of population earn income 9.5 times higher than the lowest 10 percent and the Gini index also increased from 0.29 in the 1980s to 0.32 in 2011. Indonesia also experiences a similar condition. Central Bureau of Statistics of Indonesia documents that the lowest coefficient value of the Gini index was 0.31 point, which occurred in 1999. This value, however, slowly increased by about 0.1 point to 0.3 points every year and reached the highest level at 0.41 from 2011 to 2015. Latest data reports that the Gini index decreases by 0.1 point (0.40) in 2016. Figure (1.1.) also clearly shows that on average Gini index in each province in Indonesia is still high, especially in Papua. Figure 1. Gini Index in Each Province 2014-2015 Sources: BPS. Authors’ calculation Table 1. Gini Index in Urban and Sub Urban Area in Indonesia Year Urban Sub Urban Urban + Sub Urban 2010 0.38 0.32 0.38 2011 0.42 0.34 0.41 2012 0.42 0.33 0.41 2013 0.43 0.32 0.41 2014 0.43 0.32 0.41 Source: Statistic Bureau of Indonesia Prastowo. Income Inequality and Regional Index of Financial Inclusion 137 http://journal.uinjkt.ac.id/index.php/iqtishad In addition, table (1.1) shows income inequality occurs both in urban and suburban areas. The coefficient value of the Gini index in urban areas is slightly higher than in suburban areas. Specifically, the difference value of the Gini index in urban and suburban areas is approximately 0.6 to 0.9 point. Graph (1.1) shows that from the mid-2013 to mid-2016, the percentage of poor people in Indonesia remains at average 8 percent and 14 percent in urban and suburban areas, respectively. Although, the value of Gini index in the urban areas is higher than in suburban (table 1.1. above), however, the percentage of poor people in a suburban area is almost double than in urban area. This indicates that, basically, the level of income in suburban areas is much lower than in urban areas. This indication can be traced from the reports of Central Bureau Statistics of Indonesia in 2015, i.e. between 2010 and 2014. The richest 20 percent of the population spent more than three times and two times than the poorest 40 percent in an urban and suburban area, respectively. Figure 2. Percentage of Poor People in Indonesia 2013 to 2016 Source: Statistic Bureau of Indonesia The consequences of high-income inequality are significant, which will not only affect the stability of the economy but also will threaten social security. Previous empirical evidence shows that the relationship between income inequality and crime is positive, which means that higher inequality causes more crimes and, thus, a loss in business ((Dadzie, Blanco, and Dony 2014; Fajnzylber 2002; Levitt 1999; Pshisva and Suarez 2010; Stolzenberg, Eitle, and Alessio 2006). As society 138 http://journal.uinjkt.ac.id/index.php/iqtishad Al-Iqtishad: Jurnal Ilmu Ekonomi Syariah (Journal of Islamic Economics) Vol. 11 (1), January 2019 is not stable, the investors will confront high risks, high crimes, and conflict areas. The urgency to minimize the gap of income is basically to stabilize the economy of a country. Previous research shows that income inequality is considered as the main determinant factor to create economic stability (Berg and Ostry 2011; Berg, Ostry, and Zettelmeyer 2012; Kumhof and Rancière 2010; Ostry, Berg, and Tsangarides 2014). Previous empirical studies reveal that income inequality exists due to limited access to financial resources (Akimov, Wijeweera, and Dollery 2006; Kenourgios and Samitas 2007; Levine 2003; Park and Shin 2015). In other words, the financial sector may help to minimize the level of income inequality; that is by expanding its access, especially for the low-level income group. As more individuals can access to the financial sector, the level of financial inclusion will increase (Dabla-Norris and Kochhar (2015). World Bank (2015) further highlights that financial inclusion is an important factor in enhancing the welfare of the people. The greater level of financial inclusion is expected to minimize the level of income inequality and promote higher economic growth, employment, or even better health insurance. In this regards, a high level of income inequality in Indonesia is, perhaps, caused by the low level of financial inclusion, even after islamic banking was firstly established in the 1990s. At this point, the establishment of the islamic banking system is basically to promote income and wealth distribution amongst individuals in society (Ismail 2010). Unfortunately, it seems that the benefit of islamic banks in Indonesia is accessed merely by a small percentage of Indonesian Muslims. Considering this problem, there is a need to measure to what extent the level of financial inclusion of an islamic bank in Indonesia has been achieved. To do so, this research assesses the financial inclusion index (FII) introduced by Sarma (2008). FII is a multi-dimensional index which provides some financial dimension inclusive information within a single-digit number between 0 to 1. A value of 0 means perfect financial exclusion, while the value of 1 means perfect financial inclusion. This index is plotted to see which region has a high and low value of financial inclusion. It is, thus, expected that the result of this research could be used for estimating the effectiveness of the financial inclusion strategy in islamic banks in order to reduce income inequality in Indonesia. Literature Review Financial inclusion index is firstly established by Sarma (2008). There are two fundamental objectives of financial inclusion index (FII) as explained by C. Park and Mercado (2015); Sarma (2012); and Yorulmaz (2013), i.e. (1) to estimate Prastowo. Income Inequality and Regional Index of Financial Inclusion 139 http://journal.uinjkt.ac.id/index.php/iqtishad and monitor the level of financial inclusion and (2) to understand factors affecting financial inclusion. In addition, FII can also be utilized for cross country analysis and can be used to show the relationship between economic development and financial inclusion. C. Park and Mercado (2015) develop financial inclusion indicators to investigate macroeconomic variable, which affects level or degree of financial inclusion in 37 countries within the Asian region. They find that income per capita, the rule of law, and demographic characteristic positively affect financial inclusion in developing countries in Asia. In addition, they also find that financial inclusion may reduce the level of poverty as well as income inequality contemporaneously. Yorulmaz (2013) develops financial inclusion indicators in the case of regional Turkey. He finds that income has a positive relationship with the degree of financial inclusion in each region in Turkey. That is, a higher level of income in such regions lead to a higher degree of financial inclusion, and vice versa. Ardic, Heimann, and Mylenko (2011) measure access to financial services amongst World bank member countries by using CGAP database. They involve three elements, i.e., computing number of unbanked people, computing access to deposits as well as loans services, and measuring financial inclusion mandate in the member countries. The result shows that there is only 44 percent of adults who have access to financial services, and about 64 percent are unbanked people. It is also observed that there is one unbanked person in every five adults in developed countries. In addition, access to deposit services increased even during the global financial crisis in 2009. In the case of India, Kumar and Mishra (2011) assess financial inclusion from the supply and demand side. The supply side or bank factors include several deposits, credit accounts, branches, average deposit, and amount of credit per account and credit utilized. Meanwhile, the demand side or household factors include saving, credit proposed to the bank, and insurance. The data is ranged from 2002-2003. The study also compares the compare the condition of financial service outreach in both rural and urban are. Empirical result shows that there is a significant difference in financial development among the state. The result shows that in the urban area of top state Chandigarh, the index of financial inclusion of banking outreach is six times higher than in another bottom area, Manipur. In addition, there is also a significant gap amongst the rural area of the top state in Delhi and bottom state in Manipur; that is almost 8 times. It is observed that in the rural area, the outreach of financial services is low and, reversely, is high in the urban area. Meanwhile, financial inclusion index for households in the urban area (0.29) is greater than in the rural area (0.18). It is found that in the country 140 http://journal.uinjkt.ac.id/index.php/iqtishad Al-Iqtishad: Jurnal Ilmu Ekonomi Syariah (Journal of Islamic Economics) Vol. 11 (1), January 2019 level, only one person out of every three individual households has access to saving. Less than one person out of every five individual households has access for credit. Furthermore, in every eight individual households, only one person has insurance. In the rural area, only 25 percent and 20 percent of individual households have access to saving and insurance facilities from financial services, respectively. Data and Methodology Due to the availability of data, this research retrieves secondary data, i.e., 33 provinces cross-section data from 2014 and 2015. There are five groups of data involved in this research: 1. Total number of bank branches in each province 2. Total bank account volume in each province 3. Total third-party fund in each province 4. Total credit in each province 5. Gross regional domestic product (GRDP) each province. All data are obtained from the Bank of Indonesia, the Financial Services Authority (Otoritas Jasa Keuangan, OJK), and the Central Bureau of Statistics (Badan Pusat Statistik, BPS). To measure FII, this research follows the work of Sarma (2008), with some adjustments depending on the provinces’ condition. Basically, Sarma’s work has similarities with the Human Development Index (HDI), the Gender-related Development Index (GDI), and Human Poverty Index (HPI) in its computation. Figure 3. Dimension of Financial Inclusion Index Prastowo. Income Inequality and Regional Index of Financial Inclusion 141 http://journal.uinjkt.ac.id/index.php/iqtishad Figure (3.1) shows that FII consists of 3 dimensions, i.e., banking penetration, availability of banking services, and usage of banking services. a. Dimension 1: Banking Penetration (BP) Banking penetration measures the size of the “banked” population that is several people who have bank accounts. b. Dimension 2: Availability of banking services (ABS) The second dimension calculates the total number of bank branches per 100,000 people or ATM per 100,000 people and the total number of bank branches (per 1000 km2) of the population). c. Dimension 3: Usage of banking services (UBS) Usage of banking services is measured by the volume of credits, total deposits, gross domestic, and regional product (GDRP) ratio. This research only uses two dimensions due to the unavailability of “banked population” data. To compute FII at the regional level, there are three steps of calculation. First step: Computing Dimension index, di for each dimension. (1) Notes: Ai = actual value from dimension i mi = minimum value from dimension i Mi = maximum value from dimension i Second step: Calculating FII with n-dimensional space for the i country as follows: (2) Third step: Calculating FII with 3dimensional space for province i. In the third step, there is a need to identify a city or regional area by point (BPi, ABSi, UBSi) into three dimensions of Cartesian space, where BPi, ABSi, and UBS are dimension index, d for province i computed with equation (1). FII for province i is measured by normalized inverse Eucthe lidean distance from point (pi ai,ui) and point (1,1,1) becomes the best (or ideal) condition (perfectly financial inclusion) as follows. 142 http://journal.uinjkt.ac.id/index.php/iqtishad Al-Iqtishad: Jurnal Ilmu Ekonomi Syariah (Journal of Islamic Economics) Vol. 11 (1), January 2019 (3) The value of FII is between 0 and 1. If FII equals 1, it means that a province has ideal financial inclusion. Reversely, if FII equals 0, it implies that a province has financial exclusion. Higher FII (close to 1) means a better condition of financial inclusion. From this FII formula, then we can categorize FII into three groups as follows: 0.6 < FII ≤ 1 : high level of financial inclusion 0.3 ≤ FII≤ 0.6 : middle level of financial inclusion FII ≤ 0.3 : low level of financial inclusion Result and Discussion Result Dimension: Availability of Banking Services The existence of banking services is essential in the formation of financial inclusion. Indicators used for forming this dimension are the number of branches of the islamic Bank and Sharia Business Unit. Numbers of bank branches (per 100,000 populations) and the total number of bank branches (per 1000 km2) of the population are the indicators of the dimensions of this availability of banking services. Accessibility to banking services by people is closely related to the number of bank branches. The existence of a large number of branch offices has not adequately described the extent of the spread of banking services. Thus, ABS dimension can be used to measure the public uses, extent deployment and affordability of banking services. Table 2. Detail Value of ABS Dimension in 2014-2015 No. Province 2015 2014 Average 1. Jawa Barat 1.0000 1.0000 1.0000 2. Banten 0.2843 0.2772 0.2808 3. DKI Jakarta 0.7549 0.8614 0.8081 4. D.I Yogyakarta 0.2059 0.2079 0.2069 5. Jawa Tengah 0.7745 0.7822 0.7783 6. JawaTimur 0.9020 0.9109 0.9064 Prastowo. Income Inequality and Regional Index of Financial Inclusion 143 http://journal.uinjkt.ac.id/index.php/iqtishad 7. Bengkulu 0.0686 0.0693 0.0690 8. Jambi 0.0686 0.0594 0.0640 9. Aceh 0.2451 0.2475 0.2463 10. Sumatera Utara 0.4020 0.3960 0.3990 11. Sumatera Barat 0.1961 0.1980 0.1970 12. Riau 0.1373 0.1584 0.1478 13. Sumatera Selatan 0.1863 0.1980 0.1921 14. Bangka Belitung 0.0196 0.0198 0.0197 15. Kepulauan Riau 0.0980 0.0891 0.0936 16. Lampung 0.1569 0.1683 0.1626 17. Kalimantan Selatan 0.1373 0.1386 0.1379 18. Kalimantan Barat 0.0980 0.0891 0.0936 19. Kalimantan Timur 0.1863 0.1881 0.1872 20. Kalimantan Tengah 0.0588 0.0594 0.0591 21. Sulawesi Tengah 0.0392 0.0396 0.0394 22. Sulawesi Selatan 0.2647 0.2574 0.2611 23. Sulawesi Utara 0.0196 0.0198 0.0197 24. Gorontalo 0.0000 0.0000 0.0000 25. Sulawesi Barat 0.0098 0.0000 0.0049 26. Sulawesi Tenggara 0.0490 0.0495 0.0493 27. Nusa Tenggara Barat 0.1176 0.1287 0.1232 28. Bali 0.0784 0.0792 0.0788 29. Nusa Tenggara Timur 0.0098 0.0099 0.0099 30. Maluku 0.0000 0.0000 0.0000 31. Papua 0.0098 0.0099 0.0099 32. Maluku Utara 0.0294 0.0198 0.0246 33. Papua Barat 0.0000 0.0000 0.0000 Indonesia 0.2002 0.2040 0.2021 Sources: OJK, BI. Authors’ calculation Value dimension of ABS in 2014 is about 0.2040, while in 2015 is around 0.2002. Value dimension of ABS is very low, which equals ≤ 0.3. Table (3.1) indicates that as many as 4 Provinces, i.e., Central Java, East Java, West Java, and DKI Jakarta have a dimension value of 0.6