Jurnal Ekonomi & Studi Pembangunan Volume 21 Nomor 2, Oktober 2020 Article Type: Research Paper Dynamic Financial Inclusion in ASEAN 8: Do Macroeconomics and Financial Technology Matter? Tunjung Sekar Laksmi Pandhit Abstract: This study aims to estimate the effects of macroeconomic indicators and financial technology on financial inclusion in ASEAN 8 during 2010-2018. There are three financial inclusion indicators, which include debit card ownership (Model 1), credit card ownership (Model 2), and domestic credit to GDP ratio (Model 3). Furthermore, the dynamic panel is applied to demonstrate dynamic financial inclusion models. The findings show that the domestic credit to GDP ratio is influenced by the unemployment rate, inflation, and financial technology. In addition, Model 1 and 2 show that the FEM is a robust model, while Model 3 indicates that REM is a robust model. This study encourages governments in ASEAN 8 to manage macroeconomic indicators progressively and stably to expand equal financial inclusion for the community. Keywords: Financial Inclusion; Macroeconomy; Financial Technology; Dynamic Panel JEL Classification: C23; E58; G21; O33 Introduction Financial inclusion encourages and facilitates all individuals to engage in a broad and integrated financial system (Berry, 2015; Appleyard, Rowlingson, & Gardner, 2016; Salignac, Muir, & Wong, 2016). In general, the definitions of financial inclusion tend to vary or are not universal. Put simply, Lenka and Barik (2018) described that financial inclusion is identical to the process of providing various financial products and services such as deposit and credit facilities, check services, mobile/internet banking and insurance facilities for poor and low-income households at affordable costs. At the high-level conference held in Seoul, South Korea in November 2010, financial inclusion became one of the nine main pillars of the Global Development Agenda (GPFI, 2011). The access to finance through financial inclusion will improve savings among people who are not familiar with formal finance such as farmers, so that they can manage their expenses. Demirguc-Kunt, Klapper and Singer (2017) said that this access is important for people living in the poor category, because the financial inclusion will help them reduce inequality and poverty. Financial inclusion can be measured by several indicators. The latest financial inclusion indicators have been published by the World Bank since AFFILIATION: PT Putra Alam Teknologi, Bekasi, Indonesia *CORRESPONDENCE: sekarpandhit@gmail.com THIS ARTICLE IS AVALILABLE IN: http://journal.umy.ac.id/index.php/esp DOI: 10.18196/jesp.21.2.5037 CITATION: Pandhit, T. S. L. (2020). Dynamic Financial Inclusion in ASEAN 8: Do Macroeconomics and Financial Technology Matter?. Jurnal Ekonomi & Studi Pembangunan, 21(2), 146-160. ARTICLE HISTORY Received: 5 June 2020 Reviewed: 29 June 2020 21 Sep 2020 Revised: 1 July 2020 22 Sep 2020 Accepted: 9 Oct 2020 mailto:sekarpandhit@gmail.com http://journal.umy.ac.id/index.php/esp https://journal.umy.ac.id/index.php/esp/article/view/8963 http://journal.umy.ac.id/index.php/esp Pandhit Dynamic Financial Inclusion in ASEAN 8: … Jurnal Ekonomi & Studi Pembangunan, 2020 | 147 2011 in the form of Global Findex. Chikalipah (2017) utilized bank account ownership as an indicator of financial inclusion. Similarly, Raza, Tang, Rubab, and Wen (2019) used indicators such as the number of bank accounts and the number of bank branches. Furthermore, Inoue (2018) used several financial inclusion indicators such as the number of bank branches, the number of bank account ownership, and financial deepening. Meanwhile, the composite approach to the financial inclusion index was carried out by Sharma (2016); Lenka and Barik (2018). Sharma (2016) employed three dimensions of financial inclusion indicators, namely: (a) banking penetration such as the number of deposit and loan accounts, (b) the availability of banking services such as the number of bank branches and the number of ATMs, and (c) the practice of banking services such as the ratio deposits per GDP and credit ratio per GDP. In addition, the three dimensions of financial inclusion used by Lenka and Barik (2018), namely: (a) banking penetration including the number of bank account ownership, (b) the availability of financial services including the number of ATMs, bank branches, and the number of employees, and (c) the practice of banking services among other such as the volume of credit per GDP ratio and the volume of debits per GDP ratio. Figure 1 Financial Inclusion, Financial Technology and Macroeconomic Indicator in ASEAN Countries during 2010-2018 Source: The World Bank, Findex and Google Trend (processed) Note: crd = domestic credit/GDP (%); dc = debit card ownership (%); cc = credit card ownership (%); fto = financial technology observer (index); gdpg = economic growth (%); povr = poverty rate (%); inf = inflation (%); and uer = unemployment rate (%). Figure 1 describes the development of financial inclusion, macroeconomic, and financial technology indicators in ASEAN 8 during 2010-2018. All financial inclusion indicators have upward trends, especially the debit card ownership. This indicates that the public has responded to the existence of financial institutions by saving for daily transactions. However, the increase in credit card ownership and domestic credit to GDP ratio is relatively slow. This means that there is a business risk and relatively low financial transactions in ASEAN 8 which become obstacles to the acceleration and expansion of financial inclusion. Similarly, the condition has happened in the development of financial technology. Meanwhile, developments illustrated in macroeconomic indicators show an upward trend. A significant decrease occur in the poverty rate. The development of economic growth, inflation, and unemployment rates tend to be stable. Pandhit Dynamic Financial Inclusion in ASEAN 8: … Jurnal Ekonomi & Studi Pembangunan, 2020 | 148 Based on GDP growth data (annual in %) published by the World Bank, ASEAN countries experience accelerated growth, with an average of 6.02% during 2010 - 2018. Le, Chuc, and Taghizadech-Hesary (2019) described that Asia has strong growth. Therefore, policymakers should improve the poor access to financial services to ensure normal growth. On 7-8 April 2016, the Asian Development Bank Institute, the APEC Business Advisory Council, and the Foundation for Development Cooperation held a forum to discuss the issue of Financial Inclusion in the Digital Age. The forum discussed the importance of accessing financial services to individuals and groups to be able to get benefit from broad and integrated financial service products. Many empirical studies estimate the financial inclusion models both at the level of a country and cross-country analysis. In addition, various approaches or methods have been used to estimate the determinants of financial inclusion. Chikalipah (2017) identified several factors that influenced financial inclusion in Sub-Saharan Africa (SSA) in 2014 using the OLS method. Those factors were literacy, GDP growth rate, population density, infrastructure index, and GNI per capita. Meanwhile, Inoue (2018) employed independent variables such as poverty, real GDP per capita, inflation, and trade openness. Specifically, Lenka and Barik (2018) found that changes in financial inclusion in India did not produce significant growth in rural areas compared to cities. There is a gap of financial inclusion in rural and urban areas which can be caused by a large number of multinational companies in urban areas that drive financial services in urban areas to be more adequate. The development of the financial inclusion index (FII) was conducted by Goel and Sharma (2017) that a value of 0 0, while β3 and β4 are < 0. Furthermore, the i is the cross-section of ASEAN 8 countries. Model 2 will estimate the effect of macroeconomics and financial technology (FTO) on dynamic credit card ownership (CC). This model is also used as a robustness test against Model 1. Macroeconomic indicators consist of economic growth (GDPG), the poverty rate (POVR), and inflation (INF). The dynamic panel model to be estimated is as follows: CCit = α0 + β1CCit-1 + β2GDPGit + β3POVRit + β4INFit + β5FTOit + εit (2a) Equation (2a) is a Pooled OLS or Common Effects Model (CEM). Meanwhile, FEM is explained by Equation (2b) while REM is described by Equation (2c). CCit = α0 + α1Dni+β1CCit-1 + β2GDPGit + β3POVRit + β4INFit + β5FTOit + εit (2b) CCit = α0 + β1CCit-1 + β2GDPGit + β3POVRit + β4INFit + β5FTOit + wit (2c) The α0 is the intercept while β1, β2, β3, β4, and β5 are the parameters/slopes of the equation. The values of β1, β2, and β5 are> 0, while β3 and β4 are <0. Furthermore, the i is the cross-section of ASEAN 8 countries. Model 3 will estimate the effect of macroeconomic and financial technology (FTO) on the dynamic domestic credit to GDP ratio (CRD). This model is also utilized as a robustness test for Models 1 and 2. Macroeconomic indicators used to consist of economic growth (GDPG), unemployment rate (UER), and inflation (INF). The dynamic panel model to be estimated is as follows: CRDit = α0 + β1CRDit-1 + β2GDPGit + β3UERit + β4INFit + β5FTOit + εit (3a) Equation (3a) is a Pooled OLS or Common Effects Model (CEM). Meanwhile, FEM is explained by Equation (3b) while REM is described by Equation (3c). CRDit = α0 + α1Dni+β1CRDit-1 + β2GDPGit + β3UERit + β4INFit + β5FTOit + εit (3b) CRDit = α0 + β1CRDit-1 + β2GDPGit + β3UERit + β4INFit + β5FTOit + wit (3c) The α0 is the intercept while β1, β2, β3, β4, and β5 are the parameters/slopes of the equation. The values of β1, β2, and β5 are> 0, while β3 and β4 are <0. Furthermore, the i is the cross-section of ASEAN 8 countries. Result and Discussion This empirical study estimates the effects of macroeconomic indicators and financial technology on financial inclusion in ASEAN 8. Financial inclusion is proxied by three indicators namely debit card ownership (DC), credit card ownership (CC), and domestic credit to GDP ratio (CRD). The mean DC value is 572.3225%. It means that on average, an Pandhit Dynamic Financial Inclusion in ASEAN 8: … Jurnal Ekonomi & Studi Pembangunan, 2020 | 153 ASEAN community member has more than 1 debit card. The countries that have relatively low percentages of debit card ownership are Cambodia, Myanmar, and Lao PDR while the countries that have high percentages of debit card ownership are Vietnam, Thailand, the Philippines, Malaysia, and Indonesia. From 2010-2018, the mean CC value in ASEAN 8 was 121.4883%. Countries that have a relatively low CC percentage are Cambodia, Myanmar, and Lao PDR. Meanwhile, the mean CRD value is 67.9301%. Countries that have relatively high CRD percentages are Vietnam, Thailand, and Malaysia. Thus, the development of financial inclusion in Vietnam, Thailand, and Malaysia tend to be more progressive than the five ASEAN countries. Table 2 Descriptive Statistics Variable Mean Std. Dev. Min Max DC overall 572.3225 561.2078 0.6995 1887.9820 between 579.7744 4.2654 1689.8750 within 129.1920 151.2323 810.5426 CC overall 121.4883 148.4165 0.0010 431.1013 between 156.4689 0.4534 395.5370 within 17.4221 51.0766 177.9990 CRD overall 67.9301 47.0383 4.7700 149.3700 between 48.2342 15.4278 139.5656 within 12.1741 34.2201 106.2301 GDPG overall 6.1244 1.5921 0.8400 9.6300 between 1.2239 3.7667 7.5189 within 1.0980 3.1978 9.8678 POVR overall 15.6360 9.6199 0.4000 42.2000 between 9.2512 0.7889 32.1200 within 4.0745 2.4993 26.2093 INF overall 3.9250 2.8271 -0.9000 18.6800 between 1.7174 1.6856 6.5678 within 2.3185 -1.7628 16.0372 UER overall 1.9632 1.4490 0.3900 5.6100 between 1.5060 0.6022 4.5689 within 0.2954 1.1399 3.0043 FTO overall 27.2138 25.0743 0.0000 72.8000 between 22.3322 1.6522 58.9178 within 13.6451 6.2093 66.0715 Source: Secondary data (processed) The mean value of economic growth in ASEAN 8 is 6.1244%. During 2010-2018 several ASEAN countries had economic growth rates above 6% such as Cambodia, Myanmar, and Lao PDR. Meanwhile, the mean inflation rate is 3.9250%. Myanmar and Vietnam have experienced inflation rates above 6% for several years. The mean poverty rate in ASEAN 8 is 15.6360%. ASEAN countries that have poverty rates above 15% are Cambodia, Myanmar, Loa PDR, and the Philippines. This condition indicates that the economic conditions of ASEAN 8 countries are still full of the economic development cycle problems which are based on high levels of poverty and low-income distribution. Furthermore, the mean unemployment rate is 1.9632%. The three countries that are still obstructed by unemployment rate control constraints are the Philippines, Malaysia, and Indonesia. Pandhit Dynamic Financial Inclusion in ASEAN 8: … Jurnal Ekonomi & Studi Pembangunan, 2020 | 154 Thus, the three countries are relatively difficult to absorb labor in the domestic market compared to other countries in ASEAN 8. Table 3 describes the estimated results of dynamic debit card ownership (DC). DC is an indicator of financial inclusion in the dynamic panel model (Model 1). Based on the Hausman test results, it can be seen that FEM is the best panel model. FEM estimation results show that the DC lag has a significant and positive effect on DC. It means that the development of debit card ownership in ASEAN 8 is currently closely related to the dynamics of debit card ownership in previous periods. Meanwhile, macroeconomic and financial technology indicators have no significant effect. This finding is different from the Pooled OLS and REM estimation results which indicate that macroeconomic indicators (such as economic growth and inflation) and financial technology have a significant effect. However, an increase in economic growth and financial technology led to a decrease in debit card ownership in ASEAN 8. Simply stated, this condition indicates that economic growth and financial technology achieved have not been able to encourage significant and evenly distributed public savings activities for all people. Other indications show that an increase in inflation causes an increase in debit card ownership (Pooled OLS and Random Effects estimation results). It means that people tend to reduce the risk of monetary value at the time of inflation by saving with the hope that they can obtain the appropriate interest rate on savings/deposits. Table 3 Financial Inclusion under Dynamic Debit Card Ownership Variable Pooled OLS Fixed Effect Random Effect DC(-1) 1.013 (0.025) [40.86]*** 0.738 (0.079) [9.33]*** 1.012 (0.026) [39.48]*** GDPG -11.240 (5.646) [-1.99]* -7.381 (7.394) [1.00] -11.208 (5.780) [- 1.94]* POVR 0.491 (1.520) [0.32] 1.272 (1.886) [0.67] 0.503 (1.547) [0.33] INF 5.954 (2.842) [2.10]** -2.325 (3.551) [0.65] 5.801 (2.861) [2.03]** FTO -0.658 (0.360) [-1.83]* 0.169 (0.581) [0.29] -0.690 (0.367) [-1.88]* Constant 79.605 (48.120) [1.65]* 207.135 (71.591) [2.89]** 81.582 (49.566) [1.65]* R- square: Within 0.7959 0.7766 Betwee n 0.9978 0.9991 Overall 0.9901 0.9860 0.9901 Wald Chi-square 1164.16*** 39.78*** 5228.64*** (F-statistics) LM Test 0.34 Hausman Test 12.51** Observations 64 64 64 Source: The authors’ estimation Note: () denotes standard error [ ] denotes Z-statistics ***, ** and * denote significant levels at 1%, 5% and 10%, respectively Pandhit Dynamic Financial Inclusion in ASEAN 8: … Jurnal Ekonomi & Studi Pembangunan, 2020 | 155 The R-square of the Fixed Effects Model (FEM) is about 0.7959 (within-group). It means that 79.59% of the dependent variable is influenced by variations in the dependent variable. Furthermore, the R-square of cross-sectional estimation is 99.78% (between groups). Meanwhile, the R-square of the overall estimation is 98.60%. Table 4 Financial Inclusion under Dynamic Credit Card Ownership Variable Pooled OLS Fixed Effect Random Effect CC(-1) 0.997 (0.0162) [61.47]*** 0.661 (0.084) [7.92]*** 0.998 (0.0219) [45.57]*** GDPG -2.743 (1.139) [-2.41]** -0.643 (1.175) [0.55] -1.750 (1.231) [-1.42] POVR -0.162 (0.232) [-0.70] -0.066 (0.298) [0.22] -0.209 (0.269) [-0.78] INF 0.569 (0.509) [1.12] -0.034 (0.498) [0.07] 0.672 (0.511) [1.32] FTO 0.090 (0.063) [1.42] 0.322 (0.086) [3.75]*** 0.122 (0.071) [1.71]* Constant 17.910 (9.434) [1.90]* 39.838 (13.343) [2.99]** 11.349 (10.645) [1.07] R-square: Within 0.7409 0.6975 Between 0.9949 0.9992 Overall 0.9966 0.9899 0.9955 Wald Chi-square 2620.38*** 29.17*** 4279.82*** (F-statistics) LM Test 0.06 Hausman Test 13.35** Observations 64 64 64 Source: The authors’ estimation Note: () denotes standard Error [ ] denotes Z-statistics ***, ** and * denote significant levels at 1%, 5% and 10%, respectively A dynamic panel of financial inclusion estimates is carried out on Model 2 to obtain robust estimation results (Table 4). The Hausman test shows that FEM is the right panel model. FEM estimation results describe that credit card ownership (CC) is significantly influenced by lagged of CC and financial technology. The number of credit card ownership (CC) in the previous period led to an increase in the current CC period. Furthermore, financial technology (FTO) has a significant and positive effect on CC. This finding is following the hypothesis developed in Model 2. It means that the higher the community seeks and utilizes financial technology, it will encourage an increase in financial inclusion in ASEAN 8. The parameter of constant also indicates a significant and positive influence. Thus, FEM estimation results are better than Pooled OLS and REM estimation results. The R-square of FEM is 74.09% (within-group). It means that 74.09% of the dependent variable is influenced by variations in the independent variable. Besides, the R-square of cross-sectional estimation is 99.49% (between groups). Meanwhile, the R-square of the overall estimation is 98.99%. Pandhit Dynamic Financial Inclusion in ASEAN 8: … Jurnal Ekonomi & Studi Pembangunan, 2020 | 156 Previous empirical studies indicate that macroeconomic indicators such as economic growth, poverty (unemployment), and inflation have a significant effect on financial inclusion. Based on Tables 3 and 4, it can be seen that the results of the dynamic panel estimation show that economic growth, poverty, and inflation have no significant effect. For this reason, this empirical study carries out an estimation in Model 3 and obtains a more robust estimation model. Table 5 Financial Inclusion under Dynamic Domestic Credit to GDP Ratio Variable Pooled OLS Fixed Effect Random Effect CRD(-1) 0.969 (0.016) [59.67]*** 0.956 (0.048) [20.04]*** 0.964 (0.026) [36.88]*** GDPG 0.328 (0.447) [0.73] 0.040 (0.560) [0.07] 0.091 (0.497) [0.18] UER -1.562 (0.510) [- 3.06]*** -2.171 (2.032) [-1.07] -1.629 (0.931) [-1.75]* INF -0.887 (0.203) [- 4.38]*** -0.945 (0.215) [- 4.40]*** -0.931 (0.201) [- 4.62]*** FTO -0.115 (0.301) [- 3.74]*** -0.099 (0.037) [-2.70]** -0.106 (0.032) (- 3.28)*** Constant 12.789 (4.151) [3.08]*** 16.372 (6.278) [2.61]** 14.603 (4.803) [3.04]*** R- square: Within 0.8965 0.8963 Betwee n 0.9972 0.9978 Overall 0.992 0.9914 0.9919 Wald Chi-square 1439.71*** 88.39*** 1558.65*** (F-statistics) LM Test 4.17** Hausman Test 0.24 Observations 64 64 64 Source: The authors’ estimation Note: () denotes standard Error [ ] denotes Z-statictics ***, ** and * denote significant levels at 1%, 5% and 10%, respectively Table 5 shows the estimated results of dynamic domestic credit to GDP ratio as one indicator of financial inclusion. The LM test indicates that the results of REM estimation are correct. REM estimation results inform that the domestic credit to GDP ratio (CRD) is influenced by the lag of CRD, unemployment rate, inflation, and financial technology. Moreover, the constant parameter of estimation also has a significant and positive effect. An increase in the domestic credit to GDP ratio (CRD) in the previous period was able to stimulate an increase in the CRD of the current period. This condition indicates the expansion of credit transactions in each ASEAN 8. Furthermore, the increase in the unemployment rate and inflation will have implications for the reduction in CRD. These results are in line with the hypothesis developed in Model 3. For this reason, ASEAN 8 governments are expected to be careful in formulating macroeconomic policies to control the amount of unemployment as well as low and stable inflation rates. However, an Pandhit Dynamic Financial Inclusion in ASEAN 8: … Jurnal Ekonomi & Studi Pembangunan, 2020 | 157 increase in financial technology led to a decrease in CRD. This needs to be explored in- depth on how people utilize financial technology so that domestic credit transactions are reduced. Furthermore, the estimation results are not following the hypothesis formulated in Model 3 that financial technology will encourage an increase in domestic credit. The R-square of REM is 89.63% (within-group). It means that 89.63% of the dependent variable is influenced by variations in the independent variable. Furthermore, the R- square of cross-sectional estimation is 99.78% (between groups). Meanwhile, the R- square of the overall estimation is 99.19%. Previous empirical studies conducted by Sharma (2016), Lal (2017), Inoue (2018), Lenka and Barik (2018), Raza et al. (2019) and Anarfo, et al. (2019) found that macroeconomic variables had significant effects on financial inclusion. Meanwhile, this empirical study shows that economic growth has no significant effect. This can happen due to the inability of the level of economic growth to stimulate the public to increase banking and financial activities broadly and evenly. Furthermore, Lashitew et al. (2018), Mushtaq and Bruneau (2019), and Sabir et al. (2019) described that financial inclusion has close links with financial technology. These findings are in line with the results of the Model 3 estimation of this study. Thus, the estimation results of Model 3 dynamic panel in this study are considered robust estimation models. Acknowledgement Many thanks to Mr. Malik Cahyadin from Universitas Sebelas Maret for comments, analytical supports and motivations to improve quality of the study. Conclusion Financial inclusion can stimulate more efficient economy, deepen financial markets, and broaden banking activities in the community. This paper estimates the impact of macroeconomic and financial technology on financial inclusion in ASEAN 8. The dynamic panel method is chosen to identify past financial inclusion interactions in the current financial inclusion period. In addition, three financial inclusion indicators are used including debit card ownership, credit card ownership, and domestic credit to GDP ratio. The selection of these indicators is already relevant to previous empirical research. The empirical development that has been carried out is the use of dynamic panel methods and financial inclusion variables. Model 1 shows that the fixed effect model is more appropriate. The estimation results explain that debit card ownership is significantly influenced by the lag of debit card ownership. Besides, Constant also has a significant effect. Meanwhile, macroeconomic and financial technology variables have no significant effect. This means that the independent variable does not have implications for dynamic financial inclusion in ASEAN 8 during the study period. Pandhit Dynamic Financial Inclusion in ASEAN 8: … Jurnal Ekonomi & Studi Pembangunan, 2020 | 158 Model 2 describes that the fixed effects model is more appropriate. Financial inclusion is a proxy by credit card ownership indicators. Credit card ownership is significantly influenced by lagged credit card ownership, financial technology observer, and constant. The results of this estimation provide a better illustration than Model 1. It means that financial technology has significant implications for dynamic financial inclusion in ASEAN 8 during 2010-2018. The final model is the random effects model as a more appropriate panel model. Domestic credit to GDP ratio is influenced by the lagged of domestic credit to GDP ratio, unemployment rate, inflation, financial technology observer, and constant. This means that macroeconomic indicators and financial technology have significant implications for financial inclusion in ASEAN 8 during the study period. This empirical study provides inputs to economic and financial policymakers in ASEAN 8 to keep inflation rates low and stable. Furthermore, governments in the ASEAN 8 region should encourage and facilitate the expansion and acceleration of access to financial technology to the public to accelerate the implementation of financial inclusion on a massive and equitable basis for the wider community. However, this study has limitations in identifying non-economic factors that have significant implications for financial inclusion in ASEAN 8. Thus, further empirical research is expected to develop a model for estimating financial inclusion under non-economic factors. Furthermore, the selection of more appropriate dynamic models can also be used. References ADBI. (2016). Financial Inclusion in The Digital Age. Policy Brief No. 7. 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