The Corona Virus Disease 2019 (Covid-19) outbreak had a tremendous impact on the global economy in 2020. Covid-19, which first appeared in Wuhan, China, in December 2019, spread very quickly to 178 countries or 99.5% of world GDP. With its vast scale and speed of spread, Covid-19 was declared a global pandemic by the World Health Organization in March 2020. In 2020, this pandemic had infected more than 85 million people and resulted in the deaths of more than 1.8 million people, causing a crisis in the increasing number of poor people in the world. This health and humanitarian crisis has caused contractionary economic growth evenly distributed in various parts of the world [1]. 

The Indonesian economy in the first quarter of 2021 contracted 0.74 percent (YoY), improving compared to the contraction in the previous quarter due to improved performance in the external sector in line with the economic recovery in major trading partner countries, particularly China and the United States. From a business perspective, the economic recovery drive by positive growth in six sectors: industry, water supply, financial services, agriculture, electricity and gas trading, and real estate. Meanwhile, other sectors showed a slight contraction [2]. 

Figure 1: Indonesia's Economic Growth in the First Quarter, 2010 – 2020

Source: Central Bureau of Statistics

Figure 1 shows a slowdown in Indonesia's economic growth from 2010 to 2019 due to the external challenges of world economic growth. In the first quarter of 2020, Indonesia's economic growth slumped to 2.97% due to the COVID-19 pandemic. The phenomenon of the problems in 2015 and 2016, Indonesia's GDP growth experienced a slowdown due to the external challenges faced by lower-than-expected world economic growth, which ultimately affected the trade and financial channels [3,4]. Meanwhile, the domestic challenge is due to the economic structure that relies on commodities and the shallow domestic financial market (Figure 2). 

The World Bank report states that despite the global economic downturn, extreme poverty in the world continues to decrease. Improvements to eradicate extreme poverty are driven mainly by the East Asia and Pacific region, especially China, Indonesia, and India [3]. From 2004 to 2019, the number of poor people in Indonesia continued to decline. The reduction in the poverty rate is one indicator of improving the community's welfare, which requires more and more financial products and services following the community's needs and abilities [5].

Literature Review

Theory of Financial Development-Economic Growth

Studies on financial development have identified four distinct areas as driving forces of economic growth:

• Drivers of economic growth from providing reliable and low-cost means of payment for all, especially low-income groups

• The role of financial intermediation in increasing the volume of transactions and the allocation of resources from surplus units to economic deficit units and improving the distribution of resources [6]

• The financial system provides the effects of risk management by reducing liquidity risk, thereby enabling the financing of riskier but more productive investments and innovations in the economy [7,8]

• The financial sector offers information about investment possibilities and the availability of capital in the system, thereby improving asymmetric information [9]

From the perspective of the aggregate production function, the above-mentioned financial effects contribute significantly to the change of investment and saving inputs into more significant outputs in the economy, either through capital accumulation or technological change. Taking the channel of capital accumulation as an example, Babajide, Adegboye, and Omankhanlen adopted the Odeniran and Udaeja model in developing the Solow growth model. The model assumes that an increase in the saving rate will increase capital (k) and per capita output (y). Shifts in, as illustrated in Figure 2. 

Figure 2: Percentage of Indonesia's Poor Population, 2010 – 2020

Source: Central Bureau of Statistic

The change from δ1 to δ2 causes the steady-state, k increases from k*1 to k*2, and output per capita increases from y*1 to y*2 [10,11].

This analysis implies the elimination of financial repression and reduction of financial market failures which will improve investment quality as only projects with returns more excellent than the interest rate (IR) will be fund. Thus, the entire production function will shift from f(k) to g(k). Increasing economic efficiency will further increase savings because 2 g (k )>2 f (k), as shown in Figure 2.6. It can be seen from Figure 2 that the new steady-state levels of capital stock per worker and output per worker, k*3 and y*3, exceed not only initial levels, k*1 and y*1 but also higher levels. Higher due to an increase in saving and investment, k*2 and y*2. The financial sector also plays a vital role in improving the production technological progress or long-term economic growth. The limitations of the Solow growth model gave rise to the Schumpeterian growth model. Schumpeter argues that a well-developed financial sector is necessary. New projects require financing because the entrepreneur cannot always bear the upfront investment. Innovation will be nearly impossible without the financial sector to channel funds, and there will be little permanent economic growth. In this situation, financial inclusion becomes indispensable for economic growth, as it provides innovative financial products to encourage low-income people to save more [6].

Financial Inclusion and Poverty

One of the goals of financial inclusion is to make it part of a grand strategy of economic development, poverty reduction, income distribution, and financial system stability [12] Several previous studies that have been conducted show different results in each country. Increasing access to finance in rural Malawi through savings commitments improves the welfare of poor households by providing access to their savings for agricultural inputs [13]. Research conducted in Kenya found that commercial banks can help improve access to finance for the poor by leveraging underprivileged families [14]. In developing Asia, it was found that financial inclusion, in turn, reduces poverty and income Inequality [15]. Several developing countries reported that financial inclusion is a viable tool for fighting poverty and income distribution [16]. Increasing access to financial services for those on low incomes can reduce the number of working poverties [17]. Financial inclusion in Indonesia is significant in poverty. Still, the effect is more pronounced in urban areas than rural areas due to the concentration of financial service providers in urban centers [18]. However, Anwar and Amrullah found that the impact of financial inclusion indirectly reduces poverty but increases income inequality significantly. Massively due to geographic, gender, and age biases in financial inclusion [19]. The expansion of BPR branches in India has helped reduce poverty [20]. Similar to Hanohan, higher access to finance significantly reduces income inequality as measured by the Gini coefficient [21].

Dimensions of Financial Inclusion

Several literature studies examine financial inclusion by using several indicators. These indicators refer to three dimensions of financial inclusion: access, usage, and quality of banking services. The dimension of access relates to the financial aid reach as depicted through ATMs (Automatic Teller Machines) and bank branch offices. The usage dimension relates to financial products such as the number of savings, credit, number of depositors, and borrowers. In contrast, the quality dimension refers to financial product availability to meet the needs of society.

Figure 3: Effect of Savings on Capital Accumulation [11]

Financial inclusion is a policy solution in many developing countries, especially Indonesia, which is in line with statements at international forums such as the G-20 (most recently in 2016 in Hangzhou), that financial inclusion is one factor in reducing poverty increase prosperity. Still, the problem is the low financial inclusion index in Indonesia. The year 2019 recorded Indonesia's financial inclusion index at 76.2%, which is low compared to other emerging market countries [22].

The Financial Services Authority (OJK) continues to carry out various programs to increase public financial inclusion, which is expected to improve the community's economy and is in line with the National Economic Recovery (PEN) program carried out by the Government [23]. During the COVID-19 pandemic, the acceleration of financial inclusion by distributing working capital loans to the MSME sector moved during the pandemic [22]. A member of the OJK Board of Commissioners for Consumer Education and Protection also explained that financial inclusion has an essential and strategic role. It can be a surefire solution in accelerating economic recovery due to the COVID-19 pandemic [23].

The urgency of this research is critical in seeing the strength of the Seemingly Unrelated Regression (SUR) model in detecting how the impact of financial inclusion on economic growth and poverty levels in Indonesia.

This research approach is quantitative research with SUR (Seemingly Unrelated Regression) model. Data using various sources, namely the World Bank, Bank Indonesia, and the Central Statistics Agency period 2004-2019. The data processing uses Eviews 10 software.

Data Analysis Technique

he Seemingly Unrelated Regression (SUR) model is an extension of linear regression analysis in the form of a system of equations consisting of several regression equations that are interconnected because the errors are contemporaneously correlated. Contemporaneous error correlation occurs when, at the same time unit, errors in different equations are connected [24]. The SUR model is used to detect economic growth and poverty rates based on financial inclusion in Indonesia. The equation of the SUR model in this study is as follows:

Figure 4: Effect of Savings on Output [11]

Y_1t-p = α₀ + α₁X_1t-p + α₂X_2t-p + α₃X_3t-p + α₄X_4t-p + e₁ (1)

Y_2t-p = γ₀ + γ₁X_1t-p + γ₂X_2t-p + γ₃X3_t-p + γ₄X_4t-p + γ₅Y_1t-p +e₃ (2)

Information:

Y1 = Economic growth (%)

Y2 = Poverty rate (%)

X1 = Commercial bank branch office (per 100,000 adults)

X2 = Savings (Billion USD)

X3 = Credit disbursed (% of GDP)

X4 = Number of ATMs (per 100,000 adults)

t = Number of time (15 years, 2004 – 2019)

p = Optimal lag length

e = Error

Results of the SUR Model

The SUR model consists of several unrelated systems of equations. The results of the SUR analysis with the FGLS estimation procedure System equation I, namely Y1 economic growth and equation II, namely Y2 poverty level as the dependent variable in Table 1.

Table 1: Results of SUR

Source: Researchers computation from Eviews

Table 1 shows the results of the first equation SUR. Both X1, X2, X3, and X4 have no significant effect on Y1 at = 5%. Commercial bank branch offices and savings have a positive impact on economic growth but are not substantial. Credit and ATM hurt economic growth but are also not significant. While the second equation SUR results, only X1 and X2 significantly affect Y2 at = 5%. The X1 coefficient value of -0.48 means that if the number of bank branch offices increases by 1%, it will reduce the poverty rate by 0.48%. The X2 coefficient value of -0.766 means that if savings increase by 1%, it will reduce the poverty rate in Indonesia by 0.766%. The size of the model's goodness in the first equation is 0.477 (47.7%), while the measure of the second equation is 0.954 (95.4%).

Normality Test

The results of the normality test using Eviews software can be seen in the Table 2.

Table 2: Cholesky Normality Test

Source: Researchers computation from Eviews

The results of the Cholesky Normality Test show both the chi-sq skewness and kurtosis values, and the probability value is less than 0.05. It means the residue is not spreading commonly. Therefore, this problem will be overcome by changing the research variable data into natural logarithms.

Results of the SUR Model Using Natural Logarithms

Table 3 shows the results of the first equation SUR. Both X1, X2, X3, and X4 have no significant effect on Y1 at = 5%. Commercial bank branch offices and savings positively impact economic growth but are not substantial, same as credit, and ATM affects economic growth but are also not significant. This result is the same as the previous SUR result. While the results of the second equation SUR, X1, X2, and X3 significantly affect Y2 at = 5% with coefficient values ​​of -0.249, -0.445, and -1.637, respectively. It means the increasing number of bank branch offices will reduce the poverty rate in Indonesia. Likewise, with savings and credit, the higher the savings and the higher the credit disbursed, the lower the poverty rate in Indonesia. The size of the model's goodness in the first equation seen through the R-square is 0.441 or 44.1%, while the measure of the integrity of the second equation is 0.956 or 95.6%.

Table 3: Results of SUR (Natural Logarithms)

Source: Researchers computation from Eviews

Normality Test Using Natural Logarithms

The results of the Cholesky Normality Test show both the chi-sq skewness and kurtosis values, and the probability value is more significant than 0.05. It means the residue has spread typically.

The trend of residual values ​​for both Y1 and Y2 fluctuates in positive and negative positions without forming the same pattern. It means that the residual variance of each equation is free from the heteroscedasticity problem.

Table 4: Cholesky Normality Test (Natural Logarithms)

Source: Researchers computation from Eviews

The results of the SUR model in the first equation show that financial inclusion has little impact on economic growth in Indonesia. This result contradicts the theory and aligns with research [25] and [26]. 

On the other hand, the results of the SUR model in the second equation show that financial inclusion can reduce the poverty rate in Indonesia through a significant negative relationship on the variables of bank branch offices, savings, and credit which is in line with research [13,20,27,28].

Financial inclusion will create a culture of saving, taking loans no longer to moneylenders but financial institutions. For this reason, providing access to financial services is very important and can potentially pull the poor out of the poverty circle. As stated by the President of the World Bank Group, Jim Yong Kim, access to financial services can help people get out of poverty [29]. The finding of a significant negative relationship between credit and poverty levels is in line with [30]. It is supported by research [31] and [17]. Of course, this further strengthens that proper and targeted credit distribution plays a vital role in reducing poverty in Indonesia as it enables marginalized households to access essential financial resources.

RECOMMENDATION

The policy implications recommended for stakeholders in Indonesia are to optimize the distribution and equity of financial inclusion throughout Indonesia, both urban and rural. If people in urban areas have been able to access or use formal financial services, they tend not to do so in rural areas. For this reason, the government and related institutions are more aggressive in socializing financial literacy because financial literacy is the first step for the community to recognize financial inclusion to increase economic growth and reduce poverty in Indonesia.

Figure 5: Residual graphs of Y1 and Y2

Source: Researchers computation from Eviews

Acknowledgment

The authors would like to thank the Ministry of Research and Technology – the National Research and Innovation Agency (RISTEK-BRIN) for funding this research grant, to the Universitas Pembangunan Panca Budi as the place where the author works, and to fellow writers who have helped the research process, so that finished on time.

1. Bank Indonesia. Bersinergi Membangun Optimisme Pemulihan Ekonomi. Laporan Perekonomian Indonesia. 2020.

2. Kementerian PPN/BAPPENAS. “Perkembangan ekonomi indonesia dan dunia triwulan i tahun 2021.” Perkembangan Ekonomi Indonesia, vol. 5, no. 1, May 2021.

3. Bank Indonesia. Laporan Perekonomian Indonesia. Jakarta, 2016.

4. Bank Indonesia. Perkembangan Ekonomi Keuangan dan Kerja Sama Internasional. Jakarta, 2019.

5. Soetiono, K.S. and C. Setiawan. Literasi dan Inklusi Keuangan Indonesia. Rajawali Press, 2018.

6. Odeniran, Samson O. and E.A. Udeaja. “Financial sector development and economic growth: empirical evidence from Nigeria.” Economic and Financial Review, vol. 48, no. 3, September 2010, pp. 91–124.

7. Greenwood, Jeremy, and Boyan Jovanovic. “Financial development, growth, and the distribution of income.” The Journal of Political Economy, vol. 98, no. 5, 1990, pp. 1076–1107.

8. Bencivenga, Valerie R. and Bruce D. Smith. “Financial intermediation and endogenous growth.” Review of Economic Studies, vol. 58, 1991, pp. 195–209.

9. Levine, Ross. Finance and Growth: Theory and Evidence. NBER Working Paper Series, no. 10766, September 2004.

10. Babajide, A.A. et al. “Financial Inclusion and Economic Growth in Nigeria.” International Journal of Economics and Financial Issues, vol. 5, no. 3, 2015, pp. 629–637.

11. O., S.O. and E. Udaeja. “Financial Sector Development and Economic Growth: Empirical Evidence from Nigeria.” Econ. Finance Rev., vol. 48, no. 3, 2010, pp. 91–124.

12. Zia, I.Z. and P.E. Prasetyo. “Analysis of Financial Inclusion Toward Poverty and Income Inequality.” Jurnal Ekonomi Pembangunan, vol. 19, no. 1, 2018, pp. 114–125.

13. Brune, L. et al. Commitments to Save: A Field Experiment in Rural Malawi. World Bank Policy Research Working Paper No. 5748, August 2011.

14. Allen, Franklin, et al. Resolving the African Financial Development Gap: Cross-Country Comparisons and a Within-Country Study of Kenya. NBER Working Paper Series No. 18013, April 2012.

15. Park, Cyn-Young, and Rogelio V. Mercado Jr. Financial Inclusion, Poverty, and Income Inequality in Developing Asia. ADB Economics Working Paper Series, no. 426, January 2015.

16. Fadun, S.O. “Financial inclusion, tool for poverty alleviation and income redistribution in developing countries: evidences from Nigeria.” Academic Research International, vol. 5, no. 3, May 2014, pp. 137–146.

17. Coulibaly, A. and U.T. Yogo. Access to Financial Services and Working Poverty in Developing Countries. Études et Documents No. 20, November 2016.

18. Gunarsih, T. et al. “Financial inclusion and poverty alleviation: evidence from Indonesia.” International Journal of Economics, Business and Management Research, vol. 2, no. 3, 2018, pp. 468–480.

19. Anwar, A.I. and A. Amrullah. “Impact of financial inclusion towards poverty in Indonesia.” Advances in Economics, Business and Management Research, vol. 40, November 2017.

20. Burgess, Robin, and Rohini Pande. “Do rural banks matter? evidence from the indian social banking experiment.” American Economic Review, vol. 95, no. 3, 2005, pp. 780–795.

21. Honohan, Patrick. “Cross-country variation in household access to financial services.” Journal of Banking & Finance, vol. 32, no. 11, November 2008, pp. 2493–2500.

22. Media Indonesia. “Inklusi keuangan berperan penting dalam pemulihan ekonomi.” E-paper Media Indonesia, 2020.

23. “OJK Dorong Inklusi Keuangan untuk Pemulihan Ekonomi di Masa AKB.” Website Resmi Pemerintah Jawa Barat, October 2020, jabarprov.go.id.

24. Dwiningsih, E. Model Seemingly Unrelated Regression (SUR). Universitas Negeri Yogyakarta, 2012.

25. Mwaitete, C.P. and L.A. George. “Financial inclusion and economic growth: A regression analysis.” Imperial Journal of Interdisciplinary Research, vol. 4, no. 1, 2018, pp. 265–288.

26. Iqbal, B.A. and S. Sami. “Role of banks in financial inclusion in India.” Contaduría y Administración, vol. 62, 2017, pp. 644–656.

27. Kusuma, A.A. and I.G. Indrajaya. “Analisis pengaruh inklusi keuangan terhadap tingkat kemiskinan dan ketimpangan pendapatan masyarakat di kabupaten/kota provinsi bali.” E-Jurnal EP Unud, vol. 9, no. 5, 2020, pp. 993–1022.

28. Williams, H.T. et al. “Role of financial inclusion in economic growth and poverty reduction in a developing economy.” International Journal of Research in Economics and Social Sciences, vol. 7, no. 5, May 2017, pp. 265–271.

29. Pasopati, G. “Bank dunia: 2 miliar orang tidak memiliki rekening bank.” CNN Indonesia, 2015, cnnindonesia.com.

30. Bakari, P.I. et al. “An Examination of the Impact of Financial Inclusion on Poverty Reduction: An Empirical Evidence from Sub-Saharan Africa.” International Journal of Scientific and Research Publication, vol. 9, no. 1, January 2019, pp. 239–252.

31. Abimbola, A. et al. “Financial inclusion as a catalyst for poverty reduction in Nigeria.” International Journal of Scientific Research and Management, vol. 6, no. 6, 2018.