The COVID-19 pandemic has had a multi-sectoral impact on the economy. The weakening of people's purchasing power is one of the most felt impacts. In addition, the increasing number of people affected by the COVID-19 virus reduces their ability to meet their daily needs. Furthermore, the uncertainty caused by the pandemic has caused many people to hesitate to invest. These various impacts led to the weakening of the Indonesian economy. It was recorded that in July 2020, real GDP growth reached minus 5 percent [1]. The percentage of poor people also increased significantly during the pandemic. In September 2020, BPS recorded that the rate of Indonesia's poor was 10.19 percent, an increase of 0.97 percent from September 2019.

One of the reasons for the decline in household welfare (based on per capita expenditure) was a decrease in income. Studies conducted by UNICEF, UNDP, Prospera, and SMERU show that 75 percent of households experienced a decline in revenue during the pandemic (Figure 1). 

Figure 1: Percentage of Indonesia's Poor (%)

Source: Central Bureau of Statistics 

As many as 66 percent of households that have small businesses also experienced a decrease in the number of buyers and business turnover. In addition, in August 2020, there was an increase in the unemployment rate by 2.7 million people. The average nominal wage for workers or laborers also decreased by -5.2 percent from the nominal wage before the pandemic [2].

Even though the economy is weakening and poverty is increasing, one thing that experiences positive changes namely the use of digitalization technology. The pandemic has forced everyone to carry out conventional activities no longer. Restrictions on gatherings and restrictions on crowding activities trigger the need for innovation with technology. Figure 2 shows the volume of electronic money transactions and ATM/debit cards from 2019 to 2021. In the first semester of 2020, both transaction volumes experienced a downward trend, but in the second semester, the showed an increasing trend (Figure 2). 

Figure 2: The volume of Electronic Money Transactions and ATM/Debit Card Transaction 

Source: Bank Indonesia [2]

Although the development is quite volatile, it will continue to increase at the end of 2021. The use of digital-based payment systems is growing during the pandemic because it makes people's activities more efficient and effective. Bappenas and UNDP revealed that the use of digitalization technology has proven to be successful in effectively helping efforts to reduce poverty in developing countries such as Peru, China, Solomon Islands, Zimbabwe, and India. India, for example, has been driven by public sector efforts to extend the reach of traditional financial institutions to the countryside. Digital payment systems have also helped trade through e-commerce which has finally increased micro-enterprises. Faye & Niehaus mention that using digital payment infrastructure has made it possible to put more than 90 percent of donations into the hands of the poor [3].

But what about Indonesia? Developing countries are designated as the fourth country with the most smartphone users in 2020, according to Databoks [4], where smartphones are one of the media to make digital payments quickly. Indonesia's main problem is the poverty rate, which is still relatively high. For this reason, this research is urgent to analyze whether the digital payment system can reduce poverty in Indonesia during the pandemic. We use simultaneity modeling to answer this question. 

Literature Review

Digital Payment System

Payments are a whole area of ​​economics. A well-functioning payment system is a crucial prerequisite for financial stability and economic prosperity in a country, as it facilitates the efficient exchange of goods and services between consumers and businesses [5]. From the user's point of view, payment systems provide a way to deposit money in one account and then transfer it, withdraw or deposit cash, and receive funds from other charges. Both bank checking accounts and cell phone-based mobile money are such costs [6].

From a complete system view, a payment system is a set of instruments, banking procedures, and, usually, a system of transferring funds between banks that ensures money circulation. A country's entire payment system is a collection of all the ways this can happen. The complete picture is complex, involving multiple players (e.g., banks, mobile money operators, processors), channels for accessing cash or making transactions (e.g., ATMs, point-of-sale terminals, online interfaces, cell phones), and the means of payment that can be used to perform transactions (e.g., credit transfer, debit card, credit card).

The facilitation of digital payments is critical to developing e-commerce in the European Union [7]. The invention of blockchain, the success of Bitcoin, its subsequent replication in various other cryptocurrencies, and the proliferation of services to support it have attracted the interest of regulators, especially in developed countries, where digital payment surveillance systems have become much more sophisticated [8]. Digital payment solutions serve as digital platforms that facilitate direct interaction between several types of customers with whom they are affiliated [9]. Digital payment platforms are scalable with high development costs and low marginal costs [9].

Digital Payments and Poverty

Based an analysis of payment systems in more than thirty countries, including China, India, Kenya, Nigeria, the Netherlands, and the United States, found that digital payment systems such as mobile money and electronic account deposits can reduce transaction costs by up to 90 percent and offer the most significant potential for financial inclusion [6]. Given that 84 percent of the 2.5 billion people living on less than $2 a day do not have access to a formal bank account, which makes it difficult for them to receive payments, pay bills, or send money to relatives, expanding access to financial services is a crucial element, in fighting poverty. Moreover, while there is currently little economic incentive for financial providers to serve the poor, lessons learned from developed countries suggest that digital payments are cheaper, more efficient, and ultimately more sustainable.

Bill & Melinda Gates says that for a payment system to serve the poor successfully, it needs to meet three criteria [6]:

• Strong functionality. Users need reliable access to trusted systems and providers. A wide variety of users must accept the system and offer them a range of payment services.

• Low cost and low price. The provider requires relatively low fees, and the possibility of attractive returns is higher. Lower fees allow them to offer services at lower prices. Higher returns will attract them to start serving the poor and developing the system.

• Effective coordination. The market structure requires effective coordination to ensure providers achieve better results and systems evolve successfully over time. Effective coordination will include cooperation and competition among providers.

Data

To obtain the purpose of this study, we used secondary data with a quantitative approach monthly period from January 2019 to December 2021. Data sourced from Bank Indonesia was processed using Eviews 10.0 software. The variables of this study are described in Table 1.

Table 1: Research Variables

Data analysis technique

This study uses a simultaneous equation model analysis technique. A simultaneous equation model is an equation model consisting of more than one dependent variable and more than one related equation [10].

Simultaneous equation model identification

The simultaneous structural equations formed are:

Equation I :          POVt = ao + a1VCCt + a2VTRFt + a3VUEt + a4PEt + e1 (1)

Equation II :         PEt = b0 + b1VUEt + b2VATMt + b3POVt + e2 (2)

Where: POV: percentage of poor people (percent), PE: economic growth (percent), VCC: credit card transaction volume (transaction unit), VTRF: fund transfer volume (thousand), VUE: electronic money transaction volume transaction unit), VATM: ATM/debit card transaction volume (transaction unit), a and b are coefficients, and e is error term. The results of the identification of each equation are (Table 2).

Table 2: Simultaneous Equation Identification

The results of the identification of the first equation are exact identified, while the results of the identification of the second equation are over-identified. Then the TSLS method can then be used to estimate the study's results. According to Gujarati, the TSLS method is specifically made for models that are too identified but are still used for identifying correct equations [10].

Simultaneous Equation Model Results

The results of the simultaneous equation for equation one is (Table 1). Based on the output results of the first equation in Table 13 above, it can be seen that the volume of electronic money affects economic growth negatively and insignificantly. In contrast, the volume of ATM transactions significantly positively affects economic growth. Furthermore, poverty affects economic growth significantly negatively. The Jarque-Bera probability value on the normality test results is 0.9956, which is greater than 0.05. Then the data can be said to be normally distributed, and the normality assumption is met (Figure 3).

Table 3: Results of Simultaneous Equation I

Table 4: Autocorrelation test results I

Figure 3: Normality test results I

Source: Output Eviews 10.0

The autocorrelation test using the serial correlation LM test resulted in an obs*R-squared value of 21.129 while the probability value of chi-square (2) was 0.0000. It indicates that the model has an autocorrelation problem. Therefore, it will be overcome by the method of differentiation. The results are as (Table 5). 

Table 5: Autocorrelation test results for differentiation method I

After using the differentiation method, the result of obs*R-squared is 0.5346, while the probability value of chi-square (2) is 0.7654. It means that the non-autocorrelation assumption has been met. The results of the simultaneous equation for equation two are (Table 6). 

Table 6: Results of Simultaneous Equation II

For the second equation, it can be seen that the volume of credit card transactions has a significant negative effect on poverty and the volume of fund transfers, but the effect is not significant. The volume of electronic money and economic growth affect poverty in a positive and insignificant way (Figure 4).

Figure 4: Normality test results – II

Source: Output Eviews 10.0

The Jarque-Bera probability value on the normality test results is 0.9956, which is greater than 0.05. Then the data can be said to be normally distributed, and the normality assumption is met (Table 7). 

Table 7: Results of autocorrelation test II

The autocorrelation test using the serial correlation LM test resulted in an obs*R-squared value of 11.882 while the probability value of chi-square (2) was 0.0026. It indicates that the model has an autocorrelation problem. Therefore, it will be overcome by the method of differentiation. The results are (Table 8). 

Table 8: Autocorrelation test results for the differentiation method II

After using the differentiation method, the results of obs*R-squared are 1.2458, while the probability value of chi-square (2) is 0.5364. It means that the non-autocorrelation assumption has been met.

Based on the results of the first equation, the digital payment system significantly impacted poverty alleviation in Indonesia during the COVID-19 era through the variable volume of credit card transactions. During the pandemic in Indonesia, the number of credit card transaction volumes experienced a decline. However, throughout 2021, credit card transactions have increased again. The policy of reducing the maximum limit of credit card interest rates by Bank Indonesia has caused credit card performance to be maintained, and its use increased during the pandemic. These results support the findings of [11] that credit cards have a statistically significant negative effect on poverty in India. The variable volume of funds transfer also harms the poverty level, but the relationship is insignificant. Furthermore, the variable volume of electronic money transactions and economic growth have an insignificant effect on poverty.

Then, in the second equation, it is found that the volume of ATM card transactions significantly positively affects Indonesia's economic growth in the COVID-19 era. The findings are consistent with the results [12-17]. ATM/debit card transactions are in great demand by the public during the pandemic, partly because of the government's recommendation to reduce cash transactions to avoid direct contact with other people and prevent the spread of the COVID-19 virus. Furthermore, the poverty rate affects economic growth significantly negatively, meaning that when the poverty rate increases, economic growth will decrease. Of course, this result follows the theory because if a country's poverty level is high enough, it will reduce people's purchasing power. As a result, domestic producers cannot sell many goods and services. Indeed, the percentage of poor people increased when the pandemic hit Indonesia. With the work from home policy, shopping centers were closed, which reduced staff during the pandemic, causing the unemployment rate to increase. Furthermore, the electronic money transaction variable has an insignificant negative effect on economic growth. This finding contradicts the results of [18-20].

The conclusion from the results of this study is that digital payment systems can have a good impact on poverty alleviation and economic growth in Indonesia during the COVID-19 era by using the simultaneity model. However, unfortunately, not all parts of the digital payment system that we studied had a significant impact. The policy implications that we can recommend for the government are further to optimize the use of the current digital payment system and expand access and facilities such as extensive internet for using digital payments. Moreover, the Indonesian people are mostly smartphone users. It is excellent potential so that all levels of society can make payments efficiently through their respective smartphones.

1. ceicdata.com. “Real GDP growth: Indonesia.” CEIC Data, 2021, https://www.ceicdata.com/id/indicator/indonesia/real-gdp-growth.

2. bi.go.id. “Uang Elektronik—Jumlah.” Bank Indonesia, 2021, https://www.bi.go.id/id/statistik/ekonomi-keuangan/ssp/uang-elektronik-jumlah.aspx.

3. Faye, M. and Paul Niehaus. “Ending poverty with electronic payments.” Brookings, 2016, https://www.brookings.edu/wp-content/uploads/2016/07/FayeNiehausEndingPovertywithElectronicPayments.pdf.

4. Pusparisa, Yosepha. “Daftar negara pengguna smartphone terbanyak.” Databoks, edited by Dwi Hadya Jayani, 2021, https://databoks.katadata.co.id/datapublish/2021/07/01/daftar-negara-pengguna-smartphone-terbanyak-indonesia-urutan-berapa.

5. Hanegraaf, R. et al. “Life cycle assessment of cash payments in the Netherlands.” International Journal of Life Cycle Assessment, vol. 25, no. 1, 2020, pp. 120–40.

6. Bill and Melinda Gates Foundation. Fighting Poverty, Profitably: Transforming the Economics of Payments to Build Sustainable, Inclusive Financial Systems. 2013.

7. Donnelly, Matt. “Payments in the digital market: Evaluating the contribution of payment services directive II.” Computer Law and Security Review, vol. 32, no. 6, 2016, pp. 827–39.

8. Papadopoulos, George. “Blockchain and digital payments: An institutionalist analysis of cryptocurrencies.” Handbook of Digital Currency, 2015.

9. Staykova, K.S. and J. Damsgaard. “The race to dominate the mobile payments platform: Entry and expansion strategies.” Electronic Commerce Research and Applications, vol. 14, no. 5, 2015, pp. 319–30.

10. Gujarati, Damodar N. and Dawn C. Porter. Dasar-Dasar Ekonometrika. Jakarta: Salemba Empat, 2012.

11. bps.go.id. “Kemiskinan dan Ketimpangan.” Badan Pusat Statistik, 2021, https://www.bps.go.id/subject/23/kemiskinan-dan-ketimpangan.html.

12. Siddik, M.N. et al. “Does financial permeation promote economic growth? some econometric evidence from Asian countries.” SAGE Open, July–Sept. 2019, pp. 1–13.

13. Kim, D.W. et al. “Financial inclusion and economic growth in oic countries.” Research in International Business and Finance, 2018, pp. 1–14.

14. Van, L.T.H. et al. “Financial inclusion and economic growth: an international evidence.” MPRA, 2019, pp. 1–35.

15. Iqbal, B. A. and Samiya Sami. “Role of banks in financial inclusion in India.” Contaduría y Administración, vol. 62, 2017, pp. 644–56.

16. Singh, Tarlok. “Does domestic saving cause economic growth? time-series evidence.” Journal of Policy Modeling, vol. 32, 2010, pp. 231–53.

17. Masih, R. and S. Peters. “A re-visitation of the savings–growth nexus in Mexico.” Economics Letters, vol. 107, 2010, pp. 318–20.

18. Mwinzi, David M. The Effect of Financial Innovation on Economic Growth in Kenya. University of Nairobi, 2014.

19. Aziz, Noor, and Athoillah. “Fintech contribution to indonesia’s economic growth.” MPRA, 2019, pp. 1–8.

20. Migap, J.P. et al. “Financial inclusion for inclusive growth: The Nigerian perspective.” International Journal of Information Technology and Business Management, vol. 37, no. 1, 2015, pp. 1–8.