The researchers examined how three monetary and payment-related factors—non-cash payments, the BI Rate, and the narrow money supply known as M1—were associated with Indonesia’s economic growth. Together, the three variables explained 61.32% of the variation in economic growth in the study’s statistical model.
Indonesia’s Economy and the Rise of Digital Payments
Indonesia has experienced a major shift from cash-based transactions toward non-cash payments. Initiatives such as the National Non-Cash Movement, the National Payment Gateway, the Indonesia Payment System Blueprint 2025, and QRIS have supported the expansion of digital payment systems.
The shift is important because digital payments can make transactions faster and potentially reduce transaction costs. However, Olivia and Sishadiyanti found that the rapid growth of non-cash payment transactions did not move in the same direction as economic growth throughout the period they examined.
Indonesia’s economic growth remained relatively stable at around 5% annually during much of 2014–2025, but the economy contracted sharply during the COVID-19 pandemic in 2020. Economic growth subsequently recovered and reached 5.11% in 2025.
At the same time, the average value of non-cash transactions increased from approximately Rp1,175.86 trillion in 2014 to Rp2,261.39 trillion in 2025. M1 also increased substantially, from approximately Rp905.43 trillion to Rp2,984.46 trillion over the same period.
How the Researchers Conducted the Study
Olivia and Sishadiyanti used a quantitative approach based on 48 quarterly observations from the first quarter of 2014 through the fourth quarter of 2025.
The data came from official publications of Bank Indonesia and Statistics Indonesia (BPS). Economic growth was measured using real GDP growth, while the explanatory variables consisted of non-cash payment transactions, the BI Rate, and M1 money supply.
The researchers then used multiple linear regression to examine the relationship between the three variables and economic growth. They also conducted statistical tests to check whether the regression model met the necessary conditions for analysis.
Three Major Findings
The study produced three main findings:
1. Non-cash payments had a significant negative relationship with economic growth.
A 1% increase in the value of non-cash payment transactions was associated with approximately a 0.0155% decrease in economic growth, with other variables held constant. The relationship was statistically significant.
The researchers noted that the result differs from the expectation that digital payment technology should increase productivity and economic activity.
Olivia and Sishadiyanti suggest that the increase in non-cash transactions may have reflected a change in payment behavior rather than a proportional increase in the production of goods and services. In other words, people and businesses may simply have shifted from cash to digital payment methods without that shift necessarily creating additional economic output.
2. The BI Rate had a significant negative relationship with economic growth.
A 1-percentage-point increase in the BI Rate was associated with an approximately 0.1421-percentage-point decline in economic growth, holding other variables constant.
The researchers relate this finding to the cost of borrowing. Higher benchmark interest rates can increase financing costs, potentially discouraging business investment and reducing credit-financed household consumption. These changes can weaken overall economic demand and slow output growth.
3. M1 money supply had a significant positive relationship with economic growth.
A 1% increase in M1 was associated with an approximately 0.0059% increase in economic growth, with other variables held constant.
According to the researchers, greater liquidity can provide households and businesses with more capacity for consumption and investment. Increased demand can then encourage producers to expand output when sufficient labor and production resources are available.
What the Findings Mean for Indonesia
The findings suggest that the expansion of digital payments should not be evaluated only by the value or volume of transactions. The economic impact also depends on whether digital payment activity is connected to productive activity in the real economy.
For businesses, digital payment growth may provide greater transaction convenience, but increased digital transactions alone do not necessarily translate into higher production or broader economic growth.
For monetary authorities, the findings highlight the relationship between interest rates, liquidity, investment, consumption, and economic activity. The researchers recommend careful management of the BI Rate and M1 liquidity, particularly during periods of economic uncertainty.
The study also recommends strengthening the transmission of liquidity into productive sectors, including micro, small, and medium enterprises. Greater access to financing could help ensure that available liquidity supports consumption, investment, and production rather than creating macroeconomic imbalances.
A Key Insight From the Researchers
The findings from Alifa Zerlinda Olivia and Sishadiyanti of Universitas Pembangunan Nasional “Veteran” Jawa Timur indicate that Indonesia’s growing digital payment activity should be understood separately from real-sector economic expansion. Their analysis suggests that the migration from cash to non-cash payments can represent a change in the method of payment without necessarily producing a proportional increase in goods and services output.
The researchers emphasize that the connection between financial technology and economic growth becomes stronger when payment-system development is accompanied by productive investment and increased real-sector activity.
Study Limitations and Future Research
The study is limited to three main explanatory variables: non-cash payments, the BI Rate, and M1 money supply. It also uses a static multiple linear regression approach.
Olivia and Sishadiyanti recommend that future research incorporate indicators such as fintech adoption, financial inclusion, exchange rates, and inflation. They also suggest using dynamic economic models and more detailed monthly data to better capture short- and long-term responses in the economy.
Author Profile
Alifa Zerlinda Olivia — Corresponding author and researcher, Universitas Pembangunan Nasional “Veteran” Jawa Timur.
Sishadiyanti — Co-author and researcher, Universitas Pembangunan Nasional “Veteran” Jawa Timur.
The journal article identifies both authors with Universitas Pembangunan Nasional “Veteran” Jawa Timur. The source does not provide their academic degrees or detailed individual fields of expertise, so those details are not added here.
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