arXiv subjects

Wishnu Badrawani

Publications and source records attributed to Wishnu Badrawani.

4 recordsLinked to original sources

arXiv2026

Do we still need coins? The role of payment system innovation, the pandemic, and the coin's purchasing power on coin demand in Indonesia

This study investigates the relationship between coin demand, payment innovation, COVID-19, and a coin's purchasing power, particularly in emerging countries like Indonesia. The rapid advancement of payment platforms, combined with high adoption during the pandemic, has positioned non-cash payments as a complement or substitute for coin money for transactions. However, there is notably limited coin-money-related research in the economic literature. Employing the autoregressive distributed lag (ARDL) bounds test methodology's cointegration approach using monthly data from 2011 to 2022, our findings reveal a long-term relationship between coin demand and its determinants: payment innovations, the pandemic, coin depreciation, and income. Despite the swift advancement of payment innovations and their usage, coins remain vital to the economy and are unlikely to become obsolete soon. Our study offers essential policy recommendations and enriches the field of knowledge on coin money demand. Policymakers must understand the driving factors of coin demand in both economic and non-economic contexts to improve coin production-related issues and coin circulation policies. Reviewing the Rupiah denomination structure is crucial in addressing the problem of ineffective coin circulation in the economy.

econ.GN↗

arXiv2025

The Role of Digital Payments in Driving Regional Economic Growth: A Panel Data Analysis with Structural Break

Using a panel payment system dataset of thirty-three provinces in Indonesia, we examine the impact of digital payment on the regional economy, considering structural breaks induced by unprecedented events and policies. Digital payments were determined to significantly affect regional income and consumption before and after the identified breakpoint, with the impact greater following the break. Employing a novel method for structural break analysis within interactive effects panel data, we demonstrate that the break in retail payment models is due to COVID-19, and the break in the wholesale payment model is associated with the central bank's payment system policy.

econ.GN↗

arXiv2025

Digital payment policy impact analysis on the intention to use QRIS (quick response code Indonesian standard) during COVID-19 pandemic

This study aims to evaluate the adoption of Bank Indonesia's QRIS (Quick Response code Indonesian Standard) payment system policy. The evaluation is hindered by the contemporaneous emergence of the COVID-19 pandemic, which acts as a confounding factor in adopting the new payment instrument. To disentangle the impact of central bank policy from the pandemic, a novel variation of the model of Unified Theory of Acceptance and Use of Technology (UTAUT) is proposed and is estimated using purposive sampling from an online survey with 572 respondents during the pandemic. The result of the study successfully disentangles the policy effect from the pandemic effect, and also separate the risk of pandemic with common risks (PR) and other technology adoption determinants. The results indicate that perceived central bank policy and pandemic risk are the most influential variables affecting the intention to use QRIS. The findings suggest that this measurement approach can be appropriately used as a complementary tool to examine the effectiveness of the central bank's policy in influencing people's behavior.

econ.GN↗

arXiv2025

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia

This paper evaluates the performance of prominent machine learning (ML) algorithms in predicting Indonesia's inflation using the payment system, capital market, and macroeconomic data. We compare the forecasting performance of each ML model, namely shrinkage regression, ensemble learning, and super vector regression, to that of the univariate time series ARIMA and SARIMA models. We examine various out-of-bag sample periods in each ML model to determine the appropriate data-splitting ratios for the regression case study. This study indicates that all ML models produced lower RMSEs and reduced average forecast errors by 45.16 percent relative to the ARIMA benchmark, with the Extreme Gradient Boosting model outperforming other ML models and the benchmark. Using the Shapley value, we discovered that numerous payment system variables significantly predict inflation. We explore the ML forecast using local Shapley decomposition and show the relationship between the explanatory variables and inflation for interpretation. The interpretation of the ML forecast highlights some significant findings and offers insightful recommendations, enhancing previous economic research that uses a more established econometric method. Our findings advocate ML models as supplementary tools for the central bank to predict inflation and support monetary policy.

econ.GN↗