Preview

Finance: Theory and Practice

Advanced search

Can an Electronic Money Transaction Raise the Inflation Rate? (Indonesian Pre-Pandemic)

https://doi.org/10.26794/2587-5671-2023-27-5-205-218

Abstract

Along with the rapid growth of technology, payment instruments are also changing. Electronic money is slowly but surely replacing the role of paper money and coins. The emergence of electronic money can provide convenience for consumers, it can lead to an increase in the demand for goods and services that ultimately leads to demand-pull inflation.

The purpose of this study is to determine the impact of electronic money transactions (both in natural and in value terms) on inflation growth. By using the Chow Breakpoint Test, Difference-in-Differences and Propensity Score Matching shows that the inflation trend has tended to decline since the Bank of Indonesia launched its national non-cash campaign.

By using the ordinary least squares (OLS) method was revealed that an increase in the volume of electronic money transactions in the long-term may affect a decrease in inflation, but not in the short-term. The rate of interest of the Bank of Indonesia, the growth of lending and GDP led to the decline in inflation.

It was concluded that the Bank of Indonesia could expand the use of electronic money to manipulate inflation levels in the long-term. The policy that can be implemented by Bank Indonesia is to distribute electronic money infrastructure services more evenly and increase the socialization of the use of electronic money, especially in remote areas.

About the Authors

F. Fadli
Brawijaya University
Indonesia

Faishal Fadli —  PhD in Econ., Head of International Undergraduate Program Economics Departement; Lecturer, School of Economics and Business

Malang


Competing Interests:

The authors have no conflicts of interest to declare.



V. Devia
Brawijaya University
Indonesia

Vietha Devia — PhD in Finance, Head of Quality Assurance Unit Economics Departement; Lecturer, School of Economics and Business

Malang


Competing Interests:

The authors have no conflicts of interest to declare.



References

1. Kochergin D. A., Yangirova A. I. Central Bank digital currencies: Key characteristics and directions of influence on monetary and credit and payment systems. Finance: Theory and Practice. 2019;23(4):80–98. DOI: 10.26794/2587–5671–2019–23–4–80–98

2. Gunawan H., Sinaga B. L., Purnomo W. P.S. Assessment of the readiness of micro, small and medium enterprises in using e-money using the Unified Theory of Acceptance and Use of Technology (UTAUT) method. Procedia Computer Science. 2019;161:316–323. DOI: 10.1016/j.procs.2019.11.129

3. Kartika V. T., Nugroho A. A.B. Analysis on electronic money transactions on velocity of money in ASEAN-5 countries. Journal of Business and Management. 2015;4(9):44–56.

4. Luo S., Zhou G., Zhou J. The impact of electronic money on monetary policy: Based on DSGE model simulations. Mathematics. 2021;9(20):2614. DOI: 10.3390/math9202614

5. Mele A., Stefanski R. Velocity in the long run: Money and structural transformation. Review of Economic Dynamics. 2019;31:393–410. DOI: 10.1016/j.red.2018.09.004

6. Calson-Öhman F. The effect of increased e-commerce on inflation. Master degree thesis. Stockholm: Institution for Social Sciences; 2018. 31 p. URL: https://sh.diva-portal.org/smash/get/diva2:1214628/FULLTEXT01.pdf

7. Ren X., Shao Q., Zhong R. Nexus between green finance, non-fossil energy use, and carbon intensity: Empirical evidence from China based on a vector error correction model. Journal of Cleaner Production. 2020;277:122844. DOI: 10.1016/j.jclepro.2020.122844

8. Wang L., Hou H., Weng J. Ordinary least squares modelling of urban heat island intensity based on landscape composition and configuration: A comparative study among three megacities along the Yangtze River. Sustainable Cities and Society. 2020;62:102381. DOI: 10.1016/j.scs.2020.102381

9. Simamora R. H. Socialization of information technology utilization and knowledge of information system effectiveness at Hospital Nurses in Medan, North Sumatra. International Journal of Advanced Computer Science and Applications. 2019;10(9):117–121. DOI: 10.14569/IJACSA.2019.0100916

10. Ayinde K., Lukman A. F., Rauf R. I., Alabi O. O., Okon C. E., Ayinde O. E. Modeling Nigerian COVID-19 cases: A comparative analysis of models and estimators. Chaos, Solitons & Fractals. 2020;138:109911. DOI: 10.1016/j.chaos.2020.109911

11. Petrevska B. Predicting tourism demand by A.R.I.M.A. models. Ekonomska Istraživanja = Economic Research. 2017;30(1):939–950. DOI: 10.1080/1331677X.2017.1314822

12. Fadli F., Maski G., Sumantri V. D.S. Earmarking tax: Can it increase public trust in the Indonesian government? Institutions and Economies. 2020;12(2):1–40. URL: https://ijie.um.edu.my/index.php/ijie/article/view/17016/11582

13. Yan Y., Hongbing O. Effects of house-sale restrictions in China: A difference-in-difference approach. Applied Economic Letters. 2018;25(15):1051–1057. DOI: 10.1080/13504851.2017.1394968

14. Bhattarai S., Eggertsson G. B., Schoenle R. Is increased price flexibility stabilizing? Redux. Journal of Monetary Economics. 2018;100:66–82. DOI: 10.1016/j.jmoneco.2018.07.006

15. Rhee H. J., Song J. Wage rigidities and unemployment fluctuations in a small open economy. Economic Modelling. 2020;88:244–262. DOI: 10.1016/j.econmod.2019.09.033

16. Shu Y., Cai J. “Alcohol bans”: Can they reveal the effect of Xi Jinping’s anti-corruption campaign? European Journal of Political Economy. 2017;50:37–51. DOI: 10.1016/j.ejpoleco.2017.09.004

17. Brown M., Hentschel N., Mettler H., Stix H. The convenience of electronic payments and consumer cash demand. Journal of Monetary Economics. 2022;130:86–102. DOI: 10.1016/j.jmoneco.2022.06.001

18. Eo Y., Lie D. Average inflation targeting and interest-rate smoothing. Economics Letters. 2020;189:109005. DOI: 10.1016/j.econlet.2020.109005

19. Chen H. Nominal GDP targeting, real economic activity and inflation stabilization in a new Keynesian framework. The Quarterly Review of Economics and Finance. 2020;78:53–63. DOI: 10.1016/j.qref.2020.01.002

20. Alvarez F., Lippi F. Cash burns: An inventory model with a cash-credit choice. Journal of Monetary Economics. 2017;90:99–112. DOI: 10.1016/j.jmoneco.2017.07.001

21. Alvarez F., Lippi F., Robatto R. Cost of inflation in inventory theoretical models. Review of Economic Dynamics. 2019;32:206–226. DOI: 10.1016/j.red.2018.11.001

22. Švigir M., Miloš J. Relationship between inflation and economic growth; comparative experience of Italy and Austria. FIP: Financije i pravo. 2017;5(2):91–101. URL: https://hrcak.srce.hr/file/285035

23. Alberola E., Urrutia C. Does informality facilitate inflation stability? Journal of Development Economics. 2020;146:102505. DOI: 10.1016/j.jdeveco.2020.102505

24. Kumar A., Dash P. Changing transmission of monetary policy on disaggregate inflation in India. Economic Modelling. 2020;92:109–125. DOI: 10.1016/j.econmod.2020.07.016

25. Balima H. W., Kilama E. G., Tapsoba R. Inflation targeting: Genuine effects or publication selection bias? European Economic Review. 2020;128:103520. DOI: 10.1016/j.euroecorev.2020.103520

26. Davoli M., Rodríguez-Planas N. Culture and adult financial literacy: Evidence from the United States. Economics of Education Review. 2020;78:102013. DOI: 10.1016/j.econedurev.2020.102013


Review

For citations:


Fadli F., Devia V. Can an Electronic Money Transaction Raise the Inflation Rate? (Indonesian Pre-Pandemic). Finance: Theory and Practice. 2023;27(5):205-218. https://doi.org/10.26794/2587-5671-2023-27-5-205-218

Views: 599


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2587-5671 (Print)
ISSN 2587-7089 (Online)