Digital Financial Inclusion, Transaction Costs, and MSME Productivity: Micro Evidence from Indonesia
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
Abstract
This study examines the impact of digital financial inclusion on
transaction costs and productivity of MSMEs in Indonesia using
micro-panel data (≈1,400 MSMEs; multiple waves) and a quasiexperimental
approach. The identification strategy combines
difference-in-differences (DiD) with fixed effects, event studies to
examine parallel trends, and instrumental variables (IV) based
on supply-side variations (agent density and signal quality) to
address endogeneity of adoption. Key variables include adoption
and depth of use of digital financial services (payments,
bookkeeping, sales channels), transaction cost indices
(monetary, time, and coordination costs), and productivity
indicators (output per worker, operating margin). Results show
that digital adoption increases output per worker by ≈ Rp2.1
million/year (p<0.001) and operating margin by ≈ +2.3
percentage points; IV estimates confirm causality with a slightly
larger impact (≈ Rp2.6 million/worker/year). Incorporating a
transaction cost index into the model reduces the adoption
coefficient, suggesting mediation through reduced transaction
friction—specifically savings in reconciliation time and monetary
costs (MDR/cash-out). Nonlinear analysis identifies a benefit
threshold of around –0.5 SD on the cost index: cost reductions
below the threshold trigger a productivity surge, while above the
threshold the effect weakens. The impact is stronger in micro-
SMEs, the food and beverage sector, players with high digital
depth, and urban areas. The findings are consistent across
robustness tests (PSW/entropy balancing, pre-adoption placebo,
and alternative outcome/index definitions). Practically, inclusion
policies need to shift from simply encouraging adoption to
reducing the most “binding” cost components per segment and
encouraging digitalization depth (payments–bookkeeping–sales
channels), accompanied by infrastructure expansion and training
in disadvantaged areas.
##plugins.themes.bootstrap3.article.details##
(English edition).
Bank Indonesia. (nd). Quick Response Code Indonesian Standard (QRIS)—features &
policies (including QRIS TUNTAS).
Financial Services Authority (OJK). (2024). Indonesian Fintech Lending Statistics
(LPBBTI, December 2024 edition).
Financial Services Authority (OJK). (2025). Directory of Licensed Fintech Lending
Providers as of December 31, 2024 (97 companies).
GSMA. (2024). State of the Industry Report on Mobile Money 2024. London: GSMA.
World Bank. (2025). Indonesia Economic Prospects (IEP)—a biannual report series.
Asian Development Bank (ADB). (2021). Asia Small and Medium-Sized Enterprise
Monitor 2021, Volume I. Manila: ADB.
ADB. (2024). Wihardja, MM Business Digitalization of MSMEs during COVID-19: The
Digital Financial Inclusion, Transaction Costs, and MSME Productivity: Micro Evidence from
Indonesia – Iwan Saragih
Page 83 of 11
Case of Indonesia (ADB Economics Working Paper No. 725).
Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money.
Science, 354(6317), 1288–1292. https://doi.org/10.1126/science.aah5309
Jack, W., & Suri, T. (2014). Risk sharing and transaction costs: Evidence from Kenya's
mobile money revolution. American Economic Review, 104(1), 183–223
Aker, J.C., Boumnijel, R., McClelland, A., & Tierney, N. (2016). Payment mechanisms
and antipoverty programs: Evidence from a mobile money cash transfer
experiment in Niger. Economic Development and Cultural Change, 65(1), 1–37.
https://doi.org/10.1086/687578
Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies
with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175–199.
https://doi.org/10.1016/j.jeconom.2020.09.006
Callaway, B., & Sant'Anna, P.H.C. (2021). Difference-in-Differences with multiple time
periods. Journal of Econometrics, 225(2), 200–230.
https://doi.org/10.1016/j.jeconom.2020.12.001
Wooldridge, J. M. (2023). Simple approaches to nonlinear difference-in-differences with
panel data. The Econometrics Journal, 26(3), C31–C66.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical
and powerful approach to multiple testing. Journal of the Royal Statistical Society:
Series B, 57(1), 289–300.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing
discriminant validity in variance-based SEM. Journal of the Academy of Marketing
Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
IDNFinancials. (2025, Jan 15). QRIS transaction volume saw a 175.2% surge in 2024—
statement by the Governor of BI regarding digital payments in 2024.
Ministry of Communication & Informatics (Kominfo). (2024). QRIS transactions surge
226.54%: The digital payment revolution in Indonesia (BI data summary: 50.5
million users).
OJK. (nd). Fintech Statistics (summary page & periodic releases).
T20 Brazil. (2024). Digitalizing MSMEs in Indonesia—policy brief (MSMEs contribute 61%
of GDP & 97% of the workforce)