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All Journal Jurnal Tata Kelola dan Akuntabilitas Keuangan Negara
Hendra Hendra
ANU College of Business and Economics, Australian National University, Canberra, Australia
Economics, Econometrics & Finance Social Sciences

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Assessing Indonesia’s net tax revenue resilience after COVID-19: Controlled interrupted time series analysis Hendra Hendra
Jurnal Tata Kelola dan Akuntabilitas Keuangan Negara Vol. 12 No. 2 (2026): Issue in Progress
Publisher : Badan Pemeriksa Keuangan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28986/jtaken.v12i2.2713

Abstract

Revenue resilience is central to public financial management, yet post-crisis revenue growth may reflect cyclical rebound rather than genuine fiscal adaptation. This study addresses that gap by examining whether real net tax revenue administered by Indonesia’s Directorate General of Taxes exhibits resistance, recovery, and renewal after COVID-19 shocks and whether post-2022 strengthening is clearer relative to fiscal comparators. Using annual interrupted time series data for 2015–2025 (constant 2015 IDR), the study estimates segmented and controlled interrupted time series models. Non-tax revenue served as the primary comparator, while customs and excise revenue served as a robustness comparator. Intervention timing follows the policy calendar, incorporating the 2020–2021 tax incentives regime, VAT implementation in 2022, and the Voluntary Disclosure Program. The results indicate an immediate 2020 shock of approximately IDR 222.9 trillion below the counterfactual trajectory, followed by a stronger post-2020 trend of about IDR 75.6 trillion per year. Post-2022 strengthening is more evident relative to non-tax revenue than to customs and excise revenue. The study concludes that annual fiscal resilience can be assessed through deviations from a pre-shock counterfactual and interpreted with caution against fiscal comparators. The article contributes a practical low-frequency framework for monitoring revenue resilience using routinely available public data.
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