Examining the Relationship Between Tax Revenue and Economic Growth in Indonesia Through the Endogenous Growth Model

The aim of this study is to analyze the causal relationship between tax revenues and economic growth in Indonesia using an endogenous growth economic model. The causality analysis employed a multivariate setup using a vector autoregression approach, with the Toda–Yamamoto method serving as the causality test. Using time series data from 1983 to 2021, the research findings indicate that the control variables — capital, labor, foreign direct investment, government spending, inflation, and exchange rates — reflect innovation mechanisms and technological progress or total factor productivity in the endogenous growth model, which captures the relationship between tax revenues and economic growth in Indonesia. The results of the causality test using the Toda–Yamamoto method show that tax revenues and economic growth influence each other; tax revenues help boost economic growth, and at the same time, higher economic growth leads to more tax revenues. The authors concluded that, in addition to tax revenue causing or encouraging economic growth through financing economic activities, increased economic growth and activity will also raise the amount of tax revenue, both from the tax base and from nominal tax revenue determined by economic growth.

Authors

Institutions

Publication Details

Journal
Review of Business and Economics Studies
Published
2025-07-26
DOI
https://doi.org/10.26794/2308-944x-2025-13-2-129-140
Citations
1
Primary Topic
Fiscal Policy and Economic Growth
Type
article
Field-Weighted Citation Impact
3.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Examining the Relationship Between Tax Revenue and Economic Growth in Indonesia Through the Endogenous Growth Model

Harta Haryadi, Yossinomita Yossinomita, Saidin Nainggolan, Zulfanetti Zulfanetti
1 citations
Review of Business and Economics Studies
Fiscal Policy and Economic Growth
3.13
article

Examining the Relationship Between Tax Revenue and Economic Growth in Indonesia Through the Endogenous Growth Model

Harta Haryadi, Yossinomita Yossinomita, Saidin Nainggolan, Zulfanetti Zulfanetti
article en
1 citations

Abstract

The aim of this study is to analyze the causal relationship between tax revenues and economic growth in Indonesia using an endogenous growth economic model. The causality analysis employed a multivariate setup using a vector autoregression approach, with the Toda–Yamamoto method serving as the causality test. Using time series data from 1983 to 2021, the research findings indicate that the control variables — capital, labor, foreign direct investment, government spending, inflation, and exchange rates — reflect innovation mechanisms and technological progress or total factor productivity in the endogenous growth model, which captures the relationship between tax revenues and economic growth in Indonesia. The results of the causality test using the Toda–Yamamoto method show that tax revenues and economic growth influence each other; tax revenues help boost economic growth, and at the same time, higher economic growth leads to more tax revenues. The authors concluded that, in addition to tax revenue causing or encouraging economic growth through financing economic activities, increased economic growth and activity will also raise the amount of tax revenue, both from the tax base and from nominal tax revenue determined by economic growth.

Review of Business and Economics StudiesVol. 13(2)
Universitas Jambi (ID), Universitas Nusa Bangsa (ID)
Openalex Percentile: Top 7%
Fiscal Policy and Economic Growth
3.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.