economies

Article
Tax Buoyancy in Indonesia: An Evaluation of Tax Structure and
Policy Reforms
Suhut Tumpal Sinaga 1,2, * , Mahjus Ekananda 2 , Beta Yulianita Gitaharie 2 and Milla Setyowati 3

1 State Finance Polytechnic of Stan, Kota Tangerang Selatan 15222, Indonesia

2 Department of Economics, Faculty of Economics and Business, University of Indonesia,
Depok 16424, Indonesia; mahyusekananda@gmail.com (M.E.); beta.yulianita@ui.ac.id (B.Y.G.)
3 Faculty of Administrative Sciences, University of Indonesia, Depok 16424, Indonesia;
milla_setyowati@yahoo.com
* Correspondence: stsinaga@pknstan.ac.id

Abstract: This study investigates tax buoyancies in Indonesia. It analyzes the cointegration relation-
ship between the regional gross domestic product (RGDP), along with several control variables, and
tax revenues. Focusing on personal income tax (PIT), corporate income tax (CIT), and value-added
tax (VAT), it employs a dynamic analysis from 2015 to 2021. This research utilizes the Wald test to
evaluate long-term buoyancies and the PMG-ARDL model to assess tax dynamics and cointegra-
tion coefficients. The results revealed tax revenues’ consistent tendency toward equilibrium in the
long term, with fluctuations across Indonesian provinces. PIT displayed the highest buoyancy and
adjustment speed, followed by VAT and CIT. This analysis highlights tax types’ diverse responses
to underlying factors, offering crucial insights into fiscal dynamics. The research illuminates the
intricate relationship between economic indicators and tax categories, providing valuable lessons
for future policies, especially concerning structural changes like tax amnesty programs and tax
rate modifications.

Keywords: tax buoyancy; tax structure; tax policy; panel ARDL; Indonesia

Citation: Sinaga, Suhut Tumpal,

Mahjus Ekananda, Beta Yulianita
1. Introduction
Gitaharie, and Milla Setyowati. 2023.
Tax Buoyancy in Indonesia: An The complex relationship between tax revenues and economic fluctuations, a subject
Evaluation of Tax Structure and extensively explored by scholars, is influenced by diverse factors (Ahmed and Muhammad
Policy Reforms. Economies 11: 294. 2010; Baiardi et al. 2019; Dudine and Jalles 2018; Filipova and Tanchev 2021; Jalles 2017).
https://doi.org/10.3390/ One crucial dimension shaping this complex interplay is the concept of tax buoyancy,
economies11120294 denoting the degree to which tax revenues respond to changes in economic activity. This
Academic Editor: Sanzidur Rahman
phenomenon, coupled with various factors like economic structures, formal vs. informal
economies, and demographic elements, intricately shapes tax revenue dynamics. Moreover,
Received: 3 November 2023 the construction and administration of the tax system, encompassing elements like the
Revised: 28 November 2023 breadth of the tax base, exemptions, tax rates’ progressivity, and mechanisms for taxpayer
Accepted: 29 November 2023 registration, significantly contribute to the intricate web of influences. Against this back-
Published: 5 December 2023
drop, the case of Indonesia stands out, as the nation continually refines its tax policies,
including initiatives like the 2016 tax amnesty policy, where participants could absolve
their tax liabilities by disclosure and payment, and the recent adjustments in response to
Copyright: © 2023 by the authors.
the COVID-19 crisis, alongside permanent alterations such as reduced corporate income tax
Licensee MDPI, Basel, Switzerland. rates and revamped VAT collection mechanisms for e-commerce transactions. Within this
This article is an open access article context, understanding tax buoyancy is vital, offering nuanced insights for policymakers
distributed under the terms and and researchers.
conditions of the Creative Commons The connection between tax revenues and economic growth is intricate and often
Attribution (CC BY) license (https:// defies simple expectations. Government attempts to boost economic growth through
creativecommons.org/licenses/by/ increased spending do not always lead to the expected rise in tax revenues. Delays in tax
4.0/). revenue responsiveness to economic growth further complicate this relationship, prompting

Economies 2023, 11, 294. https://doi.org/10.3390/economies11120294 https://www.mdpi.com/journal/economies

Economies 2023, 11, 294 2 of 18

inquiries into the reasons behind these delays. Additionally, the analysis becomes more
complex when considering different tax types. The timing of tax revenue adjustments
is crucial, especially during economic recoveries. Although economic resurgence can
increase tax revenues, reaching equilibrium is a prolonged process. Furthermore, the rates
of adjustment vary across tax categories, adding further complexity to this phenomenon.
Understanding how taxes respond to economic changes is crucial for various purposes:
analyzing tax collection trends, making revenue projections, national budgeting, and inter-
national comparisons (Arachi et al. 2015; Baiardi et al. 2019; Blanchard et al. 2010; Blanchard
and Perotti 2002). Government decisions on tax policies require a deep grasp of these
dynamics, guiding adjustments in tax rates and bases. In Indonesia, studying tax buoyancy
is particularly significant. It helps policymakers predict the impact of economic growth
on tax revenues, enabling well-informed tax policy decisions. Additionally, examining the
buoyancy of different tax types highlights the tax system’s strengths and weaknesses and
provides insights into how various tax categories adapt to economic fluctuations.
This study employs the autoregressive distributed lag (ARDL) technique, specifically
the pooled mean group (PMG) ARDL model developed by Pesaran et al. (1999), to delve
into the intricate relationship between regional GDP growth and tax revenues in Indonesia
over the long term. The use of this model, which accommodates varying cointegrating
terms across regions, ensures robust and reliable estimations. Through dynamic panel
heterogeneity analysis, this research scrutinizes the enduring effects of regional GDP
growth on tax revenues, shedding light on the rate at which adjustments move toward
long-term equilibrium.
The subsequent sections of this paper are organized as follows: Section 2 provides a
brief literature review, while Section 3 is devoted to an overview of Indonesia’s tax system
and policy alterations between 2015 and 2021. Section 4 outlines the data and methodology
employed in this research. The findings derived from the panel ARDL estimations are
explicated in Section 5, while Section 6 furnishes the paper’s conclusions.

2. Literature Review
The literature encompasses multifaceted and crucial aspects regarding tax buoyancy
in various countries and economic contexts. For instance, Gupta et al. (2021) estimated
tax buoyancy in 44 sub-Saharan African countries during the period 1980 to 2017. Their
findings revealed that the long-term tax buoyancy in most of these countries approached
or slightly exceeded one. However, nations with fragile institutions exhibited lower short-
term tax buoyancy, indicating weaknesses within their institutional systems. These findings
signify a diminished automatic stabilizer effect in the short term and fiscal sustainability
in the long term. Jalles’ (2017) study on 37 sub-Saharan African countries indicated that
only a small fraction of these nations had tax systems functioning as stabilizers during the
period 1990 to 2015. On the other hand, Dudine and Jalles’ (2018) research highlighted the
impact of business cycles, particularly in the context of the global financial crisis. Their
study found that advanced economies tended to have higher tax buoyancy in corporate
income tax during economic contractions, indicating this tax’s stabilizer function in times
of economic downturn.
Specifically, Hill et al.’s (2022) research delved into tax buoyancy amid economic shifts,
notably after the significant impacts of the COVID-19 pandemic. Analyzing developing
Asian economies spanning from 1998 to 2020, the study revealed a tax buoyancy close to
one, indicating a strong correlation between GDP and tax incomes. However, the pandemic
led to a tenth reduction in tax revenue growth, signaling a negative effect. Individual
analyses at the economy level echo these regional patterns, confirming the proximity to a
tax buoyancy coefficient of around one in most instances. An alternate analysis estimated
the excess loss of tax revenues in 2020 due to the pandemic, highlighting that, on average,
these economies experienced a half percentage point decrease in tax revenues equivalent to
their GDP. This finding correlates with observations linking the size of COVID-19 fiscal
measures to declines in tax buoyancy.
Economies 2023, 11, 294 3 of 18

Furthermore, studies have explored various factors influencing tax buoyancy. For in-
stance, Sheikh et al.’s (2018) research in Pakistan identified economic variables affecting tax
buoyancy in the country from 1996 to 2016. Their results showed diverse economic factors
affecting tax buoyancy. Similarly, Ahmed and Muhammad’s (2010) study underscored vari-
ables such as imports, the manufacturing sector, and budget deficits as influential factors
in tax buoyancy in developing countries. Moreover, research has extended to non-oil and
environmental taxation. Cotton’s (2012) study in Trinidad and Tobago exhibited a positive
response of non-oil tax revenue to economic growth, yet it highlighted administrative
challenges impacting tax revenue during specific periods. Additionally, De Pascale et al.’s
(2021) study evaluated the environmental tax responsiveness in Europe to economic cycles,
finding that environmental taxes served as effective economic stabilizers with varying
buoyancy in both the short and long term.
However, in the context of Indonesia, the literature on tax buoyancy remains limited.
Classic studies like Tandjung’s (1987) utilized a simple model by Singer, showing an
elasticity coefficient of around 0.93 and a buoyancy coefficient of 1.03. Nevertheless, this
performance is deemed in need of improvement for Indonesia to enhance its revenue
collection efficiency. These studies provide profound insights into the dynamics of tax
buoyancy in diverse global and local contexts, offering critical perspectives for policymakers
and researchers to enhance the effectiveness of tax systems in supporting economic growth
and sustainable development.

3. Indonesia Tax System

In Indonesia, tax administration falls under the purview of the Directorate General of
Taxes (DGT), operating under the Ministry of Finance. The DGT comprises two primary
components: the head office, responsible for policy formulation, technical regulations,
coaching, and administrative support; and operational offices, which handle technical
operations and support functions. DGT’s operational structure includes 34 regional offices,
4 large taxpayer offices (LTO), 9 special taxpayer offices, 38 medium taxpayer offices
(MTO), 301 small taxpayer offices (STO), 204 tax counseling and consultation service
offices, and 4 technical implementation units. Between 2015 and 2021, the organizational
structure underwent adjustments in response to regional economic developments, resulting
in mergers, removals, additions, and reductions of offices. Taxpayers in Indonesia are
categorized into LTO, MTO, STO, or Special Taxpayer Office registrants. LTOs consist of
select large taxpayers, while Special Taxpayer Office registrants include foreign investment
entities, foreigners, listed companies, and oil and gas firms in Indonesia. MTO registrants
are significant taxpayers within specific regional areas, not registered with LTOs or Special
Taxpayer Offices. STO registrants are taxpayers within STO’s administrative regions, not
registered with LTOs, MTOs, or Special Taxpayer Offices.1

3.1. Personal Income Tax

In Indonesia, the taxation system operates within a family-based framework, where
the combined income of all family members is considered a single economic entity for
taxation purposes, typically managed by the father. A family can consist of a maximum of
three dependent children and the wife. Individuals in Indonesia are subject to income tax
through various methods, depending on income sources and amounts. For instance, income
from business activities below IDR 4.8 billion annually incurs a final tax of 0.5% of the gross
income, eliminating the need for recalculations at year-end. Freelance business income
below this threshold is taxed based on a deemed taxable income at the rate specified
in Article 17 of the Income Tax Law. If their income exceeds IDR 4.8 billion per year,
individuals are subject to a progressive tax rate based on net income. Additionally, payroll
tax is applied to wages and salaries.
Taxable income encompasses all economically valuable earnings, regardless of origin
within or outside Indonesia. However, specific categories, such as aid, grants, and inheri-
tance, remain exempt from taxation. A portion of income remains non-taxable, determined
Economies 2023, 11, 294 4 of 18

by marital status and the number of dependents at the start of the year. Notably, the
non-taxable income threshold was adjusted once from 2015 to 2021, specifically in June
2016, as indicated in Table 1.

Table 1. Pre-specified non-taxable income.

Yearly Non-Taxable Income (IDR)

Taxpayer’s Status
Before June 2016 Starting from June 2016
Single 36,000,000 54,000,000
Married 39,000,000 58,500,000
Married, and spouse earning income 75,000,000 112,500,000
Additional for each dependent (max 3) 3,000,000 4,500,000

Indonesia’s progressive income tax system employs varying rates based on income
levels, ranging from 5% to 30% between 2015 and 2021, as depicted in Table 2. The
calculation involves multiplying taxable income by the applicable rate. Taxpayers may
qualify for deductions, such as taxes already paid, which can offset owed taxes. Tax
payments must be made to the state treasury before filing an annual tax return, which
includes all income sources, assets, investments, and foreign income. The deadline for
filing the tax return is within three months after the end of the tax year.

Table 2. Personal income tax rates.

Taxable Income Brackets (IDR) Tax Rate

0–50 million 5%
more than 50 million–250 million 15%
more than 250 million–500 million 25%
more than 500 million 30%

3.2. Corporate Income Tax

In the realm of taxation, entities or corporations denote groups of individuals or
capital functioning collectively, irrespective of their engagement in commercial activities.
Comparable to individuals, corporations are accountable under income tax regulations.
Taxable income is computed based on net income, derived by deducting business activity
costs from gross income. These costs encompass various expenditures like materials,
labor, interest, rent, royalties, travel, waste treatment, insurance premiums, promotions,
administrative expenses, and more. However, specific costs such as dividend distributions,
personal shareholder interest, and payments exceeding a fair value to related parties cannot
be deducted. In cases where gross income minus costs results in a loss, this loss can be
carried forward to offset income in subsequent years, typically for up to five years, or in
specific industries or regions, up to ten years.
Effective from the 2020 tax year, the corporate income tax rate was uniformly set at
22%, indicating a reduction from the previous rate of 25%. Public companies meeting
specific criteria and trading their shares on the Indonesia Stock Exchange are subject to a
reduced tax rate of 3%. Furthermore, small companies may be eligible for a 50% discount
on the tax rate for sales revenue up to IDR 4.8 billion. A small company is defined as a local
business generating annual earnings of up to IDR 50 billion. A distinctive tax mechanism
imposes a 0.5% tax rate on businesses earning IDR 4.8 billion annually. Corporate taxpayers
can benefit from this facility for three years, while individual taxpayers are eligible for up to
seven years. Similarly to individual taxpayers, corporate entities are mandated to file their
annual tax returns, with the submission deadline set at four months after the conclusion of
the tax year.
Economies 2023, 11, 294 5 of 18

3.3. Value-Added Tax

Value-added tax (VAT) is obligatory for businesses offering taxable goods or services,
necessitating their registration as VAT taxpayers. Entities earning less than IDR 4.8 billion
annually have the option to abstain from registration. Registered taxpayers must collect
VAT from buyers, providing a tax invoice as evidence. Multiple business locations typically
mandate individual registration based on their respective locations. However, specific
criteria allow registration at a single location.
A pivotal characteristic of VAT lies in its non-cumulative multi-stage levy. It is imposed
at every production, distribution, and consumption stage, with the VAT owed calculated
by deducting VAT paid on purchases (VAT input) from the tax charged to customers (VAT
output). This mechanism ensures efficient VAT collection, even if the collection chain is
disrupted. Non-registration or tax avoidance may escalate VAT revenue losses due to
cascading effects. VAT computation utilizes the indirect subtraction method, deducting
VAT input from VAT output for each taxable transaction. The DGT employs an automated
system to verify accurate VAT collection and payment, ensuring regulatory compliance.
Taxes generally apply to most goods and services unless explicitly exempted. The
negative list outlines goods and services exempt from taxation, including natural resources
acquired through mining, essential items, restaurant food, money, gold, securities, as well
as services in health, social sectors, finance, education, religion, arts, broadcasting, public
transport, labor, hotels, and government services.
During the study period, a 10% VAT rate was applied, except for exported goods and
services, which were zero-rated. Starting on 1 July 2020, a gradual VAT collection process
commenced for 148 online businesses, which were predominantly foreign, delivering goods
within Indonesia’s customs area. These businesses collected VAT totaling IDR 731.4 billion,
IDR 3.9 trillion, and IDR 5.51 trillion in 2020, 2021, and 2022, respectively. VAT payable is
calculated monthly and must be reported by the end of the subsequent month following
the tax period’s conclusion.

3.4. Tax Policy Changes

In July 2016, Indonesia launched a comprehensive tax amnesty program, enabling
individuals and businesses to disclose undisclosed assets and settle outstanding taxes by
paying a specified redemption amount. This initiative, spanning income tax and VAT,
allowed taxpayers to participate until 31 March 2017, with varying redemption rates
based on the location of undisclosed assets. Notably, micro, small, and medium-sized
(MSME) taxpayers were eligible for special rates, outlined in Table 3. The program resulted
in substantial revenue, with a total of IDR 130 trillion collected, involving nearly one
million taxpayers, including both new registrations and existing participants (Kominfo
2017; Kontan 2017).

Table 3. Tax amnesty redemption rates.

Location of Undisclosed Assets Undisclosed Assets of MSME

Tax Amnesty Periods
Domestic Overseas <10 billion >10 billion
July–September 2016 2% 4% 0.50% 2%
October–December 2016 3% 6% 0.50% 2%
January–March 2017 5% 10% 0.50% 2%

Furthermore, amid the economic challenges posed by the COVID-19 pandemic, In-
donesia implemented exceptional tax policies to support national recovery efforts. These
policies encompassed adjustments in corporate income tax rates, modifications in taxa-
tion procedures for electronic trading activities, extensions in the implementation period
for rights and tax obligations, and customs facilities such as duty exemptions or reduc-
tions. Specifically, from the tax year 2020 onwards, the corporate income tax rate for
Economies 2023, 11, 294 6 of 18

local businesses and permanent establishments was reduced from 25% to 22%, with a 3%
reduced rate applicable to specific domestic public companies meeting defined criteria.
Additionally, starting from 1 July 2020, the import of intangible taxable goods and services
through electronic trading was subject to collection, remittance, and reporting by desig-
nated foreign traders or domestic marketplaces. These measures represent Indonesia’s
multifaceted approach to addressing both fiscal challenges and the evolving landscape of
international trade.

4. Methodology
Utilizing a cointegration method, this study follows the method used by Dudine
and Jalles (2018) as the main reference, to determine the long-term relationship between
economic growth and tax revenues. The analysis employs quarterly data from 2015 to
2021 and focuses on the tax revenues of 34 provinces in Indonesia. In addition to total tax
revenue, this research delves into various types of taxes, including personal income tax,
corporation tax, and value-added tax.
This study categorizes tax codes 411121 (as outlined in Article 21 of Income Tax) and
411125 (as outlined in Article 25/29 of PIT) as PIT revenue. Additionally, tax codes 411122
(as outlined in Article 22 of Income Tax), 411123 (as outlined in Article 22 of Import Income
Tax), and 411126 (as outlined in Article 25/29 of CIT) are classified as CIT revenue. Lastly,
tax codes 411211 (relating to domestic VAT), 411212 (relating to Import VAT), and 411219
(other VAT), as VAT revenue2 . In this study, we selected inflation, inequality, population,
and trade openness as control variables based on previous literature (Dudine and Jalles
2018; Furceri and Jalles 2018; Jalles 2017; Lagravinese et al. 2020; Sheikh et al. 2018). These
variables are province-specific and were not gathered at the national level, aligning with
the provincial collection of tax revenues contextual to our study. Descriptive statistics of all
data are shown in Table 4.

Table 4. Descriptive statistics.

VARIABLES DESCRIPTION MEAN SD MINIMUM MAXIMUM

LNTOTAL All types of tax revenues 28.11 1.27 25.51 32.00
LNPIT Personal income tax revenues 26.46 1.26 21.31 30.02
LNCIT Corporate income tax revenues 25.89 1.62 20.77 30.37
LNVAT VAT revenues 27.21 1.30 24.19 31.19
LNRGDP Regional GDP 31.59 1.15 29.48 34.26
LNCPI Inflation rate 4.89 0.06 4.74 5.05
LNGINI Gini ratio −1.04 0.11 −1.41 −0.81
LNPOP Population 15.28 1.01 13.37 17.75
LNTRADE Trade openness −2.05 1.49 −7.75 1.01
Number of observations = 952
Notes: all variables are in natural logarithm.

Based on the assumption of symmetric response behavior, this study aims to measure
the buoyancy of a tax system, which represents the total change in tax revenues in response
to changes in regional income and discretionary changes in tax policy over time. Generally,
this is represented as the percentage amount that tax revenue increases or decreases when
income changes by one percent. If T represents tax revenue and Y represents RGDP, both
expressed in rupiah constant prices, tax buoyancy can be calculated as

∂T Y
bT,Y = × (1)
∂Y T
The tax buoyancy can be computed through the utilization of the following regression
equation:
T = α + βY + eit (2)
Economies 2023, 11, 294 7 of 18

∂T
where α is a constant, β is the marginal tax rate, and e is the error term. Since ∂Y = β, the
 
Y
buoyancy is bT,Y = β T . Additionally, another approach that can be employed for this
purpose exists, as rewritten in Equation (2):

T = αY β ε (3)

In this study, buoyancy is measured by regressing the logarithm of tax revenue (either
in total or per tax type) on the logarithm of RGDP.

ln T = ln α + βln Y + ε (4)

When estimating buoyancy, there are a couple of challenges to consider. First, it

is important to determine the appropriate timeframe for measuring the response of tax
revenue to RGDP. While buoyancy is expected to become equal to one in the long run, in
the short term, it may vary between tax types. For example, personal income tax revenue
may grow at a faster rate than the proportion of RGDP growth if inflation is not taken into
account in the taxable income or non-taxable income layer. Similarly, after a recession, the
growth in corporate income tax revenues may be slower than that of VAT due to tax rules
that allow companies to use their losses from the recession to offset their taxes in the future.
Our next topic of concern pertains to the correlation between tax revenues and national
income over time, and whether this correlation remains stable in the long term. Generally,
we integrate the logarithms of tax revenues and RGDP, and assume that they are cointe-
grated. To investigate this matter, cointegration techniques are necessary. Accordingly, we
construct the primary model (Model I) in the following way:

taxit = ai + β 1 rgdpit + β 2 cpiit + β 3 giniit + β 4 popit + β 5 tradeit + ηit (5)

The PMG ARDL method is a widely employed statistical technique that serves to
address the issue of panel data heterogeneity, while also accounting for both the short- and
long-term dynamics of all variables under consideration (Attiaoui et al. 2017; Hafsi et al.
2021; Pesaran et al. 1999). The PMG estimator takes into account individual heterogeneity,
such as slope and intercept, in the short term and homogeneity in the long term. The PMG
model for ECM can be defined as
p −1 q −1
∆taxit = αi taxit−1 − β0 i Xit−1 + ∑ λij0 ∆taxit− j + ∑ δik0 ∆Xit− j + vi + ε it (6)
j =1 k =0

where taxit−1 is tax revenue in the natural logarithm. The long-term elements are the
residues of
αi taxit−1 − β0 i Xit−1 = eit−1 (7)
Dividing Equation (7) by αi , we obtain

taxit−1 − φi Xit−1 = ηit−1 (8)

β0 eit−1
where φi = α i , and ηit−1 = αi is the error term. If φi < 0 then the following long-term
i
relationship exists:
taxit−1 = φi Xit−1 + ηit−1 (9)
Model II is obtained by combining the equations with their respective control variables.

taxit = αi + α1 taxit−1 + β 1 rgdpit + β 2 cpiit + β 3 giniit + β 4 popit + β 5 tradeit + ηit (10)

Model III is the residual equation:

Economies 2023, 11, 294 8 of 18

 
α + α1 taxit−2 + β 1 rgdpit−1 + β 2 cpiit−1 +
ηit−1 = taxit−1 − i (11)
β 3 giniit−1 + β 4 popit−1 + β 5 tradeit
where ηit−1 is the error correction term and αi is a measure of the speed of adjustment
towards long-term equilibrium. Finally, model IV is the ARDL equation:

p −1 q −1
∆taxit = αi ηit−1 + ∑ λij0 ∆taxit− j + ∑ δik0 ∆Xit− j + vi + ε it (12)
j =1 k =0

Equation (12) can be estimated on a per-province basis or for the entire provincial
panel. Exploitation of panel dimensions has several advantages. First, it mitigates the
limitations posed by a limited number of degrees of freedom inherent in the short time span
at the cross-section level. Second, its hypothesis testing and inference are more powerful
than time series techniques in a single province. Third, cross-sectional information reduces
the tendency of pseudo-regression (Banerjee 1999).
To assess the potential issue of homogeneity, our analysis begins by estimating the
parameters in Equation (10) through fully modified ordinary least squares (FMOLS) sepa-
rately for each province. Then, we examine the key statistics of these estimates, categorized
based on various types of tax revenue. FMOLS, as introduced by Philips and Hansen
(1990), incorporates semi-parametric corrections to eliminate problems caused by long-run
correlations between (a) deviations from long-run equilibrium and (b) innovations in the
stochastic process characterizing each regression.
The parameter estimation in Equation (12) is determined using the panel data method.
Specifically, we employ the group mean estimator proposed by Pesaran and Smith (1995),
as well as the combined group mean estimator introduced by Pesaran et al. (1999). These
methods, suited for dynamic panel analysis characterized by long time spans and cross-
sectional dimensions, offer the advantage of accommodating both long-term equilibrium
and the dynamic adjustment processes of heterogeneous contingencies. This estimator
allows the correction of bias that may result from the estimation of the tax buoyancy
coefficient using the standard fixed effect model. It achieves this correction by incorporating
a non-stationary error term, thereby emphasizing the homogeneity of the parameters
incorporated in the estimation equation.
Utilizing the long-term equation, the cointegration coefficient is shown by the pa-
rameter αi . Models II and III are referred to as long-term equations, with the equation
length being determined by p and q based on the optimal model with the lowest AIC value.
Then, in order to discern the tax structure and examine the potential influences of any
changes in tax policy on the regression outcomes, an exhaustive review of tax regulations
and pertinent literature will be undertaken.

5. Results and Discussion

Prior to conducting the unit root test to ascertain stationarity, it is imperative to
assess the presence of cross-sectional dependence within the dataset. The findings of this
examination may influence the selection of appropriate unit root tests. Employing two
widely recognized methodologies for assessing cross-sectional dependence, namely the
Breusch–Pagan LM test and the Pesaran CD test, Table 5 shows the results that validate the
existence of cross-sectional dependence across all variables under scrutiny.
Furthermore, to ensures robust estimation and inference, it is imperative for each
variable to exhibit stationarity, while considering the cross-sectional dependence among
the units (cross-sectional dimension) and allowing for individual-specific heterogeneity. In
the realm of balanced panel data analysis, the Pesaran CIPS (cross-sectionally augmented
IPS) tests, as elucidated by Pesaran (2007), serve as indispensable tools for assessing the
presence of unit roots and thereby confirming stationarity, while considering cross-sectional
dependence among the units. The results in Table 6, derived from panel unit root tests,
affirm the stationarity of all variables, except the population and RGDP variables.
Economies 2023, 11, 294 9 of 18

Table 5. Cross-sectional dependence test.

Variables Breusch–Pagan LM Pesaran CD

LNTOTAL 4597.598 *** 60.948 ***
LNPIT 2868.389 *** 37.791 ***
LNCIT 3295.809 *** 43.706 ***
LNVAT 2948.336 *** 37.361 ***
LNRGDP 13,954.80 *** 117.916 ***
LNCPI 15,348.57 *** 123.886 ***
LNGINI 3878.853 *** 31.143 ***
LNPOP 9871.887 *** 92.179 ***
LNTRADE 3676.743 *** 12.580 ***
Notes: Statistics marked *** indicate significance at 1% level.

Table 6. Panel unit root test with cross-sectional dependence: Pesaran–CIPS.

Variables CIPS Test Statistic Critical Value (1%)

LNTOTAL −3.107 *** −2.80
LNPIT −3.253 *** −2.80
LNCIT −3.600 *** −2.80
LNVAT −2.924 *** −2.80
LNRGDP −2.525 −2.80
LNCPI −3.056 *** −2.80
LNGINI −2.926 *** −2.80
LNPOP 2.420 −2.80
LNTRADE −3.011 *** −2.80
Notes: Statistics marked *** indicate significance at 1% level.

Our inquiry into the long-term relationship among variables employed the panel
cointegration test method proposed by Kao (1999). As presented in Table 7, employing the
Newey–West and Bartlett kernel automatic bandwidth selection, our findings reject the
null hypothesis of no cointegration at a 1% significance level.

Table 7. Panel cointegration test.

Kao Residual Cointegration Test t-Statistic

ADF −4.166 ***
Notes: Statistics marked *** indicate significance at 1% level.

We examined the long-term tax buoyancies’ unity using a Wald test (H0: tax buoyancy = 1),
as presented in Table 8. At a 1% confidence level, our analysis found no significant evidence
to reject the null hypothesis. Hence, it can be inferred that all tax categories adhered to a
tax buoyancy of one.

Table 8. Wald test of long-term buoyancies equaling one.

Test Statistic Total Tax PIT CIT VAT

t-statistic 2.111 ** 2.213 ** −2.239 ** 1.577
F-statistic 4.456 ** 4.896 ** 5.011 ** 2.487
Chi-square 4.456 ** 4.896 ** 5.011 ** 2.487
Notes: Null-hypothesis is C(1) = 1. Statistics marked ** indicate significance at 5% level.

Theoretical postulations suggest that long-term tax buoyancy should align with unity
(Dudine and Jalles 2018; Lagravinese et al. 2020). However, empirical studies, confined to
specific time frames, have demonstrated a fluctuating pattern in tax buoyancy (Belinga et al.
2014; Choudhry 1979; De Pascale et al. 2021; Gupta et al. 2021). These analyses highlight
considerable variability, with the tax buoyancy surpassing or falling below the unitary
Economies 2023, 11, 294 10 of 18

threshold. Such variability stems from diverse macroeconomic, budgetary, cultural, and
political contexts, shaping tax responsiveness to GDP changes, as elucidated by Lagravinese
et al. (2020). The Wald test results in Table 8 bolster these observations. Significantly,
at a 99% confidence level, the analysis failed to provide compelling evidence to reject
the hypothesis that all tax types exhibit unity in buoyancy. These findings underscore
the intricate factors influencing the relationship between taxes and economic growth,
emphasizing the nuanced nature of tax buoyancy in diverse socio-economic contexts.
In examining the potential long-term relationship between RGDP and tax revenues,
the selection of appropriate models is crucial. The Akaike information criterion (AIC)
is employed to assess various candidate models and determine the most suitable one,
ensuring a delicate balance between model fit and complexity. Lower AIC values indicate
a better fit, making the model with the lowest AIC preferable in comparative analyses
(Akaike 1974). Table 9 presents the results of the dynamic model estimation, focusing on the
four distinct tax types, with tax revenues serving as the dependent variable in the equation
under scrutiny.

Table 9. Long-run tax buoyancies.

Total Tax
Variables PIT (1,1,1,1,1,1) CIT (3,3,3,3,3,3) VAT (3,1,1,1,1,1)
(1,1,1,1,1,1)
LNRGDP 1.252 *** 1.348 *** 0.937 *** 1.281 ***
LNCPI −1.283 *** −0.964 *** 3.539 *** −1.148 **
LNGINI 0.007 −0.306 3.000 *** 2.239 ***
LNPOP −0.118 −0.213 −9.772 *** −0.001
LNTRADE −0.024 ** 0.084 *** 0.132 *** −0.025 **
Speed of adjustment −0.789 *** −0.779 *** −0.594 *** −0.654 ***
Notes: coefficients marked *** and ** indicate significance at the 1% and 5% levels, respectively.

To ascertain the robustness of outcomes concerning model and methodology choices,

we introduce inflation as a supplementary control to investigate the potential dependency
of tax buoyancy on price fluctuations. Should there be no discernible association, an analo-
gous relationship would emerge when employing real variables. The findings presented in
Table 10 indicate that inflation introduces a significant negative coefficient, albeit exhibiting
a significant positive association for CIT. Furthermore, the coefficients pertaining to buoy-
ancy are notably augmented, save for CIT yet again. Consequently, tax buoyancy does not
exhibit neutrality in relation to inflation, suggesting that tax buoyancy in real measures
surpasses its nominal counterpart.

Table 10. Tax buoyancies with and without controlling for inflation.

Total Tax PIT CIT VAT

Variables
(1,1,1,1,1,1) (1,1,1,1,1,1) (3,3,3,3,3,3) (3,1,1,1,1,1)
Tax Buoyancy 1.252 *** 1.348 *** 0.937 *** 1.281 ***
without controlling for inflation 0.727 *** 0.958 *** 2.148 *** 0.863 ***
Speed of adjustment −0.789 *** −0.779 *** −0.594 *** −0.654 ***
without controlling for inflation −0.798 *** −0.790 *** −0.704 *** −0.677 ***
Notes: coefficients marked *** indicate significance at the 1% level.

Utilizing dynamic analysis, our study reveals a long-term relationship between tax
revenue and various factors. While short-term fluctuations occur, the tax revenue tends
to return to its equilibrium. Multiple elements, including RGDP, inflation, inequality,
population, and trade openness, influence these fluctuations. Our focus was on RGDP’s
impact on taxation, showing a positive effect exceeding unity. Among tax categories,
personal income tax (PIT) demonstrated the highest buoyancy, at 1.348, followed by VAT at
1.281, total tax at 1.252, and CIT at 0.937. Notably, inflation, population, and trade openness
reduced total taxes, while higher inequality increased overall tax revenue. The concept of
Economies 2023, 11, 294 11 of 18

tax buoyancy encompasses tax elasticity regarding RGDP changes and reflects complex tax
structure dynamics, policy changes, and compliance factors.
The elevated buoyancy of PIT implies that the alterations in the tax structure and
policies related to PIT during the evaluated period positively influenced the increase in
tax revenues. PIT operates on a progressive rate system, where a rise of 1% in GDP can
lead to a PIT increase exceeding 1%. This phenomenon arises due to the application of
higher tax rates corresponding to higher income levels. Additionally, the PIT framework
incorporates a withholding mechanism within payroll tax, a strategic approach that ensures
systematic tax collection, while concurrently mitigating instances of tax evasion. Despite
existing tendencies indicating a correlation between higher incomes and elevated levels of
tax avoidance, the withholding mechanism compels taxpayers to adhere to tax regulations,
fostering greater compliance (Lang et al. 1997; Prasetyo and Sinaga 2014).
Two significant changes occurred in the personal income tax (PIT) framework. First,
the tax amnesty program implemented from 2016–2017 generated substantial revenue,
totaling IDR 130 trillion. Individual taxpayers contributed the majority, with IDR 90.36
trillion from non-MSME taxpayers and IDR 7.56 trillion from MSME taxpayers (Kominfo
2017). This initiative also attracted 44,232 new registrants, and asset declarations reached
IDR 4813.4 trillion, consequently expanding the tax base for subsequent fiscal periods. Sec-
ond, the specified non-taxable income threshold was raised from IDR 36 million to IDR 54
million in June 2016. The impact on tax revenue depended on the extent of this adjustment.
Conversely, the diminished buoyancy of corporate income tax (CIT), falling below
unity, underscores that the structural modifications and policy changes within CIT during
the examined period had an adverse impact on CIT revenues. Unlike personal income tax
(PIT), CIT follows a uniform single-rate system without progressivity. The computation
of tax liability is conducted through a self-assessment method, entrusting taxpayers with
the responsibility of calculating their tax obligations. This mechanism provides ample
opportunities for the practice of tax avoidance. Two significant alterations were made to
the CIT structure during the review period: first, the implementation of the tax amnesty
program from 2016–2017; and second, the reduction in the CIT rate from 25% to 22%, effec-
tive from the 2020 tax year. Despite a substantial IDR 130 trillion being collected through
the amnesty program, corporate contributions were limited. According to Kominfo (2017),
non-MSME corporate taxpayers contributed IDR 4.31 trillion, whereas SME corporate
taxpayers contributed IDR 0.62 trillion. This limited contribution rendered the impact of
the tax amnesty program on CIT buoyancy negligible. The rate reduction is expected to
further reduce CIT buoyancy.
During the review period, the value-added tax (VAT) system underwent significant
changes due to two key policies: the tax amnesty program and a new VAT collection method
for online businesses. The tax amnesty primarily attracted individual taxpayers, resulting
in a modest impact on VAT revenue. While specific data are lacking, it is reasonable to
assume that many individuals may not be VAT-registered. Conversely, the introduction of
the VAT collection system for online businesses, launched in July 2020, showed remarkable
success, with revenue increasing from IDR 731 billion in 2020 to IDR 3.9 trillion in 2021, as
reported by the Directorate General of Taxes (DJP 2023).
The strong VAT buoyancy highlights its effectiveness. Indonesia’s VAT system features
a non-cumulative, multi-stage levy, serving as a protective measure and regulatory check.
This structure allows for the detection of issues like broken VAT chains or tax evasion.
Additionally, the use of the indirect subtraction method and the invoice system ensures
meticulous monitoring of tax reporting and VAT payments, ensuring compliance with
regulations. Furthermore, the gradual adoption of electronic tax invoices for VAT collection,
starting in July 2014, has enhanced monitoring and enforcement, aligning the system with
technological advancements and regulatory requirements.
The speed at which the four tax categories tend to return to equilibrium over the long
term is indicated by negative cointegration coefficients (αi < 0), revealing an inherent cor-
rective mechanism. When taxes exceed their long-term trajectory, they naturally decrease,
Economies 2023, 11, 294 12 of 18

while if they fall below it, they autonomously increase, to restore equilibrium. Total tax has
the highest coefficient (−0.789), followed by PIT (−0.780), VAT (−0.654), and CIT with the
lowest value (−0.594). This discrepancy underscores the diverse tax structures governing
each category, highlighting the nuanced nature of the inherent correctional mechanisms in
the tax system.
Specifically, PIT demonstrates the most significant negative coefficient (−0.780), indi-
cating its rapid return to long-term equilibrium. In contrast, both VAT and CIT also tend to
revert to their equilibrium, albeit at a comparatively slower pace than PIT. This variation
in adjustment speeds reflects the distinct characteristics of each tax type. For example,
PIT’s payroll tax mechanism enables swift settlement, unlike CIT’s lengthy tax calculation
process. CIT regulations governing financial loss compensation prolong the time needed
for the CIT revenue to realign with its long-term trajectory, even after economic recovery.
In contrast, VAT exhibits a relatively quicker return to equilibrium, highlighting distinct
recovery dynamics among tax categories.
This study calculated cointegration coefficients (αi ) to assess adjustment speed across
provinces. While Table 7 shows the overall tax revenue coefficients for Indonesia, Table 11
details province-specific coefficients. This analysis revealed nuanced tax revenue patterns
within provinces, influenced by RGDP, inflation, inequality, population, and trade openness.
Notably, not all provinces exhibited high tax adjustment. Cointegration conditions shed
light on these variables’ role in future tax revenue. When tax revenues deviate from long-
term patterns, they naturally readjust. Further research is essential to understand the
specific adjustment factors in each province.

Table 11. Cointegration coefficient per province.

Province Totax PIT CIT VAT

1 Nangroe Aceh Darussalam −1.097 *** −0.428 *** −0.612 *** −1.262 ***
2 North Sumatera −1.244 *** −0.808 *** −3.306 *** −2.061 ***
3 Riau −1.201 *** −0.769 *** −0.115 *** 0.097 *
4 West Sumatera −0.671 *** −1.007 *** −0.722 *** −0.525 ***
5 Jambi −1.171 *** −0.716 *** −0.614 *** −0.581 ***
6 South Sumatera −1.258 *** −0.961 *** 0.440 *** −0.765 ***
7 Bangka Belitung Islands −0.391 *** −0.557 *** −0.265 ** −0.098
8 Bengkulu −0.720 *** −1.222 *** −3.863 *** −1.869 ***
9 Lampung −0.875 *** −0.265 *** −0.342 *** −0.529 ***
10 Riau Islands −1.115 *** −0.601 *** 0.530 *** −0.581 ***
11 Jakarta −1.114 *** −0.761 *** −0.075 ** −0.364 ***
12 Banten −0.156 *** −0.882 *** −0.614 *** 0.002
13 West Jawa −0.618 *** −1.229 *** −0.321 *** −0.215 ***
14 Central Jawa −1.225 *** −1.041 *** −1.633 *** −1.126 ***
15 Yogyakarta −1.277 *** −0.580 *** −0.151 ** −1.405 ***
16 East Jawa −1.399 *** −0.935 *** −0.312 *** 0.137 ***
17 West Kalimantan −0.911 *** −0.663 *** −0.040 −0.22 **
18 South Kalimantan −0.648 *** −0.726 *** −0.153 ** −0.387 **
19 Central Kalimantan −0.475 *** −0.783 *** −0.038 *** 0.092
20 East Kalimantan −1.135 *** −0.888 *** −0.487 −0.575 ***
21 North Kalimantan −0.383 *** −1.015 *** −0.474 *** −0.528 ***
22 South Sulawesi −0.431 *** −0.567 *** −1.757 *** −0.870 ***
Economies 2023, 11, 294 13 of 18

Table 11. Cont.

Province Totax PIT CIT VAT

23 West Sulawesi −0.266 *** −0.130 ** 0.041 −0.253 ***
24 South East Sulawesi −0.313 *** −0.358 *** −0.046 −0.513 ***
25 North Sulawesi −0.360 *** −0.795 *** −0.304 *** −0.472 ***
26 Central Sulawesi −0.826 *** −0.675 *** −1.413 *** −0.999 **
27 Gorontalo −0.559 *** −1.018 *** −0.378 *** −0.575 ***
28 North Maluku −0.640 *** −1.256 *** −0.353 *** −0.168 **
29 Bali −0.803 *** −1.198 *** −0.579 −1.288 ***
30 West Nusa Tenggara −0.707 *** −0.617 *** −0.304 ** −1.247 ***
31 East Nusa Tenggara −0.887 *** −0.644 *** −1.616 *** −1.218 ***
32 Papua −1.009 *** −0.991 *** −0.619 *** −1.122 ***
33 West Papua −0.489 *** −0.828 *** 0.177 *** −0.439 ***
34 Maluku −0.435 *** −0.587 *** 0.107 ** −0.290 ***
Average −0.789 *** −0.780 *** −0.594 *** −0.654 ***
Notes: coefficients marked ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

6. Conclusions
This study investigated the impact of RGDP and several control variables, i.e., in-
equality, inflation, population, and trade openness, on tax revenue through a dynamic
analysis. The study established a cointegration relationship between tax revenues and these
determinants, indicating a long-term association. Detailed fluctuations in tax revenues
across Indonesian provinces were observed.
According to the Wald test results, the long-term buoyancies of personal income tax
(PIT), corporate income tax (CIT), and total tax aligned with theoretical expectations, ex-
hibiting no significant deviation from unity. However, this conformity was not observed in
the case of value-added tax (VAT). Among the tax categories, PIT demonstrated the highest
tax buoyancy coefficient (1.348), followed by VAT (1.281) and total tax (1.252), with CIT
registering the lowest value (0.937). These findings shed light on the varied responsiveness
of different tax types to the underlying factors, providing valuable insights into the fiscal
dynamics of Indonesian provinces. In contrast to antecedent studies exploring analogous
inquiries within disparate national contexts, the revelations derived from this study within
the framework of Indonesia’s economic landscape offer a novel dimension of understand-
ing. Indonesia’s economy, characterized by distinctive features, policy frameworks, and
economic structures, served as a unique backdrop for this investigation, thereby yielding
fresh insights discernible in the ensuing elucidation.
The elevated buoyancy of personal income tax (PIT) signifies that alterations in the tax
structure and its subsequent modifications during the studied period yielded a favorable
impact on augmenting tax revenues. The progressive rate system employed in PIT played a
pivotal role in this regard. Under this system, a one percent increase in RGDP can result in
a PIT revenue growth of more than one percent, owing to the higher rates imposed on ad-
ditional income. Additionally, the implementation of a withholding mechanism in payroll
tax was instrumental in ensuring efficient tax collection, while mitigating instances of tax
evasion. During the period under examination, two significant changes were introduced
in the PIT structure. First, the implementation of the tax amnesty program in 2016–2017,
aimed at providing a framework for taxpayers to regularize their tax affairs. Second, there
was an increase in the non-taxable income threshold, effective from June 2016, which further
influenced the dynamics of PIT collection. These structural adjustments played a pivotal
role in shaping the trajectory of PIT revenues during the specified timeframe.
Economies 2023, 11, 294 14 of 18

Conversely, the diminished buoyancy, slightly below unity, observed in corporate

income tax (CIT) also mirrors the tax structure and its modifications throughout the exam-
ined period. Unlike personal income tax (PIT), CIT adheres to a fixed-rate system. Within
the scope of the study, two notable alterations were introduced to the CIT framework. First,
the implementation of the tax amnesty program in 2016–2017 represented a significant
structural shift. Second, a reduction in the CIT rate, from 25% to 22%, was implemented
commencing from the 2020 tax year. These changes significantly impacted the dynamics of
CIT collections during the specified timeframe.
The elevated buoyancy observed in value-added tax (VAT) underscores the continued
effectiveness of its tax structure. One of the defining features of VAT in Indonesia is its multi-
stage levy system, which is non-cumulative in nature. Furthermore, the application of the
indirect subtraction method alongside an invoice system ensures rigorous oversight of tax
invoice reporting and VAT payments, aligning with regulatory requirements. Additionally,
the Directorate General of Taxes (DGT) mandated the gradual adoption of electronic tax
invoices for VAT collection, beginning in July 2014, further enhancing the monitoring and
enforcement mechanisms. Within the timeframe studied, two significant tax policies were
implemented. The first was the tax amnesty program, aimed at encouraging tax compliance,
and the second involved the introduction of a new mechanism for VAT collection in online
businesses. These initiatives played a pivotal role in shaping the VAT landscape during the
specified period.
The control variables exhibited diverse impact orientations. Inflation escalation led
to a decrease in personal income tax (PIT) and value-added tax (VAT) but resulted in an
increase in corporate income tax (CIT). The rationale behind these relationships can be
elucidated by considering the nominal nature of the GRDP and tax revenue data utilized
in this study, coupled with the inherent characteristics of each tax type. Given its nature
as a consumption tax, value-added tax (VAT) experienced a negative influence due to
inflation. Inequality contributed to the escalation in both CIT and VAT, with no statistically
significant effect discerned in the case of PIT. Furthermore, population growth exerted
a discernible negative impact solely on CIT. Conversely, trade openness demonstrated
a positive influence across variables, with the exception of VAT, where its impact was
non-beneficial. These nuanced patterns underscore the complexity of the interplay between
economic indicators and different tax categories, highlighting the intricate dynamics at
play in the fiscal landscape.
The cointegration coefficients, indicating the speed of adjustment, exhibited negative
values for all four tax categories, signifying a consistent tendency for tax revenues to revert
to equilibrium. The cointegration coefficient for total tax stood at −0.789. Among the three
tax types, personal income tax (PIT) displayed the most substantial absolute value (−0.780),
followed by value-added tax (VAT) at −0.654, with corporate income Tax (CIT) registering
the lowest value of −0.594.
This research reveals that tax revenues within individual provinces exhibit fluctuations
but consistently revert to equilibrium. The assessment of tax revenue can be conducted
using specific determinants such as RGDP, inequality, inflation, population, and trade
openness at the provincial level. Alterations in these determinants can serve as guidelines
for formulating future tax revenue strategies. It is anticipated that by modifying these deter-
minants, an enhancement in tax revenue can be achieved. Consequently, tax authorities in
each province need to devise more suitable strategies, to ensure higher future tax revenues.

Author Contributions: Conceptualization, S.T.S. and M.E.; methodology, S.T.S., M.E., B.Y.G. and
M.S.; software, S.T.S. and M.E.; validation, S.T.S., M.E., B.Y.G. and M.S.; formal analysis, S.T.S., M.E.,
B.Y.G. and M.S.; investigation, S.T.S., M.E., B.Y.G. and M.S.; resources, S.T.S.; data curation, S.T.S. and
M.E.; writing—original draft preparation, S.T.S.; writing—review and editing, S.T.S., M.E., B.Y.G. and
M.S.; visualization, S.T.S.; supervision, M.E., B.Y.G. and M.S.; project administration, S.T.S.; funding
acquisition, S.T.S. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Economies 2023, 11, 294 15 of 18

Informed Consent Statement: Not applicable.

Data Availability Statement: 3rd Party Data. Restrictions apply to the availability of these data. Data
was obtained from the Directorate General of Taxes of the Republic of Indonesia and are available
from the authors with the permission of the Directorate General of Taxes of the Republic of Indonesia.
Conflicts of Interest: The authors declare no conflict of interest.

Appendix A

Table A1. Tax account codes in Indonesia.

Number Tax Account Code Tax Category

I Non-Oil and Gas Income Tax
1 411121 Article 21 Income Tax
2 411122 Article 22 Income Tax
3 411123 Article 22 Import Income Tax
4 411124 Article 23 Income Tax
5 411125 Article 25/29 Personal Income Tax
6 411126 Article 25/29 Corporate Income Tax
7 411127 Article 26 Income Tax
8 411128 Final Income Tax
9 411129 Others Non-Oil and Gas Income Tax
10 411131 Overseas Fiscal (has been removed since 2011)
11 411141 Borne by Government Income Tax
II Value-Added Tax (VAT) and Luxurious Sales Tax (LST)
1 411211 Domestic VAT
2 411212 Import VAT
3 411219 Others VAT
4 411221 Domestic LST
5 411222 Import LST
6 411229 Others LST
III Property Tax
1 411313 Plantation Property Tax
2 411314 Forestry Property Tax
3 411315 Mineral Mining Property Tax
4 411316 Oil Mining Property Tax
5 411319 Others Property Tax
IV Other Taxes
1 411611 Stamp Duty
2 411612 Sale of Stamp Objects
3 411613 Coal Sales Tax
4 411619 Other Indirect Taxes
5 411621 Income Tax Collection Interest
6 411622 VAT Collection Interest
7 411623 LST Collection Interest
Economies 2023, 11, 294 16 of 18

Table A1. Cont.

Number Tax Account Code Tax Category

V Oil and Gas Income Tax
1 411111 Crude Oil Income Tax
2 411112 Natural Gas Income Tax
3 411119 Other Oil and Gas Income Tax

Appendix B

Table A2. DGT regional offices and related provinces.

No Provinces DGT Regional Offices Codes

1 Nangroe Aceh Darussalam Aceh 010
2 North Sumatera North Sumatera I 020
North Sumatera II 030
3 Riau Riau 040
4 West Sumatera West Sumatera and Jambi 050
5 Jambi
6 South Sumatera South Sumatera and Bangka Belitung Islands 060
7 Bangka Belitung Islands
8 Bengkulu Bengkulu and Lampung 070
9 Lampung
10 Riau Islands Riau Islands 340
11 Jakarta Central Jakarta 080
West Jakarta 090
South Jakarta I 100
East Jakarta 110
North Jakarta 120
Special Jakarta 130
South Jakarta II 320
12 Banten Banten 140
13 West Jawa West Jawa I 150
West Jawa II 160
West Jawa III 330
14 Middle Jawa Middle Jawa I 170
Middle Jawa II 180
15 Yogyakarta Yogyakarta 190
16 East Jawa East Jawa I 200
East Jawa II 210
East Jawa III 220
17 West Kalimantan West Kalimantan 230
18 South Kalimantan South Kalimantan and Middle Kalimantan 240
19 Middle Kalimantan
20 East Kalimantan East Kalimantan and North Kalimantan 250
21 North Kalimantan
South Sulawesi, West Sulawesi, and South
22 South Sulawesi 260
East Sulawesi
Economies 2023, 11, 294 17 of 18

Table A2. Cont.

No Provinces DGT Regional Offices Codes

23 West Sulawesi
24 South East Sulawesi
North Sulawesi, Middle Sulawesi, Gorontalo,
25 North Sulawesi 270
and North Maluku
26 Middle Sulawesi
27 Gorontalo
28 North Maluku
29 Bali Bali 280
30 West Nusa Tenggara Nusa Tenggara 290
31 East Nusa Tenggara
32 Papua Papua, West Papua, and Maluku 300
33 West Papua
34 Maluku
Large Taxpayers 310
Note: This research did not consider the Special Jakarta Regional Office (code 130) or the Large Taxpayer Regional
Office (code 310).

Notes
1 The listing of the regional offices of the Directorate General of Taxes and the respective provinces they are situated in can be
referenced in the Appendix B.
2 A comprehensive categorization of tax codes in Indonesia is available in Appendix A.

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