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Does Trade Openness Affect Taxation? Evidence from
BRICS Countries
Article in Millennial Asia · October 2023
DOI: 10.1177/09763996231199310
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Original Article
Does Trade Openness
Affect Taxation?
Evidence from
BRICS Countries
Md. Mominur Rahman1
Millennial Asia
1–29
© 2023 Association of
Asia Scholars
Article reuse guidelines:
in.sagepub.com/journals-permissions-india
DOI: 10.1177/09763996231199310
journals.sagepub.com/home/mla
and Mohammad Ekramol Islam1
Abstract
This study aims to investigate the relationship between trade openness and taxation
in BRICS (Brazil, Russia, India, China and South Africa) countries. This study uses a
panel dataset for 2000–2021 and employs various econometric techniques such as
the cross-sectional dependence test, unit root test, panel regression selection
criteria, robustness checking fully modified ordinary least square and dynamic
ordinary least square to validate the research model. The study finds that trade
openness positively impacts taxation in BRICS countries. Specifically, the study
finds that trade freedom, trade ratio and average trade increase the tax-to-GDP
ratio and tax collection. Additionally, the study finds that financial development
(FDV), financial openness (FON), GDP per capita (GPR) and political stability (PLS)
positively impact taxation, but inflation has a negative effect. The results imply
support for comparative advantage theory, suggesting that trade openness can
positively impact taxation. The findings also highlight the importance of FDV, FON,
GPR and PLS for tax revenue collection. From a managerial perspective, the results
suggest that policymakers in BRICS countries should prioritize measures that
promote trade openness and economic growth to improve their taxation systems.
Keywords
Trade openness, tax revenue, political stability, financial openness, tax-to-GDP ratio
I. Introduction
In the twenty-first century, almost every organization tries to develop a robust supply
chain network to reduce risk and improve supply chain surplus (Kumari & Bharti,
2021; Morrow et al., 2022). As a result, it significantly impacted the mobility of raw
1
Department of Business Administration, Northern University Bangladesh, Dhaka, Bangladesh
Corresponding author:
Md. Mominur Rahman, Department of Business Administration, Northern University Bangladesh,
Dhaka, Bangladesh.
E-mail: mominurcou@gmail.com
2
Millennial Asia
materials, labour and other resources, including trade flow and capital movement.
Global trade proliferated, reaching about 26.02 trillion in 2012 and 28.5 trillion in
2021 (Beverelli & Ticku, 2022). The flow is drastically reduced due to the COVID-19
pandemic, which was only 18 trillion in 2019. The growth rate of trade in goods has
been higher in developing countries than in developed countries, indicating that trade
openness has played a significant role in the economic development of these countries
(Gnangnon, 2022b).
In our present article, we are addressing the problem of whether trade openness
in the BRICS (Brazil, Russia, India, China and South Africa) countries has
affected the taxation scenario. According to Raghutla (2020), trade openness
refers to a country’s economy being oriented towards trading with other countries.
Outward orientation means a country takes advantage of opportunities to trade
with other countries. Shrestha et al. (2021) identified three reasons for the growth
in world trade and predicted continued growth. These reasons are (a) advancements in transportation and communication technology reducing the cost of transporting goods and resources, (b) increased demand for goods and services due to
changes in consumer preferences and trust and (c) global economic cooperation
leading to reduced barriers to trade.
Trade openness is a concept that refers to a country’s ability to import and
export goods and services to other countries (Baunsgaard & Keen, 2010). This
type of openness allows countries to take advantage of their comparative advantages by exporting goods and services that they can produce efficiently and
importing goods and services that they can produce less efficiently (Bowdler &
Malik, 2017). This results in lower prices for consumers, an increase in real
income and an overall increase in consumer and producer welfare. Furthermore,
trade openness can lead to gains in total factor productivity as it exposes countries
to new production technologies that can foster higher productivity at both the firm
and industry levels (Gnangnon, 2021c). Additionally, trade openness enables lowincome countries to raise their income levels in order to compete with highincome countries.
However, trade openness can also indirectly affect imports and revenue performance (Gnangnon, 2022b). This is because trade reforms can trigger changes in
consumption and production decisions, which affect prices. Additionally, the ease
of collecting trade tax revenue compared to the taxation of domestic goods has
historically been one of the reasons for trade protection (Gnangnon, 2020).
However, since the inception of the World Trade Organization (WTO) in 1995,
both developed and developing countries have experienced a significant degree of
liberalization of their trade regimes, which has led to a decline in international
trade tax revenue and hence a fall in public revenue (Morrow et al., 2022).
Gnangnon (2021c) has reported empirical evidence of the public revenue losses
associated with greater trade liberalization. This study explores the situation only
for the BRICS countries.
Countries open their markets to trade; they may have to lower import tariffs or
taxes on imported goods to comply with trade agreements or remain competitive
with other countries (Keho, 2017). This can reduce government revenue from
tariffs and may require the government to find other sources of revenue, such as
Rahman and Islam
3
increasing taxes on domestic goods or services. Trade openness can also lead to
increased cross-border trade, making it more difficult for governments to enforce
tax laws and regulations (Raghutla, 2020). This can lead to tax evasion and avoidance by businesses and individuals, further reducing government revenue. Trade
openness can also increase foreign investment and economic growth, hence
increasing tax revenue. This can be especially true in developing countries, where
foreign investment can help to spur economic development and create jobs
(Gnangnon, 2021b). Therefore, trade openness can affect taxation both positively
and negatively by reducing government revenue from the tariff band. This study
focuses on the BRICS region for taxation and trade openness issue.
Gnangnon and Brun (2019) discovered that trade openness significantly
impacts revenue performance in developing countries. The study also found that
revenue performance is higher in economies that are more open to trade. However,
a second group of studies argues that removing trade barriers can lead to an
increase in imports but a decline in revenue performance (Beverelli & Ticku,
2022; Keho, 2017). These findings suggest that the relationship between trade
openness and revenue performance is complex and can vary depending on the
economic structures of a particular economy or region (Amna Intisar et al., 2020;
Beverelli & Ticku, 2022; Fenira, 2015; Keho, 2017; Sabina & Eldin, 2018).
Therefore, the effects of trade openness on revenue performance cannot be generalized. In this study, we aim to explore further the relationship between trade
openness and taxation in BRICS countries to understand how these factors
interact.
This study aims to investigate the impact of international trade factors on taxation in BRICS countries. Using panel data for the period of 2000–2021, it will
examine the effects of trade freedom (TFR), trade ratio (TRO), average tariff rate
(ATR), financial development (FDV), inflation (INF), financial openness (FON),
GDP per capita (GPR) and political stability (PLS) on taxation in BRICS countries. The study aims to provide valuable insights for BRICS countries in making
export–import decisions by answering questions such as: Does TFR positively
affect taxation in BRICS countries? What role does corporate tax play in promoting long-term economic development in BRICS countries? And what are the most
influential factors, including TFR, that affect taxation among BRICS countries?
The study aims to contribute to the current literature by providing evidence-based
policy recommendations for BRICS countries. This study addresses the gaps by
conducting a more in-depth analysis of the specific mechanisms through which
trade openness leads to improved tax revenues and by considering the potential
impact of other factors such as political and economic instability, industryspecific analysis and different levels of development in the BRICS countries on
the relationship between trade openness and taxation.
Emerging nations have seen significant progress in terms of industrial and economic development (Gnangnon, 2022b). These countries can serve as suppliers of
labour or resources to more developed nations. In order to establish the necessary
infrastructure for production processes and improve supply chain networks, these
countries must collect revenue and enhance their capital (Gnangnon, 2020; Keho,
2017). The BRICS countries (Brazil, Russia, India, China and South Africa) are
4
Millennial Asia
ideal for this study, as they are all emerging economies facing the challenges of
the Fourth Industrial Revolution and looking for ways to improve their production
and service capabilities. Furthermore, these countries are looking for ways to
develop their infrastructure and supply chain networks and increase their revenue
to support the growth of their economies (Sabina & Eldin, 2018; Shrestha et al.,
2021). By focusing on the BRICS countries, this study aims to provide a comprehensive understanding of the challenges and opportunities facing emerging economies in today’s globalized world in the context of international trade.
This study employs various panel econometric approaches to examine the
research questions, such as the cross-sectional dependence (CSD) test, Breush–
Pagan Lagrange Multiplier (LM), Pesaran scaled LM, Pesaran CSD and the unit
root test. The study also applied the Chow and Breush–Pagan tests to determine
the appropriate model, either pooled ordinary least square (OLS) or the fixed- and
random-effects model suggested by Schunck (2013). Additionally, to ensure the
robustness of the main findings, the study employed both the fully modified ordinary least square (FMOLS) and the dynamic ordinary least square (DOLS) panel
estimate methods, similar to the studies conducted by Halim and Rahman (2022)
and Rahman et al. (2021a). This study finds that trade openness has a positive
association with taxation. Specifically, the study found that an increase in TFR,
TRO and average trade leads to an improvement in the tax-to-GDP ratio (TGR).
These findings suggest that as countries become more open to trade, they can
generate more revenue through taxation, supporting economic growth and development. Additionally, this highlights the importance of trade as a key driver of
economic development and the need for countries to continue to promote and
facilitate trade to support their economies’ growth.
This study contributes in at least three ways. First, it provides new evidence
of the positive relationship between trade openness and taxation in BRICS
countries. While this relationship has been reported in previous studies, our
study utilizes a novel methodology to uncover it. Specifically, we use panel data
regression analysis to account for unobserved heterogeneity and endogeneity
issues, which were not addressed in earlier studies. Therefore, our study offers
a more robust and reliable analysis of the relationship between trade openness
and taxation in these countries. Second, our findings on the positive effect of
TFR, TRO and average trade on the TGR in BRICS countries contribute to the
literature by emphasizing the importance of trade openness in driving economic
development. While this relationship has also been reported in earlier studies,
our study expands on the existing literature by providing new evidence on the
specific trade-related factors (TFR, TRO and average trades) influencing the
TGR. Finally, the study’ focus on BRICS countries is significant because these
countries represent a unique context for studying the relationship between trade
and taxation. In recent years, BRICS countries have undergone significant economic transformations and become major global players. Therefore, understanding the relationship between trade and taxation in these countries is
essential for informing policy decisions in other emerging economies and providing a broader perspective on the challenges and opportunities facing these
countries in today’s globalized world.
Rahman and Islam
5
The article is structured as follows: Section I contains the introduction. Section
II describes literature review. Section III describes the methodology. Section IV
presents the results and discussions. Section V discusses the robustness of results,
and Section 6 concludes.
II. Literature Review
Trade Openness and Taxation in BRICS Countries
Trade openness measures the ease with which goods and services can be traded
across borders (Brueckner & Lederman, 2015; Fenira, 2015; Rahman et al., 2023;
Sabina & Eldin, 2018). It often indicates a country’s integration into the global
economy. BRICS countries, which include Brazil, Russia, India, China and South
Africa, have varying levels of trade openness and different approaches to taxation.
Brazil has been a member of the WTO since 1995 and has a relatively open trade
policy. However, the country has been criticized for its high tariffs and non-tariff
barriers, making it difficult for foreign companies to do business in Brazil
(Shrestha et al., 2021). In recent years, the government has tried reducing tariffs
and simplifying the tax system to attract foreign investment. Russia is also a
member of the WTO and has been working to liberalize its trade policies (Amna
Intisar et al., 2020). However, the country’s dependence on natural resources,
such as oil and gas, has made it difficult for other sectors of the economy to
develop. The Russian government has also been criticized for using import tariffs
and other trade barriers to protect domestic industries (Adebayo et al., 2022).
India has been gradually liberalizing its trade policy since the 1990s and has
made significant progress in reducing tariffs and other trade barriers (Balavac &
Pugh, 2016). However, India still has a relatively high level of protection for
certain sectors, such as agriculture and textiles (Kawadia & Suryawanshi, 2021).
Additionally, the country’s tax system is complex and has been criticized for
being burdensome for businesses (Gnangnon, 2020). China has been one of the
world’s fastest-growing economies in recent years, and the country has made significant progress in liberalizing its trade policy. China is a member of the WTO
and has reduced tariffs and other barriers to trade. However, the country has been
criticized for using state-owned enterprises and other government support for
domestic industries, making it difficult for foreign companies to compete
(Habibullah & Eng, 2006). South Africa is also a member of the WTO and has a
relatively open trade policy. However, the country has been criticized for its high
tariffs and non-tariff barriers, which can make it difficult for foreign companies to
do business in South Africa. Additionally, the country’s tax system is complex and
has been criticized for being burdensome for businesses (Ramzan et al., 2019).
BRICS countries have varying levels of trade openness and different approaches
to taxation (Gnangnon, 2021a). While all five countries are members of the WTO,
they have been criticized for using tariffs and other trade barriers to protect
domestic industries. Additionally, all five countries have complex tax systems
that have been criticized for being burdensome for businesses (Bowdler &
6
Millennial Asia
Malik, 2017). Trade openness and taxation are critical factors influencing the economic growth and development of BRICS countries. While all five countries are
members of the WTO and have made progress in liberalizing their trade policies,
they still have a relatively high level of protection for specific sectors (Menyah
et al., 2014).
In the context of emerging countries like the BRICS, trade openness can significantly impact the TGR. These countries often seek to increase their integration
into the global economy and attract foreign investment to support economic
growth and development (Redmond & Nasir, 2020). A more open trade policy can
be an important tool in achieving this goal. An increase in trade openness in
BRICS countries can lead to a rise in economic growth and development, which
can lead to an increase in the tax base (Çevik et al., 2019; Naito, 2006). As more
businesses and individuals can participate in the economy, more taxpayers will
contribute to the government’s revenue. This can increase the TGR. For example,
as foreign companies invest in BRICS countries and establish operations there,
they will contribute to the local economy through their taxes and generate income
and employment opportunities, increasing the tax base (Cetin et al., 2018). As a
result, the TGR can increase.
Additionally, trade openness can lead to new industries developing in BRICS
countries, increasing the tax base (Cagé & Gadenne, 2018). As new industries
emerge, more businesses and individuals will participate in the economy and pay
taxes. However, it is important to note that trade openness can also lead to a
decrease in tax revenue if it results in increased competition from foreign companies, leading to lower prices and profit margins for domestic companies
(Gnangnon, 2022a). This, in turn, can lead to a decrease in the tax base and potentially a reduction in the TGR. Therefore, trade openness can significantly impact
the TGR in emerging countries like the BRICS. A more open trade policy can lead
to an increase in economic growth and development, which can increase the tax
base and lead to a higher TGR (Capasso et al., 2022). However, trade openness
can also lead to increased competition, decreasing the tax base and a lower TGR.
Therefore, it is important for BRICS countries to carefully consider the potential
impacts of trade openness on their TGR when formulating trade policies.
BRICS is a significant group of countries to test the research question of ‘Does
trade openness affect taxation?’ for several reasons: First, BRICS countries have
diverse economies, ranging from developing to industrialized, which allows for a
wide range of economic conditions to be studied and compared (Bayale et al.,
2022; Rani & Kumar, 2019). Second, the BRICS countries are home to over 40%
of the world’s population and account for more than 25% of global GDP. This
makes them an important group of countries to study regarding their economic
impact on the global economy (Arif et al., 2022; Rahman et al., 2021a). Third,
BRICS countries have experienced rapid economic growth in recent years, which
has led to significant changes in their trade policies and economic structures. This
makes them an ideal group of countries to study regarding the impact of trade
openness on taxation (Capasso et al., 2022; Rahman & Halim, 2022). Fourth,
BRICS countries have different trade policies, which allows for comparing the
effects of trade openness on taxation in different policy environments (Rahman &
Rahman and Islam
7
Halim, 2022). Fifth, BRICS countries are a mix of developed and developing
countries, which allows for a comparison of the effects of trade openness on taxation at different development levels (Bayale et al., 2022; Burange et al., 2018;
Fenira, 2015; Gnangnon, 2022b; Gries et al., 2009; Ramzan et al., 2019). Overall,
BRICS countries provide a unique opportunity to study the relationship between
trade openness and taxation in a diverse and dynamic economic setting, which can
provide valuable insights into the potential impacts of trade policy on tax systems
in other emerging and developing economies.
Theoretical Background
The relationship between trade openness and taxation has been the subject of much
debate in literature. One of the theories that can be used to analyse the relationship
between trade openness and taxation is the theory of comparative advantage
(Banday et al., 2021; Grant, 1991). Developed by economist David Ricardo in the
early nineteenth century, this theory posits that countries can benefit from trade
by specializing in the production of goods and services in which they have a comparative advantage and then trading with other countries for goods and services in
which they have a comparative disadvantage (Beaudreau, 2016). The theory of
comparative advantage explains how trade openness can improve a country’s TGR.
This specialization can lead to increased efficiency and economic growth.
When a country has high levels of trade openness, it allows for greater specialization and the ability to trade goods and services at a lower cost, increasing economic growth and leading to a higher GDP (Fenira, 2015). A higher GDP can
result in more revenue for the government through taxes, which can then improve
the TGR (Capasso et al., 2022). Additionally, trade openness can lead to increased
competition, which can increase productivity and innovation, which can also
boost GDP and, in turn, increase the TGR (Cetin et al., 2018; Gnangnon, 2022b;
Sabina & Eldin, 2018). As firms become more productive and efficient, they can
generate more revenue, which the government can tax. Furthermore, trade openness can attract foreign investment, which can also boost GDP and tax revenue.
Through specialization, increased efficiency and economic growth, trade openness can lead to a higher GDP and more tax revenue, resulting in an improved
TGR (Gnangnon & Brun, 2019; Menyah et al., 2014).
Further, the concept of dynamic comparative advantage can be added to the
theory of comparative advantage as it relates to trade openness and taxation
(Redding, 1999). The traditional comparative advantage theory assumes that countries have a static set of resources and technology and that these factors do not
change over time (Bond et al., 2003). However, in reality, countries can develop
new resources and technologies and improve their existing ones. Dynamic comparative advantage theory argues that countries can increase their comparative
advantage over time through investments in education, research and development
and infrastructure (Nishimizu & Page, 1986). These investments can lead to developing new industries and expanding existing ones, increasing a country’s GDP and
tax revenue.
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Millennial Asia
The theory of comparative advantage, developed by David Ricardo in the early
nineteenth century, posits that countries can benefit from trade by specializing in
the production of goods and services in which they have a comparative advantage
and then trading with other countries for goods and services in which they have a
comparative disadvantage. This specialization can increase efficiency and economic growth, resulting in higher GDP and tax revenue. Trade openness allows
for greater specialization and the ability to trade goods and services at a lower
cost, increasing economic growth and leading to a higher GDP. Additionally, trade
openness can lead to increased competition, increased productivity and innovation, further boosting GDP and tax revenue. However, the theory of comparative
advantage only offers one perspective on the relationship between trade and taxation. New growth theories, trade theories, economic geography and structural
economics offer additional channels through which trade can affect economic
growth and tax revenue.
New growth theories emphasize the role of technological progress and knowledge spillovers in driving economic growth (Aghion & Howitt, 1990; Romer,
1990). Trade openness can facilitate knowledge and technology transfer across
borders, leading to increased productivity and innovation. This, in turn, can lead
to higher GDP and tax revenue. On the other hand, new trade theories emphasize
the role of economies of scale and imperfect competition in driving economic
growth (Helpman & Krugman, 1987; Krugman, 1980). Trade openness can facilitate the entry of firms into new markets, leading to increased competition and
economies of scale. This, in turn, can lead to higher GDP and tax revenue. New
economic geography theory emphasizes the role of agglomeration effects and
spatial spillovers in driving economic growth (Fujita & Krugman, 2004). Trade
openness can facilitate the concentration of economic activity in certain regions,
leading to agglomeration effects and spatial spillovers. This, in turn, can lead to
higher GDP and tax revenue. Finally, the new structural economic theory emphasizes the role of industrial upgrading and structural transformation in driving economic growth (Lin, 2011). Trade openness can facilitate the upgrading of
industries and the transformation of economies from low-productivity to highproductivity activities. This, in turn, can lead to higher GDP and tax revenue.
In addition to these theories, dynamic comparative advantage highlights the
importance of human capital, technology and infrastructure investments in
increasing a country’s comparative advantage over time (Bond et al., 2003). By
investing in these areas, governments can increase their ability to produce goods
and services at a lower cost with higher quality, making them more competitive in
the global market. This can lead to increased trade and economic growth, which
can, in turn, increase tax revenue and improve the TGR. In terms of trade
openness, dynamic comparative advantage theory suggests that countries can
improve their comparative advantage and increase their exports by investing in
human capital, technology and infrastructure (Bond et al., 2003; Redding, 1999).
By investing in these areas, countries can increase their ability to produce goods
and services at a lower cost and with higher quality, making them more competitive in the global market. This can lead to increased trade and economic growth,
increasing tax revenue and improving the TGR (Nishimizu & Page, 1986).
Rahman and Islam
9
Dynamic comparative advantage theory adds an important perspective to the traditional comparative advantage theory by emphasizing the role of investments in
human capital, technology and infrastructure in increasing a country’s comparative advantage over time, which can improve trade openness and tax revenue
(Bond et al., 2003).
The conceptual framework (see Figure 1) for examining the association
between trade openness (independent variable) and taxation (dependent variable)
can be based on the dynamic comparative advantage theory. According to this
theory, countries can improve their comparative advantage over time through
investments in human capital, technology and infrastructure (Bond et al., 2003).
These investments can lead to the development and expansion of new industries,
increasing a country’s GDP and tax revenue. In this context, trade openness can
be seen as a way for countries to access new markets and customers, leading to
increased exports and economic growth (Fenira, 2015; Keho, 2017). As a result,
trade openness can positively influence the TGR, as increased economic growth
can lead to increased tax revenue. However, other factors such as FDV, INF, FON,
GPR and PLS can also affect the association between trade openness and taxation
(Balavac & Pugh, 2016; Gnangnon, 2021b). These variables can be controlled for
in the analysis to isolate the effect of trade openness on taxation.
Hypothesis Development
Trade openness refers to the degree to which a country allows goods and services
to be imported and exported (Keho, 2017). Trade policy indicators, such as tariffs
and non-tariff barriers, can measure this. Countries with high levels of trade openness tend to have lower tariffs and fewer non-tariff barriers, making crossing their
borders easier for goods and services. Taxation, conversely, refers to the process
by which a government imposes levies on individuals and businesses (Raghutla,
2020). Taxation can take many forms, such as income tax, sales tax and tariffs. In
the context of trade openness, taxes and tariffs can act as non-tariff barriers to
Figure 1. Conceptual Model of the Research.
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trade, making it more difficult for goods and services to cross borders. The relationship between trade openness and taxation is complex. On the one hand, trade
openness can lead to increased economic growth and increased competition,
which can result in lower prices for consumers (Gnangnon, 2020). On the other
hand, trade openness can lead to the loss of domestic jobs and industries as companies relocate to countries with lower taxes and labour costs. Additionally, taxes
and tariffs can be used to protect domestic industries, but they can also lead to
retaliation and trade conflicts with other countries. In recent years, there has been
increasing debate about the effects of trade openness and taxation on the global
economy. Menyah et al. (2014) argue that trade openness and lower taxes lead to
increased economic growth, while others argue that they lead to increased inequality and the erosion of domestic industries. Ultimately, the ideal balance
between trade openness and taxation will depend on a country’s specific economic, political and social circumstances.
Several studies have found evidence that trade openness can improve the TGR
or taxation (Baunsgaard & Keen, 2010; Gnangnon, 2021a; Gnangnon & Brun,
2019). One study by the International Monetary Fund (IMF) found that trade
openness positively correlates with the TGR in 135 countries from 1990 to 2011
(Cagé & Gadenne, 2018). The study found that an increase in trade openness of
10 percentage points is associated with an increase in the TGR of about 0.5 percentage points. Another study by the World Bank found that trade openness was
positively associated with the TGR in a sample of 114 developing countries over
the period of 1990–2010 (Shrestha et al., 2021). The study found that a 10-percentage point increase in trade openness is associated with an increase in the TGR
of about 1.5 percentage points.
Aydin and Turan (2020) suggest that tax reform positively and significantly
impacts tax revenue as a percentage of GDP. Countries at a higher level of development got to experience a greater magnitude of the positive effect of tax reform
on tax revenue performance (Burange et al., 2018). The study also suggests that
trade openness can affect tax revenue through the export revenue of trading firms.
As firms oriented towards international trade activities would have higher corporate revenue and employees working in these firms would have higher personal
income, the government could collect more personal income tax revenue and corporate tax revenue (Ramzan et al., 2019). Additionally, the rise in corporate and
personal income can lead to higher consumption, generating more value-added
tax revenue, excise tax revenue and possibly higher tariff revenue if the rise in
these incomes leads to higher imports.
In this study, H1 posits that trade openness, measured by various indicators
such as TFR, TRO and average trade, positively impacts taxation. This hypothesis
is based on the idea that increased trade leads to greater economic activity, resulting in higher corporate and personal income and, ultimately, higher tax revenue
for the government. This hypothesis is supported by studies such as Bayale et al.
(2022), Cagé and Gadenne (2018), Cetin et al. (2018), Gnangnon (2022a) and
Amna Intisar et al. (2020), which found that tax reform has a positive and significant effect on tax revenue as a percentage of GDP and that trade openness could
affect tax revenue through trading firms’ export revenue. However, it is important
11
Rahman and Islam
to note that trade openness may also have negative effects on taxation, as seen in
the studies by Bayale et al. (2022), Beverelli and Ticku (2022) and Banday et al.
(2021), which found an inverse relationship between TFR and tax revenue collection in Pakistan. Therefore, further research is needed to fully understand the relationship between trade openness and taxation.
H1: There is an association between trade openness and taxation.
III. Data and Methodology
In this section, this study will provide an overview of the data used, including the
description of the variables and their sources, the period of analysis, the estimated
model and various statistical tests such as the descriptive statistics, correlation
matrix, CSD test, unit root test and panel regression selection criteria. The aim is to
provide a thorough understanding of the data and methods used in the analysis.
Data and Sample
The data for this research are collected from secondary sources such as the World
Bank, the IMF and the global economy. The sample for this study includes the
BRICS countries: Brazil, Russia, India, China and South Africa. Including these
countries in the analysis is significant because they are emerging economies that
have proliferated in recent years and had a significant impact on the global economy
(Burange et al., 2018; Halim & Rahman, 2022). They are also considered key
players in the global trade system and have been experiencing significant economic
growth in recent years. Additionally, the BRICS countries have different levels of
trade openness and varying tax systems, which makes them ideal for studying the
relationship between trade openness and taxation. The study period will be
from 2000 to 2021, providing a long-term perspective on the relationship between
trade openness and taxation in these countries. Table 1 shows the details of the
variables.
Model Specification
In this research, our main objective is to examine the effects of TFR, TRO, ATR,
FDV, INF, FON, GDP and PLS on the effect of taxation in BRICS countries based
on panel data for 2000–2021. The study will specify an empirical model to
examine the effects of taxation in the BRICS countries (see Equation (1)).
C
X it C 1Trade Opennessit c Yitc it , (1)
c 1
Table 1. Descriptions of Dependent, Independent and Control Variables.
Variables
Sign
Measurement
Sources
Dependent Variable (Tax Revenue)
Tax-to-GDP ratio
TGR
TGR indicates the total tax revenue and GDP ratio of the sample countries each
year.
TCL indicates the log of total tax collection in the sample countries each year.
WDIa
TFR measures the extent of trade openness based on the sum of exports and
imports % of the GDP of the sample countries each year.
TRO measures the ratio of the export price index to the import price index
of the sample countries each year.
ATR equals the average tariff rate for all traded goods in the sample countries
each year.
WDI and World
Bank
WDI and World
Bank
WDI and World
Bank
The FDV index is a broad measure of FDV by considering its efficiency, accessibility
and depth. It takes value from each country each year.
The inflation rate of each country at each year
The KAOPEN (the Chinn–Ito Index) index measures constraints on capital and
current account transactions, the requirement for surrendering export proceeds
and multiple exchange rates. The index ranges from 0 to 1, where a higher value
indicates more financial openness.
Each country’s GPR (constant 2010 US$) each year.
The PLS index reflects the possibility of politically motivated violence, including
terrorism. The variable ranges from –2.5 to 2.5, with higher values indicating
more PLS.
IMFb
Taxation
TCL
Independent Variable (Trade Openness)
Trade freedom
TFR
Trade ratio
TRO
Average tariffs rate
ATR
Control Variables
Financial development
FDV
Inflation
Financial openness
INF
FON
GDP per capita
Political stability
GPR
PLS
Source: ahttps://databank.worldbank.org/source/world-development-indicators.
b
https://www.imf.org/en/Data.
c
https://knoema.com/NBERCIFOI2017/the-chinn-ito-financial-openness-index.
d
https://www.theglobaleconomy.com/rankings/wb_political_stability/.
Note: WDI, World Development Indicators.
WDI
WDI
KNOEMAc
WDI
Global economyd
13
Rahman and Islam
where C is a constant term, i and t subscripts represent the country and year,
respectively. X is the dependent variable that indicates tax revenues. The dependent variable in this study is tax revenues, which are measured by the TGR and tax
collection (TCL). The independent variable of interest is trade openness, which is
proxied by TFR, TRO and ATR. Y with superscripts c are the vectors of control
variables. Control variables such as FDV, INF, FON, GDP and PLS are considered in the analysis, and eit is the error term. The definitions and data sources for
each variable are provided in Table 1.
This article uses three measures of trade openness and two measures of tax
revenues and rewrites Equation (1) as follows:
C
TGRit C TFRit cYitc it , (1.1a)
c 1
C
c
TCLit C TFRit cYit it . (1.2a)
c 1
Here, TGR, TCL and TFR are tax-to-GDP ratio, tax collection and trade freedom,
respectively.
C
TGRit C TROit cYit it , (1.1b)
c
c 1
C
TCLit C TROit cYitc it . (1.2b)
c 1
Here, TRO is the trade ratio.
C
TGRit C ATRit cYitc it , (1.1c)
c 1
C
TCLit C ATRit cYit it . (1.2c)
c
c 1
Here, ATR is the average tariff rate.
Further, the researchers employed the combined effects of all the independent
variables and rewrite Equation (1) as follows:
C
TGRit C 1TFRit 2TROit 3 ATRit cYitc it , (2a)
c 1
C
TCLit C 1TFRit 2TROit 3 ATRit cYitc it . (2b)
c 1
14
Millennial Asia
Diagnostic Tests
Descriptive Statistics and Correlation Matrix
In this research, the study presents summary statistics and a correlation matrix in
Table 2, which includes the overall mean and standard deviation of the TGR, TFR
and other variables. Regarding the TGR, the overall mean is 0.50 with a standard
deviation of 0.14, and in the case of TFR, the overall mean is 0.47 with a standard
deviation of 0.20. The moderate levels of TGR and TFR indicate that the BRICS
countries have room for improvement in their taxation scenario (Morrow et al.,
2022). The mean values of other variables, such as TFR, TRO, ATR, FDV, INF,
FON, GPR and PLS, are also presented in the table. The mean taxation proxies
TFR, TRO, ATR, FDV, INF, FON, GPR and PLS are 0.23, 0.29, 0.31, 0.44, 0.12,
0.37, 0.22 and 0.23, respectively. Additionally, the correlation matrix shows the
correlation between different variables, which is less than 80%, indicating that
multicollinearity is not an issue in this study (Rahman et al., 2021a). This indicates that the results of the study are reliable and valid.
Test of CSD and Unit Roots
The CSD test checks for cross-sectional dependence, which can occur in panel
data and lead to biased results (Sarafidis & Wansbeek, 2012). It ensures that the
panel regression results are reliable and not affected by CSD. Further, unit root
tests are used to determine whether a time series is stationary or non-stationary
(Strauss & Yigit, 2003). This is important because many statistical techniques
assume that the analysed data is stationary. If a time series is non-stationary, it can
lead to incorrect conclusions and invalid inferences. Therefore, conducting a unit
root test allows the study to determine whether the data is suitable for further
analysis and to ensure the results’ validity.
The CSD test, presented in Table 3, is used to determine dependency among
the study’s cross-section units. The null hypothesis of no CSD is rejected at a
significance level of 1%, 5% and 10% using Breush–Pagan LM, Pesaran scaled
LM and Pesaran CSD criteria (Sarafidis & Wansbeek, 2012). The results indicate
the presence of CSD among the units, which is further examined through a test for
stationarity.
This study finds CSD in the series. Thus, conducting a second-generation unit
root test on the trade openness and taxation data is important to determine whether
the panel dataset is stationary or non-stationary (Breitung & Das, 2005; Pesaran,
2007). The second-generation unit root test, also known as the panel unit root test,
is used to test for the presence of a unit root in a panel dataset, where a panel
dataset consists of multiple cross-sectional units and time series observations
(Strauss & Yigit, 2003). This type of unit root test allows for the presence of both
CSD and heteroscedasticity, which can affect the stationarity of the series
(Breitung & Das, 2005). Using a second-generation unit root test in this context is
important because it allows for a more accurate determination of stationarity,
which is essential for proper econometric analysis and interpretation of the results.
Table 2. Summary Statistics and Correlation Matrix of the Variables.
Criteria
TGR
TCL
TFR
TRO
ATR
FDV
INF
FON
GPR
PLS
Mean
Median
Maximum
Minimum
SD
Skewness
Kurtosis
Obs.
Variables
0.50
0.47
0.59
0.32
0.14
1.40
1.64
110
0.47
0.42
0.48
0.35
0.20
0.96
3.16
110
0.23
0.18
0.33
0.24
0.13
–0.62
2.09
110
TFR
0.29
0.26
0.35
0.17
0.08
0.87
2.64
110
TRO
0.31
0.28
0.40
0.20
0.05
0.97
1.63
110
ATR
0.44
0.38
0.50
0.23
0.10
–0.22
2.33
110
FDV
0.12
0.10
0.24
0.14
0.02
1.58
1.79
110
INF
0.37
0.32
0.38
0.17
0.06
0.89
1.67
110
FON
0.22
0.17
0.32
0.23
0.12
0.55
2.80
110
GPR
0.23
0.21
0.24
0.17
0.10
0.83
2.34
110
PLS
1
0.39*
0.21*
0.27*
0.32*
1
0.16*
0.24*
0.34*
1
0.47*
0.22*
1
0.20*
1
TFR
TRO
ATR
FDV
INF
FON
GPR
PLS
Note: *Indicates 1% level of significance.
1
0.41*
0.20*
0.16*
0.25*
0.18*
0.13*
0.21*
1
0.32*
0.28*
0.36*
0.17*
0.16*
0.31*
1
0.45*
0.33*
0.17*
0.13*
0.37*
16
Millennial Asia
Table 3. Results of Cross-Sectional Dependence (CSD) test with three tests (Breusch–
Pagan LM, Pesaran Scaled LM and Pesaran CSD).
Variables
Breusch–Pagan LM
Pesaran Scaled LM
Pesaran CSD
TGR
TCL
TFR
TRO
ATR
FDV
INF
FON
GPR
PLS
–6.6465**
–8.6926*
–6.9433*
–7.3052**
–8.8018***
–8.288**
–7.0742**
–7.1568*
–6.4505**
–6.6465**
–6.65**
–8.673**
–6.923*
–7.294**
–8.792**
–8.267***
–7.056*
–7.14*
–6.433*
–6.65*
–6.6612**
–8.7122***
–6.9601**
–7.3227
–8.82**
–8.3076**
–7.0875**
–7.1736**
–6.4617*
–6.6612*
Note: *, ** and *** indicate 10%, 5% and 1% level of significance, respectively.
Table 4. Results of the Unit Root Test with Second-Generation Approach (CrossSectional Augmented Dickey–Fuller [CADF] Test and Cross-Sectional Augmented IPS
[CIPS] Test).
CADF Test
CIPS Test
Variables
C
C+T
C
C+T
Decisions
DTGR
DTCL
DTFR
DTRO
DATR
DFDV
DINF
DFON
DGPR
DPLS
–10.003**
9.758**
9.450**
–9.772****
–9.373*
9.597**
9.611**
–9.933**
10.395*
12.861***
–9.989**
–9.744**
–9.443***
9.751***
9.380*
9.646**
–9.870**
–10.269**
10.423**
12.843**
–6.328**
6.406**
6.587**
6.819***
–6.561*
–5.318*
6.742***
–6.934***
5.821**
10.045**
–7.232***
7.322**
7.528**
7.794***
–7.498**
–6.078***
7.705**
–7.924**
6.652**
8.929***
I (1)
I (1)
I (1)
I (1)
I (1)
I (1)
I (1)
I (1)
I (1)
I (1)
Note: *, ** and *** indicate 10%, 5% and 1% level of significance, respectively. C, Constant; T, Trend.
The cross-sectional augmented Dickey–Fuller (CADF) and cross-sectional
augmented IPS (CIPS) tests are considered good options for unit root testing in
panel data, as they consider CSD and provide accurate results (Westerlund et al.,
2016). Thus, the study investigated the stationary nature of the series by applying
the second-generation unit root test, which is shown in Table 4. The null hypothesis for these tests is that the series is non-stationary, meaning it has a unit root
(Pesaran, 2007). If the null hypothesis is rejected, the series is stationary and does
not have a unit root. The first differencing method is used to make a time series
stationary. In this case, the study found that the trade openness and taxation data
were non-stationary at the level but became stationary after the first differencing
17
Rahman and Islam
(Westerlund et al., 2016). This means the trade openness and taxation data had a
unit root at the level but not after the first differencing. This is essential information for the econometric analysis, as the study can now estimate the panel data
model using the stationary data.
Selection of Fitted Model
The Chow and BP tests are used to determine the appropriate panel model selection for analysing the relationship between trade openness and taxation in BRICS
countries (Candelon & Lütkepohl, 2001). The Chow test, also known as the structural break test, compares the fit of a larger model with a smaller model by looking
at the change in goodness-of-fit statistics between the two models (Schunck,
2013).
The null hypothesis for the Chow test for panel model selection is that there is
no structural change in the model’s coefficients across different subsamples. In
other words, it tests whether the model’s coefficients are the same across different
subsamples. The null hypothesis for the BP test is that the model’s coefficients are
not different across subsamples, meaning there is no structural change in the coefficients (Schunck, 2013). Suppose the null hypothesis is rejected for both tests. In
that case, it suggests that the OLS model is appropriate for the data, and the model
coefficients are consistent across the samples (see Table 5).
The Hausman test is a statistical test used to determine whether a fixed- or
random-effects model is more appropriate for a given dataset (Schunck, 2013).
The null hypothesis of the Hausman test is that the random-effects model is
preferred, while the alternative hypothesis is that the fixed-effects model is preferred. This test is important in this respect because it allows the researcher to
determine whether the differences between the groups (in this case, the BRICS
countries) are due to random variation or if they are due to unobserved, timeinvariant factors. If the fixed-effects model is preferred, it suggests that the differences between the countries are due to unobserved factors, while if the
random-effects model is preferred, it suggests that the differences are due to
random variation (Schunck, 2013). In this study, by accepting the null hypothesis, it means that the random effects estimator is the appropriate choice for the
panel data analysis of the relationship between trade openness and taxation in
BRICS countries (see Table 6).
Table 5. Appropriateness of Ordinary Least Square (OLS) Model Selection.
Test
p Value
Decision
Chow (F-test)
0.025**
BP (|2 test)
0.012***
Reject null hypothesis (pooled OLS is the
effective model)
Reject null hypothesis (pooled OLS is the
effective model)
** and *** indicate 5% and 1% level of significance, respectively.
18
Millennial Asia
Table 6. Results of Hausman Test to Select Fixed-Effects or Random-Effects Model.
Test
p Value
Decision
Cross-section random
0.411
Period random
0.326
Accept null hypothesis (use
random-effects model)
Accept null hypothesis (use
random-effects model)
Cross-section and period
random
0.642
Accept null hypothesis
(use random-effects model)
IV. Finding and Discussion
This section presents the main findings of the study using a random-effects model.
Additionally, the robustness of these findings is examined using FMOLS and
DOLS methods. The results of these models are compared and contrasted to
provide a thorough analysis of the relationship between trade openness and taxation in BRICS countries. The section also discusses how these findings contribute
to the existing literature.
Table 7 shows the relationship between trade openness and taxation in the
context of BRICS countries. This finding suggests that greater TFR, higher TROs
and lower ATRs positively impact tax revenues in BRICS countries, as measured
by both the TGR and TCL. This aligns with existing studies on the relationship
between trade openness and tax revenues, which have generally found that greater
trade openness leads to higher tax revenues (Gnangnon & Brun, 2019; Raghutla,
2020; Sabina & Eldin, 2018). One possible explanation for this relationship is that
increased trade leads to more remarkable economic growth and development, as
well as higher tax revenues as more economic activity is subject to taxation.
Additionally, greater trade openness may increase foreign investment and a more
diversified economy, contributing to higher tax revenues. These findings are particularly relevant for BRICS countries as they are among the most rapidly growing
economies in the world, and trade openness is considered an important driver of
their economic development (Banday et al., 2021; Burange et al., 2018).
However, some studies have found an inverse or no significant relationship
between trade openness and taxation. Brueckner and Lederman (2015) found that
trade openness had a negative impact on tax revenue collection, while a study by
Cetin et al. (2018) found no significant relationship between trade openness and
tax revenue collection. Increased trade liberalization may lead to a decrease in tax
revenues due to the erosion of tax bases and increased competition among countries to attract foreign investment by offering low tax rates. Additionally, trade
openness may lead to an increase in tax evasion and avoidance, as individuals and
businesses may be able to take advantage of cross-border trade to evade taxes.
However, the findings of this study, which indicate a positive relationship between
trade openness and taxation in BRICS countries, add to the literature by suggesting that the relationship between trade and taxation may vary depending on the
specific context and economic conditions of a country or region.
0.380
0.630
0.283
0.187
0.098**
0.169**
–0.196**
0.292
0.147**
0.232*
0.408
0.395
2.478
27.508***
1.879
0.035*
TGR
0.494
0.526
0.126*
0.023**
0.034*
0.235**
0.241**
–0.196*
0.385*
0.210**
0.299*
0.782
0.776
3.444
26.401***
2.619
Combined
Effects
(TGR)
0.794
0.299
0.311**
–0.281*
0.553*
0.301*
0.417**
0.824
0.799
4.848
56.745***
3.721
0.044*
0.207**
TCL
0.263
0.174
0.157**
–0.182*
0.272*
0.137
0.216*
0.379
0.367
2.305
25.589***
1.748
0.007**
0.014**
TCL
Individual Effects
0.429
0.185
0.120***
0.192***
–0.173
0.341*
0.186**
0.257*
0.509
0.494
2.994
35.048***
2.298
0.019**
TCL
0.700
0.745
0.179**
0.032*
0.048**
0.333**
0.342**
–0.277**
0.546*
0.298*
0.423**
0.825
0.798
4.879
37.402***
3.710
Combined
Effects (TCL)
Note: The Wooldridge autocorrelation test’s null hypothesis (absence of autocorrelation) was accepted, and the null hypothesis (error terms are normally distributed) of the
Greene heteroscedasticity test was accepted. The dependent variables are the tax-to-GDP ratio (TGR) and tax collection (TCL). *, ** and *** indicate 10%, 5% and 1% level of
significance, respectively.
Greene heteroscedasticity test p
value
0.416
0.205
0.012***
0.049**
0.273**
–0.270**
0.429*
0.239**
0.352*
0.592
0.571
3.815
43.447***
2.940
0.022**
0.146**
Constant
DTFR
DTRO
DATR
DFDV
DINF
DFON
DGPR
DPLS
R-squared
Adj. R-squared
SE of regression
F-statistic
Durbin–Watson statistic
Diagnostic tests
Wooldridge autocorrelation test
p value
TGR
0.236**
–0.246**
0.383*
0.215**
0.314*
0.574
0.557
3.442
39.097***
2.601
TGR
Variables
Individual Effects
Table 7. Results of Regression Estimation Test with Random-Effect Model.
20
Millennial Asia
A positive relationship between the ATR and TGR may seem surprising initially, as one might expect that higher tariffs would discourage trade and economic activity, ultimately resulting in lower tax revenue for the government (Lal
& Myint, 1998). However, there are several potential explanations for this positive relationship. First, higher tariffs may increase government revenue through
import taxes, contributing to higher TGRs. Additionally, higher tariffs may protect
domestic industries, allowing them to grow and generate more taxable income.
Furthermore, countries with higher TGRs may be more likely to implement protectionist trade policies, such as higher tariffs, in order to shield domestic industries from foreign competition and maintain their revenue streams. Overall, while
a positive relationship between the ATR and the TGR may seem counterintuitive,
it is important to consider the factors that could be driving this relationship.
India’s TGR has been stagnant at around 15%–17% over several decades
(Burange et al., 2018). This finding may raise questions about the relationship
between trade openness and taxation in India and the factors contributing to this
stagnation. It is important to note that several structural factors may contribute to the
stagnant TGR in India, such as a large informal economy, weak tax administration
and a narrow tax base. Additionally, the effects of trade openness on tax revenues
may vary depending on the specific economic conditions of a country. Therefore,
while the findings of this study suggest a positive relationship between trade openness and taxation in the context of BRICS countries, it is necessary to consider the
unique circumstances of individual countries and regions when interpreting these
results. Further research is needed to explore the relationship between trade and
taxation in India and other countries with similar economic conditions.
This study finds that FDV, FON, GPR and PLS positively impact taxation.
These findings are consistent with previous studies that have found a positive
relationship between these factors and tax revenue. For example, a study by
Brueckner and Lederman (2015) found that FDV is positively associated with tax
revenues in developing countries. Raghutla (2020) also found that GPR is positively associated with tax revenues, and a study by Gnangnon (2021b) found that
PLS is positively associated with tax revenues. On the other hand, this study also
finds that INF has a negative effect on taxation, which is consistent with previous
studies that have found that high INF can negatively impact tax revenues
(Baunsgaard & Keen, 2010; Bowdler & Malik, 2017; Morrow et al., 2022).
It is important to check the robustness of the main findings because it helps to
ensure that the results are not sensitive to small changes in the model specification
or data (Bowdler & Malik, 2017; Deb et al., 2022; Rahman et al., 2021b, 2021c;
Sabina & Eldin, 2018). By conducting robustness checks, the study can be more
confident that the findings are robust and generalizable to other contexts.
Additionally, by using different methods and models, it is possible to identify any
potential outliers or issues that may have affected the initial results and make
adjustments accordingly (Gnangnon & Brun, 2019). Robustness checks can also
be used to identify any potential sources of bias or errors in the data or analysis
and ensure that the results are not driven by any specific assumptions or conditions (Çevik et al., 2019). These robustness-checking findings are presented in
Tables 8 and 9. The use of FMOLS and DOLS in this research ensures that the
0.015**
0.100**
Constant
DTFR
DTRO
DATR
DFDV
DINF
DFON
DGPR
DPLS
R-squared
Adj. R-squared
SE of regression
Long-run variance
0.185**
–0.184**
0.292*
0.162*
0.240*
0.402
0.388
0.754
0.007
0.008**
0.033*
TGR
0.067*
0.115*
–0.133**
0.199*
0.100
0.158**
0.277
0.269
0.710
0.005
0.024*
TGR
Note: *, ** and *** indicate 10%, 5% and 1% level of significance, respectively.
0.161*
–0.167**
0.260*
0.146**
0.214
0.391
0.379
1.610
0.001
TGR
Variables
Individual Effects
0.086**
0.016**
0.023*
0.160**
0.164**
–0.133*
0.262*
0.143**
0.203*
0.396
0.378
0.800
0.013
Combined
Effects (TGR)
0.212*
–0.191
0.376**
0.205*
0.283*
0.560
0.543
0.533
0.011
0.030*
0.141**
TCL
0.107**
–0.124*
0.185*
0.093
0.147**
0.258
0.250
1.447
0.032
0.005*
0.010**
TCL
Individual Effects
0.081**
0.131**
–0.118*
0.232**
0.126*
0.175**
0.346
0.336
0.624
0.035
0.013**
TCL
Table 8. Results of Regression Estimation Tests with Fully Modified Ordinary Least Square (FMOLS) Model for Robustness Checking.
0.121**
0.022**
0.032**
0.227***
0.232*
–0.188**
0.371*
0.202**
0.288*
0.561
0.536
1.416
0.007
Combined
Effects (TCL)
0.020*
0.132**
Constant
DTFR
DTRO
DATR
DFDV
DINF
DFON
DGPR
DPLS
R-squared
Adj. R-squared
SE of regression
Long-run variance
0.247*
–0.244*
0.388*
0.216**
0.319*
0.535
0.517
1.003
0.009
0.011*
0.044*
TGR
0.089**
0.152
–0.177*
0.264**
0.133*
0.210*
0.369
0.357
0.944
0.006
0.032*
TGR
Note: *, ** and *** indicate 10% 5% and 1% level of significance, respectively.
0.214*
–0.222*
0.346
0.194*
0.284**
0.519
0.503
2.141
0.002
TGR
Variables
Individual Effects
0.114**
0.021**
0.030**
0.213**
0.218*
–0.177*
0.348
0.190*
0.270**
0.727
0.703
1.064
0.017
Combined
Effects (TGR)
0.281*
–0.254*
0.500**
0.272*
0.377*
0.745
0.733
0.709
0.014
0.040
0.188**
TCL
0.142**
–0.165*
0.246*
0.124**
0.195*
0.343
0.332
1.925
0.043
0.007**
0.013*
TCL
Individual Effects
Table 9. Results of Regression Estimation Test with Dynamic Ordinary Least Square (DOLS) Model for Robustness Checking.
0.108*
0.174*
–0.157*
0.309**
0.168*
0.233*
0.460
0.446
0.830
0.046
0.017
TCL
0.161*
0.029**
0.043*
0.301**
0.309*
–0.251*
0.494
0.269*
0.383
0.846
0.822
1.884
0.010
Combined
Effects (TCL)
Rahman and Islam
23
study’s main findings are robust and not affected by any omitted variable bias or
model specification errors (Halim & Rahman, 2022; Keho, 2017). These techniques are commonly used in panel data analysis to check the robustness of the
results. Using these techniques in the context of BRICS countries is important as
it can provide more robust evidence of the relationship between trade openness
and taxation. This is particularly relevant in the literature on trade openness and
taxation, as there have been mixed findings in previous studies. Using FMOLS
and DOLS, this study can provide more robust evidence of the relationship
between trade openness and taxation in the context of BRICS countries, which
can contribute to the existing literature on this topic.
V. Conclusion, Policy Implications and Future
Research Direction
This study finds that trade openness positively impacts taxation in BRICS countries.
Specifically, the study finds that TFR, TRO and average trade increase TGR and
TCL. This finding is consistent with previous literature in the field, which also
shows that trade openness can positively impact taxation (Gnangnon & Brun, 2019;
Morrow et al., 2022). The study also finds that FDV, FON, GPR and PLS positively
impact taxation, while INF has a negative effect on taxation. This study uses panel
data and employs various econometric techniques such as the CSD test, unit root
test, panel regression selection criteria and robustness checking FMOLS and DOLS
to investigate the relationship between trade openness and taxation in BRICS countries. The robustness check confirms the validity of the main findings. This study
contributes to the existing literature by providing evidence of the positive impact of
trade openness on taxation in BRICS countries and highlighting the importance of
considering other factors such as FDV, FON, GPR, PLS and INF in understanding
the relationship between trade openness and taxation.
The findings of this study provide theoretical implications for comparative
advantage theory. The results indicate that trade openness, measured by TFR and
TRO, positively impacts taxation in the BRICS countries. This supports the idea
that countries with higher levels of trade openness can improve their taxation
systems, potentially through increased revenue from trade. This aligns with the
comparative advantage theory, which states that countries should specialize in
producing and exporting goods and services in which they have a comparative
advantage and import those they do not. As a result, countries with higher levels
of trade openness may have access to more resources and knowledge, which can
be used to improve their taxation systems (Rahman et al., 2021a). This study also
finds that FDV, FON, GPR and PLS positively impact taxation, which INF negatively affects taxation. This highlights these factors’ importance in the BRICS
countries’ taxation systems.
The findings of this study provide strong evidence for the comparative advantage theory, which suggests that countries should specialize in the production and
export of goods and services in which they have a comparative advantage to
24
Millennial Asia
achieve economic growth and stability (Beaudreau, 2016). This research also
highlights the importance of FDV, FON, GPR and PLS for tax revenue collection.
The results of this study can be used by policymakers in BRICS countries to
inform their economic development strategies and trade policies.
Based on the findings of this study, several key managerial implications can be
drawn. First, the results suggest that trade openness positively impacts taxation in
BRICS countries. This means that policymakers in these countries should prioritize measures that promote trade and investment, such as reducing tariffs and
non-tariff barriers and promoting a stable and predictable business environment.
The findings also suggest that FDV, FON, GPR and PLS positively impact taxation. Therefore, policymakers should focus on implementing policies that promote
FDV and stability and measures that increase GPR, such as investments in infrastructure and education. Finally, it is important to note that INF has a negative
effect on taxation (Bowdler & Malik, 2017). Therefore, policymakers should prioritize measures that help to control INF, such as monetary policy measures, and
implement policies that promote long-term economic growth. Overall, the findings of this study suggest that policymakers in BRICS countries should prioritize
efforts that promote trade openness and economic development to improve their
taxation systems.
This study has the following limitations that create future research directions:
First, this research focused on BRICS countries and did not consider other developing countries. Therefore, generalizing the findings to other developing or
developed countries would be inappropriate. Second, this study relies on secondary data and may not reflect the true picture of the economy. Additionally, this
study only considers trade openness and taxation as the main variables, while
other factors like government expenditure, corruption and PLS may also affect the
relationship. Finally, this research only uses panel data regression analysis, while
other methods like structural equation modelling or machine learning could
provide more comprehensive results. Therefore, future researchers can consider
these limitations and incorporate them into their studies to provide more robust
and accurate findings. Primary data-based studies with PLS-SEM analysis can
also be conducted in the future (Deb et al., 2022; Rahman, 2023; Rahman &
Akhter, 2021; Rahman & Islam, 2023).
Acknowledgement
The authors are thankful to the colleagues and reviewers who assist them to amplify the
quality of the article.
Author Contribution
Md. Mominur Rahman conceptualized, developed the research framework, collected
data, performed statistical analysis, interpreted findings, wrote implications, performed
referencing, wrote the thesis and prepared the drafted report. Mohammad Ekramol Islam
performed partly preparation of the introduction, literature review and conclusions. As the
corresponding author, Md. Mominur Rahman bears full responsibility for the submission
and confirms that all authors listed on the title page have contributed significantly to the
work. Finally, all authors read and approved the final manuscript.
25
Rahman and Islam
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research,
authorship and/or publication of this article.
Funding
The authors received no financial support for the research, authorship and/or publication
of this article.
ORCID iD
Md. Mominur Rahman https://orcid.org/0000-0001-5726-6123
References
Adebayo, T. S., Rjoub, H., Akinsola, G. D., & Oladipupo, S. D. (2022). The asymmetric
effects of renewable energy consumption and trade openness on carbon emissions in
Sweden: New evidence from quantile-on-quantile regression approach. Environmental
Science and Pollution Research, 29(2), 1875–1886. https://doi.org/10.1007/
s11356-021-15706-4
Aghion, P., & Howitt, P. (1990). A model of growth through creative destruction (Working
Paper No. 3223). National Bureau of Economic Research.
Amna Intisar, R., Yaseen, M. R., Kousar, R., Usman, M., & Makhdum, M. S. (2020).
Impact of trade openness and human capital on economic growth: A comparative
investigation of Asian countries. Sustainability, 12(7), 2930.
Arif, A., Sadiq, M., Shabbir, M. S., Yahya, G., Zamir, A., & Bares Lopez, L. (2022).
The role of globalization in financial development, trade openness and sustainable
environmental–economic growth: Evidence from selected South Asian economies.
Journal of Sustainable Finance & Investment, 12(4), 1027–1044. https://doi.org/10.10
80/20430795.2020.1861865
Aydin, M., & Turan, Y. E. (2020). The influence of financial openness, trade openness,
and energy intensity on ecological footprint: Revisiting the environmental Kuznets
curve hypothesis for BRICS countries. Environmental Science and Pollution Research,
27(34), 43233–43245. https://doi.org/10.1007/s11356-020-10238-9
Balavac, M., & Pugh, G. (2016). The link between trade openness, export diversification,
institutions and output volatility in transition countries. Economic Systems, 40(2),
273–287. https://doi.org/10.1016/j.ecosys.2016.02.001
Banday, U. J., Murugan, S., & Maryam, J. (2021). Foreign direct investment, trade openness
and economic growth in BRICS countries: Evidences from panel data. Transnational
Corporations Review, 13(2), 211–221. https://doi.org/10.1080/19186444.2020.1851162
Baunsgaard, T., & Keen, M. (2010). Tax revenue and (or?) trade liberalization.
Journal of Public Economics, 94(9), 563–577. https://doi.org/doi.org/10.1016/j.
jpubeco.2009.11.007
Bayale, N., Tchila, P., Yao, J.-P. A., & Tenakoua, H. (2022). Do tax administration reforms
improve tax revenue performance in Togo? Empirical insights from experimental
approaches. South African Journal of Economics, 90(2), 196–213. https://doi.org/doi.
org/10.1111/saje.12316
Beaudreau, B. C. (2016). Competitive and comparative advantage: Towards a unified
theory of international trade. International Economic Journal, 30(1), 1–18. https://doi.
org/10.1080/10168737.2015.1136664
26
Millennial Asia
Beverelli, C., & Ticku, R. (2022). Reducing tariff evasion: The role of trade facilitation.
Journal of Comparative Economics, 50(2), 534–554. https://doi.org/doi.org/10.1016/j.
jce.2021.12.004
Bond, E. W., Trask, K., & Wang, P. (2003). Factor accumulation and trade: Dynamic
comparative advantage with endogenous physical and human capital. International
Economic Review, 44(3), 1041–1060. https://doi.org/doi.org/10.1111/1468-2354.
t01-1-00099
Bowdler, C., & Malik, A. (2017). Openness and inflation volatility: Panel data evidence.
The North American Journal of Economics and Finance, 41, 57–69. https://doi.org/doi.
org/10.1016/j.najef.2017.03.008
Breitung, J., & Das, S. (2005). Panel unit root tests under cross-sectional dependence.
Statistica Neerlandica, 59(4), 414–433. https://doi.org/doi.org/10.1111/j.1467-9574.
2005.00299.x
Brueckner, M., & Lederman, D. (2015). Trade openness and economic growth: Panel data
evidence from Sub-Saharan Africa. Economica, 82(S1), 1302–1323. https://doi.org/
doi.org/10.1111/ecca.12160
Burange, L. G., Ranadive, R. R., & Karnik, N. N. (2018). Trade openness and economic
growth nexus: A case study of BRICS. Foreign Trade Review, 54(1), 1–15. https://doi.
org/10.1177/0015732518810902
Cagé, J., & Gadenne, L. (2018). Tax revenues and the fiscal cost of trade liberalization,
1792–2006. Explorations in Economic History, 70, 1–24. https://doi.org/doi.
org/10.1016/j.eeh.2018.07.004
Candelon, B., & Lütkepohl, H. (2001). On the reliability of Chow-type tests for parameter
constancy in multivariate dynamic models. Economics Letters, 73(2), 155–160. https://
doi.org/doi.org/10.1016/S0165-1765(01)00478-5
Capasso, S., Cicatiello, L., De Simone, E., & Santoro, L. (2022). Corruption and tax
revenues: Evidence from Italian regions. Annals of Public and Cooperative Economics,
93(4), 1129–1152. https://doi.org/doi.org/10.1111/apce.12356
Cetin, M., Ecevit, E., & Yucel, A. G. (2018). The impact of economic growth, energy
consumption, trade openness, and financial development on carbon emissions:
Empirical evidence from Turkey. Environmental Science and Pollution Research,
25(36), 36589–36603. https://doi.org/10.1007/s11356-018-3526-5
Çevik, E. İ., Atukeren, E., & Korkmaz, T. (2019). Trade openness and economic growth in
Turkey: A rolling frequency domain analysis. Economies, 7(2), 41.
Deb, B. C., Rahman, M. M., & Rahman, M. S. (2022). The impact of environmental
management accounting on environmental and financial performance: Empirical
evidence from Bangladesh. Journal of Accounting & Organizational Change, 19(3),
420–446. https://doi.org/10.1108/JAOC-11-2021-0157
Fenira, M. (2015). Trade openness and growth in developing countries: An analysis of the
relationship after comparing trade indicators. Asian Economic and Financial Review,
5(3), 468–482. https://doi.org/10.18488/journal.aefr/2015.5.3/102.3.468.482
Fujita, M., & Krugman, P. (2004). The new economic geography: Past, present and
the future. In R. J. G. M. Florax & D. A. Plane (Eds), Fifty years of regional science
(pp. 139–164). Springer.
Gnangnon, S. K. (2020). Export product diversification and tax performance quality in
developing countries. International Economics and Economic Policy, 17(4), 849–876.
https://doi.org/10.1007/s10368-020-00462-6
Gnangnon, S. K. (2021a). Export product diversification, poverty and tax revenue in
developing countries. The Journal of International Trade & Economic Development,
30(7), 957–987. https://doi.org/10.1080/09638199.2021.1919182
Rahman and Islam
27
Gnangnon, S. K. (2021b). Financial development and tax revenue in developing countries:
Investigating the international trade channel. SN Business & Economics, 2(1), 1.
https://doi.org/10.1007/s43546-021-00176-0
Gnangnon, S. K. (2021c). Tax reform and public debt instability in developing countries:
The trade openness and public revenue instability channels. Economic Analysis and
Policy, 69, 54–67. https://doi.org/doi.org/10.1016/j.eap.2020.11.005
Gnangnon, S. K. (2022a). Tax revenue instability and tax revenue in developed and
developing countries. Applied Economic Analysis, 30(88), 18–37. https://doi.
org/10.1108/AEA-09-2020-0133
Gnangnon, S. K. (2022b). Tax transition reform and economic growth in developing
countries. The International Trade Journal, 1–24. https://doi.org/10.1080/08853908.
2022.2154719
Gnangnon, S. K., & Brun, J.-F. (2019). Trade openness, tax reform and tax revenue in
developing countries. The World Economy, 42(12), 3515–3536. https://doi.org/doi.
org/10.1111/twec.12858
Grant, R. M. (1991). Porter’s ‘competitive advantage of nations’: An assessment.
Strategic Management Journal, 12(7), 535–548. https://doi.org/doi.org/10.1002/
smj.4250120706
Gries, T., Kraft, M., & Meierrieks, D. (2009). Linkages between financial deepening,
trade openness, and economic development: Causality evidence from sub-Saharan
Africa. World Development, 37(12), 1849–1860. https://doi.org/doi.org/10.1016/j.
worlddev.2009.05.008
Habibullah, M. S., & Eng, Y.-K. (2006). Does financial development cause economic
growth? A panel data dynamic analysis for the Asian developing countries. Journal of the
Asia Pacific Economy, 11(4), 377–393. https://doi.org/10.1080/13547860600923585
Halim, M. A., & Rahman, M. M. (2022). The effect of taxation on sustainable development
goals: Evidence from emerging countries. Heliyon, 8(9), e10512. https://doi.org/doi.
org/10.1016/j.heliyon.2022.e10512
Helpman, E., & Krugman, P. (1987). Market structure and foreign trade: Increasing
returns, imperfect competition, and the international economy. MIT Press.
Kawadia, G., & Suryawanshi, A. K. (2021). Tax effort of the Indian states from 2001–2002
to 2016–2017: A stochastic frontier approach. Millennial Asia, 14(1), 85–101. https://
doi.org/10.1177/09763996211027053
Keho, Y. (2017). The impact of trade openness on economic growth: The case of Cote
d’Ivoire. Cogent Economics & Finance, 5(1), 1332820. https://doi.org/10.1080/2332
2039.2017.1332820
Krugman, P. (1980). Scale economies, product differentiation, and the pattern of trade. The
American Economic Review, 70(5), 950–959.
Kumari, M., & Bharti, N. (2021). Linkages between trade facilitation and governance:
Relevance for post-COVID-19 trade strategy. Millennial Asia, 12(2), 162–189. https://
doi.org/10.1177/0976399620972346
Lal, D., & Myint, H. (1998). The political economy of poverty, equity and growth: A
comparative study. Oxford University Press.
Lin, J. Y. (2011). New structural economics: A framework for rethinking development. The
World Bank Research Observer, 26(2), 193–221.
Menyah, K., Nazlioglu, S., & Wolde-Rufael, Y. (2014). Financial development,
trade openness and economic growth in African countries: New insights from a
panel causality approach. Economic Modelling, 37, 386–394. https://doi.org/doi.
org/10.1016/j.econmod.2013.11.044
28
Millennial Asia
Morrow, P., Smart, M., & Swistak, A. (2022). VAT compliance, trade, and institutions.
Journal of Public Economics, 208, 104634. https://doi.org/doi.org/10.1016/j.
jpubeco.2022.104634
Naito, T. (2006). Growth, revenue, and welfare effects of tariff and tax reform: Win–win–
win strategies. Journal of Public Economics, 90(6), 1263–1280. https://doi.org/doi.
org/10.1016/j.jpubeco.2005.07.004
Nishimizu, M., & Page, J. M. (1986). Productivity change and dynamic comparative
advantage. The Review of Economics and Statistics, 68(2), 241–247. https://doi.
org/10.2307/1925503
Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section
dependence. Journal of Applied Econometrics, 22(2), 265–312. https://doi.org/doi.
org/10.1002/jae.951
Raghutla, C. (2020). The effect of trade openness on economic growth: Some empirical
evidence from emerging market economies. Journal of Public Affairs, 20(3), e2081.
https://doi.org/doi.org/10.1002/pa.2081
Rahman, M. M. (2023). The effect of business intelligence on bank operational efficiency
and perceptions of profitability. FinTech, 2(1), 99–119.
Rahman, M. M., & Akhter, B. (2021). The impact of investment in human capital on bank
performance: Evidence from Bangladesh. Future Business Journal, 7(1), 61. https://
doi.org/10.1186/s43093-021-00105-5
Rahman, M. M., Chandra Deb, B., Rahman, M. S., Uddin, M. M. M., Ramzan, M.,
Hossain, M. J., & Uddin, G. (2023). Does trade openness affect global entrepreneurship
development? Evidence from BRICS Countries. Annals of Financial Economics,
18(3), 2350001. https://doi.org/10.1142/S201049522350001X
Rahman, M. M., & Halim, M. A. (2022). Does the export-to-import ratio affect
environmental sustainability? Evidence from BRICS countries. Energy & Environment,
0958305X221134946. https://doi.org/10.1177/0958305X221134946
Rahman, M. M., & Islam, M. E. (2023). The impact of green accounting on environmental
performance: Mediating effects of energy efficiency. Environmental Science and
Pollution Research, 30, 69431–69452. https://doi.org/10.1007/s11356-023-27356-9
Rahman, M. M., Rahman, M. M., Rahman, M., & Masud, M. A. K. (2021a). The impact of
trade openness on the cost of financial intermediation and bank performance: Evidence
from BRICS countries. International Journal of Emerging Markets (ahead-of-print).
https://doi.org/10.1108/IJOEM-04-2021-0498
Rahman, M. M., Rahman, M. S., & Deb, B. C. (2021b). Competitive cost advantage: An
application of environmental accounting and management approach with reference to
Bangladesh. The Cost and Management, 49(3), 47–59.
Rahman, M. M., Rahman, M. S., & Deb, B. C. (2021c). Impact of corporate governance
on tax management: Evidence from DSE listed banks. Bangladesh Economia, 1(1).
Ramzan, M., Sheng, B., Shahbaz, M., Song, J., & Jiao, Z. (2019). Impact of trade openness
on GDP growth: Does TFP matter? The Journal of International Trade & Economic
Development, 28(8), 960–995. https://doi.org/10.1080/09638199.2019.1616805
Rani, R., & Kumar, N. (2019). On the causal dynamics between economic growth, trade
openness and gross capital formation: Evidence from BRICS countries. Global
Business Review, 20(3), 795–812. https://doi.org/10.1177/0972150919837079
Redding, S. (1999). Dynamic comparative advantage and the welfare effects of trade.
Oxford Economic Papers, 51(1), 15–39. https://doi.org/10.1093/oep/51.1.15
Redmond, T., & Nasir, M. A. (2020). Role of natural resource abundance, international
trade and financial development in the economic development of selected countries.
Resources Policy, 66, 101591. https://doi.org/doi.org/10.1016/j.resourpol.2020.101591
Rahman and Islam
29
Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy,
98(5, Part 2), S71–S102.
Sabina, S., & Eldin, M. (2018). Trade openness and economic growth: Empirical evidence
from transition economies. In B. Vito (Ed), Trade and Global Market (Ch. 2).
IntechOpen. https://doi.org/10.5772/intechopen.75812
Sarafidis, V., & Wansbeek, T. (2012). Cross-sectional dependence in panel data analysis.
Econometric Reviews, 31(5), 483–531. https://doi.org/10.1080/07474938.2011.611458
Schunck, R. (2013). Within and between estimates in random-effects models: Advantages
and drawbacks of correlated random effects and hybrid models. The Stata Journal,
13(1), 65–76.
Shrestha, S., Kotani, K., & Kakinaka, M. (2021). The relationship between trade openness
and government resource revenue in resource-dependent countries. Resources Policy,
74, 102332. https://doi.org/doi.org/10.1016/j.resourpol.2021.102332
Strauss, J., & Yigit, T. (2003). Shortfalls of panel unit root testing. Economics Letters,
81(3), 309–313. https://doi.org/doi.org/10.1016/S0165-1765(03)00210-6
Westerlund, J., Hosseinkouchack, M., & Solberger, M. (2016). The local power of the
CADF and CIPS panel unit root tests. Econometric Reviews, 35(5), 845–870. https://
doi.org/10.1080/07474938.2014.977077
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