Convergence in tax revenues across ASEAN and Asia Pacific and
Oceania countries: Evidence from club convergence
Nicholas Apergis
Department of Banking and Financial Management
University of Piraeus
Piraeus, Greece
napergis@unipi.gr
and
Arusha Cooray (corresponding author)
School of Economics
University of Wollongong
NSW 2552, Australia
arusha@uow.edu.au
March 2013
Abstract
The goal of the present paper is to investigate the degree of convergence in tax revenues for a
panel of 16 ASEAN and Asia Pacific and Oceania countries spanning the period 1990-2012. We
apply the methodology of Phillips and Sul (2007) to various categories of tax revenues to assess the
presence of convergence clubs. We consider five alternative categories of tax revenues. Overall, the
results do not support the hypothesis that all countries converge to a single equilibrium state in such
tax revenues.
Keywords: convergence; tax revenues; ASEAN countries; club convergence approach
JEL Classification Codes: H20; C33
1
1. Introduction
The ASEAN encompasses ten Southeast Asian nations – Brunei, Cambodia,
Indonesia, Lao, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam. The
ASEAN is currently in the process of launching a single common market by 2015 through
increased economic integration. In order to achieve this objective, the ASEAN member
nations have established mechanisms through which to strengthen the operation of its
prevailing economic initiatives including the ASEAN Free Trade Area (AFTA), the ASEAN
Framework Agreement on Services (AFAS) and the ASEAN Investment Area (AIA)
(Jogarajan, 2001). The ASEAN countries planned to complete a network of bilateral tax
treaties across all member nations by 2010. Although significant progress has been made
towards achieving this objective, the process is still not complete. Some argue that an
ASEAN multilateral tax treaty may be better for promoting intra-regional investments
(Rogarajan 2001; ADBI 2009).
Against this backdrop, the goal of this study is to investigate, for the first time, tax
policy convergence among ASEAN countries, as greater integration between the tax policies
of these countries would accelerate the process of achieving a single common market. We
examine tax policy convergence for income-profits-capital gains, social security
contributions, property and general taxes on goods and services. The ASEAN group is also
exploring the means through which economic integration can be increased amongst its
members and the Asia Pacific and Oceania including economies, such as, Japan, South
Korea, China, Australia and New Zealand. Therefore, tax policy convergence between the
ASEAN and these countries is also examined.
An additional novelty of this paper stems from the implementation of the new
methodology of panel convergence testing, recommended by Phillips and Sul (2007). The
philosophy of the methodological approach is based on the club convergence hypothesis,
which considers that certain countries, states, sectors or regions that belong to a club move
from disequilibrium positions to their club-specific steady-state positions. This methodology
has several appealing characteristics. To begin with, no specific assumptions concerning the
stationarity of the variable of interest and/or the existence of common factors are necessary.
Nevertheless, this convergence test could be interpreted as an asymptotic cointegration test
without suffering from the small sample problems of unit root and cointegration testing. In
addition, the methodology is based on a quite general form of a nonlinear time varying factor
model.
2
Our results suggest evidence of club divergence in tax revenues with three to four
clubs depending on the type of tax investigated. This study will allow policy makers to
determine the countries in which tax systems have converged and help implement measures
to promote greater integration between tax systems of countries which have not converged.
Tax convergence permits increasing the efficiency of public policies and prevents member
states from being exploited due to excessive taxes (Fuest and Huber, 1999), member states
are able to compete strategically for mobile factors (Winner, 2005), and member states are
able to prevent a race to the bottom due to tax harmonization (Winner, 2005).
The rest of the paper is organized as follows. Section 2 reviews the recent empirical
literature on tax revenues convergence. Section 3 presents the new methodology employed.
Section 4 reports and discusses the results of the empirical analysis, while Section 5
summarizes the paper and offers some policy implications.
2. The Literature
The focus of much of the literature on tax convergence has been on the European
Union (EU) and OECD nations. Empirical studies on tax convergence employing cross
sectional data have examined beta and sigma convergence (Esteve et al., 2000; SosvillaRivera et al., 2001). Studies using time series data have used unit root tests (Delgado and
Presno 2011) and studies based on panel data have used random effects estimation
(Bretschger and Hettich, 2002). Delgado and Presno (2011) investigate tax policy
convergence (for income and profits, social security contributions, property, general taxes on
goods and services, taxes on specific goods and services) in the EU over the period 19652005 taking as reference Germany, the United Kingdom and the European average. Using
time series methodology, they find little evidence of tax convergence across the countries in
the sample under investigation. Bretschger and Hettich (2002) investigating the impact of
globalisation on corporate taxes for a panel of 14 OECD countries over the period 1967-1996
and a random effects model, similarly find evidence of a negative and significant impact of
globalisation on corporate taxes. They additionally find evidence of an increase in labour
taxes and social expenditures due to globalisation.
In a different strand of the literature, that exemplifies the β-and σ-convergence
approaches, Sosvilla-Rivera et al. (2001) investigate the degree of beta and sigma
convergence in the tax burden for 14 EU countries over the period 1965-1995. They show
that there is some evidence in favour of convergence in the sub-period 1967-1974, but
3
evidence of divergence from 1974-1984 and evidence of convergence again beyond 1974.
Similar conclusions are reached by Esteve et al. (2000) for fiscal policy convergence in the
EU over the period 1967-1994. Both beta and sigma convergence in tax policy show
evidence of divergence over the period 1967-1979 and convergence over the period 19791994. Examining if international tax competition leads to a shift of tax burden from mobile to
immobile tax bases in 23 OECD economies, in particular, for small open economies over the
period 1965–2000, Winner (2005) finds that capital mobility has a negative effect on capital
tax burden and a positive impact on labour tax burden. Country size is found to have a
positive effect with small open economies imposing lower capital and labour taxes compared
to larger ones. He finds evidence of an increase in tax competition rather than convergence
between the countries since the mid 1980s. However, critics on β-convergence question the
adequacy of -convergence regressions. Binder and Pesaran (1999) show that -convergence,
even when used to study the growth path of a given economy towards its own steady state,
can collapse in the case of a stochastic technological progress, while Durlauf et al. (2005)
note that a negative coefficient (β) on initial income in a cross-section framework could
simply imply that economies converge to their own different steady states. At the same time,
critics of σ-convergence argue that it provides a necessary, but not sufficient, ingredient for
observing reductions in any variable dispersion (Quah 1993). If countries converge to a
common equilibrium with shared global technologies and identical internal structures, then
the dispersion of income should disappear in the long-run as all countries converge to the
same real per capita income. If, however, countries converge to convergence clubs or to their
own unique equilibrium, the dispersion of real per capita income will not approach zero
(Miller and Upadhyah, 2002).
Devereux et al. (2002) and Devereux et al. (2008) similarly find evidence of tax
competition rather than convergence across the EU and OECD countries. Devereux et al.
(2002) examine the effect of taxes on corporate income in the EU and G7 nations over a
period of two decades. They conclude that effective tax rates on marginal investment have
remained relatively unchanged but that those on more profitable investments have fallen due
to tax-cutting and base-broadening restructuring. They argue that this could be due to
government reaction to a fall in the cost of income shifting by placing downward pressure on
the statutory tax rate and the competition for more profitable projects. Devereux et al. (2008)
in a study of whether OECD countries compete with each other over corporate taxes to attract
investment, find evidence of countries competing for the statutory tax rate, the effective
4
average tax rate and the effective marginal tax rate, but in particular, over the statutory tax
rate and effective average tax rate. They state that this is due to locational choices by
multinational firms.
Sorensen (2000; 2001; 2004) examines fiscal competition for mobile capital and its
effect on resource allocation, income distribution and social welfare within a general
equilibrium framework. He argues in favour of a fully harmonised corporate tax within the
EU, observing the differences between global and regional tax coordination. Fuest and Huber
(1999) similarly argue that a coordination of regional policies can help internalizing positive
externalities of domestic policies. Theoretical models of international taxation have also been
put forward by Edwards and Keen (1996), Keen and Marchand (1997), Fuest and Huber
(1999), Becker and Fuest (2010), Marchand et al. (2003), Kanbur and Keen (1993), Razin
and Sadka (1991), among others. Razin and Sadka (1991), in a model of tax coordination
between a small country and the rest of the world, show that that if competing countries are
well coordinated with the rest of the world, then tax competition leads each country to apply
the residence principle of taxation and there will be no gains from tax harmonization. If,
however, there is no proper coordination, then tax competition can lead to low capital income
taxes and the tax burden falls on internationally immobile factors. The study of Becker and
Fuest (2010), for instance, shows that coordination of investment in transport cost cutting
infrastructures within union countries, increases welfare and reduces tax competition.
Marchand et al. (2003) argue that partial coordination of withholding taxes on interest income
can be welfare reducing as opposed to no coordination at all. Keen and Marchand (1997)
show that welfare can be increased through a coordinated decrease in the provision of local
public inputs and an increase in the public provision of local public goods, and Kanbur and
Keen (1993) find that smaller countries lose from coordination of tax rates between those set
in the non-cooperative equilibrium, but both small and large countries gain from the
enforcement of a minimum tax.
To the best of our knowledge, there are no studies which have empirically examined
tax convergence across the ASEAN and Asia Pacific and Oceania nations. Additionally,
studies have also not employed the Phillips and Sul (2007) convergence formulation to
investigate tax policy convergence. Therefore, we extend upon the literature by empirically
investigating tax policy convergence across the ASEAN and the Asia Pacific and Oceania
nations using this particular convergence methodological approach.
5
3. Econometric methodology
In this section, we outline the methodology proposed by Phillips and Sul (2007) (henceforth
PS) to test for convergence in a panel of countries. Let us have panel data for a variable X it ,
where i = 1,...N and t = 1,...T , with N , T the number of countries and the sample size,
respectively. Often X it is decomposed into two components, one systematic, g it , and one
transitory ait
(1)
X it = g it + ait
PS transforms (1) in a way that common and idiosyncratic components in the panel are
separated. Specifically,
 g  ait
X it   it
 t

  t   it  t , for all i, t

(2)
In this way, the variable of interest, Xit, is decomposed in two components, one common, μt,
and one idiosyncratic, δit, both of which are time varying. This formulation enables testing for
convergence by testing whether the factor loadings δit converge. To do so, PS define the
relative transition parameter, hit, as:
X
δ
hit = 1 it = 1 it
∑X it
∑δit
N
N
(3)
which measures the loading coefficient δit in relation to the panel and, as such, the transition
path for the economy i relative to the panel average. The relative transition curves depict the
relative transition coefficients hit, calculated from Equation (3).
In the context of macroeconomic data, and since our interest is on the long-run
behaviour, we first remove the business cycle component of Xit by employing the HodrickPrescott (1997) filter. The only input required is a smoothing parameter determined mainly
by the frequency of the data.1 Having extracted the trend component from the series denoted
1
In our application with quarterly data, the smoothing parameter λ, is set equal to 1600.
6
Xˆ it
as X̂ it , we calculate the estimated transition paths as hˆit = 1
. Next we construct the
∑Xˆ it
N
cross-sectional variation ratio H 1
Ht =
Ht
where:
1 N ˆ
∑(h - 1) 2
N i =1 it
PS show that the transition distance Ht has a limiting form of
Ht ∼
A
L(t ) 2 t 2α
as t → ∞
where A is a positive constant, L(t) is a slowly varying function, such as log( t  1) and α
denotes the speed of convergence. To test for the null hypothesis of convergence,
H0 : δi = δ and α ≥0
against the alternative
H A : δi ≠δ for all i, or α  0
PS run the following log t regression:
log  H 1   2 log L(t )  cˆ  bˆ log t  ut
 Ht 
(4)
where L(t) = log(t + 1). The standard errors of the estimates are calculated using a
heteroskedasticity and autocorrelation consistent (HAC) estimator for the long-run variance
of the residuals. In this study, we employ the quadratic spectral kernel and determine the
bandwidth by means of the Andrews (1991) data-dependent procedure. By employing the
conventional t-statistic tb the null hypothesis of convergence is rejected if t b  1.65 . In
practice, this regression is run after a fraction of the sample is removed. PS recommend
starting the regression at some point t = [rT ] , where [rT ] is the integer part of rT , and
r = 0.3. 2
This null hypothesis implies relative convergence (conditional convergence) rather
than absolute convergence (convergence in level). If we change the null hypothesis to α ≥1,
2
Extensive Monte Carlo simulations conducted by Phillips and Sul (2007) show that
terms of both size and power.
r = 0.3 is satisfactory in
7
which is equivalent to b̂ ≥2 , we can test for absolute convergence. Given that rejection of
the null for the panel as a whole does not imply the absence of club convergence, PS go one
step beyond and develop an algorithm for club convergence. We next briefly outline the basic
steps of the respective algorithm.
4. Empirical analysis and results
4.1 Data
Quarterly data on a number of tax revenues classifications as a percentage share of GDP,
such as total, income-profits-capital gains, social security contributions, property, and general
taxes on goods and services -all measured as percentages of their corresponding GDP- are
obtained from the Datastream database for a number of ASEAN and Asia Pacific and
Oceania countries, they are in local currency and spanning the period 1990-2012. All tax
revenues and the GDP are measured at 2000 constant prices. The countries included in the
sample are: Australia, China, Indonesia, Japan, Malaysia, Myanmar, New Zealand,
Philippines, Republic of Korea, Singapore and Thailand. Taxes on income-profits-capital
gains are levied on the actual or presumptive net income of individuals, on the profits of
corporations and enterprises, and on capital gains, whether realized or not, on land, securities,
and other assets. Social contributions include social security contributions by employees,
employers, and self-employed individuals plus actual or imputed contributions to social
insurance schemes operated by governments. Taxes on goods and services include general
sales and turnover or value added taxes, selective excises on goods, selective taxes on
services, taxes on the use of goods or property, taxes on extraction and production of
minerals, and profits of fiscal monopolies.
4.2 Empirical results
Table 1 reports the results for total tax revenues. We reject the null hypothesis of full
convergence for this variable at the 5-percent significance level. The results of the club
convergence algorithm indicate the presence of two clubs plus a non-converging group.
Three countries (Australia, New Zealand, Singapore) form the first club, whereas the second
club includes six countries (Indonesia, Malaysia, Myanmar, Philippines, the Republic of
Korea and Thailand). China and Japan do not converge at all.
8
Table 2 reports the results for tax revenues on income, profits and capital gains. We
reject the null hypothesis of full convergence for this variable at the 5-percent significance
level. The results of the club convergence algorithm indicate the presence of three clubs with
3 countries, Australia, New Zealand, Singapore, in the first, Indonesia, Malaysia and
Myanmar in the second club, and 5 countries (China, Japan, Philippines, the Republic of
Korea and Thailand) in the third. The strongest speed of convergence is between countries in
the second group, Indonesia, Malaysia and Myanmar with b̂  3.328. Compared to the total
tax revenues variable, we see more dispersion of countries with more in the third group.
Table 3 reports the results for tax revenues on social security contributions. We reject
again the null hypothesis of full convergence at the 5-percent significance level. The results
of the club convergence algorithm indicate the presence of three clubs with 4 countries
(Australia, New Zealand, Singapore and the Republic of Korea) in the first club, 2 countries
(Indonesia and Malaysia) in the second, and 3 countries (Japan, Philippines and Thailand) in
the third. China and Myanmar form a non-converging group. The speed of convergence for
tax revenues on social security contributions is highest between member countries in the
second group, Indonesia and Malaysia with b̂  3.874.
The last two tables (Tables 4 and 5) report the convergence findings for tax revenues
on property and tax revenues on general taxes on goods and services, respectively. In
particular, Table 4 displays the presence of four converging clubs plus a non-converging
group in terms of tax revenues on property. Australia and New Zealand form the first club,
Indonesia, Malaysia and Myanmar the second, the Republic of Korea and the Philippines the
third, and Japan, Singapore and Thailand the fourth. China falls into the non-converging
group. These results document high dispersion across the countries included in the sample
under investigation. The highest speed of convergence is recorded between Australia and
New Zealand ( b̂  3.782). Finally, similar results are obtained in Table 5 that shows
divergence in terms of tax revenues on general taxes on goods and services. Table 5 indicates
the presence of four clubs with Australia and New Zealand in the first, Indonesia and
Malaysia in the second, Japan, the Republic of Korea and Singapore in the third and the
Philippines and Thailand in the fourth. This time the non-convergence group, in addition to
China, contains the country of Myanmar. The highest speed of convergence for general taxes
on goods and services is between Australia and New Zealand ( b̂  3.672).
9
The results for all tax revenue groups are summarized in Table 6. There is strong
evidence of convergence in all tax revenues between Australia and New Zealand. This is not
surprising given their geographical proximity and similar economic conditions. Singapore
forms part of this club with regard to tax revenues on income-profits-capital gains and social
security contributions. There is also evidence of tax convergence between Indonesia and
Malaysia. These two countries are also geographically closely located and have similar
cultural and economic backgrounds. Additionally, the taxable income (chargeable
income/employment income less exemptions and deductions) of Indonesia, Myanmar and
Malaysia are similar (National Tax Research Journal (NTRC, 2007). The tax revenues of
Thailand and the Philippines also appear to move together. This perhaps is because the tax
base of employment income, that is, the annual gross income less deductions of the
Philippines is similar to the assessable income (gross employment income less personal
reliefs and standard/itemized deductions) tax base of Thailand (NTRC, 2007).
There is little evidence to suggest that China’s tax revenues will converge with those
of the ASEAN in the near future. Japan and the Republic of Korea show only weak evidence
of convergence. Japan’s tax revenues appear to be most closely aligned with those of
Thailand and the Republic of Korea’s with that of the Philippines. Overall, although we
observe evidence that supports club divergence across the ASEAN and the Asia Pacific and
Oceania countries and in terms of disaggregated tax revenues, this divergence does not seem
substantial when it comes to the total tax revenues, denoting that a specific mechanism in tax
collection can generate tendencies for convergence.
4.3 Robustness tests
Figure 1 depicts the relative transition curves for total tax revenues of each convergence club.
Visual inspection of these curves enables us to gain some insight on the outcomes of the
testing methodology and monitor the convergence of total tax revenues for each club,
relatively to the sample average. In particular, the transition curves report a graphical picture
about the tendency of the cluster participants to converge or diverge from above or below 1,
which is the convergence path reference point during the period under study. Both transition
curves show that at 2012 (the end point of our sample) the tendency is towards convergence,
though curves 1 seems to approach convergence from above and curve 2 seems to approach
convergence from below.
10
Finally, Phillips and Sul (2009) argue that their convergence club methodology tends
to overestimate the number of clubs than their true number. To avoid this overdetermination,
they run the algorithm across the sub-clubs to assess whether any evidence exists in support
of merging clubs into larger clubs. The results of the new converging tests are reported in
Table 7. The empirical findings display that for the two sub-clubs there is no evidence to
support mergers of the original clubs.
5. Conclusions
This paper investigated the issue of tax convergence across a sample of 16 ASEAN and Asia
Pacific and Oceania countries spanning the period 1990-2012. To serve this objective, the
novel methodology of Phillips and Sul (2007) was used. This methodology used a non-linear
factor model with a common and an idiosyncratic component – both time-varying, which
allowed for technical progress heterogeneity across these countries. In terms of total tax
revenues, the empirical findings suggested that the 16 ASEAN and Asia Pacific and Oceania
countries under study did not form a homogeneous convergence club. In addition to the total
tax revenues variables, we also used four different disaggregated tax revenues variables. We
found no evidence supporting full convergence across all countries in our sample. Rather, we
discovered club divergence with a number of different clubs, depending on the definition of
the variable measured.
Club divergence could suggest that differences in tax revenues between clubs could
exist over a period of time. The potential adverse effects of a non-harmonised tax system
include tax competition and harmful bidding wars between nations leading to a race to the
bottom. The ASEAN and Asia Pacific and Oceania countries could follow the example of
other regions which have been successful in tax policy cooperation. For example, Nordic tax
cooperation is strongly aligned with the region’s overall cooperation objectives. The treaty on
administrative assistance among these countries specifies the requirements for countries with
respect to the exchange of information and tax collections (Jogarajan, 2011). These countries
could follow a similar practice to increase transparency and accelerate the process of tax
cooperation. Similarly, ensuring that tax policies are tightly aligned with the objectives of
these countries would permit maximizing the welfare gains from tax coordination.
11
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13
Table 1. Total tax revenues
Full sample
Countries
t-stat
b
coefficient
Australia, China, Indonesia, Japan, Malaysia,
Myanmar, New Zealand, Philippines, Republic
of Korea, Singapore, Thailand
-0.537
-9.342
Australia, New Zealand, Singapore
-0.155
-1.375
Indonesia, Malaysia, Myanmar, Philippines,
Republic of Korea, Thailand
2.327
2.482
1st club
2nd club
China, Japan
Non - converging
14
Table 2. Tax revenues on income, profits and capital gains
Countries
t-stat
b
coefficient
Full sample
Australia, China, Indonesia, Japan, Malaysia,
Myanmar, New Zealand, Philippines, Republic
of Korea, Singapore, Thailand
-0.792
-9.672
1st club
Australia, New Zealand, Singapore
0.136
0.573
2nd club
Indonesia, Malaysia, Myanmar
0.371
3.328
3rd club
China, Japan, Philippines, Republic of Korea,
Thailand
0.564
1.893
15
Table 3. Taxes revenues on social security contributions
Full sample
Countries
t-stat
b
coefficient
Australia, China, Indonesia, Japan, Malaysia,
Myanmar, New Zealand, Philippines, Republic
of Korea, Singapore, Thailand
-2.782
-6.548
1st club
Australia, New Zealand, Republic of Korea,
Singapore
2.518
3.509
2nd club
Indonesia, Malaysia
1.472
3.874
3rd club
Japan, Philippines, Thailand
1.815
2.326
Non - converging
China, Myanmar
16
Table 4. Tax revenues on property
Countries
t-stat
b
coefficient
Full sample
Australia, China, Indonesia, Japan, Malaysia,
Myanmar, New Zealand, Philippines, Republic
of Korea, Singapore, Thailand
-0.584
-21.328
1st club
Australia, New Zealand
0.471
3.782
2nd club
Indonesia, Malaysia, Myanmar
0.152
2.673
3rd club
Philippines, Republic of Korea
0.094
0.793
4th club
Japan, Singapore, Thailand
1.683
3.861
Non - converging
China
17
Table 5. Tax revenues on general taxes on goods and services
Countries
t-stat
b
coefficient
Full sample
Australia, China, Indonesia, Japan, Malaysia,
Myanmar, New Zealand, Philippines, Republic
of Korea, Singapore, Thailand
-1.384
-23.906
1st club
Australia, New Zealand
0.086
3.672
2nd club
Indonesia, Malaysia
-0.037
-0.1941
3rd club
Japan, Republic of Korea, Singapore
0.493
1.785
4th club
Philippines, Thailand
0.381
0.852
Non - converging
Myanmar, China
18
Table 6: Summary of Results
Tax Revenues
Clubs
Income,
Profits
1st club
Social
Property
Goods
and Security
Services
Capital Gains
Contributions
Australia,
Australia,
New
New Zealand,
Zealand,
Singapore,
Singapore
Republic
and Total
Australia
Australia,
Australia,
New Zealand
New
New Zealand
Zealand,
of
Singapore
Korea
2nd club
Indonesia,
Indonesia,
Indonesia,
Indonesia,
Indonesia,
Malaysia,
Malaysia
Malaysia,
Malaysia
Malaysia,
Myanmar
Myanmar
Myanmar,
Philippines,
Republic of
Korea,
Thailand
3rd club
China, Japan, Japan,
Philippines,
Philippines,
Republic
Republic
Philippines,
of Thailand
Korea
Korea,
Japan,
of Republic
of
Korea,
Singapore
Thailand
4th club
-
-
Japan,
Philippines,
Singapore,
Thailand
-
Thailand
Nonconverging
-
China,
Myanmar
China
China,
China,
Myanmar
Japan
19
Figure 1. Total tax revenues: relative transition curves of convergence clubs
Table 7. Convergence club classification (total tax revenues)
___________________________________________________________________________
Club
Tests of club merging
___________________________________________________________________________
1
Club 1+2 = -0.083*
(-6.71)
2
___________________________________________________________________________
Notes: * denotes statistical significant at the 5% level, while it rejects the null hypothesis of
convergence. The figures in parenthesis denotes t-statistics.
20