IER-08-04-02-pp025--2091-Ficek,Gawlik
2022, Vol. 8, No. 4 10.15678/IER.2022.0804.02
A comparative analysis of regional integration potential
in the Asia-Pacific Region
Wojciech Ficek, Remigiusz Gawlik
A B S T R A C T
Objective: The objective of the article is to verify whether the EU can be perceived as a benchmark for
further integration of Regional Comprehensive Economic Partnership Participating Countries (RCEP PC) of
the Asia-Pacific region.
Research Design & Methods: We adopted a quantitative research methodology. It employed cluster analysis
through Ward’s minimum-variance method to analyze the Euclidean distances of eight GDP-based World Devel-
opment Indicators and build two synthetic development measures: SMDRCEP and SMDEU. They were used to group
countries according to their level of economic advancement. Standard deviation was used to measure the differ-
ences in the structure of GDP in both environments (σRCEP and σEU). The research sample was composed of all
RCEP PC and European Union Member States (EU MS). The data sets came from the World Bank database.
Findings: The integrity level of RCEP PC is lower than that of EU MS; however, the differences are less
significant than expected. Nevertheless, the possibility of RCEP reaching the next integration levels in the
foreseeable future is limited.
Implications & Recommendations: As RCEP PC do not seem to be able to engage in further integration in the
near future, RCEP PC policymakers and business entities should focus on keeping the agreement alive in its
current form (FTA). We recommend analyzing whether integration in smaller and more homogeneous groups
of countries is possible and desirable. Another factor worth further research is whether the inadequate size
of the Chinese economy within the agreement has a pro- or anti-integrational influence on RCEP.
Contribution & Value Added: Our research provides an actual insight into the development possibilities of the
‘youngest’ regional integration agreement, the RCEP, based on the experiences related to the integration of
the most advanced regional integration agreement, the EU.
Article type: research article
Keywords:
Regional Comprehensive Economic Partnership; European Union; regional integration;
economic integration; economic integration indexes
JEL codes: F13, F15, D81
Received: 25 May 2022 Revised: 20 July 2022 Accepted: 15 August 2022
Suggested citation:
Ficek, W., & Gawlik, R. (2022). A comparative analysis of regional integration potential in the Asia-Pacific Re-
gion. International Entrepreneurship Review, 8(4), 25-39. https://doi.org/10.15678/IER.2022.0804.02
INTRODUCTION
Since the mid-1990s, the World has witnessed a rapidly growing number of regional integration
agreements. It is sometimes referred to as the Spaghetti Bowl Effect (Bhagwati, 1995). Until now,
the most advanced form of regional integration is the European Union (EU), formed by the Member
States of the European Union (EU MS). However, in the last five years, it was the Transatlantic Trade
and Investment Partnership (TTIP) that attracted the attention of most experts. It is believed this
agreement would create not only ‘winners’ but also many ‘losers’ at the same time. Plenty of sectors
both in the EU and in the US would be in danger if the deal kicked in. Therefore, we can see contro-
versies in the microeconomic sphere that impact the macroeconomic environment (Huettinger &
Zirgulis, 2020). It is also considered that the removal of tariff barriers would de facto make the US a
International Entrepreneurship Review
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26 | Wojciech Ficek, Remigiusz Gawlik
part of the EU. Little progress in the TTIP negotiations was mainly caused by the success of Brexit
and Trump’s election (Huettinger & Zirgulis, 2020). That is why it has been the Regional Comprehen-
sive Economic Partnership (RCEP) which in turn gained enough momentum to significantly impact
the global economy. This fact became the main motivation for our research.
The RCEP agreement was signed on 15 November 2020 by 15 countries and entered into force
on January 1, 2022. It took 8 years of negotiation to create the largest FTA in the world (Francois &
Elsig, 2021). Headquarters are in Hanoi (Vietnam). With almost 2.3 billion people (30% of the global
population) and GDP reaching 26 trillion USD equal to 30% of the global GDP (World Bank, 2022),
RCEP should be considered bigger than the EU. Sometimes RCEP is called ASEAN Plus Five, because
it contains ASEAN countries (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines,
Singapore, Thailand, and Vietnam (Chander & Sunder, 2018)) plus Australia, China, Japan, South Ko-
rea, and New Zealand (Gaur, 2021). Chaisse and Pomfret (2019) define RCEP as “A vehicle for Eco-
nomic Integration and Inclusive Development” (p. 186).
The economic potential of the RCEP is neutralized by some critical remarks. I.e., Francois and Elsig
(2021) state that RCEP is a new tool to exercise economic violence for the world’s second-largest econ-
omy, China. Gaur (2021) sees its weakness in the tremendous economic differences between the RCEP
PC. We have found very little literature discussing the possibilities of the RCEP for further integration.
Dieter (2021) observes that although the RCEP has shown such potential, its PC are currently far from
it. This is the research gap that we intended to fill with the presented research. Park (2017) concludes
that “many East Asian countries need to participate in the RCEP in order to solve the noodle bowl
effect because their regional bilateral FTAs overlap” (p. 149).
The highest level of regional integration was achieved by a group of countries grouped in the EU.
The integration, which started as a common market for two goods, with the formation of the European
Coal and Steel Community (Treaty of Paris, 1951), turned into an FTA, the European Economic Com-
munity (Treaty of Rome, 1957), converted into its actual form, an economic union, the European Union
(Treaty of Maastricht, 1992), with some countries participating also in the European Monetary Union
(established by the same Treaty). The integration started with six countries (Belgium, France, Germany,
Italy, Luxembourg, and the Netherlands) and was later enlarged 7 times: in 1973 (Denmark, Ireland,
the United Kingdom), 1981 (Greece), 1986 (Spain, Portugal), 1995 (Austria, Finland, Sweden), 2004
(the EU-10 enlargement: Czechia, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia,
Slovakia), 2007 (Bulgaria, Romania) and 2013 (Croatia). After the United Kingdom decided to leave the
EU (Brexit), the block counts 27 EU MS. Its headquarters are in Brussels (Belgium), Luxembourg (Lux-
embourg), and Strasbourg (France). With 448 million inhabitants (5.8% of the global population) and a
15 trillion USD GDP equal to 18% of the global GDP (World Bank, 2020), despite being more advanced
in the stage of economic integration, the block is significantly smaller than RCEP. However, European
integration exceeds the economic dimension. Foster (2013) describes it as “the ‘civilized zone’ of the
European continent” (p. 375) with Browning (2018) adding a surrounding “buffer zone of a so-called
ring of friends” and a “threatening world” outside (p. 127).
Critical votes to the EU are called Euroscepticism, which is defined as “opposition to the process
of European integration” or “opposition to European integration and/or the EU” (Treib, 2020, p. 3).
Their nature seems to be more political, than economic – most Euroscepticism comes from the far-
right-wing parties in the European Parliament (Götz et al., 2018). The recent Russian invasion of
Ukraine also shed some new light on the geopolitical interests of autocratic Russia in impeding Eu-
ropean integration and destabilizing the EU.
The objective of the article is to verify whether the EU can be perceived as a benchmark for further
integration of RCEP Participating Countries (RCEP PC) – above the recently signed Free Trade Area
(FTA). The research gap in the existing literature is the lack of analysis of RCEP’s potential for further
integration. Therefore, the research problem of this study is to explore the possibilities of RCEP’s de-
velopment into higher forms of economic integration in the foreseeable future.
We formulate three research questions:
A comparative analysis of regional integration potential in the Asia-Pacific Region | 27
RQ1: What indicators of the economic conditions have been previously analyzed in the liter-
ature and why the issue is still not resolved?
RQ2: Does the GDP structure of the RCEP PC show similarities that provide reasons to consider
their further economic integration following the EU MS model?
RQ3: Is the European Union a good benchmark for Asian regional integration agreements?
We applied a quantitative research methodology with cluster analysis using Ward’s minimum var-
iance method to analyze the economic integration potential of the RCEP PC. We used quantitative data
sets from the 2015-2020 World Development Indicators of the World Bank.
Our article starts with a review of the literature on regional integration, followed by a section on
materials and methods, a presentation and discussion of the results of the research, and a conclusion.
LITERATURE REVIEW (AND HYPOTHESES DEVELOPMENT)
Regional Integration
Mlambo (2019) defines regional integration as “a significant initiative with regard to stimulating eco-
nomic growth amongst member states and enhancing intraregional trade, security initiatives, and bi-
lateral and multilateral agreements”(p.1). It can also be interpreted as the concept of a functional re-
gion with strengths and trends in evolving political, economic, and cultural relations compared to re-
lations with external structures (Pacuk et al., 2018). Mlambo and Mlambo (2018) postulate for inter-
governmental cooperation within regional integration agreements to benefit from “jointly implement-
ing policies that are aimed at spurring regional development” (p. 258).
From the entrepreneurial perspective, Głodowska (2017) confirms the dependence of regional in-
tegration and economic growth on the business environment using quantitative analysis. Startups be-
come creators of the new business model of the 21st century. Their development brings advantages
to the entire economy in regional, national, and world dimensions. An increase in the number of micro-
enterprises contributes to the increase in GDP level per capita (Szarek & Piecuch, 2018). Balawi (2021)
shows entrepreneurship performance (ecosystem) and its influence on the economy.
Cluster Analysis
Cluster analyses aim to examine convergence (or divergence) within a group (of countries). In the EU
we can see a high level of convergence between countries regarding business structure and demogra-
phy. On the other hand, the EU cannot be seen as a homogeneous group, since there is a significant
difference between EU-15 (before Brexit) and EU-13 countries. We can observe the decrease in dis-
similarities. Hence, the process of constant convergence is a thing in the EU (Kamińska & Zielenkiewicz,
2019). When it comes to RCEP, also two intra-groups can be distinct in terms of bilateral trade. RCEP-
4 (Japan, South Korea, Taiwan, and Australia) and RCEP-11 (other RCEP PC). Taiwan is not widely rec-
ognized as a country. In our research, we do not separate Taiwan from the People's Republic of China.
RCEP-4 countries seem to be relatively more integrated, based on their intra-trade flows (Chang et al.,
2020). The similarity between the EU and the RCEP in the field of cluster analyses is significant. In both
environments, we can observe a line between two intra-major groups. With that being said, EU-15
may be seen as a benchmark for RCEP-4 and EU-13 for RCEP-11. It thoroughly fits in the ‘two-speed
Europe’ concept that assumpts the divergence between countries within the EU (Kundera, 2019).
We decided to ground our set of indexes on GDP, as the GDP structure is crucial to examine those
potential differences between countries, both in the EU and in the RCEP. Therefore, nine indicators
were chosen to measure the dissimilarities between the members of both blocks. Our research focuses
on differences based on the structure of GDP between RCEP and the EU.
The empirical evidence from prior literature studies allowed us to build the following Main Re-
search Thesis (MRT):
MRT: In the foreseeable future, the RCEP will evolve into a Common Market.
28 | Wojciech Ficek, Remigiusz Gawlik
To prove (or reject) the MRT, we assumed the following hypotheses:
H1: It is possible to create a consistent set of socio-economic indicators that assess the potential
of countries participating in regional integration agreements for further integration.
H2: The discrepancy in the GDP structure between the RCEP PC is low enough to engage in the
next level of economic integration, the Customs Union.
H3: The European Union is a good benchmark for Asian regional integration agreements.
The following section discusses the research methodology and data.
RESEARCH METHODOLOGY
The research design is quantitative. It aimed to identify more homogeneous subgroups within RCEP PC
to exclude the countries with the highest divergence. Then, the RCEP's potential for further economic
integration was derived from its comparison with the EU.
Data sets on the chosen GDP-based World Development Indicators of RCEP PC and EU MS came
from the World Bank database. We used Microsoft Excel (v2203, build 15028.20228) for data analysis
and forecasting, ADE-TAX (created by Pisulak & Bauer) for minimum-variance method calculations, and
Statistica (v13.3) for graphic presentations. Literature selection is derived from in-depth studies of re-
search articles on regional integration agreements, cluster analysis, and European Treaties.
We used Ward’s minimum-variance – a cluster analysis method. Kovacova et al. (2019) state that
“the use of the cluster analysis focuses on the identification of homogeneous subgroups of explanatory
variables to sort the variables into clusters so that the variables within a common cluster are as similar
as possible” (p. 744). Our application consisted of the following steps.
First, data were standardized with Maciejewski’s (2017) Formula (1):
��� = ��� − �̅��� (1)
where: ��� - standardized value of the j-th index in the i-th country; ��� - value of the j-th index in the i-th country; �̅� - arithmetic mean of index j; �� - the standard deviation of index j; - RCEP PC and EU MS,
��
= �1,2, … , �
��
�, �
��
= 15; �� = �1,2, … , ��� �, ��� = 27; � - number of indicators, � = �1,2, … , ��, � = 8.
Second, we apply Ward’s minimum-variance method for hierarchical clustering to obtain a hierar-
chical structure of similarities between RCEP PC and EU MS (within each block independently, not be-
tween two blocks). The resulting dendrogram illustrates the arrangement of the clusters (Ward, 1963),
which can be quantified. The initial cluster distances are the Euclidean distance between the countries,
defined after (Kovacova et al., 2019, p. 752) by Formula (2):
��� = � !��" − ��" #$""%& (2)
where: ��" - value of variable k for the in the country referred as i; ��" - value of variable k of the j-th index (interpreted as a maximum value).
Fallucchi et al. (2019) observe that Ward’s method, contrary to the k-means method, specifies
the number of types (groups). In our research, we split the research samples into four groups (sep-
arate for RCEP and EU).
Third, we calculate (3) the Synthetic Measure of Development (SMD). Mazur and Witkowska (2006)
point at SMD, providing a more transparent visualization of such aggregated Euclidean distances.
A comparative analysis of regional integration potential in the Asia-Pacific Region | 29
'() = 1 − *+*, (3)
where: '() - Synthetic Measure of Development (SMD); �� - Euclidean distance for = {1,2, … , �}, where i stands for an individual country; �0 - Euclidean distance max value.
The maximum value of the Euclidean distance can be calculated using Formula (4). �0 = max � {�� } (4)
The SMD values range from 0 to 1, with greater values reflecting a higher level of development. As
a result, we obtained a grading of economic development distances between members of researched
regional integration agreements, RCEP and EU, separately within each agreement.
Additionally, after obtaining SMDs, we used the standard deviation to measure the divergence be-
tween countries within the RCEP and the EU (Formulas 5 & 6).
4
��
= 5∑ |89:;<=>9:;<|?@9:;<=& (5)
where: 4
��
- the standard deviation for RCEP PC; �
��
- specific value in a data set; A
��
- arithmetic mean; B
��
- the number of countries (15).
4�� = 5∑ |8;C=>;C|?@;C =& (6)
where: 4�� - the standard deviation for EU MS; ��� - specific value in a data set; A�� - arithmetic mean; B�� - the number of countries (27).
As variables, we employed eight GDP-related indicators, presented as GDP %. The initially consid-
ered set of socio-cultural-legal-economic indexes proved to be too large and not homogeneous
enough. Therefore, after preliminary research, we decided to limit this set to GDP-related indexes,
addressing various spheres of economic well-being. Their presentation as GDP % provides greater
transparency. GDP-based indicators seem to be the most appropriate and adequate indexes when it
comes to empirical analysis. As they do not measure the social sphere itself (Giannakitsidou, 2016), we
treated GDP as a benchmark for our indicators. We decided not to employ the most popular economic
integration indexes, such as openness to foreign trade or capital flows, because of countries like Sin-
gapore. Justification: the openness to the foreign trade of Singapore extends over ten times the same
index for China or Japan (World Bank, 2021b). Although it is impossible to carry out an economic anal-
ysis with Singapore not suppressing the results, we concluded, that our set of indicators protects ex-
tensively the examination from data distortion.
We employed the following indicators, defined by the World Bank (2021a):
− Final Consumption Expenditure (FCE) – expenditure by resident institutional units, including house-
holds and enterprises whose main economic center of interest is in that economic territory, on
goods or services that are used for the direct satisfaction of individual needs or wants or the collec-
tive needs of members of the community;
− Foreign Direct Investments (FDI) – net inflows of investment to acquire a lasting management inter-
est (10% or more of voting stock) in an enterprise operating in an economy other than that of the
investor; it is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-
term capital as shown in the balance of payments;
30 | Wojciech Ficek, Remigiusz Gawlik
− Military Expenditure (ME) – all current and capital expenditures on (i) the armed forces (including
peacekeeping forces); (ii) defense ministries, and other government agencies engaged in defense
projects; (iii) paramilitary forces, if these are judged to be trained and equipped for military opera-
tions; (iv) military space activities (military and civil personnel, including retirement pensions of mil-
itary personnel and social services for personnel); (v) operation and maintenance; (vi) procurement;
(vii) military research and development; (viii) military aid;
− Current Health Expenditure (CHE) – estimates of current health expenditures include healthcare
goods and services consumed during each year, excluding capital health expenditures such as build-
ings, machinery, IT, and stocks of vaccines for emergencies or outbreaks;
− Exports of Goods and Services (EX) – the value of all goods and other market services provided to
the rest of the world; they include the value of merchandise, freight, insurance, transport, travel,
royalties, license fees, and other services, such as communication, construction, financial, infor-
mation, business, personal, and government services;
− Services Value-Added (SVA) – includes the value-added (VA) in wholesale and retail trade (with ho-
tels and restaurants), transportation, and government, financial, professional, and personal services
such as education, health care, and real estate services;
− Industry Value-Added (IVA) – it comprises VA in mining, manufacturing (also reported as a separate
subgroup), construction, electricity, water, and gas;
− Agriculture Value-Added (AVA) – includes VA in forestry, hunting, and fishing, the cultivation of
crops, and livestock production.
Five of the above indicators are stimulants (raising the indicator value increases the dependent
variable), and three are destimulants (raising the indicator value decreases the dependent variable).
The three last indicators (SVA, IVA, AVA) are calculated as value-added, which is the net output of a
sector after adding all outputs and subtracting intermediate inputs. It is calculated without making
deductions for the depreciation of fabricated assets or the depletion and degradation of natural re-
sources (World Bank, 2021a).
Despite some limitations enumerated in the Conclusions section, we managed to propose and
standardize a set of economic development indexes applicable for both integration agreements.
This confirms H1.
The third section contains a presentation of our research results and their discussion.
RESULTS AND DISCUSSION
Table 1 presents the calculated Euclidean distances between RCEP PC for each index individually.
Table 2 presents similar calculations for the Euclidean distances between EU MS for the same
set of indexes.
The application of the minimum-variance method allowed us to create two Synthetic Measures of
Development (SMDRCEP and SMDEU) one for each regional integration agreement. Table 3 presents the
SMD for RCEP PC (SMDRCEP).
The SMDRCEP values presented in Table 3 prove a relatively similar economic advancement of
RCEP PC, with two exceptions: Singapore and Lao PDR. Singapore’s outstanding economic perfor-
mance is mainly due to the highest scores on FCE, FDI, EX, SVA, and AVA indexes (Table 1). The
other extreme is represented by Lao PDR, due to the lowest values of ME and CHE indexes (Table
1). We can therefore state that the differences between GDP structures of RCEP PC are not thwart-
ing further integration. However, tighter integration circles (various integration fields and speeds)
could prove necessary because of the two mentioned exceptions. Therefore, H2 can be condition-
ally confirmed.
Table 4 presents the SMD calculated for EU MS (SMDEU).
A comparative analysis of regional integration potential in the Asia-Pacific Region | 31
Table 1. Economic development indicators for the Regional Comprehensive Economic Partnership Participat-
ing Countries
RCEP PC FCE FDI ME CHE EX SVA IVA AVA
Australia 75.09 3.21 1.98 9.38a 21.76 66.91 24.07 2.34
Brunei Darussalam 46.26 2.52 3.25 2.36a 53.10 40.04 60.54 1.10
Cambodia 77.83 12.61 2.11 6.24a 61.26 39.06 31.55 23.30
China 54.97 1.59 1.75 5.10a 19.44 52.98 39.39 7.62
Indonesia 66.99 1.78 0.82 2.92a 19.51 43.77 39.28 13.23
Japan 74.49 0.64 0.95 10.71a 17.05 70.00a 28.42a 1.05a
Korea, Rep. 64.01 0.67 2.57 7.28a 40.25 55.97 33.75 1.83
Lao PDR 81.59b 6.63 0.15d 2.44a 36.94b 42.14 30.41 16.35
Malaysia 69.44 2.80 1.19 3.76a 66.91 53.06 37.66 8.05
Myanmar 71.76 4.53 3.22 5.05a 26.08 39.99 35.80 24.21
New Zealand 76.33 1.11 1.26 9.30a 26.34 65.61c 20.27c 5.37c
Philippines 85.23 2.43 1.02 3.97a 27.87 59.96 30.03 10.01
Singapore 45.90 25.55 3.01 4.22a 173.90 70.29 24.20 0.03
Thailand 67.03 1.23 1.40 3.77a 62.86 56.84 34.71 8.45
Vietnam 74.52 6.14 2.37c 4.82a 100.53 41.05 33.64 15.36
Variables: D S S S S S D D
a data set: 2015-2019 (forecasted for missing data); b data set: 2015-2016 (forecasted for missing data);
c data set: 2015-2018 (forecasted for missing data); d data set: 1992-2013 (forecasted for missing data);
variables – statistical features (stimulants – S or destimulants – D)
Source: own elaboration based on World Bank (2022), indicator codes: NE.CON.TOTL.ZS; BX.KLT.DINV.WD.GD.ZS;
MS.MIL.XPND.GD.ZS; SH.XPD.CHEX.GD.ZS; NE.EXP.GNFS.ZS; NV.SRV. TOTL.ZS; NV.IND.TOTL.ZS; NV.AGR.TOTL.ZS (accessed
on March 25, 2022).
Table 2. Economic development indicators for the European Union Member States
EU MS FCE FDI ME CHE EX SVA IVA AVA
Austria 71.66 -3.27 0.75 10.37a 53.64 62.88 25.33 1.13
Belgium 74.62 -2.70 0.93 10.68a 80.94 69.43 19.30 0.65
Bulgaria 76.72 3.39 1.69 7.38a 63.27 59.57 23.08 3.71
Croatia 78.88 2.03 1.69 6.83a 47.39 59.50 20.42 3.00
Cyprus 81.14 67.82 1.64 6.75a 73.55 74.07 11.68 1.89
Czechia 66.51 3.63 1.08 7.36a 76.76 55.89 32.32 2.01
Denmark 71.24 1.00 1.24 10.09a 55.73 65.01 20.76 1.13
Estonia 70.24 5.44 2.07 6.56a 74.99 60.85 23.60 2.38
Finland 76.70 2.90 1.41 9.27a 37.05 60.16 23.85 2.34
France 77.57 1.64 1.91 11.30a 30.49 70.38 17.24 1.56
Germany 72.42 2.56 1.21 11.38a 46.25 62.25 27.16 0.74
Greece 89.63 1.64 2.63 8.06a 34.93 69.39 14.12 3.81
Hungary 69.68 28.31 1.15 6.70a 84.13 55.75 25.24 3.61
Ireland 42.85 24.44 0.31 7.07a 124.45 55.22 37.11 0.96
Italy 79.07 0.86 1.36 8.72a 30.36 66.55 21.33 1.97
Latvia 77.74 2.53 1.75 6.11a 60.51 64.67 18.94 3.65
Lithuania 78.42 4.13 1.74 6.63a 72.66 60.97 25.73 3.21
Luxembourg 49.36 24.00 0.59 5.28a 197.05 79.52 11.39 0.22
Malta 63.78 28.98 0.51 8.75a 146.94 75.73 12.51 0.78
Netherlands 68.74 4.16 1.24 10.18a 81.78 70.03 17.78 1.70
Poland 76.19 3.00 2.04 6.45a 53.67 56.87 28.70 2.49
Portugal 82.31 3.11 1.87 9.39a 41.26 65.47 19.23 2.08
Romania 78.91 2.67 1.76 5.28a 40.78 57.41 28.44 4.14
Slovakia 75.41 2.57 1.35 6.87a 92.49 58.52 29.14 2.02
Slovenia 71.45 2.91 1.01 8.39a 80.76 56.62 28.49 2.04
32 | Wojciech Ficek, Remigiusz Gawlik
EU MS FCE FDI ME CHE EX SVA IVA AVA
Spain 77.17 2.74 1.25 9.03a 33.90 67.87 20.09 2.81
Sweden 71.47 3.10 1.08 10.85a 44.71 65.46 21.84 1.41
Variables: D S S S S S D D
a data set: 2015-2019 (forecasted for missing data); variables – statistical features (stimulants – S or destimulants – D).
Source: own elaboration based on World Bank (2022), indicator codes: NE.CON.TOTL.ZS; BX.KLT.DINV.WD.GD.ZS;
MS.MIL.XPND.GD.ZS; SH.XPD.CHEX.GD.ZS; NE.EXP.GNFS.ZS; NV.SRV. TOTL.ZS; NV.IND.TOTL.ZS; NV.AGR.TOTL.ZS (accessed
on March 25, 2022).
Table 3. Synthetic Measure of Development for Regional Comprehensive Economic Partnership Participating
Countries
RCEP PC !DEF − DGF#HFF%I � !DEF − DGF#H
F
F%I SMDRCEP
Australia 33.75 5.81 0.253
Brunei Darussalam 56.37 7.51 0.035
Cambodia 40.42 6.36 0.183
China 42.37 6.51 0.164
Indonesia 57.88 7.61 0.022
Japan 41.50 6.44 0.172
Korea, Rep. 33.19 5.76 0.260
Lao PDR 60.55 7.78 0.000
Malaysia 41.29 6.43 0.174
Myanmar 51.55 7.18 0.077
New Zealand 38.72 6.22 0.200
Philippines 51.76 7.19 0.075
Singapore 6.32 2.51 0.677
Thailand 39.94 6.32 0.188
Vietnam 35.96 6.00 0.229
Max: – 7.78 –
Source: own calculations in Microsoft Excel.
Table 4. Synthetic Measure of Development for European Union Member States
EU MS !DEF − DGF#HFF%I � !DEF − DGF#H
F
F%I SMDEU
Austria 71.59 8.46 0.138
Belgium 57.89 7.61 0.225
Bulgaria 75.11 8.67 0.117
Croatia 76.97 8.77 0.106
Cyprus 39.98 6.32 0.356
Czechia 75.19 8.67 0.116
Denmark 58.48 7.65 0.221
Estonia 60.20 7.76 0.209
Finland 72.96 8.54 0.130
France 58.91 7.68 0.218
Germany 65.67 8.10 0.174
Greece 79.49 8.92 0.091
Hungary 66.05 8.13 0.172
Ireland 68.02 8.25 0.160
Italy 71.30 8.44 0.140
Latvia 73.45 8.57 0.127
Lithuania 73.64 8.58 0.125
Luxembourg 34.94 5.91 0.398
A comparative analysis of regional integration potential in the Asia-Pacific Region | 33
EU MS !DEF − DGF#HFF%I � !DEF − DGF#H
F
F%I SMDEU
Malta 32.27 5.68 0.421
Netherlands 47.50 6.89 0.298
Poland 78.15 8.84 0.099
Portugal 65.68 8.10 0.174
Romania 96.29 9.81 0.000
Slovakia 71.64 8.46 0.137
Slovenia 72.19 8.50 0.134
Spain 70.12 8.37 0.147
Sweden 61.60 7.85 0.200
Max: – 9.81 –
Source: own calculations in Microsoft Excel.
The creation of GDP within the EU block seems to be distributed much more equally. Even though
Malta, Luxembourg, and Cyprus score the best, and Poland, Greece, and Romania have the lowest
SMDEU values, no extremes similar to SMDRCEP can be identified. These findings are supported by stand-
ard deviation calculations: 4
��
= 0.161 vs. 4�� � 0.095 proves that the EU MS are more conver-
gent than the RCEP PC. However, we need to acknowledge a calculation bias coming from Singapore
being an indisputable leader within the RCEP.
Distances in economic development between the countries are presented in the dendrograms be-
low, for RCEP PC (Figure 1) and EU MS (Figure 2) separately.
Figure 1. Distances in economic development between RCEP PC (SMDRCEP)
Source: own elaboration with the use of Statistica based on calculations in ADE-TAX.
Both in Figures 1 and 2 we use an internal comparative unit for dendrograms imposed by the Sta-
tistica software (Formula 7).
34 | Wojciech Ficek, Remigiusz Gawlik
MNOPO � 100 ∙
R)
()
(7)
where:
MNOPO - an internal comparative unit for dendrograms imposed by the Statistica software;
BD - binding distance in a dendrogram;
MD - the maximum distance in a dendrogram.
Our analysis shows that around the distance of 15 units we can distinguish four groups of coun-
tries based on their SMDRCEP values: (i) Singapore; (ii) South Korea, Australia, Vietnam; (iii) New
Zealand, Thailand, Cambodia, Malaysia, Japan, China; (iv) Myanmar, Philippines, Brunei Darus-
salam, Indonesia, Lao PDR.
Figure 2. Distances in economic development between EU MS (SMDEU)
Source: own elaboration with the use of Statistica based on calculations in ADE-TAX.
Similar reasoning within the EU also identifies four groups, more homogeneous in their level of
economic advancement, since the distance of around 12 units is sufficient to obtain the same num-
ber of groups. These groups are (i) Malta, Luxembourg, Cyprus, Netherlands; (ii) Belgium, Denmark,
France, Estonia, Sweden, Germany, Portugal, Hungary; (iii) Ireland, Spain, Italy, Austria, Slovakia,
Slovenia, Finland, Latvia, Lithuania, Bulgaria, Czechia, Croatia, Poland, Greece, and (iv) Romania. It is
worth mentioning that group No. 3 seems to be remarkably numerous. We can find 14 countries
within; mainly PIIGS countries and new Member States (accession after 2004). The core countries of
the EU were placed in groups No. 1 and 2.
The above shows that drawing direct conclusions from EU integration towards RCEP’s potential for
further economic approach is premature. Dieter (2021) backs our findings by saying that RCEP is a
great FTA but the odds that it will become a Customs Union (CU) are rather limited.
Flach et al. (2021) claim that the value of trade between the RCEP PC has the potential to in-
crease, because: “(i) trade relations and interdependencies between RCEP countries are more prom-
inent compared to third countries, (ii) the relative importance of intra-RCEP trade has increased over
the years, (iii) complex value chains play an important role in the region, and (iv) for giant China,
A comparative analysis of regional integration potential in the Asia-Pacific Region | 35
‘Factory RCEP’ is the most important partner network”(p. 98). Nevertheless, even with such argu-
ments in favor of the growing importance of RCEP, the mentioned authors do not risk statements
about taking RCEP PC at higher integration levels.
Rahman and Ara (2015) quantified the potential economic impact of the elimination of all import
tariffs between the current RCEP PC and concluded that it could result in a drop in exports in most
of them. Surprisingly, India would gain significantly from the FTA, even though it did not sign the
RCEP agreement. A possible explanation comes from the fact that RCEP includes other trade blocks,
e.g., ASEAN+6 countries (without India).
In addition to vertical integration, the RCEP can evolve horizontally, by accepting new PC.
Chakraborty et al. (2019) claim that India would join the agreement if services became part of it. Being
beneficial to India, the agricultural and service market of this country (mainly the technological indus-
try) would bring great added value to other RCEP PC. Zhou et al. (2021) argue that “RCEP will have
huge economic effects on members, such as GDP, welfare, and trade, and India’s accession will bring
greater benefits to RCEP members”(p. 13). India’s accession would start a cycle synergy effect, as it
pushes the agreement toward a Common Market – another vertical level of economic integration.
Kurihara (2017) predicts potential conflict areas by saying that developed RCEP PC will benefit from
a larger increase in trade than the developing ones. He also predicts tensions between the Trans-Pacific
Partnership (TPP) signatories and RCEP PC, as “the members are overlapping each other, and there
might occur unexpected and unwanted effects on both and/or one of their economies”(p. 105).
Chaisse and Pomfret (2020) claim that RCEP has the potential to play an important role in the mod-
ern globalized economy but are not certain of the level of integration it will finally achieve. Chang et
al. (2020) prove that the effect created by intra-regional trade is higher within RCEP than within the
EU. They also notice the importance and similarity of sub-groups within the integrated environment.
Thus, we can see the EU as a benchmark for the RCEP. On the other hand, some countries, i.e., China,
Indonesia, Singapore, and Malaysia, can relatively easier (than EU-13) transcend and become part of
RCEP-4 because divergence between RCEP’s sub-groups (RCEP-4 and RCEP-11) is lower than in the EU
(EU-15 and EU-13). Hence, only can we confirm H3 conditionally.
CONCLUSIONS
The objective of the article was to verify whether the EU can be perceived as a benchmark for further
integration of RCEP Participating Countries (RCEP PC) in the foreseeable future. With the minimum-
variance methods, based on World Bank’s data on the GDP-related economic development indexes,
we came to the following conclusions:
− RCEP PC are less integrated than the EU MS, however, the differences between the two regional
integration agreements are less significant than expected;
− after excluding the extremes (Singapore and Lao PDR) the level of economic integration between
RCEP PC seem to be satisfactory for further integration.
We managed to confirm H1, whereas H2 and H3 were confirmed only conditionally. Therefore,
our Main Research Thesis, stating that “In the foreseeable future the RCEP will evolve into a Common
Market” cannot be confirmed.
Our research bears the following implications and recommendations for policymakers:
− the European model of economic integration can be used as a benchmark for RCEP PC;
− however, the cultural, social, and political similarities in the actual stage of development of EU MS
shall be also considered;
− therefore, the indicators employed for analyses and predictions of RCEP’s future integration poten-
tial shall include also cultural, social, and political factors;
− the scenario of RCEP’s integration in smaller, more homogeneous groups of countries is worth being
analyzed by the policymakers.
Business entities shall notice that the higher level of integration, the higher volume of trade flows
among the integrated countries. This is backed by Savinsky (2020), Ishikawa (2021), Shimizu (2021), and
36 | Wojciech Ficek, Remigiusz Gawlik
Drysdale and Garnaut (2022). Therefore, we perceive them as potentially strong pro-integrational moti-
vators. In this way, the microeconomic sphere can influence the macroeconomic environment. The more
bilateral trade relations national entities have gotten between countries within FTA, the easier integra-
tion at the governmental level will be and this can lead to greater stages of economic integration.
Our article brings value-added to the Science of Economics by providing new information on
RCEP’s development potential. It also highlights the fact, that although conclusions on EU-RCEP sim-
ilarities can be drawn from an economic perspective, direct comparisons of these two integration
blocks can be misleading.
We observed the following limitations of the presented research: (i) choice of indexes – we used
only economic indexes presented as GDP %, whereas adding social, cultural, and political factors would
enrich the picture; (ii) some of the indexes can be interdependent, e.g.: lower health expenditure may
equalize high military expenditure; (iii) dividing the indexes into stimulants and destimulants can result
in some indicators being interpreted as nominants where “normal” (nominal) values of a given factor
are desirable, while any deviations from the “normal” level are perceived as a negative phenomenon
– e.g.: ME – difficult to be classified strictly as stimulant or destimulant, but treated here as a stimulant;
(iv): lack of recent data from some RCEP PC, notably from the Lao Statistics Bureau, e.g. GHDx data –
we had to forecast missing data, which can cause data distortion and calculation bias.
Future research on both RCEP and EU should concentrate on: (i) empirical research on up-to-date
effects of economic integration; (ii) exploring further integration opportunities (including enlargements)
and their limitations; (iii) simulating intra-RCEP trade after an alleged accession of India; (iv) anticipating
the geopolitical threats for economic integration, e.g. under the light of Russia-China or China-Taiwan
relations; (v) repeating our calculations with a broadened set of indicators, e.g. with focus on FDI struc-
ture; (vi) creating integration criteria hierarchies, e.g. with the use of Analytic Hierarchy Process method.
REFERENCES
Balawi, A. (2021). Entrepreneurship ecosystem in the United Arab Emirates: An empirical comparison with Qatar
and Saudi Arabia. International Entrepreneurship Review, 7(2), 55-66. https://doi.org/10.
15678/IER.2021.0702.05
Bhagwati, J.N. (1995). US trade policy: the infatuation with FTAs. Columbia University.
Browning, C. (2018). The construction and deconstruction of the EU’s neighbourhood. In T. Schumacher, A. Mar-
chetti, & T. Demmelhuber (Eds.), The Routledge handbook on the European Neighbourhood Policy (pp. 119-
129). Routledge.
Chaisse, J., & Pomfret, R. (2019). The RCEP and the Changing Landscape of World Trade. Law and Development
Review, 12(1), 159-190. https://doi.org/10.1515/ldr-2018-0058
Chakraborty, D., Chaisse, J., & Xu, Q.I.A.N. (2019). Is it finally time for India’s free trade agreements?
the ASEAN “Present” and the RCEP “Future”. Asian Journal of International Law, 9(2), 359-391.
https://doi.org/7101.01/S3400009131524402
Chander, A., & Sunder, M. (2018). The Battle to Define Asia’s Intellectual Property Law: TPP to RCEP. UC Irvine
Law Review, 8(3), 330-362.
Chang, S.M., Huang, Y.Y., Shang, K.C., & Chiang, W.T. (2020). Impacts of regional integration and maritime
transport on trade: with special reference to RCEP. Maritime Business Review, 5(2), 143-158. https://doi.
org/10.1108/MABR-03-2020-0013
Drysdale, P., & Garnaut, R. (1993). The Pacific: an application of a general theory of economic integration. In C.F.
Bergsten & M. Noland (Eds.), Regional Institutional Arrangements (pp. 183-223). Institute for International
Economics.
Dieter, H. (2021). RCEP-Countries create Asia-Pacific free trade zone: trade facilitation but no integrated bloc.
SWP Comment 2021/C 03, 1-4. https://doi.org/10.18449/2021C03
Fallucchi, F., Luccasen, R.A., & Turocy, T.L. (2019). Identifying discrete behavioural types: a re-analysis of public
goods game contributions by hierarchical clustering. Journal of Economic Science Association, 5, 238-254.
https://doi.org/10.1007/s40881-018-0060-7
A comparative analysis of regional integration potential in the Asia-Pacific Region | 37
Flach, L., Hildenbrand, H.M., & Teti, F. (2021). The Regional Comprehensive Economic Partnership Agreement and Its
Expected Effects on World Trade. Intereconomics, 56(2), 92-98. https://doi.org/10.1007/s10272-021-0960-2
Foster, R. (2013). Tabula Imperii Europae: A cartographic approach to the current debate on the European Union
as Empire. Geopolitics, 18(2), 371-402. https://doi.org/10.1080/14650045.2012.716466
Francois, J., & Elsig, M. (2021). Short overview of the Regional Comprehensive Economic Partnership (RCEP). Eu-
ropean Parliament, Directorate General for External Policies of the Union, PE 653.625, 1-25. https://doi.
org/10.2861/001684
Gaur, P. (2021). Regional Comprehensive Economic Partnership (RCEP): a Trade Agreement among Equals? Jour-
nal of Asia Pacific Studies, 6(3), 403-416.
Giannakitsidou, O., Tsagkanos, A., & Giannikos, I. (2016). Correlation of municipal solid waste production and
treatment with socioeconomic indexes. International Journal of Environment and Waste Management,
18(4), 303-316.
Głodowska, A. (2017). Business Environment and Economic Growth in the European Union Countries: What Can
be Explained for the Convergence?. Entrepreneurial Business and Economics Review, 5(4), 189-204.
http://doi.org/10.15678/EBER.2017.050409
Götz, M., Jankowska, B., Matysek-Jędrych, A., & Mroczek-Dąbrowska, K. (2018). Governmental change and FDI
inflow to Poland and Hungary in 2010-2016. Entrepreneurial Business and Economics Review, 6(1), 153-173.
https://doi.org/10.15678/EBER.2018.060109
Huettinger, M., & Zirgulis, A. (2020). Controversies Regrading the TTIP Agreement in the Academic Literature.
Review of Economics and Economic Methodology, 4(1), 79-103.
Ishikawa, K. (2021). The ASEAN Economic Community and ASEAN economic integration. Journal of Contemporary
East Asia Studies, 10(1), 24-41. https://doi.org/10.1080/24761028.2021.1891702
Kamińska, T., & Zielenkiewicz, M. (2019). Changes in the similarity of business structure and demography after
European Union accession. Research Papers of Wrocław University of Economics, 63(6), 51-63.
https://doi.org/10.15611/pn.2019.6.04
Kovacova, M., Kliestik, T., Valaskova, K., Durana, P., & Juhaszova, Z. (2019). Systematic review of variables applied
in bankruptcy prediction models of Visegrad group countries. Oeconomia Copernicana, 10(4), 743-772.
https://doi.org/10.24136/oc.2019.034
Kundera, J. (2019). The future of EU: Towards a two Speed Europe. European Research Studies Journal, 22(3),
261-281. https://doi.org/10.35808/ersj/1469
Kurihara, Y. (2017). Are RCEP and TPP Effective? American International Journal of Contemporary Research, 7(3),
102-108.
Maciejewski, M. (2017). The problems of the economic development of the European Union Member States.
Scientific Journal of the University of Economics in Katowice, 319, 117-126.
Mazur, A., & Witkowska D. (2006) Application of the taxonomic measures to estimate of the real estate. Econom-
ics and Organization of Agri-Food Sector, 60, 251-258.
Mlambo, D.N. (2019). Unearthing the challenges and prospects of regional integration in Southern Africa. Journal
of Public Affairs, 19(1), e1882, 1-7. https://doi.org/10.1002/pa.1882
Mlambo, V.H., & Mlambo, D.N. (2018). Challenges impeding regional integration in Southern Africa. Journal of
Economics and Behavioral Studies, 10(2(J)), 250-261. https://doi.org/10.22610/jebs.v10i2(J).2234
Pacuk, M., Palmowski, T., & Tarkowski, M. (2018). The emergence of Baltic Europe: An overview of Polish research
on regional integration. Quaestionaes Geographicae, 37(2), 47-60. https://doi.org/10.2478/quageo-2018-0013
Park, S.C. (2017). RCEP versus TPP with the Trump Administration in the USA and Implications for East Asian
Economic Cooperation. Entrepreneurial Business and Economics Review, 5(4), 135-152. http://doi.org/10.
15678/EBER.2017.050406
Rahman, M.M., & Ara, L.A. (2015). TPP, TTIP, and RCEP: implications for South Asian economies. South Asia Eco-
nomic Journal, 16(1), 38-43. https://doi.org/10.1177/1391561415575126
Savinsky, A.V. (2020). Economic Integration in ASEAN: trade-related aspects. In W. Szkutnik, A, Sączewska-Pi-
otrowska, M. Hadaś-Dyduch, & J. Acedański (Eds.), Proceedings of the 14th International Scientific Confer-
ence Analysis of International Relations 2020 – Methods and Models of Regional Development, Summer Edi-
tion (pp. 178-188). Publishing House of the University of Economics in Katowice.
38 | Wojciech Ficek, Remigiusz Gawlik
Shimizu, K. (2021). The ASEAN Economic Community and the RCEP in the world economy. Journal of Contempo-
rary East Asia Studies, 10(1), 1-23. https://doi.org/10.1080/24761028.2021.1907881
Szarek, J., & Piecuch, J. (2018). The importance of startups for construction of innovative economies. Interna-
tional Entrepreneurship Review, 4(2), 69-78. https://doi.org/10.15678/PM.2018.0402.05
Treib, O. (2020). Euroscepticism is here to stay: what cleavage theory can teach us about the 2019 European Parlia-
ment elections. Journal of European Public Policy, 28, 1-16. https://doi.org/10.1080/13501763.2020. 1737881
Ward, J.H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical
Association, 58. https://doi.org/10.1080/01621459.1963.10500845
World Bank (2021a). World Bank Metadata Glossary: <https://databank.worldbank.org/metadataglossary/all/
series>.
World Bank (2021b). World Bank Open Data database: <https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS>.
World Bank (2022). World Bank Open Data database: <https://data.worldbank.org/>.
Zhou, L., Pan, C., He, J., & Li, S. (2021). The Impact of RCEP on Chinese Regional Economy From Global Value
Chains Perspective (tentative results). Law and Development Review (Working Paper). Retrieved on March
25, 2022 from https://www.gtap. agecon.purdue.edu/resources/download/10412.pdf.
A comparative analysis of regional integration potential in the Asia-Pacific Region | 39
Authors
The contribution share of authors is equal and amounted to 50% for each of them. WF – literature review,
application of the research methodology, calculations, writing. RG – research design and methodology, con-
ceptualization, literature review, discussion, writing.
Wojciech Ficek
Bachelor of International Economics in International Trade, alumnus and master’s program student at the
Cracow University of Economics (Cracow, Poland). Scientific interests: regional integration, European unifica-
tion process, financial economics, monetary integration, international trade relations, international economic
and political relations. Applied research methods: synthetic measures of development, Ward’s method, stand-
ardized sum method, correlation analysis, linear arrangement.
Correspondence to: Mr. Wojciech Ficek, Cracow University of Economics, College of Economics, Finance and
Law, Institute of Economics, International Trade Department, ul. Rakowicka 27, 31-510 Kraków, Poland, e-
mail: wojciech.ficek@pm.me
ORCID http://orcid.org/0000-0002-2816-5830
Remigiusz Gawlik
Associate Professor at Cracow University of Economics (Cracow, Poland) and Extraordinary Associate Pro-
fessor at the North-West University Business School (Potchefstroom, South Africa). Scientific interests:
modelling and enhancing decision-making processes in management, international economic and political
relations, social costs of global economic development, the economy of happiness and work-life balance,
geopolitics. Applied research methods: Analytic Hierarchy Process, Analytic Network Process, Artificial Neu-
ral Networks, Fuzzy Sets.
Correspondence to: Prof. UEK, dr hab. Remigiusz Gawlik, Cracow University of Economics, College of Econom-
ics, Finance and Law, Institute of Economics, International Economics Department, ul. Rakowicka 27, 31-510
Kraków, Poland, e-mail: remigiusz.gawlik@uek.krakow.pl
ORCID http://orcid.org/0000-0003-3934-2131
Acknowledgements and Financial Disclosure
The publication was financed from the subsidy granted to the Cracow University of Economics - Project no.
075/EEG/2022/POT. The authors would like to express their gratitude to Jakub Janus, PhD, Stanisława Klima,
PhD, and Agnieszka Wałęga, PhD for their valuable comments, which allowed to increase the value of this
article.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relation-
ships that could be construed as a potential conflict of interest.
Copyright and License
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