The Impact of Geopolitical Influence on Regional Cooperation and Integration in East Asia

Abstract

Recent developments in regional studies argue that geopolitical influence is one factor affecting the regional order. However, studies on geopolitical influence have yet to cover East Asia to explain East Asia's regional order. The quantitative approach to geopolitical influence studies still faces a methodological challenge because it uses an arbitrary weighting of geopolitical influence in developing an index. In order to address those challenges, this research deploys factor analysis as a non-arbitrary weighting system to measure the geopolitical influence of China, Japan, Russia, and the US in East Asia during the period from 2005 to 2018. Additionally, this research explores how the geopolitical influence of those countries affects East Asia's regional cooperation and integration. The research shows that: (1) China has been able to compete with the US for geopolitical influence in East Asia since 2014, and (2) Chinese, Japanese, Russian, and US geopolitical influence positively contributes to regional cooperation and integration in East Asia, with the US and China as the main contributors. The research highlights three possible causes to explain the results: China's regional infrastructure initiatives, rejuvenation of China's view on globalization, and the relative decline of US relations with the allies in the region.

Keywords

ARCII East Asia geopolitical influence regional order

Introduction

Current studies on the regional order in East Asia have shown that the new regional order is transitioning from US hegemony and domination to a possible co-existence of US influence with China's increasing influence in the region (Beeson, 2009; Ikenberry, 2016). The dynamics of the East Asian regional order resemble possible scenarios somewhere between the continuation of US primacy, a new Cold War, the return of a Sino-centric system, a concert of powers, and regionalism (see Acharya, 2008). Most recent studies on regional order have argued that geopolitical influence is one of the essential determinants of regional order (Barthwal-Datta and Chacko, 2020; Koga, 2020). However, studies measuring geopolitical influence have yet to cover the East Asia region to explain East Asia's regional order. The existing studies on geopolitical influence have focused on South Asia (Shidore and Busby, 2019; Wang et al., 2015) and Southeast Asian sub-regions (Qin et al., 2019; Wang et al., 2017).

Moreover, one of the main challenges for studies of geopolitical influence is the methodology used for measuring influence. While some studies have focused on a qualitative approach to measuring influence (Cadier, 2019; Shidore and Busby, 2019), other studies have developed a quantitative approach to measuring influence (Gu and Wang, 2015; Mou et al., 2017; Qin et al., 2019; Wang et al., 2015). Most quantitative studies of geopolitical influence have used an arbitrary weighting system to measure influence. However, some studies have used a factor analysis method to weight factors in their model (Gu and Wang, 2015; Mou et al., 2017). Nevertheless, these studies were analogous to a “snapshot” observation because they focused only on one year of data. The challenge for this new research is to expand that application into a time series analysis.

Therefore, this research further develops the existing geopolitical influence model by deploying factor analysis as a non-arbitrary weighting system to measure the geopolitical influence of China, Japan, Russia, and the US in the East Asian region from 2005 to 2018. Next, the research estimates the geopolitical influence index of those countries in East Asia using the Asian Regional Cooperation and Integration Index (ARCII) (Park and Claveria, 2018) to learn how geopolitical influence affects regional cooperation and integration in East Asia. After the preliminary background provided in this introduction, the following section provides a literature review, focusing on the core concepts of this research: geopolitics, power, interdependence, and the relationship between them. The third section describes the model specifications and the research data. The fourth section interprets the results and provides a discussion and analysis related to the results. The article ends by summarizing the research and identifying possible areas for future research.

Literature review

The first central concept of the article is geopolitics. The broad and general meaning of geopolitics is simply a political relationship (of the relevant actors) related to spatial or geographical aspects (Kjellen and Langfeldt, 1917). Geopolitical studies consist of two main branches: classical geopolitics and critical geopolitics. Classical geopolitics sees the quality of the land as the resource (Haushofer, 1938; Mackinder, 1904; Ratzel, 1897; Spykman, 1942; Wallerstein, 2004). Critical geopolitics sees the states and relevant actors as resources. In critical geopolitics, both the quality of the land (resources) and the actors (policy and statecraft) determine states’ relationships on the international stage (Dalby, 2008; Tuathail and Agnew, 2014).

The next concept is power and its relationship to influence, as well as the source of power. Realists’ concept of power is based on three essential principles: (1) the interest of a state drives power, (2) power is closely related to resources, and (3) animus dominandi (power over others), or influence (Mearsheimer, 2001; Morgenthau and Thompson, 1993; Waltz, 1979). Pluralist Robert Dahl explored the concept of power in 1957 when he proposed the idea of relational power, reasoning that power is “to get whom to do what.” Relatedly, Singer (1963) and Keohane and Nye (1973) described power as the ability to get others to do something they are expected not to do (i.e. influence). Regarding the source of power, realists believe that relative independence and dependence create a state's vulnerability, which other states can exploit as sources of power (Mearsheimer, 2001; Morgenthau and Thompson, 1993; Waltz, 1979). From the liberalists’ perspective, asymmetrical interdependence is the origin of a state's power over another state in international relations (Hirschman, 1945; Keohane and Nye, 1973; Rosecrance and Stein, 1973).

Model specification and data description

The article will expand on the concept of power and interdependence using new themes related to globalization. Additionally, it will explore the concept of power and interdependence as a basis for measuring geopolitical influence and its relationship with regional cooperation and integration. As illustrated in Figure 1, this research assumes that geopolitical influence consists of three elements: degree of acceptance, national strength, and geographical proximity.

Figure 1.

The conceptual framework of geopolitical influence and regional cooperation and integration. Source: Author.

Geopolitical influence of the United States, Russia, China, and Japan in East Asia

The recent development of quantitative methods for geopolitical influence studies combines size, power, and geographical factors into measures of geopolitical influence. Gu and Wang (2015) proposed the basis of gestation, means of transformation, and exercising geopolitical power as the core concepts of geopolitical influence formation. They also constructed an initial quantitative model for measuring the geopolitical influence that consists of hard power, soft power, and interdependent power, divided by the friction of the distance between countries. Qin et al. (2019) modified this model by adopting a new approach to include reciprocality in measuring geopolitical influence. First, they expanded the concept of interdependent power into the degree (ratio) of acceptance. Second, they modified the geopolitical influence model into the function of the degree of acceptance (with both hard power and soft power as variables), divided by the comprehensive friction of the distance between countries. However, the model in their research used an arbitrary method to determine the index.

The challenge of developing an index is to justify the validity of the index. The way a model chooses variables and determines the weight to assign each one is the main challenge of developing an index. Therefore, after proposing a conceptual framework and translating it into a model, the next step is to determine which weighting system the research should adopt: arbitrary or non-arbitrary. Hohn (2011) identified several ways to create a non-arbitrary weighting system: a statistical approach, a survey-consultation approach, and an empirical approach. The statistical approach includes principal component analysis, factor analysis, and discriminant analysis, using statistical software to process the data. In the research presented here, statistical software is the tool for extracting variables into components and determining the appropriate weight for those variables and components in measuring the index (i.e. factor analysis).

Gu and Wang (2015) and Mou et al. (2017) deployed a factor analysis method to conduct a geopolitical influence assessment in the oil & gas and climate change field. The factor analysis focused on identifying latent factors underlying the observed data. Therefore, the model in this article adopts the latest modification by Qin et al. (2019), but with a further application of factor analysis in determining the weight of data and observations over time. The research also covers the broader region of East Asia and analyzes the impact of geopolitical influence on regional cooperation and integration in East Asia. The model measures geopolitical influence using three components: degree of acceptance, national strength, and geographical proximity. The degree of acceptance represents one country's response to other countries’ growing influence. National strength represents the national power of a country. Last, geographical proximity measures the physical distance (both the land and sea distance) between countries. Thus, the model of geopolitical influence is expressed as:

G I_{i j} = D A_{i j} \cdot N S_{i j} \cdot e^{- d_{i j}}

(1)

where: DA_ij is the degree of acceptance of country i towards country j; NS_ij is the national strength of country je is base of the natural logarithm; and d_ij is geographical distance between country i and country j.

The first part of the model is the degree of acceptance. Schirm (2005) identified both recognition and acceptance of leadership status by other countries in the region as one of the criteria for a country to gain regional power status. Meanwhile, Prys (2010) highlighted the role of regional acceptance in determining regional power hood typology. Acceptance itself is determined by how the regional power could bear both the cost and responsibility for the region's financial and administrative issues (Flemes and Lobell, 2015). Therefore, most recent geopolitical influence studies have recognized the importance of incorporating the degree of acceptance factor in the geopolitical influence model.

Gu and Wang (2015) adopted the geopolitical environment advantage to represent regional acceptance. Wang et al. (2015) measured interdependence power as the total trade and investment between countries. Qin et al. (2019) included not only economic indicators (bilateral trade and foreign direct investment (FDI)) but also political indicators (political similarity and weapons trade) in the degree of acceptance factor. This research includes the ratio of total trade, the ratio of incoming FDI, the ratio of weapons trade, and the degree of political similarity between countries in the degree of acceptance factor. Next, as described below, the research uses a factor analysis method to determine which variables are relevant and the appropriate weight for those variables in the degree of acceptance factor.

The second part of the model is national strength. Some studies have focused on variables such as population, labor force, territory, production, and military budget and personnel (Alcock and Newcombe, 1970; Beckman, 1984; Cline, 1977; Fucks, 1965; Singer and Small, 1972). Other studies have also incorporated variables such as world population, world production, and foreign aid (Beckman, 1984; Kugler and Domke, 1986; Organski and Kugler, 1980). Recent studies on geopolitical influence translated those approaches into the concepts of hard power and soft power. Gu and Wang (2015) used territorial scale, economic capacity, naval power, diplomatic capability, and domestic stability as national strength factors. Wang et al. (2015) included population, area, military expenditure, research and development budget, and high-tech exports as hard power factors, while the political system, peacekeeping activities, official development assistance, cultural exports, and international students represented the soft power of a country. Qin et al. (2019) used population, land area, GDP, research and development budget, high-tech exports, and military expenditure as hard power factors, and political stability, government effectiveness, number of peacekeepers, contribution to the UN, cultural goods exports, and international students as soft power factors.

This research adopts population, land area, GDP, international trade, research and development expenditure, number of patent applications, government effectiveness, political stability, military expenditure, number of armed forces personnel, contribution to United Nations, and number of peacekeepers as national strength factors. However, cultural interactions could be an alternative way of projecting a nation's soft power. Tourism appeal measures human travel, while art goods exports measure cultural movement and activities related to cultural interactions. Therefore, this research also includes international tourist arrivals and art goods exports as the variables to measure national strength. Again, to deploy a non-arbitrary weighting system in creating the index, this research uses a factor analysis method to determine which variables are relevant to national strength and the appropriate weight for those variables.

The third part of the model is geographical proximity. While Gu and Wang (2015) used a transportation factor instead of a geographic factor, Wang et al. (2015) incorporated physical and diplomatic factors to represent the frictional distances between two countries. However, Qin et al. (2019) focused only on land and sea distance as the comprehensive measure of distance. They assumed that sea distance is one-fifth of land distance. This research focuses only on physical distances because the concept of diplomatic distance is represented in the degree of acceptance factor. Thus, the model of geographical proximity (d_ij) consists of the total sea and land distance, as follows:

d_{i j} = 0.2 d_{s e a_{i j}} + d_{l a n d_{i j}}

(2)

where:

d_{s e a_{i j}}

is the sea distance between country i and country j, measured by the distance of the largest port of the country i to the largest port in country j; and

d_{l a n d_{i j}}

is the land distance between country i and country j, measured by the road distance of the capital city of country i to the capital city of country j.

Given the different geographical relationships between countries, these additional rules will apply to measure geographical proximity in this research:

Sea distance is equivalent to one-fifth of the land distance.

Sea distance from the US will be measured from the largest port on the West Coast, and from Russia will be measured by the largest port on the East Coast.

If both land distance and sea distance are available, the shorter distance will be used.

Geopolitical influence, regional cooperation, and integration in East Asia

This research estimates the relationship between the geopolitical influence index and the regional cooperation and integration index; specifically, the relationship between the geopolitical influence of China, Japan, Russia, and the US in East Asia and the ARCII of East Asian countries during the period from 2006 to 2018 (see Table 1). This article proposes two initial hypotheses. First, a rivalry, or competition, exists between powers within the region (China and Japan) and powers outside the region (Russia and the US). Second, it is possible for China (as a regional power) and the US (as a global power) to co-exist, despite the competition between them in East Asia's regional order.

Table 1.

Asian Regional Cooperation and Integration Index (ARCII) of East Asian countries, 2006–2018.

Countries	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
Cambodia	0.48	−	0.46	0.48	0.47	0.47	0.47	0.46	0.45	0.47	0.46	0.46	0.46
China	0.47	0.47	0.49	0.48	0.48	0.48	0.48	0.49	0.50	0.50	0.51	0.49	0.49
Indonesia	0.51	0.50	0.52	0.50	0.51	0.51	0.52	0.52	0.52	0.51	0.50	0.51	0.54
Japan	0.46	0.45	0.46	0.47	0.46	0.46	0.46	0.46	0.46	0.46	0.47	0.46	0.45
Lao	0.50	−	−	−	−	−	0.51	0.52	0.54	0.53	0.52	0.46	0.55
Malaysia	0.55	0.55	0.53	0.54	0.54	0.56	0.54	0.56	0.56	0.57	0.56	0.54	0.55
Mongolia		0.41	0.40	0.39	0.39	0.39	0.45	0.45	0.47	0.43	0.50	0.48	0.43
Philippines	0.45	0.44	0.44	0.44	0.41	0.45	0.44	0.45	0.45	0.45	0.45	0.46	0.47
S. Korea	0.50	0.49	0.51	0.50	0.50	0.51	0.49	0.50	0.50	0.50	0.50	0.50	0.50
Singapore	0.50	0.52	0.52	0.53	0.53	0.52	0.51	0.51	0.51	0.52	0.52	0.52	0.51
Thailand	0.51	0.51	0.51	0.51	0.51	0.54	0.52	0.53	0.52	0.53	0.52	0.51	0.51
Vietnam	0.46	0.45	0.47	0.45	0.47	0.44	0.46	0.47	0.49	0.47	0.49	0.49	0.48

Source: Park and Claveria (2018) for the Asian Development Bank (ADB).

This research deploys a panel data analysis with a fixed-effect model to analyze the relationship between both indices. The ARCII serves as the dependent variable, and the geopolitical influence index is the independent variable for the panel data estimation. Since there could always be a potential endogeneity issue in regressing panel data, the model applies the Instrumented Variables estimation with the Two-Stage Least Squares method (IV-2SLS) to address this problem (Semykina and Wooldridge, 2010; Sovey and Green, 2011; Wooldridge, 2005) (Table 2). The research uses a one-year lag for the independent variables as the instrumental variables for the endogenous variables, as follows:

A R C I I_{i} = β_{0} + β_{1} {\hat{G I_U S}}_{i} + β_{2} G I_R u s s i a_{i} + β_{3} {\hat{G I_C h i n a}}_{i} + β_{4} G I_J a p a n_{i} + u_{i}

(3)

where:

Table 2.

Data, measurement, and sources.

Data	Measurement	Source
Degree of acceptance
Total trade	Share of global export + import of country i with country j to global export + import of country i	International Trade Center
FDI	Share of FDI inflow of country i from country j to FDI inflow of country i from the rest of the world	UNCTAD-Bilateral FDI Statistics and www.investmentmap.org
Weapon Trade	Share of imported HS-93 (arms and ammunition; parts and accessories thereof) of country i from country j to imported HS-93 of country i from the rest of the world	WITS database – World Bank
Political similarity	Freedom score of country i compared to freedom score of country j (values closer to 1 indicate political similarity)	Freedom House
National strength
International trade	Total export + import of country j (US$)	International Trade Center
Population	Total population of country j (persons)
Land area	Total land area of country j (square km)
GDP	Real GDP of country j (constant 2010 US$)
R&D expenditure	Research and development expenditure (as a percentage of GDP)	World Bank Database
Patent applications	Number of patent applications by residents (applications)	World Bank Database
Military expenditure	Military expenditure (US$)
Armed forces personnel	Number of armed forces personnel (persons)
International tourist arrivals	Number of tourist arrivals (persons)
Government's effectiveness	Index of government effectiveness	Worldwide Governance Indicators
Political stability	Index of political stability and absence of violence	Worldwide Governance Indicators
Cultural goods trade	Export of cultural goods of country j (H.S. code classified by UNESCO) (US$)	UNESCO
Contribution to the UN	Financial contribution to the annual UN regular budget (US$)	UN official website
Number of peacekeepers	Number of peacekeepers sent (persons)	UN Peacekeeping official website
Geographical proximity
Geographical distance	Land or sea distance between country i and country j (km)	www.searates.com
Asian Regional Cooperation and Integration Index
ARCII of East Asian countries	Index of regional cooperation and integration of East Asian countries	Asian Development Bank Publications

Source: Compiled by the author.

ARCII_i is the Asian Regional Cooperation and Integration Index of East Asian countries; β₀ and γ₀ are constant; GI_US_i is the geopolitical influence of the United States in East Asia; GI_Russia_i is the geopolitical influence of Russia in East Asia; GI_China_i is the geopolitical influence of China in East Asia; GI_Japan_i is the geopolitical influence of Japan in East Asia; β₁, β₂, β₃, and β₄ are coefficients of GI_US_i, GI_Russia_i, GI_China_i, and GI_Japan_i, respectively; and u_i is the error term.

The data includes the relevant countries’ annual data between 2005 and 2018 (see Table 3). In order to balance the data, variables representing the degree of acceptance are processed in a ratio-based dimension (between zero and one). Meanwhile, variables representing the national strength have been standardized into a dimensionless system of two-digit values (between 0 and 99). Geographical proximity is the natural logarithm of geographical distance.

Table 3.

Statistical analysis of the main components of degree of acceptance.

	Non-rotated			Orthogonal rotation
Component	Eigenvalues	Contribution rate (%)	Cumulative contribution rate %	Eigenvalues	Contribution rate (%)	Cumulative contribution rate %
1	1.725	43.126	43.126	1.460	36.511	36.511
2	1.161	29.034	72.160	1.426	35.649	72.160
3	0.670	16.756	88.916
4	0.443	11.084	100.000

Source: Calculated by the author.

Results and discussions

Geopolitical influence of the United States, Russia, China, and Japan in East Asia

One proposed alternative weighting scheme for developing a geopolitical index is the maximum-minimum bound approach (Seth and McGillivray, 2018). That approach applies the upper and lower bounds by weighted allocation in a ratio between 0 and 1. Therefore, the research in this article deploys a factor analysis process using statistical software to extract relevant components for the degree of acceptance and national strength factors and to determine the weights for each variable under the related components.

Four variables related to the degree of acceptance have been extracted into three components of the degree of acceptance. The contribution of variance will be utilized as the weight for each component’s contribution to the degree of acceptance factor. The KMO index was 0.513, with a Bartlett's test of sphericity value of 390.46 and a sig value of 0.00, indicating that the correlation between variables is significant enough for the factor analysis process. Based on the factor analysis process, the top two components have a cumulative variance contribution rate of 72.16%. Even though the desirable total variation is between 80% and 90% (Jolliffe, 2011), given the small number of the components and commonalities between variables (closer to one), the results indicate minimal information loss in describing the components.

The preferable cut-off value for the loading factor inside the components is somewhere between 0.700 and 0.900 (Jolliffe, 2011). Building on that, the first component, with a contribution rate of 36.51%, is associated with political similarity (see Table 4). The second component, with a contribution rate of 35.65%, is associated with the variable of trade (see Table 4). The other two variables, FDI and weapon trade, do not associate with those two components. This model assumes that those variables are associated with the rest of the components; thus, the rest of the components are treated together as the third component.

Table 4.

Loading matrix of the main components of degree of acceptance.

Variables	Component		Associate with
Variables	1	2	Associate with
Trade	−0.122	0.909	Component 2
FDI	0.486	0.691	Rest/others
weapon_trade	0.682	0.304	Rest/others
political_similarity	0.863	−0.175	Component 1

Source: Calculated by the author.

This research identifies the first component as economic acceptance, the second component as political acceptance, and the third component as cooperative acceptance (see Table 5). Based on the components and weights provided by the factor analysis, the degree of acceptance model is:

D A_{i j} = 0.3651. P_{i j} + 0.3565. E_{i j} + 0.2784. C_{i j}

(4)

where: DA_ij is the degree of acceptance of country i towards country j; P_ij is the political acceptance of country i towards country j (political similarity between the two countries); E_ij is the economic acceptance of country i towards country j (share of imports between the two countries); and C_ij is the cooperative acceptance of country i towards country j (share of FDI and weapons trade between the two countries).

Table 5.

Contribution rates of degree of acceptance components.

Components	Contribution rate (%)	Variables
Political acceptance	36.51	Political similarity
Economic acceptance	35.65	Trade
Cooperative acceptance	27.84	FDI
Cooperative acceptance	27.84	Weapon trade

Source: Calculated by the author.

Fourteen variables related to national strength have been extracted into three components of national strength. Again, the contribution of variance will be the weight for the contribution of each component to the degree of acceptance factor. The KMO index was 0.694, with a Bartlett's test of sphericity value of 1856.90 and a sig value of 0.00, indicating that the correlation between variables is significant enough for the further factor analysis process. Based on that process, the top two components have a cumulative variance contribution rate of 94.14%. The commonality between variables produces a value closer to one, indicating minimal information loss in describing the components (Table 6).

Table 6.

Statistical analysis of the main components of national strength.

Component	Non-rotated			Orthogonal rotation
Component	Eigenvalues	Contribution rate (%)	Component	Eigenvalues	Contribution rate (%)	Component
1	5.832	41.657	41.657	4.767	34.051	34.051
2	5.300	37.860	79.517	4.257	30.407	64.458
3	2.047	14.620	94.136	4.155	29.678	94.136
4	0.397	2.834	96.970
5	0.192	1.368	98.339
6	0.102	0.727	99.066
7	0.045	0.318	99.384
8	0.027	0.192	99.576
9	0.023	0.166	99.742
10	0.015	0.106	99.849
11	0.011	0.079	99.928
12	0.006	0.044	99.972
13	0.002	0.017	99.989
14	0.002	0.011	100.000

Source: Calculated by the author.

Given the high number of observations and components, the loading factor's cut-off value is permitted to be less than 0.700 (Jolliffe, 2011). Therefore, the first component, with a contribution rate of 34.05%, is associated with the variables of government effectiveness, political stability, and R&D expenditure (see Table 7). The second component, with a contribution rate of 30.41%, is associated with the variables of population, number of patents, number of armed forces personnel, cultural goods exports, and number of peacekeepers. The third component, with a contribution rate of 29.68%, is associated with the variables of GDP, international trade, military expenditure, international tourist arrivals, and financial contribution to the UN Another variable, land area, does not associate with the other three components. This research assumes that the variable is associated with the rest of the components and treats the rest of the components together as the fourth component.

Table 7.

Loading matrix of the main national strength components.

Variables	Component			Associate with
Variables	1	2	3	Associate with
population	−0.235	0.942	0.047	Component 2
land_area	−0.959	−0.120	0.147	Rest/others
gdp	0.372	0.131	0.914	Component 3
intl_trade	0.194	0.635	0.729	Component 3
rnd_expend	0.971	−0.031	0.219	Component 1
patents	0.299	0.813	0.118	Component 2
military_exp	0.099	−0.052	0.985	Component 3
armed_force	−0.610	0.714	0.194	Component 2
tourism_arrivals	−0.222	0.430	0.837	Component 3
govt_effectiveness	0.892	−0.074	0.416	Component 1
political_stability	0.952	−0.218	0.128	Component 1
cult_goods_exp	−0.010	0.742	0.555	Component 2
un_contribution	0.639	−0.228	0.699	Component 3
peacekeepers	−0.203	0.959	−0.092	Component 2

Source: Calculated by the author.

Thus, the model identifies the first component as inward strength, the second component as outward strength, the third component as international recognition, and the fourth component as potential and resources (Table 8). Based on the components and weights provided by the factor analysis, the national strength model is as follows:

N S_{j} = 0.3405. I S_{j} + 0.3041. O S_{j} + 0.2968. I R_{j} + 0.0586. P R_{j}

(5)

where:

Table 8.

Contribution rate of the national strength components.

Components	Contribution rate (%)	Variables
Inward strength	34.05	Government effectiveness
		Political stability
		R&D expenditure
Outward strength	30.41	Population
		Number of patents
		Number of armed force personnel
		Art goods exports
		Number of peacekeepers
International recognition	29.68	GDP
		International trade
		Military expenditure
		International tourist arrivals
		UN contribution
Potential and resources	5.86	Land area

Source: Calculated by the author.

NS_j is national strength of country j; IS_j is inward strength of country j (government effectiveness, political stability, and R&D expenditure); OS_j is outward strength of country j (population, number of patents, number of armed force personnel, art goods exports, and number of peacekeepers); IR_j is international recognition of country j (GDP, international trade, military expenditure, international tourist arrivals, and financial contribution to the UN); and PR_j is potential and resources of country j (land area).

The factor analysis process provided a model for the degree of acceptance and national strength for the over-arching geopolitical influence model. Each variable in each respective model is calculated using the data for the variables inside each of the components. Together, the degree of acceptance, national strength, and geographical proximity measure the geopolitical influence of the US, Russia, China, and Japan in East Asia from 2005 to 2018 (Figure 2).

Figure 2.

Degree of acceptance of the United States, Russia, China, and Japan in East Asia, 2005–2018. Source: Index calculated by the author.

Overall, East Asian countries show a greater degree of acceptance towards the US compared to the other countries studied. China displayed a strong positive trend after 2009 but has experienced a significant decline since 2015. Increased tension surrounding the South China Sea dispute followed by the trade war with the US might explain this phenomenon. Japan used to be an important player in the region (especially in Southeast Asia), but since the decline of their official development assistance (ODA), they have lost recognition from other East Asian countries during the period from 2005 to 2018 (Liu and Liu, 2019). Since the end of the Cold War, Russia has focused its efforts on Eurasian countries rather than the East Asia region. This explains Russia's declining trend of acceptance in the region.

Some studies argue that the rise of acceptance towards China from the region was because China managed to top the US and Japan in initiating a regional infrastructure development strategy, which was marked by the adoption of the Belt and Road Initiative (BRI) in 2013, followed by the establishment of the Silk Road fund in 2014 and the Asian Infrastructure Investment Bank (AIIB) in 2015 to support the operation of the BRI (de Graaff and van Apeldoorn, 2018; Weissmann, 2020; Yuan, 2018; Zhao, 2019). Those events contribute to the superior involvement of China in infrastructure projects throughout East Asia. Nevertheless, China elaborates its geographical advantage over the US (and Japan) to ‘buy’ acceptance from countries in the region by exploring their economic interdependencies towards China (Lim and Cooper, 2015; Mao et al., 2019; Ross, 2019; Yilmaz and Li, 2020; Zhao, 2019; Zhexin, 2018). Another point of view is that China undertakes those initiatives to show its readiness to play more roles, not only in the regional scope but also in the global scope. China proposes the BRI as a new form of globalization (turning marine-based globalization into comprehensive globalization), as the current global international order favors US domination (Ekman and Nicolas, 2019; Lim and Cooper, 2015; Ratner, 2013; Yilmaz and Li, 2020; Yuan, 2018).

The US is considered a global power and still has the greatest national strength in the world, while China has managed to close the gap and compete with the US since 2014 (see Figure 3). The year 2014 marked the peak of China's GDP growth of the post-Cold War era, when it became a dominant power in the East Asian economy, serving as the main export destination country of most East Asian countries (Ross, 2019; Singh et al., 2017). Globally, China has managed to surpass the US as the largest exporting country, as well as the largest FDI destination country, since 2014 (Pan, 2018) and to complement this status by strengthening its role in the global international order with an increasing contribution to the United Nations (UN), both financially (contribution to the UN regular budget) and physically (number of peacekeepers). A study by Yuan (2018) measures the relative capabilities of the major powers, following the work of George Modelski in 1974. His finding confirms the year 2014 as the lowest point of US relative capabilities, while China had peaked its relative capabilities by 2015. However, while China's strength is more quantitative than qualitative, political stability issues remain a concern. For Japan, a long period of economic stagnation beginning in the 1990s, directly and indirectly, affected its capability and capacity as a country. Moreover, after the Cold War, Russia has been a very different country than the former USSR but has still grown steadily (Figure 4).

Figure 3.

National strength of the United States, Russia, China, and Japan, 2005–2018. Source: Index calculated by the author.

Figure 4.

Geopolitical influence of the United States, Russia, China, and Japan in East Asia, 2005–2018. Source: Index calculated by the author.

In the 2005 to 2018 period, China's geopolitical influence in East Asia grew on average by 6.3% annually, while the US, Russia, and Japan grew 1.8%, 0.2%, and 0.2%, respectively. Country by country, China's influence is somewhat more significant in its neighboring countries, such as Laos, Mongolia, Myanmar, and Vietnam. On the other hand, the US has maintained a greater geopolitical influence with its traditional allies such as Japan, the Philippines, and South Korea. Japan has sustained a comfortable position in the region through its ODA; however, the somewhat stalled economic growth during the last few decades has prevented Japan from maintaining or increasing its influence. Russia has managed to stay under the radar, focusing only on bilateral security-military cooperation. While the region has a lack of economic interdependence with Russia, the political similarity with some regional countries becomes the main tool to maintain its geopolitical influence in the region.

China's geopolitical influence growth displayed a positive trend from 2009 to 2015. In 2014, China's influence surpassed US influence and it has competed tightly since then. Previous studies support this finding, such as the 2014 poll by the Pew Research Center. The poll found a power shift in the balance of power of China–US between 2008 and 2014. In 2008, 49% of respondents saw the US as the leading global economic power, compared to the 19% who favored China. However, only 40% were still in favor of the US in 2014, while 31% of respondents saw China as the leading global economic power. Again, the study by Yuan (2018) on major power relative capabilities proposes that the rise of Chinese economic power, together with its upward trend in the military budget, enabled China to oppose the US in a new bipolar system in 2018. This research found a relatively similar result, using geopolitical influence as the parameter.

Geopolitical competition in East Asia is one of the China–US rivalry chapters, but probably the most important one since it is located in China's neighborhood. Some studies argue that the rise of China in the region was because it was not until 2017, under the Trump administration, that the US revamped its political and economic focus towards the threat coming from China (Campbell and Ratner, 2018; Friedberg, 2018; Liu and Liu, 2019; Yang, 2017). This would later be reflected by the focus of the US on China in its 2017 National Security Strategy (NSS) and 2018 National Defense Strategy (Tan, 2020).

Other studies believe China's national rejuvenation significantly changes its policy and approach towards the global international order. The old Deng Xiaoping ethos of “hiding strength and awaiting opportunities” was replaced by new ones such as “The Chinese Dream” in 2012 and “Socialism with Chinese Characteristics for a New Era” in 2017, as China's thoughts turned to a manifestation of a new form of international relations with a focus on community-shared humanity, a harmonious world, and peaceful development (Callahan, 2013; Nordin, 2016; Nordin and Weissmann, 2018; Shin, 2018; Weissmann, 2020).

This rejuvenation focuses on adopting a win-win and mutual approach, maintaining peace with neighboring countries, and pursuing regional stability (Heath, 2013; Liu, 2016; Xiao, 2016; Zhao, 2019). This has been further materialized by engaging in deeper cooperation with neighboring countries in the region. In 2013, China held a Conference on the Diplomatic Work with Neighboring Countries, highlighting its awareness of the importance of its relations with neighboring countries. As a result, China had either negotiated or signed various economic partnerships and free trade agreements with ASEAN, South Korea, and Australia by 2014. In the physical aspect, the BRI and the establishment of the AIIB served as the follow-up actions and became crucial policy tools for China's policy on the geopolitics of Asia, including East Asia (Han, 2017; Li, 2020; Ross, 2019; Weissmann, 2020; Yuan, 2018). Contrary to that, the US under the Obama administration experienced worsening relations with its allies; for example, related to the US position on the 2014 Thailand military coup, the US role in the Malaysia 1MDB scandal in 2015, the position of the US towards the Philippines’ aggressive anti-drug policy in 2016 (Tan, 2020).

Another reason explaining China's rise in the geopolitics of East Asia rests on the growing power of China as a nation, economically and militarily. Internally, the long adoption of the ‘China Model’ contributes to the development of China as a globally competitive and compelling country, which adds to its status as the largest exporting country and surpasses the US GDP, based on purchasing power parity, since 2014 (Horesh and Lim, 2017; Ross, 2019). Externally, the growing national strength and capacity boost China's confidence to exercise its power in the regional and global scope. China always relates the great power political dynamics in the region with the North Korea issue (Christensen, 2006; Moore, 2017; Sinkkonen, 2019; Zhu, 2016). North Korea is one of the most (if not the only) important allies in the region, where China both enjoys benefits and suffers from the consequences. However, having its ally become one of the nuclear-weapon states redefines the China–North Korea relations (and the China–US rivalry) in favor of China. As a result, China would have its ally become more dependable (have more bargaining power); simultaneously, it elevates the relative importance of the Chinese role in negotiating peace and stability in the region (Kim and Cha, 2016; Xiao, 2016; Yang, 2019).

Moreover, on the issue of security, the rejuvenation was translated into the expanding sphere of influence of China as a leading regional power in East Asia, in the form of a transformation from a land power to a hybrid sea-land power (Dian, 2015; Erickson, 2016; Walton and McGrath, 2014). The transformation is focused on upgrading its naval military capacity to control the ‘Near Seas’ (Erickson and Wuthnow, 2016). China realizes the importance of having political and security stability in the ‘Near Seas’ to support its growing influence as a leading power in the region. Therefore, one of the focuses of the 2015 Military Strategy of China is on enhancing military and security cooperation, including joint military exercises, arms trade, and exchange of technologies to balance the US and its allies in the region (Sinkkonen, 2019; Zhu, 2016). In addition, China has started to exercise a more assertive foreign policy in the form of a state-by-state approach and extending the leadership on non-traditional securities in response to the US security leadership in East Asia (Gong, 2020; Kim and Cha, 2016; Liff, 2018; Sun, 2018).

Nevertheless, the US is still considered a dominant player, especially in military and security matters and in its involvement and contribution to international organizations. The study of Tan (2020) supports the findings of this research related to the US geopolitical influence in East Asia: despite Obama's pivot to Asia, it is not until the US under the Trump administration intensified its involvement in various regional initiatives such as the Indo-Pacific Initiative, the Asia Maritime Security Initiative, and the Asia Reassurance Initiative Act that it regained its influence in the region. Nevertheless, the complexity and instability of the China–US rivalry in the region would be intensified during the intersection of the Trump and Xi administrations in 2018–2020 (Nordin and Weissmann, 2018; Shin, 2018). Despite that, the US has been at a disadvantage in economic and cultural relations because of its geographical distance from the region. Thus, the US should maintain its status as the provider of security and stability in the region, either through military power or influence on international organizations.

China's rejuvenation as a country covers all the economic, political, and military-security issues. As a result, it has significantly increased its political and economic influence while simultaneously approaching the US in the military-security realm and its role in the international order. In the economic and political realm, the BRI and the supporting initiatives pursue a more inclusive approach. Meanwhile, in the military-security realm, Chinese security-military policies pursue a more expansive approach. However, gaining support and acceptance from neighboring countries will be crucial for successfully implementing China's rejuvenation as a country. Nevertheless, China needs to convince the neighboring countries to support or even join the initiatives. At the same time, China has to gain trust from them and prove that its expansive security-military policies will not threaten the peace and political stability in the region.

Geopolitical influence, regional cooperation, and integration in East Asia

Estimation using panel data models carries a risk of various statistical problems such as autocorrelation, heteroscedasticity, multicollinearity, and endogeneity. The Variance Inflation Factors (VIF) analysis (see Table 9) shows no multicollinearity on the model (the VIF values of all independent variables are < 10.0).

Table 9.

Variance inflation factors analysis.

Variable	VIF
C	−
GI_US	1.2566
GI_Russia	1.0701
GI_China	1.0555
GI_Japan	1.2139

Source: Calculated by the author.

The residual test results (see Table 10) show endogeneity on some of the independent variables (P-value < 0.05). The observation period was from 2006 to 2018 (T = 13) and included 12 countries (N = 12), T > N. Therefore, the Instrumented Variables (IV) estimation with a Two-Stage Least Squares (2SLS) method is more compatible to use in the model, rather than the Generalized Method of Moment (GMM) estimation (Arellano and Bover, 1995; Bond and Arellano, 1991; Holtz-Eakin et al., 1988; Semykina and Wooldridge, 2010; Sovey and Green, 2011; Wooldridge, 2005). For the correlation and heteroscedasticity issue, the application of IV estimation in panel data nullifies both serial correlation and heteroscedasticity (Wooldridge, 2002, 2005). Therefore, applying the IV-2SLS estimation method to this research model will mitigate autocorrelation and heteroscedasticity issues.

Table 10.

Endogeneity check.

Variable	P-value
C	−
Resid_GI_US	*0.0168
Resid_GI_Russia	0.0797
Resid_GI_China	*0.0072
Resid_GI_Japan	0.3623

Source: Calculated by the author.

Table 11 shows the estimation results for the geopolitical influence of the countries studied. The Hausman test showed a P-value > 0.05, suggesting that the fixed effect model is the better fit for this model. However, to solve the endogeneity issue, this research goes further by adopting the IV-2SLS method for the estimation. The results show that the geopolitical influence of the US, Russia, China, and Japan contributes positively to regional cooperation and integration in East Asia. However, the different level of significances between China and Japan, and the US and Russia, represents the competition between them. Indeed, relevant studies of political regionalism in East Asia have shown that both China and Japan have focused on improving regional integration in East Asia (Kim, 2010; Teh, 2011). China and Japan compete through their regional initiatives, either the East Asian Summit or the ASEAN Plus Three. Even though the regional countries are always skeptical about China's long-term goals, they also see Japan as neither truly Asian nor truly Western (Harun, 2015).

Table 11.

Estimation results.

Dependent variable: ARCII of East Asian countries
	LS	RE	FE	IV-2SLS
C	0.512	0.425	0.408	0.348
	(0.019)	(0.021)	(0.022)	(0.052)
GI_US	^a−0.028	^b0.036	^a0.049	^c 0.088
	(0.010)	(0.016)	(0.019)	(0.046)
GI_RUSSIA	−0.035	0.054	^c0.059	0.084
	(0.064)	(0.035)	(0.035)	(0.054)
GI_CHINA	−0.010	^a0.029	^a0.029	^b 0.037
	(0.010)	(0.008)	(0.009)	(0.015)
GI_JAPAN	^c0.029	0.035	0.056	0.116
	(0.017)	(0.045)	(0.061)	(0.155)
P-value (cross-section)	−	0.251	0.000	−

Source: Estimated by the author.

significant at 1% level; ^bsignificant at 5% level; ^csignificant at 10% level.

On the other hand, the US–Russia relationship in East Asia has changed since the Cold War ended. However, the US retreat would allow the Sino–Russian partnership to challenge the US-led order in East Asia (Wishnick, 2018). While Russia lacks importance on East Asia regionalization and integration, it contributes to a stable political-security climate of the region by not taking part in the existing conflicts in the region (Koldunova, 2016). Eventually, East Asian countries (and even Russia itself) realized Russia's tendency to focus on the European continent rather than East Asia, leaving the US with more influence in the region. Therefore, the insignificant positive relationship of Russia's geopolitical influence with the cooperation and integration of East Asia found by this research supports Kuldunova's argument: it will not be until Russia adopts a strategic policy to solve the domestic disparity between its European and Asian counterparts that deep and strategic cooperation with East Asian countries will materialize.

For US–China competition in the region, the results showed the importance of both the US's and China's geopolitical influence in East Asia. While the results identify the US and China as the main actors in the region, a positive relationship between them reveals the possibility for them to co-exist in the region. With the two great powers competing for influence in the regional order, the existence of both powers in the regional order is supported by both China enjoying the relatively ineffective regional institutional architecture and the US reluctantly committing itself to doing something about it (Beeson, 2015, 2019). However, both China and the US play a significant role in facilitating a stable environment for the regional countries. Other studies support these findings and propose a possibility of having multiple hierarchies in the East Asian regional order: a security hierarchy led by the US and China's economic hierarchy (Ikenberry, 2016). Furthermore, in discussing the impact of the China–US relationship on East Asia's regional order, Rosecrance (Diebold, 1986) identified two competing perspectives on national power. One country focuses on territory, alliance, and military forces (the US), while the other concentrates more on commercial and financial issues (China). Thus, the dual hierarchical orders create a dynamic competition between the two powers: a ‘hot economics’ accompanied by cooperative security interactions (Liu and Liu, 2019). Even though East Asia's regional order is currently in the transitional phase, its dynamics have made it difficult for any single power to dominate in the region. Hence, the regional powers prefer to hedge themselves towards the competition to benefit both competing powers (Brown, 2018; Chung, 2016; Sutter, 2010). Since the regional countries do not want to choose sides amid US–China competition, the influence of both countries can co-exist to share leadership and responsibilities (Curtis, 2010; Wang, 2015).

Conclusions

The article aims to help shape the future study of international relations, especially in measuring geopolitical influence and the regional order in East Asia, by developing a non-arbitrary way of measuring and analyzing geopolitical influence. The factor analysis method successfully extracts variables into components and provides weights for each of them. The results gained from this geopolitical influence model reliably explain the geopolitics of East Asia from 2005 to 2018. The US is still considered the dominant player, especially in the military and security realms, and in its involvement in and contribution to international organizations. However, the US experienced disadvantages in economic and cultural relations due to its geographical distance from the region. Thanks to the regional infrastructure initiatives and Chinese rejuvenation (which covers all economic, political, and military-security issues) as a country, China has significantly improved its influence in other areas while at the same time approaching the US in military and security matters and involvement in the international order. However, political stability and bilateral relations with neighboring countries are still an issue for China.

Furthermore, this research estimated the geopolitical influence of the ARCII in describing East Asia's regional cooperation and integration. The results highlight the positive relationship between geopolitical influence and regional cooperation and integration in East Asia. The results also show that the US and China are the main actors in terms of geopolitical influence in the region. The results showed a positive and significant relationship between their geopolitical influence and East Asia's regional cooperation and integration. What should be competition between the US and China seems to present a possibility of co-existence between them. Given the alignment of the results to the initial hypotheses and the previous research supporting the results and conclusions, it is fair to conclude that the geopolitical influence index (together with the ARCII) can explain cooperation to some extent and integration in East Asia.

Factor analysis is not the only method to achieve a non-arbitrary approach in measuring a geopolitical influence index. Likewise, estimating geopolitical influence towards the ARCII is also not the only way to analyze the regional order, but it helps to explain further how geopolitical influence affects regional cooperation and integration. Therefore, future research could further identify (1) alternative, non-arbitrary methods for measuring geopolitical influence using a quantitative approach and (2) alternative ways to analyze how geopolitical influence affects the dynamics of the regional order.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Arianto Christian Hartono

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