Spatial correlation effect of China's outward foreign direct investment in countries along the One Belt and One Road

• Date: 01 May 2020
• Author: Shuzhong Ma,Mengheng Liu
• Published date: 01 May 2020
• DOI: http://doi.org/10.1111/1468-0106.12328

SPECIAL ISSUE ARTICLE

Spatial correlation effect of China’s outward

foreign direct investment in countries along

the One Belt and One Road

Shuzhong Ma | Mengheng Liu

School of Economics, Zhejiang

University, Zhejiang, China

Correspondence

Mengheng Liu, School of Economics,

Zhejiang University, 866 Yuhangtang

Road, Xihu, Hangzhou, Zhejiang 310027,

China.

Email: mhliu@zju.edu.cn; mashuzhong@

zju.edu.cn

Abstract

Promoted by both the “going out”policy and the “One

Belt and One Road”(OBOR) Initiative, China’s out-

ward foreign direct investment (OFDI) has grown rap-

idly in the past two decades. A spatial network has

gradually formed that facilitates global production,

sales, and innovation. This paper studies the spatial

correlation of China’s OFDI network along the OBOR

and its determinants. The block model analysis sug-

gests that China’s OFDI spatial network comprises four

gradient sections. In addition, the analyses of quadratic

assignment procedure and exponential random graph

models demonstrate a tendency of agglomeration of

China’s OFDI, which may hinder the further develop-

ment of its spatial correlation networks. Finally, we

present some policy suggestions stemming from the

study in the hope of helping both the government and

firms to optimize their OFDI spatial layout strategy

along the OBOR.

KEYWORDS

One Belt One Road

1|INTRODUCTION

Network analysis methods have been widely used in the field of international economics. Ser-

rano and Boguna (2003) use social network methods to analyse trade networks in different

countries, and they argue that the trade relations formed by countries around the world typi-

cally involve complex networks. Garlaschelli and Loffredo (2005) argue that the world trade

Received: 3 April 2020 Accepted: 9 April 2020

DOI: 10.1111/1468-0106.12328

228 © 2020 John Wiley & Sons Australia, Ltd wileyonlinelibrary.com/journal/paer Pac Econ Rev. 2020;25:228–249.

network is closely related to the GDP of countries. Based on neoclassical trade models, such as

Armington or Hecksher–Ohlin, Fajgelbaum and Schaal (2017) develop a framework to study

optimal transport networks in general equilibrium spatial models. They find that the optimal

expansion of current road networks reduces regional inequalities. Fagiolo, Reyes, and

Schiavo (2010) study the pattern of world trade by means of social network analysis based on

international trade data. Egger and Larch (2008) find the formation mechanism of the global

free trade agreement from the perspective of complex networks. Lusher, Koskinen, and

Robins (2013) use the exponential random graph model (ERGM) to study the formation and

impacts of high-end manufacturing trade networks and the influencing factors; they believe

that institutional factors such as trade, finance, and government will significantly influence the

formation of high-end manufacturing networks. Xu and Cheng (2016) use the quadratic assign-

ment procedure (QAP) method to analyse the spatial relevance of global service trade. They

argue that there is a significant spatial link in global service trade. With the deepening of eco-

nomic globalization, the spatial correlation of service trade is constantly strengthening, and the

global service trade network still has an agglomeration effect. Hanson, Mataloni, and Slaugh-

ter (2005) argue that in recent decades, growth of world trade has been driven largely by rapid

growth of trade in intermediate inputs. Much of input trade involves multinational firms locat-

ing input processing with their foreign affiliates, thereby creating global vertical production net-

works. This research conclusion is particularly important for understanding the performance of

the outward foreign direct investment (OFDI) network.

Although Hanson et al. describe research on trade networks, there is little discussion on for-

eign direct investment (FDI) networks. However, the results from Hanson et al. (2005) provide

theoretical evidence for the existence of an FDI network. Through their research, we can put

forward a conjecture: because there is a trade network, the FDI network also exists. Through a

literature review, we also find relevant research on the FDI network. Javorcik, Özden,

Spatareanu, and Neagu (2011) argue that the US FDI abroad is positively correlated with the

presence of a migrants’network from the host country. Based on a panel data analysis, Garas,

Lapatinas, and Poulios (2016) find a strong positive correlation between the migration network

and the FDI network by using a gravity equation enriched with variables that account for com-

plex network effects. These two studies seem to pay more attention to the influence of cultural

factors on FDI. The results of research of Bekes and Bisztray (2017) confirmed the existence of

the FDI network more directly. They analyse the Central Asian and East Asian countries and

find that countries with similar backgrounds (e.g., both sides are members of the business alli-

ance) are more likely to have connections that facilitate FDI. This conclusion seems to directly

prove that cultural factors drive the formation of the FDI network at the theoretical level.

Trade networks, immigration networks, and cultural networks are all important factors

affecting FDI. They cannot be used as direct evidence to prove the existence of FDI networks,

but we can still obtain some enlightenment from these factors. A very natural idea is: Does the

FDI network exist? What factors may affect the network?

According to the social network theory, the formation of a network is closely related to the

spillover effect (White, Boorman, & Breiger, 1976). A more intuitive fact is that spillover is the

basis of network formation and also a manifestation of network dynamic evolution. Spillover

emphasizes the third-party effect, while the network can be understood as a multi-party effect,

because it connects multiple third-party effects. Thus, spatial spillover effects play a key role in

the formation of OFDI and its spatial association network (Ekholm, Forslid, & Markusen,

2007; Nwaogu & Ryan, 2014). From 2011, scholars have begun to pay attention to the spatial

spillover effect of China’s OFDI. Based on the perspective of new economic geography, Xie and

MA AND LIU 229