Research Articles

The location choice of Chinese investment in the United States: Industrial agglomeration, ethnic networks and firm heterogeneity

  • SI Yuefang , 1, 2 ,
  • SUN Hanyan 1, 2 ,
  • WANG Junsong , 2, 3, * ,
  • LIANG Xinyi 4
  • 1. The Center for Modern Chinese City Studies, East China Normal University, Shanghai 200062, China
  • 2. School of Geographic Sciences, East China Normal University, Shanghai 200241, China
  • 3. Institute for Global Innovation & Development, East China Normal University, Shanghai 200062, China
  • 4. Department of Geography, Hong Kong Baptist University, Hong Kong, China
*Wang Junsong (1983-), PhD and Associate Professor, specialized in industrial location, and regional innovation development. E-mail:

Si Yuefang (1982-), PhD and Professor, specialized in Chinese outward FDI, firm innovation and innovation networks. E-mail:

Received date: 2023-07-29

  Accepted date: 2024-02-07

  Online published: 2024-05-31

Supported by

National Natural Science Foundation of China(42130510)

National Natural Science Foundation of China(41871110)

The National Social Science Fund of China(23BJL113)


China’s outward foreign direct investment (FDI) is different from traditional FDI in various ways, for example being rooted in “Guanxi” in Chinese culture, influenced by government, and located in developed economies where they have limited ownership advantages compared with local firms. Chinese investment in the United States (the U.S.) is an example of how the location is influenced by economic factors, social linkages, as well as geopolitical events, such as the U.S.-China trade conflict, which deserves more academic attention. It is such a complex phenomenon that cannot be fully explained by traditional FDI theories, which mainly focus on economic factors. In this paper, we illustrate the historical development, distribution and firm heterogeneity of Chinese investment in the U.S. from 2000 to 2020, and use a conditional logit model to investigate the location factors. Our study reveals that the number of Chinese investment projects in the U.S. peaked in 2017 and has declined year-over-year since then. These projects are mainly located along the East and West coasts of the U.S. and around the Great Lakes, with the largest numbers in California and New York. Previous Chinese investment agglomeration and ethnic networks both influence the location choice of China’s outward FDI, even when controlling for regional attributes and economic embeddedness. In terms of firm heterogeneity, Chinese firms that enter the American market with greenfield investment modes, state-owned enterprises and firms in high-tech sectors are more likely to follow previous Chinese investment, but place less emphasis on Chinese ethnic linkages, implying that previous Chinese investment agglomeration can replace the role of Chinese ethnic networks for these firms. Finally, the U.S.-China trade conflict has significantly lessened the active role of Chinese ethnic networks and has reduced Chinese investment in states with higher industrial output.

Cite this article

SI Yuefang , SUN Hanyan , WANG Junsong , LIANG Xinyi . The location choice of Chinese investment in the United States: Industrial agglomeration, ethnic networks and firm heterogeneity[J]. Journal of Geographical Sciences, 2024 , 34(5) : 985 -1006 . DOI: 10.1007/s11442-024-2236-9

1 Introduction

Foreign Direct Investment (FDI) holds a pivotal position within the realm of geography research, manifesting its significance in both theory and practice (Perkins and Neumayer, 2005; Sheng et al., 2023). Traditional FDI location theories, originating from international business and organizational behavior, include models such as the Ownership-Location-Internalization Advantage model and the Uppsala Internationalization Process model (Johanson and Vahlne, 1977; Dunning, 1988; Dunning, 1993). However, FDI location choice is often complex and transcends business behavior, which also takes into account multiple dimensions such as social culture, institutions, and geopolitics (Liang et al., 2019; Liu and Yao, 2021; Wang et al., 2021; Zhao et al., 2022). Therefore, we contend that the theoretical progress in FDI at the present stage necessitates integration with the research characteristics of geography, which are comprehensive and multi-dimensional.
Studying China’s outward FDI requires a more multidimensional perspective due to distinct characteristics of Chinese multinational enterprises (MNEs). Firstly, Chinese MNEs do not have superior technological and management advantages at the early stage of internationalization, and their investment in developed countries is mostly strategic-resource-driven, aiming for technological catching-up, brand marketing, and enhancing management capacity (Deng, 2004, 2007; Ling and Ying, 2008; Klossek et al., 2012). Secondly, rooted in “Guanxi” in Chinese culture, China’s outward FDI relies heavily on ethnic networks, which is claimed to be a particularly important channel for Chinese firms seeking knowledge abroad (Chen and Chen, 1998; Lin et al., 2018). Thirdly, China’s outward FDI is highly influenced by its government, which implements a catch-up strategy (Fan, 2011). For example, this influence takes the form of organizational support (e.g. providing lists of potential candidates for acquisitions), capital assistance from state banks and verbal encouragement from the government. Based on the above characteristics, there are two major trends when Chinese MNEs invest overseas: the critical role of the strategic asset-seeking motivation through increasing use of mergers and acquisitions (M&As) as a mode of entry, and the high presence of state-owned enterprises (SOEs) (Wu et al., 2023). Due to its importance and peculiarity, it is necessary to understand the location choice of China’s outward FDI along with the differences in entry modes, firm ownership, and other firm heterogeneity (Kang and Jiang, 2012; Kolstad and Wiig, 2012; Lin et al., 2018).
Among all host countries of China’s outward FDI, the United States (the U.S.) is an essential one which deserves greater attention. China and the U.S. stand as the world's largest economies and important economic partners. FDI between China and the U.S. has shifted from the one-way flow from the U.S. to China to a two-way exchange, with tens of billions of dollars of FDI flowing in each direction. In 2015, China’s FDI in the U.S. exceeded that of the U.S. in China for the first time. In 2018, the U.S. imposed 25% duties on $34 billion worth of Chinese imports, resulting in a more than 80% year-on-year decrease in China’s outward FDI to the U.S. that year, and triggering ongoing trade conflict (Hanemann et al., 2019). The U.S.-China trade conflict influences the FDI flow amount deeply. Does it influence the importance of ethnic linkages and industrial agglomeration as location factors for China’s outward FDI? The U.S.-China trade conflict, as the black swan in the international economy, remains a heated topic up to the present day. The research on its impact on China’s outward FDI can improve our understanding of the impact of this black swan event.
Compared with China’s ever-changing outward FDI, the existing empirical research on its location choice within a country is sparse. The research is mainly from the following perspectives: firstly applying Dunning’s Ownership-Location-Internalization advantage model to examine the influences of natural resources, labor, market potential, and strategic assets on the location choice of China’s outward FDI, and to test the validity of classic theories (Yeung and Liu, 2013); secondly testing the roles of SOEs and the government involvement, mainly under the umbrella of Belt and Road Initiatives (Zheng and Liu, 2015; Wuzhati et al., 2017; Chen et al., 2020; Li, 2023; Meng et al., 2023); thirdly examining the importance of ethnic linkages to understand its peculiarity resulting from Chinese culture. Some scholars believe that the importance of overseas ethnic linkages is universal, while others state that it is much less important in the U.S., Germany and other economies with advanced institutions and weak cultural and ethnic linkages (Child and Rodrigues, 2005; Lin et al., 2018). Due to the limited availability of statistics on China’s outward FDI, quantitative research in recent years, whether published in geographical journals or economic and management journals, has primarily focused on national-scale research, or has been based on the analysis of geographical units within the European Union NUTS-1 region, resulting in a lack of exploration on sub-national scales (Karreman et al., 2016; Liang et al., 2019; Wang et al., 2020). Concerning the location choice of China’s outward FDI in the U.S., the empirical research remains under development (Kelley et al., 2013; Anderson and Sutherland, 2015; Yang and Bathelt, 2021). The lack of empirical studies limits further understanding of this new emerging FDI from China to developed economies.
More importantly, it is common to apply economic traditions rooted in the trade theory and industrial organization, as well as behavioral traditions inspired by theories of firm behavior and firm growth, to explain location choices of FDI (Kim and Aguilera, 2016). However, geographic studies tend to analyze phenomena in multiple dimensions. The implicit atomism perspective on firm decision and international expansion cannot fully explain the increasingly complex phenomenon, which calls for a supplementary network or relational perspective (Glückler, 2006). In this paper, we synthesize the three dimensions of culture, institutions and geopolitics to analyze the location choice of Chinese enterprises investing overseas.
Hence, this paper intends to investigate the location choice of Chinese investment in the U.S. and its influencing factors. The remainder of the paper is organized as follows: In the next section, we present hypotheses based on a short literature review about the influencing factors for location choice of China’s outward FDI in the U.S. This is followed by a section describing our database of 2100 cases from 2001 to 2020 and the methodology used for testing the hypotheses. The next two sections introduce the methodology used for testing the hypotheses and describe our database of 2100 cases from 2001 to 2020. The estimation results to answer the major research questions are reported in section 5. Finally, we conclude and discuss the policy implications of our findings and point out future research directions.

2 Theoretical background and hypotheses

2.1 Research framework

In the absence of ownership advantages, the extensive overseas investment by Chinese enterprises presents a challenge to traditional FDI theories (Si et al., 2013; Paul and Benito, 2018). Increasingly, scholars have recognized the limitations of the single perspective offered by traditional international business views (Buckley et al., 2018). Faced with the rapid internationalization of Chinese businesses, it is imperative to employ a multidimensional approach. This involves analyzing the complex economic geography phenomenon from dimensions such as culture, institutions, and geopolitics. In this section, we mainly explore the socio-cultural dimension, beginning with a review of the ethnic network of FDI location among individuals. Subsequently, we extend the network perspective from individual social ties to mutual emulation among firms, organizing literature about the influence of previous Chinese investment on the firm level. In the institutional dimension, we explore the role of market adjustment strategies through firm heterogeneity, while we delve into the geopolitical dimension through the U.S.-China trade conflict (Figure 1).
Figure 1 Research framework of the paper

2.2 Ethnic network and China’s outward FDI

Given the promotion of information exchange and resource flow within and between organizational members, the social networks that are conceptualized as relations or networks of relationships among individuals and organizations have been incorporated to analyze the location choice of FDI (Jean et al., 2011). Specifically, some scholars provide a detailed account of the working of FDI location choice of Chinese Taiwan in terms of network linkage (Chen and Chen, 1998). They indicate that external relational linkages built upon cultural and ethnic linkages that create trust and mutual understanding facilitate Taiwanese investment in the Chinese mainland. In a similar vein, ethnic Chinese networks play a facilitating role in China’s outward FDI for overcoming informal barriers to international trade and investment (Gao, 2003). There is also evidence that co-national immigrants do play a pivotal part in affecting the location choice and survival of foreign subsidiaries established in the U.S. through processes of local learning and knowledge transfer (Hernandez, 2014). According to the existing studies, social networks, especially ethnic networks - the specific type of the former that is characterized by informal social or personal relationship elements such as native language, national origins, ethnic groups and region of birth - have been considered to provide strategic value for firms entering foreign markets (Zhou, 1998; Zaheer et al., 2008; Jean et al., 2011).
Drawing from the existing conceptual and empirical work, ethnic Chinese networks are also applied to address the location choice of China’s outward FDI. Based on the interviews in eight Chinese provinces and three African countries, local Chinese business and community networks in both China and Africa are important sources of information, which help Chinese private investors identify business opportunities to invest in Africa (Gu, 2009). The bulk of the quantitative evidence shows the critical role of ethnic networks for Chinese MNEs’ internationalization as well (Ramasamy et al., 2012; Amighini et al., 2013; Brienen et al., 2013). The earliest study using quantitative methods to measure the impact of ethnic Chinese networks on China’s outward FDI can be traced back to the work of Buckley et al. (2007). In their investigation of the determinants of China’s outward FDI, they find that China’s outward FDI is associated positively with the proportion of ethnic Chinese in the host population, indicating that network effects are important in attracting Chinese investors and that relational assets constitute a special ownership advantage. Indeed, several studies confirm that ethnic Chinese networks serve as important channels of information about business conditions and opportunities abroad. Ethnic networks help reduce transaction costs and risk, which constitutes a specific location advantage held by Chinese MNEs, especially for small and medium-sized firms when they enter and operate in a foreign business environment (Ramasamy et al., 2012; Amighini et al., 2013; Schüller and Schüler-Zhou, 2017; Zhang, 2017).
Hence, we propose the following hypothesis:
H1a: Chinese MNEs are more likely to locate their FDI in a region where they have stronger ethnic linkages.
Entry modes may yield impacts on the location preferences. Compared to cross-border acquisitions, greenfield investment entails higher risks, exposing enterprises to challenges such as cultural differences, government regulations, and initial-stage issues such as the exclusion of host country enterprises (Zhou et al., 2021). Scholars have deeply explored the location choices of greenfield investment made by Chinese MNEs across European regions (Karreman et al., 2016). They highlight that the role of overseas communities in the location choices of Chinese firms is based on increased access to strategic information. Concerning enterprise ownership, state-owned MNEs generally have larger scales than their private counterparts, possessing sufficient domestic strategic assets and governmental support to withstand greater risks (Gaur et al., 2018; He et al., 2020). But the special political context also results in state-owned MNEs facing greater obstacles to legitimacy in host countries, especially those emphasizing free market economies, and needing more help from local ethnic networks. In contrast to high-tech MNEs, ethnic networks often assume a more crucial role for traditional MNEs. The latter, operating in highly competitive traditional markets, have to leverage ethnic networks to overcome uncertainties (Buckley et al., 2018). Finally, current studies prove that anti-dumping trade conflict can promote Chinese investment in host countries, potentially weakening the role of ethnic networks to some extent (Yu and Chen, 2019). Therefore, it is essential to test the effects of firm heterogeneity and temporal heterogeneity in the relationship between ethnic linkages and location choices of Chinese FDI.
Hence, we propose the following hypothesis:
H1b: The difference in entry modes, ownership and technical types may affect the degree of Chinese MNEs’ attention to ethnic linkages in choosing FDI location.

2.3 Previous investment agglomeration and China’s outward FDI

Although ethnic networks can help Chinese MNEs gain access to information on the local context, what Chinese MNEs need more is the professional and industry-related knowledge that ethnic networks may not provide (Lin et al., 2018). Along this line of reasoning, Chinese MNEs may not only rely on ethnic networks, but also consider firm agglomeration more when they choose where to invest. Scholars find that new MNEs tend to locate themselves in an industrial agglomeration where they can gain access to a local pool of skilled labor, local input-output linkages, industry-specific knowledge, as well as prior experience from their counterparts (Krugman, 1991; Kim and Aguilera, 2016).
It is not only FDI agglomeration of the same industry, but also country-of-origin agglomeration that provides an effective channel for the sharing of sensitive and tacit knowledge about the local business environment (Jean et al., 2011). Compatriot FDI firms develop both formal networks, such as country-based business associations, and informal social networks, such as expatriates’ personal and family involvement in the local expatriate community. Based on the analysis of the nature of local FDI networks of production and R&D activities in China, some studies found a significant influence of industrial agglomeration on FDI location (Wei et al., 2012). Similarly, scholars have shown that in the U.S., the agglomeration of FDI has a positive effect on FDI size when FDI arrives there from the same home country (Halvorsen, 2012).
For Chinese MNEs, the previously mentioned benefits are particularly likely because Chinese MNEs are recognized as latecomers, as they lack competitive advantages in technology and management, and the intention of FDI is understood as the search for strategic resources and capabilities, which may cause them to attach great importance to industry knowledge and prior experience. Based on the dataset of Chinese listed firms from 2004 to 2015 in 129 host countries, Liu et al. (2018) prove that Chinese FDI location choice is heavily affected by the agglomeration on the national level. Therefore, we argue that Chinese MNEs prefer locations with a higher FDI stock of the same industry. Furthermore, given that the prior experience from existing investors and the challenges that Chinese MNEs face vary according to entry modes, Chinese MNEs are inclined to invest in the focal state of the U.S. where there is more FDI stock within an industry and in the same entry mode.
Hence, we propose the following hypotheses:
H2a: Chinese MNEs are more likely to locate their FDI in a region where they have more Chinese FDI stock.
H2b: Chinese MNEs are more likely to locate their FDI in a region where they have more Chinese FDI stock of the focal industry.
H2c: Chinese MNEs are more likely to locate their FDI in a region where they have more Chinese FDI stock of the focal industry in the same entry mode.
Moreover, differences in entry modes may result in varying impacts of previous investment agglomeration on location choices. In contrast to acquisitions, greenfield investment incurs higher sunk costs, which amplifies the significance of the investment experience of predecessors (Shen and Puig, 2018). Owing to differences in scale and political context, state-owned MNEs and private MNEs may have distinct considerations regarding previous investment agglomeration. Previous studies show that state-owned MNEs tend to mitigate the adverse effects of FDI by emulating the practices of other Chinese MNEs in the same industry (Li et al., 2018). Besides, the motivations for overseas investment differ between high-tech MNEs and traditional MNEs, potentially leading to variations in their emphasis on agglomeration (Pelegrín and Bolancé, 2008). Finally, studies have confirmed that China’s outward FDI has a significant incentive to cross anti-dumping barriers in host countries, suggesting that the trade conflict may make Chinese MNEs more dependent on previous investment experience (Zhang and Long, 2018). It is important to explore whether firms’ entry modes, ownership and industry influence the relationship between agglomeration and location choice of Chinese FDI in different periods.
Hence, we propose the following hypothesis:
H2d: The difference in entry modes, ownership and technical types may affect the degree of Chinese MNEs’ attention to previous investment agglomeration in choosing FDI location.

3 Data and methodology

3.1 The definition of variables

3.1.1 Dependent variable

The dependent variable is the situation that state j is selected as the host region by project i during the period 2001-2020. For each project, there are 51 potential states (including Washington D.C.) to select as the host region, therefore we identify the selected state j as 1 and otherwise 0. The final estimated data total is 2100 × 51. For each project, when the dependent variable is 1, the independent variable is the attribute of the state where the project is located; when the dependent variable is 0, the independent variable is the attribute of the other 50 states not chosen.

3.1.2 Independent variables

Chinese ethnic networks. Chinese ethnic networks may reduce the impediments and risk when conducting business in host countries (Karreman et al., 2016; Wang et al., 2020). Chinese depend particularly on “Guanxi” in business operations. We introduce the variable measured by the number of Chinese in the host state (Chinese) to indicate Chinese ethnic social networks and expect a positive effect on the location choice.
Previous FDI investment. To overcome operational risk in the host country, firms tend to agglomerate with other similar firms. We expect that Chinese MNEs also tend to locate their FDI in the states with previous Chinese investment, especially in locations with similar Chinese investment in the same sector or the same mode. Therefore, we introduce three agglomeration variables to indicate such impacts: state’s previous Chinese investment, state’s previous Chinese investment in the same sector, state’s previous Chinese investment in the same sector and the same entry mode. We expect positive signs in the result, and expect that the influence of the last agglomeration variable will exert the highest influence, since it is able to exert the largest Marshall externalities.

3.1.3 Control variables

We also control for factors that might influence MNEs’ location choices according to traditional international and regional location choice literature. Firstly, the output of the local sector of the state is introduced to represent the local agglomeration of the sector. Larger local sector output indicates better local accumulation of the sector and skilled labor and is expected to attract more Chinese investment, but it might also crowd out the FDI due to competitive effects. Secondly, we consider supply factors including labor cost, resources and efficiency that might affect the probability of attracting Chinese investment. Labor cost measured by average wage per worker is controlled for and a negative relationship is expected between wages and Chinese MNEs’ location behavior, but higher wages also mean higher efficiency of the labor and are likely to attract more Chinese MNEs. In addition, Labor unions are an important factor that could raise the business costs (Brady and Wallace, 2000). The factor of labor unions is taken into account by means of the ratio of labor union members to the total number of each state’s non-agricultural employees, with a negative sign expected. The union data is from
Taxes and subsidies can play a significant role in business production and investment. Taxes are levied to raise the business costs and reduce the investment willingness, while subsidies are given to reduce cost levels. Taxes and subsidies are controlled for by means of the ratio of taxes or subsidies of the sector to employment at the state level respectively. The sector output, wage, tax and subsidy data are all taken from the Bureau of Economic Analysis (BEA).
Some Chinese investment might be made in search of innovative or natural resources. High-tech companies intend to invest in places where there are many skilled researchers and developers and cutting-edge industrial development information; therefore, we measure the innovative capabilities of states by the number of patents for the state based on data from the U.S. Patent and Trademark Office. Regarding the presence of natural resources, we include the share of resource industries in the regional economy, such as forestry, fishing and related activities, mining, and oil and gas extraction, in the output based on data from the Bureau of Economic Analysis.
All variable definitions and sources are shown in Table 1.
Table 1 Variable descriptions and sources
Variable Description Source
Ethnic links ln Chinese Log of the number of ethnic Chinese in the host state U.S. Census Bureau
ln cn_invest Log of the previous FDI Stock of China provinces in the U.S. The U.S.-China
Investment Project
ln cn_invest_sector Log of the previous FDI Stock of China provinces in the U.S. in the same industry The U.S.-China
Investment Project
ln cn_invest_sector_
Log of the previous FDI Stock of China provinces in the U.S. in the same industry in the same entry mode The U.S.-China
Investment Project
ln sector output Log of the output of the same industry of the states BEA
ln wage Log of the average wage per worker of the states BEA
ln tax Log of tax of the states BEA
ln subsidy Log of subsidies of the states BEA
Labor union The percentage of each state’s non-agricultural wage and salary employees who are union members
Resource rate The ratio of the resource industry to the whole industry BEA
Innovation ln patent Log of the number of the patents of the states The U.S. Patent and Trademark Office

3.2 Data sources

The data is from the China Investment Monitor dataset collected by the Rhodium Group and the National Committee on U.S.-China Relations that records Chinese investment to the U.S. with transaction amounts over $500,000. With the assistance of the American Chamber of Commerce in Shanghai and the China General Chamber of Commerce USA, the dataset was collected in a bottom-up manner in which the original data comes from multiple channels including commercialization datasets, media reports, company announcements, industrial associations, etc. Specifically, the dataset includes investment that transits from Hong Kong or Macau, China, which is rarely recorded in Chinese official datasets in terms of China-U.S. investment. Therefore, the dataset we use can better reflect the true investment realities between the two countries.

3.3 The conditional logit model

We use 2100 Chinese investments in the U.S. during the period 2001-2020. The location choice of Chinese MNEs in different U.S. states can be considered as a discrete choice that is expected to generate the maximum profit from among the various potential alternative locations. Chinese MNEs may choose the most profitable location based on a series of attributes of states when deciding where to locate from among 51 alternative U.S. states. Therefore, we employ a conditional logit model to estimate this discrete location choice. The conditional logit model, which assumes that individuals have rational consideration and choose the location based on maximizing utility level (McFadden, 1974), has been widely used in location choice studies. Regarding the attributes that attract Chinese MNEs in the U.S., three groups of conditional variables are included to estimate the impact of the probability of project i choosing state j. The following model is proposed:
${{P}_{ij}}=f\left( {{N}_{j}},{{A}_{j}},{{C}_{j}} \right)$
where Pij is project i choosing state j as its preferred location to maximize utility. Nj represents Chinese ethnic networks and is measured as the logarithm of the number of ethnic Chinese in the state. Aj represents agglomeration attributes of investment from China in state j, and Cj are control variables for state j. The choice model with n mutually exclusive alternatives is as follows:
where Vij is a utility function of the explanatory variables related to state j. The utility functions are determined as follows:
${{V}_{ij}}={{\beta }_{0}}+{{\beta }_{1}}{{N}_{j}}+{{\beta }_{2}}{{A}_{j}}+{{\beta }_{3}}{{C}_{j}}+{{\varepsilon }_{i}}$
where β0, …, β3 are the estimated coefficients and εi is an error term. It is hypothesized that attributes of countries such as ethnic networks, Chinese previous investment, as well as other attributes of states will affect the Chinese MNEs’ location choices in the U.S.

4 The description of Chinese investment in the U.S.

4.1 Statistics on Chinese investment in the U.S.

The sample includes 2100 Chinese strategic investment projects with over 10% stock during the period 2001-2020, of which 1417 are greenfield investment projects and 683 are acquisition projects (Table 2). About 72% of investment projects stem from private MNEs, while 28% originate from state-owned MNEs. 44% of the investment projects are in high-tech industries. According to the dataset, Chinese MNEs invested in 14 sectors during the period (Table 3), with the majority of investment projects distributed in Real Estate and Hospitality, Information and Communications Technology (ICT), and Automobile sectors, accounting for 18.48%, 12.43%, and 10.57% respectively.
Table 2 Chinese investment in the U.S., 2001-2020
Number of Chinese investment projects in the U.S. Share (%)
Entry mode Greenfield 1417 67
Acquisition 683 33
Ownership Private 1505 72
State-owned 595 28
Technical types High-tech industry 916 44
Traditional industry 1184 56

Note: Following Michael Wolf and Dalton Terrell (2016), we consider high-tech industries to include Automotive, Aviation, Electronics and Electrical Equipment, Health, Pharmaceuticals and Biotechnology, ICT, and Machinery. The remaining industries subject to investment are considered traditional industries.

Table 3 Sector distribution of Chinese investment in the U.S. (2001-2020)
Sector Number of Chinese investment projects in the U.S. Share (%)
High-tech industries Automotive 261 12.43
Aviation 23 1.10
Electronics and electrical equipment 95 4.52
Health, pharmaceuticals and biotechnology 201 9.57
ICT 222 10.57
Machinery 114 5.43
Traditional industries Agriculture and food 40 1.90
Basic materials, metals and minerals 165 7.86
Consumer products and services 178 8.48
Energy 154 7.33
Entertainment, media and education 53 2.52
Financial and business services 103 4.90
Real estate and hospitality 388 18.48
Transport, construction and infrastructure 103 4.90
Total 2100 100.00
Figure 2 shows the trend of Chinese investment project numbers from 1990 to 2020. It can be seen that the number of investment projects has grown dramatically since 2000, reaching more than 220 in 2017, and then plummeted in 2018 when the U.S.-China trade conflict occurred.
Figure 2 The number of Chinese investment projects in the U.S. (1990-2020)

4.2 Spatial distribution of Chinese investment in the U.S.

Figures 3, 4 and 5 present the geographical distribution of the 2100 Chinese investment projects from 2001 to 2020. 48 of 51 states received direct investment from China. The top five recipients of Chinese investment are California, New York, Texas, Illinois and Michigan, with California alone attracting 27% of Chinese investment in the U.S. Most Chinese investment is located on the coasts of the southwest and south regions as well as the northeast regions that surround the Great lakes. The specific analysis of the entry mode shows that on the east and west coasts of the U.S., Chinese MNEs mostly choose greenfield investment, while in inland states, the proportion of acquisition is generally higher than that of greenfield investment. In terms of ownership, Chinese state-owned MNEs conduct a relatively large proportion of investment in Montana, Wyoming, Indiana, and Alaska in the northern U.S., while Chinese investment in other states is mainly led by private MNEs. In terms of industry categories, in the northeastern states of the U.S., the proportion of Chinese investment in high-tech industries is greater than that in traditional industries.
Figure 3 Distribution of Chinese investment via acquisition and greenfield in the U.S. (2001-2020)
Figure 4 Distribution of Chinese investment from private investors and SOEs in the U.S. (2001-2020)
Figure 5 Distribution of Chinese MNEs in high-tech industry and traditional industry investment projects in the U.S. (2001-2020)

5 Results

We obtain the results utilizing the conditional logit model. Firstly, we conduct a regression analysis across all samples, focusing on the influence of ethnic networks and industry agglomeration on China’s outward FDI in the U.S. Subsequently, we perform segmented regressions based on varying entry modes, ownership structures, and technology types of enterprises to delve into the impact of firm heterogeneity. Finally, recognizing the potential influence of significant historical events such as the financial crisis and the U.S.-China trade conflict on China’s outward FDI, we further conduct segmented regressions analyzing investment samples across different time periods.

5.1 The role of ethnic networks and industry agglomeration

Table 4 presents the results of the econometric model. Specifically, models 1 and 2 do not include agglomeration variables and the industrial output variable, while models 3 and 4 exclude the Chinese ethnic variable based on models 1 and 2 respectively in order to avoid multi-correlation problems. Model 5 incorporates all variables. The results roughly coincide with our expectations. Firstly, Chinese ethnic networks have a significant positive effect on the attraction of Chinese MNEs across specifications, confirming H1a. This is in line with the previous finding that ethnic Chinese play a crucial role in attracting Chinese investment (Karreman et al., 2016). They act as an important bridge linking Chinese MNEs and local information on business opportunities, customs, regulations, etc. The patent variable is positive but not significant in models 1 and 2, but turns significantly positive in models 3 and 4 when excluding the Chinese ethnic networks variable. The difference may be due to high multicollinearity between the two variables. This shows that Chinese MNEs do seek advanced technology resources when not considering the Chinese ethnic linkages.
Table 4 Estimation for Chinese MNEs in the U.S.
Variables (1) (2) (3) (4) (5)
ln Chinese 0.395*** 0.248*** 0.244***
(0.046) (0.048) (0.048)
ln patent 0.053 -0.006 0.448*** 0.164*** -0.061
(0.054) (0.051) (0.031) (0.032) (0.054)
ln cn_invest 0.162*** 0.181*** 0.159***
(0.017) (0.017) (0.017)
ln cn_invest_sector 0.194*** 0.175*** 0.179***
(0.022) (0.022) (0.023)
ln cn_invest_sector_mode 0.372*** 0.373*** 0.372***
(0.024) (0.024) (0.024)
ln sector output 0.655*** 0.703*** 0.116*** 0.108***
(0.038) (0.037) (0.036) (0.036)
ln wage 0.265*** 0.163*** 0.322*** 0.136*** 0.119***
(0.048) (0.040) (0.048) (0.044) (0.042)
Labor union -0.012** -0.035*** 0.008 -0.020*** -0.033***
(0.006) (0.006) (0.005) (0.005) (0.006)
ln tax 0.198*** 0.156*** 0.246*** 0.141*** 0.117***
(0.047) (0.043) (0.048) (0.045) (0.044)
ln subsidy 0.097 0.093 0.125** 0.093 0.079
(0.059) (0.058) (0.059) (0.058) (0.058)
Resource rate -6.371*** -6.225*** -6.498*** -6.876*** -7.018***
(1.034) (0.998) (1.059) (1.060) (1.057)
Observations 107,100 107,100 107,100 107,100 107,100
Pseudo R2 0.274 0.393 0.269 0.392 0.393

Note: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

The results also confirm the importance of industrial agglomeration and original country agglomeration. Firstly, the agglomeration of previous Chinese investment, whether covering all investment or belonging to the same sector or the same entry mode, has a significant positive relevance toward Chinese MNEs, suggesting that previous Chinese investment will have a demonstration effect on subsequent investment. The mimic behavior is especially remarkable in the same industry or the same entry mode, indicating that Chinese MNEs are willing to reduce the risk and obtain information through following similar previous Chinese investment. Secondly, own sector output turns out to be strongly correlated with the Chinese MNEs’ location choice, confirming that Chinese MNEs prefer to invest in regions where there are already industrial foundations in order to obtain benefits from industrial linkages, labor pools or information spillovers. Such results confirm H2a-H2c. Furthermore, the coefficient of Chinese ethnic networks decreases when the agglomeration variables are introduced in models 2 and 5, demonstrating that the agglomeration factors weaken the role of Chinese ethnic networks.
Regarding the results of the control variables, average wage of the sector in the state is positive and significant across the specifications, proving that Chinese MNEs might target regions with more efficient labor and more highly developed local markets. This is in line with the evidence from predecessors that market is an important factor impacting Chinese MNEs’ choice. The average taxes are positively related with the location choices across the models, while the average subsidies only have a positive relevance with the location choice of Chinese MNEs in model 3. This might be due to the fact that Chinese MNEs seek to invest in regions with more competitive and less protected sectors. The natural resources do not appear to be associated with MNEs’ choices, possibly because Chinese MNEs do not attach much importance to American natural resources, which is different from the investment in Latin America.

5.2 The role of firm heterogeneity

The determinants of MNEs’ location choice are also influenced by the heterogeneity of firms. To address this problem, we separate the samples according to the entry modes, ownership and technical types respectively and re-estimate the speculations shown in Table 5. As expected, firm heterogeneity does affect the MNEs’ decisions on location choices. In columns 1 and 2, regressions are run for the entry modes of the greenfield investment and acquisitions respectively. Firstly, Chinese ethnic networks are significant for Chinese MNEs via greenfield investment, but not significantly positive for acquisitions, which means that greenfield investment depends more on ethnic linkages in dealing with business risks. Controlling for other variables, the effect of patents is negative for greenfield investment, but positive for acquisitions, demonstrating that more emphasis has been placed on strategical assets by Chinese MNEs through acquiring firms in innovative regions.
Table 5 Conditional logit estimation for Chinese MNEs in the U.S. according to firm heterogeneity
Variables (1)
ln Chinese 0.308*** 0.050 0.187*** 0.408*** 0.169** 0.288***
(0.058) (0.087) (0.058) (0.089) (0.074) (0.067)
ln patent -0.191*** 0.188* 0.082 -0.457*** 0.058 -0.128*
(0.065) (0.097) (0.065) (0.101) (0.097) (0.067)
ln cn_invest 0.187*** 0.110*** 0.144*** 0.201*** 0.160*** 0.158***
(0.022) (0.027) (0.019) (0.037) (0.027) (0.022)
ln cn_invest_sector 0.131*** 0.136*** 0.184*** 0.168*** 0.172*** 0.181***
(0.031) (0.040) (0.025) (0.051) (0.036) (0.029)
ln cn_invest_sector_mode 0.562*** 0.263*** 0.344*** 0.452*** 0.387*** 0.361***
(0.038) (0.038) (0.027) (0.056) (0.038) (0.032)
ln sector output 0.053 0.245*** 0.057 0.321*** 0.122** 0.110**
(0.044) (0.065) (0.042) (0.076) (0.056) (0.052)
ln wage 0.224*** -0.020 0.145*** 0.083 0.159** 0.063
(0.055) (0.067) (0.053) (0.074) (0.076) (0.061)
labor union -0.032*** -0.015 -0.041*** -0.026** -0.037*** -0.034***
(0.007) (0.010) (0.007) (0.012) (0.009) (0.008)
ln tax 0.234*** -0.024 0.096* 0.153* 0.162* 0.131**
(0.058) (0.067) (0.052) (0.079) (0.083) (0.058)
ln subsidy 0.125* -0.188* 0.075 0.039 0.018 0.088
(0.069) (0.101) (0.075) (0.093) (0.155) (0.062)
resource rate -9.943*** -3.535** -9.912*** -1.875 -5.955*** -7.324***
(1.403) (1.680) (1.350) (1.613) (1.630) (1.453)
Observations 72,267 34,833 76,755 30,345 46,716 60,384
Pseudo R2 0.426 0.357 0.376 0.458 0.385 0.401

Note: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

Previous Chinese investment and investment from the same sector and mode play significant roles in attracting Chinese greenfield investment and acquisitions. The coefficient of previous accumulation of investment from the same sector and mode is particularly high for greenfield investment, suggesting such agglomeration is more important for greenfield investment given that it faces more risk and sunk cost. The insignificant coefficient of sector output for greenfield investment indicates that Chinese MNEs try to avoid directly competing with domestic U.S. firms in their advantage industries. Greenfield investment is also likely to occur in states with high wages and few labor union members, the former meaning higher local demand and higher efficiency and labor costs, the latter meaning less interference of labor unions and lower labor costs. Such inclination does not occur in acquisitions for which the choice of location is less free.
The choice strategy also varies according to ownership, as is shown in the regression results in columns 3 and 4 for private and state-owned MNEs respectively. Private MNEs tend to invest in states where there are more ethnic Chinese, more previous Chinese investment, higher average wage, and fewer labor union members, which is in line with prior expectations.
Chinese state-owned MNEs also tend to locate their investment in states with more ethnic Chinese and previous Chinese investment in the same sector and entry mode and less labor union interference. The difference is that they attach more weight to ethnic networks and industrial foundation of the states and less to innovation capabilities, as is shown in the coefficients of sector output and patents. Compared with SOEs, the private MNEs are more profit-oriented and local-market-oriented. This is consistent with previous research showing that Chinese private MNEs are more concerned about maximizing profit, while state-owned MNEs’ investment abroad places more emphasis on the government strategies and is less sensitive to risk than that of private ones (Wang et al., 2020).
High-tech MNEs and traditional MNEs also differ in choosing the location in the U.S. High-tech MNEs place less emphasis on Chinese ethnic linkages, with the significant level dropping to 5%, but attach more importance to industrial agglomeration and highly skilled and highly paid labor. The innovative capacity in the form of patents is not significant in column 5, but extremely significant when dropping the Chinese ethnic variable. Considering the high correlation between the two variables, it is suggested that Chinese high-tech investment still stresses the innovative capabilities of the states, but their importance is lower than that of Chinese ethnic networks and agglomeration. In contrast, Chinese MNEs in traditional sectors attach more importance to Chinese ethnic networks and less to innovative capabilities and skilled labor. Relational networks are particularly important for traditional investment for Chinese MNEs, as they have to overcome the uncertainty and market risk in an intensely competitive traditional market. In addition to traditional factors, high-tech investment attaches more importance to skilled talent in order to obtain strategic assets in the United States. However, with the competition between the U.S. and China in the field of technology intensifying in recent years, the U.S. authorities have placed more restrictions and regulations on Chinese high-tech investment to avoid technology spillover to Chinese enterprises. How such transformation affects Chinese outward investment deserves more exploration both theoretically and empirically. To summarize, such results confirm H1b and H2d.

5.3 The role of temporal heterogeneity

The determinants influencing MNEs’ location choices may also be impacted by temporal heterogeneity. We designate 2008 (financial crisis) and 2018 (U.S.-China trade conflict) as pivotal time points to separate the samples and to re-estimate the putative values as shown in Table 6. The results indicate a notable increase in the positive impact of local Chinese ethnic linkages after the 2008 financial crisis. However, the 2018 trade conflict considerably tempers the active role of Chinese ethnic networks. In contrast, the positive effect of previous investment agglomeration remains consistently significant across all three time periods. After the trade conflict, the role of prior Chinese investment with the same mode and sectors is amplified. This suggests that agglomeration in the same sector and mode of previous Chinese investment can offer more precise experiential insights for coping with uncertainty which serve as a harbor in managing potential to cope with political risks, especially in 2020, when trade conflict and the COVID-19 pandemic significantly increased investment risks.
Table 6 Conditional logit estimation for Chinese MNEs in the U.S. according to temporal heterogeneity
Variables (1)
ln Chinese 0.057 0.193*** 0.140
(0.126) (0.060) (0.134)
ln patent 0.092 0.008 -0.092
(0.173) (0.067) (0.149)
ln cn_invest 0.265*** 0.144*** 0.224***
(0.053) (0.020) (0.038)
ln cn_invest_sector 0.044 0.211*** 0.245***
(0.109) (0.027) (0.047)
ln cn_invest_sector_mode 0.700*** 0.351*** 0.422***
(0.133) (0.028) (0.050)
ln sector output 0.580*** 0.086** -0.321***
(0.110) (0.044) (0.085)
ln wage 0.323 0.094* 0.283***
(0.220) (0.052) (0.104)
Labor union -0.016 -0.034*** -0.040**
(0.014) (0.007) (0.016)
ln tax 0.027 0.123** 0.277**
(0.113) (0.055) (0.111)
ln subsidy -0.151 0.036 0.416**
(0.174) (0.068) (0.209)
Resource rate -3.838 -6.964*** -7.834**
(2.778) (1.219) (3.318)
Observations 13056 72012 22032
Pseudo R2 0.353 0.390 0.497

Note: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

The significance of regional industrial development shifts from positive to negative after the trade conflict, suggesting that Chinese MNEs investing in the U.S. begin to avoid competition from local firms. Notably, the increasing significance of wage per capita suggests a growing inclination of Chinese MNEs towards investing in economically developed states in the U.S. after the trade conflict. Finally, we observe the growing importance of subsidy policies. While subsidies are not particularly influential in attracting Chinese investment before the conflict, their role begins to ascend in significance after the outbreak of conflict.

6 Conclusions and discussion

The established FDI theories in the realm of international business, such as the Ownership-Location-Internalization Advantage model and the Uppsala model, predominantly adopt a neoclassical or evolutionary perspective. These theories primarily concentrate on the internal organizational dynamics of enterprises, with their empirical basis largely derived from developed countries (Aspelund and Butsko, 2010; Jones et al., 2020). However, a comprehensive examination of the multidimensional facets of geography reveals a notable gap in traditional FDI theories. The omission of perspectives from dimensions such as culture, institutions, and geopolitics is apparent. These dimensions, often disregarded in conventional FDI frameworks, play a pivotal role in comprehensively analyzing the overseas investment strategies of Chinese MNEs.
In this study, we have presented evidence on the location choice of Chinese MNEs in developed economies, using the example of the U.S., and have tested the moderate effects of bilateral trade conflict since 2018. Our study provides three findings. Firstly, Chinese MNEs do seek advanced technology resources when Chinese ethnic linkages are not considered. But when considering relational factors, Chinese ethnic networks play an important role in attracting China’s outward FDI, and the role becomes weaker when agglomeration factors are included in the model. Secondly, in terms of firms’ heterogeneity, previous Chinese investment agglomeration can act as a substitute for the role of Chinese ethnic networks for MNEs of different ownership and technology. Finally, the trade conflict between the U.S. and China has significantly lessened the active role of Chinese ethnic networks. However, the agglomeration of previous investment in total or previous investment in the same entry mode and same sector could hedge against the adverse effect of the trade conflict, indicating that the agglomeration could help enterprises cope with uncertainty and act as a harbor for handling potential political risk.
According to our research, FDI from China is different from that from developed countries in the following aspects: Firstly, FDI from developed countries tends to be cost-oriented and market-oriented, while access to advanced technology and acquisitions of established brands are important influencing factors for Chinese overseas branches in developed economies. These companies are eager to establish better presences and to shed the negative image of “Made in China” (Si et al., 2013). Secondly, MNEs from developed countries employ independent consultants in host countries, while Chinese MNEs, being latecomers to foreign investment, highly value previous investment experience and rely on local ethnic networks (Paul and Benito, 2018). Thirdly, FDI from developed economies is a kind of enterprise behavior aimed at gaining economic benefits, while Chinese foreign investment is a long-term development plan led by the Chinese government, in which SOEs play an important role (Liu et al., 2023; UNCTAD, 2023).
Our research has made the following contributions. Firstly, we extend beyond traditional FDI theories by incorporating a cultural perspective, specifically delving into the unique ‘Guanxi’ culture prevalent in China (Hsu and Saxenian, 2000; Lin et al., 2018). This approach integrates elements such as ethnic networks and previous investment agglomeration, surpassing the previous quantitative studies that separately explore the correlation between overseas ethnic networks or previous FDI agglomeration and FDI location. Our study examines the synergy between ethnic networks and previous FDI agglomeration in determining FDI location, highlighting their complementary relationship with patenting and other economic factors. These discoveries reaffirm the pivotal role of relationships in shaping the investment strategies of Chinese MNEs. The observed inclination may derive from a deeply ingrained preference in Chinese culture for establishing networks of associates and trusted sources to gather ‘intelligence’ on potential investment sites (Kelley et al., 2013). In contrast, Western MNEs tend to rely on independent consultants or advisors for similar purposes. This reliance on relationship networks among Chinese MNEs serves as a channel to acquire essential business information and to offset their disadvantage as latecomers in developed markets.
Secondly, our research highlights the significance of institutional and geopolitical perspectives in the analysis of FDI location choice. Considering their distinct political context and substantial scale, Chinese SOEs are particularly subject to heightened scrutiny by the U.S. government during investment (Lin, 2000; Alami and Dixon, 2020). Consequently, they pay more attention to relying on ethnic networks for information acquisition. At the same time, Chinese investment proves to be exceptionally responsive to trade conflicts between nations. Macro-level political tensions significantly diminish the reliance of Chinese enterprises on micro social connections and strengthen imitation of investment behavior to a certain extent. The increased focus on geopolitical risks makes Chinese investment more pragmatic, steering it away from direct competition with local American companies. This strategic shift reflects an astute response to the complex interplay of political and economic factors, emphasizing adaptability and risk mitigation in the face of evolving geopolitical landscapes (Yang, 2012).
Thirdly, the conclusion serves as an inspiration to adopt a multidimensional perspective when analyzing FDI location choice. This approach enables scholars to move beyond singular interpretations of organizational behavior. It encourages an examination through the lenses of cultural and institutional contexts of the home country as well as the political dynamics between the home country and the host country. This comprehensive approach facilitates a more nuanced and thorough representation of the realities of outward FDI, enriching our understanding by considering a series of influential factors.
However, due to limited data, this study uses the scale of the state level, not the metropolitan level, which makes the results impossible to verify for small-scale areas. Further research is needed to explore this aspect. In the future, we can explore more databases related to China’s outward FDI and strive to obtain investment data of Chinese MNEs in smaller spatial scale units so that we can more accurately analyze the location choice of Chinese investment.
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