China’s Outward Direct Investment in the United States: From the perspective of agglomeration economies

Xinyi Liang; Junsong Wang; Bingquan Lin; Xianzhong Cao; Yuefang Si

doi:10.1371/journal.pone.0269602

Abstract

Over most of the last two decades, China’s Outward Direct Investment (CODI) has reshaped the global economic landscape and attracted considerable attention. Although extensive research shows that CODI features agglomeration, there is limited research from the perspective of different patterns of agglomeration economies at the subnational level. It is unclear which patterns of agglomeration economies play a role in the location choice of CODI, especially with the variations of CODI in terms of entry mode and ownership. Therefore, based on the data of the CODI in the United States in the period 2000–2016, we use a conditional logit model to investigate the influence of specialized and diversified agglomeration of local firms as well as industry-specific and industry-diverse agglomeration of Chinese investors on the location choice of CODI, and further explore the heterogeneous influence concerning the entry mode and ownership. Our results show that among a variety of agglomeration economies, the specialized agglomeration of local firms is the premier factor influencing the location choice of CODI, even exceeding the influence of industry-specific agglomeration of CODI in the same industry. Industry-diverse agglomeration of CODI plays a weak role, while diversified agglomeration of local firms has no effect. Moreover, the location choice of acquisition is more sensitive to the specialized agglomeration of local firms than that of greenfield investment, and the influence of the industry-diverse agglomeration of CODI has no effect on the location choice of acquisition. In terms of the ownership, the location choice of state-owned enterprises (SOEs) is more sensitive to the specialized agglomeration of local firms and industry-specific agglomeration of CODI than that of private investment, and the industry-diverse agglomeration of CODI has no significant impact on the location choice of SOEs’ offshoring subsidiaries.

Citation: Liang X, Wang J, Lin B, Cao X, Si Y (2022) China’s Outward Direct Investment in the United States: From the perspective of agglomeration economies. PLoS ONE 17(6): e0269602. https://doi.org/10.1371/journal.pone.0269602

Editor: Ning Du, DePaul University, UNITED STATES

Received: September 23, 2021; Accepted: May 24, 2022; Published: June 10, 2022

Copyright: © 2022 Liang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant datasets are publicly available on The US-China Investment Project [1] and US Census Bureau. [1] https://www.us-china-investment.org/.

Funding: The work sponsored by the grants from National Natural Science Foundation of China (Nos.41871110), and the funders played the role in study design, decision to publish the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Driven by the “Go Global” policy initiated in 1999, CODI has grown quickly over most of the last two decades. By the end of 2019, CODI stock had reached $2198.88 billion, increasing from $2.99 billion in 2002 and accounting for 6.4% of the global stock, third only to the United States and the Netherlands. Against this background, scholars have been extensively exploring the drivers, entry modes, and location choice of CODI [1, 2]. Despite the fact that CODI is mainly concentrated in the United States from the country level, as well as the heated discussion concerning the trade and investment relationship between China and the United States, the research on the location choice of CODI in the United States is mostly incorporated into that of CODI in developed countries in general [3], which cannot reflect the specific mechanism of the location choice of CODI in the United States.

The location choice of CODI has received significant attention from scholars, since it is often regarded as a big challenge for Chinese multinational enterprises to face the huge liability of foreignness when investing abroad [2, 4]. Under such circumstances, scholars found that agglomeration economies wield significant influence on the location choice of CODI [5–7]. Given the limited knowledge of foreign markets and lack of competitive advantages in technology and management, the accessibility of more information is crucial to the location choice of CODI when entering a “primitive” market [8]. To decrease uncertainties and attain agglomeration externalities, Chinese investors tend to locate where the local firms agglomerate or follow the existing location decision of CODI [5, 8]. Although the influence of agglomeration economies on the location choice of CODI at the country level has been verified [9, 10], there has been little attention paid to the subnational level analysis. This is a significant omission given that knowledge spillovers occur locally and regional heterogeneity is regarded as the main factor in determining the specific investment location of multinational enterprises [11, 12]. With respect to the location choice of CODI in the United States, the small amount of research has mainly focused on the location advantages, while little attention has been paid to the role of agglomeration economies at the subnational level [13]. Furthermore, there is a consensus among scholars that entry mode and ownership influence the location choice of CODI [14–16].

Therefore, this study intends to use a conditional logit model based on the data from the US-China Investment Project to investigate the role of agglomeration economies for CODI in the United States at the regional level and to further explore the heterogeneous influence concerning the entry mode and ownership. More precisely, this study investigates whether the specialized or diversified agglomeration of local firms and the industry-specific or industry-diverse agglomeration of CODI can influence the location choice of CODI. In terms of research period, because CODI in the United States was rare and negligible before 2000 and the pace of CODI in the United States has slowed sharply since the inauguration of Donald Trump in 2017, this study selects the period between 2000 and 2016.

The remainder of the study proceeds as follows: In the next section, we conduct a literature review and propose hypotheses based on this. The data of CODI in the region of the United States and the methodology employed to test the hypotheses are clarified in section 3. Section 4 presents and explains the main results. The final section contains the conclusion and discussion.

2. Theoretical reviews

Industrial differences and foreign investors make the patterns of agglomeration economy complicated. Agglomeration economy refers to the benefits generated by firms and individuals being close to each other in a region [17]. The traditional agglomeration economies refer to the advantages arising from the specific industry as suggested by Alfred Marshall, including pools of skilled labour, specialized suppliers, and knowledge inflows from competitors [18]. In contrast to Alfred Marshall, Jane Jacobs argued that diverse and varied industries foster opportunities to imitate, share and recombine ideas and practices across industries, which may create the potential for economic growth [19]. Later, with the expansion and deepening of globalization of multinational enterprises, scholars found that agglomeration economies arose not only from the local firms, but also from foreign investors, especially those from the country-of-origin [20]. In the existing research conducted on the agglomeration economies, there are some distinctions between different patterns of agglomeration economies, which become more prominent after considering the strategy of firms [21]. To clarify the influence of the different patterns of agglomeration economies on the location choice of CODI, we mainly divide agglomeration economies into specialized and diversified agglomeration of local firms, as well as industry-specific or industry-diverse agglomeration of Chinese investors.

2.1 Specialized agglomeration or diversified agglomeration of local firms

For Chinese investors, agglomeration economies arising from local firms are important for their location choice. In order to maintain normal operations abroad, considering the unfamiliarity with culture, regulations, and competition in the host market, foreign investors need to pay the additional costs that local firms would not incur, known as “liability of foreignness” [4, 22]. Moreover, the liability of foreignness increases when foreign firms lack the strength of ownership advantages relative to their competition in the host country [23]. For example, because of inexperience in international markets and limited ownership advantages (such as weaker marketing resources and technology resources), CODI face a heavy liability when they enter developed countries that constitute a completely distinctive institutional and social environment [5, 24]. In this case, locations that can provide more information become foreign investors’ preferred place to invest [25]. Agglomeration economies are conducive to reducing information asymmetry, since foreign investors can access knowledge spillovers from the region where local firms aggregate. Local firms’ possession of location-specific advantages relating to the local economy helps foreign investors to overcome barriers in the new market [26]. In addition, with the background of the host-country market, local firms have stronger inter-firm linkages because they hire domestic workers and cooperate with local firms frequently, generating more significant agglomeration economies [27].

Furthermore, the influence of agglomeration economies in the United States on the location choice of CODI needs to be further distinguished based on the industry. Knowledge spillovers, local market pooling and inter-firm linkages are the three advantages for the formation of agglomeration economies that can help multinationals do business efficiently [28]. The same advantages that give rise to agglomeration economies mean that foreign investors can benefit not only from the specific industry (i.e., Marshallian externalities), but also from the diversity of industries (i.e., Jacobs externalities) [22]. Although existing research indicates that both Marshallian and Jacobs externalities play an important role in the location decisions of foreign firms, it is worth noting that not all firms are subject to the same economies of agglomeration, which thus cannot be expected to have an identical impact [20, 29]. As Ning et al. suggested, specialized agglomeration promotes knowledge spillovers, while diversified agglomeration may booster innovation in a vibrant environment [30]. The influence of diversification agglomeration has been observed amongst ‘mature’ multinational enterprises from developed countries, however, specialized agglomeration seems to align the interests with the strategy of CODI, which is characterized by learning-based knowledge-seeking strategies [3]. Chinese firms’ low technology absorptive capacity may hinder the inter-industry spillovers from a diversified industrial structure within the foreign environment [30]. Under the assumption of strategic-asset-seeking, CODI are more likely to choose regions characterized by specialization agglomeration of local firms to better augment their production and technological capabilities [31]. As Barrios et al. pointed out, high-value-added and innovative firms are prone to first locate in urban centres in order to take advantage of knowledge-related spillovers from the diversity of industries [29]. In contrast, low-tech firms seem to be only influenced by agglomeration economies created by Marshallian externalities. Therefore, the location choice of CODI may tend more towards the region where the local firms of the same industry aggregate. Hence, we propose the following hypothesis:

H1: The higher the degree of specialized agglomeration of local firms in a state, the more likely CODI of the same industry will enter.

2.2 Industry-specific or industry-diverse agglomeration of Chinese investors

In addition to the agglomeration economies of local firms benefiting CODI, the agglomeration economies of Chinese investors can also benefit later investors from China. In contrast to the advantages of local firms in terms of local knowledge and resources, foreign firms from the same origin country possess knowledge that may be more targeted to new entrants. Given the same background, new foreign entrants may face similar obstacles with prior foreign entrants from the same country [32]. In this case, foreign firms from the same origin country can provide some certain firm-specific (home-based) resources to enable survival and higher performance in the foreign market [24]. Furthermore, when foreign investors face high liability of foreignness, the knowledge they seek may concern sensitive cultural and institutional aspects, which may not be easily obtained from local firms [21]. In addition, new entrants can find it easier to gain legitimacy when they co-locate with firms from the same origin country because of the same ethnic identity [21]. They can learn from the early entrants from the same origin country not only how to gain legitimacy, but also to build on the legitimacy they enjoy back home. Along this line of reasoning, the agglomeration economies arising from foreign investors from the origin country are important to the location choice of new foreign entrants.

Similar to the classification of agglomeration economies of the host countries, a further distinction needs to be made between industry-specific and industry-diverse agglomeration of Chinese investors. Admittedly, with the same ethnic identity, any kind of agglomeration of CODI can support new entrants from China in accessing the knowledge spillovers [5]. However, because of the industrial differences, industry-specific agglomeration of CODI can provide more industry-specific knowledge and inter-firm linkages for the later entrants from China in the same industry. As Florida pointed out, the co-location of backward- and forward-linked manufacturing enterprises from the same country is a significant factor that plays a great role in the industrial location of multinational enterprises investing abroad [33]. Furthermore, in order to reduce competition and avoid uncertainties from the industry side in the foreign context, firms tend to follow the investment decisions of the same industry pioneer firms [34]. Therefore, we propose the following hypothesis:

H2: The more CODI stock of a specific industry in a state, the more likely it is that CODI of the same industry will enter.

3. Data and methodology

3.1 Data sources and the description of CODI in the United States

The data for CODI in the United States mainly used is from the US-China Investment Project, which is led by Rhodium Group and the National Committee on US-China Relations in partnership with the American Chamber of Commerce in Shanghai and the China General Chamber of Commerce in the United States. The data set was collected in a bottom-up way, with the original data coming from multiple channels including business service databases, disclosure reports, officially published information, and social media, which means the dataset can provide information that is as complete as possible. Every transaction amount over 1 million dollars was recorded. Specifically, the dataset captures three types of transactions: (1) acquisitions of existing assets that result in at least 10% ownership stakes; (2) greenfield projects with at least 10% ownership stakes (newly built facilities such as factories, warehouses, offices, and R&D centres); (3) the expansion of existing CODI operations. This not only provided flow data and number of investment projects, but also detailed information about the industrial and geographical distribution, entry mode and investor ownership.

Looking back at the history of CODI in the United States, three important time points are the introduction of China’s ‘Go Global’ policy in 1999, the Financial Crisis in 2008 and Donald Trump’s Inauguration in 2017, deeply influencing the historical development of CODI in the United States, and accordingly dividing the historical development into four stages. Although China introduced the ‘Reform and Opening up’ policy in 1978, the first CODI occurred in the United States in 1986. At this stage, CODI in the United States was rare and negligible. The investigation of the data collected by Rhodium Group shows that CODI in the United States during the period 1990 to 1999 was negligible, with only 12 investments totalling 24 million dollars. This is not only because Chinese per capita income was still low at that time, but also because of the Chinese government’s cautious attitude towards CODI approval. Specific analysis of that time reveals that China only invested in eight states, and that New York attracted most of the investment. After the Chinese government implemented the ‘Go Global’ policy in 1999, CODI expanded to more states, with 29 states having accepted CODI by the end of 2007. However, the transaction volume in this period is generally low, and the average annual investment is still less than 160 million. Most CODI was concentrated on the coasts in California and New York, as well as a few other states hosting large firms or investments, including North Carolina, Michigan, and Texas.

2008 was the beginning of stage 3, not only because of the turbulent international economic situation caused by the financial crisis, but also due to the officially launched BIT negotiations between China and the United States. The financial crisis hit the United States economy hard, which led to its increasing emphasis on foreign direct investment, especially from emerging markets. At the same time, CODI maintained rapid growth while other countries reduced investments. During this period, CODI flow into the United States increased dramatically, from $771 million in 2008 to $46.215 billion in 2016, with an average annual increase of nearly 59%. In terms of the geographical distribution, the scope of investment covers almost the entire United States. By the end of 2016, Chinese firms had investments in 46 of the 50 states. Since the inauguration of Donald Trump in 2017, the pace of CODI in the United States has slowed sharply. According to the report Two-Way Street: 2019 Update US-China Investment Trends, punitive tariffs, a trade war, tighter capital controls by the Chinese government and increased scrutiny of foreign acquisitions in the United States are among the main reasons why CODI in the United States has plummeted.

Considering the negligible volume of CODI in the United States before 2000 and unpredictable changes after 2017, 2000 to 2016 was chosen as the time span. Table 1 shows the geographical distribution of CODI over the United States during this period. Although CODI spread over 47 states in the United States, when looking at the share of CODI location among states, most of these investments were concentrated in California (23.3%). New York (14.3%), Illinois (8.6%), Kentucky (8.4%), Virginia (8.2%) and Texas (6.5%) also show relatively high regional concentration of CODI. It was also found that the amount of investment is not proportional to the number of investment projects. Some regions, such as Kentucky, Oklahoma, and Kansas, receive fewer projects but a large investment amount, while other regions, such as Washington, New Jersey, and South Carolina, receive many projects but only a small investment amount.

Download:

Table 1. Distribution of CODI over the states of the United States, 2000–2016.

https://doi.org/10.1371/journal.pone.0269602.t001

From a dynamic perspective, the distribution of CODI from 2000 to 2016 shows a tendency of agglomeration towards the Northeast Corridor and the Midwest. Prior to 2005, China had a small amount of investment in the United States. Only North Carolina became an outstanding state, since a big project was undertaken there by Beijing in the Information and Communications Technology industry through acquisition in 2005. In the period 2006–2010, Chinese firms increased their investment in the Northeast Corridor and the Midwest, and the investment industry was dominated by the energy and automotive industries. After 2011, the expansion of Chinese firms broadened further, including to the Pacific Northwest, the South, and parts of the Midwest, and became more diversified, including not only the traditional resource-based industries, but also emerging industries such as Entertainment, Media, and Education, as well as the Financial and Business Services industry. Taken together, by comparing the three sub-plots, the change of the distribution of CODI in the United States seems to show a trend of agglomeration.

3.2 Methodology

The conditional logit model of McFadden [35] was adopted to analyse the location factors of 1358 projects of Chinese investment across fifty-one regions (fifty states and a federal district) in the US during the period between 2000 and 2016. The conditional logit model is derived from profit-maximizing firm behaviour under appropriate assumptions concerning the stochastic term in the profit function, and the model has been widely used in previous empirical studies of location choice. It was assumed that CODI would choose the region that would yield the highest profit with the consideration of a series of attributes of states when deciding where to invest. For every project, CODI could choose any of the fifty-one regions. In this case, if the state was selected, the dependent variable was assigned as 1, otherwise the dependent variable was assigned as 0, which meant that there were many states not selected. In addition, referring to Ben-Akiva et al.’s research, only 5 states that have not been invested in were randomly selected to estimate the model [36]. Since the state that had not been invested in was randomly selected, the result would not be affected. The final estimated sample data was 1358*6.

Regarding the attributes that attract CODI in the United States, two groups of conditional variables were included to estimate the impact of the probability of the project i choosing the state j. The following model was proposed: (1) Where P_ij was the project i choosing state j as its preferred location to maximize utility. A_j represented agglomeration attributes in state j, and C_j were control variables for state j. The choice model with n mutually exclusive alternatives was as follows: (2) Where V_i was a utility function of the explanatory variables related to state j. The utility function was determined as follows: (3) Where β₀, β₁, β₂ were the estimated coefficients and ε_i was an error term. It was hypothesized that the above-mentioned agglomeration economies as well as other attributes of states would affect the location choices of CODI in the United States.

3.3 Variables

3.3.1 Whether to invest as dependent variable.

Because the model adopted was a conditional logit model, the dependent variable was a dummy variable indicating whether an investment was made in the NUTS 2 area: 1 if yes, 0 if no. The data was from the US-China Investment Project.

3.3.2 Four patterns of agglomeration as independent variables.

In accordance with our previous hypotheses, Local specialized agglomeration, Local diversified agglomeration, Specialized_CN and Diversified_CN were mainly investigated in the model. The explanations of different patterns of agglomeration were as follows.

In order to capture specialized agglomeration and diversified agglomeration in the states, with reference to Li and Song [37], the relative value to measure was used. If S_jk was defined as the percentage of the output value number of industry k in state j, the specialization index for the individual state was: (4)

In order to obtain a horizontal comparison of specialization between different states, the level of relative specialization (rather than absolute specialization) was needed, so the relative specialization index (Local specialized agglomeration) was defined as: (5)

S_k referred to the industry’s share of the country. The most common diversification index to use is the reciprocal of the Herfindahl-Hirschman Index (HHI). The HHI index refers to the sum of the squares of the output value share of all industries. The diversification index measured by its reciprocal was: (6)

The relative diversification index (Local diversified agglomeration) was defined as: (7)

The output value data of various industries were obtained from the database of the Bureau of Economic Analysis.

The third agglomeration variable (Specialized_CN) was the log of the CODI stock in the industry k in the United States. The variable captured industry-specific agglomeration of CODI. The measurement of the industry-diverse agglomeration of CODI (Diversified_CN) was the log of the CODI stock in the other industries (i.e., not industry k) in the United States. The data of CODI was from the US-China Investment Project.

3.3.3 Control variables.

Based on a long string of studies on the location choices of CODI, some control variables that might influence the location decision of CODI in the United States were selected. Because of the proximity of the culture and language, overseas Chinese act as an interface and function as localized sources of social and human capital to assist the link-up [8, 38], helping Chinese investors reduce the impediments and risk when investing in the host country. Therefore, the potential influence of the overseas Chinese was controlled. The variable was measured by the proportion of the percentage of overseas Chinese in each state to the percentage of overseas Chinese in the United States, with the data coming from the United States Census Bureau.

In addition to the influence of overseas Chinese, the strategic asset-seeking, market-seeking, and resource-seeking motivations suggested by the theory on the eclectic paradigm were also considered as control variables. Existing studies show that the motivation of CODI in developed countries is mainly strategic asset-seeking [39, 40]. Therefore, CODI in the United States tends towards the states with strong innovation capabilities. The innovation capability of the states was measured by three indicators: patents, R&D expense, and labour quality. The log of the number of patents of the states was used to measure the innovation output of the states. The proportion of R&D expense to the GDP of the states was used to measure the innovation input. Moreover, higher quality of the labour of the states may induce higher innovation efficiency. Therefore, the proportion of the labouring population with a bachelor’s degree was used as the proxy of the labour quality. The data of these three variables were from the National Science Foundation, Science and Engineering Indicators.

Some scholars also found that CODI was driven by the market attraction of developed countries [41, 42]. Therefore, the potential influence of the market-seeking motivation was controlled. The measurement of the market-seeking motivation was the growth rate of the GDP, per capita tax, and per capita income. The data used were obtained from the Bureau of Economic Analysis.

Some Chinese investors may seek natural resources in developed countries through acquisition [43]. Therefore, the proportion of the resource industry to the entire industry of the states was used as the proxy of resource-seeking motivation. The resource industries include forestry, fishing and related activities, mining, as well as oil and gas extraction. The data were from the Bureau of Economic Analysis.

All variable definitions and sources are listed in Table 2. It is worth mentioning that considering the lag of variables, all variables (except the dependent variable) used in the model were from the data of the previous year. In addition, there is no serious multicollinearity problem because the variance inflation factors of the variables are all less than 5. The descriptive statistics of variables are presented in Table 3.

Download:

Table 2. Variable descriptions and sources.

https://doi.org/10.1371/journal.pone.0269602.t002

Download:

Table 3. Descriptive statistics for variables.

https://doi.org/10.1371/journal.pone.0269602.t003

4. Results

As Table 4 shows, the results confirm the above-mentioned hypotheses. Table 4 presents the results of the econometric model. Specifically, Model 1 contains only the control variables, Model 2 introduces Local specialized agglomeration and Local diversified agglomeration and control variables, Model 3 introduces Specialized_CN, Diversified_CN and control variables, and Model 4 encompasses all the variables. Firstly, through comparing the difference of the Pseudo R2 between the four models, the inclusion of four patterns of agglomeration economies is found to improve the model specification, indicating that the agglomeration economies do influence the location decision of CODI, and the inclusion of agglomeration economies from the US local firms as well as from CODI are crucial in terms of the clarification of the location choice of CODI in the United States. Next, Model 4 shows that Local specialized agglomeration is significantly positively related to the investment, while Local diversified agglomeration has no relation with the investment, verifying H1. This result may indicate that knowledge spillovers from specialized agglomeration may better satisfy Chinese knowledge-seeking needs [3].

Download:

Table 4. Conditional logit estimation for CODI in the United States.

https://doi.org/10.1371/journal.pone.0269602.t004

Thirdly, the industry-specific agglomeration and industry-diverse agglomeration of CODI both show a significantly positive correlation with the investment. To further clarify their role, with reference to studies by Cheng and Stough [44], the average probability elasticity was calculated to assess the magnitude of estimated coefficients. Here, the elasticity of industry-specific agglomeration of CODI (Specialized_CN), 0.233, means that a 10% increase of previous CODI stock of a specific industry in a state leads to a 2.33% increase in the probability that CODI of the same industry will choose that particular state on average. As Table 4 indicates, the elasticity of Specialized_CN (0.233) is larger than Diversified_CN (0.085), suggesting that regarding the influence of previous CODI stock, although both industry-specific and industry-diverse agglomeration of Chinese investors play a positive role in the location choice of CODI, the industry-specific agglomeration of CODI is more instructive. Therefore, H2 is verified. Furthermore, it is noteworthy that the elasticity of Local specialized agglomeration (0.340) is larger than Specialized_CN (0.233), suggesting that the attractive effect of the specialized agglomeration of local firms surpasses the industry-specific agglomeration of CODI of the same industry. This finding is in line with the study by Lee et al. [45], who found that when deciding on the focal state in the United States, Korean multinational enterprises are more sensitive to the agglomeration of local firms than to the agglomeration of Korean firms, suggesting that the impact of factor endowments exceeds that of nationality. This may also imply that in a specific industry, CODI is more sensitive to the knowledge spillovers resulting from the local firms of the United States rather than from the CODI stock in the same state, especially in the context of Chinese investors following learning-based knowledge-seeking strategies [3, 46].

Regarding the results of the control variables, Overseas Chinese do influence the location choice of CODI, which corresponds to past studies [8, 38]. The number of patents of the states also has positive effects on the location decision, proving that Chinese investors are seeking strategic resources. Higher taxes also lead to lower CODI, to some extent implying that the United States market is an important factor that affects the location choice of CODI. However, natural resources have a significantly negative impact on attracting CODI, possibly because the expense of resource-seeking CODI is too high in the United States.

Furthermore, as Table 5 showed, four patterns of agglomeration economies have varied influences on the location choice of CODI in the context of different entry modes and ownerships. Firstly, as shown in Model 5, the elasticity of Local specialized agglomeration (0.409) in the context of acquisition investment is larger than that of greenfield investment. One plausible reason is that Chinese investors can quickly gain strategic resources through acquisition [31], resulting in more focus on the specific industry knowledge from the local firms. This inference is also evidenced by Chinese transnational acquirers focusing more on the greater elasticity of the number of patents of the states and significantly lower R&D expense. In addition, in contrast to greenfield investment, the location choice of acquisition is not sensitive to the industry-diverse agglomeration of CODI. Moreover, the growth rate of the GDP of the states has a significantly positive impact on attracting CODI, revealing Chinese transnational acquirers’ attention to the market [47].

Download:

Table 5. Conditional logit estimation for CODI in the United States according to entry modes and ownerships.

https://doi.org/10.1371/journal.pone.0269602.t005

With respect to the choice difference of the ownerships, as Model 6 shows, compared to the private CODI, the location choice of SOEs’ offshoring subsidiaries is more sensitive to the United States firms and industry-specific agglomeration of CODI than that of private investment, and the industry-diverse agglomeration of CODI has no significant impact on the location choice of SOEs’ offshoring subsidiaries. The rationale behind this finding may be that the strategic need of SOEs to acquire unique resources and capabilities [48] leads to the focus on the specific industry agglomeration. In addition, private investors tend to invest more in the states with more patents than state-owned investors, while being reluctant to locate in the states with abundant natural resources.

5. Conclusion and discussion

Based on the data of the US-China Investment Project, we use a conditional model to investigate the influence of different patterns of agglomeration economies on the location choice of CODI in the United States in the period 2000–2016. Our study confirms that agglomeration economies do influence the location decision of CODI. The specialized agglomeration of local firms exerts more influence on the location choice of CODI than industry-specific agglomeration of CODI in the same industry. Industry-diverse agglomeration of CODI plays a weak role, while diversified agglomeration of local firms has no effect. Moreover, the heterogeneity of firms in terms of the entry mode and ownership also impacts the influence of agglomeration economies on the location decision of CODI. The location choice of acquisition is more sensitive to the specialized agglomeration of local firms than that of greenfield investment, while the influence of the industry-diverse agglomeration of CODI has no effect on the location choice of acquisition. The location choice of SOEs’ offshoring subsidiaries is more sensitive to specialized agglomeration of local firms and industry-specific agglomeration of CODI than that of private firms, and the influence of the industry-diverse agglomeration of CODI has no effect on the location choice of SOEs’ offshoring subsidiaries.

Under the Belt and Road Initiative, more Chinese firms are going abroad to participate in economic globalization. In this case, this paper can contribute impulses for CODI in three ways. Firstly, it expounds which types of agglomeration play a role in the location selection of CODI and shows the difference in the influence of the agglomeration with respect to the entry mode and ownership, which not only responds to the call by Head et al. [20] to account for different patterns of agglomeration in the location decision, but also provides reference for CODI to make location decisions from the perspective of industrial agglomeration. Secondly, this paper focuses on the location choice of CODI in different regions within a country and provides empirical evidence for the research on the location choice of CODI at a more detailed level. Thirdly, this paper reveals the current location choices of CODI in the United States and provides reference for subsequent CODI there. In addition, this study may have the following policy implications: firstly, the government needs to consider the differences of the invested regions’ factor endowments when formulating the incentive policies for investing abroad in order to better integrate global resources; secondly, the government should formulate more specific investment policies based on the ownership of Chinese multinational enterprises in order to encourage them to actively participate in foreign direct investment.

This study confirms that the influence of different patterns of agglomeration economies on the location choice of CODI in the United States varies, however it does not address an important question, namely the sources of agglomeration economies affecting the location choice of CODI. The heterogeneity of industry of the firms also impacts the agglomeration economies in their location decision, an issue that has also not been solved in this paper. Further research is recommended to investigate these questions in order to better understand the location choice of CODI. Moreover, due to different investment behaviours of CODI in developed and developing countries, more research must also be conducted to determine whether the findings of this paper can be applied to other countries.

References

1. Buckley P. China goes global: provenance, projection, performance and policy. International Journal of Emerging Markets. 2019; 14(1): 6–23. https://doi.org/10.1108/IJoEM-01-2017-0006
- View Article
- Google Scholar
2. Alon I, Anderson J, Munim Z H, Ho A. A review of the internationalization of Chinese enterprises. Asia Pacific Journal of Management. 2018; 35(3): 573–605. https://doi.org/10.1007/s10490-018-9597-5
- View Article
- Google Scholar
3. Jindra B, Hassan S S, Cantner U. What does location choice reveal about knowledge-seeking strategies of emerging market multinationals in the EU?. International Business Review. 2016; 25(1): 204–220. https://doi.org/10.1016/j.ibusrev.2014.11.008
- View Article
- Google Scholar
4. Zaheer S. Overcoming the liability of foreignness. Academy of Management Journal. 1995; 38(2): 341–363. https://doi.org/256683
- View Article
- Google Scholar
5. De Beule F, Somers D, Zhang H. Who follows whom? A location study of Chinese private and state-owned companies in the European Union. Management International Review. 2018; 58(1), 43–84. https://doi.org/10.1007/s11575-017-0330-2
- View Article
- Google Scholar
6. Mariotti S, Piscitello L, Elia S. Spatial agglomeration of multinational enterprises: the role of information externalities and knowledge spillovers. Journal of Economic Geography. 2010; 10(4): 519–538. https://doi.org/10.1093/jeg/lbq011
- View Article
- Google Scholar
7. Jones J. Agglomeration economies and the location of foreign direct investment: A meta‐analysis. Journal of Regional Science. 2017; 57(5): 731–757. https://doi.org/10.1111/jors.12335
- View Article
- Google Scholar
8. Chen H, Chen T J. Network linkages and location choice in foreign direct investment. Journal of international business studies. 1998; 29(3): 445–467. https://doi.org/10.1057/palgrave.jibs.8490002
- View Article
- Google Scholar
9. Amighini A A, Franco C. A sector perspective on Chinese outward FDI: The automotive case. China Economic Review. 2013; 27: 148–161.
- View Article
- Google Scholar
10. Liu H, Jiang J, Zhang L, Chen X. OFDI agglomeration and Chinese firm location decisions under the “Belt and Road” initiative. Sustainability. 2018; 10(11): 4060. https://doi.org/10.3390/su10114060
- View Article
- Google Scholar
11. Beugelsdijk S, Mudambi R. MNEs as border-crossing multi-location enterprises: The role of discontinuities in geographic space. In Location of international business activities (pp. 8–34). 2014. Palgrave Macmillan, London. https://doi.org/10.1057/9781137472311_2
12. Li P, Bathelt H. Headquarters‐subsidiary knowledge strategies at the cluster level. Global Strategy Journal. 2020; 10(3): 585–618. https://doi.org/10.1002/gsj.1356
- View Article
- Google Scholar
13. Anderson J, Sutherland D. Entry mode and emerging market MNEs: An analysis of Chinese greenfield and acquisition FDI in the United States. Research in International Business and Finance. 2015; 35: 88–103. https://doi.org/10.1016/j.ribaf.2015.03.008
- View Article
- Google Scholar
14. Wang J, Wei Y D, Lin B. Functional division and location choices of Chinese outward FDI: The case of ICT firms. Environment and Planning A: Economy and Space. 2021; 53(5): 937–957. https://doi.org/10.1177/0308518X20973016
- View Article
- Google Scholar
15. Ramasamy B, Yeung M, Laforet S. China’s outward foreign direct investment: Location choice and firm ownership. Journal of world business. 2012; 47(1): 17–25. https://doi.org/10.1016/j.jwb.2010.10.016
- View Article
- Google Scholar
16. Tang R W, Shu C, Zhou K Z. State ownership of Chinese firms and their outward foreign direct investment: Political and economic contingencies. Asia Pacific Journal of Management. 2021; 1–27. https://doi.org/10.1007/s10490-021-09756-y
- View Article
- Google Scholar
17. Glaeser E L. Agglomeration economics. Chicago: University of Chicago Press. 2010.
18. Marshall A. Principles of economics. London: Macmillan. 1920.
19. Jacobs J. The economy of cities. New York: Random House. 1969.
20. Head K, Ries J, Swenson D. Agglomeration benefits and location choice: Evidence from Japanese manufacturing investments in the United States. Journal of international economics. 1995; 38(3–4): 223–247. https://doi.org/10.1016/0022-1996(94)01351-R
- View Article
- Google Scholar
21. Tan D, Meyer K E, Country-of-origin and industry FDI agglomeration of foreign investors in an emerging economy. Journal of International Business Studies. 2011; 42(4): 504–520. https://doi.org/10.1057/jibs.2011.4
- View Article
- Google Scholar
22. Beaudry C, Schiffauerova A. Who’s right, Marshall or Jacobs? The localization versus urbanization debate. Research policy. 2009; 38(2): 318–337. https://doi.org/10.1016/j.respol.2008.11.010
- View Article
- Google Scholar
23. Gaur A S, Kumar V, Sarathy R. Liability of foreignness and internationalisation of emerging market firms. In Dynamics of globalization: Location-specific advantages or liabilities of foreignness?. Emerald Group Publishing Limited. 2011. https://doi.org/10.1108/S1571-5027(2011)0000024016
24. Miller S R, Thomas D E, Eden L, Hitt M. Knee deep in the big muddy: The survival of emerging market firms in developed markets. Management International Review. 2008; 48(6): 645–666. https://doi.org/10.1007/s11575-008-0101-1
- View Article
- Google Scholar
25. He C F. Information costs, agglomeration economies and the location of foreign direct investment in China. Regional studies. 2002; 36(9): 1029–1036. https://doi.org/10.1080/0034340022000022530
- View Article
- Google Scholar
26. Guimaraes P, Figueiredo O, Woodward D. Agglomeration and the location of foreign direct investment in Portugal. Journal of Urban Economics. 2000; 47(1): 115–135. https://doi.org/10.1006/juec.1999.2138
- View Article
- Google Scholar
27. Crozet M, Mayer T, Mucchielli J L. How do firms agglomerate? A study of FDI in France. Regional Science and Urban Economics. 2004; 34(1): 27–54. https://doi.org/10.1016/S0166-0462(03)00010-3
- View Article
- Google Scholar
28. Parr JB. Missing elements in the analysis of agglomeration economies. International Regional Science Review. 2002; 25(2): 151–168. https://doi.org/10.1177/016001702762481221
- View Article
- Google Scholar
29. Barrios S, Görg H, Strobl E. Multinationals’ location choice, agglomeration economies, and public incentives. International Regional Science Review. 2006; 29(1): 81–107. https://doi.org/10.1177/0160017605281516
- View Article
- Google Scholar
30. Ning L, Wang F, Li J. Urban innovation, regional externalities of foreign direct investment and industrial agglomeration: Evidence from Chinese cities. Research Policy. 2016; 45(4): 830–843. https://doi.org/10.1016/j.respol.2016.01.014
- View Article
- Google Scholar
31. Deng P. Why do Chinese firms tend to acquire strategic assets in international expansion?. Journal of world business. 2009; 44(1): 74–84. https://doi.org/10.1016/j.jwb.2008.03.014
- View Article
- Google Scholar
32. Shaver J M, Mitchell W, Yeung B. The effect of own firm and other-firm experience on foreign direct investment survival in the United States, 1987–92. Strategic Management Journal. 1997; 18(10): 811–824. https://doi.org/10.1002/(SICI)1097-0266(199711)18:10<811::AID-SMJ924>3.0.CO;2-8
- View Article
- Google Scholar
33. Florida R. Agglomeration and industrial location: An econometric analysis of Japanese-affiliated manufacturing establishments in automotive-related industries. Journal of urban economics. 1994; 36(1): 23–41. https://doi.org/10.1006/juec.1994.1024
- View Article
- Google Scholar
34. Gimeno J, Hoskisson R E, Beal B D, Wan W P. Explaining the clustering of international expansion moves: A critical test in the US telecommunications industry. Academy of Management Journal. 2005; 48(2): 297–319. https://doi.org/10.2307/20159657
- View Article
- Google Scholar
35. McFadden D. Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, Massachusetts: Academic Press, 1974; 105–141.
36. Ben-Akiva M E, Lerman S R. Discrete choice analysis: theory and application to travel demand (Vol. 9), MIT press, 1985.
37. Li J Y, Song D Y. Specialization, diversification and urban agglomeration economy: an empirical study based on panel data of prefecture-level units in China (in Chinese). Management World. 2008; 0(2): 25–34.
- View Article
- Google Scholar
38. Karreman B, Burger MJ, van Oort F. Location choices of Chinese multinationals in Europe: The role of overseas communities. Economic Geography. 2017; 93(2): 131–161. https://doi.org/10.1080/00130095.2016.1248939
- View Article
- Google Scholar
39. Si Y F, Liefner I, Wang T. Foreign direct investment with Chinese characteristics: A middle path between Ownership-Location-Internalization model and Linkage-Leverage-Learning model. Chinese geographical science. 2013; 23(5): 594–606. https://doi.org/10.1007/s11769-013-0603-z
- View Article
- Google Scholar
40. Child J, Rodrigues S B. The internationalization of Chinese firms: A case for theoretical extension. Management and organization review, 2005; 1(3): 381–410. https://doi.org/10.1111/j.1740-8784.2005.0020a.x
- View Article
- Google Scholar
41. Kolstad I, Wiig A. What determines Chinese outward FDI. Journal of World Business. 2012; 47(1): 26–34. https://doi.org/10.1016/j.jwb.2010.10.017
- View Article
- Google Scholar
42. Lin L, Wang D, Si Y F. The limits of guanxi for Chinese nationals doing business abroad: empirical investigation into Chinese companies in Germany. Tijdschrift voor economische en sociale geografie. 2018; 109(3): 420–433. https://doi.org/10.1111/tesg.12301
- View Article
- Google Scholar
43. Yang M, Deng P. Cross‐Border M&As by Chinese companies in advanced countries: Antecedents and implications. Thunderbird International Business Review. 2017; 59(3): 263–280. https://doi.org/10.1002/tie.21767
- View Article
- Google Scholar
44. Cheng S, Stough RR. Location decisions of Japanese new manufacturing plants in China: a discrete-choice analysis. The Annals of Regional Science. 2006; 40(2): 369–387. https://doi.org/10.1007/s00168-005-0052-4
- View Article
- Google Scholar
45. Lee K D, Hwang S J, Lee M H. Agglomeration economies and location choice of Korean manufacturers within the United States. Applied Economics. 2011; 44(2): 189–200. https://doi.org/10.1080/00036846.2010.502109
- View Article
- Google Scholar
46. Hilber C A, Voicu I. Agglomeration economies and the location of foreign direct investment: Empirical evidence from Romania. Regional studies. 2010; 44(3): 355–371. https://doi.org/10.1080/00343400902783230
- View Article
- Google Scholar
47. Boateng A, Qian W, Tianle Y. Cross‐border M&As by Chinese firms: An analysis of strategic motives and performance. Thunderbird international business review. 2008; 50(4): 259–270. https://doi.org/10.1002/tie.20203
- View Article
- Google Scholar
48. Rudy BC, Miller SR, Wang D. Revisiting FDI strategies and the flow of firm‐specific advantages: A focus on state‐owned enterprises. Global Strategy Journal. 2016; 6(1): 69–78. https://doi.org/10.1002/gsj.1106
- View Article
- Google Scholar

[ref1] 1. Buckley P. China goes global: provenance, projection, performance and policy. International Journal of Emerging Markets. 2019; 14(1): 6–23. https://doi.org/10.1108/IJoEM-01-2017-0006
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Alon I, Anderson J, Munim Z H, Ho A. A review of the internationalization of Chinese enterprises. Asia Pacific Journal of Management. 2018; 35(3): 573–605. https://doi.org/10.1007/s10490-018-9597-5
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Jindra B, Hassan S S, Cantner U. What does location choice reveal about knowledge-seeking strategies of emerging market multinationals in the EU?. International Business Review. 2016; 25(1): 204–220. https://doi.org/10.1016/j.ibusrev.2014.11.008
View Article
Google Scholar

[8] View Article

[9] Google Scholar

[ref4] 4. Zaheer S. Overcoming the liability of foreignness. Academy of Management Journal. 1995; 38(2): 341–363. https://doi.org/256683
View Article
Google Scholar

[11] View Article

[12] Google Scholar

[ref5] 5. De Beule F, Somers D, Zhang H. Who follows whom? A location study of Chinese private and state-owned companies in the European Union. Management International Review. 2018; 58(1), 43–84. https://doi.org/10.1007/s11575-017-0330-2
View Article
Google Scholar

[14] View Article

[15] Google Scholar

[ref6] 6. Mariotti S, Piscitello L, Elia S. Spatial agglomeration of multinational enterprises: the role of information externalities and knowledge spillovers. Journal of Economic Geography. 2010; 10(4): 519–538. https://doi.org/10.1093/jeg/lbq011
View Article
Google Scholar

[17] View Article

[18] Google Scholar

[ref7] 7. Jones J. Agglomeration economies and the location of foreign direct investment: A meta‐analysis. Journal of Regional Science. 2017; 57(5): 731–757. https://doi.org/10.1111/jors.12335
View Article
Google Scholar

[20] View Article

[21] Google Scholar

[ref8] 8. Chen H, Chen T J. Network linkages and location choice in foreign direct investment. Journal of international business studies. 1998; 29(3): 445–467. https://doi.org/10.1057/palgrave.jibs.8490002
View Article
Google Scholar

[23] View Article

[24] Google Scholar

[ref9] 9. Amighini A A, Franco C. A sector perspective on Chinese outward FDI: The automotive case. China Economic Review. 2013; 27: 148–161.
View Article
Google Scholar

[26] View Article

[27] Google Scholar

[ref10] 10. Liu H, Jiang J, Zhang L, Chen X. OFDI agglomeration and Chinese firm location decisions under the “Belt and Road” initiative. Sustainability. 2018; 10(11): 4060. https://doi.org/10.3390/su10114060
View Article
Google Scholar

[29] View Article

[30] Google Scholar

[ref11] 11. Beugelsdijk S, Mudambi R. MNEs as border-crossing multi-location enterprises: The role of discontinuities in geographic space. In Location of international business activities (pp. 8–34). 2014. Palgrave Macmillan, London. https://doi.org/10.1057/9781137472311_2

[ref12] 12. Li P, Bathelt H. Headquarters‐subsidiary knowledge strategies at the cluster level. Global Strategy Journal. 2020; 10(3): 585–618. https://doi.org/10.1002/gsj.1356
View Article
Google Scholar

[33] View Article

[34] Google Scholar

[ref13] 13. Anderson J, Sutherland D. Entry mode and emerging market MNEs: An analysis of Chinese greenfield and acquisition FDI in the United States. Research in International Business and Finance. 2015; 35: 88–103. https://doi.org/10.1016/j.ribaf.2015.03.008
View Article
Google Scholar

[36] View Article

[37] Google Scholar

[ref14] 14. Wang J, Wei Y D, Lin B. Functional division and location choices of Chinese outward FDI: The case of ICT firms. Environment and Planning A: Economy and Space. 2021; 53(5): 937–957. https://doi.org/10.1177/0308518X20973016
View Article
Google Scholar

[39] View Article

[40] Google Scholar

[ref15] 15. Ramasamy B, Yeung M, Laforet S. China’s outward foreign direct investment: Location choice and firm ownership. Journal of world business. 2012; 47(1): 17–25. https://doi.org/10.1016/j.jwb.2010.10.016
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref16] 16. Tang R W, Shu C, Zhou K Z. State ownership of Chinese firms and their outward foreign direct investment: Political and economic contingencies. Asia Pacific Journal of Management. 2021; 1–27. https://doi.org/10.1007/s10490-021-09756-y
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref17] 17. Glaeser E L. Agglomeration economics. Chicago: University of Chicago Press. 2010.

[ref18] 18. Marshall A. Principles of economics. London: Macmillan. 1920.

[ref19] 19. Jacobs J. The economy of cities. New York: Random House. 1969.

[ref20] 20. Head K, Ries J, Swenson D. Agglomeration benefits and location choice: Evidence from Japanese manufacturing investments in the United States. Journal of international economics. 1995; 38(3–4): 223–247. https://doi.org/10.1016/0022-1996(94)01351-R
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref21] 21. Tan D, Meyer K E, Country-of-origin and industry FDI agglomeration of foreign investors in an emerging economy. Journal of International Business Studies. 2011; 42(4): 504–520. https://doi.org/10.1057/jibs.2011.4
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref22] 22. Beaudry C, Schiffauerova A. Who’s right, Marshall or Jacobs? The localization versus urbanization debate. Research policy. 2009; 38(2): 318–337. https://doi.org/10.1016/j.respol.2008.11.010
View Article
Google Scholar

[57] View Article

[58] Google Scholar

[ref23] 23. Gaur A S, Kumar V, Sarathy R. Liability of foreignness and internationalisation of emerging market firms. In Dynamics of globalization: Location-specific advantages or liabilities of foreignness?. Emerald Group Publishing Limited. 2011. https://doi.org/10.1108/S1571-5027(2011)0000024016

[ref24] 24. Miller S R, Thomas D E, Eden L, Hitt M. Knee deep in the big muddy: The survival of emerging market firms in developed markets. Management International Review. 2008; 48(6): 645–666. https://doi.org/10.1007/s11575-008-0101-1
View Article
Google Scholar

[61] View Article

[62] Google Scholar

[ref25] 25. He C F. Information costs, agglomeration economies and the location of foreign direct investment in China. Regional studies. 2002; 36(9): 1029–1036. https://doi.org/10.1080/0034340022000022530
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref26] 26. Guimaraes P, Figueiredo O, Woodward D. Agglomeration and the location of foreign direct investment in Portugal. Journal of Urban Economics. 2000; 47(1): 115–135. https://doi.org/10.1006/juec.1999.2138
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref27] 27. Crozet M, Mayer T, Mucchielli J L. How do firms agglomerate? A study of FDI in France. Regional Science and Urban Economics. 2004; 34(1): 27–54. https://doi.org/10.1016/S0166-0462(03)00010-3
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref28] 28. Parr JB. Missing elements in the analysis of agglomeration economies. International Regional Science Review. 2002; 25(2): 151–168. https://doi.org/10.1177/016001702762481221
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref29] 29. Barrios S, Görg H, Strobl E. Multinationals’ location choice, agglomeration economies, and public incentives. International Regional Science Review. 2006; 29(1): 81–107. https://doi.org/10.1177/0160017605281516
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref30] 30. Ning L, Wang F, Li J. Urban innovation, regional externalities of foreign direct investment and industrial agglomeration: Evidence from Chinese cities. Research Policy. 2016; 45(4): 830–843. https://doi.org/10.1016/j.respol.2016.01.014
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref31] 31. Deng P. Why do Chinese firms tend to acquire strategic assets in international expansion?. Journal of world business. 2009; 44(1): 74–84. https://doi.org/10.1016/j.jwb.2008.03.014
View Article
Google Scholar

[82] View Article

[83] Google Scholar

[ref32] 32. Shaver J M, Mitchell W, Yeung B. The effect of own firm and other-firm experience on foreign direct investment survival in the United States, 1987–92. Strategic Management Journal. 1997; 18(10): 811–824. https://doi.org/10.1002/(SICI)1097-0266(199711)18:10<811::AID-SMJ924>3.0.CO;2-8
View Article
Google Scholar

[85] View Article

[86] Google Scholar

[ref33] 33. Florida R. Agglomeration and industrial location: An econometric analysis of Japanese-affiliated manufacturing establishments in automotive-related industries. Journal of urban economics. 1994; 36(1): 23–41. https://doi.org/10.1006/juec.1994.1024
View Article
Google Scholar

[88] View Article

[89] Google Scholar

[ref34] 34. Gimeno J, Hoskisson R E, Beal B D, Wan W P. Explaining the clustering of international expansion moves: A critical test in the US telecommunications industry. Academy of Management Journal. 2005; 48(2): 297–319. https://doi.org/10.2307/20159657
View Article
Google Scholar

[91] View Article

[92] Google Scholar

[ref35] 35. McFadden D. Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, Massachusetts: Academic Press, 1974; 105–141.

[ref36] 36. Ben-Akiva M E, Lerman S R. Discrete choice analysis: theory and application to travel demand (Vol. 9), MIT press, 1985.

[ref37] 37. Li J Y, Song D Y. Specialization, diversification and urban agglomeration economy: an empirical study based on panel data of prefecture-level units in China (in Chinese). Management World. 2008; 0(2): 25–34.
View Article
Google Scholar

[96] View Article

[97] Google Scholar

[ref38] 38. Karreman B, Burger MJ, van Oort F. Location choices of Chinese multinationals in Europe: The role of overseas communities. Economic Geography. 2017; 93(2): 131–161. https://doi.org/10.1080/00130095.2016.1248939
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref39] 39. Si Y F, Liefner I, Wang T. Foreign direct investment with Chinese characteristics: A middle path between Ownership-Location-Internalization model and Linkage-Leverage-Learning model. Chinese geographical science. 2013; 23(5): 594–606. https://doi.org/10.1007/s11769-013-0603-z
View Article
Google Scholar

[102] View Article

[103] Google Scholar

[ref40] 40. Child J, Rodrigues S B. The internationalization of Chinese firms: A case for theoretical extension. Management and organization review, 2005; 1(3): 381–410. https://doi.org/10.1111/j.1740-8784.2005.0020a.x
View Article
Google Scholar

[105] View Article

[106] Google Scholar

[ref41] 41. Kolstad I, Wiig A. What determines Chinese outward FDI. Journal of World Business. 2012; 47(1): 26–34. https://doi.org/10.1016/j.jwb.2010.10.017
View Article
Google Scholar

[108] View Article

[109] Google Scholar

[ref42] 42. Lin L, Wang D, Si Y F. The limits of guanxi for Chinese nationals doing business abroad: empirical investigation into Chinese companies in Germany. Tijdschrift voor economische en sociale geografie. 2018; 109(3): 420–433. https://doi.org/10.1111/tesg.12301
View Article
Google Scholar

[111] View Article

[112] Google Scholar

[ref43] 43. Yang M, Deng P. Cross‐Border M&As by Chinese companies in advanced countries: Antecedents and implications. Thunderbird International Business Review. 2017; 59(3): 263–280. https://doi.org/10.1002/tie.21767
View Article
Google Scholar

[114] View Article

[115] Google Scholar

[ref44] 44. Cheng S, Stough RR. Location decisions of Japanese new manufacturing plants in China: a discrete-choice analysis. The Annals of Regional Science. 2006; 40(2): 369–387. https://doi.org/10.1007/s00168-005-0052-4
View Article
Google Scholar

[117] View Article

[118] Google Scholar

[ref45] 45. Lee K D, Hwang S J, Lee M H. Agglomeration economies and location choice of Korean manufacturers within the United States. Applied Economics. 2011; 44(2): 189–200. https://doi.org/10.1080/00036846.2010.502109
View Article
Google Scholar

[120] View Article

[121] Google Scholar

[ref46] 46. Hilber C A, Voicu I. Agglomeration economies and the location of foreign direct investment: Empirical evidence from Romania. Regional studies. 2010; 44(3): 355–371. https://doi.org/10.1080/00343400902783230
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref47] 47. Boateng A, Qian W, Tianle Y. Cross‐border M&As by Chinese firms: An analysis of strategic motives and performance. Thunderbird international business review. 2008; 50(4): 259–270. https://doi.org/10.1002/tie.20203
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref48] 48. Rudy BC, Miller SR, Wang D. Revisiting FDI strategies and the flow of firm‐specific advantages: A focus on state‐owned enterprises. Global Strategy Journal. 2016; 6(1): 69–78. https://doi.org/10.1002/gsj.1106
View Article
Google Scholar

[129] View Article

[130] Google Scholar

Figures

Abstract

1. Introduction

2. Theoretical reviews

2.1 Specialized agglomeration or diversified agglomeration of local firms

2.2 Industry-specific or industry-diverse agglomeration of Chinese investors

3. Data and methodology

3.1 Data sources and the description of CODI in the United States

3.2 Methodology

3.3 Variables

3.3.1 Whether to invest as dependent variable.

3.3.2 Four patterns of agglomeration as independent variables.

3.3.3 Control variables.

4. Results

5. Conclusion and discussion

References