FDI to China: An investigation of
MNE’s location strategies at the city-level
Ganling Wu 10704094 29 June 2015
MSc Business Studies: International Management University of Amsterdam
Master Thesis
First supervisor: Dr. Niccolò Pisani
Statement of Originality
This document is written by Ganling Wu who declares to take full responsibility for the
contents of this document.I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
Extensive study has been conducted about the determinants of location choice and the inflow FDI. In the previous literatures, the determinants for inflow FDI location choice at country-, province- and firm-level are well researched but the drivers at city-level are relatively unexplored. This study examined the influence of market size, human capital and transportation infrastructure on inflow FDI formulation at city-level. Moreover, the moderating effects of investment of environmental protection on these relations are studied. Using 278 Chinese preferential-cities’ relevantly economic values across ten years, the findings indicate that larger market size and higher ratio of high education labors are positively related with inflow FDI. In the same level of GDP, the city with more environmental investment attracts more inflow FDI than city with less investment on environmental protection. In addition, higher ratio of qualified labor also has positive effect on inflow FDI formulation, but more environmental investment may deter inflow FDI in this relationship.
TABLE OF CONTENT
1.INTRODUCTION ... 4
2. LITERATURE REVIEW ... 7
2.1 Location theories of FDI ... 7
2.2 Determinants of inflow FDI at inter-country level ... 8
2.3 Determinants of inflow FDI at intra-country level in China ... 10
2.4 Determinants of inflow FDI at firm level ... 11
3. THEORETICAL FRAMEWORK ... 15
3.1 Market size of cities ... 16
3.2 Human capital in cities ... 16
3.3 Infrastructure of cities ... 17
3.4 Investment on environmental protection ... 18
4. METHODS ... 21
4.1 The sample and data collection ... 21
4.2 Measures ... 21
4.2.1 Dependent variable ... 21
4.2.2 Independent variables ... 22
4.2.3 Moderator ... 23
4.2.4 Control Variables ... 24
4.3 Statistical analysis and results ... 25
5. DISSCUSION ... 34
5.1 Academic relevance ... 34
5.2 Policy implication ... 37
5.3 Managerial implication ... 38
5.4 Limitations and suggestions for future research ... 38
6. CONCLUSION ... 40
ACKNOWLEDGEMENT ... 40
REFERENCES ... 42
APPENDICES ... 46
LIST OF FIGURES Figure 1.Conceptual model ... 19
Figure 2.Moderating effect of environmental investment on inflow FDI ... 28
LIST OF TABLES Table 1. Effect of country-level determinants on FDI location choices ... 9
Table 2.Effects of intra-country level determinants on FDI location choices ... 11
Table 3. Descriptive statistics; means; standard deviation and correlations ... 26
Table 4. Results of OLS regression ... 31
1. INTRODUCTION
In latest World Investment Report (UNCTAD, 2014), the inflow of Foreign Direct Investment (FDI) to developing countries represents 54 percent of global inflows, reaching $778 billion. Within these developing economies, as the second largest recipient of inflow FDI in the world, China plays an important role (Zhao, Chan & Chan, 2012). This achievement should owe to the open-door policy in 1978 and its intention to transform socialism into marketization in early 1990s (Zhao et al., 2012). Especially the preferential treatment to foreign investment in special economic zones has accelerated the boom of China’s FDI inflow (Coughlin & Segev, 2000). Consequently, inflow FDI remarkably contributes to Chinese economic evolution since it has not only transferred capital and technology but also accelerated China’s integration into the global economy (Zhao et al., 2012).
Interest in determinants of inflow FDI in China has grown rapidly, including inter-country level, intra-country level and firm level. According to academic and empirical studies on location choice, crucial variables that are shown to determine the location choice of foreign investors for FDI into China are market size (Ang, 2008; Asiedu, 2006; Broadman & Sun, 1997; Chen & Kwan, 2000), labor factors (Groh & Wich, 2012; Jiménez, 2011; Mukim & Nunnenkamp, 2012; Sun, Tong & Yu, 2002), quality of infrastructure (Ang, 2008; Asiedu, 2006; Fung, Lizaka & Parker, 2002), agglomeration (Blanc-Brude, Cookson, Piesse & Strange, 2014), preferential government policies (Fung et al., 2002; Sethi, Judge & Sun, 2011) and geographic proximity (Coughlin & Segev, 2000). Besides, many studies identified firms’ concerns about location choices, such as information cost, agglomeration economies and heterogeneities (Belderbos & Carree, 2002; He, 2002; Rasciute, Pentecost & Ferrett, 2014). Thus, studies about FDI location determinants at city-level are still absent in this field.
identified at country-, province- and firm-level are also applicable for inflow FDI distribution different Chinese cities. The major contributions of this paper are: (1) it integrates main determinants and moderations into one framework;(2) it shifts the emphasis from broad-based country-level and province-level analyses to more specific city-level analyses of FDI inflows; (3) it uses a panel data set of Chinese 239 cities from 2005 to 2014, which makes the analysis more comprehensive and up-to-date. This study also has important policy implications for government policy makers who want to attract more inward FDI to their prefecture-cities and provides practical insights for foreign investors who want to seek more investment opportunities in China.
The paper is structured as follows. In next section, previous literatures about general motives of inward FDI, determinants of inflow FDI in inter-country level, province level and firm level will be discussed. Subsequently, a framework with development of hypotheses is stated in this part. Third, the details of dataset will be provided. Based on the dataset, the dependent, independent, moderating variables and method are defined to test the relationship between the identified factors and effects to FDI inflow in specific cities. This is followed by the results and a discussion of the findings that includes academic and managerial implications and limitations. The final section provides suggestions for future researches and also concludes the importance of prefectural location decisions of FDI firms.
2. LITERATURE REVIEW 2.1 Location theories of FDI
Foreign Direct Investment (FDI) is a concept that has been put forward by Hymer (1976) to explain why Multinational Enterprises (MNEs) are engaging in international operation. Compared with country level portfolio investment, FDI is a presence of firm-level control (Rugman, Verbeke & Nguyen, 2011). There are two reasons associated with the existence of FDI : First, foreign enterprises must own some assets to offset disadvantages of competing with local companies and make sure full returns of its capabilities; second, the market for offsetting disadvantages must be imperfect (Hymer, 1976). That is, as Rugman et al. (2011: 759) argue, “for firms to own and control value-adding activities, they must possess some kind of monopolistic advantages sufficient to outweigh the liability of foreignness (LOF)”. It seems that MNEs have preference on some locations than others and this phenomenon has attracted interest of academic scholars within the international business field. Dunning (1998) concludes that MNEs’ investments are primarily driven by four motivations: resource seeking, market seeking, efficiency seeking and strategic asset seeking. First, resource seeking includes not only natural resources, but also infrastructure and government restrictions. Second, market seeking refers to the condition of labor, related companies, infrastructure and institutional environment in large and developing domestic markets, and regional markets. Third, efficiency seeking emphasizes related production cost, agglomerative economies, removing barriers by government and specialized spatial cluster. The fourth important incentive for MNEs is strategic assets seeking, which is a demand of experience, knowledge- intensive resources and partnership with a foreign firm. Over time, the features of cross-border activities shifted from access of natural resources and market to gaining knowledge-intensive assets, learning experience and enjoying benefit of agglomeration (Dunning, 1998). In conclusion, economic environment, institutional framework together with
host country’s physical and manpower resources play important roles in FDI location decision of MNEs (Dunning, 1998).
Apart from the four motives of FDI, Dunning and Lundan (2008) classify determinants of FDI into three categories which are based on locational segments of OLI—Ownership advantage, Location advantage and Internalization advantage (Dunning, 1998). They argue that there are three categories of factors that can affect choice of FDI location: “endowment effects” which refers to strong resource seeking — natural and human resource; “agglomeration effects” indicates the attractiveness of one firm’s location choice to other companies; Last but not least, “policy-induced effects ” emphasize the preference of specific regions which are caused by policy intervention, for example, Special Economic Zones in China.
Differing from Dunning and Lundan’s opinion, Mucchielli (1998, 2008 in Mucchielli & Yu, 2011) divides determinants for the location choice made by MNEs into four types (Mucchielli, 1998, 2008, in Mucchielli & Yu, 2011):
1. Demand seeking: whether the target market has potential demand for MNEs’ products or services;
2. Costs and efficiency seeking: host country’s production cost in terms of labor, transportation and so on;
3. Strategy seeking: same as agglomeration effects, in other words, the amount of MNEs from same home country and related local companies in specific location;
4. Policy seeking: political effects generated by priorities or incentives offered by subnational or national governments in host country.
2.2 Determinants of inflow FDI at inter-country level
level can be divided into two categories: studies focused on developed countries versus developing countries (Lei & Chen, 2011). Over time, the researches’ focus of inward FDI determinants transferred from developed regions to emerging economies. (Asiedu, 2006; Ang, 2008; Quazi, 2007; Pajunen, 2008; Choong & Lam, 2010; Khadaroo & Seetanah, 2010; Groh & Wich, 2012).
Several determinants of inflow FDI have been identified in previous research: real gross domestic product (GDP) per capital (Jaspersen, Aylward, & Knox, 2000; Asiedu, 2006; Quazi, 2007; Ang, 2008), infrastructure quality (Asiedu, 2006; Quazi, 2007; Ang, 2008; Khadaroo & Seetanah, 2010), labor cost (Wheeler & Mody, 1992; Schneider & Frey, 1985; Loree & Guisinger, 1995), openness (Wheeler & Mody, 1992; Hausmann & Fernandez-Arias, 2000; Ang, 2008), taxes and tariffs (Lipsey, 1999; Ang, 2008). What is worth noting, new drivers such as corruption (Jiménez, 2011), human capital (Jiménez, 2011;Mukim & Nunnenkamp, 2012; Groh & Wich, 2012), institution factors (Mukim & Nunnenkamp, 2012; Groh & Wich, 2012) attract attention from researchers in recent years. Nevertheless, there is no consensus about the effects of these variables to attract or deter inward FDI. Building on Asiedu (2002)’s research, Table 1 presents extant research on location choices.
2.3 Determinants of inflow FDI at intra-country level in China
Although the comprehensive analyses are presented at country-level, they can’t fully address the location choices within country, especially for the large number and complexity of Chinese provinces and cities (Sethi et al., 2011). After MNEs choose emerging countries to invest, they will face tough choices about where to invest within these countries. In China for example, most of the researchers argue that there is an unevenly geographical distribution of inflow FDI within China——inflow FDI is highly concentrated in coastal regions and less in inland (Broadman & Sun, 1997;Wei, Liu, Parker & Vaidya, 1999). Thus, figuring out the explanations of the variations draws attention of academic field.
Examples include Wei et al. (1999), who use panel data covering 27 provinces in China. Result shows that high degree of international trade, R&D manpower, market size, preferential investment policy, improvement infrastructure and advances in agglomeration have add the attractiveness to the location, While high effective wage rates and high information costs will have deterring effects .
As presented in table 2, location-specific determinants such as market size, human capital, wage, geographical proximity, quality of infrastructure, preferential policy, international trade and agglomeration are highlighted in main stream of studies in terms of Chinese province-level location choice (Broadman & Sun, 1997; Chen & Kwan, 2000;Coughlin & Segev, 2000; Fung et al., 2002; Zhao et al., 2012;Sethi et al., 2011;Sun et al., 2002;Wei et al., 1999). Large market size means the rich customer base in local market, which provides a greater potential demand for MNEs’ products (Broadman & Sun, 1997; Chen & Kwan, 2000). Furthermore, quality of human capital and labor cost are important as they are keys to productivity and profit (Blanc-Brude et al., 2014). There are many possible benefits of geographical proximity: coastal cities with high quality of infrastructure, political incentives from local governments, high degree of international trade as well as historical and cultural link with investors are all contributing to attractiveness of inflow FDI. What’s more, industry agglomeration arouses advantages: related industry and service support, well-trained employees, high standard infrastructure, lower production and information cost. In addition, recruiting local managers who are familiar with institutional environment can help investors reduce LOF (Blanc-Brude et al., 2014).
2.4 Determinants of inflow FDI at firm level
As the subject of inflow FDI activities, multinationals play important roles on
inflow FDI are more relevant to firms’ interests, so this level also needs fully consideration. In previous literatures, the determinants of firms’ behavioral decisions contain not only classical country-level factors such as regional policies (Crozeta, Mayerb & Mucchiellia, 2004; Kang &Lee, 2007), tax (Cummins & Hubbard, 1994; Devereux & Griffith,1998) and market size (Blomstrom & Lipsey , 1993; Head & Mayer, 2004; Kang &Lee, 2007), but also firm-level motives such as information cost, agglomeration economies and heterogeneities (Belderbos & Carree, 2002; He, 2002; Rasciute, Pentecost & Ferrett, 2014).
When foreign investors enter a new host country, they will encounter information asymmetry (He, 2002). Compared with public information that can be easily accessed from media and internet, private-held information such as the practical implementation of foreign investment policies, strategies for selecting partners and the functioning of labor markets are more crucial for their decisions (He, 2002). An important way to get this kind of information is through spillover, thus, investors would choose to locate near to other firms within same industry to reduce cost generating from collecting information (Devereux, Griffith & Simpson 2007).
The purpose of agglomeration effects is similar with information cost, but advantages of agglomeration effects are not only including informational aspect. Through locating in same industrial cluster, multinationals enjoy the spillover of technology and well-trained labor, good quality of infrastructure and specialized service suppliers. Therefore, agglomeration effects play positive positions in firms’ behavioral decisions (Belderbos & Carree, 2002; Crozeta, Mayerb & Mucchiellia, 2004; Devereux, Griffith & Simpson, 2007; He, 2002; Wheeler & Mody 1992).
Some researchers use heterogeneity referring to that enterprises respond diversely to location choices with regard to their own situations (Belderbos & Carree, 2002). For example, differences in locational choices between small and medium-sized enterprises (SMEs) and
larger firms. Belderbos and Carree (2002) use data from Japanese plants to illustrate that SMEs are strongly associated with agglomeration compared with larger firms, because of higher pressure about cost of travel, transport and information collecting, smaller bargaining power with local government and greater influence of the risk of failure in foreign investment. By locating close to other firms in the same industry, SMEs enjoy greater benefits of agglomeration externalities (Head et al., 1995). What’s more, compared with MNEs of nontraditional industries, enterprises in traditional sectors are associated more with high labor cost but less with unemployment rate in host country (Rasciute, Pentecost & Ferrett, 2014). Moreover, more profitable firms would choose location with larger distance than less profitable firms (Rasciute, Pentecost & Ferrett, 2014).
To sum up, previous research exhibits a comprehensive analysis about determinants of inflow FDI with regard to country-level, province-level and firm-level, however, the motives at city-level are relatively unexplored. Because cities in China are now having a dramatic change, especially the second tier cities and third tier cities are getting more important. So those limited researched may not provide perfect foresight. Ma, et al. (2013) conduct a city-level research which only based on data of Beijing and Shanghai. Although the study of Blanc-Brude et al (2014) uses dataset of 224 prefecture-cities in China, to some extend the time period from 2004 to 2007 is out-of-date. There is, therefore, a need for conceptual integration of all factors that impact inflow FDI at the city-level.
3. THEORETICAL FRAMEWORK
With the increasing of cross-country investment activities, the location choices within countries become critical issues for MNEs. According to prior literature, the purpose of MNEs to allocate their investment in other countries is to maximize their risk-adjusted profit (Caves, 1996). The profit of inflow FDI mainly derives from three categories of factors: (1) internal factors of firms that enhance its competitiveness in both home and host countries, such as core technology and management expertise; (2) external factors in host country that provide a superior environment to attract inflow FDI, such as tax incentives and low labor cost; (3) factors related to enterprises’ choice between inflow FDI and exporting or licensing, such as transaction costs (Zhang, 2001). Dunning (1998) uses OLI (paradigm of eclectic theory) referring these three categories of factors to three groups of motives for an enterprise to go multinational, namely Ownership, Location and Internalization. Location advantages are the primary concern of MNEs when they decide to invest abroad (Zhang, 2001). Classical location theory focuses on determinants such as GDP, human capital and infrastructure in terms of distribution of inward FDI (Asiedu, 2006; Coughlin & Segev, 2000; Wei, Liu, Parker & Vaidya, 1999). More recently, scholars have emphasized the importance of political incentives and agglomeration (Groh & Wich, 2012; Pan & Xia, 2014; Zhao, Chan & Chan, 2012).
As the literature review of drivers of inward FDI in terms of country-level, province-level and firm-level in this study, there are many approaches to analyze variables which affect MNEs’ location choices. Based on results of these studies, the purpose of this paper is to test whether determinants that are identified in other levels are also applicable in city level. More specifically, the relationship between the degree of FDI inflow and city characteristics as market size, transportation, human capital will be examined. The moderating factor in this study is investment on environmental protection of local
governments.
3.1 Market size of cities
Size of a city’s economy can be measured by its Gross Domestic Product (GDP). It’s obvious that the market demand has positive relationship with inward FDI since it has influence on expected revenue of investment programs (Zhang, 2001). Indeed, market seeking is main reason of inward FDI (Coughlin & Segev, 2000). If other factors remain constant, the larger the market size of a specific city is, the more attractive the city to inward FDI.Based on cross-country datasets,Broadman & Sun (1997) exhibit statistic evidence that market size has highest significance among all explanatory variables they tested. Moreover, Wei et al. (1999) find province’s GDP has positive and compelling impact on inflow FDI through a panel data of Chinese 27 provinces.
Due to the presented advantages of large market demand at country- and province-level, I assumed that there is also positive effect of market size on FDI inflow at city-level. Support from research of Blanc-Brude et al. (2014) presents that positive effect of economic growth on inward FDI in Chinese 224 cities between 2004 and 2007. Having larger market size in specific location attracts more market-oriented inward FDI, because the high expected revenue in inward FDI destination. I then hypothesize:
Hypothesis1: GDP is positively related to inward FDI at the city-level.
3.2 Human capital in cities
Another indicator that should be taken into consideration when discussing city-level development is its human capital development. The FDI inflow, especially for capital and technology intensive investments, is engaging in high proportion of skilled labor. Qualified
labor means more productive workforce and quick adaptation to updated technology. It has been proved by many studies that locations are more attractive when a high level of human capital is possessed. For example, based on Chinese province-level data, Lei et al. (2012) argue that a high degree of human capital stock is a decisive motive of inward FDI. What’s more, senior high school and above education shows the best performance among all education levels (Chen & Kwan, 2000). Thus, this study assumes that labor quality is also a significant determinant for country-level or province-level to attract FDI, same result can also be displayed in city-level.
Qualified labor is one of the essential factors that investors consider, especially for companies with capital-intensive in production and skilled labor-oriented (Zhang, 2001). High qualities talents contribute to production increasing with advanced technology and then raise output, which enable competitiveness of firms in local markets (Zhang, 2001). Many studies argue that a region with more skilled workforce attracts more FDI inflow than place with less advanced talents (Coughlin & Segev, 2000; Mukim & Nunnenkamp, 2012). In this case, the following hypothesis is:
Hypothesis 2: Availability of talent is positively related to inward FDI at the city-level
3.3 Infrastructure of cities
There is no doubt that the degree of infrastructure affects investors’ location choice. Infrastructure includes many elements: the length of railroad, highways and tele- communication system (Broadman & Sun, 1997). Khadaroo and Seetanah (2010) point out that well-developed region with better transportation facilities encourage FDI inflows when other drivers of inward FDI were controlled. High standard infrastructures, especially transportation facilities, ensure easy access to both raw materials of MNEs outputs and target
customers. For manufacturing industry, it accelerates the turnover of products and reduces cost of production. However, contrast with results of Broadman & Sun (1997) and Khadaroo & Seetanah (2010), there is no significant effect of infrastructure on inward FDI in Coughlin & Segev (2000)’s study. In spite of the minority opposition, most of inward FDI destination researches are support the importance of transportation facilities in attracting inward FDI (Zhang, 2001). Thus, transportation facilitation should be one of the important factors for investors’ location decision.
A reason for MNEs to invest in well-developed cities is good quality of infrastructure, especially convenient transportation (Khadaroo & Seetanah, 2010). It facilitates commodity circulation and economic growth, which have positive impact on FDI inflow. Hence, I hypothesize:
Hypothesis 3: Quality of infrastructure is positively related to inward FDI at the city-level
3.4 Investment on environmental protection
The amount of environmental protection measures the degree of local environmental regulation (Leiter, Parolini & Winner, 2011). As an important indicator of local policies, environmental expenditures play double roles in deciding making of foreign investors. Zeng & Eastin (2012) find that more environmental investment shows local government think highly of environmental protection. As a result of policies preference, investors will earn more profit through commitments of environmental protection. In turn, it becomes advantages of these locations to attract inward FDI. Another opinion illustrates the pushing effects of regulation on efficient resource consumption and innovative technologies. As the positive reaction to environment regulation, companies foster comparative advantage from pursuing
higher efficiency of resource consumption and better quality with less pollution (Leiter et al., 2011).
However, opposition argues that tight regulation about pollution retards inward FDI. From economic perspectives, cost and profit drive firms’ behaviors. When low cost seekers encounter expensive environmental regulation, it needs careful consideration for them to enter such host countries (Leiter et al., 2011). Garofalo & Malhotra (1995) utilize data from manufacturing industry in 34 American companies between 1983 and 1989. They show a negative influence of environmental protection investment on capital formation from aboard. Greenstone (2002) supports this view that rigid regulations on pollution hinder inflow investments.
Taking both benefit and cost into consideration, high expenditures on environmental protection attract more inward FDI. Because sustainable development becomes global concern, firms with purely low cost and high pollution will be died out in historical river. Environmental regulation is beneficial for both host countries and MNEs. MNEs enjoy technologies innovation from regulation, which enhance their competitiveness and profit making. In this case, high investment on environmental protection accelerates the speed of FDI inflow.
Hypothesis 4: Investment on environmental protection positively moderates the relationships as hypothesized in H1, H2 and H3.
This set of hypotheses predicts factors in Chinese cities that attract FDI inflows and moderator that will elaborate these relationships. The following empirical model (Figure 1) integrates the combined impact of the factors (market size, human capital, transportation) and moderator (investment on environmental protection) on FDI inflows.
4. METHODS 4.1 The sample and data collection
This study uses a panel data analysis to examine the influence of GDP, human capital and infrastructure on inward FDI in Chinese city-level including one moderator, which is investment on environmental protection. The sample is based on 278 prefecture-level cities.
China is an emerging country with diverse geographic areas in terms of economic activity, infrastructure and political status. There are thirty four provincial-level administrative units in China: 22 provinces, 5 autonomous regions, 4 large municipalities and 3 special regions (Blanc-Brude, Cookson, Piesse & Strange, 2014). In this paper, I excluded the data of Hong Kong, Macau, Taiwan and Tibet because of their special circumstances and used the data of 278 prefecture-level cities under the remaining thirty provinces (see
appendices). The location distribution between provinces can’t provide a comprehensive
guidance about investment, due to the distinction within province. So I chose these 278 prefecture-level cities as the sample of this study. As the second level unit ranking below province, a prefecture-level city is consisted with an urban center and surrounding rural areas (Blanc-Brude, Cookson, Piesse & Strange, 2014). Differences in cities in terms of economic activity, political autonomy and engagement of inward FDI allow us to predict that the degree of inflow investment is relevant to it.
The data are collected from Yearbooks in 278 local statistical bureaus. Inward FDI data for each of these 278 cities is available from 2004 to 2013, but in small cities only newest year’s data are available. The final dataset is a panel of ten years (2004-2010) for 278 cities.
4.2 Measures
In this study, the dependent variable is inflow foreign direct investment in Chinese 278 prefecture-level cities. To determine the level of cities’ attractions for inward FDI the amount of foreign investment actually absorbed is used. In order to get a more comprehensive and up-to-date analysis, I took yearly FDI inflow to each city from 2004 to 2013 in US dollars.
4.2.2 Independent variables
There are three independent variables used in this study, and each variable has separate influence on inflow FDI. First variable is market size, which is measured by gross domestic product (GDP) of each city. The significant effect of market size on inflow FDI location decision-making is emphasized by many empirical studies (Sun et al., 2002; Zhang, 2001). Because this variable is viewed as a factor that attracts potential investors, the impact is supposed to be positive. This study uses aggregate data across sectors, because economic aggregate rather than output of single sector can represent the level of economic development and potential demand of investors’ output. What’s more, in order to get a more comprehensive result, this paper doesn’t select cities with particular size to represent markets for inflow FDI in China.
Human capital is second independent variable in the thesis. I used the ratio of advanced education enrollment student number in registered population to represent the level of human capital in each city that has been used byFung et al. (2002). Compared with labor cost, labor quality is more desirable for high technology industries. People who received tertiary education are more likely to be productive and qualified workforce when there are new technology and higher requirement than people who had only secondary education (Asiedu, 2002). Thus, skilled labor should be a crucial factor in inflow FDI consideration.
However, there are also some researchers that use illiteracy rates in regions to measure availability of human capital (Broadman & Sun, 1997; Coughlin & Segev, 2000). People who
are not illiterate may range from basic education receiver to tertiary education students. Not all levels of labors who received education are qualified to support technology- and capital intensive industry. Therefore, I used ratio of advanced education enrollment student number in registered population to represent the level of human capital and expected this variable to be positively associated to the level of FDI inflows to a city.
The third variable used in this study is infrastructure quality of city. In terms of infrastructure, there are many indicators contained in this concept, such as length of highways and railroads, telecommunication systems and industry-related services. It is so challenging to include all these factors in one variable, because of the unavailability of data collecting (Broadman & Sun, 1997). In this study, I used two indicators, freight transportation volume and passenger departures, to test the capability of a city’s transportation volume. High turnover of freight and passenger indicate the great capacity of transportation infrastructure in a city. It is expected to be positively related to FDI inflow. The importance of this variable should be higher for firms in traditional sectors than in nontraditional industries, because traditional sectors’ businesses depend on superior transportation to connect with suppliers and distributors.
4.2.3 Moderator
The environmental regulation is expected to play the moderator’s role in the three pairs of relationship stated above. It is defined by the yearly investment on environmental protection, because cost on environmental protection can be viewed as first indicator of environmental regulation (Leiter, Parolini & Winner, 2011). If local governments spend relatively high number of investments on environmental protection, it shows more strict restriction imposed on local firms. For MNEs in technology- and capital intensive industries, it would be an advantage to locate in such destinations. Since the pressure of existence under
such regulatory environment, MNs have to improve their technology through innovation to meet higher standards. On one hand, such environmental commitments gain preference and supports from local governments, which are beneficial for development of MNEs in China. On the other hand, self-improvements in technology enable MNEs’ competitiveness to compete with both local companies and aboard competitors. Finally, MNEs with low cost and high pollution will be eliminated from Chinese market since the trend of sustainability and sense of environmental protection.
4.2.4 Control Variables
The first control variable is city size, which is a common control variable and is associated with economic index. Specifically, city size is not a geographic concept in this study, but an economic notion. To determine the size of a city the year-end registered population is used. Larger population in city means more potential consumption, which is viewed as growing market by investors (Mukim& Nunnenkamp, 2012). What’s more, cities with large population have better educational facilities and resources to support high portion of skilled labor, which also affects location choices (Choong & Lam 2010). Last but not least, transportation infrastructures for serving large population must be well-developed to support huge passenger and freight turnover.
Industries are always used as control variable in many empirical researches (Rasciute et al., 2014). Generally, there are three categories of sectors. The primary sector is mainly focus on work with natural resources. The secondary sector is production and manufacturing oriented, while firms in the tertiary sector are mainly focused on service. The main sector the city belongs is crucial for inflow FDI invest programs. This calls for a fit between cities and investment programs. Based on the source of industry, it can be divided into manufacturing -based industries (eg. primary sector and secondary sector) and technology- or
information-based industries (e g. tertiary sector), (Rasciute et al., 2014). The former refers to traditional industries producing tangible products, such as machine, consumer goods and durable goods. For those industries, cost of labor, facilitation of transportation and productivity are key factors to success. The latter represents nontraditional industries which provide information service and high technology. Workforces who are qualified to do these jobs through training are essential for technology- or information-based firms. Thus, the proportion of each sector on total GDP is measured to represent the control effect of each sector.
The third control variable is city’s political status, which is supported by Pan and Xia (2014). Cities with special political status, such as capital in province, municipalities and special economic zones, enjoy more political preference than other cities. Tax reduction, as one of the preferential policies, becomes an important driver for MNEs to invest. Besides the political advantage, this kind of cities are always advanced in terms of economy, education and infrastructure, which are also factors considered by investors. Therefore, I used 1 to represent cities with special status and 0 otherwise.
In addition to political status, location of cities should also be considered. In most cases, the small cities near coastline take more advantage of their locations than inland cities which are capitals of provinces. Due to the geographic advantage, coastal cities have more transportation advantages and cultural links with foreign companies (Sethi, Judge & Sun, 2011). In this case, I measured cities belong to coastal province with 1 and others with 0.
4.3 Statistical analysis and results
Table 3 illustrates the descriptive statistics of the dependent, independent, and control variables that used in this thesis. To assess the correlations between all the predictor variables the multicollinearity is used to test all variables. Multicollinearity can exists when there are
very high levels of correlation (Field, 2009). In these predictor variables, none of them correlate very highly (above 0.7). However, even checking correlation for multicollinearity as the first step, there are still some subtle forms of multicollinearity exist. In this case, the variance inflation factors (VIF) and the tolerance levels are calculated. The tolerance degree means the level of variability of a certain independent variable is not explained by the other independent variables (Pallant, 2011). The unaccepted value of VIF is 10 and tolerance level is below 0.2 (Field, 2009). Thus, most of the VIF values are around 1.5 and tolerance levels are almost between 0.2 and 0.8 in this table indicate multicollinearity is not a problem in this thesis.
The descriptive statistics show that the average city in the Chinese prefecture -level city list has absorbed inflow FDI of 221.331(100 million USD) and GDP of 1246.715 (100 million USD) per year. When looking at the descriptive values concerning the human capital it shows that 2.7 percent of population received high levels of education. The variables for infrastructure show that around one hundred million tons of goods and passengers are transported through the transportation infrastructures in each city.
The control variables include population size, sector, political status of specific city and location distribution. It is not surprising that the average population is around 8.8 ten million because of huge population volume in China. The dominating sector that contributes to GDP is the secondary industry, while the tertiary sector is also growing in recent years, which accounts 40 percent of total GDP. Cities with special political status occupied 13 percent in 278 prefecture -level cities, which is only 36 cities. Furthermore, the numbers of cities which enjoy location advantages are about 111 cities (40 percent).
To test hypotheses a multiple linear regression and a hierarchical multiple regression were used. For the relationships between dependent and independent variables, I chose the Ordinary Least Squares (OLS) model, which is most suitable for variables with continuous values. Hierarchical multiple regression is used for test the effect of moderators. After standardizing the independent and moderating variables, the interaction terms between them are tested. There are two steps in regression analysis process. In the first model, I introduced the control variables to analyze their impacts on dependent variable. In order to observe the explanatory power of each independent variable, I added one explanatory variable and the interaction term at a time in model 2-5. Finally, I run totally ten models. Model 6 integrated all three independent variables into one model. Model 7-10 added moderator in each relationship of hypothesis 1- 3 to test the impact of moderator. The results of regression analysis are listed in table 4 and 5. In these tables, there are three values that need to be noticed. The Beta standardized coefficient indicates the amount the dependent variable will change by if the independent variable changes by one unit. The value of significance less than .05 means the model is significant. Finally, the R² means the goodness of fit of the model. The larger the value of the R²,the larger the proportion of variance of the dependent variables that is explained by the model.
Hypothesis 1 is tested for the impact of total GDP value on inflow FDI in cities. In table 4, the coefficient of total GDP value is significant (b=.648, p=.000) and positively associated with inflow FDI as predicted. Therefore, hypothesis 1 is strongly supported. Moreover, R² increases from .230 to .307 when adding the explanatory variable, which indicates that this model fits the data better. Hypothesis 2 stated that human capital of a city is related to its inflow FDI and I used model 3 to analyze this pair of relationship. The result (b=.375, p=.000) also shows positive effect of high educational ratio on absorbed FDI. Thus, it can be concluded that this evidence support hypothesis 2.
There are two operations to test independent variable in hypothesis 3, which presents that transportation infrastructure has positive influence on absorbed FDI. Two factors of freight transportation volume and passenger departures are tested respectively in model 4 and model 5. The impact of freight transportation volume is insignificant with the result (b=.071, p=.224). However, another operation indicates that the volume of passenger departures (b=.427, p=.006) is positively related with absorbed FDI. In conclude, hypothesis 3 is partially supported.
A hierarchical multiple regression is introduced to examine whether the three pairs of relationship above are affected by the ratio of environmental investment in GDP. After gathering the variables that measure GDP, human capital, infrastructure and environmental investment, the interaction terms were calculated. One moderator, control variables and the interaction variables were entered in to a simultaneous regression model.
The results are illustrated in Table 5, Model 7 suggests that both GDP value (b=.005, p=.000) and environmental investment ratio (b=.206, p=.000) are associated with FDI inflow. What’s more, the interaction between total GDP value and environmental investment ratio is also significant (b=.260, p=.000), illustrating that the relationship between total GDP value and absorbed FDI is positively moderated by environmental investment.
Model 8 in Table 5 indicates the moderated result of hypothesis 2 considering a city’s environmental investment ratio. Both high educational ratio in city (b=77.623, p=0.024) and environmental investment ratio (b=.171, p=.006) are related with a positive influence on absorbed FDI. However, the interaction between is significant but negative (b=-2.28, p=.024). That means environmental investment ratio affect the relationship between human capital and FDI inflow indeed, but in a negative way.
Models 9 and model 10 show the results concerning hypothesis 4. It measured the moderation effects of environmental investment ratio on relationship between transportation infrastructure and absorbed FDI. Model 9 displays the outcome that the environmental investment ratio (b=.192, p=.000) is associated with higher level of absorbed FDI. But the direct relationship between freight transportation volume and inflow FDI is not significant (b=.000, p=.182). Moreover, the interaction between freight transportation volume and environmental investment ratio is also meaningless (b=.000, p=.000). The similar result appears in Model 10 —environmental investment ratio (b=.170, p=.000) and passenger numbers(b=.000, p=.034) are positively associated with inflow FDI, while the interaction between them is insignificant (b=.270, p=.780). The outcome indicates that the relationship between passenger departures and inflow FDI is not moderated by local environmental investment. Therefore, integrated conclusion of model 7-10 is that hypothesis 4 is partially supported.
With regard to control variables, powerful support is found in population size (b=.194, p=.000). This outcome indicates that the demographic scale of city has an influence on degree of attraction for inflow FDI. The cities’ industry characteristics also have significant impact, especially for secondary sector (b=.175, p=.006). Therefore, cities with dominating industries in this group enjoy more inward FDI. Furthermore, political status and locations of cities are also crucial in Chinese background, with significance at .001. Thus, cities of provincial
capital, special economic zone and with coastal location attract more preference from investors abroad.
5. DISSCUSION
Previous studies investigated determinants of FDI inflows in China at different levels
except for city-level. They identified drivers such as gross provincial products, infrastructure quality and labor quality (Blanc-Brude et al., 2014; Coughlin & Segev, 2000). This study focuses on Chinese city-level factors that are considered by foreign investors. It assumes that market size, human capital and transportation infrastructure play important roles in city’s inflow FDI amount. However, the result shows that only the market size and labor quality have significant effect on inward FDI while the infrastructure quality has not. More specifically, higher GDP and more qualified labor in destination attract more inflow FDI in China. Compared with market size and human capital, transportation infrastructure doesn’t play a crucial role in investors’ consideration. The analysis of environmental investment and its moderating effect on three main relationships draws attention in this research. It shows that the city with greater environmental investment attracts more inflow FDI when the GDP stays constant. But environmental investment presents a negative moderating effect on the relationship of human capital and inflow FDI. Moreover, when environmental investment is used in the relationship between transportation infrastructure and inflow FDI no moderating effect appears.
In the following parts, the academic relevance, the managerial implication and limitations and suggestions for future research are discussed.
5.1 Academic relevance
This study contributes to the existing literatures by deepening the level of inflow FDI location choice in China. It tested if the determinants that are identified in country-level and province-level are also applicable in city-level by using the data of 278 prefecture-cities in China. More specifically, the relationships between market size, human capital, transportation
infrastructure and inflow FDI are measured. It fills the gap that few of inflow FDI location choice researches were concentrated in city level of China.
As expected, the results obtained show that market size has a positive and significant effect on attracting inflow FDI, which is consistent with most of previous studies.Broadman & Sun (1997) exhibit statistic evidence that market size has highest significance among all explanatory variables they tested. Moreover, Wei et al. (1999) find province’ GDP has positive and compelling impact on inflow FDI through a panel data of Chinese 27 provinces. The market demand has positive relationship with inflow FDI since it has influence on expected revenue of investment programs. In this way, market seeking is viewed as one of the most important reasons of inflow FDI. If other factors remind constant, the larger the market size of a specific city is, the more attractive the city to inflow FDI.
Existing literature indicates that high degree of human capital stock is a decisive motive of inflow FDI and senior high school and above education shows the best performance (Chen & Kwan, 2000; Lei et al., 2012). My finding is supporting those studies. Qualified labor means more productive workforce and quick adaptation to updated technology. High qualities talents contribute to production increasing with advanced technology and then raise output, which enable competitiveness of firms in local markets. Thus, the FDI inflow, especially for capital and technology intensive investments, is engaging in cities with high proportion of skilled labor.
Surprisingly, the factor of transportation infrastructure doesn’t show significant impact on inflow FDI as predicted in other literatures. As pros illustrated in previous researches, well-developed region with better transportation facilities encourage FDI inflows when other drivers of inflow FDI were controlled. High standard infrastructures, especially transportation facilities, ensure easy access to both raw materials of MNEs outputs and target customers. (Khadaroo & Seetanah, 2010). However, the outcome of this study is consistent with
Coughlin & Segev’s (2000) view that there is no significant effect of infrastructure on inflow FDI.
What’s worth noting is that the moderating effects of environmental investment on three relationships listed above generate three different consequences. The first result shows the city with higher investment on environmental protection attracts more inflow FDI than city with lower investment in the same economic level. This is not surprising since Leiter et al. (2011) find that companies foster comparative advantage from pursuing higher efficiency of resource consumption and better quality with less pollution as the positive reaction to environment regulation. These advantages will contribute to economic growth and thus attract more inward FDI.
What’s more, there is a negative moderation effect of environmental investment on the relationship between human capital and inflow FDI formulation. In cities with similar ratio of skilled labors, the city with higher environmental investment may have less inflow FDI. The explanation could be high environmental investment is an indicator of strict regulation, which means MNEs spend more expenditures with regard to regulatory cost and technology updating (Greenstone, 2002). If firms need to pay relatively higher cost for environment except the labor cost, they prefer the location with similar labor cost but lower environmental expenditures. In this case, the moderator environmental investment will hinder the formulation of inflow FDI and thus has negative impact on the relationship between labor quality and inflow FDI.
Another interesting finding obtained is that the environmental investment is not moderating the relationship between transportation infrastructure and inflow FDI. Based on the result of hypothesis 3, there is no significant effect of transportation infrastructure on inflow FDI. Therefore, the prerequisite of moderation effect is not established. Obviously, the environmental investment has no influence on this relationship.
5.2 Policy implication
The empirical results have important policy implications for economic development of individual cities in China. The differences between coastal and inland cities, large and small economic scale cities call for distinct policy incentives. Coastal cities enjoy the advantages such as geographic and economic link as well as commercial and industrial tradition with foreign companies. It enables relevant economic growth and educational resources which attract more inflow FDI. However, inland cities are less attractive to foreign investors because of small market size and relatively backward education system. The difference also appears in mainly political cities and small cities. Cities with special political status, such as capital of province and special economic zone, have priority from national policy in terms of tax privilege and fund support. Those conditions greatly contribute to the location choice of MNEs. In this case, small cities have few opportunities to gain attention of national government. Policy incentives from local government become a critical issue for the foreign investment gathering of small cities.
From government policy-makers’ perspective, in order to add attraction of local city for inflow FDI, making appropriate policy incentives is a favorable step. According to this study, economic output and educational level positively affect the amount of inflow FDI. Thus, improvements in stimulating economic growth and developing compulsory education seem to be important for continuing FDI inflows. The moderation effect of environmental investment also reminds local governments don’t underestimate the effect of environmental protection when they are pursuing high growth of GDP. This is the one of the problems demanding prompt solution in Chinese context. The consequences of developing economy without considering environment would be huge penalty and strict regulation to cure environmental problem, which deter the invest enthusiasm from investors. Although the effect the transportation infrastructure on inflow FDI is no significant in this study, I still think
policy-makers shouldn’t ignore the importance of infrastructure in developing economy and attracting inward FDI.
5.3 Managerial implication
Furthermore, investors should have a comprehensive evaluation of investment destination based on whole model but not on one factor. Market size is an important indicator that affects investors’ location decision, but it also means more competition and higher cost on land, labor and other expenses, such as Beijing and Shanghai. Thus, MNEs should choose a city with relatively lower level of economy but similar ratio of skilled labors. These cities like Wuxi and Chengdu provide high quality of workforce and lower R&D expenditure. In addition, management of MNEs should match firm-strategy to location advantages and also integrate distinct government incentives. More specifically, this study provides general suggestions for all MNEs who are going to enter Chinese market. The decision should base on actual situation of the firm. It will become more successful and profitable if MNEs have comprehensive evaluation about the destination.
5.4 Limitations and suggestions for future research
Limitations of this research need to be acknowledged. Due to restriction of data, the model just examined the explanation effect of three independent variables on inward FDI. In this case, this model ruled out the influence of other possible variables such as labor cost, resource and openness. So it can’t be viewed as a comprehensive model for inward FDI location decision. It also happened to moderator, because environment factor is considered as the unique moderator of those relationships. Besides environment factor, I believe other factors such as agglomeration and sector will have significant moderation effect on the model. Like what I mentioned, the limitation of data collection restricted the analysis of this study.
Therefore, future researches could extend the database and integrate more explanatory variables and moderators into one model.
Another limitation is also about data collection. The targeted research period of each city is 10 years (2004-2013). However, not all Yearbooks across 10 years in 278 local statistical bureaus are available in the websites. So in some small cities’ statistical bureaus websites, we can only find data of the latest four or five years. This reduced the sample size and, to some degree, the reliability of some results. Studies, which are also focus on inward FDI location choice in Chinese city-level in the nearly future, could collect more comprehensive data from various approaches to provide more reliable analyses for this topic.
With regard to the explanatory outcome of transportation infrastructure on inward FDI, there are still some improvements need to be done. One of the improvements is the choice of measurement items for infrastructure quality. In this study, freight transportation volume and passenger departures of cities are chosen to test the quality of transportation infrastructure. However, the outcome is not as expected to be positive or negative to inward FDI. The insignificance relationships remind us that maybe there are some inappropriateness in the items of measurement for infrastructure. The turnover of freight and passenger could just represent the convenience of the location, but not necessary to represent the quality of infrastructure. The suggestion for further researches is paying attention to this problem and choosing more relevant items about quality such as the length of road and investment on transportation infrastructure.
6. CONCLUSION
The purpose of this thesis was to explore the relationships between the determinants of location choice and the inflow FDI at the city-level. In the previous literatures, the determinants for FDI location choice at country-, province- and firm-level are well researched but the drivers at city-level are relatively unexplored. One of the identified determinants is market size. It said that market demand has positive relationship with FDI since it has influence on expected revenue of investment programs (Zhang, 2001). Researchers tested the effect of market size on FDI based on both country- and province-level dataset (Broadman & Sun, 1997; Wei et al., 1999). Other factors such as human capital and infrastructure also play crucial role on FDI attraction. Qualified labor means more productive workforce and quick adaptation to updated technology, thus it becomes one of the essential factors that investors consider in targeted country, especially for companies with capital-intensive in production and skilled labor-oriented (Lei et al., 2012). What’s more, high standard transportation facilities ensure easy access to both raw materials of MNEs outputs and target customers. Therefore, well-developed province with better transportation facilities encourages FDI inflows when other drivers of FDI were controlled (Khadaroo and Seetanah, 2010). However, the academic field didn’t address the gap that analyzes main determinants of FDI in Chinese city choice and consider the moderation effect of environmental factors.
Therefore, this study assessed the influence of market size, human capital and transportation infrastructure on inflow FDI formulation. It focuses on the conditions of cities because city is an important political unit in China. The panel dataset was constructed using 278 Chinese preferential-cities’ relevantly economic values across ten years.
The hypotheses were examined with a multiple regression analysis and it shows different results in these relationships. More specifically, the larger market size is positively associated with FDI attraction. In the same level of GDP, the city with more environmental investment
attracts more inflow FDI than city with less investment on environmental protection. In addition, higher ratio of qualified labor also has positive effect on FDI formulation, but more environmental investment may deter inflow FDI in this relationship. To be surprising, transportation infrastructure is the only variable that has insignificant effect on FDI.
The findings of the present study have both academic, policy and managerial implications. First, it provides evidences that determinants that are identified at other levels are also applicable at city level. Although the outcomes show that only the relationship between infrastructure and FDI is not significant, further researches can address this question by choosing better measurement items. Second, environmental investment plays an important moderator role in relationships above and it can generate different effect on them. Third, policy-makers should take the location and political status of the city into consideration, and make appropriate incentives in terms of GDP growth and skilled labor development. Fourth, when they are using research model, MNEs managements need to have a comprehensive evaluation about all conditions of FDI destination. Moreover, matching firm-strategy to location advantages and also integrating different government incentives are also necessary for investors’ decision making.
ACKNOWLEDGEMENT
I gratefully acknowledge the valuable comments, feedback and guidance of my thesis supervisor Dr. Niccolò Pisani, Assistant Professor of International Management at the University of Amsterdam.
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