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国际贸易硕士论文 企业生产力与对外直接投资 一基于中国工业企业的实证研究

发布时间:2020-02-06 12:45
Introduction
Research Background
Since the implementation of the epoch-making policy-the reform and opening up, China has been engaging in the integration with the global economy. Sequentially, in the early 1990s, a stream of foreign direct investment (FDI) started to flow into China and a number of Sino-foreign joint-ventures were established. China’s economy, thenceforth, has experienced a remarkable increase, part of which can be attributed to the positive effects of inward foreign direct investment (IFDI). Chinese companies, from the perspective of microlevel, have obtained a great deal of energy and vitality from IFDI as well. Nowadays, China has been one of the most attractive and desirable destinations for FDI. In 2017, against the backdrop of a decline in worldwide FDI, China continued to be the largest FDI recipient among developing countries and the second largest in the world, just following the United States (UNCTAD, World Investment Report 2018). A large body of literature, therefore, has been investigating the impacts of IFDI on China5 s economy with respect to the technology spillover effects, the alteration of industrial structure, the employment issue, etc.
In the early 2000s,the Chinese government launched the “Go globally” strategy to further integrate into the globalization, inspiring Chinese firms to export and invest abroad. After the global financial crisis in 2008, an increasing number of Chinese firms conducted mergers and acquisitions (M&As) in foreign markets. Subsequently, with the launching and proceeding of “The Belt and Road Initiative”,China’s outward foreign direct investment (OFDI) has captured a continuous increase. Last decade, so to speak, has witnessed an imposing growth of China9 s OFDI. Today, China has been one of the main capital exporters just behind Japan and the United States (UNCTAD, World Investment Report 2018). Consequently, this phenomenon has caught growing attention from researchers both at home and abroad to investigate its unconventional patterns and rapid growth. (Buckley et al., 2008; Zhang & Daly et al., 2011; Wang et al., 2012; Kolstad & Ivar et al., 2012; Li & Jian et al., 2016; Yao et al., 2016; Li & Luo et al., 2018). Amid the existing literature, however, a large scale of the spotlight has been cast on the macro-level characteristics and effects, which substantially dwarfs the micro-level studies. Therefore, inspired by the pros or cons of previous studies, this paper aims to shed more light on this topic based on a firm-level dataset.

Research Objectives
From the perspective of theoretical evolution, theories regarding FDI, initially, are to cope with multinational enterprises’(MNEs) overseas investment practice which dominantly occurred in developed countries. Taking the relationship between firm productivity and foreign investment propensity as an example, Helpman et al. (2004) have theoretically and empirically shown that only the most productive firms are able to invest outwards. Empirical analysis ensuing based on European and American firm-level data has further validated this viewpoint (Eaton et al., 2004; Girma et al., 2005; Mayer and Ottaviano, 2007; Bernard and Jensen, 2007; Yeaple, 2009). The reason why these empirical tests based on the firm-level data of advanced countries, by and large, are consistent with theoretical expectations is that firms born in these countries have specific advantages that can be transferred. These advantageous enterprises either exploit the international market via their competitiveness or make the best of the low-cost factors of host countries to promote their efficiency. With the upsurge of developing countries, especially emerging economies (EE) during the 1970s and 1980s, however, the power of classical FDI theories based on the developed countries seems to be not that competent to explain developing countries5 OFDI activities. For instance, Ryubei and Takashi (2012) by analyzing Taiwan’s micro-level data found that the productivity of firms who invested abroad was not necessarily higher than that of firms without FDI.
Compared with firms from advanced economies, Chinese firms as latecomers, tend not to have the apparent “specific advantages” that can be transferred. In addition, China as a transition economy, its economic system, policies, ownership characteristics, etc. are quite distinguished from other countries. Thus, the initial objective of paper is to give more insights into these issues: (1) Compared with non-OFDI firms, what are the characteristics of the OFDI firms; (2) What are the impacts of productivity on firms5 OFDI decision and to what extent can HYM model explain Chinese’ firms’ OFDI behavior; (3) Is there significant productivity disparity between firms tending to invest high-income countries and those being apt to invest in low-middle income countries. (4) What is the impact of OFDI on Chinese firms’ productivity growth? Additionally, China still remains as a developing country at present with a certain social-economic gap between advanced economies. How to make the best of external resources and market, particularly the advanced technology and innovation resources of developed countries, to promote firms5 productivity and profitability, to optimize the industrial structure, and to escalate the quality of economic growth? This is one of the main missions of

reform and opening up in a new and upgraded stage. Under the background that a large scale of companies is investing abroad, therefore, to get a further understanding of the characteristics and impacts of Chinese MNEs5 OFDI under the context of firm heterogeneity is of great significance.
Research Methods and Main Contents
This paper mainly employs documentation, descriptive, and empirical analysis. Based on the theoretical evolution of OFDI, we first listed some literature concerning OFDI practices of developed countries, then the ones pertaining to developing countries. In the end, we went through a bunch of literature about firm heterogeneity. Then we conducted a descriptive analysis of the development of China5 s OFDI in different stages. On the basis of the previous theoretical mechanism and empirical research, we empirically analyzed the nexus between firm productivity and their OFDI by employing a logit model, the propensity score matching method and the difference-in-difference method.
The remaining chapters mainly encompass the following parts: Chapter 1 is to review the theoretical and empirical literature related to the main issues in conjunction with some comments, which provides a clearer perspective on theory evolution and referable research thoughts and methods for Chinese firms5 OFDI activities; Chapter 2 sheds light on the development of China9 s OFDI in terms of history, current situation, and some urgent problems from a macroscopic viewpoint, aiming at displaying some general characteristics of China5 s OFDI; Chapter 3 intends to give more insights into the relationship between firms5 heterogeneity and their OFDI decision, to test whether the Chinese case is compliant with the predictions of current FDI theory or not; Chapter 4 mainly investigates the influences of OFDI on firms productivity growth; The last part is comprised of a conclusion and discussion of the limitations of this paper and the future research directions in this field.

Chapter One Literature Review
This chapter mainly deals with the relevant literature review of OFDI and multinational enterprises. Initially, the author looked back on the theoretical part, which serves as the foundation of this study. Specifically, the author went through the traditional OFDI theories regarding both advanced economies and merging economies in conjunction with some comments on their pros and cons; Subsequently, certain of existing literature on firm heterogeneity and firms5 internationalization decision was reviewed from both the theoretical and empirical perspective; Ultimately, centering on the core issues of this study, I carried on the documents review to the impacts of OFDI on firms5 productivity. All in all, based on the theory of firm heterogeneity, this chapter reviewed two categories of study respectively: one is about the motivation of MNEs5 OFDI behavior; another is associated with the impacts of OFDI on parent firms.
1.1 Traditional Theories about OFDI and MNEs
MNEs and their OFDI activities have always been an intriguing and prevailing topic in the sphere of international economics. It is widely acknowledged that literature focusing on OFDI theories and MNEs starts from the 1960s, which contrasts sharply with the neoclassical trade and financial theory that made little distinction between international portfolio investment flows and OFDI. Due to the fact that the early OFDI is dominantly conducted in MNEs of developed countries, thus, the following theories are initially intended to shed light on the OFDI behavior of MNEs belonging to the advanced economies, and mainly focusing on the emergence of OFDI. In accordance with the evolution of OFDI theory, the theoretical framework mainly composes monopolistic advantage theory, product life cycle theory, internalization theory, and marginal industry theory, etc. Each theory, to some extent, has explained the OFDI practices of developed countries from a specific perspective. However, the literature of this field is relatively general and scattered due to a lack of a unified theoretical and logical framework. In this section, I reviewed an array of traditional and influential OFDI theories related to both developed and emerging economies, and then briefly commented on their merits and demerits.
1.1.1 Traditional OFDI Theories about MNEs of Developed Countries

Stephen Hymer^ seminal dissertation (1960) made a great contribution to OFDI theory, leading us to an analysis of MNEs based on an industrial-organizational theory by jumping over the intellectual straitjacket of neo-classical-type trade and financial theory. Multinationals firms were just arbitrageurs that transferred the capital from countries where returns were low to countries where returns were higher based on traditional international trade and investment theory. Escaping from the arid mold of orthodox international economics theory, Hymer^ pioneering conceptual insight put emphasis upon the MNEs per se. Hymer's monopolistic advantage theory demonstrated that monopolistic advantage (special assets) is the immediate driving force of MNEs5 FDI instead of the financial structure decisions based on the assumption of market imperfections. He reckoned that there existed at least four types of market imperfections: the imperfection in the product market, in the factor market, in the economies of scale and the externality and deficiency of management. The core of monopolistic advantages is the technical advantage. Although Hymer^ theory identified the structural market failure presciently, unfortunately, it overlooked the distinction between structural and transaction-cost market imperfections. In addition, not only did he neglected the significance of the spatial and geographical dimension of the MNEs he also paid little attention to the policymaking and the political or social issues of developing nations.
1.1.1.2 Product Life Cycle (PLC) Theory
Raymond Vernon (1966) introduced the product cycle model to explain firms5 marketseeking production of a particular nationality or ownership from a dynamic perspective of technology development and international trade. According to PLC theory, product life was divided into three stages, namely, innovation stage, standardization stage, and mature stage. Countries and their firms were positioned in different places within the hierarchy of international trade, where firms5 location choice, overseas production, and export were combined into a dynamic and systematic analysis. Initially, relying on their strong capital and R&D capabilities, developed countries produced a new product (or more precisely, the value- added activities based on a firm^ proprietary assets) mainly for the domestic consumption and exported a small portion to the sub-developed markets. Therefore, enterprises did not need outbound investment in this phase. At the following stage, on the one hand, the technology of new products was maturing; on the other hand, the demand from foreign countries was increasing. Thus, new products were exported to foreign countries with similar demand patterns

and supply capabilities. Simultaneously, for one thing, firms of import countries started to imitate the new product; for another thing, authorities of importers set out to limit the inflow of those innovative products for sake of protecting their domestic infant industry by escalating the import barrier, which gradually shifted the innovative firms from exporting to outward investing. Later, as the product becomes standardized or mature, the competitive advantages of the supplying firms were seen to change from those to do with the uniqueness of product per se, to their ability to minimize the costs of value-adding activities and/or their marketing expertise. At the same time, as consumer demand turns more price-elastic, labor becomes a more important ingredient of costs, and foreign markets keep expanding, the desire to site value- added activities in a foreign rather than a domestic location grows stronger. Eventually, Vernon deduced, if the conditions in the host countries were suitable, the affiliate might replace exports from the parent company or even export back to it.
Like Hymer, Vernon provides a theory that incompletely addressed the issues related to MNEs9 activities. The product life cycle, on the one hand, is a pioneering and dynamic elucidation of the determinants of, and the relationship between, international trade and foreign production. In addition, it brings in some novel hypotheses concerning demand stimuli, technology leads and lags, and information costs, which have been sequentially testified to be useful tools for the study of foreign production and exchange. On the other hand, as Vernon (1979) himself acknowledged, by the 1970s, the explanatory ability was insufficient towards the phenomenon that the further geographical reach of MNEs coupled with a growing convergence in the advanced markets of the world. Furthermore, it does not explain the occurrence of resource-based, efficiency-seeking or strategic-asset-acquiring OFDI.
1.1.1.3 Internalization Theory
Buckly and Casson (1976) argued that due to the market imperfections in intermediate products, notable knowledge, firms tended to create an internal market (internalizing the external market) to increase profits and avoid certain costs on the basis of profit maximization and growth principles of firms. There are two ways to create an internal market: "First, internalization of market refers to the replacement of an arm's length contractual relationship (i.e. external market); Second, internalization of an externality refers to the creation of a market of any kind were non-existent before,/ (Casson, 1986). Under such circumstances, internalizing markets across national borders gives rise to MNEs. Later on, the concept of "transaction costs" which referred to all the costs in organizing an economic activity was brought in and got
prevalent. The logic of transaction cost is that if firms seek to lower costs and uncertainty and
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higher revenues, then they will try to internalize markets across national boundaries. There are two typical cases: one is that information asymmetry among transaction parties will spark uncertainty to firms' production of intermediate goods. In order to guarantee the product quality, firms tend to internalize this part to mitigate transaction costs; another is, in the context of an imperfect market, to prevent the leakage of strategic technology, innovation, and knowledge, firms prefer to produce by themselves instead of outsourcing.
If Hymer's theory well explained the horizontal FDI seeking market expanding, then internalization theory illustrated the vertical FDI seeking for efficiency improvement to a large degree. Meanwhile, internalization theory sheds some light on the global value chain and internal trade of MNEs as well. However, it is not strong enough to explain the horizontal FDI and firms' FDI of developing countries.
1.1.1.4 Eclectic Paradigm
Due to the respective deficiency of above theories in fully explaining the FDI issues, Dunning (1979, 1980, 1988) integrated a string of different theories and developed an eclectic paradigm or so-called "OLI (Ownership, Location, Internalization) paradigm to determine the "extent,’,’'form,' and "pattern,' of international value-adding operations of firms.
Dunning argued that if foreign firms wanted to compete with domestic firms in the host countries, they must hold certain edges specific to the nature and/or nationality of their ownership, for which they had to undertake additional costs of setting up and conducting foreign value-added activities besides facing the competition form domestic firms. As for the locational advantages, Dunning (1988) indicated that firms tended to get involved in foreign production whenever they realized that it was in their best interests to combine spatially transferable intermediate products produced in the home country, with at least some immobile factor endowments or other intermediate products in another country. Last, internalization advantages encompassed the advantages of controlling, coordinating ownership and location- specific advantages within the MNEs instead of licensing or selling them to domestic firms in the host country.
Eclectic paradigm not only absorbs the nutrition of monopoly advantage theory and internalization theory but also incorporates the location factors that international trade theory underlines. Thus, it manifests strong explanatory power and openness and for quite a long time, it has been regarded as a general theory to explain MNEs5 FDI behavior. However, it still has some flaws. That is, for one thing, it overlooks the facilitation effect of the home country^
policy; for another, it is invalidated to explain the MNEs5 FDI behavior of developing countries.
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After the comparison between American firms’ and Japanese firms’ FDI practices,Kojima (1978) put forward FDI comparative advantage theory or marginal industry expansion theory to explain firms9 FDI behavior. Here, marginal industries refer to the sunset industries in developed countries, meanwhile, they might be the promising industries in developing countries due to the imbalanced development between the north and the south. Thus, by utilizing the comparative advantages of sunset industries in technology, management, and brand plus the lower factor price in recipient countries, firms in those industries of developed countries were still able to gain some growth/profits when investing in developing countries. In addition, Kojima intrinsically held that FDI should work as an efficient conduit for trading intermediate products, but the timing and direction of such investment should be determined by market forces rather than by hierarchical control.
To sum up, Kojima’s prescription is that FDI should commence from investor country’s industries that are or will be at a disadvantage compared with other domestic industries but still hold some advantages in comparison with those industries of the recipient country. Sequentially, firms of developed countries gradually start to invest outbound in accordance with their marginal industries. By following this pattern, not only can it improve the home country^ industrial structure, facilitate the foreign trade but also it is conducive for the host country to upgrade its industrial structure and enhance the development of relevant industries.
Kojima almost followed Vernon’s doctrine,although to some extent he employed the trade models to explain the patterns of Japanese firms5 FDI. However, he ignored the influence of transaction costs on allocation on international resources and hence failed to explain the FDI phenomenon among developed countries and the FDI behavior of developing countries.
A brief summary of OFDI theories related to MNEs of developed countries is listed in Table 1.
1.1.2 Traditional OFDI Theories about MNEs of Developing Countries
With the economic development of developing countries, especially the rise of emerging market countries, their outward direct investment has experienced a substantial increase in the last two decades. However, unlike MNEs of developed countries especially MNEs of the U.S.A., firms of developing countries may well not possess those core competitive advantages from market monopoly, sophisticated technology or advanced management. Thus, OFDI theories

Theory Author Main Points Comments
Monopolistic
Advantage
Theory
Hymer (1960),
Kindleberger
(1969)
Based on the assumption of market imperfections, monopolistic advantage (special assets) is the main driving force of FDI instead of the financial structure decisions of multinational firms. This theory escapes from the arid mold of orthodox international economics theory and puts emphasis upon the MNEs per se; however, it overlooks the distinction between structural and transaction-cost market imperfections and neglecting the significance of spatial and geographical dimension of the MNEs '
Product Life Cycle Theory Vernon (1966) This theory divides the product life cycle into three stages: innovation, maturity, and standardization. At each stage, countries have different roles, thus, they have a different location, export, and investment strategies. The product life cycle, on the one hand, was the pioneering dynamic elucidation of the determinants of, and relationship between, international trade and foreign production; However, it is frail to explain the increasing geographical reach of MNEs coupled with a growing convergence in the advanced markets of the world and it doesn't offer explanation for the resource-based, efficiency-seeking or strategic-asset-acquiring FDI.
Internalization
Theory
Buckley & Casson(1976) This theory holds that the imperfection of intermediate market leads to the transaction cost in the market exceeding the internalization cost, which promotes the internalization advantage of enterprises and provides the impetus for enterprises to carry out international direct investment activities. Internalization theory illustrated the vertical FDI seeking for efficiency improvement to a large degree. Meanwhile, internalization theory sheds some light on the global value chain and internal trade of MNEs as well. However, it is not strong enough to explain the horizontal FDI and firms’ FDI of developing countries.
The Eclectic OLI Framework Dunning
(1981,1988)
Firms, who possess ownership advantage, location advantage, and internalization
advantage, are more likely to be involved in international production. Furthermore, the scale of production, geographical distribution and industrial structure of MNEs are based on the interaction of these three advantages.
This paradigm integrates the nutrition of monopolistic advantage theory and internalization theory and incorporates the location factors that international trade theory mainly focuses on. Thus, it displays stronger explanatory power. However, it overlooks the facilitation effect of the home country’s policy, and it is invalidated to explain the MNE's FDI behavior of developing countries.
Marginal
Industry
Expansion
Theory
Kojima (1978) Based on the comparative advantage theory and experience of Japanese firms’ FDI, this theory points out that OFDI of a country should start from the relatively less competitive industries (marginal industries), and then moves forward according to the marginal industries. Kojima almost follows the Vernon tradition, to the extent that he employs the trade models to explain the patterns of Japanese firms’ FDI. However, he ignores the influence of transaction costs on the allocation of international resources and hence failed to explain the FDI phenomenon among developed countries and the FDI behavior of developing countries.
 
 
 
based on advanced countries, practices are insufficient to explain their new features. Under such kind of circumstances, starting from the 1970s, a couple of researchers began to shift their focus on the OFDI behavior of developing countries and put forward some consequential theories.
1.1.2.1 Small-scale Technology Theory
Wells (1977) proposed the small-scale technology theory to shed light on the firms5 FDI
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phenomenon of developing countries. He argued that MNEs9 comparative advantages of developing countries principally stemming from lower production costs. Further, the low-cost advantage mainly comes from the small-scale technology catering for the small-scale market and the market similarity with the host countries. By contrast, the technological and economies- of-scale advantages of MNEs in developed countries primarily serving the large-scale market, however, are not as applicable as in small-scale markets where they are not able to make the best of the advantage of economies of scale. In addition, unlike the sumptuous marketing strategies of MNEs of developed countries, MNEs of developing countries usually take the low- price marketing strategy, which is a powerful weapon to stab the low-end market.
Wells5s theory combines the market similarity between the home country and host country and MNEs5 own comparative advantages of developing countries, which offers inspiration to those firms of developing countries without highly-advanced technology and economies of scale to participate in global competition by investing abroad.
However, small-scale technology theory essentially is a passive technology theory following Vernon^ tradition. It implies that developing countries simply utilize the obsolete technologies of advanced countries to serve other developing countries with parallel market features. On the one hand, this might leave developing countries5 MNEs hovering on the brink of global value-chain and product life cycle; on the other hand, it is disabled to explain the reverse FDI from developing countries to developed countries, especially for the investment in the sphere of high technology.
1.1.2.2 Investment Development Path Theory
The investment development path (IDP) theory was proposed by Dunning in the 1980s, which is a dynamic evolution of the eclectic paradigm. The core proposition holds that the net OFDI position of developing countries is systematically related to its level of economic development and the advantages of ownership, location, and internalization.
Dunning categorized economic development into four stages according to the GNP per capita of each country. Countries at the first stage (GNP per capita<$400) neither possess many ownership advantages, internalization advantages nor an attractive business environment. Thus, in such context, the net OFDI is most negative due to the infrequent IFDI and almost empty OFDI; At the second stage ($400<GNP per capita<$2000), thanks to the development of domestic economy, the expand of domestic market and the improvement of domestic business

environment, the location advantages get strengthened. Under such circumstances, the IFDI grows significantly while the OFDI is still in a start-up state because of the limited ownership and internalization advantages. A majority of developing countries are at this stage; When countries enter into the third stage ($2000<GNP per capita<$4750), their economy has experienced a thriving development. Thus, a segment of firms has gained some ownership and internalization advantages to invest abroad. As a result, both IFDI and OFDI has reached a certain scale. A number of newly industrialized countries belong to this group; The fourth stage (GNP per capita>$4750) mainly refers to the advanced counties or regions on account of their remarkable ownership and internalization advantages, and their globalized strategies to utilize the location advantages of host countries.
IDP theory, combining the abilities of countries to attract IFDI and invest beyond boundary due to the domestic economic development, holds that the position of the country^ international investment is positively correlated with its GNP per capita. However, if we look into the issue from a dynamic perspective, we will discover that there is a brunch of modern international investment practices, which are contrary to this theory. Besides, the method per se just using the GNP per capita as an indicator to category the economic development level has some limitations.
1.1.2.3 Technical Innovation and Industrial Promotion Theory
Since the middle of the 1980s, developing countries’ OFDI has seen a significant increase. Especially in those newly industrialized economies, firms began to target the developed country as a destination and then became mighty competitors for local firms. Based on the prerequisite of technology accumulation, Cantwell and Tolentino (1990) put forward technical innovation and industrial promotion theory to explain this phenomenon from a dynamic perspective. This theory offered two propositions: for one thing, the upgrading of industrial structure in developing countries and regions shows that firms5 technological capability has been steadily improved, which is the result of continuous technology accumulation; another thing is that the improvement of technological capabilities of developing countries is directly related to the growth of their OFDI. Existing technology capability is one of the main factors affecting international production activities, as well as the form and growth rate of MNEs5 OFDI in developing countries. Based on these two preconditions, Cantwell and Tolentino argued that the industrial and geographical distribution of developing countries5 OFDI was gradually evolving over time and were predictable.
Generally speaking, this theory holds that the accumulation of technology is the internal
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driving force of MNEs’ OFDI in developing countries which results in firms5 regional expansion according to the psychological distance or cultural distance. Further, with the reinforcement of the inherent technological capability, for one thing, the OFDI will be gradually evolved from resource-oriented to technology-oriented; for another thing, the industries involved will get upgraded and the composition of the industry is closely correlated with the change of regional distribution. So to speak, this theory comprehensively explains the phenomenon of OFDI in developing countries, especially in Asia’s emerging industrial countries and regions after the 1980 to a large extent, not only including the rising of this kind of OFDI but also the trajectory-from developing countries to developed countries, from traditional industries to high-tech industries. Therefore, this theory has a certain guiding significance for developing countries in strengthening technology accumulation and innovation through OFDI and further upgrading the industrial structure and international competitiveness.
1.1.2.4 Linkage-Leverage- Learning (LLL) Framework
To unveil the puzzle—how firms as latecomers and newcomers from developing countries that lack strategic resources and are distant from major markets become MNEs, challenge even defeat incumbents in the global economy—Mathews dedicated himself to the study on Asia- Pacific business dynamics over the past decade. Mathews (2006) found some common characteristics of those firms and proposed the linkage-leverage-learning (LLL) framework, which offered some clues to unraveling this question. He pointed out that firstly, the early international expansion of firms in developing countries did not rely on their own advantages, but on obtaining such advantages through external linkages; secondly, the links could be established with incumbents or partners so that resources could be leveraged, for example, to form a strategic alliance or to establish a joint-venture; thirdly, firms learned an gained some advanced technologies, managerial skills to bolster their efficiency and development through such repeated applications of linkage and leverage. Therefore, the innovative patterns of outward expansion in search of new resources can be captured by this process. To sum up, this framework, based on the extended resource-based view (RBV), pays more attention to firms9 acquisition of external resources rather than their own advantage to unravel the OFDI phenomenon of MNEs in developing countries. A summary of OFDI theories related to MNEs of developed countries is expressed in Table 2.
 1.2Firm Heterogeneity and OFDI
1.2 Firm Heterogeneity and OFDI
1.2.1 Firm Heterogeneity Theory
In the 1980s, in order to shed more light on the widespread phenomenon of intraindustry trade or trade between countries with parallel factor endowment, Dixit and Stiglitz (1977) primarily developed the modeling of monopolistic completion and product differentiation. Then it was employed by Krugman (1979, 1980), who opened the door to formally modeling multinational firms within the general-equilibrium framework (Antras and Yeaple, 2014). To capture more reality, Melitz (2003) incorporated firm heterogeneity (firms differ in productivity) within an industry to illustrate that when exposed to trade, only the relatively more productive firms are able to export to foreign markets while the less productive firms will continue to serve the domestic market in conjunction with the least
13

productive firms being kicked out the industry. Focusing on firms5 choice between exports and horizontal FDI (HFDI), Helpman, Yeaple and Melitz (2004) (HYM model) extended the trade model of Melitz and proved that: first, only the most productive firms tend to engage in foreign activities; second, after weighing the transportation costs of export and the fixed costs of FDI, the most productive firms are more likely to invest abroad, while the relatively less productive firms choose to export; third, other low-productivity firms opt to serve the domestic market only and the least productive firms are forced to leave the industry. This is the first time to demonstrate firms5 horizontal FDI behavior from the perspective of firm heterogeneity.
Grossman et al. (2006) developed a complementary strategy model incorporating firm heterogeneity as a determinant of horizontal and vertical FDI. They showed that even within the same industry where each firm faced the same market size, fixed costs and transportation costs, firms differing in productivity have different optimal strategies. In addition, they summarized that the least productive firms produce in the home market while firms engaging in FDI are more productive, and the most productive firms will move both intermediate and assembly stages to the developing countries. Integration strategies depend on the scale of trading costs and fixed costs of the intermediate and assembly stages related.
1.2.2 Empirical Evidence from Developed Countries
Helpman et al. (2004), by using American firms’ data, found that exporters outperformed non-exporters in productivity, while the productivity of firms with OFDI was greater than export firms. Bernard et al. (2006), Bernard and Jensen (2007), by using American firms9 data, also discovered that firms with lower productivity only operated in the home market, those more productive ones chose to export, while the most productive firms decided to invest outbound. Yeaple (2009) reported that: first, the most productive firms invested more foreign countries and gain more revenues in these investment destinations; second the country-specific characteristics, for instance, economic development level, geographical and cultural distance and so on, had a significant influence on American OFDI activity. Eaton et al. (2004) by using French firms5 data, showed that the more foreign markets a firm invested, the higher its productivity, which indicated that the increasing costs of OFDI required firms to be more productive. Grima et al. (2005), by using English firms5 data, found similar evidence. Mayer and Ottaviano (2008) also manifested that only a small proportion of firms export or invest overseas, whose productivity was significantly higher than those only served the domestic market after investigating the international activity of European firms. By and large, the evidence from developed countries is in line with the prospection of Helpman^ theory. Because a large scale of FDI emerging from advanced countries also goes predominately to developed countries, especially in the early stage (now the share of developing countries has been enlarging), most of which is HFDI (Navarreti et al., 2006).
Compared with European and American countries, Japan as a latecomer displays some heterogeneity with respect to OFDI, where not only exists a bunch of HFDI but also a relatively large proportion of vertical FDI (VFDI). Aimed at expanding market share in host countries, Japanese firms, from the automobile and electronics industry to the household electrics industry, have made plenty of investments in other developed countries based on their comparative advantages. Meanwhile, they also locate their low and middle-end production in some peripheral developing countries, like Malaysia, Indonesia, and mainland China in order to reduce the production costs of intermediate inputs. By investigating Japanese firms5 data, Head and Ries (2003) found not only the consistent results mentioned before but also the fact that firms who invested in developed countries were more productive than those invested in low-income countries. As presented in the HYM model (2004), the productivity cutoff of OFDI is determined by host countries’ specific factors, like market share, factor prices, productivity and so on. Therefore, the productivity cutoff of firms for investing in developing countries tends to be lower than that in developed countries. Likewise, Ryubei and Tanaka (2009), after comparing the productivity heterogeneity of Japanese firms in Europe, America, and East Asia, discovered that firms investing in the United States and Europe simultaneously were more productive than those investing only in the United States or Europe, while those investing in East Asian countries were not necessarily more productive than exporters or those only operate in home market. Therefore, the characteristics of Japanese firms5 OFDI activities indicate that the efficiency of firms investing in developed countries, such as Europe and the United States, is consistent with the theoretical expectations, while the productivity of those investing in developing countries tends to remain in uncertainty.
1.2.3 Empirical Evidence from Developing Countries
With the development of globalization, the OFDI share of developing countries has been expanding in recent decades. Compared with the OFDI from advanced countries, what is the feature of OFDI from developing countries? Is there anything unexpected? Thus, this topic has caught increasing attention from researchers. Damijan et al. (2007), by exploring the rich dataset of Slovenian manufacturing firms, found that the productivity of firms investing abroad, of exporters and of firms only operating domestically decreased successively, which were in accord with the HYM model. Aw and Lee (2008) investigated the data of Taiwanese firms and presented that the productivity of Taiwanese firms, whether investigating in the United States or in the mainland of China, exhibited consistent pattern with the prediction of HYM model. In addition, firms investing only in the U.S. had higher productivity than those investing exclusively in China due to the lower fixed investment cost in China. Ryuhei and Takashi (2012) utilizing the Taiwanese firms5 data as well, revealed some new evidence: first, firms conducting OFDI in high-income countries are more productive than those in low- income destinations, but not definitely more efficient than non-OFDI firms; second, the more productive, the more destinations they invest regardless of the market attributes. This paper also suggested that developing countries can speed up the internationalization of firms by lowering the productivity cutoff of OFDI through government backup.
Recently, the relationship between Chinese firms,productivity heterogeneity and their OFDI activity has been a hot topic among researchers both at home and abroad. Wang and Zhao (2012) based on the firm-level data of Guangdong province, found that OFDI firms tended to be more productive, which was in line with the HYM?s theoretical model. In addition, the more productive the OFDI firms, the more countries they invested in. Based on firms5 data of Zhejiang province, Tian and Yu (2012) also found similar results which further showed that the more productive the OFDI firms were, the larger the volume they invested. Xiao and Xiao and Liu (2012) by using Chinese firms’ data (2000〜2010) and incorporating geographic factor into the model, found that OFDI firms with higher productivity were more able to invest further, while less productive firms are more likely invest in adjacent countries. Chen and Tang (2014) found that OFDI was associated with better firm performance, including higher productivity, employment, export intensity, and greater product innovation, but over half of OFDI deals resided in service sectors. Chen (2014) specifically examined the OFDI in service sectors and found that the HYM theory was also applicable to service industries. Jiang(2015), by testing Chinese firms5 data, found not only the supportive evidence towards the firm heterogeneity theory but also additional features: first, firms investing high-income destinations were not necessarily more productive than those just investing low-income countries; second, more efficient OFDI firms were more technology- seeking-oriented relative to expanding their market share in host countries; third, state-owned OFDI firms were not definitely more productive than other OFDI firms with different
ownership.
On the basis of the current literature on Chinese firms5 OFDI behavior, there still remain two major concerns requiring further investigation. One is the issue related to ownership. State-owned firms, who possess specific ownership advantages and “dual motivation”,have predominately conducted over half of China5 s OFDI. As presented in previous documents, the encouragement and support from the government may lower the productivity cutoff of OFDI, resulting in some unqualified firms investing abroad. Because state-owned firms tend to be easier to get access to the financial credit from state-owned banks or other external financial sources and are more likely to be sheltered by government policies compared with private firms. In addition, productivity, capital intensity, and labor intensity vary from industry to industry, which could also affect the productivity cutoff of OFDI. For instance, the high-tech industry definitely requires a higher productivity threshold than the labor- intensive manufacturing industry. Therefore, the inter-industry distinctions cannot be ignored, and we need to give more insights into the intra-industry OFDI activity.
1.3 Impacts of OFDI on firmsProductivity
Productivity, as an important indicator of efficiency, has been referred to as the driving force of the firm^ sustained competitiveness and growth (Lieberman & Dhawan, 2005; Syverson, 2011). Thus, it is vital for emerging economies to catch up with the advanced world (Kharas & Kohli, 2011). At present, the study on productivity inequality across firms has come a long way (Bartelsman & Doms, 2000) and a large and growing body of literature has been seeking to identify the factors affecting firm^ productivity from various angles and fields, especially the levers that firms can utilize to increase their productivity (Bertrand & Capron, 2015; Syverson, 2011). OFDI, one of those levers, has been utilized as a mechanism by which firms can not only exploit ownership advantages, but also gain resource variety, fulfill resource reallocation, stir up competition, and facilitate the development of productivity (Bertrand & Capron, 2015; Cantwell, 1989; Dunning, 1988; Frost, 2001).
Due to the accessibility of firm-level data, the majority of early literature examining the productivity effect of OFDI on the home country just focuses on the practice of developed countries from the macro-level perspective. Potterie and Lichtenberg (2001) estimated the technology spillover effects of trade, IFDI, and OFDI by using country-level panel data of 13 developed countries from 1971 to 1990. The empirical results showed that OFDI flowing into countries with advanced technology indeed had positive effects on the productivity of the home country by absorbing technological knowledge from the host country. Driffield,
Love, and Taylor (2009) showed that both efficiency-seeking OFDI and technology-sourcing OFDI would strengthen domestic productivity. Hezer (2011) also revealed that, by and large, OFDI was beneficial to the productivity of developing countries in the long run.
As for the industry-level studies, by utilizing the data of the Swedish manufacturing industry, Braconier, Ekholm, and Knarvik (2001) hardly found any indication of FDI-related R&D spillovers. Bitzer and Kerekes (2008) reported that host countries of FDI got remarkable benefits from the knowledge of spillovers of IFDI, while little positive OFDI-led technology souring effects were found. Further, by investigating a similar dataset Bitzer& Gorg, (2009) even found that on average, a country^ stock of OFDI had a negative correlation with its productivity. However, Driffield and Chiang (2009) unveiled that there indeed existed a positive correlation between Taiwan’s OFDI to mainland China and its labor productivity.
Compared with analysis with country-level and industry-level data, firm-level study is deemed to be more rigorous for exploring the productivity effect of OFDI as it is free from the constraints of aggregation bias (Holz, 2004) and offers perspectives for discerning firm heterogeneity (Helpman et al., 2004), contributing to providing more insights into firm-level variations in OFDI-led productivity effect. Barba Navaretti and Castellani (2004) found that in contrast with domestic firms, OFDI of Italian multinationals improved their total factor productivity growth by adopting the propensity matching score method. Branstetter (2006) also confirmed that OFDI was a channel of knowledge spillover for Japanese MNEs investing in the United States. Hijzen et al. (2007) suspected the positive results might result from the neglect of endogeneity bias that probably could emerge when more productive firms self-select into going abroad. To address this problem, Hijzen et al. (2007) employed propensity score matching and difference-in-difference techniques to examine the Japanese firms’ data from 1995〜2002, however, they failed to find a significant positive impact of OFDI on productivity.
Despite the fact that there is a bulk of empirical studies coping with the productivity effect of OFDI in developed countries, by contrast, this topic in developing countries is still under-researched. There are a few scattered attempts based on micro-level data from Taiwan, mainland China, and Estonia, but the empirical results of these investigations are still miscellaneous.
Therefore, what are the reasons behind those controversial results even based on similar firm-level datasets and estimation methodologies? Helpman et al. (2004) proposed that firm- level specific features may well diversify firms5 investment strategy and performance.
Kokko and Kravtsova (2008) also affirmed that it is not automatic for productivity premium and technology diffusion. Firms that have higher R&D and absorptive capability and invest in relatively developed countries/regions tend to obtain higher productivity premium. (Dosi, 1988; Kim, 1997; Almeida & Kogut, 1997; Cantwell & Janne, 1999; Cohen & Levinthal, 1990). Besides, the diverse ownership of parent firms (Ramasamy et al., 2012) and OFDI destination (Branstetter, 2006; Li, 1995; Nocke & Yeaple, 2007) may significantly moderate emerging country’s multinational enterprises (EMEs)’ leaming-by-OFDI effect. At present, however, there has been no comprehensive report on how the firm-level heterogeneity moderates the OFDI-led productivity effect. (Almeida & Kogut, 1997; Bitzer & Kerekes, 2008; Branstetter, 2006; Chen & Yang, 2013; Chuang & Lin, 1999; Potterie & Lichtenberg, 2001; Herzer, 2008)
In the context that China is one of emerging market economies and increasing Chinese companies are going abroad, whether Chinese firms5 OFDI has improved their productivity and competence or not is an urgent question requiring more systematic research on their OFDI behavior. By introducing and testing an extended learning-by-OFDI model, which takes Chinese firms5 heterogeneity into consideration, this paper attempts to shed more light on this topic. Theories related to developing countries MNEs5 OFDI will be employed as the theoretical foundation for this work. The analytic framework is demonstrated in Fig 1, which can be more robust to explain and predict the Chinese MNEs5 OFDI-productivity effect.
A summary of the preceding study results on the productivity effect of OFDI at the firm- level has been displayed in Table 3.
Figure 1 Analytic framework
 
 

Main Findings
OFDI improves the firms5' TFP but this effect is just significant in non-state-owned firms not in state-owned firms. Besides, the productivity effect of OFDI varies due to the different political environment in host countries, i.e., firms investing in a country with low political risk can gain the positive productivity effect, while that in high or medium destines is not significant.
They find that OFDI has a positive effect on firms' productivity, but this effect is subject to the heterogeneity of parent firms and their investment strategy.
Parent firms5 productivity indeed rises after OFDI, but this effect varies remarkably with a firm、heterogeneity. More precisely, a firm、 absorptive ability is crucial for the own-firm effect. Additionally, technology-seeking OFDI in developed countries manifestly bolster the own-firm effect, in contrast, supports from government show insignificant impacts.
Chinese firms' OFDI is significantly conducive to enhance their productivity and scales of operation. More precisely, OFDI by M&A facilitates early access to invisible assets, but has an adverse effect on financial performance, while greenfield investments show a more significant impact on the productivity and scale of Chinese MNEs investing in Europe.
This study finds that there exist positive productivity spillovers from OFDI to domestic firms, especially to domestic suppliers of home-country MNEs; The results also demonstrate that absorptive capacity plays a pivotal role in explaining the extent (if any) of productivity spillovers to recipient firms.
Cross-border acquisitions have a positive effect on the acquirers' labor productivity at home, and these effects will be greater when there are learning opportunities in the destinations and when acquirers contemporaneously invest in domestic productivity-enhancing activities
The results reveal that the technical efficiencies of the Taiwanese firms have got improved over the sample period, which suggests that there exists a positive correlation between Taiwanese firms' OFDI and their technological advances and technical efficiency.

 

Chapter Two Overview of China’s OFDI
The spring breeze of reform and opening-up stirred up China5 s outward foreign direct investment (OFDI). In 1979, China^ State Council promulgated an array of economic reform policies, one of which was "to establish enterprises overseas". For the first time, OFDI was formally marked as a public policy, which opened the way for the OFDI of Chinese enterprises. For years, however, purchasing foreign securities, foreign trade credit, and foreign loans remained as the main forms of capital outflow of China, and the proportion of foreign direct investment was quite small. In 2001, China became a member of the World Trade Organization (WTO). Thereafter, the development speed of China5 s OFDI has become faster in conjunction with her economic development, and it plays an increasingly important role in the domain of international investment. By the end of the year 2017, more than 20000 domestic investors had set up 39200 foreign direct investment enterprises abroad, distributed in 189 countries (regions) around the world, and the total assets of those firms amounted to $6 trillion.
World Investment Report 2018 (UNCTAD) shows that in 2017, China^ OFDI accounted for 11.1% and 5.9% worldwide in terms of flows and stocks respectively. Flows ranked third place and stocks jumped to the second from sixth in 2016. During the period 2002-2017, the average annual growth rate of China5 s foreign direct investment is 36.5%. Obviously, China has become an increasingly important exporter of capital. This chapter mainly exhibits the different historical stages and the current situation of China5 s OFDI, including the investment scale, location, industries distribution, entry modes, main entities, etc. Besides, this chapter also analyses the motivation of China5 s OFDI at the current stage and some issues to be solved urgently. Thus, this chapter provides a realistic basis for the following theoretical and empirical research.
2.1 History of China^s OFDI
It has been 40 years, since China5 s reform and opening-up. Chinese firms have fulfilled remarkable achievements in these four decades in terms of going abroad and undertaking international operations. Chinese firms are still enthusiastic to enter the global market via both trade and investment in conjunction with their accumulated strength and experiences. Specifically, Chinese firms5 OFDI roughly can be phased into the following stages.
2.1.1 Initial Development Stage (1979-2000)
In this stage, promoted by the State Council’s policy of “setting up enterprises abroad”, some professional foreign trade companies engaged in international trade for a long period of time and international economic and technological companies conducting foreign economic cooperation first invested abroad by virtue of their affluent foreign experiences. With the promulgation of succeeding OFDI policies, a couple of influential non-trading firms, industrial magnates, and comprehensive international trust companies, such as Capital Iron and Steel Corporation, China International Trust and Investment Corporation, gradually got on the board of foreign direct investment. Later, quite a few powerful and ambitious private companies joined in.
During this period, China5s OFDI presents the following features: (1) the scale of investment is relatively small; (2) in terms of geographical distribution, a majority of them located in those countries or regions which are adjacent to China both from the perspective of geographical distance and cultural distance, such as Hongkong, Taiwan, and Southeast Asian countries. This is in line with the initial OFDI model of developing countries in the theory of foreign direct investment; (3) As for the industries involved, it mainly concentrates on catering, service and other industries, and gradually begins to expand to resources development, machinery manufacturing and processing, transportation, health care, and other industries; (4) the main entities are those large state-owned trading companies, quite a few manufacturing enterprises from other industries and private sector; (5) With respect to the entry modes, the joint venture is the main form of investment, and the scale of Chinese firms5 investment is relatively small. Greenland investment only accounts for quite a small proportion. Thus, the entry mode is relatively monotonous.
2.1.2 Juvenile Stage (2001-2008)
In 2001, China officially became a member of the World Trade Organization (WTO). Thenceforth, China9 s opening-up took a great step up to a new stage no matter from the perspective of depth and breadth. In 2002, the Chinese central government proposed that to comprehensively improve China5 s opening-up and to participate in global cooperation and competition at a higher level, it was of great importance to combine ''introducing in,? strategy with “going out”. Since 2002, China’s OFDI has seen an increase at astonishing speed with a bunch of Chinese firms9 international mergers and acquisitions (M&As). In 2005, China implemented the exchange rate reform, then the Chinese currency appreciated substantially, which was greatly conducive to Chines firms5 international investment. By the end of the year 2005, nearly 4,000 Chinese investors had set up more than 6,000 non-financial enterprises in 163 countries (regions). For the first time, China5 s outward FDI flows exceeded 10 billion US dollars to 12.26 billion US dollars, and the year-on-year jumped to 123%. This period witnessed a strikingly surge of China9 s OFDI. In 2007, the stock of OFDI exceeded the $100 billion; The project amount also risen remarkedly from hundreds of millions of dollars to billions of dollars; The range of industry was also enlarged, involving construction, transportation, petrochemical, metallurgical engineering, ICT, power equipment, etc., though business services, manufacturing, wholesale, retail and mining industries were still the dominating parts; The recipients of investment were still clustered in Asian countries or regions, accounting for nearly 70% of the stock of China5 s OFDI. In addition, large-sized state-owned companies were still the main forces investing abroad.
2.1.3 Growth Stage (2009 to present)
In the context of the global financial crisis after 2008, international capital flows, cross- border investment, and mergers & acquisitions (M&A) activities contracted dramatically. Global outward FDI declined by 30% in 2009, according to the analysis of the World Investment Report 2010 (UNCTAD). During the “Twelfth Five-Year Plan” period, outward foreign direct investment was upgraded as a national strategy by the Chinese government. In 2013, with the promulgation of “the Belt and Road” strategy, a series of policies has been released to support Chinese firms <4going out?,. First is to standardize and simplify the process of OFDI, by means of the policy guidance, streamlining the approval process, and decentralization. In addition, the Ministry of Commerce has compiled the Guidelines for Countries (Regions) of Foreign Investment Cooperation, which analyses the political, economic and humanistic factors of the recipient countries, points out the potential risks of investment projects and provides references for domestic investors. The establishment of the Information Service System for Foreign Investment Cooperation more conveniently provides additional information for Chinese firms, and further saves their information searching cost for their OFDI activities. On top of that, the development of China5 s financial industry has offered more convenient and faster financial services for Chinese investors. As China5s government proactive arrangement and planning, China5 s OFDI in non-financial sectors had shown certain growth against the downward trend. With the full implementation of “The Belt and Road” strategy after 2013, China5s OFDI flows keep increasing until 2017.
2.2 Current Status of China’s OFDI

As a latecomer in the domain of international investment, the development pattern of China^ OFDI displays its own features in terms of motivation, location, industry, entity, so on and so forth. It is crucial to shed more light on these features in order to get a more vivid picture of China’s OFDI. This section conducts a comprehensive analysis and summary of the characteristics and trends of China5 s OFDI from the aspects of scale, location, industry, entry mode and ownership, in order to provide a policy basis for the development strategy of China5 s OFDI.
2.2.1 Scale
During the period 2002〜2017, the average yearly growth rate reached to 36.5%. By the end of the year 2017, more than 20000 domestic investors had set up 39200 foreign direct investment enterprises distributed in 189 countries (regions) around the world. The total assets of those firms amounted to $6 trillion. Figure 2 displays the trend of China5 s OFDI from 2002 to 2017 in terms of the amount and world ranking of stocks. In 2017, the stock surged to $1089.04 billion, which was $451.65 billion more than it in last year (2016), the global ranking climbed to the second place from the sixth in 2016. However, compared with other countries, China’s OFDI stock is still far behind the U.S. (7799 $ billion), only accounting for 23% of the U.S., though it is quite close to the number of other countries (regions), such as Hongkong, Germany, and Netherland.

 
 
Fig 2 Trend of ranking and amount of Chinese OFDI stocks
Table 4 Outward FDI Stock of China and Other Major Countries (Regions), 2017
 
 

With respect to the flow of China5s OFDI, Fig 3 displays its trend from 2002 to 2017. It is the first time that China^ OFDI shows a negative growth with the amount of $1582.29 billion, but it was still in the second highest place in the historical record, which was 58.6 times as many as that in 2002. Figures 4 exhibits that starting from 2014, China5 s OFDI began to surpass the IFDI and China achieved to be a net capital exporter under two-way direct investment for four consecutive years. In 2017, China5s OFDI was still greater than IFDI.
Figure 3 Trend of ranking and amount of Chinese OFDI Figure 4 OFDI and FDI Comparison in China, 2002-2017 flows
 
Unit: USD 100 million Unit: USD 100 million

The Flows of OFDI —•— Global Ranking s OFDI ■ IFDI
Source: Author^ arrangement based on 2017 Statistical Bulletin of China's Outward Foreign Direct Investment
2.2.2 Location
The geographical distribution of China5 s OFDI is quite unbalanced, which is mainly centralized in Asian countries. By the end of the year 2017, the stock of China5 s OFDI in Asian countries was $1139.32 billion accounting for 63%; Meanwhile, Hongkong took up 86.1% of the stocks in Asia. The second major recipient was Latin America with a stock of $386.89 billion, accounting for 21.4%. In terms of the flow, Asia was still the largest recipient continent in 2017 with a proportion of 69.5%, although it declined 15.5% compared with last year. The investment in Europe hit a record with the amount of $18.46 billion and a 72.7% year-on-year increase, accounting for 11.7% of the total OFDI flow in 2017. The investment in Africa reached $4.1 billion, with a year-on-year increase rate of 70.8% accounting for 2.6% of the total OFDI flow. On other continents, however, the OFDI flow showed a decline to a different degree,
26

which led to the decline of 2017. Further, these figures revealed the fact that the main destinations of China5 s OFDI are developing countries, the share of which exceeded 80% both from the perspective of the stock and the flow. Among these investments in Asia, Hongkong took a relatively large share both from the perspective of stock (86.1%) and flow (82.8%). This phenomenon can be attributed to the following reasons. Firstly, due to the geographical and cultural adjacency, Chinese firms are more willing to invest in Hongkong; Secondly, via investing in Hongkong and reinvesting in the mainland of China, these firms can obtain a bunch of preferential treatments as foreign investors; thirdly, Hongkong as a tax haven, it is an ideal place to establish a control center.
Table 5 Regional Distribution of China’s Outward Foreign Direct Investment, 2017
Area Stock Flow
  Amount Share (%) Amount Share (%) YOY Growth Rate(%)
Asia 1139.3 63.00 110.0 69.50 -15.5
Latin America 386.9 21.40 14.1 8.90 -48.3
Europe 110.9 6.10 18.5 11.70 72.7
North America 86.9 4.80 6.5 4.10 -68.1
Africa 43.3 2.40 4.1 2.60 70.8
Oceania 41.8 2.30 5.1 3.20 -1.9
Total 1809.0   158.3    
 
Source: Author^ arrangement based on 2017 Statistical Bulletin of China's Outward Foreign Direct Investment
 
 
By comparison, by the end of the year 2017, China’s OFDI in developed countries was $229.1billion, only accounting for 12.7%. The reasons behind are as follows. First, the technological threshold of investing in developed countries is higher than that in developing countries; Second, the institutional and market environments are quite different from domestic ones, leading to greater potential risks. But with the development of China5s OFDI and fluctuation of the international market, the location strategy of China5 s OFDI will get changed. For example, the investment in Europe in 2017 witnessed a sharp increment with the year-on- year growth rate of 72.7 %. The proportion of China5 s OFDI in Africa is also quite small, mainly focusing on energy exploration and other low-end industries by using the cost advantages and comparative advantages.
2.2.3 Industrial Distribution

In recent years, the industries involved in China5 s OFDI have continuously become more diverse. In 2017, China^ OFDI covered 18 industries in the national economy. However, the intensity across each industry varied a lot. There were four industries, the rental and business services industry, the manufacturing industry, financial services industry, and the wholesale and retail trade industry, whose OFDI flow exceeded $10 billion (two fewer compared to that of the previous year), and their total shares accounted for 81.4% of the whole flow. Among them, the rental and business services industry remained the first, with the amount of $54.27 billion accounting for 34.3% of the total flow in that year, though it declined 17.5% year-on- year. The investment was mainly distributed in Hong Kong China, British Virgin, Singapore,

 
 

Figure 5 China's OFDI flows in Manufacturing Main Secondary Category, 2017
Source: Author^ arrangement based on 2017 Statistical Bulletin of China's Outward Foreign Direct Investment
the United States, the United Kingdom, and other countries (regions). The reasons behind might be that the business service industry has the characteristics of flexible form and easy to evade supervision. On the one hand, it can help exporters diversify their origin and avoid the hostility of the third countries to China’s investment; on the other hand, it can reasonably “evade taxes” and even help capital to escape.
The manufacturing industry ranked second place with the amount of $29.51 billion accounting for 18.6% of the total flow. Further, these investments mainly went to the chemical raw materials and chemical manufacturing, automotive manufacturing,
computer/communication, other electronic equipment manufacturing, pharmaceutical manufacturing, railway/ship/aerospace, and other transportation equipment manufacturing, etc. Among them, $11.03 billion went to the equipment manufacturing industries, which fell down 22.6% year-on-year and accounted for 37.4% of total investment in the manufacturing sector. With continuous adjustment and optimization of industrial structure and the upscaling of the manufacturing industry, China5 s strategic emerging industries have quickened the pace of internationalization and proactively explored new cooperation modes to enhance the core competitiveness and independent R&D capabilities.
2.2.4 Entry Modes
At present, Chinese firms conduct OFDI mainly via the following modes: First, set up
overseas sales companies. By doing so, firms can globalize their sales network and reduce the
intermediate costs, such as an agent fee, which is conducive to export. Establishing a sales
company is the most prevalent way for China5 s OFDI though it is a relatively simple way.
However, China5 s export of manufacturing is facing an increasing restriction of the tariff, which
especially has exerted a threat to the labor-intensive industries; Second, build factories overseas.
This mode can effectively utilize the raw materials and labor of the host country, and most
importantly, can reduce the impacts of tariff barrier. In addition, the local factories can also
serve as a center of the surrounding countries, so as to make the sales network closer
and reduce the transportation cost; Third, cross-border M&As. This mode involves a wide Table 6 China’s M&As via Direct Investment, 20042007

range of industries, including the traditional manufacturing industry, the energy industry, and some newly emerging industries.
In recent years, Chinese firms have become more proactive in participating in global market competition. In such a context, M&As have been frequently adopted as the main mode of Chinese firms5 OFDI. In 2017, Chinese overseas M&As were till thriving, 431 M&As completed in 56 countries (regions), with an actual transaction amount of $119.62 billion. More precisely, $33.47 billion were direct investment4, accounting for 28% of the M&As5 amount and 21.1% of China5 s total OFDI in that year; $86.15 billion were overseas financing, an increase more than 70% of the previous year, accounting for 72% of the M&As amount and being the record high in terms of enterprise overseas financing. China National Chemical Corporation’s acquisition of 98.06% of Swiss Syngenta’s equity with $42.1 billion was the largest overseas M&As of China and the second-largest cross-border M&As project of the
world in 2017.
Table 7 Industrial distributions of China’s M&As, 2017
Industry Number of
M&As
Amount (Billions of US Dollars) Share (%)
Manufacturing 163 60.72 50.8
Mining 22 11.41 9.5
Production and Supply of Electricity, Heat, Gas, and Water 30 10.19 8.5
Hotels and Catering services 1 6.5 5.4
Leasing and business services 38 6.31 5.3
Information Transmission, Software, and IT Services 42 6.12 5.1
Transportation, Storage and Postal Services 13 5.58 4.7
Financial Services 4 3.42 2.9
Wholesale and Retail Trade 45 3.12 2.6
Real Estate 9 2.52 2.1
Public Health and Social Work 5 1.17 1
Scientific Research and Technical Services 28 1.12 0.9
Agriculture, Forestry, Animal Husbandry, and Fishery 13 0.81 0.7
Culture, Sports and Entertainment 5 0.58 0.5
Water Conservancy, Environment and Public Facility Management 3 0.03 -
Construction 3 0.02 -
Resident Services, Repair, and Other Services 4 0.01 -
Education 3 0.01 -
 
Source: Author^ arrangement based on 2017 Statistical Bulletin of China's Outward Foreign Direct Investment
 
 
In terms of the industries distribution of Chinese firms in 2017, 18 industrial categories were covered, including manufacturing, mining, production and supply of electricity, heat, gas,
4Direct investment refers to domestic investors5 or their overseas enterprises’ M&As, which are financed by domestic investor’s own funds and domestic bank loans (excluding loans guaranteed by domestic investors.)
30

and water, etc. With respect to the amount of M&As, the manufacturing industry was $60.72 billion, twice that of the previous year, ranking first, and involving 163 projects. The mining industry was $11.41 billion, increased by 52.1% year-on-year, ranking second. CNPC and Huaxin Group acquired a 12% stake in Abu Dhabi National Oil Company, the largest M&A project in the field of oil resources in 2017. Production and supply of electricity, heat, gas and water industry reached $12.65 billion, up 12.8% year-on-year, ranking third; State Grid Corporation's acquisition of Brazil's CPFL is the largest M&As project in the relevant field of that year.
2.2.5 Distribution of investment subjects
According to the business registration type of domestic investors (Figure 6), by the end of 2017 among the $1606.25 billion non-financial outward FDI stock, state-owned enterprises had taken a share of 49.1%, decreased by 5.2% compared with that of the previous year. While the share of the non-state enterprises had reached 50.9%. Among the non-state enterprises, limited liability companies had taken a share of 16.4%. incorporated companies 8.7%, self-employed company 7.4%, private enterprises 6.9%, Hong Kong, Macao and Taiwan-invested enterprises 5.8%, foreign-invested enterprises 3%, joint-stock cooperative enterprises 0.5%, collective enterprises 0.3%, and others 1.9%, respectively. It evidenced that state-owned companies still played a leading role in the domain of OFDI, which mainly because: for one thing, the inextricable links between state-owned enterprises and the government make them more privileged to obtain those overseas business opportunities; for another thing, as the first-mover in transactional operation, state-owned firms have more foreign-related experience compared

Figure 6 Structure of China5 s Non-financial OFDI stock, by domestic investor registration types, by the end of 2017
Source: Author^ arrangement based on 2017 Statistical Bulletin of Chinas Outward Foreign Direct Investment
with private firms. But such a large-scale overseas investment of state-owned firms can easily
31

lead to political interference of the host country due to the strong political intention behind it.
Figure 7 shows that over a decade, the proportion of state-owned firms has been shrinking, and this is the first time that the share of non-state firms exceeded the state-owned firms. It reflects that on the one hand, Chinese government has improved the management system and strengthened the support for the OFDI of non-public companies; on the other hand, private firms can develop rapidly with their clear property rights, flexible organization structure, agile
 
Source: Author5 s rearrangement based on 2017 Statistical Bulletin of Chinas Outward Foreign Direct Investment
 
response, and strong innovation power.
In terms of regional distribution, in 2017, the total provincial OFDI flows of China (Figure 8) reached $86.23 billion, with a year-on-year decrease of 42.7%, accounting for 61.8% of China’s total non-financial OFDI flows ($139.5 billion). Specifically, $64.24 billion were from eastern China, accounting for 74.5% of the total provincial investment flows. $12.47 billion was from western China, accounting for 14.5% of the total provincial investment flows and with a year-on-year (YoY) increase of 8.0%. A $7.61 billion were from central China, accounting for 8.8% of the total provincial investment flows and with a year-on-year decrease of 24.7%. Shanghai, Guangdong, Zhejiang, Shandong, Beijing, Chongqing, Jiangsu, Hainan, Fujian, Tianjin were the top 10 provinces (municipalities) in terms of the provincial OFDI flows, from which a total of $67.63 billion OFDI flows were achieved, accounting for 78.4% of China’s total provincial OFDI flows (Figure 9).
As for the stocks (Figure 10), by the end of 2017, non-financial OFDI stock by local enterprises had reached $727.46 billion, accounting for 45.3% of China5s total non-financial



OFDI stock, increased by 0.9% compared with the previous year. In particular, $611.52 billion
Note: 1. Eastern China includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Source: Author^ rearrangement based on 2017 Statistical Bulletin of China's Outward Foreign Direct Investment and Hainan; 2. Central China includes six provinces, namely, Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; 3. Western China includes Inner Mongolia, Guangxi, Sichuan, Chongqing, Guizhou, Yunnan, Shannxi, Gansu, Qinghai, Ningxia, Xinjiang, and Tibet. 4. Three Northeast Provinces include Heilongjiang, Jilin, and Liaoning; The data tag includes region, OFDI flow, share, and the year-on-year (YoY)growth rate.
came from eastern China, accounting for 84.1% of the total. $53.08 billion came from western China, accounting for 7.3% of the total. $41.55 billion came from central China, accounting for 5.7% of the total. $21.31 billion came from three provinces in northeastern China, accounting for 2.9% of the total. Guangdong was still the largest province as the source of OFDI stock with $189.71 billion, followed by Shanghai with $112 billion, and Zhejiang, Beijing, Shandong, Jiangsu, Tianjin, Liaoning, Fujian, Hainan, etc. Both from the perspective of flows and stocks, China’s OFDI mainly stems from the eastern coastal areas (Figure 11).
Figure 10 Regional weightings of China’s OFDI stocks by local enterprises, by the End of 2017
Unit: Billions of US Dollars
Source: Author^ rearrangement based on 2017 Statistical Bulletin of Chinas Outward Foreign Direct Investment

2.2.6 Current issues
(1) Increasing political and economic risks
Since the global financial crisis in 2008, the international political, economic and diplomatic situation have become more unstable and precarious. The growing anti-globalization trend marked by the withdrawal of the United States from the Trans-Pacific Partnership Agreement (TPP), and the Brexit has also led to the intensification of protectionism. Many countries have become extremely sensitive to the chain reaction of investment policies. Under the principle of investment liberalization, different sorts of restrictive policies and regulations related to investment have been widely used by several countries. Industrial protection has gradually gone beyond the scope of the economy with a political color, and the scope of protection has gradually extended from the protection of traditional industries to emerging services, financial industry, high-tech industry, etc. The purpose of protection Wois not to cultivate the ability of free competition, but to strengthen the monopoly of domestic and foreign markets. The main means of protection are invisible protection, such as technical standards, environmental protection, labor standards and so on. In addition, the change of regime and regulations caused by the general election in various countries will also lead to an increase in risks and uncertainties faced by Chinese firms9 transactional operations.
With the continuous development of China5 s economy and the large-scale OFDI of Chinese firms, "China Threat StatementM has gradually stepped in the spotlight of world opinion. Even some countries condemn China for its frenzied plunder of foreign resources and neocolonial tendencies in its foreign investment. Because of the differences in ideology and national strategy, the West World always tends to associate the “going out” of Chinese enterprises with the will of the state and has a great political tendency when scrutinizing China’s investment. In addition, the instability of the global economic recovery and the financial turmoil has rendered exchange rate risk become one of the main risks of Chinese firms9 OFDI.
(2) Insufficient policy support
Firstly, investment facilitation still needs to be improved urgently. Although the examination and approval system for overseas investment projects has been changed to the record-keeping system, the whole procedure is still very complicated and time-consuming, which will cost at least 90 days to finish and then lead to missing the business opportunity. Secondly, there is no clear industrial policy or guidance for Chinese firms5 foreign investment activities currently. In terms of the stock of Chinese firms5 OFDI, industrial distribution is relatively concentrated in the leasing and business service industry, mining industry, financial industry, and the wholesale and retail industry. The growth of OFDI is mainly driven by the primary industry, low-end service industry, and the financial industry .While the proportion of the manufacturing industry is relatively small, although China5 s investment in technologyintensive industries has also increased in recent years. In addition, many domestic investors, especially for private firms, lack sufficient foreign-related experiences and reasonable evaluation of their projects in terms of market prospect, trade friction, political risks, culture and so on, which can affect firms9 business safety and sustainable development. Thirdly, Chinese firms5 foreign investment lacks proper coordination—Sometimes several Chinese enterprises compete with each other in the same project. They often adopt low-price promotion, competitive bidding even vicious competition, which not only damages their own interests but also easily causes economic and trade disputes with foreign governments or enterprises.
(3) Financial constraints
Domestic financial institutions fail to fulfill their role as bridges and ties in the process of Chinese firms5 foreign investment, lagging in providing investment proposals and financial services, and lack of follow-up support, resulting in Chinese enterprises having to be subjected to foreign investment banks. Compared with developed countries, China5s enterprises have more restrictions on domestic financing. In 2017, $86.15 billion of M&As were overseas financing, an increase of more than 70% than the previous year, accounting for 72% of the total M&As amount and being the record high in terms of enterprise oversea financing. For private firms, they are more suffering with respect to financing restrictions due to the high financing threshold of policy banks and the discrimination of commercial banks. In addition, China5s mechanism of providing credit guarantees and insurance for foreign investment is still unsound. Firms5 overseas projects can only be mortgaged by domestic assets when financing at home, while overseas assets cannot be mortgaged. What is more, in terms of financial credit, domestic enterprises are often limited by the loan quota and specific foreign exchange quota, which has a particularly prominent impact on private enterprises. Taking the State Administration of Foreign Exchange as an example, the main purpose of some exchange regulations is to effectively crack down on the international illegal capital flows, however, it will hinder private enterprises5 OFDI and financing to a certain extent, and may miss out on good opportunities for cross-border M&As.
(4) Lack of managerial talents in multinational corporations
The lack of excellent overseas project management personnel has become an obstacle to the further development of enterprises’ overseas business. Many Chinese MNEs are facing human resource bottlenecks, and lack of enough managerial talents who are experienced in transnational cooperation. Compared with developed countries, most of the Chinese expatriates are not qualified, experienced and have no comprehensive knowledge of international trade and investment, and local cultures. As a result, overseas enterprises cannot achieve their foreign business very smoothly and, which can seriously affect their investment benefits.

Chapter Three Firms Productivity and OFDI
This chapter mainly aims to empirically examine the relationship between Chinese manufacturing firms9 OFDI and their productivity based on the firm heterogeneity theory (Helpman et al. 2004). More specifically, this chapter mainly includes the following contents. First, the author briefly reviewed the mechanism of firms5 OFDI based on HYM model and then untied some presumptions, for example, countries are no longer symmetric, to show different potentials. Second, by using the dataset of Chinese manufacturing firms the author tests whether the HYM model is applicable for explaining China5 s case and which kind of firms are more likely to undertake OFDI when considering firms5 characteristics.
3.1 Mechanism of firms9 OFDI
3.1.1 Consumer preferences
Helpman et al. (2004) proposed that firm-level specific features may well diversify firms9 investment strategy and performance, which is the theoretical framework of this study. CES utility function represents consumer preferences.
U = , 0 <a<l (3.1)
where x(a>) is the demand function of goods co, Q denotes a set of differentiated goods co. s=l /(I-a) and £>1, represents the substitution elasticity. With budget constraint,
= 1 (3.2)
where p(() is the price of good co, and I denotes the income level of a country. By maximizing the utility function, we can yield the Marshallian demand function of good co
p ⑼一 %■ p/"£
Pj is the price index of country;, whose formula is as follows: PJ =
3.1.2 Firms9 production and market strategy
Suppose that labor is the only input factor of the manufacturer^ production. If the output of each unit labor is cp, then ^denotes a firm^ productivity. Prior to entering the market, firms face uncertainty about how productive they will turn to be. To start producing a particular variety, a firm has to bear a fixed cost,/e units labor. Upon paying this sunk cost, we assume that firms5 productivity level is exogenous and subjected to Pareto distribution.
G{cp) = 1 — cp~k, k > a — 1 37

If the wage level of country/ is W/, then the marginal cost a firm is C/= W/ /^9, which varies across firms. By profit maximization, a firm sets a price such that:
Pi =-=—
a acp
Let9s postulate domestic fixed cost is fd{ , then the total cost TC —
R = p. x(〇))and profit of a firm for producing units product are as
nf = x{(x))wt/acp - (/^ + x<i))Wi/(p)
Substitute formula (3.3) into (3.7), we can obtain:
^ = (1-a)(奶广 -/? (3.8)
where a firm^ profit is the function of its productivity. As e>l, a firm^ profit is positively associated with its productivity. To simplify formula (3.9), set Ai = (1 — a)(aP^)£_1/^ , then we can get:
Atcp8-1 -ftd (3.9)
Melitz (2003) introduced trade frictions, the fixed cost for exporting to country; is ff. Besides, firms need to pay for the transport costs (iceberg costs) Tj > 1. The fixed cost of investing in countryj is f-nv , and the relationship is fjUV > > ftd . Thus, in such context, the profit of a
firm for exporting and investing are as follows:
tt/ = (1/TjWiy-1 Aj-cp8-1 -// (3.10)
njnv=(l/wjy~1 Ajcp8-1 -flnv (3.11)
Under the condition of market clearing, firms5 economic profits equal to zero. Thus, according to formula (3.9), (3.10) and (3.11), we can derive the productivity level of different production modes:
^d = (^:)£-iwi (3.12), cpexp = (^—(3.13), cpinv = (3.14)
where 7p,7p and 7p respectively denote firms5 zero-profit productivity of serving in the domestic market, exporting and OFDI. When a firm^ productivity (pis below 7pd, it will be forced to exit the domestic market. When a firm^ profit of exporting equals that of OFDI, namely when 7rJ(cp)= ^^^(cp) , it can choose either to export or invest overseas. Thus, from

formula (3.10) and (3,11), we can get the OFDI cut-off productivity:

cp < cp < cp (3.16)
which means that if the difference in the fixed cost of OFDI and export is greater than that of the variable cost, then the zero-profit productivity of OFDI is higher than that of exports. Firms will choose OFDI rather export when firms5 productivity cp > ^ . This is in line with the expectation of HYM model, which is based on the horizontal FDI practices of developed countries. Because they assume that (1) countries are symmetric, namely, there is little disparity between the host country and home country in terms of market scale and wage level (^4^ ^ Aj, T{Wi ^ Wj) and (2) export and OFDI are substitutive for each other. Thus, under these assumptions and from equations (3.13), (3.14), (3.15) we can obtain such relationship:
7pd <^exv<7^nv
 
   
which reveals that the most productive firms serve the foreign market via subsidiary sales; lower productivity firms serve foreign markets via exports; still lower productivity firms only serve the domestic market. We can also catch the intuition from Fig 12 below. HYM model well explained the horizontal FDI among developed countries, which had been heavily tested by subsequent empirical analysis (Mayer and Ottaviano, 2007; Yeaple, 2009).
However, the real situation is different from what the model portrayed. Countries are not symmetric, developed countries and developing countries are different from market scale or wage level. In addition, compared with the majority of developing countries, China5s wage level actually is relatively higher. Therefore, let?s assume that the wage level of the home


which means that if the difference of variable cost between OFDI and export is greater than that of fixed cost, then the zero profit productivity of export is higher than that of OFDI (Fig 13).This
dfjnv ^ dlj ' dwj
which implies that a firm^ zero-profit productivity of OFDI is positively correlated with the fixed costs producing in the host country and its wage level. On the contrary, the larger the market size of the host country, the lower the zero-profit productivity of OFDI. If the productivity threshold of the host country is higher, the fewer enterprises will satisfy the entry conditions, thus the fewer enterprises will choose OFDI. This reduces the total number of entrants but increases the average productivity of OFDI-firms. On the contrary, the lower the threshold of productivity of entering the host country, the more enterprises will meet the entry conditions and the more enterprises will engage in. This augments the number of entrants but reduces the average productivity of OFDI-firms. In addition, as countries are asymmetric, there are differences in productivity thresholds for entering different host countries. Obviously, firms with higher productivity can get access to more host countries than those with lower
40

productivity.
3.2 Empirical Analysis of Firms9 Productivity and OFDIDecision
3.2.1 Data
This study relies on two disaggregated databases. One is the uChina Industrial Enterprises Database” which is derived from the “Chinese Annual Survey of Industrial Firms” conducted by China9 s National Bureau of Statistics, This dataset contains the basic and financial information of China s manufacturing firms whose annual sales exceed RMB 5 million with the time span from 1997 to 2013. But starting from 2011, the annual sales threshold was lifted up to RMB 20 million. Given this fact, this paper employs this dataset from 2011 to 2013, covering more than 200,000 firms each year. In summary, this dataset incorporates more than 40 useful variables that can be used. However, this dataset lacks the firms5 OFDI information. In addition, on account of non-standardized financial statements or report errors from some firms, noisy observations exist in this dataset as well. Hence, it is necessary to clean the original data before doing further analysis. The author conducts the data cleaning via the following filtering criteria. First, following Feenstra, Li, and Yu (2014), observations with missing primary financial variables (such as total asset, gross industrial output value, and net fixed assets) are omitted; Second, firms with fewer than 10 workers are excluded from the sample because they are under a different legal regime (Brandt, Van Biesebroeck, & Zhang, 2012; Yu, 2015); Third, observations satisfying the following criteria are excluded according to the basic rules of the Generally Accepted Accounting Principles (GAAP): (a) liquid assets are greater than total assets; (b) fixed assets are greater than total assets; (c) net fixed assets are greater than total assets; (d) an invalid founded time exists (i.e., the opening month is earlier than January or later than December.); (e) the firm's identification number is missing.
Another dataset utilized in this paper comes from the uList of Foreign Investment Enterprises (Organizations)’’ provided by China’s Ministry of Commerce. It covers rich information of Chinese MNEs that have conducted non-financial OFDI, including parent firm names, investment destinations, registration addresses, foreign subsidiary names, approval dates, and business scopes except for the financial information of those firms. All the Chinese MNEs engaging in non-financial OFDI from 2011 are covered in the dataset. Thus, by combining these two datasets through matching their firms9 names, we can investigate the firms5 productivity and their OFDI behavior.

 

Regarding the measurement of Total Factor Productivity (TFP), normally it is estimated by the OLS method as follows:
vait = a〇 + plit + Skit + sit (3.20)
Where vait denotes a firm^ industrial added value, l and k respectively represents the labor input (measured by the yearly average employment) and capital stock, £it is the error term. However, there is a two-way causal relationship between TFP and firms5 factor input level. For example, the higher the enterprise^ productivity, the more investment, and employees it may have. Thus, in order to deal with the endogenous issue, this paper utilizes the method of Levinsohn and Petrin (2003):
vau = Pht + (Kht + mu) + sit (3.21)
in which mit means a firm^ intermediate inputs; (p(kit + mit) is the function of capital stock and intermediate inputs, which is the third-order polynomial approximation represented by k and m. We can obtain the coefficient of labor and capital stock through the estimation of formula (3.21), then calculate the TFP via the following formula:
TFPit = vait — piit — Skit (3.22)
In practical calculation, variables in (3.22) and (3.23) are taken in logarithmic form.
Table 8 Summary statistics of main variables
    Variables   Observation Mean S.D Min Max
1 OFDI outward foreign direct investment dummy 498,723 0.001 0.036 0 1
2 SOE state-owned enterprises dummy 498,723 0.034 0.181 0 1
3 FIE foreign-invested enterprises (including Hong, Taiwan, and Macao) dummy 498,723 0.175 0.380 0 1
4 Export(exp) export status dummy 498,723 0.587 0.492 0 1
5 East East China dummy 498,723 0.766 0.423 0 1
6 In (TFP) total factor productivity   498,723 7.052 0.684 6.105 15.326
7 In (TFP)t-l TFP of period(t-l)   332,482 7.021 0.672 6.105 13.814
8 InM intermediate inputs   498,723 11.317 1.175 2.708 18.817
9 InY total output   498,723 5.642 0.805 2.398 11.899
10 InL number of employments (firm size)   498,723 11.666 1.126 7.546 19.267
11 age firm age   498,723 10.317 7.552 1 413
12 ROS Return of Sales=total profit/total sales   498,723 0.058 0.234 -6.600 146.521
13 k_density capital_intensity=ln (net value of fixed assets / No. of employment)   498,723 3.820 1.455 -6.805 13.443
14 Absorptivity TFPi/TFPmax   498,723 0.670 0.080 0.399 1
 
Based on the previous literature, the author incorporates firm characteristics as control
 
 
variables, including the capital intensity (measured by the ratio of fixed assets to the number of employees), firm size (measured by the annually average employment), foreign experience (pre-OFDI export based on export status), firm ownership (foreign-invested firm or state-owned enterprise), firm age (Wang, Hong, Kafouros, & Wright, 2012), and the dummy variable for regions and industries. Table 8 displays the summary statistics of the main variables.
3.2.3 Model
As we can solely obtain a firm^ OFDI status but not the scale of its OFDI from the current dataset, thus based on the previous research and observable characteristics, this paper will estimate the probability of Chinese firms investing abroad by means of Logit model:
Logit(ofdiijkt = l') = at + Sk + rjj + plnTFPtj^.^ + 6nCijkt + £ijkt (3.23)
where is a dummy denotes whether a firm conducts OFDI or not, if so, = 1,
otherwise = 0; / represents firm entity, j. denotes the industry a firm belonging to,众
implies a firm’s domestic location, and f denotes the time (year); 7^,5/^ indicates the industrial fixed effect, regional fixed effect, and time fixed effects to control those unobservable factors that may affect firms5 OFDI decision. TFP is the core explanatory variable, total factor productivity. C represents those control variables, as suggested by preceding literature, including capital intensity, firm size, firm age, pre-OFDI export status, ownership and so on. s is the error term.
As we know, there might exist a “learning effect” for firms that have already had OFDI experience to improve their productivity. Then firms with high productivity are more likely to invest abroad. To tackle the endogenous problem, the author takes the following method: (1) Concerning OFDI firms, only those conducted their first OFDI are selected into the research sample. The mechanism is shown in the following Table 9. Because it is not sure whether the OFDI only occurred in 2011 is firms9 first OFDI or not, the author only selected firms of type 1, 6, 7, and 8 into the sample. To a certain extent, it avoids the possibility that OFDI enterprises can enhance their productivity through “learning effect”;(2) the firms’ TFP are lagged one period to avoid the influence of two-way effect between firms5 productivity and OFDI.
Table 9 Types of firms according to their OFDI statues
  1 2 3 4 5 6 7 8
2011 0 1 1 1 1 0 0 0
2012 0 0 1 0 1 1 1 0
2013 0 0 0 1 1 0 1 1
 

To verify whether there exists a collinearity problem among the independent variables, the author first reports the correlation matrix (Table 10). The greatest correlation coefficient of independent variables is 0.4666, less than 0.5, thus, there is no strong collinearity between independent variables. Besides, we use the robust standard error to reduce the impacts of heteroscedasticity.
Table 10 Correlation matrix
    1 2 3 4 5 6 7 8 9 10
1 OFDI 1                  
2 SOE 0.0155 1                
3 FIE 0.0020 -0.0868 1              
4 Export 0.0239 -0.0020 0.1315 1            
5 East 0.0161 -0.0793 0.1838 -0.1507 1          
6 lnTFP(t-l) 0.0561 0.1415 0.0774 0.0779 -0.0622 1        
7 InL 0.0409 0.1198 0.1694 0.1284 -0.0004 0.4456 1      
8 age 0.0183 0.2019 0.0187 0.0350 0.0499 0.0868 0.1663 1    
9 ROS 0.0120 -0.0182 -0.0456 0.0112 -0.0634 0.0657 -0.0147 -0.0237 1.0000  
10 k-density 0.0349 0.1097 0.0140 0.0642 -0.1007 0.4666 -0.0963 0.0163 0.0904 1
 
 
 
Table 11 shows the regression results. At the very beginning, I just use the naive simple regress with robust standard error, then add other control variables, and regional effect, time effect, and industrial effect. The coefficients represent the odds ratio. Compared with column (1) and (2), the coefficient of lagged TFP has a big slip but it is still strongly significant. After we add the regional effect, time effect and industrial effect one after another, the coefficients of TFP just vary slightly (column (3) to (5)). In addition, concerning the joint significance of the model, the Pseudo R2 is not very high. Thus, I suspect whether it is due to the sample bias. Because in the sample of 353393 observations, merely 0.4% of firms have OFDI behavior. Given this situation, I randomly sampled 2500 firms by their OFDI behavior, in which firms with OFDI and without OFDI have the same proportion. Column (6) shows the resits: both the coefficient of TFP and the joint significance of the model increase substantially. The coefficient of TFP indicates that the odds ratio of OFDI will increase by 74% for every one percent increment in TFP. Generally, the results have evidenced that the higher the productivity of enterprises, the more likely they are to conduct OFDI, which is consistent with the HYM model and current empirical research.
Regarding other control variables, it is obvious that firms of east China have remarkably a higher propensity to invest abroad. Because the majority of firms in the eastern coastal areas once obtained the priority to development plus the advantageous geographical location, they
Variables (1)
OFDI
(2)
OFDI
(3)
OFDI
(4)
OFDI
(5)
OFDI
(6)
OFDI
LnTFP(t-i) 2.489*** 1.443*** 1.441*** 1.481*** 1.576*** 1.736***
  (0.072) (0.079) (0.079) (0.080) (0.084) (0.176)
InL   1.493*** 1 499*** 1.441*** 1.389*** 1.500***
    (0.066) (0.067) (0.062) (0.059) (0.111)
age   1.007*** 1.007*** 1.006*** 1.005** 1.011*
    (0.002) (0.002) (0.002) (0.002) (0.007)
ROS   1.156** 1.157** 1.133** 1.135** 5.382**
    (0.069) (0.069) (0.060) (0.066) (3.619)
k_density   1.342*** 1.341*** 1.390*** 1.415*** 1.334***
    (0.035) (0.035) (0.037) (0.038) (0.062)
SOE   0.949 0.945 0.933 0.815* 0.699
    (0.112) (0.112) (0.111) (0.100) (0.175)
FIE   0.613*** 0.611*** 0.496*** 0.403*** 0.541***
    (0.049) (0.049) (0.043) (0.039) (0.076)
exp   2.408*** 2.404*** 2.344*** 2.902*** 2.291***
    (0.174) (0.174) (0.171) (0.259) (0.287)
East   3.099*** 3.096*** 2.705*** 1.954*** 2.818***
    (0.272) (0.272) (0.239) (0.423) (0.813)
Year effect No No Yes Yes Yes Yes
Industrial effect No No No Yes Yes Yes
Reginal effect No No No No Yes Yes
Obs. 353393 353393 353393 353393 351511 2490
Pseudo R2 0.049 0.082 0.082 0.101 0.113 0.237
 
Note: Standard errors are in parenthesis p<0.01, *^p<0.05, *p<0.1
 
 
are more productive than firms in the Midwest area. Thus, they are more likely to invest abroad. The results also testify that firms with export experiences are more willing to engage in OFDI. Melitz (2003) has theoretically analyzed that only efficient enterprises could export. Helpman et al. (2004) claimed that the most efficient enterprises not only export but also invest abroad. In addition, from the practical experience, the more enterprises get familiar with the international market, the more likely they are to invest abroad. As for ownership, foreign- invested enterprises (FIE) are less likely to do OFDI. Because they are already as foreign investors when doing business in China, for most of them, there is no need to undertake the OFD again. For state-owned enterprises, they are less inclined to invest overseas as well, which is at odds with the reality that nearly half of Chinese MNEs are SOEs. This can be attributed to the sample bias. Because in the database, SOEs only accounts for a tiny proportion. In addition, the coefficient of SOE is not significant. With respect to capital intensity and returns on sales (ROS), both of them have a significantly positive effect on firms5 OFDI decision, which proves that firms with higher capital intensity and profitability are more apt to undertake OFDI to seek more business opportunities or expand their business scope in the global market. Firm age is also significantly positively correlated with the OFDI odds ratio, though the effect is relatively small. It might be on account of the fact that the longer an enterprise operates, the more comprehensively it knows about the industry and the external market or gains more experience,
so the more likely it is to invest abroad. In terms of firm size, the results exhibit that it has a significant and positive effect on firms OFDI decision due to the fact that firms with bigger sizes may have some advantages in human capital and market competitiveness when going global.
3.2.5 Host country income level and firms9 productivity
According to the HYM model, ruling out the least productive firms exiting the industry, among the remaining firms that are capable of serving the domestic market and foreign market, the most productive firms will invest abroad and the less productive ones choose to export. In addition, previous theoretical analysis has mentioned that host countries, income level, market share, and economic development will affect the productivity threshold of a firm^ OFDI decision. The higher the host country's income, the higher the marginal cost and fixed cost of the enterprise^ foreign investment, thus, the higher the productivity threshold of the enterprise^ investment. Hence, from the theoretical point of view, we may speculate that the difference in wage level in the host country may lead to the disparity of OFDI-firms’ productivity, such that firms5 productivity in low-wage countries may be relatively lower. Therefore, this study will empirically test the following three propositions: (1) Whether the productivity of exporters is greater than that of the pure domestic firms; (2) Whether the productivity of OFDI firms both in low-income and high-income countries is higher than that of domestic firms? (3) Are OFDI firms are more productive than sheer exporters? (4) OFDI firms investing in high-income countries (HICs) are more productive than those investing in low-middle income countries (LMICs)?
Table 12 exhibits the mean of lagged TFP (column (1)〜(4)) and the mean difference of different groups (column (5)〜(10)), which also takes control variables, year effect, industrial effect, and regional effect into consideration for the robust test. The results show that OFDI firms indeed are more productive than pure exporters and firms only serving in the domestic market, namely OFDI firms are the most productive. However, the result in column (5) reveals that the productivity of exporters is not necessarily greater than that pure domestic firms according to Melitz model (2003), which again verifies the “productivity paradox” of Chinese export enterprises. This can be mainly attributed to the fact that Chinese exporters are still in lower-end of the global value chain, and the labor-intensive products still play a leading role among China’s export commodities.

Variable (1)
Mean
Domestic
(2)
Mean
Export
(3)
Mean
High
(4)
Mean
Low
(5)
Domestic vs Export
(6)
Domestic vs HIC

Domestic vs LMIC

Export vs HIC
(9)
Export vs LMIC
(10) LMIC vs HIC
TFP(t-i) 6.959 7.064 7.659 7.657 -0.012*** 0.143*** 0.211*** 0.217*** 0.217*** -0.036
P-value (0.622) (0.702) (0.999) (1.007) (0.000) (0.000) (0.000) (0.000) (0.000) (0.313)
Controls _ _ _ _ Yes Yes Yes Yes Yes Yes
Year
effect
        Yes Yes Yes Yes Yes Yes
Industry
effect
        Yes Yes Yes Yes Yes Yes
Regional
effect
        Yes Yes Yes Yes Yes Yes
Obs. 142,099 210,038 952 304 352,137 143,051 142,403 210,990 210,342 1,256
 
 
 
Note: Countries have been classified by their income level as measured by per capita gross national income (GNI). Accordingly, countries have been grouped as high-income, upper middle income, lower middle income and low-income Countries with less than $1005 GNI per capita are classified as low-income countries, those with between $1,006 and $3,975 as lower- middle-income countries, those with between $3,976 and $12,275 as upper- middle-income countries, and those with incomes of more than $12,276 as high-income countries. The GNI per capita in dollar terms is estimated using the World Bank Atlas method and the classification is based on data for 2010.

According to the report6 on “Development of China’s Foreign Trade in 2012” issued by China5 s Ministry of Commerce, the export of labor-intensive products in 2012 accounted for nearly 75% of China5 s total export value in 2012. In addition, comparing the mean of TFP of firms investing in HICs (column (3)) and LMICs (column (4)), there only exits a slight difference. Besides, the result in column (10) shows that the OFDI firms investing in HICs are not necessarily more productive than those investing in LMICs, although the mean difference is not significant. This seems at odds with the theoretical expectations that firms investing in HICs have higher productivity than those investing in LICs. This phenomenon might be due to the industrial distribution of Chinese MNEs5 OFDI. According to the 2011 Statistical Bulletin of China's Outward Foreign Direct Development, the flows of leasing and business services, mining, wholesale and retail trade accounted for 66% of the total OFDI flow, while manufacturing only accounted for nearly 10%. In addition, 62% of leasing and business services rushed into HICs and the remaining 38% flowed into low-middle income countries. While, in terms of green-land investment, 28% rushed into HICs and 72% flowed into low-middle income countries. Therefore, in contrast, China5s OFDI related to business services in HICs accounts for a larger proportion of services, while local production investment only takes up a smaller proportion. As a result, even though the local income level is high, there is still no significant

47

increase in the cost of firms5 investment so that there is no increase in the productivity threshold firms9 investment in high-income countries. By contrast, a large number of firms investing in low-middle income countries need to undertake the production and processing based on their own specific edge, which escalates the marginal cost and fixed cost of firms5 investment, further uplifts the productivity threshold. To sum up, OFDI firms are the most productive compared with pure exporters and firms only serving in domestic markets. (However, the productivity of OFDI firms investing in HICs is not necessarily greater than that of firms investing in low- middle countries.)

Chapter Four Impacts of OFDI on Firms’ Productivity
The previous chapter mainly aims to resolve the following questions: (1) Which kind of firms have a higher propensity for investing abroad; (2) Is there any ex-ante productivity disparity between firms investing in high-income countries and those investing in low-middle income countries, which are the antecedent features of Chinese firms when they decide to engage in OFDI. On this basis, this chapter will shift our focus onto the OFDI-led productivity effect, since productivity has widely been perceived as a determinant of firms9 sustained competitiveness and survival. China, as an emerging market country, its firms5 competitiveness is still inferior to that of developed countries5 firms in general as a whole. Chinese firms have been are increasingly going global in the recent decade, seeking strategic resources such as R&D, technology and human capital of developed countries. Thus, under this circumstance, the core of this chapter is to verify whether Chinese MNEs OFDI is beneficial to their productivity growth.
4.1 Transmission mechanism of OFDI and hypothesis
This section is mainly to summarize the transmission mechanisms for how OFDI will affect firms5 productivity. Based on previous literature, five mechanisms are adopted to enhance firms9 productivity through OFDI, specifically including economies of scale, coordination mechanism, learning effect, cross-border M&As, and joint R&D mechanism.
4.1.1 Economies of scale
Companies can achieve economies of scale by increasing production and lowering costs when production becomes efficient. How does a firm fulfill the economies of scale through OFDI? Export! Lispey and Wesis (1981), Blonigen (2001), Fontagne and Pajot (2002) by using the data from the U.S., Japan, and France respectively, they founded that OFDI and export were mutually complementary, which verified that OFDI seemed to enhance export. In addition, China^ domestic empirical research evidence also supported this contention. The increase of export will undoubtedly elevate firms9 output, and the increase of output will inevitably reduce the fixed cost of per unit product, so the effect of scale economy of enterprises come into being. Further, the reduction of fixed cost per unit product not only apportions the cost of R&D but also improves the productivity of enterprises.
4.12 Profit feedback mechanism
Firms5 productivity improvement is mainly driven by technology upgrade, while
49

technology upgrade benefits form R&D investment and technology innovation, both of which, requires massive monetary and human capital input. Then, how do the overseas subsidiaries contribute to enhancing parent firms5 productivity? The significance of foreign subsidiaries5 capabilities to develop, create and integrate knowledge via both their internal and external networks has been underlined in the international management literature (Andersson et al., 2002; Phene and Almeida, 2008). According to Ambos and Schlegelmich (2007),foreign subsidiary’s role can be categorized into three types: (1) units that adapt products to local market needs, (2) units that exploit the MNEs9 technological competencies on a global basis, and (3) units established to augment or create new technological competencies abroad. Sorts of typologies of these roles have been utilized in existing studies. The conceptualization of foreign units used here is adopted from Ghoshal (1986), and they are subsequently referred to as (1) implementer subsidiaries, (2) contributor subsidiaries and (3) innovator subsidiaries. In terms of the first type of subsidiary, they just follow the command of the parent firm and strive to cater to the local tastes. Their contribution to parent firms’ productivity improvement is to make as many as profits for R&D and technology innovation investment. Thus, the profit feedback mechanism is mainly applied to the implementor subsidiaries.
4.13 Learning effect and reverse knowledge transfer
Apropos of contributor and innovator subsidiaries, especially when they are established in advanced countries, they can get access to the advanced technology, product information, managerial tactics and integrate into a vigorous and innovative environment. Thus, no matter by learning, absorbing these external advantages or by creating new technology or new product in such an innovative environment and then transferring them back to their parent firms, they can help parent firms to escalate their productivity. Potterie and Lichtenberg (2001) discovered that investment in R&D-intensive countries contributed to productivity growth in home countries. They contended that home countries can benefit from the reverse spillover effect of technology through OFDI. Regarding product innovation, developed countries such as Europe and the United States were the birthplace of the latest products in the world. Investment in these countries would help enterprises learn and absorb the latest product design, customer demand, and future innovation directions, and then promote enterprises to carry out relevant R&D and innovation. Finally, advanced management and marketing skills. Developed countries are not only the providers of advanced technology and the latest products but also the cutting-edge business and management models. Thus, exposing to such a vigorous business environment, firms will learn to optimize their own management, marketing skills and business models.
Blomstrom et al (2000) revealed that Japanese firms5 investment in developed countries like Europe and the U.S. were conducive for them to learn technology and product innovation, and then to ameliorate their productivity and the industrial restructuring.
4.1.4 Purchase and cross-border merger and acquisitions
For later-comer countries, the most straightforward way to obtain the advanced technology of developed countries is to purchase and cross-border M&As. For instance, Chem China acquired Syngenta in 2017, a Swiss agrochemical and seed company, with a total value of $43 billion; Midea Group, a Chinese household appliance manufacturer, has always attached great importance to industrial robotics. In 2016, he offered to buy Kuka Group, a German manufacturer of industrial robots with €4.5 billion; Qingdao Haier acquired the household appliances department of American company General Electric. This enables acquirers to directly learn and absorb the advanced technology of foreign enterprises, quickly narrow the technological gap with foreign companies, and then improve their productivity. Branstetter (2006) found that the number of patents filed by Japanese firms has increased significantly after the M&As of American firms, which showed that Japanese enterprises promoted their technological progress after learning, absorbing and mastering the technology of American enterprises. Pradhan and Singh (2017) studied the impact of cross-border M&As on the automotive industry. The results evidenced that Indian automotive companies5 cross-border M&As had successfully integrated foreign R&D resources and advanced technology, thus promoting the productivity of the Indian automotive industry.
4.15 Joint research and development
Joint R&D refers to R&D activities that are jointly invested by enterprises from two or more nations. Joint R&D generally affects enterprise productivity through three channels. Firstly, joint R&D can help to absorb high-quality R&D resources from developed countries. Developed countries possess a better innovation environment, human resources, and technological level. Joint R&D is conducive for late-developing enterprises to make use of these high-quality resources to promote their productivity progress. For example, Lenovo Group has set up a joint R&D center in Silicon Valley of the United States, employing local R&D technicians and utilizing local technology and equipment for R&D innovation. Huawei has set up several joint innovation centers in Europe, America, and other developed countries. Secondly, joint R&D may well promote parent firms5 technological progress through reverse technology transfer, which enables the parent companies to quickly master the advanced technology, and then elevate their productivity. Thirdly, Joint R&D among enterprises is conducive to reducing R&D costs and risks, thus it will help enterprises to carry out more R&D activities, and then promote the technological progress.
To sum up, OFDI can enhance firms’ productivity through the above mechanisms, however, it is also a risky activity as the previous discussion we mentioned in Chapter two. Thus, whether Chinese firms OFDI is conducive for their productivity is worthy of shedding more light.
4.2 Data and Methodology
42.1 Data and variables
The dataset utilized in this chapter is consistent with that of the previous chapter, but here the author just selects those firms only started to invest in 2012 and those firms never had OFDI activity into the sample to avoid the learning effect. Further, to cope with the endogeneity and self-selected bias, the author used the propensity score matching method picked out a more qualified sub-sample. In total there are 649 firms started to invest abroad in 2012. Regarding the variables, besides those variables representing firms5 characteristics, firms9 knowledge absorptivity is also taken into consideration, which is measured by the ratio of a firm^ TFP and the leading firm^ TFP in this industry. It is suggested that technology diffusion is not automatic and firms need to possess basic technical knowledge to adopt advanced technology (Glass and Saggi, 2000 ; Zahra and Hayton, 2008). In accordance with this view, firms near the technological frontier tend to have greater capacities to identify, assimilate, and exploit other firms’ technology.
422 Methodology
The main challenges for unraveling the causality between firms5 productivity mark-up and their OFDI behavior are endogeneity and self-selected bias, as firms with high productivity are more likely to undertake OFDI. Thus, the simple least square estimation might be untenable. Enlighted by previous studies, the author uses propensity score matching (PSM) method, which creates the missing counter facts of firms that have foreign subsidiaries, to pick out the OFDI firms and non-OFDI. It does so by paring up a firm that undertakes OFDI with a non-OFDI firm on the basis of the similar pre-OFDI observable firm characteristics that have explanatory power in firms OFDI decision, such as TFP, capital intensity, returns on sales, firm age, and export status. Then to cope with the causal effect between OFDI and firms5 productivity growth, the author utilizes the difference-in-difference (DID) approach. Based on the logic of PSM and
DID, the sample is divided into treatment group (firms only started to invest abroad in 2012) and control group (firms have never done OFDI during this period), by which the OFDI-led productivity effect can be captured by the average treatment effect on the treated (ATT).
EiAtfpilofdii = 1) = E(itfpls\ofdi = 1) - E{tfp°is\ofdi = 1) (4.1)
where the dummy variable ofdi means whether a firm has OFDI or not, ofdi=l means a firm starts to invest abroad, and ofdi=0 means a firm has no OFDI; The base period of a firm^ OFDI is denoted 0, and s>0 denotes the number of the year after a firm starts to invest abroad. t/p& denotes the productivity if a firm begins to undertake OFDI, and t/p& means the pre- OFDI productivity. The crux of getting the ATT is to find out the counter facts of the treatment group, namely, the control group by employing the propensity score matching method. Because E(tfpis\ofdi = 1), in fact, is unobservable after a firm has conducted OFDI. On the basis of the information (TFP, capital intensity, returns on sales, firm age, and export status) prior to the year when a firm started to invest abroad, we use the logit model to predict the propensity score of each firm. If the balancing condition is satisfied, the kernel matching method is to adopted to find out the counterfactual observations. There are 649 firms starts to invest abroad in 2012, and the ratio OFDI firms and non-OFDI firms is 1:3. The matching results are presented the following tables and figures :
Table 13 Means difference between raw data and matched data of matching variables
Means Treated Raw
Untreated
StdDif Treated Matched
Untreated
StdDif
TFP(t-l) 7.6549 7.0689 0.6595 7.1167 7.0905 0.0295
lnL(t-l) 6.1339 5.5657 0.5044 5.6134 5.5858 0.0244
kdensity(t-l) 4.6361 3.9557 0.4473 4.0927 4.0320 0.0400
export 0.7889 0.6079 0.4021 0.6251 0.6343 -0.0204
age 12.6918 10.8398 0.2182 10.4966 10.8676 -0.0437
Share control 3.4068 3.3861 0.0142 3.4566 3.3977 0.0403
Industry 31.8891 28.3692 0.4226 28.4556 28.8592 -0.0485
Table 14 Variances ration between raw data and matched data of matching variables  
Variances Treated Raw
Untreated
Ratio Treated Matched
Untreated
Ratio
TFP(t-l) 1.0446 0.5344 1.9548 0.5080 0.5125 0.9913
lnL(t-l) 1.7831 0.7549 2.3621 0.9235 0.7202 1.2822
kdensity(t-l) 2.4649 2.1625 1.1398 2.2353 2.0134 1.1102
export 0.1668 0.2384 0.6996 0.2344 0.2320 1.0103
age 92.9820 51.1144 1.8191 38.0367 48.0940 0.7909
Share control 2.6059 1.6592 1.5706 1.8318 1.6654 1.0999
Industry 68.4043 70.3441 0.9724 83.4508 67.9121 1.2288
 
 

   
Table 13 shows the mean difference of matching variables between raw data and matched data, where the mean difference of matching variables in the treated groups becomes smaller compared with that in the untreated groups except for export status. Table 14 presents the variance ratio of matching variables between raw data and matched data, similarly, except for the export status and the industry, the variance ration of matching variables in the treated group is less than that in the untreated group. Figure 15 and Figure 16 respectively exhibit the distribution patterns of the propensity score of the treated and untreated observations in the raw data and matched data.
After obtaining the matched control group, we combine the PSM and DID method to get a more precise estimation of the OFDI-led productivity effect. If the productivity growth of the treatment group after OFDI is systematically higher than that of the control group, then it is strongly evidenced that firms5 OFDI has a positive impact on their productivity growth. The DID model is as follows:
^fPit - + Prdu + p2 dt + p3 du dt + sit (4.2)
tfpit and £it respectively denote firms5 productivity and the error terms, and E (£it)=0. du, is a dummy variable, denoting whether a firm start to invest or not; du-l represents the treated group, namely firms with OFDI record, while du=0 means firms have never conducted OFDI. dt represents the time dummy variable, dt=l means the period when a firm starts to investment, and dt=0 means the period before firms5 first OFDI. The implication of each coefficient is presented in table 15. In equation (4.2), the productivity of the treated group before and after OFDI are (BO+B) and (BO +B1+B2+B3), thus the productivity difference of the treated group

before and after OFDI is (P2+P3). Similarly, the productivity difference of the control group is (P2). Thus, the difference of productivity growth of the OFDI firms and non-OFDI firms is (P3), which is presented as the following equation:
h = E(Atfp\du = 1) - E(Atfp\du = 0) = (^2 + /?3) - (^2) (4.3)
Table 15 The coefficients of equation (4.2)
  Period before OFDI (dt=0) Period after OFDI (dt=l) Difference
Treatment group (du=l) Po+Pi P〇 +P1+P2+P3 P2+P3
Control group (du=0) P P0+P2 P2
Difference Pi P1+P3 Ps(DID)
 
 
 
Thus, /33 , the coefficient of the interaction term in equation (4.2), captures the impact of OFDI on firm^ productivity growth. If > 0 , it proves that the productivity mark-up of OFDI firms is greater than that of non-OFDI firms. Out of the concern of robustness check and on the basis of the antecedent research the author takes other control variables and fixed effects into the equation (4.2), where control variables include capital intensity, firm size, firm age, export, fdi, and absorptive capability and fixed effects consist of regional effects and industrial effects.
4.3 Estimation results and analysis
4.3.1 Initial test
Based on the matched sample and the DID method, the author conducts the initial tests on the model in equation (4.2). Table 16 displays the estimated results. At the very beginning, the control variables are excluded, and then we gradually add the control variables, regional and industrial fixed effects. The key independent variable is did, namely the interaction item du^dt. From the test results column (1) and (2), the coefficients of did are significantly positive. After adding additional control variables, the coefficients and significance are still robust, which shows that the productivity growth rate of OFDI firms is larger than that of non-OFDI firms. Thus, it suggests OFDI has a significant positive on firms5 productivity mark-up. Due to the variations among regions and industries, firms5 productivity may be affected. For example, differences in market size, external market demand, and supply of specific factors among regions may affect enterprise productivity; the productivity of high-tech industries may be higher than that of traditional industries. From column (3) to (4), the coefficient of did is still significantly positive, which again shows that the OFDI has significantly promoted the productivity of Chinese MNEs.

  (1) (3) (4)
  tfpJp tfp_lp tfpJp tfpJp
ofdi(du) 0.062 0.009 -0.088*** -0.083***
  (0.057) (0.029) (0.027) (0.027)
time(dt) 0.078*** 0.031*** 0.064*** 0.059***
  (0.019) (0.010) (0.008) (0.008)
did (du*dt) 0.401*** 0.106*** 0.102*** 0.114***
  (0.044) (0.023) (0.023) (0.023)
InL   0.275*** 0.176*** 0.178***
    (0.008) (0.007) (0.007)
absorptivity   4.549*** 6.724*** 6.630***
    (0.091) (0.109) (0.110)
k_density   0.164*** 0.108*** 0.110***
    (0.005) (0.005) (0.005)
ROS   0.140*** 0.107** 0.097**
    (0.052) (0.042) (0.042)
age   -0.001* -0.000 0.000
    (0.000) (0.000) (0.000)
exp   ■0.044_ -0.018** -0.013
    (0.010) (0.008) (0.010)
SOE   0.112*** 0.049* 0.046*
    (0.034) (0.026) (0.026)
FIE   -0.013 -0.010 -0.010
    (0.012) (0.010) (0.010)
cons 7.113*** 1.825*** 1.154***  
  (0.014) (0.044) (0.047) (0.053)
Industrial fixed        
  No No Yes Yes
effect        
Regional effect No No No Yes
Obs. 7327 7327 7327 7327
R-squared 0.044 0.749 0.843 0.846
F-Value 88.05 1700.17 1029.04 628.73
 
Note: Standard errors are in parenthesis, *** p<0.01, ** p<0.05, * p<0.1. About the number of observations, the number of treated firms is 1548, while the number of untreated firms is 5779, the ration is around 1:3.
 
 
The coefficient of ofdi(du) measures the difference between the treated group and the untreated group. In column (1) and (2), under the condition of ceteris paribus, the productivity of OFDI firms is higher than non-OFDI firms although it is not significant. Further, as the industrial and regional fixed effect is added, the coefficient of OFDI firms turns significantly negative. This probably because, breaking them down into industry and region, non-OFDI firms are more productive than OFDI firms. In general, the sign and the salience of ofdi (du) are not rigorous when we add control of firm characteristics and fixed effects. If the time effect is not taken into account, the productivity of the treated group is not necessarily higher than that of the control group. Time (du) is a dummy variable denoting the period before and after OFDI. The sign and significance of variable time(dt) is positive and stable, which means that the productivity of the treated group and the control group increases over time without considering the impact of OFDI.
Regarding other control variables, firm size, absorptivity, capital intensity and return on sales are significantly and positively correlated with firms9 productivity growth, of which absorptivity^ magnitude is the most remarkable one. It evidences that the larger the firm size, the higher the capital density, the higher the profit margin of sales, and the smaller the productivity gap between the enterprise and the industry benchmark, the higher the enterprise productivity. State-owned firms also show a positive effect, which may be owing to the fact that state-owned firms are less constrained by financing, have higher credibility and stability and “special relationship” with the government. The impacts of firm age and foreign ownership on firms9 productivity is insignificant. The coefficient of export is significant and negative, showing that export firms are not necessarily more productive than non-export firms especially when we take the industry and region fixed effect into consideration. This result is at odds with the expectation of the Melitz model (2003),while verifies the “productivity paradox” of Chinese exporters. Due to the segregation of domestic market and local protectionism, the cost of domestic sales may be higher than that of foreign markets. This requires firms serving in the domestic market to be more productivity. Besides, Lu (2010) believes that the costs of domestic sales and export of Chinese firms depend on the characteristics of the industry. For specific industries, domestic competition is more severe than that of a foreign market. Thus, exporting enterprises may not be more productive than non-exporting enterprises.
To sum up, through the initial test, it is suggested that after controlling the firm's characteristic variables and the fixed effects of region and industry, the firm's OFDI still significantly improves its productivity. This evidences that the OFDI-led productivity effect is significant in Chinese OFDI firms.
4.3.2. Other robustness checks
(1) State-ownership and OFDI-led productivity
Due to the special relationships between Chinese SOEs and government, SOEs5 OFDI will draw more attention at home and abroad. According to previous studies, the OFDI of Chinese SOEs not only involves market motivation such as maximizing profits and shareholders5 equity, but also the “non-market motivation”,such as obtaining the strategic resources related to the national economy and peopled livelihood, promoting the political relationship with the host countries. In addition, because of the principal-agent problem, the investment decision-making of SOEs5 executives not only have ''market purpose", but also the "non-market purpose". For example, irrational investment based on ''political reputationM, ''political future1' and "position promotion" etc. Such OFDI often fails to meet the market expectations, leading to investment failure and posing a negative impact on the development of the enterprise, let alone the improvement of the productivity of the enterprise. Out of this consideration, this paper examines the relationship between SOEs5 OFDI and their productivity growth by adding the interaction item FSOE (ofdi*SOE). In addition, we also add the fexp (ofdi*exp) to test whether firms conducting both OFDI and export are more productive or not.
(2) Host country effect test
As discussed before, the host country is a very import factor to affect OFDI firms5 productivity. Firms investing in high-income countries are likely to get access to the most advanced technology, business models and management experiences. Thus, OFDI-led productivity may be more significant when firms investing in developed countries. De Loecker (2007) found that the destination of export is a very important factor when he studied the impacts of export on firms9 productivity growth. He claimed that the productivity increase of enterprises exporting to developed countries was greater than that of developing countries. Similarly, this logic might suit firms5 OFDI behavior as well. The reason is that compared with the export of enterprises, firms5 OFDI are only closer to the advanced technology and management experience at present, but also to the users or creators of the advanced technology and management experience. In addition, there also exits another phenomenon—Some Chinese firms have the “institution evasion or speculation” motivation, for example, some enterprises establish subsidiaries in Hong Kong, Macao or the traditional tax avoidance "paradise" and reinvest to the mainland of China after obtaining the status of a foreign investor. The aim of these enterprises probably is to obtain more policy preferential as a foreign investor. Therefore, whether this investment significantly promotes the productivity of enterprises or not is still worth investigating. The following pictures depict in the sample of 547 OFDI firms There are 417 firms investing in high-income countries, among which 173 firms invested in utax paradise^ regions, like Hong kong.
Figure 16 Distribution of invest countries by income level
 
high income upper middle lower middle low income

  (1)
tfpJp
(2)
tfP-lp
(3)
tfpJp
(4)
High-
income
(5)
Without tax paradise
(6)
Only tax paradise

Low-
middle
income
ofdi 0.007 -0.089*** -0.084*** -0.093*** -0.098*** -0.081** -0.054**
  (0.030) (0.027) (0.027) (0.028) (0.032) (0.034) (0.038)
time 0.058*** 0.076*** 0.074*** 0.075*** 0.075*** 0.0766*** 0.075***
  (0.010) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)
did -0.004 0.057** 0.057** 0.055** 0.053** 0.052** 0.051轉
  (0.029) (0.026) (0.026) (0.026) (0.026) (0.026) (0.026)
exp -0.072*** -0.030*** -0.031*** -0.031*** -0.032*** -0.029*** -0.030***
  (0.010) (0.008) (0.010) (0.010) (0.010) (0.010) (0.010)
fexp 0.133*** 0.052*** 0.071* 神 0.073*** 0.076構 0.077*** 0.076***
  (0.021) (0.015) (0.016) (0.016) (0.017) (0.017) (0.017)
FSOE 0.206*** 0.161*** 0.157*** 0.145** 0.143** 0.166** 0.175**
  (0.071) (0.056) (0.055) (0.061) (0.066) (0.066) (0.063)
SOE 0.043 -0.002 -0.004 -0.004 -0.002 -0.0005 -0.0006
  (0.037) (0.029) (0.029) (0.029) (0.029) (0.029) (0.029)
_cons 1.911*** 1.194*** 1.266*** 1.272*** 1.276*** 1.286*** 1.286***
  (0.044) (0.048) (0.054) (0.054) (0.055) (0.055) (0.055)
Firm features Yes Yes Yes Yes Yes Yes Yes
Industrial effect No Yes Yes Yes Yes Yes Yes
Regional effect No No Yes Yes Yes Yes Yes
Obs. 7327 7327 7327 7205 7029 6969 6916
R-squared 0.751 0.844 0.847 0.845 0.842 0.844 0.843
 
Note: Standard errors are in parenthesis, *** p<0.01, ** p<0.05, * p<0.1. Here model (4) only includes firms investing high- income countries and non-OFDI firms; model (5) 4ttax paradise^ means Hongkong (China), Macao and the British Virgin Islands; model (7) only contains firms investing in low-middle income countries and non-OFDI firms.
 
 
Column (1)〜(3) in Table 17 evidence that SOEs do not necessarily have a positive effect on firms5 productivity growth. However, the coefficients of interaction item FSOE (ofdi*SOE) prove that SOEs engaging in OFDI are conducive for their productivity. It explains why the coefficients of SOE in the previous initial test are significantly positive. In addition, as we mentioned before, there exists a “productivity paradox” among Chinese export firms. Indeed, if we study the OFDI effect and export effect separately, they are not helpful to improve firms9 productivity. However, if a firm conducts export and OFDI simultaneously, this situation really improves their productivity. Column (4)〜(7) examines the host country’s effect on firms’ productivity increase. The coefficients of did(du*dt) all are significantly positive no matter firms invest in high-income countries or low-middle income countries. In addition, the coefficients of SOEs investing in high-income countries are greater than that of low-middle income countries, whether the utax paradise55 regions are excluded or not. In brief, SOEs engaging in OFDI have a positive effect on their productivity growth and the OFDI-led productivity effect is greater for firms investing in high-income countries than firms investing in low-middle income countries.
Conclusion and Discussion
Chinese firms have actively engaged in OFDI in the recent decade, which has excited the strong interests of scholars both at home and abroad. By combining the Chinese manufacturing dataset and the “List of Foreign Investment Enterprises (Organizations)’’ from 2011 to 2013. In order to cope with the “learning effect”,only those firms conducting their first OFDI behavior in 2012 and 2013 and those firms have never undertaken OFDI are selected into the sample. This study firstly tested the impact of firms9 productivity on their OFDI decision based on the HYM model (2004). The results are consistent with the prediction of the HYM model, namely, the more productive a firm, the more it is likely to invest abroad. OFDI firms are the most productive compared with pure exporters and firms only serving in domestic markets. However, export firms are not necessarily more productive than those firms only serving the domestic market, which verifies the ''productivity paradox55 phenomenon of Chinese exporters. When the income level of the host country factor is taken into consideration, however, the productivity of OFDI firms investing in high-income countries is not necessarily greater than that of firms investing in low-middle countries due to the industrial structure and country distribution of China’sOFDI.
Secondly, we investigated the impacts of OFDI on firms5 productivity. To address the endogenous issue and self-selection bias, the propensity matching score method is adopted to establish an appropriate comparison group—where the firms in the treated groups and controls share similar characteristics. Sequentially, the difference-in-difference approach to estimate the OFDI-led productivity effect. The results evidence that in general, OFDI is conducive for firms5 productivity mark-up. Regarding the control variables, firm size, absorptivity, capital intensity and return on sales are significantly and positively correlated with firms5 productivity growth, of which absorptivity^ magnitude is the most remarkable one. Further, we conducted some other robustness checks. From the perspective of state-owned firms, we find that SOE has a positive effect on firms5 productivity, but the contribution mainly comes from those firms conducting OFDI. Similarly, although pure exporter is not beneficial for firms5 productivity, however, the results suggest that if a firm doing export and OFDI at the same time, the joint productivity effect is significant and positive. In addition, when testing the impact of the income level on OFDI-led productivity, the results generally suggest that no matter including the utax paradise55 regions or not, the OFDI-led productivity growth effect in high-income countries is greater than that in low-middle income countries.
The findings in this study have important implications for a firm’s strategic decisionmaking of internationalization and a country^ investment policy. First, when facing the domestic market and the foreign market, a firm should adjust their operating strategies on the basis of their own features, strengths and their investment destinations to maximize the benefits from they can obtain from OFDI; Second, this study finds that the absorptive ability, which plays an important role in shaping an OFDI-firm^ technology recognition, assimilation, and application, has a remarkable positive moderating effect on OFDI firms5 productivity. Thus, a company manager should be always aware of those companies at the frontier of corresponding industries and pay more attention to their own R&D input and human capital to enhance their own absorptive ability. In term of SOEs, the results show exhibit SOEs is not necessarily more productive than non-SOEs, thus government should keep the reform of SOEs to enhance their vitality and encourage to compete effectively both at home and abroad. In addition, as OFDI also involves a great many risks and uncertainty in different countries, thus, it is necessary for the government to help offer some guidance related to risk pre-warning and risk prevention mechanisms.
As with most studies, there are several limitations in this paper, which also imply the opportunities for future research. First, as we derive the dataset in this study from the merge of China Industrial Enterprises Database and List of Foreign Investment Enterprises (Organizations) ranging from 2011 to 2013, however, the former dataset is just comprised of those firms whose annual sales exceed 20 million RMB. Thus, we are unable to obtain the information of non-industrial firms and industrial firms whose annual sales are less than 20 million RMB. Besides, it does not contain the information of R&D input and output, hence we have to use the technology absorptivity as a proxy variable. Future studies could attempt to merge China5s listed firms5 dataset with the dataset List of Foreign Investment Enterprises (Organizations) to shed more light on the OFDI-led productivity effect based on the firms-level data. In addition, the latter dataset just contains the status of a firm^ OFDI behavior but not the amount or the entry mode of investment. This impedes our further investigation on the role of entry mode as a moderator for the OFDI-productivity nexus. Different entry modes indeed will affect subsidiaries5 managerial pattern, corporate culture, technique-learning channels, and their OFDI results (Nocke & Yeaple, 2007) according to existing study. Second, the time span of our sample just crosses 2 years (because we just select firms firstly start to invest abroad in 2012 in the sample), therefore, it hinders our process of investigating the OFDI-productivity effect from a more dynamic viewpoint. Therefore, this topic requires a longer period of time, more specific data, and a more sophisticated model to support the empirical study. Another limitation of this study is owing to the absence of detailed subsidiary level data. Hence, further subsidiary level studies in this topic are strongly recommended, to clearly track the mechanisms with which MNEs’ OFDI promote parent firms’ productivity (Rugman, Verbeke,& Nguyen, 2011).

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