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Haverford College Senior Thesis Determinants of China’s OFDI in European Union and North America Are the determinants for European Union different from the determinants for North America? Supervisor: Author: Biswajit Banerjee Chunan Liu A thesis submitted in fulfilment of the requirements for the degree of Bachelor of Art in the Department of Economics April 2014 HAVERFORD COLLEGE Abstract Department of Economics Bachelor of Art Determinants of China’s OFDI in European Union and North America Are the determinants for European Union different from the determinants for North America? by Chunan Liu The role of Foreign Direct Investment (FDI) is more and more critical in a globalized world. China’s Outward FDI (OFDI) has received increasing interest in empirical research since it started surging in 2004. China’s OFDI to European Union (EU) and North America countries has been substantially growing recently, especially during financial crisis period. This thesis investigates the key determinants of China’s OFDI to EU Member States and North America Countries and compares if the determinants for EU countries are different from the determinants for North America Countries. Statistical analyses are applied to seek evidence of general considerations of FDI and specific considerations on China’s OFDI, and on EU and North America Countries. Acknowledgements I take this opportunity to express my profound gratitude and deep regards to my advisor Prof. Biswajit Banerjee for his exemplary guidance, monitoring and constant encouragement throughout the course of this thesis. The blessing, help and guidance given by him time to time shall carry me a long way in the journey of life on which I am about to embark. I cannot express enough thanks to my instructors Prof. Anne Preston, Prof. David Owens, Prof.Giri Parameswaran, Prof. Steven Lindell, Prof. David Wonnacott, who provide countless support and encouragement to all seniors in the department, and Prof. John Dougherty, who gave me valuable guidances as my Computer Science thesis advisor. I offer my sincere appreciation for the learning opportunities provided by them. Thanks to my friends Zhen, Ethan, Tom and Bryan, without your understanding and support, I could not have gone this far today. Finally, to my caring, loving, and supportive parents and grandparents: my deepest gratitude. Your encouragement when the times got rough are much appreciated and duly noted. ii Contents Abstract i Acknowledgements ii Contents iii List of Figures iv List of Tables v 1 1 1 Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Literature Review and Hypotheses 2.1 General considerations of FDI . . . . . . . . . . 2.2 General Considerations on China’s OFDI . . . 2.3 Considerations of China’s OFDI in European America . . . . . . . . . . . . . . . . . . . . . . 2.4 Hypotheses . . . . . . . . . . . . . . . . . . . . . . 5 5 6 . . 8 9 3 Data and Methodology 3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 11 11 12 4 Results 13 5 Conclusions 15 A Supplementary Tables 17 Bibliography 26 iii . . . . . . . . Union . . . . . . . . . . . . . . . . . . . . (EU) and . . . . . . . . . . . . . . . . . . . . North . . . . . . . . List of Figures 1.1 1.2 1.3 1.4 China’s OFDI flow in percent of its current GDP . China’s OFDI stock in percent of its current GDP Regional Weights of China’s OFDI Flow . . . . . . Regional Weights of China’s OFDI Stock . . . . . iv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 3 4 List of Tables A.1 A.2 A.3 A.4 A.5 A.6 A.7 A.8 Summary findings of Earlier Studies on China’s Summary findings of Earlier Studies on China’s Summary findings of Earlier Studies on China’s Data Specifications . . . . . . . . . . . . . . . . Data Specifications continued . . . . . . . . . . Results: Full Sample . . . . . . . . . . . . . . . Results: EU Countries . . . . . . . . . . . . . . Results: Complete Specifications . . . . . . . . v OFDI I. . OFDI II. OFDI III. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 19 20 21 22 23 24 25 Chapter 1 Introduction This thesis aims to examine the key determinants of China’s outward foreign direct investment (OFDI) in the European Union and North America. 1.1 Motivation Foreign direct investment (FDI) is important in a globalized world. Firms have gone beyond international trade and built (green-field FDI) or acquired (brown-field FDI) plants abroad to carry out production and distribution. Dunning [1](1980) explained the incentives of corporations to invest abroad in terms of Ownership, Location and Internalization (OLI). Firms decide to invest abroad because it facilitates greater access to market via ownership of financial capital, technology, trademarks, natural resources and patents. By choosing appropriate destinations for FDI, firms can acquire economic benefits, and political and social advantages. By internalizing cross border assets, firms can reduce transaction costs via FDI. Although FDI requires firms to put in sunk costs for building plants and setting up for production, it also brings tremendous benefits over time. Importance of China’s OFDI has also been significantly growing over time. Figure 1.1 shows China’s OFDI flow in percent of its current GDP from year 2004 to year 2012. In year 2004, China’s OFDI is only about 0.3 percent of China’s current GDP, but it surged to around 1.2 percent of current GDP in 2008. Although during the Financial Crisis and Sovereign Debt Crisis period, the OFDI flow to GDP ratio declined slightly, it started recovering since 2011. China’s OFDI stock to current GDP ratio tells a similar story (see Figure 1.2). During year 2004-2008, the growth of China’s OFDI stock was faster than the growth during the crisis period. 1 Chapter 1. Introduction Figure 1.1: China’s OFDI flow in percent of its current GDP Figure 1.2: China’s OFDI stock in percent of its current GDP 2 Chapter 1. Introduction 3 The flow of FDI into China and out of China has received increasing interest in empirical research. China’s OFDI has surged in the last decade from a very low base. In 2012, China’s OFDI flow was US dollar 87.8 billion, or 1.07 percent of its current GDP, ranked the third place in the world. In terms of stock, according to 2012 statistical Bulletin of China’s Outward Foreign Direct Investment, China still has a long way to catch up. United States cumulated US dollar 5191 billion of OFDI stock up to 2012, ranked the first place in the world. China has only cumulated US dollar 531.9 billion of OFDI, ranked the seventh place in the world. The increasing trend of China’s OFDI reflects significant changes in geographical distribution. Although North America and European Union are not the most important destinations of China’s OFDI, their importance has been potentially increasing over the years and most importantly, did not decline during crisis period. Figure 1.3 shows the weight of each destination region in China’s OFDI flow during the period of 2004-2012. Investments into Asian areas except Hong Kong are slightly more important than those into North America and EU, but declined during the crisis period. The weight of EU and North America was very little in 2004, but the weights grew substantially over the years. The growth rates appear to be greater after the financial crisis in 2008 took place. Figure 1.4 shows the regional weight of China’s OFDI in terms of stock. It tells a similar story. Therefore, it will be interesting to look into what drives Chinese investors to invest in EU and North America. Figure 1.3: Regional Weights of China’s OFDI Flow Note: OFDI to Hong Kong was excluded in the graph because China’s OFDI to Hong Kong follows very different principles than to the rest of the world. Hong Kong, as the top recipient of China’s OFDI, is used as a tax haven and a spring broad of China’s OFDI. Chapter 1. Introduction Figure 1.4: Regional Weights of China’s OFDI Stock 4 Chapter 2 Literature Review and Hypotheses 2.1 General considerations of FDI Dunning [1](1980) addressed three major determinants of OFDI location choice in his eclectic OLI paradigm. One of the determinants is the incentive of expanding foreign market. Foreign market seeking FDI takes place through both defensive and offensive channels. In defensive foreign market seeking, the goal of Multinational Corporations (MNCs) is usually hopping trade barriers. Offensive foreign market seeking FDI, in contrast, refers to MNCs building plants abroad as intermediary for export to countries beyond host countries. The second determinant emphasized by Dunning [1](1980) is efficiency seeking OFDI. Home countries build factories in countries where labor, operation and transaction costs are lower, so that they can reduce their total production costs. The ability of producing products at a lower cost enables MNCs to gain a comparative advantage over production costs. According to Dunning [1](1980), the third determinant is that MNCs invest abroad in order to acquire resources. They seek either nature resources or technologies. In addition to the three major determinants in Dunning [1](1980)’s eclectic paradigm, recent scholars suggested that location choice of OFDI also depends on macroeconomic environment of the both the home country and the host country. Pain and Welsum [2](2003) have highlighted the role of exchange rate on FDI flows and the impact occurs through multiple channels. Considerations in financing options suggest a negative relationship between exchange rate and FDI flows into the host country. Holding assets denominated in a depreciating currency increases relative cost of capital and reduces the easiness of financing abroad. As other factors are held constant, the 5 Chapter II. Literature Review and Hypotheses 6 weaker is the host country currency, the higher is the level of FDI flow into the host country. Another way that the exchange rate could affect level of FDI inflow is through uncertainty of investor. When investors expects the host country currency to appreciate, and they are eventually interested in collecting the profit in appreciated currency, locations with appreciated currency will be more attractive to FDI investors. In this channel, the exchange rate level of the host country is positively correlated with level of FDI flow into the host country. Duanmu and Guney [3] (2009) addressed the importance of host country and home country institution environment in location choice of OFDI. Taxes, fiscal incentive structure, legal and macroeconomic infrastructure in both of the home country and the host country will have potential affect on FDI from the home country to the host country. 2.2 General Considerations on China’s OFDI Existing literature has confirmed the market seeking theory on China’s OFDI. Buckley et al[4] (2007), Duanmu and Guney [3] (2009) and Cheng and Ma [5] (2007) specified market size by the nature log of host country’s current Gross Domestic Product. They all found significant and positive sign on lnGDP. Fung et al Fung and Siu [6](2007) and Kolstad and Wiig [7] (2009) estimated the same motivation, but in terms of nominal current GDP of host country, and found positive and significant signs on host country’s current GDP.Korniyenko and Sakatsume [8] (2009) used Purchasing Power Parity adjusted GDP per capita to estimate the effect of market seeking, and found a significant and positive sign. Cheung and Qian [9] (2009) used the ratio of host country GDP to China’s GDP as a proxy for market seeking and found significant and positive as well. The results confirmed that Chinese investors are seeking destinations with greater market size. Trade openness is another proxy for market seeking motivation of OFDI. Fung et al Fung and Siu [6](2007), Duanmu and Duanmu and Guney [3] and Kolstad and Wiig [7](2009) included host country’s net trade to current GDP ratio, as a proxy for trade openness, in their study. Only Duanmu and Guney [3](2009) found a significant and positive sign on trade openness of host country, indicating that Chinese investors are more attracted to countries that are more open to international trade. Two studies have considered exchange rate as a determinant of China’s OFDI. Both Buckley et al[4](2007) and Duanmu and Guney [3] (2009) included nominal bilateral exchange rate between host country and china in their regression equations. Buckley et al (2007) did not find any significant relationship between bilateral exchange rate and Chapter II. Literature Review and Hypotheses 7 China’s OFDI, while Duanmu and Guney [3](2009) found a significant and positive relationship. The positive relationship suggests that Chinese investors are more interested in the long-term value of their investment projects, and therefore, more attracted to host countries with appreciating currencies. Another major motivation of investing abroad suggested by Dunning [1](1980) is technology seeking in the host country. Earlier studies (e.g. Fung et al Fung and Siu [6] (2007)) have used three proxies to test the hypothesis on China’s OFDI. One variable is the Research and Development (R&D) expenditure in the host country. One variable is secondary education enrollment and another variable is annual number of patent registration in host country. Fung et al Fung and Siu [6] (2007) found that both R&D expenditure of host country and secondary education enrollment in the host country are statistically significant and have negative sign. Buckley et al[4](2007) included annual number of patent registration in host country as another proxy for technology seeking. However, they did not find it significant in any steps. The results suggest, contrary to expectation, that Chinese investors are not interested in host countries with high technology capability. In order to account for the gradual changes taking places over the sample period, Cheung and Qian [9](2009) included a time trend variable in their regression equation. They found an significant positive sign on the time trend variable, meaning that Chinese OFDI is increasing over years because of factors that are not captured in the equation. For instance, the variable may capture the loosening restriction of OFDI from China’s government over the years. Fung et al Fung and Siu [6] (2009) found that none of the proxies for natural resource seeking hypothesis is significant in the full sample. However, in their step-wise regression, the food seeking parameter was found to be significant. Buckley et al[4] (2007) found a the significant and positive correlation between the variable measuring share of ore in exports of host country and China’s OFDI for the period 1992-2001, and argued that securing nature resources is a key factor for China’s recent growth and that the investments to secure nature resources have been redirected to political risky countries. In contrast, Duanmu and Guney [3] (2009) found an insignificant and counter-intuitive negative sign for resource seeking variable. They explained the phenomenon by arguing that nature resources attract some industries but not necessarily the others, and therefore the coefficient is insignificant for the whole sample. Kolstad and Wiig [7](2009) confirmed that the coefficient of nature resource seeking proxy is significant and positive as expected in the non-OECD sub-sample. Fung et al[6] (2009), Cheng and Ma [5] (2008) and Duanmu and Guney [3] (2009) found geographical distance as a deterrent of China’s OFDI. Buckley et al[4](2007), in Chapter II. Literature Review and Hypotheses 8 contrast, found an unexpected positive sign in the 1984-1991 sample of OECD and nonOECD countries. They interpreted the phenomenon as a result of increased maturity of Chinese market-seeking investors and increased propensity of seeking natural resources all over the world. Kolstad and Wiig [7](2009) found distance significant and negative in non-OECD countries sub-sample, indicating that distance only affect location choice for non-OECD countries. Political risk rating of the host country has been commonly used as a proxy to study the effect of host country institution environment to China’s OFDI. Buckley et al[4](2007) found a counter-intuitive and significant sign on political risk rating for sub-sample of 1992-2001, implying that during the period, Chinese enterprises prefer to invest in politically risky countries. Cheung and Qian [9](2009) found that Chinese OFDI is insensitive of host country political risk ratings. However, Duanmu and Guney [3] (2009) found that China’s OFDI are attracted to host countries with good institutional environment. Besides unconditional effects of institution quality of host country and natural resources on China’s OFDI, Kolstad and Wiig [7](2009) also believed that there is a joint effect of institution and nature resource in host country on China’s OFDI because given the competitive advantage that Chinese enterprises operating in emerging market or underdeveloped countries, the return is greater in countries with poorer institution ratings if nature resource is present in those countries Other explanation of the interaction term is also possible. For summary of literature findings, see Table A.1 A.2 and A.3 2.3 Considerations of China’s OFDI in European Union (EU) and North America Hanemann [10]( 2012) suggested that, nature resource, as an overall goal of Chinese global acquisition, is also a target of China’s OFDI in the EU and United States. Therefore, Chinese enterprises have been eagerly involved in investments in energy sector. However, I will not consider nature resource as a motivation in my study because Chinese investors are seeking opportunities to secure their position in nature resource by taking over more European and American energy companies instead of exploiting their nature resources. Moreover, Apoteker et al[11] (2013) suggested that Chinese investment in utility sector of EU and North American have gone through a spring board in Hong Kong, then to the destination country. Big investment in utility sector will not be shown in the data for Mainland China. Chapter II. Literature Review and Hypotheses 9 Hanemann [10] (2012) found that Chinese investors are seeking advanced industrial assets in EU and USA. However, Chinese enterprises seem to be more interested in the advanced technologies in EU because they are taking the opportunity of economic crisis in EU to acquire assets in lower prices. The utility sector is also more preferable than the utility sector in US because privatization in EU forced EU enterprises to attract foreign capitals. Although the US is more advanced in several high-tech clusters than EU, it does not attract more investment in those sectors then EU does. Hanemann [10] (2012) observed that although commercial opportunity is important to Chinese enterprises when making investment decisions, policy of Chinese government also plays an important role in location selection. Concerns about national security of China drive Chinese enterprises to invest defense industries in both the EU and US. Policy is expected to be more influential on Chinese OFDI in the future, as Chinese government and enterprises become more familiar with doing business abroad and their skill of measuring the risk of doing international business is improved. From previous literatures (see Table A.1 A.2 and A.3 ) and specific considerations on EU Member States and North America countries, several determinants can be eliminated by intuition for the case of China’s OFDI in EU and North America. First of all, I will not consider the contribution of distance to OFDI since there is not much variation of distance between EU a country and China or between North American countries and China. Second, I will not count the effect of political risk in my study because both EU and North American countries are relatively politically stable. The political risk rating will be similar for most of observations. Last but not the least, nature resource seeking will not be included in the study for two reasons: One, natural resources in EU and North American countries are not being exploited by Chinese enterprises. The host countries do not have much reserve of natural resources. Two, although Chinese investors have been devoting their wealth into utility sectors in EU and North America, they are not interested in taking the resource from EU and North America, but interested in stepping into the management of huge utility or energy firms and getting easier access to natural resources from rest of the world. 2.4 Hypotheses I would like to make following hypothesis prior to my estimations: 1. Chinese investors will seek market opportunities in EU Member States and North American countries. They will invest more in countries with greater market size Chapter II. Literature Review and Hypotheses 10 and trade openness. Therefore, I expect positive signs on both host countries’ GDP and trade openness. 2. Exchange rate has two channels to influence FDI inflow into host country. Therefore, what we will end up with will be a mixed effect of the two and we can only see which side is more dominant than the others. 3. Since EU countries and North American countries are known for their advanced level in technology. Chinese investors might be seeking technologies in these countries. Therefore, I expect positive sign on technology seeking proxies. 4. It is also reasonable to consider the growth of host country’s productivity as a determinant of China’s OFDI besides technology seeking proxies. Greater productivity growth in host countries may attract more investment from China. Therefore, I expect a positive sign on the growth of real GDP of host country. I chose a one year lag in my specification because evaluation of investment is usually done with the data of previous year. 5. Since China has become more open to its trade and foreign investment policies over the years, the economic environment is like to be more and more supportive of China’s OFDI. Time trend may have a significant effect on China’s OFDI to account for factors taking places gradually over time and not captured by other factors in the equation. Therefore, I expect a positive sign on time trend variable. 6. Since China’s OFDI to EU and North America did not decrease during crisis period, Chinese investors have been blamed as taking the chance of buying cheap assets during the crisis period. Therefore, I expect a positive sign on the dummy variable of crisis period. Chapter 3 Data and Methodology 3.1 Data In my estimation, I will be considering macroeconomic level determinants of China’s OFDI to EU Member States and three North American Countries (Canada, Mexico and U.S.A) during the period of 2004 - 2012. 3.2 Specifications Specification 1: OFDIj,t = β0 + β1 GDPj,t + β2 GDPGrowthj,t−1 + β3 TradeOpenessj,t + β4 REERj,t Specification 2: OFDIj,t = β0 + β1 GDPj,t + β2 GDPGrowthj,t−1 + β3 TradeOpenessj,t + β4 REERj,t + β5 R&Dj,t + β6 2ndEduj,t + β7 TimeTrendt All variables are defined in Table A.4 . OFDIj,t is the OFDI flow from China at year t, to host country j, collected from the statistical bulletin of China’s Out ward Foreign Direct Investment. 11 Chapter III. Data and Methodology 12 GDPj,t is the current GDP adjusted by Purchasing Power Parity of host country j at year t, collected from World Economic Outlook. GDPGrowthj,t−1 is the real GDP growth rate of host country j at one year prior to year t, that is , year (t-1), collected from World Development Indicator. TradeOpenessj,t is the value of imports plus value of exports in percentage of current GDP in host country j at year t. Imports and exports data are collected from World Development Indicator. REERj,t is the real effective exchange rate calculated by consumer price indices in host country j at time year t, collected from World Economics Outlook. R&Dj,t is the Research and Development expenditure in percentage of current GDP in host country j at year t. 2ndEduj,t is defined as the enrollment in secondary education in percentage of total labor force in host country j at time t. Both indicators were collected from World Development Indicator. TimeTrendt is defined as the distance of current year to the year 2004 plus one. For instance, in year 2004, the time trend will be 1; in year 2005, it is 2, and so on. More detailed explanation of variables can be found in Table 2. 3.3 Methodology In order to examine whether the determinants of OFDI to EU countries are different from the determinants to North American countries, I decided to include a EU dummy variable, equal to 1 when the country is EU Member States, while 0 if it’s North American countries. Dummy variable for crisis period can be defined as 1 if the year is from 20092012, other wise zero. The variable is aiming at accounting for impact of crisis period China’s OFDI. First step of my estimation is to examine Specification 1 and Specification 2 for the full sample, with both EU countries and North American countries. Then I will repeat the same steps on the EU sub-sample to see if the result is significantly different from the full sample. Chapter 4 Results Table A.6 shows the estimation results on the full sample. Equation 1 is the baseline estimation of the sample. GDP of host country is positively and significantly correlated with OFDI from China to host country, indicating that Chinese investors are attracted to countries with bigger market size. Trade openness is also found to be statistically significant and positively correlated with China’s OFDI flow. However, the sign of Real GDP growth rate is counter intuitive. The negative and significant sign can be interpreted that Chinese investors are not interested in countries with fast productivity growth. REER of host country has a negative sign but it is not significant. The result corresponds to my hypothesis that the effect of REER might be ambiguous. Equation 2 adds the R&D Expenditure variable to the baseline equation. It did not change the sign and robustness of the variables in baseline equation. Although the R&D Expenditure in percent of GDP of host country is not statistically significant. Equation 3 adds in secondary education variable to baseline equation instead of the R&D Expenditure variable. It did not change the sign and robustness of baseline equation variables. The secondary education variable itself was not significant but has a positive sign, corresponding to my hypothesis that Chinese investors are more interested in countries with better technology background. Equation 4 adds time trend specification to the baseline equation. The sign and robustness of GDP and trade openness remained the same. However, when adding time trend variable, real GDP growth rate became insignificant while time trend obtained a significant and positive sign. There is no evidence that Real GDP growth rate is a determinant of China’s OFDI, while China’s OFDI growth over time because of factors cannot be captured in the equation, such as policy changes in China and the world economic environment. REER is always not significant, meaning that the effect of REER on OFDI is ambiguous. 13 Chapter IV. Results 14 Equation 5 includes the baseline equation and the crisis dummy variable. The signs and robustness of other variables did not change after adding the crisis variable. A positive and significant sign of crisis variable was obtained, meaning that Chinese investors intentionally increased the amount of FDI into EU and North America during the crisis period. Notice that crisis variable and time trend variable cannot be included in the equation at the same time because they are highly correlated. Crisis period took place in the second half of study period, when the time trend value is greater. However, crisis only explains part of external factors of China’s OFDI growth, while time trend shows a net effect of all factors cannot be counted by other variables. Time trend accounts for the effect that open policy in China increases China’s OFDI in EU and North America over time, as well as the effect that Chinese investors increases their investment during crisis period, which occurs in the second half of study period. Equation (1) in Table A.8 is a full estimation including corresponding to specification 2. All signs and robustness remained the same as previous estimations. In the EU sample estimation, the results (See Table A.7 and Table A.8 ) are very similar from the estimation for the full sample. The robustness and coefficients are close to the estimation for the full sample, except in Equation 4 and 5, REER has a negative sign and statistically significant in 10 percent level, meaning that Chinese investors are attracted to depreciating currencies in EU. However, it became insignificant in full specifications. Therefore, same conclusions can be drawn for China’s OFDI in EU countries and there is no evidence that the determinants of China’s OFDI in EU countries are different from those in North American countries. Chapter 5 Conclusions This paper attempts to test if conventional determinants of China’s OFDI can be applied to China’s OFDI in advanced economies, such as North American countries and EU Member States. Formal statistical analysis was conducted to prove or disprove my hypotheses. Some of my findings conform perfectly to my predictions, but also reveal rather interesting stories. I found conventional results for market-seeking hypothesis via the host country’s PPP adjusted nominal GDP and trade openness. However, the insignificant coefficient of real GDP growth of the host country indicates that investors from China are not interested in investing in destinations where productivities are catching up. Although earlier studies demonstrated the ambiguous connection between exchange rate and FDI (Pain and Welsum [2](2003)), I found that one of the channels does dominate in the case of China’s OFDI to EU countries. The significant and negative relationship indicates that Chinese investors are interested in host countries with weaker currencies so that financing of capitals will be easier. However, the results were not robust when complete specification is applied to the dataset. I did not find evidence that Chinese investors are seeking technologies in North America and EU countries. It is possible that the technology-seeking motivation might be captured in the time trend variable or crisis variable rather than the two technology-seeking proxies in my specifications, because China’s OFDI significantly increased during the period of 2009-2012, indicating that Chinese investors might be taking the opportunity of financial crisis to acquire cheap technology asset in North America and EU countries. It is also possible that although it is intuitive that Chinese investors are interested in technological assets in advanced economy, the investments are not captured in the data, 15 Chapter V. Conclusion 16 because some Chinese firm conduct their OFDI via their Hong Kong branches, but the data only captures investments from China Mainland. Time trend variable was found to be significant and positive. It captures any factor that changed over the study period. One favorable explanation is that the time trend variable reflects the policy liberalization of China on OFDI over the study period. The Chinese government became more and more supportive to OFDI projects and therefore China’s OFDI increases over time. Since crisis took place during 2009-2012, the second half of study period, when time trend values are larger, the time trend variable is highly correlated with the crisis dummy variable and can catch the investor behaviors during the crisis period as well. Thus, the positive correlation between time trend and China’s OFDI also indicates that Chinese investors increased their investments in EU and North America countries during the crisis period, possibly for the sake of acquiring cheaper assets. No factor has been found significantly different for the full sample and for the EU subsample, indicating that the determinants of China’s OFDI to EU countries and to EU and North America countries are the same. Unfortunately, since there are only three countries and less than thirty observations for North America sub-sample, I was not able to test whether the determinants for North America countries are significantly different from those for EU countries, but comparing the EU countries and all countries in my sample, I found that the determinants for EU countries are not significantly different from the determinants for all EU and North America countries. Appendix A Supplementary Tables 17 Appendix. Supplementary Tables 18 Table A.1: Summary findings of Earlier Studies on China’s OFDI I. Authors/ GDPhost Variables Buckley et Trade R&D Openess lnGDP (+) - 2nd Education - - al (2007) Secondary Fung et al (2007) GDP (+) Net trade of R&D education host country over Expenditure enrollment in its current GDP in host country percent of (not significant) (-) total population in host country Net trade of Duanmu and lnGDP (+) Guney(2009) host country - - - - - - - - - - - - over its current GDP (+) Cheng and Ma lnGDP (+) - (2008) Net trade of Kolstad and GDP(+) Wiig (2009) host country over its current GDP (+) Cheung and Qian (2009) GDPhost GDPChina (+) Log (GDP Korniyenko, per capita Sakatsume PPP (2009) adjusted) (+) Appendix. Supplementary Tables 19 Table A.2: Summary findings of Earlier Studies on China’s OFDI II. Authors/ Exchange Time Nature Variables Rate Trend Resource Distance Nature log of Bilateral lnShare of exchange rate ore and metal Buckley et between exports in Ln(Distance) al (2007) host country total exports (-) and China of host (not country (+) - significant) Shares of food, fuel, Fung et al (2007) GDP (+) - and ores Distance in total (-)(+) exports of host country Bilateral Share of exchange ore and Duanmu and rate between Guney(2009) Cheng and Ma metals in Ln(Distance) host country total exports (-) and China of host (+) country - - - - (2008) Kolstad and Ln(Distance) (-) - - - Wiig (2009) Distance (-) Share of raw materials Cheung and - Qian (2009) Time exports in trend (+) total exports - of host country Korniyenko, Sakatsume (2009) - - - - Appendix. Supplementary Tables 20 Table A.3: Summary findings of Earlier Studies on China’s OFDI III. Authors/ Patent Host Country Institution Variables Registrition Political Stability ∗Resource lnAnnual ln Host country patent registrations political stability in host country rating(-) GDP (+) - Buckley et - al (2007) Fung et al (2007) - Host country Duanmu and - political stability - Guney(2009) rating(+) Cheng and Ma - - - - - - (2008) Kolstad and Wiig (2009) Cheung and - Qian (2009) Time - trend (+) Korniyenko, Sakatsume (2009) - - - Appendix. Supplementary Tables 21 Table A.4: Data Specifications Variables Definition N Source Proxy for 225 - - Annual FDI OFDI from flow from China China to host country Annual current GDP of host GDP − P P Phost country adjusted World 225 Market Economy seeking by Purchasing Outlook Power Parity One time lag of Real Real GDP Growth Lag1host GDP growth World 200 Productivity Development seeking of host Indicators country Net trade of host World Trade in % GDP host country in 219 Market Development percent of seeking Indicators its current GDP Real Effective Exchange World REER host Rate of 216 Macroeconomics Economy Host country condition Outlook calculated by CPI. Appendix. Supplementary Tables 22 Table A.5: Data Specifications continued Variables Definition N Source Proxy for Percent of population World 2nd edu host finished 200 Technology Development seeking secondary Indicators education in host country Research and Development World R&Dhost expenditure 218 Technology Development in percent seeking Indicators of current GDP in host country EU = 0 if host country EU is not a EU 225 - EU Countries Member State , otherwise EU =1 Year 2004 = 1, time trend is Time incremented by 225 - trend Policy changes one for the next year. Crisis = 1 if year is Crisis 2009-2012, otherwise, crisis = 0 225 - Crisis Appendix. Supplementary Tables 23 Table A.6: Results: Full Sample Variables 1 2 3 4 5 .0936 .2191 .0615 .0898 .0912 (.0146)∗∗∗ (.0518)∗∗∗ (.0141)∗∗∗ (.0141)∗∗∗ (.0142)∗∗∗ -30.1782 -27.1997 -34.1109 -10.58195 -2.6714 (12.6106)∗∗ (11.527)∗∗ (12.0133)∗∗∗ (13.3055) (14.7258) Trade 2.6289 3.4302 2.3278 2.4055 2.4637 Openess (.6884)∗∗∗ ( .6718)∗∗∗ (.6604)∗∗∗ (.6678)∗∗∗ (.6706)∗∗∗ -7.7508 -7.5251 -3.2366 -8.3107 -8.4231 (5.9278) ( 5.8961) (5.6710) (5.7289) (5.7627) - - - - - - - GDP Real GDP Growth REER R&D - -.0936 ( 2.5913) 2nd - - Edu EU 20.7368 (41.3073) - - - - - - Crisis - - - - N 179 164 156 179 179 R-square 0.2483 0.2044 0.1939 0.3025 0.2946 0.2311 0.1792 0.1670 0.2823 0.2742 0.0000 0.0000 0.0000 0.0000 0.0000 Time Trend Adjusted 66.9385 ( 18.2702)∗∗∗ 306.7772 (91.1130)∗∗∗ R-square Prob>F Appendix. Supplementary Tables 24 Table A.7: Results: EU Countries Variables 1 2 3 4 5 .2152 .2154 .1782 .2006 .2071 (.0520)∗∗∗ (.0533)∗∗∗ (.0550)∗∗∗ (.0515)∗∗∗ (.0513)∗∗∗ -26.7747 -26.7541 -28.2359 -13.6762 -6.8852 (11.71431)∗∗ (11.9410)∗∗ (12.3471)∗∗ (12.7061) (14.0430) Trade 3.4583 3.4588 3.2725 3.2464 3.3019 Openess (.6801)∗∗∗ ( .6841)∗∗∗ (.7327)∗∗∗ (.6746)∗∗∗ (.6717)∗∗∗ -9.0876 -9.0710 -5.2620 -10.1379 -10.3063 (5.8285) ( 6.0901) ( 6.1508) (5.7499)∗ (5.7524)∗ - - - - - GDP Real GDP Growth REER R&D - -.0269 ( 2.7654) 2nd - - Edu Time 14.5085 (43.8353) 44.12603 - - - Crisis - - - - N 155 155 135 155 155 R-square 0.2125 0.2125 0.1892 0.2429 0.2436 0.1915 0.1861 0.1577 0.2175 0.2182 0.0000 0.0000 0.0000 0.0000 0.0000 Trend Adjusted ( 18.0286)∗∗∗ 220.8656 (89.1986)∗∗∗ R-square Prob>F Appendix. Supplementary Tables 25 Table A.8: Results: Complete Specifications Variables (1) Full Sample (2) EU Countries .1681 .1661 (.0551)∗∗∗ (.0557)∗∗∗ .-16.2109 -13.4988 (13.6580 (13.9964)∗∗ Trade 3.0884 3.0677 Openess (.7268)∗∗∗ ( .7336)∗∗∗ .-5.6385 -8.4830 (6.3462) ( 6.6555) -.5047 -.3340 (2.7413) ( 2.8061) 2nd 19.1938 3.3909 Edu (42.7836) (44.6864) GDP Real GDP Growth REER R&D EU -108.5363 - (164.1356) Time 43.0090 49.7984 Trend (21.1660)∗∗∗ (22.3654)∗∗∗ N 144 135 R-square 0.2079 0.2197 0.1609 0.1767 0.0000 0.0000 Adjusted R-square Prob>F Bibliography [1] John H. Dunning. Trade, location of economic activity and the mne: A search for an eclectic approach. 1980. [2] N. Pain and D. V. Welsum. Untying the gordian knot: The multiple links between exchange rates and foreign direct investment. JCMS: Journal of Common Market Studies, 41(5):823–846, 2003. [3] J. Duanmu and Y. Guney. A panel data analysis of locational determinants of chinese and indian outward foreign direct investment. 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