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Master programme in Economic History Determinants of Location in Outward U.S. Foreign Direct Investment Frank Ix [email protected] Abstract: The flow of capital across borders through foreign direct investment has become a major driver in the world economy and continues to gain prominence due to the rise of Asia and other emerging markets. The United States, as the largest economy in the world for over half a century, has played a crucial role in this capital flow. Everyday, millions of private U.S. dollars pour across borders towards direct investments in foreign companies and development of international subsidiaries and joint ventures. This thesis uses panel regression data to assess what factors determine which markets receive U.S. investment and why. Out of a sample of fifteen initial independent variables, six are proven to be significant, most notably exports, exchange rates, population size, CPI, and CO2 emissions. Because of the possible limitations of a panel regression, further research is also recommended for many variables. Keywords: foreign direct investment, FDI, American investment, cross-border M&A, international economics EKHR21 Master thesis (15 credits ECTS) June 2011 Supervisor: Håkan Lobell Examiner: Lars-Olof Olander 1 Table of Contents Page 1. Introduction 03 1.1 Research Problem 04 1.2 Aim and Scope 04 1.3 Outline of the Thesis 05 1.4 Defining FDI 05 1.5 Types of FDI 06 1.6 The American Case 07 2.0 Literature Review/Theory Background 09 2.1 Introduction of Variables 19 2.2 Hypotheses 30 3. Data and Methodology 32 3.1 Data 32 3.2 Model 32 4. Empirical Analysis 35 4.1 Results 35 4.2 Discussion 40 5. Concluding Remarks 44 6. References 47 7. Appendices 49 Acknowledgements Special thanks to my supervisor Dr. Håkan Lobell and Dr. Jonas Helgertz from the Department of Economic History at Lund University, as well as Chris Culvern and Michelle Snow, whose suggestions and advice helped significantly towards the completion of this thesis. 2 1. Introduction With the integration of world markets over the last three decades, the role of foreign direct investment (FDI) has deservedly garnered increased academic attention. The movement of capital across borders in private transactions unlocks economic growth potential not only in the FDI recipient country, but in the investing country, as well. While individual cases may result in differing conclusions on the impact of FDI on both firm and economic growth, the growing role of cross-border investment in global finance is readily apparent. For the United States, outward FDI reached an all-time high in 2008, with $3.1 trillion invested on foreign soil and investment income totaling $346B (U.S. Bureau of Economic Analysis, or BEA). Although most countries in the world receive FDI from the United States in one form or another, the recipients of most of this investment are those that hold the most promise of delivering returns, as deemed by individual investors. However, the political and economic environments that promote these returns differ greatly between countries. Also, the dynamics within individual firms are different from industry to industry. Therefore, the most attractive combination of environments for a firm and recipient country vary greatly over time and space. Over the last decade, countries that once received a large portion of American investment have decreased-while other emerging markets have increased- their share. Obvious explanations for this would point to the diverse landscape of political, economic, and financial stabilities between countries, while other explanations could include wage levels, natural resource wealth, and exchange rate volatility. At the same time, countries that once received investment due to cheap labor resources now receive investment for access to unique technologies. In other words, the makeup of outward FDI determinants appears to have changing importance over time and space. In many cases, neighboring countries will promote certain domestic characteristics to attempt to attract FDI, so that a small country (like Singapore) might offer tax breaks, while a larger country (like China) with more natural resources might not need to focus on tax incentives because FDI will be attracted regardless. Whether participating in cross-border mergers and acquisitions (M&A), joint equity ventures, or overseas expansion, American investors must weigh many factors when 3 deciding among future investment locations. While delivering on shareholder returns is usually the goal, these investments and returns come in many forms, with each one having unique impacts on an investor’s portfolio. Some companies may be going abroad to avoid domestic tax rates or restrictive trade barriers, to attract unique technologies and talent, or to keep up with the competition. More often, perhaps, companies invest in other countries to diversify risk, take advantage of growth opportunities in high growth markets, or to tap into a new group of previously unreachable consumers. In order to understand why some countries have developed a certain talent for attracting FDI, and why some companies have become adept at deciding which markets to invest in, it is crucial first to understand the possible answers to these questions as determinants. Furthermore, which of these determinants carries the most weight in investment decisions, and do these decisions change over time? This paper will analyze the factors that may affect the share of U.S. outward FDI a country receives over the period of 19822008. 1.1 Research Problem The topic of research is how national political and economic environments affect the amount of foreign investment that that country receives and how this may change over time. Although the United States provides a great example because of the enormity of its outward FDI and diversity of recipient countries, the hope with this study is that the factors that attract FDI should be transferable across cases. In this sense, the results from this study are aimed at contributing to the field of FDI determinants rather than the specific case of the United States and its relationship with FDI. Because there have been few recent studies using the United States in this type of experiment, this study should contribute to the depth of research into FDI determinants, with particular benefit of timing given that the dynamics of the global economy have changed greatly since 2008. 1.2 Aim and Scope Toward the pursuit of determining the right economic and political atmosphere for attracting FDI, this paper will use a panel regression to study 51 recipient countries of US FDI over a 26-year period (1982-2008) to gain insight into why American firms 4 invest in some foreign countries more than others. Therefore the aim of this paper is to tackle this topic and provide evidence for multiple determinants. As stated, this paper will hopefully contribute to the FDI determinants literature, which to date has yet to find a general consensus. This paper does not aim to provide grounds for a general consensus, but instead to simply add one large, important case study to the growing quantity of research done previously in the hope that it will respond to some areas of debate. 1.3 Outline of thesis The paper will proceed as such: in order to be able to speak fluently about the determinants of FDI, it will be first necessary to fully define FDI and describe the historical reasons that have driven FDI in the most basic sense. This description will be followed by a review of similar research, focusing mostly on case studies on the determinants of FDI around the world, as well as specific studies on the dynamics of U.S. FDI. These two sections will lay the foundation for the rest of the paper: introducing the variables to be used, a description of the data, methodology and framework of this sample, followed by regression analysis, the results, and further concluding remarks. 1.4 Definition of FDI Foreign direct investment is a term economists have used for decades to describe investments in physical assets in international markets. Initially, this definition was limited to a company purchasing or building a physical facility in another country, most typically used as a production factory. As the nature of international markets has become more complex and more entangled over the last few decades, this definition has expanded to incorporate three other types of direct investment: 1. direct acquisition of a foreign company or facility (further referred to as cross-border M&A); 2. joint venture with a foreign company in their local market; or 3. strategic alliance with a foreign company in their local market. This is opposed to an indirect (or portfolio) investment: an investment in real estate or a corporate entity through stocks, bonds, or an investment fund. The important distinction between the two is that the company making a direct investment has a direct influence on the returns on their investments, whereas an indirect investor simply provides capital for another entity to provide these returns. This study will focus solely 5 on direct investment transactions. The dependent variable will be the U.S. FDI position in each country, which measures “the value of U.S. direct investors' equity in, and net outstanding loans to, their foreign affiliates. The position may be viewed as the U.S. direct investors' net financial claims on their foreign affiliates, whether in the form of equity (including reinvested earnings) or debt (BEA).” 1.5 Types of FDI Another important component of defining FDI is the historical reasons that companies have been compelled to invest in other markets. The most straightforward breakdown of these reasons came from Chryssochoidis, Millar & Clegg’s 1997 book, in which they provide the following five types of incentives for a company to commit resources to FDI: 1. To gain access to currently unavailable factors of production. These could be natural resources, technical skill, or patents owned by a company in the recipient country 2. To gain access to low-cost factors of production, such as labor or natural resources 3. International competitors creating a partnership across borders to gain access to the other's product lines and client lists 4. To gain access to new consumers in the recipient country’s market 5. To avoid tariffs or other trade barriers (Chryssochoidis, Millar, Clegg, 1997) Which of these incentives drives FDI often varies depending on the home market from which the investment derives, such that a company based in a low labor cost economy would not invest abroad for low-cost labor, and a company based in a large market would not prioritize investing in small foreign markets to gain more market access. These priorities can also shift within a group of investors over time. Because this thesis focuses on the U.S. as its home market, it will be important to analyze which of these five factors has influenced FDI determination for Americans and which will be important moving forward, as these factors ultimately determine the location of 6 investment. This study will use multiple variables representing each of these five categories to determine which have the most influence on U.S. FDI. 1.6 American Case For United States outward FDI, investment position and direct investment income have grown steadily over the last three decades and reached an all time high in 2008 (BEA). As stated, this investment is spread to all corners of the world, albeit unevenly (as shown in Graph 1.). The European Union, for example, receives roughly half of U.S. investment, coming in at $1.6 trillion, while China and India combined only received $62 billion. Despite this large gap, China and India have both had a huge boost in investment over the last decade. In 2000, they received only a combined $13 billion, thus doubling their US FDI income twice in the last eight years (BEA). The countries that receive the most FDI are from Western Europe, such as the Netherlands ($426 billion) and the UK ($449 billion), who top the list each taking about a 15% share of American FDI. Canada and Mexico benefit from their proximity to the U.S., with the former receiving $239 billion while the latter receives $89 billion. In contrast, lesser recipients include South American countries such as Brazil ($44.5 billion) and Peru ($4.7 billion), Africa countries like Nigeria ($3.2 billion) and Egypt ($8.3 billion), and the Middle Eastern countries Saudi Arabia ($5.1 billion) and United Arab Emirates ($3.4 billion). Within those countries that receive large amounts of U.S. FDI, the types of transactions can vary across many industries for many different purposes. For example, about half of investments in the Netherlands are directed at holding companies, with the remaining investment spread around to industries such as IT, finance, and manufacturing. In the UK, the lion’s share goes towards the banking industry, with the service and manufacturing industries receiving most of the remainder. Between Canada and Europe, the U.S. invests more heavily in the Canadian mining and manufacturing industries than the same industries in Europe. But when it comes to finance and holding companies, the 7 8 Graph 1. Distribution of American FDI, Year 2009, as % of selected BEA dataset, not total U.S. invests far more in Europe than in Canada. Perhaps to an even greater degree, Mexico shares the manufacturing pull with Canada. African and Middle Eastern investment tends to be directed at the natural resources and the service industries. China receives over half of its U.S. FDI for manufacturing, while India’s investment has focused on information and service industries (BEA). From these breakdowns, we’re able to see that certain industries tend to receive more investment than others, and that certain countries have competitive advantages in certain industries. For example, because of advanced financial markets, Europe is more prone and prepared to give returns on U.S. FDI directed at the banking industry. The Middle East has advantages in natural resources, and therefore receives investments towards natural resource extraction (oil, mining, etc.). India has a well-developed IT sector, and therefore receives funding for information services. However, despite the fact that it is relatively easy to see which industries within countries receive the most U.S. FDI, it does not answer why countries with similar industries receive different amounts. Also, it is conceivable that some countries have developed these industries with the express purpose of attracting U.S. FDI—so what makes some countries more successful at this than others? 2. Literature Review Previous research does not lead us to any simple answers to this question. Foreign direct investment is one of the most studied topics in international economics, so it is important to recognize the impact previous research will have on this study. Because of this subject’s prominence in international economics, researchers from many fields have attempted to determine which factors play a part in the decision process of investment locations. Many of these theories have been posited either by micro-level, firm-focused academics or by macro-level researchers, and consensus has yet to be reached within these fields, let alone between them. For the purpose of this study, it will be important to consider both fields in order to build a comprehensive model. Generally speaking, microeconomic scholars argue that the firm is the focal point for all decision-making processes in FDI—therefore results from these firms can then be extrapolated to explain trends in national FDI data. With the macroeconomic perspective, it is assumed that 9 individual firms’ decisions are based on larger economic trends in the hopes that they can ride on a wave of economic growth. With each of these viewpoints there are different sets of variables typically used. For example, firm-level research suggests that previous return on investments, labor costs, tax incentives, and diversifying risk are usually key determinants for firms in deciding where to invest. Macroeconomists tend to focus more on the market size of the recipient country, political stability, exchange rates, and distance between countries as the most significant possible variables. The reality is that FDI determination most likely incorporates both of these two viewpoints. To build an optimal model will require a review of the theory behind these variables so that it can be determined whether they can be meshed successfully. Three especially useful pieces of literature are theoretical overviews of previous research done on FDI determinants. Each gives broad overviews before getting into specifics of which theories have proven most successful: the first being Lizondo’s 1990 article which focuses on FDI from a micro viewpoint, then Blonigen’s 2005 review focusing on firmlevel to economic-level variables, and lastly Faeth’s study in 2009, which focuses heavily on macroeconomic research. Each successfully lays out the state of FDI research at publication time in addition to constructing their various theoretical justifications. In addition to these three works, John Dunning also presents a model based on combining many of the frameworks described by these three authors. Lizondo While Lizondo’s was written over 20 years ago, the current state of the field still resembles the structure that he describes. This structure is compromised of determinants that are broken down into three different types: 1) Perfect Market Theories 2) Imperfect Markets/Industrial Organization Theories 3) Liquidity/Currency Theories (Lizondo, Saul 1990). Each of these theoretical backgrounds considers multiple variables—even multiple types of variables—that affect the determination of FDI levels, though each is based on different initial assumptions. 1) Perfect Markets Framework The first of this group—those that are predicated on perfect market conditions—is based on the initial assumption that money will flow across borders freely, from economies with low returns on investment towards economies with high returns on 10 investment. If a firm assumes a cost of capital (or, cost of investment) that is fair when weighted with exchange rates, then the investments will be made where returns are predicted to be higher. There are, however, issues with this theory. Considering this assumption was first popularized in the 1950’s when world markets were heavily burdened with tariffs and trade barriers, it would be misguided to consider it a perfect market landscape. Even now, despite an increasing globalization that accounts for the freeing of markets over time, very real differences in culture, as well as political, legal and tax systems remain entrenched, making it difficult to assume progress toward perfect markets. Tests of this theory have attempted to measure whether companies simply make locational investment decisions based on year-over-year returns, and almost always prove to be inconclusive. One reason for this is that it is very difficult to capture return on investment data clearly. For example, many of these tests measure a company’s domestic investment versus their foreign investment in multiple countries. The studies hope to show that the company invested abroad for specific reasons in each case, namely that each foreign environment provided a unique environment, thus enabling raised expectations of rates of returns for each project. While the company may have decided to embark on these investments because of forecasting high rates of returns, it is very difficult for even the investing company to determine what these returns are over time. This is due to numerous reasons, such as a lack of standard procedure in terms of time lagging the return schedule, how financial markets and political situations can change over the course of the investment, etc. Therefore, for academics studying FDI versus domestic investment decisions, it has proven difficult to parse out the exact nature of market perfection and rates of returns. Another problem that must be faced in this type of study is the fact that rates of return must be assumed stable within each country, when this is clearly not the case. For example, if a company determines that a project carried out in China has a higher rate of return (15%) than a similar investment in their home country of the U.S. (12%), it would be assumed in such a study that the rate of return for China is 15% while the U.S. is 12%, regardless of the dynamics of the individual project. Another consideration for firms’ investment decisions is risk. Under the assumption of perfect markets, a firm may be investing abroad to diversify their 11 investment portfolio to mitigate risk. For example, a company with 10 manufacturing plants in 10 different countries faces less catastrophic risk than the company with 10 plants in a single country. Therefore, it follows that investment decisions could consider this risk in their future rates of return and invest abroad not to earn a higher profit margin, but to prevent steeper losses in the case of a catastrophic event. While risk is a productive consideration to add to this model (because it inherently accounts for each nation’s inflow and outflow of capital (which rates of returns does not)), some argue that risk cannot be logically considered while also assuming perfect markets, because there would be no reason for firms to diversify risk, only individual investors. Also, previous studies have had trouble proving that diversification of risk is a significant FDI determinant. The last consideration under the perfect market assumption is market size. Market size can sway investors from their home countries towards larger markets because of the hopes of capturing a larger consumer base. This consideration, unlike the previous two, has actually proven to have empirical merit, but is perhaps less useful than it seems. For example, a common explanation given for why China has received so much FDI over the last two decades is the size of the local market. Though the market is the largest on earth, the companies that were investing heavily in China were often building manufacturing plants that would then export products back to their home markets in the U.S. or Europe. In this situation, market size may appear to be significant but ultimately may be insignificant. In order to capture the effects of market size, it would also be necessary to measure wealth per capita to see which how many individuals could be considered viable consumers. 2) Imperfect markets/Industrial Organization Framework In 1976, Hymer’s paper introduced doubt in the perfect market assumption and argued for the individual dynamics within companies and countries that have significant influence in determining FDI distribution. Two sources of this doubt were transactioncost imperfections and structural imperfections; Lizondo defined each as such: Structural imperfections, which help the multinational firm to increase its market power, arise as a result of scale economies, knowledge advantages, distribution networks, product diversification, and credit advantages. Transaction-costs, on the other hand, make it profitable for the multinational firm to substitute an internal "market" for external transactions. (Lizondo 1990, pg. 6) 12 Each of these market imperfections has lead to a different branch of FDI determinant studies—those incorporating structural imperfections have used Industrial Organization (IO) theory, while those accounting for transaction-cost imperfections have used the Internalization Theory of FDI. The IO viewpoint stresses the advantages and disadvantages a firm faces when doing business abroad. Most importantly, the disadvantages should and do weigh heavy in terms of investment decisions. These disadvantages include language and distance barriers, as well as a potential lack of knowledge about local consumer preferences. Therefore, a company will be more likely to invest if these disadvantages are minimized (investing in countries with similar language/cultures, neighboring states, etc.), or if the advantages clearly outweigh them. Typical advantages of foreign companies, especially in the case of those from the U.S., could be dominant brand recognition, economies of scale, or management technique. As Graham and Krugman wrote in their 1989 paper, U.S. companies historically had a significant advantage in all of these areas post-World War II leading up to the 1990’s. Since then, American companies have faced increased competition with global technological and management technique on the rise. Supporting this theory, there has been a clear increase in foreign investment towards the United States over the last two decades, causing domestic fear in the U.S. over ownership and dominance compared with China and Europe. The Internalization Theory of FDI is based on the idea that because international markets are imperfect, it is less risky and less costly to have cross-border transactions within a company than across borders between two companies. In other words, it is safer and cheaper to open a foreign division or subsidiary of your company rather than counting on a foreign company to do the same work. Therefore, if a foreign country has an advantage in production (cheap labor, higher technological capability, etc.), an American company should invest directly into that country rather than indirectly. This theory leans heavily on the theory of the firm from the financial disciplines, which has a main belief that the firm is able to make better decisions for itself than the market can. Of all the theories used to describe FDI determinants, this is perhaps the most broad and all encompassing, as considerations for a firm’s motivation lies at the heart of FDI 13 determinants. An offshoot of the IO and Internalization theories is the Oligopolistic approach, which states that once some companies have been internally motivated to invest abroad, this will kick off an FDI reaction within their industry. Competitors will then race to these foreign markets so as to not lose market share, regardless of whether there are internal advantages of FDI for those companies. 3) Liquidity/Currency Frameworks Apart from these two main theories regarding determinants of FDI, several other, less comprehensive, theories have arisen to suggest the incorporation of other variables that have proven to be significant. Liquidity factors, exchange rates and currency issues, as well as government profiles all can have impacts on where companies invest abroad. There has been a large amount of documented accounts that suggest that when investing abroad, domestic firms do not make large leaps into these markets at one time. In other words, in most cases firms will make a small initial investment and then build their foreign operations over time from the profit that this division makes, therefore only putting a small amount of initial money at risk. When extrapolated to national level examination rather than firm level, this produces investment based on previous returns rather than other environmental factors. While these environmental factors no doubt have an effect on previous returns, they play less into the decision process of firms about whether to invest now than how well their investments paid off last year. Much like the theory of rates of returns in perfect markets, this liquidity theory does rely on the difficult to measure rate of returns numbers, but the incorporation of internal investment history makes more of a theoretical impact on determinations of future investments. Another factor that every company must take into account when investing abroad is the currency effects that foreign ownership will have on their balance sheet. If a country has a strong currency, it is more likely that companies in that country will invest abroad, and less likely that foreign companies will invest in that country. Because exchange rates fluctuate daily, this presents potential risk for long-term investments as investors have little influence over the currency advantages/disadvantages that their investment may inherit. And while companies must consider this upon foreign investments, studies such as Moore’s 1993 study on German FDI have proven that exchange rates often have little to no influence over FDI determination. He states that 14 because of exchange rate volatility, companies rarely have the capacity to base long-term investment decisions on short-term fluctuation possibilities. Lastly, government policy in foreign countries can pose significant challenges or incentives for investment through tax rates, tariffs, and further regulatory measures. Also, overall political stability can be a source of major risk, especially in emerging markets. When these last three considerations are added to the three major frameworks Lizondo first discusses, the article thoroughly summarizes the possible determinants of FDI that academics still face today. Blonigen proves that this is the case is his article, written fifteen years later, while formulating his own take on which frameworks may work best. Blonigen His paper, entitled “A Review of the Empirical Literature on FDI Determinants”, reviews numerous variables in depth while relating them back to theory, but theories classified in different ways from those described by Lizondo. Instead of describing the theories as firm-level versus macro-level, Blonigen uses “internal” versus “external” factors to describe the choices firms face when choosing investment locations. Internal factors are potential advantages that the firm themselves have control over, much like the micro-level factors described earlier, while external are clearly macro factors. He only briefly touches on these internal variables, and only differs from Lizondo in that he introduces “R&D intensity” and “advertising intensity”, meaning the amount spent by the firm on R&D and advertising. Blonigen rationalizes these additions through results of previous research: “R&D [and advertising] intensity is almost invariably positively correlated with multinationality”. The rest of his research reveals the importance of macroeconomic variables, which he narrows down to four: Exchange rates, Interest Rates, Institutional Policy, and Trade Policy. Each of these determinants has been proven significant in some way or another, and according to Blonigen, should not be neglected in future studies. However, in concluding his paper, he states that he has gathered that “most determinants of crosscountry FDI are fairly fragile statistically” and that “The more insightful and innovative papers in the literature have developed hypotheses about when a factor should matter and when it should not matter, and then find creative ways to test these hypotheses in the data”. Faeth’s 2009 article reveals many similar conclusions. 15 Faeth Faeth covers much of the same theoretical structure in 2009 that Lizondo and Blonigen covered years prior, but she focuses more on empirical results from both financial and economic research. Her survey of literature concludes that when trying to reveal determinants for outward FDI from a finance perspective, the following variables have been proven to be significant at times: exchange rates, tax rates, trade protection, and interest rates. The exchange rate factor is derived from two decades of research in the field, most notably Froot and Stein (1991) whose work established the significance of exchange rates and exchange rate volatility on FDI. Tax affects on FDI were brought to light with Hartman (1985), and have been honed by Blonigen and Davies (2004), with the former claiming that adverse tax regulations drive FDI and the latter finding that tax rates have little to no effect on FDI. Other financial studies that model outward determinants, such as Duanmu and Guney (2009), find currency levels, institutional support, and interest rates to be significant explanatory causes for FDI levels. Faeth also describes how economists and economic historians have contributed equally, if not more so, to the debate surrounding FDI determinants. Such models often use variables that differ from those previously stated. These variables include: GDP, GDP growth, education level of workforce, population size, political stability, distance from host to recipient country, cultural difference, and number of patents issued annually. From these variables, many different models have been employed. Faeth (2009) has helped summarize these works in her article, which discusses the nine most prominent models used in studying FDI determinants. After analyzing each model’s strengths and weaknesses, she comes to the conclusion: …Any analysis of determinants of FDI should not be based on a single theoretical model. Instead, FDI should be explained more broadly by a combination of factors from a variety of theoretical models such as ownership advantages or agglomeration economics, market size and characteristics, cost factors, transport costs, protection, risk factors and policy variables. (Faeth 2009) Dunning John Dunning wrote a seminal work on FDI determinants as well, though his study is a unique analysis rather than theoretical summary like the last three. Dunning 16 built a model based on three different historical frameworks in 1988, and though perhaps not as comprehensive as the patchwork framework Faeth suggests, it is certainly one of the most comprehensive single frameworks to emerge. His OLI model, also know as the “Eclectic Paradigm”, considers three main categories of advantages that lead to FDI: Ownership advantages, Locational advantages, and Internalization advantages (Dunning 1988). Companies investing abroad seeking ownership advantages consider the benefits of ownership higher than the cost of setting up businesses across borders. These advantages include higher returns to scale, minimized transaction costs, and the ability to ensure high quality management skills and production technique while ensuring patents and trademarks stay in house. Locational advantages are broken down into economic advantages, political advantages, and social advantages, but could be broadly measured by natural resource wealth, low-cost labor, and attractive tax or trade policy. Other common measures are size of market (measure by population, GDP, etc.), exports, and distance between countries. Internalization advantages are similar to ownership advantages, but focus more on the retaining earnings that would inevitably be lost in joint ventures or partnership agreements that can be kept by internalizing all production processes. His three-pronged framework leans heavily on transaction cost theory, and he applies this model to FDI after distinguishing three types of investment. Those investments can be efficiency seeking, strategy seeking, or support seeking. Efficiencyseeking investments usually fall into the ownership and internalization advantages columns, while strategy-seeking investments are usually made with locational advantages in mind. Support-seeking investments are those that usually have location in mind also, because these are usually foreign operations aimed at supplementing domestic operations rather than offering a separate income stream. These classifications of investment type and advantage type will be helpful in analyzing the American FDI data. Although the framework is not all encompassing, it may be the best able to describe most cases. Other References Taking all of the frameworks and variable sets that Lizondo, Blonigen, Faeth, and Dunning lay out in their studies, this research will lean heavily on pieces of each. 17 Lizondo’s theories on imperfect markets will provide the base set of assumptions, while also incorporating Industrial Organization theory. Blonigen’s key contribution will be his concluding thoughts on the appropriate use of variables depending on circumstance and interpretation, though his four variables will also make an appearance. Finally, Faeth’s description of “Aggregate variables as determinants of FDI” will be crucial because of its simplicity and openness to both micro and macroeconomic concerns. Adding to these three will be the wide scope with which Dunning brought in his OLI framework. And while these four works provide the foundation for this study, there are a handful of others that will be referenced. Also influencing this thesis are Kalotay and Sulstarolva’s 2010 case study of Russian outward FDI, Buckley, et al.’s 2007 study on Chinese outward FDI determinants, as well as Bevin and Estrin’s 2000 paper on FDI in transition economies, as each use similar subsets of variables with differing effectiveness. Kalotay and Sulstarolva’s research, done for the U.N., looked at the determinants of outward FDI for Russia with special focus given to financial considerations- especially from a firm perspective. Because of the amount of transnational corporations that grew out of the uncertainty in domestic markets during the 1990’s in Russia, the flow of outward FDI is relatively high for Russian companies and investors. The authors model this FDI using standard variables such as recipient country GDP, market size, exports, natural resources, technological assets, and geographic and cultural distance from Russia, and find GDP, natural resources, and cultural differences to be significant. However, they then also employ a model to map how Russian companies decide between countries for crossborder M&A transactions. Included in this model are new variables, such as patent levels in recipient country and exchange rates with the ruble. While this model did not find much significance with any variables in the Russian case, the authors explained tweaks that could have helped, all of which will be taken into account in this study. Other studies have focused on U.S. FDI, but often using specific subsets of variables. One such article, written by Yeaple (2003), takes a look at how skill endowments play a role in determining U.S. FDI flow by measuring market access and the availability of skilled workers recipient countries have. Another, done by Filippaios, Papanastassiou, and Pearce (2003), also studied outward FDI from the U.S., but only 18 directed at countries on the Pacific Rim. In many ways, this study is an extension of theirs to areas outside of the Pacific Rim, though some variables are of course different. This study will use these previous works as a theoretical background and foundation for the use of new data. By using these models, the study will not only add to the growing literature on determinants of outward FDI, but it will also provide valuable insight into for the American case. While studies measure outward FDI in the U.S. have been done before, the lack of recent studies as well as a lack of studies using such large samples will allow this research to add significant findings to the field. With most the theoretical background now considered, the focus shifts to the variables and data that will be specifically used in this study. The patchwork model employed here is perhaps nontraditional, but has been used repeatedly in this type of study. Davidson (1980) was a user of this framework when he wrote on U.S. FDI in North America and Europe by using a concoction of economic variables. Also, using this type of framework allows for this study to introduce and lean on many frameworks throughout, as most have something to contribute. 2.1 Introduction to Variables Variable Abbrev. FDI Position FDIP FDI Income FDII Exports EXP Exchange Rate EXCH Corp Tax Rate TAX Population POP GDP per cap GDPPC GDP Growth GDPG Geo Distance DIST Political Freedom FREE Common Lang. LANG CPI Openness to FDI CPI OPEN Description Foreign Investment Position, total amount received to date FDI Income to date in year t-1 Exports of goods and services (% of GDP) Real effective exchange rate index (2005 = 100) Corporate tax rate (% of profit) Population, total GDP per capita, PPP (constant 2005 international $) Percent annual growth in GDP Distance in miles between countries' main economic centers Political freedom rankings (Free, Partly Free, Not Free) Common Language Dummy, whether at least 9% share language Consumer price index (2005 = 100) Foreign direct investment, net inflows (% of GDP)/GDP Approximation for… Source Dependent variable BEA Amount of returns, t-1 BEA Trade policy (% of GDP) World Bank Currency issues World Bank Tax issues OECD Market size World Bank Market size, market demand World Bank Market size, market demand World Bank Locational Advantage CEPII Political stability Free. House Cultural distance CEPII Inflation, prices World Bank Incoming FDI /GDP World Bank 19 CO2 Emissions Empl. Protection CO2 EP CO2 emissions (metric tons per capita) Ranking (1-6) on strictness of Employee Protection Industrial stage, Env. Regul. World Bank Strictness of Empl. Prot. OECD Table 1. Variable Descriptions, including abbreviations to be used moving forward, what each variable is an approximation for, and source. Based on the foundation of previous literature on this topic, the empirical model to be employed in this paper will include variables for both financial and economic structural data (See Table 1). The list of variables has been chosen based on significance in previous research or particular theoretical importance in the U.S. case. As will be shown, not all variables will make it to the final model because of overlap issues or insignificance, but all will be tested in one form or another. The sample size of the data is rather large for this type of study, as it includes 51 countries over 28 years. Because of the bonuses- and limitations (lack of time observations for a time-series regression)- of such a sample size, a panel regression will be utilized. A description of this type of regression will be covered more in depth in Section 3. First, it is important to discuss the theoretical background for each of these variables before hypothesizing which will be significant and in which direction. The dependent variable in this study is the FDI position of a country in a given year in terms of FDI received from the United States. This data was sourced from the Bureau of Economic Analysis within the U.S. government. They defined the FDI position variable as such: “U.S. direct investment (position) abroad is defined as the ownership or control, directly or indirectly, by one U.S. resident of 10 percent or more of the voting securities of an incorporated foreign business enterprise or the equivalent interest in an unincorporated foreign business enterprise (BEA).”An important distinction that must be made with this statistic is that it does not necessarily represent new investment made that in a given year, but instead total investment regardless of year that remains in that foreign country. This number will rise when new investments are made, and lowered when investments are pulled out of that country, whether it is in losses or gains. Below is a statistical overview of the values of FDI positions across countries and time in this dataset (in millions USD): 20 FDIP Percentiles Smallest 1% -70 -194 10% 87 -157 Observations 1356 25% 280 -143 Sum of weight 1356 50% 1649.5 75% 7040 426762 Std. Dev. 47344.07 95% 82965 471384 Variance 2.24e+09 Largest Mean 15868.87 Table 2. Dependent Variable (FDIP) Overview Statistics, in millions USD Table 2 shows the total number of observations of the dependent variable (1356), the mean FDI received for all countries over the period 1982-2009 ($15.87 billion), the median ($16.5 billion) and the standard deviation ($47.34 billion). These numbers suggest that the FDI positions vary greatly over time and space, with recent positions in the UK and the Netherlands, for example, skewing the standard deviation to nearly three times the mean and median. This variation is neither good nor bad, as it is an accurate picture of U.S. outward FDI. If anything, the wide distribution should allow the independent variables more room for explanatory power. For the purpose of the model, however, this variable will be tested after being logged. The main reason for this is that the data given by the BEA is FDI Position rather than FDI received in year x, meaning that this statistic can accumulate year over year. Therefore, investments made in 1984 would still count in this statistic for 1985, 1986, etc. as long as the investment has not been cashed out and profits brought back to American soil. The dynamics of this statistic make it very difficult to differentiate investments made in any given year, so logging the variable will give us the ability to interpret it a percent change in FDI position, or the net change in FDI received, which will allow for the analysis of new investments in a given 21 year. The other benefit of logging this variable, as well as some of our independent variables, is dealing with potential stationarity problems. This will be discussed more in the Data and Methodology section. The first of these logged independent variables is perhaps the most closely related to FDI position, FDI Income. Relating back to Lizondo’s first framework that assumed perfect markets, the biggest factor for determining investment locations could be returns from that country in previous investments. In other words, if a subsidiary or joint venture in a foreign country has proven profitable for its parent company in the United States, that company may be more confident and willing to invest greater amounts towards that country in the future. Not only do returns speak to previous success, but it may also act as an assurance against political and economic risk. Proven profits and returns may give investors a sense, whether deserved or not, that if it was safe the last time then it is a more trustworthy environment for investment going forward. This may mean political instability, exchange rate volatility, or trade policy in the foreign country had no adverse effect on returns. Although there are obvious dangers with that (the lack of problems in previous investment periods does not determine future conditions), it is also logical for investors to consider previous investment successes or failures. The variable is noted as “FDI income”, a measure calculated by the BEA and defined as “is the return on the U.S. direct investment position abroad. It consists of earnings (that is, the U.S. parents' shares in the net income of their foreign affiliates) and the net interest received by the U.S. parents on outstanding loans and trade accounts between the U.S. parents and their foreign affiliates (BEA).” The power of this variable is counterbalanced by its potential pitfalls. There are a couple of potential dangers with this variable, the first being time lags. In other words, it is very difficult to determine how long it takes for investment to pay off. An investment made in 1982, and therefore could be contributing to returns every year leading up to 2009, or it could provide returns only in 1983. It is impossible to parse out the differences between the two in the data, meaning that investments in year t might not only be effected by income from year t-1, but also years t-2, t-3, etc. For this study, the FDI income variable will be for year t-1, meaning that income from 1983 might have an effect on 1984 FDI decisions. This construction is not perfect but may makes the most sense 22 due to the linear sequence of time and the up-to-date availability of in-house data for investing companies. Just with FDI position, this variable will also be logged to see the percent change in income year over year, but it still remains difficult to tell when the original investment was made. There is no easy way to fix this, so the issue will have to be considered moving forward. Another potential danger of including this variable in the model is that it is closely related to many others, including the dependent variable itself. It follows that FDI income is very closely tied to FDI position, in that if one million USD were invested, it would normally yield a return within 20% of that investment, negatively or positively. Therefore, those numbers could be highly correlated. If this is the case, the FDI Income variable could be dropped from the model without much theoretical damage, as the assumption of perfect markets is not seen as crucial in this study. Variable Observations Mean Stand. Dev. Minimum Maximum ln(FDIP) 1321 7.483423 2.225199 .6931472 13.06382 ln(FDII) 1315 6.5612 1.616537 .6931472 10.8715 EXP 1380 40.15725 31.12923 5.3 233.5 EXCH 1044 106.9318 63.85078 36.3 995.9 CORPTAX 684 36.01509 9.962063 12.5 61.8 POP 1428 7.85e+07 2.07e+08 365500 1.33e+09 ln(GDPPC) 1093 9866114 .8742362 -2.302585 2.785011 GDPG 1402 3.342483 3.97981 -18.78335 18.28661 DIST 1428 8916.125 3325.801 2079 15536 FREE 1400 2.560714 .6650895 1 3 LANG 1428 1.470588 .4993091 1 2 CPI 1339 69.85519 34.71952 0 228 OPEN 1334 5.04e-06 .000082 -.0002083 .0022725 ln(CO2) 1317 1.639541 .9538837 -1.203973 3.666122 EP 568 2.253011 .949557 .6 4.19 Table 3. Variable statistical overview. The potentially non-stationary variables (FDIP, FDII, GDPPC, and CO2) have been logged, so their statistics represent a percent change interpretation rather than raw data. 23 Blonigen’s four main FDI determinants were taxes, exchange rates, trade policy, and institutions, and they will all be addressed in one form or another in this model. Exchange rates and taxes are simple enough to add to the model, but perhaps a bit trickier to interpret. Exchange rates are always a concern for a multinational company as significant profits can be lost when exchanged across borders. Therefore, exchange rates are increasingly monitored, forecasted, and considered in most international transactions. FDI is presumably no different. If the currency is strong in the United States relative to world markets, it is more likely that American companies will invest abroad as the dollar goes a longer way in such investments, and should make it easier to recoup their investments. The same is true of the opposite: if the Yuan is strong relative to the dollar (a present concern), it may be less likely for American investors to consider investments in the near term. In this dataset, exchange rates were found from the World Bank and are explicitly defined as the “Real effective exchange rate index (2005 = 100)”. While this measure does not measure the exchange rates of the home currencies in each of the 51 countries against the U.S. specifically, it is a good measure of relative strength of currency against all others internationally and should therefore be sufficient. As seen in Table 3, some observations are missing for this variable and the effects of this will have to be analyzed in the Empirical section. Tax rates have long been linked as a possible FDI determinant because of corporations’ interest in avoiding high tax rates. But is it worth it for companies to move operations across borders just to shave half a percent off their tax rate? That is more difficult to answer. As Blonigen states: “An obvious hypothesis is that higher taxes discourage FDI with the more important question one of magnitude (Blonigen 2005).” While some studies have found that corporate tax rates do have a significant effect on FDI location (most notably De Mooij and Ederveen in 2003), most have a hard time determining the effect. Results are muddied by the complicated tax systems within and between countries, with double taxation, tax relief, and tax rates that vary by FDI source and industry all necessary to consider. The variable that will be used in this study is a weighted corporate tax rate taken from the OECD that will potentially face many of these issues head on. The measure is defined as “the basic combined central and sub-central (statutory) corporate income tax rate given by the adjusted central government rate plus 24 the sub-central rate (OECD Tax Database).” While this measure is great for giving international tax rates a uniform data parameter, government tax structures still vary greatly between countries. Therefore, though impossible to sift though 51 countries’ tax laws, it will be necessary to interpret the results with these possible flaws in mind. Also, just like exchange rates, the corporate tax rates were less available than most other variables. The data was only presented for roughly 35 of our countries, and even less so for the pre-2000 time period. Being the best and biggest data that could be found on corporate tax rates, and because it is a crucial variable to consider in a theoretical sense, it will still be included in initial regressions to test for significance. One interesting thing to note is that the mean tax rate for foreign countries over this time period was roughly 36%, while the U.S. tax rate over the same period averaged 41% (OECD Tax Database). A five percent decrease in taxes would certainly provide some U.S. companies sufficient reason to investment abroad, which provides further support for including this statistic in the regression, at least initially. Trade policy and institutions are the final two areas that Blonigen stresses, but application to specific variables relies on approximation. For this study, the trade policy variable will be approximated by a measure of exports. Taken from the World Bank data, it specifically measures the exports of goods and services as a percent of GDP, which allows us to grasp how open and easy it is to participate in the global market from each country. Factors that could affect this could be government policy that encourages (or discourages) trade, taxes or tariffs, or free trade zones. In many cases, U.S. FDI is directed towards the manufacturing industries, where the products will then be shipped back to the United States at a lower cost. In such a case, the statistic for exports will be very important to consider. The institutional role on FDI will be approximated by the countries’ Freedom House classification, for which each is broken down into political freedom rankings and civil freedom rankings. These rankings are then translated to give whether a country is Free, Partly Free, or Not Free, which is represented in the data set as 1, 2, or 3, respectively. The use of this variable as an approximation for institutional policy is not ideal but should suffice. As Blonigen wrote, institutional policy can influence FDI potentially if political and economic institutions draft policy that openly encourage or 25 discourage international trade, foreign ownership, or copyright/patent law. A perfect measure system would measure each of these dynamics separately. While freedom rankings cannot capture this completely, countries that are classified as free do tend to have more open policies for trade, more encouraging policies for foreign ownership, and better copyright protection. Though there are some exceptions, looking towards the membership requirements for the WTO provides ample support of this assumption. Therefore it should be an adequate approximation. Other issues that need to be considered can be traced back to the five types of FDI presented in Section 1.5. Two main considerations still unaddressed are market size and locational advantages, both of which can important drivers of FDI. Therefore, market size will be considered with three variables: population, GDP per capita, and GDP growth for the recipient countries, all of which were source from the World Bank. The population variable could be significant because of its representation of new potential consumers for American products. China, for example, has a population well over 1 billion people and represents a potentially huge market of consumers for American made (or owned) products. This may drive American companies to open production facilities, operational subsidiaries, etc. in China to get easier access to their consumers. In this dataset, populations vary from 365 thousand people, to 1.33 billion, with the average settling at 78.5 million. The importance of GDP per capita goes hand in hand with population, because in order for a population to be considered potential consumers, their GDP per capita must be at a level that allows for discretionary spending. GDP per capita, like FDI position and FDI income, will be a logged variable because of its upward trend for most countries. Regardless, this variable will allow us to see whether American investors prefer investing in wealthier countries rather than poorer ones, or the opposite; there are theoretical justifications for both. It may be that U.S. investors prefer to deal with wealthier, economically stable countries because there are cultural similarities, because they want to form relationships with consumers and other companies in advanced economies, or because wealthier countries have access to greater technology and labor skill. The opposite may be equally true: because America itself is such a large, relatively wealthy economy itself, FDI is then directed at less wealthy countries to take advantage of lower 26 wages, more relaxed environmental regulation, or tax law, or to simply get a foothold in a potentially emerging market. The results from this variable will be interesting to interpret. GDP growth is a straightforward variable that also has a potentially straightforward link to FDI. As stated earlier, macroeconomist would suggest a main determinant for FDI is the fact that companies want to take advantage of growth rates in foreign countries that are more optimistic than those in their home country. If they invest in that foreign market, they could automatically receive the benefits from the wave of high growth. Recently, China has been the largest recipient of this attention with growth rates close to 10% over the last decade (World Bank). It makes sense, then, that investors worldwide have shifted their attention towards China because it may be seemingly easier to get returns on investment in such a high growth environment. Many opportunities are to be had in such an economy, and corporate investors are well aware of this. In this dataset, there is a large variation in GDP growth, with the maximum being roughly 18%, the minimum being roughly -18%, and the average settling at 3.34% (Table 3). Now that market size has been covered, locational advantages variables are important to consider. The first of these variables included is labor dynamics, as approximated by the Employee Protection statistic at the OECD. Much like GDP per capita, it is possible that labor dynamics could attract or dissuade FDI, depending on what investors are looking to achieve. In the case of the United States, of course, it may be rare that companies invest overseas to capture unique labor talent, as there is an abundance in the domestic market. For other countries, however, it may make sense to invest abroad to attract talent that may be lacking at home. Following this logic, it has long been assumed that if labor cost and skill affect United States outward FDI, it must be towards low cost, low skilled labor that American labor laws prevent. This may be difficult to prove statistically because many investment decisions may not consider labor factors whatsoever. Employee Protection measures “the procedures and costs involved in dismissing individuals or groups of workers and the procedures involved in hiring workers on fixed-term or temporary work agency contracts (OECD EP Database)” on a scale from 0 (least stringent protection) to 6 (most stringent). Two other variables that are implemented in the model are geographic distance from the U.S. and common language, which is used as an approximation for cultural 27 distance. Both of these variables were devised and calculated by CEPII. The geographic distance variable used is a weighted average of capital cities and economic centers within countries, and their distance to the American economic center average. While far from perfect, this is a very good approximation for a simple distance data set, as it takes into account major access points, harbors, and border cities that would make distances shorter than just measuring from geographic centers. The thought with a distance variable is that FDI in neighboring countries is easier than those further away, as shipping costs, logistics, and travel expenses would be significantly less. There may also be a cultural difference tied to distance, but this is tough to determine in this statistic. Common language is better approximation for this cultural distance. It is a dummy variable based on whether the at least 9% of a foreign population shares a common language with at least 9% of the domestic country. In this case, a foreign country must have 9% of its population speak either English or Spanish, the languages that at least 9% of the American population speaks. The presumed effect on FDI this may have is that a foreign country that speaks English or Spanish would share more of a cultural identity through colonial history or modern media, and therefore would be easier to deal with and understand in a business situation. One great example of this issue is Hong Kong. As a British colony with a more diffused use of English, Hong Kong has a long tradition of strong trade and investment from the U.S. and the UK. Mainland China differs in language and history, and therefore took much longer to open up to, and take advantage of, the benefits of globalization. The apparent problem with both of these variables, however, is the fact that common language and distance between countries does not change over time, and therefore cannot be modeled in a panel, fixed-effects model. In order to overcome this, they will be tested as dynamic variables multiplied with their political freedom classifications, which do change over time. The reason why political freedom rankings work in this case is because there may be a correlation between distance to the U.S. and political freedom, as most countries in the Americas are considered politically free, while those in the Middle East, Africa, and Asia or less likely to be free. With language, the bond is even stronger, as the Americas speak English and Spanish, while countries that do not speak either are, on average, farther from the U.S. 28 Whether this proves significant as a determinant of FDI or not will be determined in the results. The World Bank CO2 data represents CO2 emissions in metric tons emitted per capita. Including CO2 emissions data in the model will hopefully tell us one of two things: either 1) U.S. investors tend to invest in well developed economies with high tech industry that gives off a lot of pollutants, and in economies with high per capita energy usage, or 2) U.S. investors tend to invest in developing economies with heavy industry (perhaps in raw materials) where environmental regulation is less strict than the U.S. Because of this dichotomy, CO2 emissions is a complicated variable to include in the data as developed, strict regulation economies and developing, lax regulation economies could have similar statistics in a per capita sense. For the case of this dataset, it is likely that the CO2 statistic will be positive because of those countries with large emissions per capita that also have strict regulations (the UK and Germany, for example). Therefore it will be a difficult variable to pinpoint an exact interpretation on, but necessary to include nonetheless. This variable will also be logged due to its typical upward trend in order to remove any potential stationarity issues. Openness to FDI is a less complicated variable, as it is simply the incoming FDI for a country divided by that country’s GDP. The formation of this variable stems from the 2007 Buckley, et al. study on Chinese outward FDI determinants, in which case this variable was proven significant in some samples and with a positive coefficient. It is a ratio that is an approximation for the host country’s policies on openness to FDI, as a large ratio represents a country that is open to, and receives, a large amount of foreign investment relative to their economy size, and a small ratio represents the opposite. Both of these input statistics (incoming FDI and GDP) were sourced from the World Bank. Lastly, CPI is a statistic also found from the World Bank and represents the consumer price index for each country annually. This index is meant to show the price of a typical basket of goods at a point in time such that it standardizes price comparisons between currencies and between countries with common currencies. It is also used to give reference to the level of inflation and the real level of wages, which makes it one of the benchmark measures of overall economic strength and health. In this study, it will be used as an approximation for inflation and wage levels, as it may be that U.S. investors 29 are looking to take advantage of opportunities in countries with undervalued currency and labor costs. 2.2 Hypotheses Now that these variables have been introduced, they can be discussed in terms of probable effect on the change in FDI position for each country. While there is justification for thinking that each and every variable may be significant, this will most likely not be the case in this model. Some will inevitably be more statistically relevant than others. Based on the theoretical background, results in previous studies, and discussion on each variable in the previous section, the hypothesis for each variable is listed below: Hypotheses Independent Variables Abbrev. Significant? Sign ln(FDI Income, t-1) FDII Y + Exports EXP Y + Exchange Rate EXCH N Corp Tax Rate TAX N Population POP Y + ln(GDP per cap) GDPPC Y + GDP Growth GDPG Y + Geographic Distance x Freedom Rankings DIST_FREE Y - Common Language x Freedom Rankings LANG_FREE Y + CPI Y + OPEN N CO2 Y EP N CPI Openness to FDI ln(CO2 Emissions) Employment Protection + H1 FDI Income (t-1) should prove to be significant and positive, meaning that a positive change in FDI income in year t-1 should lead to a positive change in FDI position in year t. H2 Corporate tax rates will prove to be insignificant because of the complications in accurately predicting the overall tax effects for investment decisions. 30 H3 Exchange rates will also prove to be insignificant because direct investment decisions are rarely short-term, and long-term exchange rate fluctuations are difficult to project. H4 Institutional and trade policy, approximated by exports, will have a positive significance on FDI received, as higher levels of exports allows for export oriented investors. H5 Market size variables (population, GDP per cap and growth) will be positively related to FDI received because American investors tend to expand overseas towards areas where they can reach as many consumers who can consume their products that they can. Growth will be interesting to analyze because it will show whether investors are attracted to simple growth statistics. H6 Employee Protection as a measure for locational advantages will be insignificant in this model, despite the amount of theoretical justification supporting this framework. While some investors may seek cheaper wages and greater labor skill, American investors should rarely seek labor skill abroad, and because most of investment goes towards Europe, clearly cheap, unregulated labor shouldn’t be a crucial attractor of FDI. H7 Cultural barriers, as measured by distance and common language will be negatively related to FDI received. This is hypothesized because although some investment decisions are almost certainly made to connect American businesses with truly foreign parts of the world, most American investors have long histories of setting up businesses, joint ventures, or subsidiaries in countries such as Mexico, Canada, and the UK. The amount of investment directed at these neighbors and culturally similar countries carries too much weight to be insignificant. H8 CO2 emissions will prove to be significant and positive, as U.S. investment is generally directed at countries with large production and manufacturing industries, as well as countries with high-energy consumption (electric, coal, etc.). Another contributing factor could be U.S. investors’ desire to invest abroad to avoid environmental regulations, which may be this case in raw materials production and other heavy industries. 31 H9 Openness to FDI (Incoming FDI /GDP) will have an insignificant effect on the model. Pressure from investors usually trumps FDI policy in host countries as foreign investment, especially from the U.S., is rarely turned away. Therefore, investment won’t seek out countries open to investment, only countries that can provide good return. Openness will prove to be a secondary consideration. H10 CPI will prove to be a positive and significant determinant of FDI position, as it is a good estimator for economic health and strength, inflation, and labor costs. It may not be something that weighs heavy on the mind of investors, but simply looking at the data, U.S. FDI definitely appears to trend towards countries with strong CPI data. 3. Data and Methodology 3.1 Data The data for these variables where chosen across 51 recipient countries to incorporate a wide geographical and characteristic range. The list of these countries can be seen in Appendix VII. Most of the FDI data was sourced from the U.S. Bureau of Economic Analysis (BEA), while most of the indexes and independent variables were sourced from databases such as the World Bank and OECD (refer to Table 1 for specific source locations). Each of the countries used has a significant U.S. FDI position and published data to prove it. Multiple countries represent each continent (excluding Antarctica, of course), which provides the sample with greater explanatory power and variation. Included are some of the U.S.’s largest trading partners (Germany, Japan) as well as some of the slower developing countries (Honduras, Philippines). It includes some of their greatest political allies (the U.K., Canada), and some with strained relationships (Venezuela, Russia). While this sample is not random (chosen based on data availability and size of U.S. FDI position), it does represent a good mix within those parameters. The inclusion of all of them will allow us to see what kind of political and economic factors are significant in determining outward FDI. 3.2 The Model As stated, these variables will be analyzed using panel analysis for the years 1982-2009. The hope is that using the last year of data available will also allow for the 32 model to capture the outlook for FDI post the 2008 global downturn, though the availability for this year is lower than the rest of the time series, which will need to be taken into account. The use of a two-dimensional panel setup allows for the examination of all of these variables across a panel set of 51 countries over the course of 28 years. This is a relatively long and wide dataset, as most comparable studies use far less countries over a period of five to ten years. The time unit is what distinguishes a panel regression from a cross-section study and what gives us the ability to interpret FDI over time within a set of nation specific characteristics. The fact that it is 28 years makes it too short of a time span to utilize a time-series model. A panel model accounts for both of these issues and allows for us to consider a long time series and large set of dependant variables. In order to decide which panel regression should be used, it is important to consider each option. The three most commonly used panel techniques are the Seemingly Unrelated Regression (SUR), the fixed-effects model, and the random-effects model (Hill, Griffith, and Lim 2008). The SUR model is optimally used when a dataset is long and narrow— or when there are few cross-section measurements. Because our dataset is wide, with 51 countries and 15 variables, this model will not provide the robustness and accuracy that the other two may. The fixed-effects model and the random-effects model both have the ability to capture wider datasets, but are based on different assumptions; namely that the randomeffects model assumes that the dataset is randomly gathered, while fixed-effects does not (Hill, Griffith, Lim 2008, pg. 398). In other words, the random-effects model in would work best if the 51 countries were selected at random. This is not the case. The countries were selected mostly based on availability of data, which is undoubtedly dependent on the size, wealth, and transparency of each country. Therefore, it was likely easier to find data on developed economies with democratic governments than developing countries with autocratic rule. This makes justifying a random-effects model in this case very difficult. The fixed-effects model, on the other hand, does not depend on randomness because it holds non-observed traits constant in its regression. In this case, any country specific constants that are unmeasured, such as historical values or innovativeness, will be held as a constant, “fixed” effect separate from the regression results. This “allows us 33 to focus on the marginal effects of the included explanatory variables (Hill, Griffith, Lim 2008, pg. 398).” For this test, unmeasured differences between countries should be held constant throughout the time period as it allows us to isolate and test only the chosen variables. While fixed-effects appears to be the model that would work best, the Hausman test can also reveal explicitly whether the sample is random (Appendix I). The test “compares the coefficient estimates from the random-effects model to those from the fixed-effects model (Adkins, Hill, 2008).” As seen, the null hypothesis for this test is that the difference in random-effects estimate coefficients is not symmetric, and the chi2 statistic is significant, meaning that the sample is not random. This makes the fixedeffects model the clear choice. The model will look as such: ln(FDI positioncountry i, time t) = αi + βln(fdiii,t) + βexpi,t + βexchi,t + βcorptaxi,t + βpopi,t + β ln(gdppci,t )+ βgdpgi,t + βcpii,t + βopeni,t + β ln(co2i,t) + βepi,t + βdist_freei,t + βlang_freei,t + ui,t Where: – αi (i=1....n) is the unknown intercept for each country (represents the country specific constant factors that do not change over time). – FDIP i,t is the dependent variable (DV) where i = country and t = year. – βx i,t represents one independent variable (IV), – β is the coefficient for that IV, – ui,t is the error term With a fixed-effects model comes a lot of important considerations not unlike other econometric models. However, because of the robustness of the model, many of these considerations present less of a danger than these other models. For example, the nature of a fixed-effects model prevents us from worrying about both heteroskedasticity and autocorrelation. This is because the individual-level robust estimators remain consistent regardless of the presence of heteroskedasticity and autocorrelation (Hill, Griffith, and Lim 2008). The danger of autocorrelation is minimized because of the fixed-effects estimator: “Any variable that does not change over time will be exactly collinear with this dummy variable (Hill, Griffith, Lim 2008, pg. 398)” and therefore would be dropped from the model, so no remaining variables would be collinear. In other words, because unseen variation in the model is accounted for, autocorrelation and heterskedasticity 34 concerns are also accounted for in ways that most econometric models are not. And even though the robustness of this model minimizes some of these dangers, it does not mitigate them all. The first of these considerations is the balance of the model, which represents whether there is an equal amount of time-dimension observations for each country in the dataset. From the outset, data was sought for the years 1982-2009 for all countries, but there are missing observations for many countries and many variables. This makes it important to distinguish between unbalanced and balanced, as the interpretations of the data will depend on this distinction. A simple test on the statistics software Stata (xtset, Appendix I) reveals that this dataset is “strongly balanced”, which should be sufficient to consider the panel balanced despite the missing observations. Stationarity is taken into consideration through the use of natural logging in the variable sequence, and the use of year dummy variables. Stationarity issues arise when variables are nonstationary, as trending variables are difficult to interpret in a regression. To counteract nonstationarity, variables with trends will be logged to give a natural log of the original variable. These variables must then be interpreted as a percent change of the original variable rather than raw change. Examples of these variables are FDI position, FDI income, CO2, and GDP per capita (all of which usually trend upward). Lastly, it is important to recognize the use of year dummy variables. Year dummy variables have been implemented in this model to minimize time fixed-effects that could be present- trends within years and within countries that could go unseen by the model. 4. Empirical Analysis 4.1 Results Four regressions were run in order to eventually gain results that are optimal for interpretation. Regression 1 included every variable, though the language-freedom and distance-freedom rankings variables were dropped due not having enough variation over time. Unfortunately, this study is unable to comment on the importance of these determinants and would need to be followed up upon in future research that crafted a regression without a time dimension. The rest of the variables were tested, however, in a first regression that only included 393 observations- a rather low number considering 35 nearly 1400 observations for our dependent variable. This is caused by the low number of observations for corporate tax rates (CORPTAX) and employment protection standards (EP). Because of this, Regression 2 was run without these variables, and the number of observations nearly doubled to 694. In Regression 3, the variables that proved significant in Regression 2 were run in isolation, with the insignificant variables dropped. This gave us 860 observations and strong significance among all the tested variables. Regression 4 was run with the variables of Regression 2, but dropping FDI income because of correlation concerns with the dependent variable. Each of these regression results can be seen in more detail in Appendix II-V. For the purpose of this analysis, it is important to interpret each variable’s significance and coefficient. If a variable is insignificant, it is not worthwhile to interpret its coefficient, though it will be worthwhile to consider their insignificance in the discussion. All results are shown in Table 4, with the R-squared (overall) and F-value at the bottom. While both of these statistics are provided, they are not considered important or as driving factors in regression analysis, as the goal with most panel regressions is to determine significant variables and interpret the results, rather than to maximize the R-squared and minimize the F-statistic. Variable Regression 1 Regression 2 Regression 3 Regression 4 (Appendix II) (Appendix III) (Appendix IV) (Appendix V) ln(FDIP) DV DV DV DV ln(FDII) .4764231*** .6725031*** .7654562*** - EXP .0072796* .0029467 - .0230847*** EXCH .004702* .044067*** .0038739*** .009865*** CORPTAX -.0012791 - - - POP 9.66e-08*** 1.64e-08*** 1.34e-08*** .0000000156*** ln(GDPPC) .0092207 .0204904 - .060502 GDPG .0440804* -.0155357 - -.0195196 DIST_FREE (dropped) - - - LANG_FREE (dropped) - - - CPI .0025988 -.0039873* -.0037509*** -.0036556* 36 OPEN -3345.844 181.1324 - 683.3816*** ln(CO2) -.760288*** -.3649243*** -.1659632*** -.4759179*** EP -.1427961* - - - Observations 393 694 860 696 R2 (overall) .52 .2112 .3473 .1313 P>F 0.00 0.00 0.00 0.00 Table 4. Regression results from three regressions. Regression 1 includes all variables, Regression 2 drops CORPTAX and EP due to observation size, Regression 3 tests only those significant from Regression 2, and Regression 4 uses Regression 2 variables while dropping ln(FDII). ***= statistically significant at the 1% level **= statistically significant at the 5% level *= statistically significant at the 10% level Regression 1 In Regression 1, three variables are significant at the three-star level (meaning their p-value <.01): FDI income (t-1), Population, and CO2. Exchange rates, Exports, and Employment Protection are significant at the one-star level (p-value <.10). Corporate tax rates, GDP per capita, GDP growth, CPI, and Openness to FDI were all proven insignificant to varying degrees (see Appendix II for t-stat and p-values for all variables). Our combined variables DIST_FREE and LANG_FREE were dropped from the regression because of a lack of variation over time. Even though the Freedom House rankings did vary over time, they only changed once or twice on average per country over the 28 year time period. These variables would need to be tested in another model in a future study to gather the information needed to determine their importance. One interesting thing to note about Regression 1 is the one-star significance of Exports. The interpretation of this statistic is that every percent increase in exports as a percent of GDP causes a .0073 percent increase in FDI position (at the one-star level). However, this significance changes in Regression 2. Previous research (covered in Section 2) shows that exports (as a percent of total GDP) have historically been found as both significant and insignificant, depending on the study and the dataset used. This occurs in these regressions as well, with significance not only changing, but also the coefficient, which changes from .0073 to .0029 between Regression 1 and 2, a relatively 37 large shift. Therefore, the results from these two Regressions would be considered inconclusive, but more analysis can be done for Regression 4, where Exports proves to be significant at the three-star level. Two variables proved to be significant in only Regression 1: GDP growth and Employment Protection. It was hypothesized that GDP growth would be significant and positive, which is the result in this regression. A one percent increase in GDP growth causes a .044 percent increase in FDI position, but with a p-value of .061. The opposite is true in Regression 2, with a negative coefficient (-.0155) and insignificant p-value (.316). This casts doubt on the significant determination of GDP growth. Employment protection is a bit different because it was dropped from Regression 2 because of a lack of observations, so Regression 1 results are all that we can go by. It’s coefficient (-.1428) is significant at a one-star level, meaning that a decrease in Employment Protection status by one causes an increase of .1428 in percent change of FDI position. While its one-star significance and lack of observations must be considered, it is at least an interesting result worth analyzing in the discussion. Regression 2 Regression 2 was run like Regression 1, except dropping CORPTAX, DIST_FREE, LANG-FREE, and EP from the equation. This allowed for a greater number of observations, which in turn gives us more confidence in the results. It also gives us the ability to deem GDP per capita, GDP Growth, and Openness to FDI as insignificant. While these were also significant in Regression 1, we can be more certain now of the results because they are consistent even with more observations added (from 393 up to 694). The added observations also strengthened the significance for Exchange Rates and CPI, with Exchange Rates moving from one-star to three-star significance, and CPI moving from insignificant to one-star significant. Regression 3 In Regression 3, all variables that have proven insignificant have been dropped, which has resulted in all remaining variables being significant at the three-star level. FDI income is the important variable to consider in this regression, as it is dropped in 38 Regression 4. The results in Regression 3 helped support the fact that an additional regression was needed without FDI Income, as the t-statistic was very large (31.74). This result, coupled with a correlation test run on all of our variables (Appendix VI), revealed that FDI Income had a strong correlation (.79) with FDI position, and could be effecting the regression. Nonetheless, it is important to analyze FDI income in Regression 3, as it clearly is significant. The variable has a coefficient of .7655, meaning that an increase in one in percent change of FDI Income (t-1) gives an increase of .7655 percent in FDI position change. Therefore, if FDI income changes 10% in one year, the FDI position should change 7.655% in the next year. CPI also reaches the height of its significance in Regression 3, achieving a three star significance level with a coefficient of (-.0038). This can be interpreted as an increase of one in the Consumer Price Index decreases FDI Position by .0038 percent. This makes sense, as investors are usually looking to take advantage of low price positions and undervalued assets. Regression 4 As stated, Regression 4 is a run of Regression 2 without FDI Income, which is dropped because of correlation issues. It is also considered the final regression in this study, in which five variables were proven significant at the three-star level: Exports (coefficient of .0231), Exchange Rates (.009865), Population (.0000000156), Openness to FDI (683.3816), and the natural log of CO2 emissions per capita (-.4759). CPI was proven significant at the one-star level, a step back from Regression 3. The natural log of GDP per capita and GDP growth were proven insignificant again, leaving little doubt in this model. It can be seen that dropping FDI Income had a large effect on the significance of Exports and Openness to FDI, as well as their coefficients. This must be attributable to the decrease in correlation problems within the regression from the previous run, where FDI Income’s presence in the model skewed significance away from other variables towards itself. These significant variables in Regression 4 can be interpreted as such: 1. An increase in one percent of exports as a percent of total GDP increases FDI position in that country by .0231 percent. 2. An increase in one in the real exchange rates of a country 39 increases FDI position in that country by .009865 percent. 3. An increase in one person in a country’s population increases FDI position in that country by .0000000156 percent. 4. An increase in one in the fraction of FDI incoming divided by total GDP increases FDI position in that country by 683.3816 percent (which seems huge, but the Openness to FDI variables are very, very small). 5. An increase in the percent change of CO2 emissions per capita by decreases the FDI position in that country by .4759 percent. 4.2 Discussion The results from these four regressions showed a lot of interesting dynamics when variables were dropped from respective runs. It also gave many significant variables with telling coefficients in terms of the American case of outward FDI. Table 5 shows the results of these regressions against the hypotheses listed in Section 2.2. Hypotheses Independent Results Abbrev. Significant? Sign Significant? Sign ln(FDI Income) FDII Y + Y* + Exports EXP Y + Y + Exchange Rate EXCH N Y + Corp Tax Rate TAX N N Population POP Y + Y ln(GDP per cap) GDPPC Y + N GDP Growth GDPG Y + N Geo Distance DIST Y - N Political Freedom FREE Y + N Common Lang. LANG Y + N CPI Y + Y - OPEN N Y + CO2 Y Y - EP N Variables CPI Openness to FDI ln(CO2 Emissions) EP + + N Table 5. Hypotheses vs. Results (with differences in italics) *significant in Regressions 1,2,3, but dropped in 4 due to correlation concerns 40 As shown, there were many incorrect hypotheses, most noticeably the insignificance of GDP per capita, GDP Growth, and Geographic Distance, Political Freedom, Common Language, measured by the variables (DIST_FREE) and (LANG_FREE). None of them were proven to be determinants of outward FDI in the American case, though, as stated, the last three of these were dropped from the regression from the beginning because of a lack of variation over time. It would be necessary to study these more in isolation before firmly concluding that they have no significant determination on outward FDI from the U.S. The other two variables, GDP per capita and GDP growth, appears to have no significance either. This could be one of two explanations: 1. These variables are insignificant in reality, because American FDI is spread throughout high GDP per capita/low GDP growth countries (such as Canada, those in Western Europe, etc.) and throughout countries bearing the opposite traits of low GDP per capita/high growth (China, India, etc.). In other words, American investors can look for markets with wealthy consumers, or they can look for markets with cheap labor (though these are not necessarily mutually exclusive). Likewise, they can be attracted to high growth economies in order to ride this growth wave, or they can be attracted to slow growth economies that are implementing policies that try to attract FDI to encourage future growth. These two variables attract FDI on both sides of their spectrums and, in this case, prove to be insignificant because of it. The other two variables that prove insignificant (corporate tax rates and employment protection) were hypothesized to prove this. In both cases, like the GDP measures, the data proves that American FDI is attracted to countries with high and low corporate tax rates, as well as high and low employment protection standards. For example, Japan and Belgium receive large amounts of American FDI each year, but in 2008 their corporate tax rates were among the highest (39.5% and 34%, respectively). Poland and Turkey received much less American FDI despite much lower tax rates (19% and 20%, respectively). Therefore, it makes sense that tax rates cannot be proven significant as a FDI determinant. Some investments are presumably made to take advantage of low corporate tax rates abroad, but most investments are most likely made because of other determinants, after which tax rates are the second, third, or fourth considerations in that investment. The same can be said for employment protection, as it 41 appears that its insignificance points to it being a secondary consideration for American investors rather than a totally non-determining factor. Four of the variables that were proven significant were either surprises, or their coefficient sign was a surprise. Exchange rates were hypothesized to be insignificant, as previous research (Faeth) pointed to the belief that investors rarely made or canceled a long-term investment because of short-term currency concerns. This proved to be incorrect, as exchange rates were significant in Regression 4 with a positive coefficient. This suggests that the stronger the real exchange rate a country has, the more FDI that country will receive from the United States. This is not a logical result in a sense because American investors should not be trying to invest abroad to get poor currency conversions from the dollar, but the reality of the international investment landscape makes this result understandable. This is because the majority of U.S. FDI still goes to the European Union and the U.K., both of which have had strong currencies in relation to the U.S. dollar over the last decade. This undoubtedly had an effect of exchange rate significance in this model. It would be interesting to isolate this variable in a follow-up study to see whether the coefficient changes in the future, especially with Europe undergoing currency struggles at the present time. Another surprise was Openness to FDI, which was insignificant in Regression 1 and 2, but jumped to three-star significance in Regression 4 when FDI income was dropped. This variable was an approximation for a country’s openness and encouragement of FDI, and in this case, American FDI. While it may make sense that this has a relationship with the amount of American FDI position, it was hypothesized to be insignificant in this model because it was presumed that investors do not consider a country’s “openness” when deciding between locations. Also, there was a worry about the correlation between this variable and the dependent variable, though this proved not to be an issue (Appendix VI). The significance proven in Regression 4 can be explained by discussing the fact that American FDI tends to go towards countries that receive a lot of FDI from all countries (not just the U.S), especially relative to their GDP. This makes sense, as countries that attract foreign investment in general should also be better at attracting U.S. investment. Consumer Price Index was also significant and positively related to FDI position. This suggests that countries with high CPI indexes receive more 42 American FDI, which could result because CPI is an approximation for prices, which are inevitably higher in countries with high GDP per capita. In this sample, the largest recipients of U.S. FDI are Western Europeans, who also were among the largest CPI’s in the sample. The last surprise was the logged variable of CO2 emissions per capita, which was hypothesized to be significant, but with a positive coefficient. The results proved that it was significant but with a negative coefficient. In Section 2.6, it was stated that CO2 emissions per capita could be positively or negatively related to FDI position, but that it was more likely positive because the countries that receive the most American FDI tend to be industrialized with large manufacturing industries, as well as high energy use per capita. This model showed that a one percent increase in CO2 emissions caused a .476 percent decrease in FDI position. This suggests that American FDI tends towards low CO2 emissions per capita economies. This is possibly caused by very high per capita emissions countries like the United Arab Emirates, a country that receives relatively little American FDI, but emits the most CO2 by far in this dataset. Countries like this (receiving little FDI but creating large emissions, such as Russia) have most likely skewed the significance despite the variable being logged. Despite a significant result, further research with this variable would help build confidence in this interpretation. The rest of the significant variables followed the logic of the hypotheses. FDI income was positively related to FDI position in Regressions 1, 2, and 3, but concerns over correlation between these two variables makes interpreting this relationship complicated. As discussed in Section 2.1, the FDI income variable faces lag issues as income is measured from investments made over long periods and short periods alike, so it is difficult to parse out whether high income in year t-1 equals a strong FDI environment in year t-1, or if it means that years t-10 through t-2 were great environments, but t-1 was worse and caused investors to pull out of foreign investments. Also, a correlation with FDI position of .79 (Appendix VI) shows that regression results could be ruined by correlation effects. Despite these warning flags, it is clear that FDI income has a strong, positive relationship with the dependent variable, meaning that an increase in FDI income in year t-1 equals an increase in change in FDI position in year t. This makes sense because positive returns on previous investments should encourage 43 future investments, especially if companies are reinvesting profits from a foreign subsidiary back into that subsidiary, as Lizindo suggested in his discussion on liquidity factors (refer to Section 2.). Although future research would be needed to clear up the correlation issues in this model, it is reasonable to conclude that FDI income is an important consideration for firm-level FDI determination. Another important consideration is Exports (as a percent of total GDP). This was hypothesized to be significant and positive, and this was the case in Regressions 1 and 4. This variable was an approximation for the importance of the ease of exportation of manufactured goods by American companies in foreign countries. In other words, for Americans investing abroad to build manufacturing facilities or to acquire manufacturing companies, it is crucial that the economic, political, and geographic characteristics allow for easy exportation of those goods back to the United States or other markets. This model proved this to be the case, as high export economies tended to receive more American FDI than those that do not export as much. Lastly, Population turned out to be a positive, significant determinant of American FDI position as hypothesized. This can be interpreted by saying that countries with bigger populations tend to receive more American FDI. This follows logic because larger populations tend to be associated with larger economies, larger consumer populations, larger segments of educated populations, etc. This may not be true worldwide, but in this data sample, in which there are few African, Middle Eastern, South American, and Asian countries, larger populations can be associated with the larger economies (China, Germany, the UK, Japan etc.). Therefore, American investors seek out countries with more possible consumers and larger labor pools. 5. Concluding Remarks The aim of this thesis was to inquire more deeply into why American investors invest abroad and—when they do—why they choose some recipient countries and not others. Previous research and theoretical frameworks have suggested many different significant determinants. Some believe firm-level considerations are the biggest determinants, while others believe macro-level structures and incentives entice FDI. The approach of this study was to consider both viewpoints while building a unique 44 composition of variables that have historically had the most empirical support. Firm-level variables (such as FDI Income and Corporate Tax Rates) were implemented to gather how foreign conditions may be beneficial to individual/corporate interests, and macrolevel variables (such as Exports, Population, and GDP Growth) were incorporated to gauge large-scale structural benefits for American companies abroad. The results of this were a fixed-effects panel regression on the determinants of American outward FDI, which gave insight into multiple variables with differing results. In total, six variables were found to be significant (though one (FDI Income) with a large asterisk beside it because of correlation issues), and seven were found insignificant. The strength of this analysis was the size of the panel data set, with 51 countries included over a 28-year time period. This allowed the regression to capture large variation between countries, and trends within countries, over time, more so than many similar studies done in the field. Previous, smaller studies on FDI determinants have suggested the significance of many of the variables used in this thesis, though some of the variables found significant in this model have been proven insignificant in other studies (like exchange rates), and some found insignificant in this model have been proven significant in other studies (such as corporate tax rates). This study does not provide conclusive answers on the significance of these variables in the study of outward FDI determinants, but instead is another case to be added to the lexicon for consideration in future research. The American case is a large and important one to consider, and the results are, by and large, clear. However, future researchers are not the only ones to benefit from this study. In addition, American corporations looking to invest abroad could gain from reviewing this study. While it does not answer which countries are most successful in delivering returns, it could allow for businesses to examine the characteristics that American investors look for among recipient countries, and how investment rates change over time. A company with a mastery of this information could analyze potential recipient countries by each variable, determining which was the most crucial in their future investment success. It is also helpful in that it tracks investments changes over time and space, giving indications on which countries FDI positions are growing faster than others, not only allowing a potential company to join in with fellow investors, but 45 also to highlight those countries that receive too few investment considerations. The size of the country panel and variable list would provide ample variation for any U.S. company looking to make their mark abroad. However, despite this model’s strength in size, it also had possible issues with correlation between variables, misinterpretations, or simple data collection errors. Two of the biggest failures of this model were the small observation numbers for corporate tax rates and employment protection, and the ineffectiveness of the distance/common language/political freedom variables. These deserve much more attention and determination from a researcher than this study provided. Nevertheless, despite all of these possible pitfalls, it is still important to recognize the possible contribution that this study can make in the FDI determination field. This study’s findings proved Exports, Exchange rates, Population, CPI, and CO2 emissions per capita to be significant FDI determinants and should offer a meaningful impact on future FDI studies, especially in the case of the American FDI. The ultimate hope of this study is that these findings will help further catalog the complex composition of FDI determinants at play, providing insight into a field of study that is becoming so vital to the integration of world economies. 46 6. References Adkins, Lee C., and Hill, R. Carter. Using Stata for Principles of Econometrics: Third Edition. John Wiley & Sons Publishing. 2008. Hoboken, NJ, USA. Bevin, Alan and Estrin, Saul. The Determinants of Foreign Direct Investment in Transition Economies. The William Davidson Institute, Working Paper 342. London Business School. October 2000. Blonigen, Bruce A. A Review of the Empirical Literature on FDI Determinants. Atlantic Economic Journal 33 (4), 383–403 (December 2005). Blonigen, Bruce A.; Davies, Ronald B. The Effects of Bilateral Tax Treaties on US FDI Activity, International Tax and Public Finance, 11, 5, 601-Y22 (2004). Chryssochoidis, G., C. Millar, and Clegg, J. Internationalisation Strategies. New York, MacMillan Press: 3-17 (1997). Buckley, Peter J., Clegg, L. Jeremy, Cross, Adam R., Liu, Xin, Voss, Hinrich and Zheng, Ping. The Determinants of Chinese Outward Foreign Direct Investment. Journal of International Business Studies. Vol. 38, No. 4, International Expansion of Emerging Market Businesses (Jul., 2007), pp. 499-518. Davidson, W.H. The location of foreign direct investment activity: country characteristics and experience effects. Journal of International Business Studies 11: 9–22 (1980). Duanmu, J.L. and Guney, Y. A panel data analysis of locational determinants of Chinese and Indian outward foreign direct investment. Journal of Asia Business Studies (2009). Dunning, John. The Eclectic Paradigm of International Production: A Restatement and Some Possible Extensions. Journal of International Business Studies. Vol. 19, No. 1 1-31 (Spring, 1988). Faeth I. Determinants of Foreign Direct Investment: A Tale of Nine Theoretical Models. Journal of Economic Surveys, 23, 1, 165-96 (2009). Filippaios, F., Papanastassiou, M. and Pearce, R. The evolution of US outward foreign direct investment in the Pacific rim: a cross-time and country analysis. Applied Economics 35, 1779-1787 (2003). . Froot, Kenneth A.; Stein, Jeremy C. “Exchange Rates and Foreign Direct Investment: An Imperfect Capital Markets Approach”, Quarterly Journal of Economics, 106, 4, 1191-1217 (1991). 47 Graham, E. and P. Krugman. Foreign direct investment in the United States. Institute for International Economics, Washington, DC. 1990. Hartman, David G. “Policy and Foreign Direct Investment. Journal of Public Economics, 26, 1, 107-121 (1985). Hill, R. Carter, Griffiths, William E., and Lim, Guay C. Principles of Econometrics: Third Edition. John Wiley & Sons Publishing. Hoboken, NJ, USA. (2008). Hymer, S. The international operations of national firms: A study of direct foreign investment. Cambridge, MA: MIT Press. (1976). Kalotay, Kalman, and Astrit Sulstarova. Modeling Russian Outward FDI, paper presented at the conference UN Conference on Trade and Development, (2010) Lizondo, J. Saul. Foreign direct investment: In Determinants and systemic consequences of international capital flows. Washington, D.C.: IMF, Research Department. (1991). Mooij De R. and S. Ederveen. Taxation and Foreign Direct Investment: A Synthesis of Empirical Research. International Tax and Public Finance, 10: 673–693 (2003). Moore, Michael O. Determinants of German Manufacturing Direct Investment: 1980– 1988. Review of World Economics. Volume 129, Number 1, 120-138 (1993). Yeaple, Stephen R. The Role of Skill Endowments in the Structure of U.S. Outward Foreign Direct Investment. Review of Economics and Statistics, 85, 3, 726-34 (2003). Data Sources CEPII FDI Database. Sourced in March 2011 from: <http://www.cepii.fr/anglaisgraph/bdd/fdi.htm> Freedom House, Freedom in the World Reports, 1982-2009. Sourced in March 2011 from: < http://www.freedomhouse.org/template.cfm?page=15> OECD Statistics. Sourced in March 2011 <from: http://stats.oecd.org> UNCTAD Database: WIR2009 FDI outflows, 17/09/09, sourced from <http://www.unctad.org/Templates/Page.asp?intItemID=3277&lang=1> U.S. Bureau of Economic Analysis (BEA). Databases sourced in February 2011 from: <http://www.bea.gov/international/index.htm> World Bank Data Indicators Database. Sourced in February 2011 from: <http://data.worldbank.org> 48 7. Appendices APPENDIX I Balance Test (xtset) . xtset country_n year panel variable: time variable: delta: country_n (strongly balanced) year, 1982 to 2009 1 unit Hausman test Coefficients (b) (B) fe re lnfdiinc exp exch corptax pop lngdppc gdpg cpi open lnco2 ep .6015543 .0575847 -.018866 .0071929 3.10e-07 -.1526944 .0334058 .0447269 -61188.12 -1.346529 -.3417275 .8062056 .028767 -.0334709 .0136201 9.43e-09 -.200789 .045087 .063209 -80925.35 -1.417195 -.205793 (b-B) Difference sqrt(diag(V_b-V_B)) S.E. -.2046513 .0288177 .0146049 -.0064272 3.01e-07 .0480946 -.0116812 -.0184821 19737.23 .0706664 -.1359345 .0537352 .0085157 .0025037 .0001231 5.22e-08 . . .0040188 . .3212093 .0881547 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(1) = (b-B)'[(V_b-V_B)^(-1)](b-B) = -1.97 chi2<0 ==> model fitted on these data fails to meet the asymptotic assumptions of the Hausman test; see suest for a generalized test 49 APPENDIX II Regression 1 Fixed-effects (within) regression Group variable: country_n Number of obs Number of groups = = 393 22 R-sq: Obs per group: min = avg = max = 9 17.9 22 within = 0.9769 between = 0.4266 overall = 0.5200 corr(u_i, Xb) F(33,338) Prob > F = -0.8092 lnfdip Coef. lnfdiinc exp exch corptax pop lngdppc gdpg dist_free lang_free cpi open lnco2 ep yd1 yd2 yd3 yd4 yd5 yd6 yd7 yd8 yd9 yd10 yd11 yd12 yd13 yd14 yd15 yd16 yd17 yd18 yd19 yd20 yd21 yd22 yd23 yd24 yd25 yd26 yd27 yd28 _cons .4764231 .0072796 .004702 -.0012791 9.65e-08 .0092207 .0440804 (dropped) (dropped) .0025988 -3345.844 -.760288 -.1427951 (dropped) (dropped) (dropped) (dropped) .1014535 .2242514 .2241552 .2964154 .3607097 .4125648 .2749009 .1346004 .1968406 .3786482 .3948142 .3468027 .3557677 2.672094 2.652093 2.8944 2.997799 2.854679 2.874933 2.831187 2.769169 2.817752 (dropped) (dropped) 1.737622 sigma_u sigma_e rho 2.6842895 .26088309 .99064271 F test that all u_i=0: Std. Err. t P>|t| = = 432.65 0.0000 [95% Conf. Interval] .0392651 .0056148 .0025607 .0033112 2.17e-08 .0438709 .0234468 12.13 1.30 1.84 -0.39 4.46 0.21 1.88 0.000 0.196 0.067 0.700 0.000 0.834 0.061 .3991883 -.0037648 -.0003349 -.0077923 5.39e-08 -.0770737 -.0020397 .5536579 .0183241 .0097389 .0052342 1.39e-07 .0955152 .0902005 .0033888 19367.03 .1675616 .0551452 0.77 -0.17 -4.54 -2.59 0.444 0.863 0.000 0.010 -.0040669 -41440.92 -1.089883 -.2512662 .0092646 34749.24 -.4306931 -.0343241 .0983792 .1013552 .1070437 .1043988 .115956 .121524 .1319559 .1547675 .1315989 .1406277 .1448218 .1499258 .1539758 .1554962 .1683403 .1784244 .1831479 .1912412 .1976175 .2103927 .2213194 .232423 1.03 2.21 2.09 2.84 3.11 3.39 2.08 0.87 1.50 2.69 2.73 2.31 2.31 17.18 15.75 16.22 16.37 14.93 14.55 13.46 12.51 12.12 0.303 0.028 0.037 0.005 0.002 0.001 0.038 0.385 0.136 0.007 0.007 0.021 0.021 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -.0920591 .024885 .0135996 .0910622 .1326235 .1735263 .0153427 -.1698285 -.0620155 .1020325 .1099487 .0518975 .0528962 2.366232 2.320967 2.543438 2.637545 2.478506 2.486218 2.417343 2.333833 2.360574 .2949661 .4236179 .4347109 .5017686 .5887959 .6516034 .5344592 .4390292 .4556966 .6552638 .6796797 .6417078 .6586393 2.977956 2.98322 3.245362 3.358052 3.230852 3.263648 3.245031 3.204506 3.27493 .8027647 2.16 0.031 .158578 3.316666 (fraction of variance due to u_i) F(21, 338) = 48.80 Prob > F = 0.0000 50 APPENDIX III Regression 2 Fixed-effects (within) regression Group variable: country_n Number of obs Number of groups = = 694 37 R-sq: Obs per group: min = avg = max = 6 18.8 25 within = 0.9538 between = 0.0532 overall = 0.2112 corr(u_i, Xb) F(34,623) Prob > F = -0.8334 lnfdip Coef. lnfdiinc exp exch pop lngdppc gdpg cpi open lnco2 yd1 yd2 yd3 yd4 yd5 yd6 yd7 yd8 yd9 yd10 yd11 yd12 yd13 yd14 yd15 yd16 yd17 yd18 yd19 yd20 yd21 yd22 yd23 yd24 yd25 yd26 yd27 yd28 _cons .6725031 .0029467 .0044067 1.64e-08 .0204904 -.0155357 -.0039873 181.1324 -.3649243 (dropped) .2174441 .3002443 .2808464 .3557171 .4118741 .5367986 .5643156 .6313968 .6909794 .5410582 .6710701 .6199449 .7760032 .8061102 .8428582 .7876777 3.106257 3.13379 3.291647 3.401238 3.125371 3.226569 3.173884 3.180208 3.319875 (dropped) (dropped) .9688947 sigma_u sigma_e rho 3.7889548 .39189047 .98941553 F test that all u_i=0: Std. Err. t P>|t| = = 378.50 0.0000 [95% Conf. Interval] .0321109 .002705 .0008456 2.05e-09 .0335265 .0154862 .0015004 157.5484 .1185072 20.94 1.09 5.21 8.03 0.61 -1.00 -2.66 1.15 -3.08 0.000 0.276 0.000 0.000 0.541 0.316 0.008 0.251 0.002 .6094443 -.0023654 .002746 1.24e-08 -.0453482 -.0459473 -.0069337 -128.2579 -.5976463 .7355618 .0082588 .0060674 2.05e-08 .086329 .0148758 -.0010409 490.5227 -.1322024 .1408246 .133607 .1346705 .1337382 .1331191 .1350099 .1369728 .1388058 .1435999 .145564 .1628429 .1429881 .1493768 .1495159 .1508223 .1583071 .1581271 .1570884 .1615665 .16556 .1703263 .1736072 .1793091 .1846384 .1884174 1.54 2.25 2.09 2.66 3.09 3.98 4.12 4.55 4.81 3.72 4.12 4.34 5.19 5.39 5.59 4.98 19.64 19.95 20.37 20.54 18.35 18.59 17.70 17.22 17.62 0.123 0.025 0.037 0.008 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -.0591043 .0378697 .0163834 .0930847 .1504575 .2716689 .2953312 .358813 .4089809 .2552026 .3512826 .3391478 .4826601 .512494 .5466766 .4767974 2.79573 2.825303 2.974366 3.076115 2.790887 2.885642 2.82176 2.817619 2.949865 .4939925 .5626189 .5453095 .6183494 .6732906 .8019283 .8332999 .9039806 .9729779 .8269137 .9908576 .9007421 1.069346 1.099726 1.13904 1.098558 3.416784 3.442277 3.608928 3.726361 3.459854 3.567495 3.526007 3.542797 3.689885 .2767044 3.50 0.000 .4255083 1.512281 (fraction of variance due to u_i) F(36, 623) = 25.82 Prob > F = 0.0000 51 APPENDIX IV Regression 3 Fixed-effects (within) regression Group variable: country_n Number of obs Number of groups = = 860 37 R-sq: Obs per group: min = avg = max = 8 23.2 26 within = 0.9528 between = 0.1033 overall = 0.3473 corr(u_i, Xb) F(30,793) Prob > F = -0.7437 lnfdip Coef. lnfdiinc exch pop cpi lnco2 yd1 yd2 yd3 yd4 yd5 yd6 yd7 yd8 yd9 yd10 yd11 yd12 yd13 yd14 yd15 yd16 yd17 yd18 yd19 yd20 yd21 yd22 yd23 yd24 yd25 yd26 yd27 yd28 _cons .7654562 .0038739 1.34e-08 -.0037509 -.1659632 (dropped) .0507193 .0860099 .0771928 .198381 .2595178 .2811882 .2991356 .3446632 .3187163 .2456882 .3779526 .3812081 .4826837 .512881 .5335212 .4832027 2.770232 2.824022 3.075363 3.140411 2.849738 2.857661 2.811093 2.806915 2.914971 (dropped) (dropped) .5903316 sigma_u sigma_e rho 3.1781846 .39483386 .98480086 F test that all u_i=0: Std. Err. t P>|t| = = 533.74 0.0000 [95% Conf. Interval] .0241128 .0005009 1.96e-09 .0012451 .0978039 31.74 7.73 6.83 -3.01 -1.70 0.000 0.000 0.000 0.003 0.090 .7181237 .0028907 9.56e-09 -.006195 -.3579484 .8127886 .0048571 1.73e-08 -.0013069 .026022 .1047984 .1042922 .1044875 .1057751 .1060062 .1070108 .107792 .109399 .1094356 .1110402 .1117812 .1140526 .1180394 .1192324 .1209777 .121175 .1226847 .1244266 .1241487 .1268828 .133285 .1387627 .1415799 .145577 .1489602 0.48 0.82 0.74 1.88 2.45 2.63 2.78 3.15 2.91 2.21 3.38 3.34 4.09 4.30 4.41 3.99 22.58 22.70 24.77 24.75 21.38 20.59 19.86 19.28 19.57 0.629 0.410 0.460 0.061 0.015 0.009 0.006 0.002 0.004 0.027 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -.1549958 -.1187116 -.127912 -.0092513 .0514318 .0711303 .0875443 .1299173 .1038986 .0277208 .1585307 .1573275 .2509771 .2788327 .2960468 .2453411 2.529406 2.579778 2.831664 2.891345 2.588105 2.585276 2.533177 2.521153 2.622568 .2564343 .2907313 .2822977 .4060134 .4676038 .491246 .5107269 .5594091 .5335341 .4636557 .5973746 .6050888 .7143902 .7469294 .7709956 .7210644 3.011057 3.068267 3.319062 3.389477 3.111371 3.130047 3.089009 3.092677 3.207374 .227818 2.59 0.010 .1431339 1.037529 (fraction of variance due to u_i) F(36, 793) = 32.58 Prob > F = 0.0000 52 APPENDIX V Regression 4 Fixed-effects (within) regression Group variable: country_n Number of obs Number of groups = = 696 37 R-sq: Obs per group: min = avg = max = 6 18.8 25 within = 0.9214 between = 0.0083 overall = 0.1313 corr(u_i, Xb) F(33,626) Prob > F = -0.8080 lnfdip Coef. exp exch pop lngdppc gdpg cpi open lnco2 yd1 yd2 yd3 yd4 yd5 yd6 yd7 yd8 yd9 yd10 yd11 yd12 yd13 yd14 yd15 yd16 yd17 yd18 yd19 yd20 yd21 yd22 yd23 yd24 yd25 yd26 yd27 yd28 _cons .0230847 .009865 1.56e-08 .060502 -.0195196 -.0036556 683.3816 -.4759179 (dropped) .4422376 .5908647 .4882761 .8308885 .96871 1.239042 1.282796 1.419921 1.51968 1.408294 1.535927 1.541526 1.856683 1.869136 1.958445 1.858248 4.208698 4.173237 4.251679 4.43751 4.468902 4.69114 4.69213 4.733571 4.902619 (dropped) (dropped) 3.285476 sigma_u sigma_e rho 3.6237858 .51075132 .98052169 F test that all u_i=0: Std. Err. t P>|t| = = 222.26 0.0000 [95% Conf. Interval] .0032886 .0010482 2.67e-09 .0436168 .0201626 .0019484 202.9418 .1542911 7.02 9.41 5.83 1.39 -0.97 -1.88 3.37 -3.08 0.000 0.000 0.000 0.166 0.333 0.061 0.001 0.002 .0166266 .0078065 1.03e-08 -.0251509 -.0591141 -.0074819 284.8525 -.7789087 .0295428 .0119235 2.08e-08 .1461549 .0200749 .0001707 1081.911 -.1729272 .1829983 .1731477 .1750107 .171737 .1699538 .1703765 .1727411 .17403 .1798044 .1817487 .2051325 .1771181 .1824719 .1830684 .1836415 .1950319 .191911 .1940727 .2017835 .2057296 .2053959 .2067599 .2133966 .2199815 .2245069 2.42 3.41 2.79 4.84 5.70 7.27 7.43 8.16 8.45 7.75 7.49 8.70 10.18 10.21 10.66 9.53 21.93 21.50 21.07 21.57 21.76 22.69 21.99 21.52 21.84 0.016 0.001 0.005 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 .0828726 .2508441 .1445969 .4936381 .6349614 .9044634 .9435741 1.078168 1.166587 1.051383 1.133096 1.193708 1.498352 1.509634 1.597817 1.475252 3.83183 3.792125 3.855425 4.033506 4.065554 4.285113 4.27307 4.30158 4.461741 .8016025 .9308853 .8319552 1.168139 1.302458 1.573621 1.622019 1.761675 1.872773 1.765205 1.938758 1.889343 2.215014 2.228639 2.319074 2.241244 4.585565 4.55435 4.647934 4.841514 4.872251 5.097167 5.11119 5.165562 5.343496 .3298384 9.96 0.000 2.637752 3.9332 (fraction of variance due to u_i) F(36, 626) = 115.58 Prob > F = 0.0000 53 54 open lnco2 ep lnfdip lnfdiinc exp exch corptax pop lngdppc gdpg dist free lang cpi open lnco2 ep 1.0000 -0.5004 lnco2 open 1.0000 -0.0149 -0.1118 1.0000 0.0247 0.0938 -0.0085 0.5318 -0.0918 -0.1066 -0.2073 . 0.3938 0.3050 -0.0720 0.3988 -0.3363 1.0000 0.7880 0.1712 0.0415 -0.3190 0.3776 -0.1290 -0.1206 -0.1280 . 0.2728 0.6209 0.0110 0.2489 -0.4406 lnfdip lnfdiinc 1.0000 ep 1.0000 -0.0511 -0.3495 -0.4801 0.0531 0.0154 -0.2839 . -0.3540 0.2786 0.2268 0.0882 -0.0661 exp 1.0000 0.1112 0.1956 -0.1676 -0.2459 -0.1061 . -0.3027 0.3282 -0.0840 0.0197 0.1358 exch 1.0000 0.3139 -0.0884 -0.1473 -0.0684 . -0.0379 -0.5324 -0.1078 0.1167 0.3315 corptax 1.0000 -0.0835 -0.1861 -0.0014 . 0.1080 0.1248 -0.1697 0.0485 -0.0570 pop 1.0000 0.8508 0.0149 . 0.0365 -0.1610 -0.4133 -0.0369 0.0389 lngdppc Correlation Table APPENDIX VI 1.0000 0.0912 . 0.1803 -0.1673 -0.2334 0.0137 -0.0445 gdpg 1.0000 . 0.1286 0.0207 0.0793 0.0688 -0.0948 dist . . . . . . free 1.0000 -0.1066 -0.0056 0.3509 -0.4664 lang 1.0000 0.1090 0.1693 -0.3293 cpi APPENDIX VII List of countries used Argentina Australia Austria Belgium Brazil Canada Chile China Colombia Costa Rica Czech Republic Denmark Ecuador Egypt, Arab Rep. Finland France Germany Greece Hong Kong SAR, China Hungary India Indonesia Ireland Israel Italy Japan Korea, Rep. Luxembourg Malaysia Mexico Netherlands New Zealand Nigeria Norway Panama Peru Philippines Poland Portugal Russian Federation Saudi Arabia Singapore South Africa Spain Sweden Switzerland Thailand Turkey United Arab Emirates United Kingdom Venezuela, RB 55