ASSESSING THE RELATIONSHIP BETWEEN TRADE ...

ASSESSING THE RELATIONSHIP BETWEEN TRADE AGREEMENTS AND FOREIGN DIRECT INVESTMENT A Thesis submitted to the Faculty of the Graduate School of Arts & Sciences at Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in the Georgetown Public Policy Institute

By

Samuel Ross Easterly, B.S.

Washington, DC April 7, 2009

ASSESSING THE RELATIONSHIP BETWEEN TRADE AGREEMENTS AND FOREIGN DIRECT INVESTMENT Samuel Ross Easterly, B.S. Thesis Advisor: Michael Clemens ABSTRACT This paper analyzes the relationship between preferential trade agreements (PTAs) signed between countries and the foreign direct investment (FDI) inflows to the member countries of the agreements. Using the most comprehensive database of PTAs available, it extends earlier research by considering the relationship between FDI flows and different types of bilateral and multilateral trade agreements, by controlling more carefully for political institutions, and by analyzing FDI flows between pairs of countries rather than FDI receipts. FDI has grown in importance to economies across the globe and establishing a link between PTAs and FDI would provide policymakers with another avenue to promote FDI inflows to their countries.

This study demonstrates that

institutional variables play a role in the relationship between trade agreements and FDI, and that relationship also differs depending on the type of trade agreement—for example, entering a customs union with an OECD country has a very different relationship with FDI than joining the WTO.

ii

I would like to thank my thesis advisor, Michael Clemens, for his invaluable support, expertise, and guidance throughout the entire process, without which this thesis would never have been completed. I would also like to thank my former colleagues at the U.S. International Trade Commission for their generous assistance in helping me find a thesis topic. I would like to thank all of my professors from the Georgetown Public Policy Institute who taught me the concepts and skills necessary to complete this type of analysis. Last, but certainly not least, I would like to thank my friends and family who have supported me throughout my education at the Georgetown Public Policy Institute and while completing the thesis-writing process.

iii

TABLE OF CONTENTS Introduction.....................................................................................................................1 Background.....................................................................................................................8 Literature Review..........................................................................................................12 Market Size and Characteristics .................................................................................12 Macroeconomic Conditions .......................................................................................14 Political and Institutional Variables............................................................................15 Population and Environmental Factors.......................................................................16 The Theory of Preferential Trade Agreements............................................................18 Trade Agreements and FDI ........................................................................................19 Conceptual Model .........................................................................................................24 PTAs Could Cause FDI..............................................................................................24 Conditions that Could Cause both PTAs and FDI.......................................................25 Conditions that Could Be Caused by FDI and PTAs...................................................28 FDI Could Cause PTAs..............................................................................................31 Analysis Plan ................................................................................................................32 Data Description............................................................................................................36 Descriptive Statistics .....................................................................................................38 Regression Results ........................................................................................................41 Results.......................................................................................................................41 Aggregate FDI Flows Regressions..........................................................................41 Bilateral FDI Inflows from All Countries (Senders) to OECD Countries (Receivers) ...............................................................................................................................46 iv

Bilateral FDI Outflows from OECD Countries (Senders) to All Countries (Receivers) .............................................................................................................53 Economic Interpretation and Policy Implications .......................................................56 Total FDI Flows Regressions .................................................................................56 Bilateral FDI Inflows from All Countries (Senders) to OECD Countries (Receivers) ...............................................................................................................................61 Bilateral FDI Outflows from OECD Countries (Senders) to All Countries (Receivers) .............................................................................................................70 Conclusions...................................................................................................................74 References.....................................................................................................................78

v

LIST OF TABLES Table 1 – Model Variables ............................................................................................32 Table 2 – Aggregate FDI Flows Model Descriptive Statistics ........................................38 Table 3 – Bilateral FDI Flows Models Descriptive Statistics .........................................39 Table 4 – Aggregate FDI Flows Regressions .................................................................44 Table 5 – Bilateral FDI Inflows from All Countries to OECD Countries Regressions ....47 Table 6 – Bilateral FDI Outflows from OECD Countries to All Countries Regressions .54

vi

Introduction In recent years, foreign direct investment1 (FDI) has acquired increasing importance to economic strength in both investor and recipient countries. After four straight years of growth, in 2007, global FDI inflows increased by 30% to $1.8 trillion, a record high (UNCTAD, 2008). Nations view FDI as a beneficial source of foreign capital in part because it is associated with a transfer of foreign technology and skills in areas like managerial techniques, marketing ideas, accounting practices, and multiple other business-relevant realms. The near consensus in the FDI literature is that, except in cases of serious market distortions, FDI increases income and social welfare in the host country (Moosa, 2002). Developed countries are the main hosts for flows of FDI, at around sixty-eight percent of the global total in 2007, and their economies benefit accordingly. Jackson (2008), for example, argues that FDI contributes to job creation, wage increases, growth in the manufacturing sector, access to new technologies and skills, increases in labor productivity, more tax revenue, and lower interest rates. Yet, at the same time, FDI flows also benefit developing countries. Flows to developing countries reached a record high in 2007 at around $500 billion, and developing countries continued to grow as an important

1 The definition of foreign direct investment taken from UNCTAD’s 2008 World Investment Report: “Foreign direct investment (FDI) is defined as an investment involving a long-term relationship and reflecting a lasting interest and control by a resident entity in one economy (foreign direct investor or parent enterprise) in an enterprise resident in an economy other than that of the foreign direct investor (FDI enterprise or affiliate enterprise or foreign affiliate).”

1

source of FDI flows, which reached a record level of $253 billion in 2007 (UNCTAD, 2008). The benefits of FDI manifest themselves a little differently in developing countries than they do in the developed world: FDI is more reliable than equity and debt capital flows, providing needed stability to developing economies; FDI has a positive relationship with savings and investment without deterioration in the current account; FDI and GDP growth are significantly correlated and develop further over time; and FDI can boost trade for developing countries through export promotion (foreign investors build plants where their goods can be produced at lower costs) and diversify exports when developing countries use technology from developed countries to produce goods normally sold by the developed world (Kumar, 2007). The nature of FDI poses a distinct challenge to the actual investors, mainly multinational companies (MNCs). FDI often entails a physical resource built by an MNC in the foreign/host country, which involves reduced liquidity and its associated higher risk. This higher risk applies to economic concerns with the investment, such as fluctuations in the exchange rate or interest rate, and political concerns, such as the possibility of expropriation or regime change that creates an inhospitable business climate. Investors will naturally want to find locations with the lowest possible risk, all other factors held equal, and nations interested in attracting FDI will present their investment environment as financially, politically, and socially stable and economically prosperous (Hicks, 2007). Over 500 investment promotion agencies in over 160 2

countries are responsible for disseminating information about investment opportunities in the country, improving the overall investment climate, and creating a positive image of the country abroad (Zanatta et al., 2006). Additionally, national governments can provide tax breaks and subsidies, and allow foreigners majority ownership of projects, free repatriation of profits, liberal employment of expatriates, and any number of other incentives to allay investor concerns related to their potential investment. Another way—which will be the focus of this paper—in which a nation can prove to the global investment community that it is financially secure and politically stable is its membership in a preferential trade agreement (PTA). Between 1989 and 2008, the number of PTAs notified to the World Trade Organization (WTO) and in force in 2008 increased by 385% from 46 to 223 agreements. The PTAs signed over the past two decades significantly differ in content from older agreements in that many of them are not exclusively concerned with merchandise trade, but instead try to integrate other areas like “investment, trade in services, setting and harmonization of standards, competition disciplines, customs cooperation, intellectual property rights (IPR), and dispute settlement” (Medvedev, 2006). The United States, for example, signed bilateral trade agreements this decade with Jordan (2000) and Morocco (2004). Neither agreement was critical to the United States from an economic perspective. In the year before each agreement, Jordan was the 100th largest trading partner (in terms of the sum of imports from and exports to Jordan) and Morocco was the 80th largest trading partner. However, both FTAs had important geopolitical implications 3

in further cementing U.S. ties in the Middle East and North Africa. Both agreements also included many “deep integration” issues such as an IPR chapter in the Jordanian FTA and an investment chapter in the Moroccan FTA. The growing prevalence of PTAs and, more importantly, PTAs that go beyond lowering barriers to merchandise trade, opens up a great number of research questions with important policy implications that can be empirically studied. This paper investigates whether or not analysis of data from the past two-and-a-half decades on trade agreements and FDI flows can reject the following null hypothesis: “The effect of preferential trade agreements on foreign direct investment flows is not statistically significantly different from zero.” This hypothesis has major policy ramifications for trade representatives and politicians in developed and developing countries who are negotiating trade agreements. While the results may or may not have external validity and instead be country-specific, in general, if the effect of PTAs on FDI inflows is found to be statistically significantly different from zero and positive, developed countries will have found a potential avenue to boost their economies outside of the traditional fiscal and monetary measures, while developing countries will have found a source of stability, technology and information-sharing, and economic growth. This paper builds upon and improves on the existing literature in several important ways. First, this paper not only studies PTAs, but also WTO accessions. Becoming a member of the WTO may be a greater sign of policy reform and stability than implementing a PTA, and, therefore, a better signal for investors to send FDI to that 4

country. Secondly, this paper considers the relationship between institutional variables and FDI flows, such as the level of human capital and level of political constraints in government. A country with high levels of human capital or a constrained system of governance with checks and balances might be more likely to attract FDI inflows. Thirdly, this paper attempts to determine whether or not different types of trade agreements have different associations with FDI. Trade agreements with the highest levels of integration (e.g.., customs unions and common markets) might also attract higher levels of investment. Finally, this paper examines the relationships associated with bilateral FDI flows specifically to and originating from the member countries involved. Past work has examined the association between trade agreements and overall FDI flows, but it would also be useful to determine whether most of the association between trade agreements and FDI flows could be attributed to the member countries of the trade agreement, since investors in those countries would have the most intimate knowledge of the investment climate and the effect of the trade agreement on it. This paper finds that membership in the WTO has a bigger partial relationship with FDI flows than the other trade agreement variables, and that in the bilateral FDI models, joining or membership in the WTO is associated with a decline in FDI flows from any one particular country because the country has multiple destinations and sources for investment. From a policy perspective, countries should spend more time negotiating agreements under the WTO than pursuing bilateral or regional agreements in their country’s trade policy, for example, by reviving the Doha round of negotiations. 5

This paper also finds that human capital and political constraints have a significant correlation with FDI flows. The results indicate that as the primary enrollment ratio in a country increases, it is associated with attracting higher levels of FDI and sending lower levels of FDI, as the economy of the country improves to the point where it can improve by itself with less foreign investment. The results for political constraints indicate that a more constrained system of government is associated with higher FDI flows, but only to a certain point that most developed countries have likely already met. Consequently, countries should continue efforts to improve their levels of education and, over the long run, countries should work towards a system of government with an adequate level of checks and balances. Finally, this paper finds that involvement in a trade agreement does have a positive partial relationship with FDI flows. However, the results are more nuanced when broken down by trade agreement type. For example, in the aggregate FDI model, only customs unions have a statistically and economically significant relationship with FDI. Similarly, in the bilateral FDI models, only trade agreements classified as “Other” and free trade agreements have statistically and economically significant relationships with FDI, with the former having a larger partial relationship when statistically significant in the OECD country FDI-sending model and specifications. Consequently, if countries choose to pursue bilateral or regional trade agreements outside of the WTO, they should try to make them as economically integrative as possible within the normal limits of political feasibility in order to attract higher levels of aggregate FDI irrespective 6

of the source. However, countries can choose to pursue much more basic agreements when considering bilateral trade strategies.

7

Background Trade agreements over the past fifty years can be classified into three “waves”. The first wave of agreements from the 1950s to late 1960s was fairly limited in scope and, since tariff levels were higher in this period than later, the preferential liberalization of merchandise trade was the main goal. The first wave included the first attempts by developing countries to create PTAs to help them lower the costs of their import substitution industrialization policies (Adams et. al., 2003). The second wave began in the 1980s as the United States entered into several PTAs. Previously, the United States had opposed to PTAs due to its support of the General Agreement on Trade and Tariffs (GATT) principle of Most Favored Nation (MFN), which holds that any trade concession granted to individual members should be extended to all members of GATT. Agreements in the second wave of trade liberalization included some non-traditional areas, such as dispute resolution, but the main focus remained on lowering tariffs and establishing free trade areas (Adams et. al., 2003). The third wave of trade agreements began in the 1990s with an explosion of PTAs worldwide. The key difference between third-wave and earlier agreements was that the majority of them began to include non-traditional areas such as “investment, trade in services, setting and harmonization of standards, competition disciplines, customs cooperation, intellectual property rights (IPR), and dispute settlement” (Medvedev, 2006). In terms of investment, provisions such as dispute settlement mechanisms and protection of intellectual property rights send a clear signal to foreign investors of the 8

institutional stability associated with the agreement and nations involved. While none of the provisions is necessary or sufficient to increase foreign direct investment (FDI), observers suggest that together they are likely to create investment (Medvedev, 2006). Indeed, when an agreement does not include any of the non-traditional areas, it could still provide a positive signal to investors by solidifying existing reforms. From this perspective, PTAs could be viewed as a stepping stone towards global free trade (Krueger, 1999) and evidence of participating countries’ greater outward orientation, leading investors to believe the countries will have lower political risk, and boosting FDI (Medvedev, 2006). One of the most important benefits of FDI to developed economies—and, over the past two decades, to developing economies as well—is the role it can play in transferring technology between nations. This boost to technology could help to foster research and development, create economies of scale, and lead to technological spillover effects. Technological spillover effects, for example, can work through several channels: 1) local firms adopt technologies introduced by multinational firms through reverse engineering or imitation; 2) workers trained by the multinational may transfer knowledge by switching employers or creating their own firms; and 3) multinationals transfer technology to firms that act as suppliers of intermediate goods or buyers for their own products (Saggi, 2000). FDI can benefit economies through more traditional economic means as well, for example, by creating new jobs and boosting growth in GDP, and, especially for developing countries, providing a source of illiquid investment that, once 9

invested, assures the host country of its long-term existence and associated increase in economic stability (Ahlquist, 2006). Policies that attract FDI flows are particularly attractive to government officials in both developed and developing economies. In the past decade, government officials interested in pursuing FDI promotion policies tried to use the WTO as a forum, especially in the “Trade and Investment” negotiations in the Doha Round. However, this most recent round of multilateral negotiations at the WTO broke down in late July 2008 and is not expected to resume until some point in 2009 at the earliest. The Doha round was dubbed the “development round” for its intended focus on issues most pressing to developing nations. It also had tried to focus on deeper integration issues for developed countries. After the failed Doha ministerial conference in August 2004 in Cancun, however, most of these issues were dropped from negotiations and the world lost an opportunity to “balance the interests of countries where foreign investment originates and where it is invested, countries’ right to regulate investment, development, public interest and individual countries’ specific circumstances.” (WTO, 2008) In terms of FDI policies at the country level, protectionism has slowly been rising in recent years. Several countries have tightened investment rules or enacted new rules to regulate FDI and protect “strategic sectors”. Host-country protectionism has led to an increase in skepticism towards and regulations for cross-border mergers and acquisitions. And even home-country protectionism—negative attitudes towards outward flows of FDI—has risen, as outsourcing has become the prime example of assumed ills associated 10

with globalization (Economist Intelligence Unit, 2008). In light of these developments, if analysis can demonstrate a link between PTAs and increased FDI flows to the member countries, policymakers in developed and developing countries still have an avenue to pursue an FDI promotion policy. This policy could circumvent the static WTO forum, while bringing some of the more traditional benefits of trade liberalization through bilateral or pluri-lateral trade agreements. Most importantly, the countries would be sending a signal to investors that, even in a time where protectionist pressures against FDI have slowly risen, their countries still provide a safe environment in which to invest.

11

Literature Review The extensive literature on the many potential determinants of FDI flowing into a country can be broken down into the four categories of market size and characteristics, macroeconomic conditions, political and institutional variables, and population and environmental factors. Dunning (1977) contributes a model for FDI determinants that has been cited often in the literature: his OLI (ownership-location-internalization) model. According to Dunning, FDI occurs when three conditions are met: 1) a multinational company (MNC) has an ownership advantage through which it is competitive in that market where it wants to invest; 2) one place has to have a location advantage (e.g., a large domestic market for the investing company’s goods) over another for an MNC to invest there; and 3) there has to be an internalization advantage where owning a plant in another country is better than licensing agreements with a firm based there (Kolstad and Tøndel, 2002).

Market Size and Characteristics The literature on FDI determinants supports a strong relationship between the size of the host country market and the amount of FDI that market receives. According to the UNCTAD (1998), larger markets are better able to accommodate increased investment, both domestic and foreign, because they have large numbers of firms, and because firms have more opportunities to develop scope and scale economies. In a literature review of surveys and econometric studies, Lim (2001) notes that “the most robust determinant [of FDI] is the size of the market. Market size proxied by real GDP…is highly significant 12

and positive in virtually all the studies.”

Lim further describes the effect of market size

in terms of the intended effect of FDI by showing that larger host markets will likely attract horizontal FDI (entire production process in a new country), but they will be indifferent to vertical FDI (part of the production process in a new country). UNCTAD (1998) proposes a slightly different explanation of FDI based on the motives of the company. Still, one of the economic determinants is market-seeking (horizontal FDI that includes “market size and per capita income” in the list). Blömstrom and Kokko (1997) believe that larger market size allows firms to grow larger than they would have in purely national markets, which could allow the firms to “invest in more R&D and marketing, which may lead to the creation of new intangible assets that stimulate FDI, within as well as outside their own region.” Another market characteristic in the FDI determinants literature is the geographic distance between the country markets. Models of bilateral FDI flows, such as the one used by Adams et al. (2003), find a significant negative impact of distance between recipient and sending countries on FDI stocks. However, Medvedev (2006) postulates that if trade and FDI are treated as substitutes by firms, there could be a positive relationship between distance and FDI. Blonigen’s (2005) literature review on FDI determinants shows that the gravity specification traditionally used to predict trade flows between countries “as primarily a function of the GDP of each country and the distance between the two countries” also fits patterns of FDI reasonably well. However, there is no support for a model of FDI flows with gravity variables as the sole determinants since 13

“intuition and theory suggests that…FDI behavior is likely much more complicated to model than trade flows.”

Macroeconomic Conditions The literature on FDI determinants is heavily focused on macroeconomic-related factors. The relationship between the level of trade openness in a country and its association with FDI, for example, has been examined by many authors. According to Blonigen (2005), higher trade protection should give firms an incentive to relocate production to the country to which they were exporting in order to avoid the higher costs from the trade protection. This phenomenon, termed “tariff-jumping”, has been documented empirically (Blonigen, 2002), and applies directly to horizontal FDI. Conversely, lower trade protection may benefit vertical FDI because the easier it is to enter the country the more a firm can take advantage of lower transportation costs and cheaper resources (Hicks, 2007). In addition to this difference related to openness, FDI and trade protection may be endogenous because a nation’s policies for trade protection could explicitly target certain import sectors where FDI is less likely (Blonigen, 2005). Stability in a country’s exchange rate is also a macroeconomic determinant of FDI. UNCTAD (1998) states that “[e]xchange-rate policy is related to stability and may influence FDI decisions by affecting the prices of host country assets, the value of transferred profits, and the competitiveness of foreign affiliate exports.” In addition, Froot and Stein (1991), using annual U.S. aggregate FDI data in an empirical study, show that currency depreciation is associated with higher inward FDI. This depreciation will 14

increase the relative wealth of foreigners and increase the relative rate of return for foreign firms investing in domestic assets since they can avoid paying a domestic monitoring penalty, thereby further encouraging additional FDI (Medvedev, 2006). Finally, Blonigen (1997) also affirms the positive link between foreign exchange rate depreciation and FDI, reasoning that if foreign firms purchase another country’s assets, they can generate returns from those assets to their benefit in currencies other than those used for the purchase.

Political and Institutional Variables Quéré et al. (2005) are exclusively concerned with institutional determinants of FDI. They find that institutions matter independently of GDP level and that public efficiency is a major determinant of FDI. The authors suggest three general reasons why institutions could matter for attracting FDI: 1) good governance raises productivity prospects, which is attractive to investors; 2) poor institutions are associated with problems such as corruption that would bring extra costs for investors; and 3) since there are high sunk costs involved with FDI, any form of uncertainty stemming from “poor government efficiency, policy reversals, graft or weak enforcement of property rights and of the legal system in general” is particularly damaging to FDI. The authors construct a gravity model with bilateral stocks of FDI as the dependent variable, and find that institutions such as tax systems, ease of creating companies, level of corruption, transparency, contract law, security of property rights, and efficiency of justice and prudential standards are all key determinants for FDI. Other studies, including one by 15

Globerman and Shapiro (2002), come to the same conclusions about the importance of institutional variables for FDI flows. All of these studies, however, note the important caveat that data for institutional variables is notoriously difficult to capture. A growing literature also focuses on the impact on FDI of countries joining institutions, such as PTAs. Büthe and Milner (2005), for example, study the impact of signing bilateral investment treaties, joining the GATT/WTO, and joining a PTA. They claim that joining these treaties and organizations requires countries to undergo economically liberalizing reforms, as well as making it much less likely that a country will renege on an economic commitment since there are now enforceable rules and another country or countries (in the case of the WTO, potentially over one hundred countries) can punish the offending country (Hicks, 2007).

Population and Environmental Factors Population and environmental factors may also play a role in determining the level of a country’s FDI. Kolstad and Tøndel (2002) note, for example, that MNCs could be attracted to areas with low levels of social development and equality if the labor is cheap. They could be attracted to areas with high levels of human capital accumulation if this condition is associated with higher levels of productivity. Therefore, proxy variables to account for the level of social development of a country’s population, such as the percentage of people who have completed secondary education, are frequently used in the FDI-determinants literature (Kolstad and Tøndel, 2002; Globerman and Shapiro, 2002),

16

and research indicates that their impact depends on the industry in which the investment is made. Aside from the social or human development characteristics of a country’s population, other factors that are almost entirely country-specific that can be associated with FDI flows. For instance, historically, the most important determinant of FDI was the level of a country’s natural resources. Although this factor has declined in significance as the importance of the primary sector in world output has declined, it can still explain a significant portion of inward FDI in natural resource-rich developing countries (World Investment Report, 1998). In addition, Adams et al. (2003) find that a number of country-specific variables are significant in their bilateral FDI model, such as how similar the two countries languages are (positive correlation with FDI), whether they have colonial ties (positive), whether they share a border (positive), and whether one or more of the countries is landlocked (negative). Investors also perceive potential threats to their investment from the political environment and act accordingly. As intrastate conflict rises, whether in the form of political turmoil or, more significantly, actual combat, the level of FDI flows to the host country drops (Nigh, 1986). Conversely, some evidence indicates that if the host country is a developed nation military conflicts fought on foreign soil might encourage FDI since they have the financial capacity to fight the war and potentially need FDI to help fund it (Hicks, 2007).

17

The Theory of Preferential Trade Agreements The three waves of PTAs have each given rise to a distinct body of theoretical work. Theoretical work during the first wave focused on the static effects of trade agreements and whether or not they would bring benefits to individual countries, the group as a whole, and/or to countries left out of the agreement. Research during the second wave tried to determine whether PTAs were “building blocks” or “stumbling blocks” to the development of the multilateral trade system. Finally, in response to the third wave, the focus has started to shift to effects of the non-trade provisions of PTAs. The following discussion of these three bodies of work draws exclusively from Adams et al. (2003). The most straightforward theoretical explanation of PTAs concerns trade creation vs. trade distortion effects. PTAs reduce the average tariff on imports for goods entering a country, thereby lowering one source of economic distortion. Yet, they simultaneously increase another distortion by making a country’s tariff schedule less geographically uniform. Consequently, PTAs can improve a country’s economic welfare “by shifting production from a higher-cost domestic source to a lower-cost PTA partner—trade creation.” However, they can also reduce a country’s economic welfare “by shifting production from a low-cost non-member to a higher-cost PTA partner—trade diversion” (Adams et al., 2003). Much of the literature devoted to the first wave of trade agreements tried to establish general “rules of thumb” that would exist in situations where the gains from trade creation in PTAs would exceed the losses from trade diversion. Unfortunately, 18

this analysis has proved generally fruitless as governments cannot easily identify PTA opportunities that meet this criterion. Four studies examine incentives for PTAs to expand their membership and whether or not that expansion will be a stumbling block or a building block to multilateral liberalization. Most literature indicates that PTAs are stumbling blocks to multilateral trade liberalization. Krishna (1998), for instance, concludes that PTAs will be stumbling blocks to the multilateral negotiations because they reduce incentives of members to liberalize tariffs with non-member countries. Levy (1997) also finds that bilateral PTAs can undermine political support for multilateral free trade, and when multilateral liberalization is not feasible without a PTA, it almost certainly will not become feasible in the presence of a PTA. Baldwin (1996) argues that PTAs serve as a building block because firms in non-member countries will see their profits decline, and will then lobby their government to enter the PTA, pushing the balance towards entry in the countries at the margin of that decision process. However, Zissimos and Vines (2000) argue that member countries have an incentive to prevent new entrants into the PTA in such a fashion that PTA formations will still fall short of multilateral liberalization.

Trade Agreements and FDI Over the past ten years, a small but growing number of studies have focused on the effects of PTAs in shaping FDI flows. This literature is the starting point for the present paper. 19

Blömstrom and Kokko (1997) examine the investment effects of regional integration in the case of three regional trade agreements: the Canada-U.S. FTA, NAFTA, and MERCOSUR. The authors hypothesize that the FDI process for a country in the context of regional integration can be mapped onto a basic template with the level of environmental change (degree to which trade and investment flows are liberalized by the agreement) as weak or strong and the advantage of location (the degree to which it is more profitable to locate a firm’s economic activity in a location) as weak or strong. In this observational study, the authors do not run regressions, but conclude that the evidence from the three agreements supports their “rough hypothesis” that an agreement in a strong position of both environmental change and advantage of location is more likely to lead to inflows of FDI from countries both outside and within the agreement. Later studies have moved beyond theoretical and observational examinations to empirical research. One of the most important contributions to this line of research is that of Adams et al. (2003) who examine the trade and investment effects of PTAs. These authors use a gravity model in which the dependent variable is “the natural logarithm of the stock of outward investment from home country to host country” in a number of developed and developing countries from 1988 to 1997. Their results show that six of the nine PTAs were investment-creating, one was investment-diverting, and two showed no clear impact. They also conclude that most of the investment impact from the agreements comes from their non-trade provisions.

20

Medvedev (2006) notes that the Adams et al. (2003) study, is “useful in determining the net FDI effect of a particular PTA, [but it] cannot establish the impact of preferential liberalization on a net FDI position of a particular country.” Jaumotte (2004) tries to address this concern in a study of developing countries from 1980 to 1999. She finds that the market size due to the PTA at the beginning of a period has a significant and positive effect on the level of FDI stock at the end of the period. She concludes that, on the whole, her evidence is insufficient to support a claim that PTAs are desirable since it is unclear whether the costs associated with trade diversion are outweighed by the benefits of increased FDI. Lederman et al. (2005), in a study of aggregate FDI flows from 1980 to 2000, find that the expectation of joining a PTA in the next two years is associated with a one-third increase in FDI flows. The most recent studies on the association between PTAs and FDI flows have expanded on the original studies through changes in model specifications and by including more countries and PTAs. Hicks (2007), for instance, improves the model specification by including a variable to account for the type of PTA: preferential trade area, free trade area, customs union, common market, monetary union, single market, or economic and monetary union. He shows that, across a small sample of 25 PTAs, higher levels of PTA economic scope (i.e., the number of financial, fiscal, and monetary stipulations the PTA can enforce upon its member countries) and independence (i.e., the legitimate supranational power of the PTA) are associated with higher inward FDI flows.

21

Medvedev (2006) provides perhaps the most comprehensive study to date on the correlation between PTAs and increased FDI flows. He builds upon the Lederman et al. (2005) model, using a panel of 143 countries from 1980 to 2003 and examining, by far, the most comprehensive list of trade agreements of any study in the literature. His baseline model, which serves as a starting point for this paper, includes FDI flows as the dependent variable and an array of time-specific independent variables: GDP, GDP growth, a measure of the country’s openness to trade, inflation, the change in the exchange rate, the level of world FDI, the world’s GDP growth, the combined GDP of the countries involved in the PTA when in force, and the distance between the countries in the PTA. This model leads to several important conclusions: 1) FDI flows from PTAs increase with the size of PTA members and their proximity to the host country; 2) the relationship between PTAs and FDI flows was strongly driven by the developing countries in the model; and 3) the link between PTAs and FDI flows is strongest in the late 1990s and early 2000s, the period when most of the new “deep integration” agreements were signed. This paper contributes to the existing literature in several important ways. First, it not only studies PTAs, but also WTO accessions, and tries to determine whether becoming a member of the WTO is a greater sign of policy reform and stability than implementing a PTA, and, therefore, a better signal for investors to send FDI to that country. Second, it analyzes the partial effect of two particularly salient institutional variables in primary enrollment ratios and political constraints indices. FDI could be 22

attracted to countries with low human capital levels if the industry involved is laborintensive, or FDI could be attracted to countries with high human capital levels if the industry involved is capital-intensive. The hypothesis as to the partial effect of political constraints on FDI flows is similarly ambiguous because a politically constrained society could either signal strong protection of individual and property rights, or a society with low levels of political constraints could signal a better opportunity for investors to influence legislation in a manner that would be advantageous to their investments. Third, it attempts to determine whether different types of trade agreements—preferential trade, free trade, customs unions, common markets, and other economic integration agreements with trade provisions—are associated with different FDI outcomes. The initial conjecture as to the magnitude of this relationship would be that the higher the degree of integration, the more likely it is that countries will commit to liberal reforms to attract FDI. Finally, this paper examines the impact of FDI flows specifically to and originating from the member countries involved. Past work in this area has examined the impact of trade agreements on overall FDI flows, but it would also be useful to know whether most of the impact on FDI flows could be attributed to the member countries of the trade agreement, since investors in those countries would have the most intimate knowledge of the investment climate and the effect of the trade agreement on it.

23

Conceptual Model This paper seeks to determine whether, holding a number of country and globallyspecific variables equal, implementation of a preferential trading agreement (PTA) is associated with an increase in foreign direct investment (FDI) flows to the member countries of the PTA. The dependent variable in question is FDI flows, while the independent variable of most interest is the establishment of a trade agreement.

PTAs Could Cause FDI There are many reasons to believe that signing a trade agreement will have a positive impact on FDI flows for countries entering that agreement. Investors may be reassured that the economic landscape in the host country is stable, and that their investment has a much lower chance to be institutionally discriminated against by the host country, because the host has established closer ties with investor countries, often including specific investment provisions in the agreement, in part under the assumption of closer economic integration (Büthe and Milner, 2004). U.S. bureaucracies that are directly impacted by trade agreements have voiced support for them by arguing they will have a positive impact on investment. Thus, the U.S. Chamber of Commerce has stated that “[the U.S.-Chile Free Trade Agreement] will immediately boost bilateral trade and investment opportunities, to the ultimate benefit of both nations” (USTR, 2002). Similarly, the Office of the United States Trade Representative states in their one-page document supporting the North American Free Trade Agreement (NAFTA) that NAFTA has benefited the United States because “trade and investment flows have substantially 24

increased” and “U.S. non-residential fixed, or business, investment has risen by 107 percent [from 1993 to 2007], compared to a 45 percent increase between 1980 and 1993 (USTR, 2007).” A growing literature supports the correlation between trade agreements and increased FDI flows. For example, Blömstrom and Kokko (1997), Adams, Gali, and McGuire (2003), te Velde and Fahnbuulleh (2003), Jaumotte (2004), Lederman, Maloney, and Serven (2005), Medvedev (2006), Hicks (2007), MacDermott (2007), and Park and Park (2007), using different methodologies and looking at different trade agreements, have all established a correlation between trade agreements and FDI flows. Of course, in order to understand the effect of PTAs on country FDI, it would be misleading and inappropriate to focus an analysis solely on the relationship between those two variables. There are three potential scenarios that could produce such a relationship aside from the relationship between PTAs and FDI. First, country-specific observable characteristics could have relationships with both PTAs and FDI. Second, unobserved country traits could cause both PTAs and FDI. Third, FDI itself could cause PTAs to form.

Conditions that Could Cause both PTAs and FDI • Openness of the Economy – The overall openness of an economy to international trade could confound the simple relationship between PTAs and FDI, because each could be correlated with openness. The a priori expectations of the overall effect of the openness of an economy on FDI flows are somewhat 25

ambiguous. In terms of the two types of FDI, as trade openness in an economy increases, horizontal FDI could fall because most companies could use trade to enter markets instead of investment. However, as trade openness in an economy increases, vertical FDI could increase as companies find it profitable to produce components and final goods in other countries (MacDermott, 2007). Whether the effects on vertical FDI are stronger than the effects on horizontal FDI, or vice versa, could depend on whether the host country is developed or developing. Andriamananjara (2003) notes that, as a country decides whether to enter a PTA, it faces a trade-off between opening its own economy (increased competition) and gaining access to the PTA’s market (preferential access). The market access gain is always larger as long as the aggregate size of the PTA’s market is larger than the market of the prospective member; therefore, increasing openness should lead to more PTAs. • Exchange Rate – The exchange rate of a country can influence FDI decisions by “affecting the prices of host country assets, the value of transferred profits, and the competitiveness of foreign affiliate exports” (World Investment Report, 1998). Therefore, as a country’s exchange rate depreciates FDI flows should increase to that country because the currency depreciation has effectively reduced production costs for foreign investors (Medvedev, 2006). Exchange rate stability has also been linked to the establishment of PTAs. Shin and Wang (2003) point out that exchange rate stability and its implications for a free trade agreement are 26

so commonly documented in the literature that the two are assumed to be subsidiary. However, the authors also indicate that certain trade agreements can lead to increased exchange rate stability and integration (e.g., the European Common Market), while others, typically bilateral agreements, can make exchange rates less stable. This inconsistency brings up an important endogeneity issue that could be present in several key variables or conditions in the core model for this paper. • GATT/WTO Membership – Unlike previously mentioned factors that impact FDI flows, whether or not a host country is a member of the GATT/WTO is only included in one of the previously mentioned studies. If a country belongs to the GATT/WTO, it could have access to many world markets as a source for inputs and outputs. Also, much like the potential effects of signing a PTA, being a member of GATT/WTO can provide a signal to investors that the country’s government is open to world markets and liberalization policies (Büthe and Milner, 2004). Therefore, acceding to the GATT/WTO should be positively correlated with FDI flows. There is also much debate in trade literature as to whether or not joining the WTO makes a country more or less likely to pursue trade agreements outside of the WTO’s purview. Algeria, for instance, joined the WTO at least in part in order to conclude a free trade agreement with the United States. The country was able to use its new trade contacts and institutional

27

knowledge as a WTO member to negotiate an agreement important to its economy on a more equal footing (Kennedy, 2004). • Shocks – Many additional factors, which are usually much less predictable, could simultaneously be causing both trade agreements and FDI flows. A sudden change to a political regime that is less friendly to FDI and/or trade, for example, could have a detrimental impact on both outcomes. Li and Resnick (2003), for example, state that political regime volatility will increase investor uncertainty about the host country’s economic policies and, more specifically, FDI policies. Countries have also been hindered from joining trade agreements by inter-state tension or conflicts. In South Asia, for example, country leaders hoped to achieve economic integration through the South Asian Association for Regional Cooperation. But continued tensions both within individual countries and between countries have blocked that integration movement (Brown et al.). Indeed, this potential effect could have multiple directions, considering that certain trade agreements are signed specifically to design conflict out of intercountry relations, with the European Union serving as an obvious example.

Conditions that Could Be Caused by FDI and PTAs • Governance/Political Environment – Some studies try to capture these empirically difficult indicators that could cause FDI, while others note its importance, but exclude them from their models due to practical concerns. 28

Institutions including tax systems, easiness to create a company, lack of corruption, transparency, contract law, security of property rights, and efficiency of justice and prudential standards can all be major determinants of FDI flows (Bénassy-Quéré, Coupet, and Mayer, 2005). As institutions worsen, costs of doing business increase, predictably diminishing FDI flows (Blonigen, 2005). However, these conditions have also been shown to be affected by signing trade agreements. For example, many recent trade agreements, especially those with a developed and developing country, include provisions that are meant to reduce corruption and increase transparency (e.g., the Canada-Peru FTA), or formally secure a substantial system of property rights (e.g., the Australia-Chile FTA). • GDP and GDP Growth Rate – Yet another set of characteristics, which could either be a cause of FDI and PTAs or be affected by FDI and PTAs, are a country’s GDP and GDP growth rate. All of the previously mentioned studies in this field have at least one variable that takes into account some form of the host country’s GDP level. Larger markets in terms of GDP are more likely to attract FDI because they have a greater expected stream of future returns. Similarly, GDP growth can induce FDI flows to a country because foreign investors are attracted to future market opportunities (Li and Resnick, 2003). Horizontal FDI, the most common type, will more likely flow to developed economies with high levels of GDP because firms desire markets with similar development levels to purchase their goods. Contrarily, vertical FDI will more likely flow to 29

developing economies with low levels of GDP where labor is cheap for the production process (Hicks, 2007). Thus, GDP should hypothetically be positively associated with FDI in developed countries and negatively associated with FDI in developing countries. FDI, however, can also be a cause of GDP growth. Carkovic and Levine (2004) summarize macroeconomic evidence on the impact of FDI on economic growth, while ultimately disagreeing with the results in some authors’ econometric studies. Borensztein et al. (1998), for instance, find that FDI positively effects growth in countries with highly educated workforces. Blomström et al. (1994) find that FDI has positive effects on growth when countries are sufficiently wealthy. Finally, Balasubramanyam et al. (1996) find a similar effect when a country has trade openness. The history of trade agreements has also clearly shown a positive impact on GDP growth. NAFTA, for example, has had a small but positive impact on American, Mexican, and Canadian GDP, averaging somewhere between $300 million and $2.1 billion per year for America in the first eight years of its existence (CBO, 2003). The murkiness of the relationship between FDI and GDP growth is best illustrated by Chowdhury and Mavrotas (2006), who find that GDP causes FDI in the case of Chile, while there is strong evidence of bi-directional causality between the two variables in Malaysia and Thailand.

30

FDI Could Cause PTAs Although the authors mentioned in the previous section have argued that signing a trade agreement leads to higher FDI flows, perhaps FDI itself could cause trade agreements to be signed. This potential for reverse causality is actually a significant concern for this paper. The concern arises from the “virtuous circle” hypothesis outlined by Medvedev (2006): faster-growing economies need more investment, including foreign, while, simultaneously, foreign investors seek to invest in countries with higher growth rates in order to maximize their expected profits. Stemming from that line of reasoning, larger and/or faster-growing economies might also be more likely to join trade agreements where they could, perhaps, be better suited to take advantage of regional integration. Therefore, “preferential liberalization may thus be an effect rather than a cause” (Medvedev 2006).

31

Analysis Plan To test the null hypothesis that there is no association between countries signing preferential trade agreements and foreign direct investment flows to the member countries of those agreements, this paper tests different specifications of a fixed-effects model using as a baseline panel data derived from Medvedev (2006). Table 1 includes the definitions of variables used in the different models and the literature justifying each. Table 1 – Model Variables Variable Name LN OF FDI FLOWS

Research Justification

Variable Definition

Hypothesized Relationship with Dependent Variable

Log of Net FDI Inflows in current US $ into Country i at Time t

LN OF GDP

Log of GDP in current US $ in Country i in t

OPENNESS

Ratio of Exports and Imports for Country i in t to GDP

GDP GROWTH

GDP Growth Rate (in Percentage Terms) of Country i between t and t-1

INFLATION

Inflation as Measured by the Percent Change in the Consumer Price Index of Country i between t and t-1

32

te Velde and Fahnbuulleh (2003) Jaumotte (2004) Lederman et al. (2005) Medvedev (2006) Hicks (2007) Jaumotte (2004) Lederman et al. (2005) MacDermott (2006) Medvedev (2006) Hicks (2007) Jaumotte (2004) Lederman et al. (2005) MacDermott (2006) Medvedev (2006) Hicks (2007) Jaumotte (2004) Lederman et al. (2005) Medvedev (2006) Hicks (2007)

Positive

Positive

Positive

Negative

EXCHANGE RATE GROWTH

Percentage Change in the Real Effective Exchange Rate Index of Country i between t and t-1

LN WORLD FDI

Log of Net World FDI Inflows in t Minus Log of Net FDI Inflows for Country i in t

WORLD GDP GROWTH

Growth Rate of GDP (in percentage terms) for the World between t and t-1

PRIMARY ENROLLMENT

Gross School Enrollment Ratio for Country i in t

POLITICAL CONSTRAINTS

Index that Measures the Feasibility of Policy Change2 for Country i in t

GATT/WTO MEMBERSHIP LANGUAGE BORDER COLONIAL ISLAND LANDLOCKED LN GDP TRADE3 LN DIST TRADE4

Dummy Variable Equal to One If Country i is a Member of the GATT or WTO in t Measure of Linguistic Similarity Between Countries i and j (For Model Using OECD) Dummy Variable Set to 1 if Countries i and j Share a Land Border (For Model Using OECD) Dummy Variable Set to 1 if Countries i and j Share Colonial Ties (For Model Using OECD) Dummy Variable Set to 1 if Country i is an Island (For Model Using OECD) Dummy Variable Set to 1 if Country i is Landlocked (For Model Using OECD) The Sum of GDP of All Trade Agreement Partners of Country i in Time t Log of the Average Distance5 Between Country i and All of its Trade Agreement Partners in t

2

Adams et al. (2003) Jaumotte (2004) Medvedev (2006) Jaumotte (2004) Lederman et al. (2005) Medvedev (2006) Jaumotte (2004) Lederman et al. (2005) Medvedev (2006) Globerman and Shapiro (2002) Kolstad and Tøndel (2002) Globerman and Shapiro (2002) Kolstad and Tøndel (2002) Büthe and Milner (2004) Hicks (2007)

Negative

Positive

Positive

Positive

Uncertain

Positive

Adams et al. (2003)

Positive

Adams et al. (2003)

Positive

Adams et al. (2003)

Positive

Adams et al. (2003)

Negative

Adams et al. (2003)

Negative

Medvedev (2006)

Positive

Medvedev (2006)

Uncertain

See Data Description section for more detailed description of this variable. LN GDP OTHER, LN GDP PTA, LN GDP FTA, LN GDP CUS UN, and LN GDP COM MKT are similarly defined for other economic integration agreements containing references to trade, preferential trade agreements, free trade agreements, customs unions, common markets, respectively. 4 Ibid. 3

33

The key variables to the analysis are the dummy variables indicating when a country joins the WTO, the school and political constraints indicators, and the variables that account for the GDP of countries involved in a PTA and the distance between the countries in the PTA. An OLS regression is subject to omitted variable bias since certain variables, such as a government’s leaders’ innate business entrepreneurial skills, that are both correlated with key variables of interest and should be related to FDI flows. Consequently, a fixed effects model is used with the panel data so that timeinvariant country-specific omitted variables will not bias the coefficient estimates. This model captures country-specific effects, but cannot capture year-specific variation because the set of independent variables includes an important regressor, world growth, that does not vary within or across countries (Medvedev, 2006). This factor to the analysis means that there is a certain degree of imprecision in the results due to the fact that any potential shocks associated with a particular year are reflected in the estimates of the coefficients. Since autocorrelation is often a key issue affecting time-series data, this paper uses the specific regression model used by Medvedev (2006), a three-step feasible generalized least squares (FGLS) estimator. As described in the aforementioned paper, this model allows for estimation in the presence of AR(1) autocorrelation within panels and heteroskedasticity across panels in a three-step process: 1) Homoskedastic errors are assumed and the model calculates consistent estimates of the AR(1) parameters; 2) A 5

Great circle distance between the largest or capital cities of each country.

34

groupwise heteroskedastic model is applied to the data in order to account for the possibility of country-specific error terms that are not normally distributed; and 3) The new moment matrix, which is no longer assumed as is the case in for ordinary least squares, obtains the accurate asymptotic variance-covariance matrix for the estimate of the FGLS coefficient. Since the FGLS regression does not distinguish between FDI from different source countries, this paper also assesses the association between trade agreements and FDI flows specifically originating from and being sent to member countries of the agreements. This model is similar in nature to the previous model in terms of the variables used, with several additional variables used to capture the more specific nature of the FDI flows and restricted to FDI flows to OECD countries due to restrictions of the data set. The key distinction of this model is that many variables are now in terms of two countries at once. For example, when the United States entered into a free trade agreement with Chile, this model would sum the GDP of the U.S. and Chile in the GDPijt and FTAGDPijt variables, and the dummy variables LINij, BORij, COLij, ISLi, ISLj, LOCKi, and LOCKj (where i = U.S. and j = Chile) would all be set equal to zero simultaneously, the U.S.-Morocco FTA of several years later would similarly sum the GDP of the U.S. and Morocco (in that later year) in the GDPijt and FTAGDPijt variables, and the dummy variables(where i = U.S. and j = Morocco) would be set equal to zero again. The regression specification used is identical to the previous model.

35

Data Description The primary source of data for this paper is the Development Indicators (WDI) from the World Bank (2008). This data source includes variables for GDP, merchandise exports and imports to calculate an index for a country’s trade openness, consumer price index for inflation, a real effective exchange rate index, population statistics, and FDI net inflows. The World Bank itself is not the primary data collection agency for the WDI, but instead compiles data from national statistical agencies, central banks, and customs services. The population covered by these data is almost every country in the world (215 countries) and twelve different country classifications (i.e., “World”, “High Income: OECD”, etc.). The time period covered by the WDI is 1960 to 2006. However, since a critical variable to the paper, real effective exchange rate, only has data coverage back to 1980, the analysis is necessarily restricted to the time period from 1980 to 2006. In order to complete the analysis for country-specific FDI flows, this paper uses the International Direct Investment Statistics from SourceOECD (2008). This source provides information on inflows and outflows of FDI for OECD countries6 to and from partner countries in the national currency as well as the U.S. dollar from 1985 to present. Several other sources detailed below are combined with the data from the WDI for the analysis in this paper. The Centre D’Etudes Prospectives Et D’Informations Internationales (CEPII, 2008) dataset is used to calculate the distance between member 6

Australia, Austria, Belgium, the Belgium-Luxembourg Economic Union, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden, Switzerland, Turkey, United Kingdom, and the United States.

36

countries to a preferential trade agreement. The distances in the dataset are calculated using the latitudes and longitudes of the most important city (in terms of population) or the country’s capital. The list of trade agreements is taken from Medvedev (2006), and it is supplemented with additional and/or more recent trade agreements found in the Dartmouth (2008) database and the McGill (2008) database. Finally, political and institutional variables are provided by Teorell et. al. (2008) compilation of freely available datasets, as well as Clemens (2004). A proxy for a country’s human capital level is available from Clemens (2004). This data contains information on the gross enrollment ratio in a country’s population, where the gross enrollment ratio is defined by UNESCO (the original source of the data) as the number of students enrolled in a level of education as a percentage of the population in the relevant age group for that level. The ratio is given in five-year increments; therefore, the present paper interpolates annual estimates. A political constraint variable is provided by Henisz (2002). The index measures the extent to which the preferences of a political actor in a country can lead to policy change. POLCON V contains the following information: 1) The number of independent branches in the government with veto power over policy changes; 2) The extent of party alignment across branches of government; and 3) The extent of preference heterogeneity within legislative branches (Teorell et. al., 2008). The data covers the period from 1960 to 2004, but, yet again, coverage is less comprehensive for developing countries. 37

Descriptive Statistics The following chart contains information on the non-dummy independent and dependent variables that are analyzed in this paper. The information represents every country for which there is data in the world over the entire time period of 1980 to 2007. The attached charts that follow present the same information, but disaggregated at the country level. Please refer to the Analysis Plan for a detailed description of each relevant independent and dependent variable. Table 2 – Aggregate FDI Flows Model Descriptive Statistics

ln of FDI Flows ln of GDP GDP Growth Openness Inflation Exchange Rate Growth ln World FDI World GDP Growth ln GDP All Trade Agreements ln Distance All Trade Agreements ln GDP Other ln Distance Other ln GDP PTA ln Distance PTA ln GDP FTA ln Distance FTA ln GDP Customs Union ln Distance Customs Union ln GDP Common Market ln Distance Common Market

Number of Observations 5,238 5,238 4,560 4,449 3,938 2,351 5,238 5,238

Mean 2.44 22.90 6.44 65.96 43.50 15.63 26.34 6.76

Standard Deviation 1.45 2.34 16.44 54.45 487.37 22.16 1.13 5.36

Minimum 0 16.84 -100 4.95 -100 -100 24.33 -0.80

Maximum 3.30 30.21 113.49 1,098.82 23,773.13 798.72 27.81 18.05

5,238

174.03

297.37

0

1,264.45

5,238 5,238 5,238 5,238 5,238 5,238 5,238 5,238

2.92 244.07 3.40 215.92 3.08 160.85 2.89 51.75

3.73 370.08 3.85 378.51 3.98 277.04 3.71 130.71

0 0 0 0 0 0 0 0

9.52 1,532.82 9.30 1,463.17 9.30 1,208.84 9.62 701.32

5,238 5,238

1.29 34.71

2.72 123.12

0 0

9.39 698.66

5,238

0.63

2.01

0

7.86

38

GATT/WTO Membership Primary Enrollment Political Constraints

5,238 4,271 2,363

0.58 95.79 0.54

0.49 21.52 0.32

0 9.90 0.01

1 162.30 0.89

Table 3 – Bilateral FDI Flows Models Descriptive Statistics

Number of Observations ln FDI Inflows to OECD Country from Another Country ln FDI Outflows from OECD Country to Another Country ln GDP OECD Countries ln GDP All Countries GDP Growth OECD Countries GDP Growth All Countries Openness OECD Countries Openness All Countries Inflation OECD Countries Inflation All Countries Exchange Rate Growth OECD Countries Exchange Rate Growth All Countries ln World FDI World GDP Growth OECD CountriesLandlocked All CountriesLandlocked OECD Countries-Island All Countries-Island Country Pair is Contiguous

Mean

Standard Deviation

Minimum

Maximum

127,380

1.56

5.04

0

25.41

127,380 126,415 115,925

2.06 26.15 22.96

5.73 1.58 2.40

0 21.80 16.84

25.87 30.21 30.21

125,836

8.53

12.19

-33.09

60.44

115,058

6.85

16.60

-100

113.49

121,976 111,478

56.34 65.24

30.18 46.53

13.64 4.95

182.45 986.65

122,555 99,865

8.62 47.45

27.81 529.19

-0.90 -100

555.38 23,773.13

112,326

0.64

7.13

-91.00

59.14

59,388 127,380 127,380

-0.02 26.34 6.76

23.27 1.00 5.08

-100 24.33 -0.80

798.72 27.81 18.05

127,380

0.20

0.40

0

1

127,380 127,380 127,380

0.20 0.10 0.19

0.40 0.30 0.39

0 0 0

1 1 1

127,380

0.02

0.13

0

1

Country Pair Shares a

39

Common Language Country Pair Shared a Colonial Relationship ln GDP All Trade Agreements ln Distance All Trade Agreements ln GDP Other ln Distance Other ln GDP PTA ln Distance PTA ln GDP FTA ln Distance FTA ln GDP Customs Union ln Distance Customs Union ln GDP Common Market ln Distance Common Market GATT/WTO Membership OECD Countries GATT/WTO Membership All Countries Primary Enrollment OECD Countries Primary Enrollment All Countries Political Constraints OECD Countries Political Constraints All Countries

127,380

0.15

0.35

0

1

127,380

0.04

0.19

0

1

127,380

3.54

9.09

0

30.30

127,380 127,380 127,380 127,380 127,380 127,380 127,380 127,380

1.04 1.28 0.32 0.34 0.13 2.88 0.83 0.77

2.74 5.76 1.50 2.99 1.13 8.32 2.42 4.52

0 0 0 0 0 0 0 0

11.03 29.29 9.99 28.90 10.92 30.30 11.03 29.29

127,380 127,380

0.20 1.21

1.22 5.62

0 0

11.03 29.29

127,380

0.31

1.46

0

9.38

127,380

0.97

0.16

0

1

127,380

0.62

0.49

0

1

125,450

102.79

6.00

82.56

128.71

104,680

96.41

20.96

9.90

162.30

110,203

0.74

0.13

0.17

0.89

57,359

0.59

0.22

0.01

0.89

40

Regression Results Results Aggregate FDI Flows Regressions Specification 1 runs a fixed effects model with the natural log of FDI flows as the dependent variable with the list of independent variables ranging from the natural log of GDP to the natural log of the distance between the countries’ trade agreement partners in the table above. This specification captures the model from Medvedev (2006) and updates it for data through the year 2006. The following variables in this model are statistically significant at the 1% confidence level with a stated partial relationship to the dependent variable while holding all other variables constant. The natural log of GDP has a positive relationship with the dependent variable, openness to trade has a positive partial relationship, inflation has a negative partial relationship, the natural log of the difference between World FDI and the FDI of the country has a positive partial relationship, and the natural log of the combined GDP of the country and its trade partners involved in any type of trade agreement has a positive partial relationship. All of these variables have the anticipated relationship with the dependent variable. A Wooldridge (2002) test for autocorrelation in panel data with the null hypothesis that there is no first-order autocorrelation produces a p-value of 0.0001. This value indicates that the null hypothesis can be rejected; consequently, a feasible generalized least squares (FGLS) estimator to correct for autocorrelation is necessary and is calculated for all specifications. 41

42 -0.0004** (0.0002) -0.015 (0.014)

-0.032* (0.019)

ln Distance Other

Specification 2 Fixed Effects FGLS Coef./(SE) Coef./(SE) 0.855*** 0.878*** (0.038) (0.016) 0.003 0.012*** (0.002) (0.003) 0.005*** 0.009*** (0.001) (0.0007) -0.0001*** -0.00008 (0.00004) (0.00005) 0.003 0.0004 (0.002) (0.003) 0.459*** 0.507*** (0.040) (0.040) -0.008 -0.013* (0.006) (0.007)

0.0002 (0.0002)

Specification 1 Fixed Effects FGLS Coef./(SE) Coef./(SE) 0.917*** 0.915*** (0.038) (0.013) 0.003 0.012*** (0.002) (0.003) 0.005*** 0.010*** (0.001) (0.001) -0.0001*** -0.0001* (0.00004) (0.00006) 0.003 0.002 (0.002) (0.003) 0.455*** 0.576*** (0.038) (0.038) -0.008 -0.015* (0.006) (0.008) 0.0005*** -0.0002*** (0.0001) (0.00009) -0.016 0.063*** (0.017) (0.018)

ln GDP Other

ln Distance Trade

ln GDP Trade

World GDP Growth

Ln World FDI

Exchange Rate Growth

Inflation

Openness

GDP Growth

ln GDP

Dependent Variable - ln FDI Flows

-0.032* (0.019)

0.0002 (0.0002)

-0.012 (0.014)

-0.0005*** (0.0002)


Table 4 – Aggregate FDI Flows Regressions

-0.032* (0.019)

0.0002 (0.0002)

-0.011 (0.015)

-0.0005*** (0.0002)


43 -14.721*** (1.090)

-18.397*** (1.025)

Number of 2,013 Observations *** - Significant at 1% level, ** - 5%, * - 10%

Intercept Term

ln GDP Customs Union ln Distance Customs Union ln GDP Common Market ln Distance Common Market GATT/WTO Membership Primary Enrollment Political Constraints

ln Distance FTA

ln GDP FTA

ln Distance PTA

ln GDP PTA

-0.0002 (0.0001) 0.018 (0.013) 0.0002 (0.0002) 0.019 (0.013) 0.001** (0.0006) 0.039 (0.025) -0.001 (0.0008) 0.071** (0.033)

-15.471*** (1.101) 2,013

-13.391*** (1.182)

0.0004** (0.0002) -0.026* (0.016) 0.001*** (0.0002) -0.018 (0.014) -0.001 (0.0008) 0.067* (0.035) -0.0003 (0.0009) 0.054 (0.045)

-0.0002 (0.0001) 0.013 (0.013) 0.0002 (0.0002) 0.017 (0.013) 0.001** (0.0006) 0.038 (0.025) -0.001 (0.0008) 0.067* (0.033) 0.287*** (0.087)

-15.413*** (1.098) 2,013

-13.389*** (1.190)

0.0004** (0.0002) -0.026 (0.016) 0.001*** (0.0002) -0.018 (0.014) -0.001 (0.0008) 0.067* (0.035) -0.0003 (0.0009) 0.054 (0.045) -0.0005 (0.108)

-0.0002 (0.0001) 0.011 (0.013) 0.0002 (0.0002) 0.017 (0.013) 0.001** (0.0006) 0.038 (0.025) -0.001 (0.0008) 0.068* (0.033) 0.287*** (0.087) 0.025 (0.034) 0.709** (0.331) -15.386*** (1.093) 2,013

0.0004** (0.0002) -0.026 (0.016) 0.001*** (0.0002) -0.018 (0.014) -0.001 (0.0008) 0.067* (0.035) -0.0003 (0.0009) 0.054 (0.045) -0.0005 (0.108) 0.027 (0.031) 0.683** (0.327) -13.385*** (1.194)

For Specification 1, the FGLS model yields the same statistically significant coefficient as the fixed effects model with some important differences. First, the sign on the relationship between the natural log of the combined GDP of the country and its trade partners involved in any type of trade agreement with the dependent variable is now negative. Second, the variable for world GDP growth is significant at the 10% confidence level with an unanticipated negative partial relationship with the dependent variable. Finally, the variable for the natural log of the distance between the country and its trade partners involved in any type of trade agreement is significant at the 1% confidence level and has an unanticipated negative partial relationship with the dependent variable. Specification 2 again runs fixed effects and FGLS regressions, but instead of combining the country pair’s GDP levels from all trade agreements, this specification breaks the type of trade agreement down into five categories ranging from the least to most economically integrated: an agreement that mentions trade-relations improvements without more specific measures (“Other”), a preferential trade agreement, a free trade agreement, a customs union, and a common market. This specification tests a new hypothesis in the literature that the more economically integrated the trade agreement is the more FDI will flow to and from the specific country pair involved in the agreement. All of the variables mentioned in Specification 1 are still statistically significant in this specification, with the exception of the inflation variable in the FGLS model. 44

In the fixed effects model, the following new variables are statistically significant. The variable for the natural log of the distance between the country and its trade partners involved in an “Other” trade agreement (at the 10% confidence level) has a negative partial relationship with the dependent variable, the natural log of the combined GDP of the country and its trade partners involved in a preferential trade agreement (5% level) has a positive partial relationship, the natural log of the distance between the country and its trade partners involved in a PTA (10% level) has a negative partial relationship, the natural log of the combined GDP of the country and its trade partners involved in a free trade agreement (1% level) has a positive partial relationship, and, finally, the natural log of the distance between the country and its trade partners involved in a customs union (10% level) has a positive partial relationship. In the FGLS model, only three new variables are statistically significant, including the natural log of the combined GDP of the country and its trade partners involved in an “Other” trade agreement (5% level) with a negative partial relationship with the dependent variable, the natural log of the combined GDP of the country and its trade partners involved in a preferential customs union (5% level) with a positive partial relationship, and the natural log of the distance between the country and its trade partners involved in a common market (5% level) with a positive partial relationship. Specification 3 runs fixed effects and FGLS models with the same dependent variable and adds a dummy variable that capture whether or not the country was a 45

member of the GATT/WTO in a particular year. This specification tests the hypothesis that joining or being a member of the GATT/WTO sends a signal to investors that their country is institutionally stable, thus leading to higher levels of FDI flows. The variables that are statistically significant in Specification 2 are still significant in this specification. In the FGLS model, the GATT/WTO dummy variable is significant at the 1% confidence level and has the anticipated positive partial relationship with the dependent variable. Finally, Specification 4 runs fixed effects and FGLS models with the same dependent variable and adds two variables that capture the level of political constraints and the gross primary school enrollment level in the FDI-receiving and FDI-sending countries. This specification tests new hypotheses that the structure and quality of a country’s government and the country’s level of human capital accumulation are associated with the level of FDI sent to that country. Again, the variables that are statistically significant in Specification 3 are still significant in this specification. In the fixed effects and FGLS models, the political constraints index variable is statistically significant at the 5% confidence level and has the anticipated positive partial relationship with the dependent variable. Bilateral FDI Inflows from All Countries (Senders) to OECD Countries (Receivers)

46

47

Dependent Variable - ln FDI Inflows to OECD Country from Another Country ln GDP Receiving Country ln GDP Sending Country GDP Growth Receiver GDP Growth Sender Openness Receiver Openness Sender Inflation Receiver Inflation Sender Exchange Rate Growth Receiver Exchange Rate Growth Sender ln World FDI World GDP Growth Receiver is Landlocked Sender is Landlocked Receiver is an Island Sender is an Island Contiguous Common Language Colonial Relationship ln GDP Trade ln Distance Trade ln GDP Other ln Distance Other ln GDP PTA Coefficient 0.698*** 1.114*** -0.009** -0.008*** 0.022*** 0.009*** -0.005*** -0.000003 0.011*** -0.001 -0.074** 0.032*** -0.703*** 0.202 0.174 1.310*** 3.960*** -0.117 2.473*** 0.062*** 0.066*

SE 0.047 0.029 0.004 0.002 0.002 0.001 0.001 0.00003 0.004 0.002 0.035 0.007 0.187 0.182 0.227 0.187 0.414 0.210 0.362 0.010 0.037

Specification 1 SE 0.046 0.029 0.004 0.002 0.002 0.001 0.001 0.00003 0.004 0.002 0.035 0.007 0.181 0.176 0.220 0.180 0.401 0.202 0.351

0.052 0.191 0.089

Coefficient 0.639*** 1.070*** -0.007** -0.005*** 0.018*** 0.007*** -0.006*** -0.00001 0.012*** -0.001 -0.098** 0.026*** -0.552*** 0.222 0.181 1.284*** 3.467*** -0.0199 2.584***

0.078 -0.180 -0.021

Specification 2

0.079 -0.184 -0.022

Coefficient 0.642*** 1.081*** -0.007** -0.005*** 0.018*** 0.007*** -0.006*** -0.00001 0.012*** -0.001 -0.077** 0.026*** -0.545*** 0.237 0.184 1.280*** 3.460*** 0.013 2.582***

0.052 0.191 0.089

SE 0.046 0.029 0.004 0.002 0.002 0.001 0.001 0.00003 0.004 0.002 0.036 0.007 0.181 0.175 0.219 0.180 0.400 0.202 0.349

Specification 3

-0.043 0.199 -0.062

0.080 0.291 0.109

SE 0.062 0.044 0.006 0.004 0.003 0.002 0.001 0.0001 0.008 0.004 0.057 0.012 0.231 0.213 0.278 0.257 0.437 0.264 0.430

Specification 4 Coefficient 0.868*** 1.557*** 0.002 -0.003 0.020*** 0.009*** -0.012*** 0.0001 -0.017** 0.0002 0.029 0.033*** -0.529** 0.880*** 0.165 0.461* 2.738*** 0.079 2.755***

Table 5 – Bilateral FDI Inflows from All Countries to OECD Countries Regressions

48

ln Distance PTA ln GDP FTA ln Distance FTA ln GDP Customs Union ln Distance Customs Union ln GDP Common Market ln Distance Common Market GATT/WTO Membership Receiver GATT/WTO Membership Sender Primary Enrollment Receiver Primary Enrollment Sender Political Constraints Receiver Political Constraints Sender -42.547*** 1.116 Intercept Term 46,422 Number of Observations *** - Significant at 1% level, ** - 5%, * - 10%

0.304 0.010 0.037 0.040 0.153 0.043 0.152

-39.064*** 1.112 46,422

-0.127 0.031** 0.069* 0.029 0.092 0.033 -0.033 0.087

-0.256***

-39.740*** 1.133 46,422

0.303 0.010 0.037 0.040 0.153 0.043 0.152

-0.128 0.032*** 0.070* 0.028 0.093 0.033 -0.033

-0.107 0.045*** -0.018*** -4.563*** 0.361 -59.949*** 25,903

-

-0.099 -0.009 0.186*** 0.037 -0.008 0.187*** -0.591**

0.148 0.010 0.005 0.656 0.268 2.122

-

0.356 0.013 0.047 0.050 0.194 0.070 0.251

Specification 1 runs a fixed effects model with the natural log of FDI flows from a single country into an OECD country as the dependent variable with the list of independent variables ranging from the natural log of GDP in the FDI-receiving country to the natural log of the distance between the countries’ trade agreement partners in the table above. This specification captures ideas from the literature (Adams et. al., 2003) and updates their work with the most comprehensive list of trade agreements and data through the year 2006. The following variables in this model are statistically significant at the 1% confidence level with a stated partial relationship to the dependent variable holding everything else constant. The natural log of GDP in the FDI-receiving and natural log of GDP in the FDI-sending country both have a positive partial relationship with the dependent variable, the GDP growth of the FDI-sending country has a negative partial relationship, the level of openness in both the FDI-receiving and sending countries have a positive partial relationship, inflation in the FDI-receiving country has a negative partial relationship, exchange rate growth in the FDI-receiving country has a positive partial relationship, world GDP growth has a positive partial relationship, the dummy variable that captures whether or not the FDI-receiving country is landlocked has a negative partial relationship, the dummy variable that captures whether or not the FDIsending country is an island has a positive partial relationship, the dummy variable that captures whether or not the country pair shares a common border has a positive partial relationship, the dummy variable that captures whether or not the countries have shared a 49

colonial bond has a positive partial relationship, and, finally, the natural log of combined GDP of the country pair if they are involved in any type of trade agreement has a positive partial relationship. All of these variables have the anticipated relationship with the dependent variable except for the GDP growth of the FDI-sending country and the exchange rate growth in the FDI-receiving country. The following variables in this model are statistically significant at the 5% confidence level with a stated partial relationship to the dependent variable. GDP growth of the FDI-receiving country has a negative partial relationship with the dependent variable, and the natural log of the difference between World FDI and the FDI of the country has a negative partial relationship. Both of these variables do not have the anticipated relationship with the dependent variable. There is one final variable, the natural log of the distance between the country pair if it was involved in any type of trade agreement, in the model that is significant at the 10% confidence level and has an unanticipated positive partial relationship with the dependent variable. Specification 2 again runs a fixed effects model with the same dependent variable, but this specification breaks the type of trade agreement down into five categories as in the Total FDI Flows Regressions. All of the variables mentioned in Specification 1 are still statistically significant in this specification. There are several new variables to this specification that are significant. Both the natural log of combined GDP of the country pair (positive partial relationship with the dependent variable) and the natural log of the 50

distance between the country pair (positive partial relationship) if it was involved in a free trade agreement are statistically significant at the 5% level and 10% level, respectively. Specification 3 again runs a fixed effects model with the same dependent variable and adds two dummy variables that capture whether or not the FDI-sending and FDIreceiving countries were members of the GATT/WTO in a particular year. The same variables that were statistically significant in Specification 2 are still significant in this specification. The GATT/WTO dummy variable for the FDI-receiving country is dropped from the regression due to perfect collinearity; however, the GATT/WTO dummy variable for the FDI-sending country is significant with an unanticipated negative partial relationship with the dependent variable. Finally, Specification 4 again runs a fixed effects model with the same dependent variable and adds four variables that capture the level of political constraints and the gross primary school enrollment level in the FDI-receiving and FDI-sending countries. The same variables that were statistically significant in Specification 3 are still significant in this specification, with the exception of GDP growth in both the FDI-receiving and FDI-sending countries, the level of FDI in the world, and the natural log of combined GDP of a country pair if involved in Free Trade Agreement. Also in this specification, the dummy variable that captures whether or not the FDI-sending country is landlocked, the natural log of combined GDP of the country pair involved in a Common Market, and 51

the natural log of the distance between a country pair involved in a Common Market are at the 1% confidence level, 1% level, and 5% level, respectively. Of the four new variables, all are significant at the 1% confidence level except the level of political constraints in the FDI-sending country. The level of primary enrollment in the FDIreceiving country has a positive partial relationship with the dependent variable, while the level of primary enrollment in the FDI-sending country and the level of political constraints in the FDI-receiving country both have a negative partial relationship with the dependent variable. A Wooldridge test for autocorrelation in panel data with the null hypothesis that there is no first-order autocorrelation produces a p-value ranging from less than 0.0001 to 0.002 in the four specifications. These values indicate that the null hypothesis can be rejected; consequently, a feasible generalized least squares (FGLS) estimator to correct for autocorrelation is necessary. Unfortunately, since this dataset contains over 46,000 observations, an advanced statistical software package, which was not readily available, is necessary to run an FGLS system. Therefore, while the estimates of coefficients are not biased, the standard errors and associated confidence levels reported in the tables are inaccurate, meaning that any coefficient that is statistically significant at the 10% confidence level might not truly be significant if an FGLS system that corrects for autocorrelation could be run instead.

52

Bilateral FDI Outflows from OECD Countries (Senders) to All Countries (Receivers) The same process outlined above is repeated with the new dependent variable as the natural log of FDI flows from an OECD country into any country. In all of these new specifications, the same variables as in the old specifications are statistically significant with several exceptions. GDP growth, inflation, exchange rate growth, and primary enrollment of the FDI-receiving country are no longer significant, while the dummy variable that captures whether or not the FDI-sending country is an island, the dummy variable that captures whether or not the country pair shares a common language, the natural log of combined GDP of the country pair involved in an “Other” agreement and Common Market, the natural log of the distance between the country pair involved in an “Other” agreement, Customs Union, and Common Market, and the level of political constraints in the FDI-sending country are now significant. Only the “Other” trade agreement variables out of the newly significant variables have the anticipated partial relationship with the dependent variable. A Wooldridge test for autocorrelation again reveals that the null hypothesis of no autocorrelation can be rejected and, therefore, an FGLS system is more appropriate. Consequently, the same caveat related to the standard errors in these specifications, as mentioned earlier, still applies.

53

54

Dependent Variable - ln FDI Outflows from OECD Country to Another Country ln GDP Sending Country ln GDP Receiving Country GDP Growth Sender GDP Growth Receiver Openness Sender Openness Receiver Inflation Sender Inflation Receiver Exchange Rate Growth Sender Exchange Rate Growth Receiver ln World FDI World GDP Growth Sender is Landlocked Receiver is Landlocked Sender is an Island Receiver is an Island Contiguous Common Language Colonial Relationship ln GDP Trade ln Distance Trade ln GDP Other SE 0.052 0.032 0.004 0.002 0.002 0.001 0.001 0.00004 0.004 0.002 0.038 0.008 0.207 0.201 0.251 0.206 0.457 0.232 0.401 0.011 0.040

Coefficient 1.398*** 1.107*** -0.015*** 0.001 0.011*** 0.011*** 0.0004 -0.00001 0.001 -0.001 -0.076** 0.023*** 0.327 -0.385* 1.005*** 0.629*** 3.583*** -0.871*** 3.016*** 0.117*** -0.119***

Specification 1

0.002 0.038 0.008 0.204 0.199 0.248 0.204 0.453 0.229 0.397

0.057

0.170***

0.004

SE 0.051 0.032 0.004 0.002 0.002 0.001 0.001 0.00004

-0.001 -0.099*** 0.021*** 0.393* -0.351* 1.039*** 0.625*** 3.624*** -0.829*** 3.111***

0.001

Coefficient 1.376*** 1.096*** -0.014*** 0.003 0.010*** 0.010*** 0.0003 -0.00001

Specification 2

0.171***

-0.001 -0.084** 0.021*** 0.397* -0.340* 1.042*** 0.622*** 3.619*** -0.806*** 3.109***

0.001

Coefficient 1.378*** 1.104*** -0.014*** 0.003 0.010*** 0.010*** 0.0002 -0.00002

0.057

0.002 0.039 0.008 0.204 0.198 0.248 0.203 0.453 0.229 0.396

0.004

SE 0.051 0.032 0.004 0.002 0.002 0.001 0.001 0.00004

Specification 3

0.057

-0.005 -0.125** 0.015 0.991*** 0.020 1.564*** -0.146 3.293*** -1.013*** 3.074***

0.006

Coefficient 1.828*** 1.360*** -0.013** 0.007* 0.009*** 0.010*** -0.0001 0.0001

0.086

0.004 0.061 0.013 0.266 0.246 0.321 0.298 0.505 0.305 0.499

0.009

SE 0.070 0.050 0.007 0.004 0.003 0.002 0.002 0.0001

Specification 4

Table 6 – Bilateral FDI Outflows from OECD Countries to All Countries Regressions

55

ln Distance Other ln GDP PTA ln Distance PTA ln GDP FTA ln Distance FTA ln GDP Customs Union ln Distance Customs Union ln GDP Common Market ln Distance Common Market GATT/WTO Membership Sender GATT/WTO Membership Receiver Primary Enrollment Sender Primary Enrollment Receiver Political Constraints Sender Political Constraints Receiver -59.388*** Intercept Term 46,422 Number of Observations *** - Significant at 1% level, ** - 5%, * - 10% 1.218

-57.849*** 46,422

-0.515** -0.019 -0.177 0.109*** -0.169*** -0.064 0.399** -0.154*** 0.548***

1.228

0.208 0.097 0.333 0.011 0.041 0.043 0.167 0.046 0.165 0.095

-0.188**

-58.350*** 1.253 46,422

0.207 0.097 0.333 0.011 0.041 0.043 0.167 0.046 0.165

-0.517** -0.019 -0.177 0.109*** -0.169*** -0.064 0.399** -0.154*** 0.548***

0.014 0.106*** 0.005 -3.653*** 1.220*** -84.770*** 25,903

-

-0.095 -0.072 0.008 0.058*** -0.038 -0.049 0.312 -0.059 0.172

0.159 0.011 0.005 0.705 0.287 2.325

-

0.312 0.118 0.391 0.014 0.052 0.054 0.210 0.054 0.267

Economic Interpretation and Policy Implications Total FDI Flows Regressions This section analyzes the aforementioned results for economic and policy relevance by breaking the variables down into four broad categories. In the FGLS models that correct for autocorrelation, the coefficient on the natural log of the difference between World FDI and the FDI of the country ranges is always statistically significant and ranges from 0.507 to 0.576. In more concrete terms, a ten percent increase in the level of World FDI would lead to about a five percent increase in FDI flows to any one particular country, holding everything else constant. Similarly, in the FGLS models, the coefficient on world GDP growth is always significant at the 10% confidence level and ranges from -0.013 to -0.015. Consequently, a one-percentage point increase in world GDP growth is associated with a 1.3 to 1.5 percent decrease in FDI flows to a particular country, ceteris paribus. These indicators do not lend themselves to making policy recommendations due to their global nature and the obvious challenges with coordinated action on that scale. The fact that the coefficient on world GDP growth is negative is counterintuitive and could potentially be explained by methodological flaws such as omitted variable bias. In the FGLS models, the coefficient on the natural log of GDP in a particular country is always statistically significant and ranges from 0.854 to 0.915. These results imply that a five percent increase in the GDP of a country, holding everything else 56

constant, is associated with about a 4.3 to 4.6 percent increase in FDI flows to a country. The economic partial associations of the other significant local associations, holding everything else constant, are summarized as follows. A one percentage point increase in the GDP growth of a country is associated with a 1.2 to 1.5 percent increase in FDI flows. A one percentage point increase in the degree of trade openness in a country is associated with about a one percent increase in FDI flows. Finally, an increase in the inflation rate of one percentage point is associated with a 0.01 percent decline in FDI flows. These results are broadly in line with past research into this topic. Yet again, it would be difficult to draw practical policy implications from these results alone beyond the standard advice that countries should follow sound macroeconomic policies, such as keeping inflation in check, and consider making the movement of goods and services to and from their country less cumbersome and freer from protectionist mechanisms like tariffs and subsidies for domestic production. This paper studies several institutional variables either new to the literature or new to these particular models. The GATT/WTO membership dummy, for example, is statistically significant in the two FGLS specifications where it is included with a value of 0.287. In other words, joining the WTO or continuing membership in the organization in one year, compared to a country that is identical in all other aspects but is not a member of the WTO, is associated with almost a 29% increase in FDI flows to that country. While the primary school enrollment variable is not significant, the political 57

constraints index is significant with a value of 0.709. This value means that as the index score (ranging from 0 to 1) increases by 0.1 points, which means that a country is becoming more politically constrained, the FDI flows to a country increase by about 7%, ceteris paribus. These results could have significant policy ramifications to certain countries. While the policy relevance of joining the WTO to potentially improve FDI flows is largely negated by the fact that 153 out of 195, or about 78%, of the world’s countries are already members of the WTO, it still serves as a valuable reminder and hedge against intransigence within the WTO to current members of one of the many degrees of economic importance associated with WTO membership. Furthermore, as will be discussed in the following section, the relationship associated with joining the WTO and a nation’s FDI inflows is far greater in magnitude than the relationship associated with involvement in any type of trade agreement or association. Thus, since association with the WTO alone is related to such a boost to FDI inflows, it is more likely that agreements conceived under its aegis will lead to broader and/or longer-lasting reforms that further attract FDI. Consequently, one important policy recommendation would be for some countries, especially those in the developing world, to focus more efforts towards WTO negotiations and less on establishing other types of trade agreements. In terms of the level of political constraints in a country, policy recommendations would have to be based in a long-term time horizon. The results imply that a higher level 58

of constraint in the government or, in other words, the more checks and balances within the system and more difficult for any one political actor to force through an agenda, is associated with a higher level of FDI flows to that country, holding everything else constant. Therefore, the policy recommendation to ensure adequate checks and balances at the federal level of government must necessarily be a long-term goal and its immediacy can certainly be questioned by developing countries facing more pressing concerns like disease, famine, and civil conflict. This paper also studies the relationships associated with trade agreements, both in aggregate and, as a new addition to the literature, by type of agreement. In Specification 1, which tests the relationship of the all trade agreements combined with the dependent variable, the coefficient on that variable in the fixed effects model is statistically significant and has a value of 0.0005. In other words, as the combined GDPs of a country and its trade agreement partners increase by ten percent, FDI flows to that country increase by about 0.005 percent, ceteris paribus. However, in the FGLS model, the coefficient on this variable is still significant, but the sign and magnitude change to 0.0002, indicating that a ten percent increase in the variable, holding everything else constant, would be associated with a decrease in FDI flows of about -0.002 percent. Regardless of the statistical method employed, these results are of very small economic significance. For example, if the United States had signed a new trade agreement in 2006, such as the one still not implemented with Colombia, that happened to increase the 59

combined GDPs of the U.S. with all of its trade agreement partners by ten percent, it would have either increased its FDI inflows, from a base of over $180 billion, by about $903,000 (fixed effects) or decreased its FDI inflows by $361,000. These results are of smaller magnitude than those found by Medvedev (2006) at 0.06 percent from the baseline model, but not unreasonably different in terms of practical economic significance (in the above example, FDI inflows would have increased for the U.S. by $108 million). In Specifications 2 through 4, trade agreements are broken down by type, ranging from the least to most economically integrative. From the FGLS models, the coefficient on the combined GDPs of a country and its customs union partners is statistically significant with a value of 0.001. This result implies that as the combined GDPs of a country and its customs union partners increases by ten percent, FDI inflows to that country increases by about 0.01 percent, ceteris paribus. At the opposite end of the spectrum, the coefficient on the combined GDPs of a country and its trade agreement partners from agreements classified in this paper as “Other” is also always significant with a value of -0.0004. As before, increasing this variable by ten percent, holding everything else constant, is associated with a decrease in FDI inflows to that country of about -0.004 percent. These results are partially in line with the hypothesis that the more economically integrated trade agreements are the more investment that countries involved in those 60

agreements are likely to receive. However, the coefficients on the other three types of trade agreements (PTAs, FTAs, and Common Markets) are never statistically significant; therefore, a definitive answer to the hypothesis cannot be reached. There is some concern that the “Other” trade agreement category is biased by a proportionally large number of agreements involving sub-Saharan African nations and their volatile FDI inflows over this time period, and this bias could be unduly influencing the other trade agreement coefficients. This possible problem should be explored in future research. In terms of policy recommendations, there is little evidence from this analysis to support a recommendation that countries pursue trade agreements with one another as a signal of economic and institutional stability to foreign investors. There is some evidence that if countries do want to join trade agreements, those that are most economically liberalizing and integrative, such as customs unions, are the type of agreement that will be most likely to attract additional FDI. Therefore, recent agreements like the Free Trade Area of the Americas that have been stuck in negotiations and bypassed by a “spaghetti bowl” of bilateral deals should have more of an impetus for renewal, especially if the Doha round of negotiations under the WTO cannot be revived. Bilateral FDI Inflows from All Countries (Senders) to OECD Countries (Receivers) Global associations under the fixed effect models for all specifications are different in nature to the previous models of total FDI flows. The natural log of the 61

difference between World FDI and the FDI of the country has a value that ranges from 0.074 to -0.091. These values imply that a ten percent increase in World FDI, holding everything else constant, is associated with about a 0.7 to 0.9 percent decrease in FDI inflows to an OECD country from any other one country. This partial relationship with the dependent variable is not in line with previous literature on this topic nor the hypotheses in this paper. The most likely explanation for this discrepancy is flaws with the methodology, possibly stemming from omitted variable bias. Values for the other global association, World GDP growth, range from 0.026 to 0.033, implying that a ten percent increase in global GDP growth, ceteris paribus, is associated with about a 0.26 to 0.33 percent increase in FDI inflows to an OECD country from any other one country. Unlike the results for World FDI, these results are in line with this paper’s hypotheses. Overall, the results for local associations are broadly in line with previous literature and the hypotheses of this paper. For example, the coefficient on the natural log of GDP for the FDI-receiving OECD country ranges from 0.639 to 0.868, translating to a ten percent increase in GDP of the receiving country, ceteris paribus, being associated with about a 6.4 to 8.7 percent increase in FDI inflows to that country. The partial relationship associated with increasing the level of GDP in the sending country by 10% is even larger, ranging from about a 7 to 11 percent increase in FDI inflows to the receiving country. Similarly, openness in the sending and receiving country (10% increase associated with a 9 to 11% increase in FDI inflows to the OECD country) and 62

inflation in the receiving country (10% increase associated with a 0.5% decrease in FDI inflows to the OECD country) prove to both have an economically significant partial relationship with FDI flows, matching previous work on this topic. The coefficient on GDP growth in both the FDI-sending and FDI-receiving countries have counterintuitive signs and values, ranging from -0.007 to -0.009. These values imply that a one percentage point increase in the GDP growth of either sending or receiving country, holding everything else constant, is related to a 0.7 to 0.9% decrease in FDI inflows to the OECD country. Perhaps as countries grow their investment relationship with one particular country becomes less important because they can attract new foreign investment sources and/or they can fulfill their demand for investment domestically. However, this result remains somewhat puzzling and does not match results from previous literature. About half of the new variables to the binary FDI models are statistically significant and most have a large association with the amount of FDI flows sent from all countries to OECD countries. The dummy variable for whether or not the FDI-receiving country is landlocked is associated with a 53 to 70 percent decrease in FDI flows sent to an OECD country compared to an FDI-receiving country that is the same in all other characteristics but is not landlocked. The dummy variable for whether or not the FDIsending country is an island is associated with a 46 to 131 percent increase in the dependent variable compared to an FDI-sending country that is the same in all other 63

characteristics but is not an island. The dummy variable for whether or not the country pair shares a border is associated with a 274 to 396 percent increase in the dependent variable compared to a country pair that is the same in all other characteristics but does not share a border. Finally, the dummy variable for whether or not the country pair has shared a colonial relationship is associated with a 247 to 276 percent increase in the dependent variable compared to a country pair that is the same in all other characteristics but has not shared a colonial relationship. In terms of policy significance, these new local variables have no direct applicability. However, in aggregate, they indicate that much of the relationship between an OECD country’s FDI inflows from a particular country is pre-determined by geographic and cultural factors that are entirely out of a country’s ability to manipulate. Several institutional variables are statistically significant in the two specifications of the OECD FDI-receiving country model. The GATT/WTO membership dummy variable for the FDI-sending country is significant in the first specification in which it is included with a value of -0.256, translating to a 26 percent decrease in FDI flows sent to an OECD country compared to an FDI-sending country that is the same in all other characteristics but is not a member of the WTO. This result could perhaps be explained by the possibility that when a country joins the WTO (which was more likely to be the case for a developing country over the studied time frame of 1980 to 2006) it no longer has to rely almost exclusively on investing in any one particular country that it has 64

probably had a long relationship with over the past. For example, China joined the WTO in December 2001 and its FDI outflows to the U.S., a growing trading and investment partner at the time, dropped precipitously in 2002 to -$129 million and remained negative in value in 2003. China’s overall FDI outflows skyrocketed in 2001, supporting the hypothesis that WTO membership is associated with an increase in aggregate FDI flows regardless of the source country, and also decreased in 2002 and 2003, but not nearly as large a decline in relative terms as the decline in FDI flows to the U.S. exclusively (59% aggregate decline compared to a 117% decline in U.S. specific outflows). This analysis ignores macroeconomic and political factors that could have influenced Chinese FDI flows to the U.S. during this time period, but it provides some evidence to explain the seemingly counterintuitive result of a negative coefficient on the GATT/WTO dummy variable for the FDI-sending country. In light of these findings, there are no immediate policy actions a country should consider in terms of joining the WTO; however, when combining this result with the result from the WTO variable in the aggregate FDI models, it should reinforce the belief that joining the WTO can attract new investment partners and destinations while also diminishing some countries’ reliance on sending FDI to a single country in their investment strategy. In the fourth specification, three of the four additional institutional variables are statistically significant. The coefficient on the primary enrollment variable for the FDI65

receiving country is 0.045, indicating that a one percentage point increase in the ratio of children in enrolled in the FDI-receiving country’s educational system to the total population in the relevant age group for that level of education, ceteris paribus, is associated with a 4.5% increase in FDI flows to the OECD receiving country. The coefficient on the primary enrollment variable for the FDI-receiving country is -0.018, indicating that a one percentage point increase in the FDI-sending country’s primary enrollment ratio is associated with a 1.8% decrease in FDI flows to the OECD receiving country, holding everything else constant. Past literature has indicated a potential association between high levels of human capital attracting more capital-intensive FDI. These results indicate that as an FDI-receiving country’s primary enrollment ratio improves, it attracts more FDI, as well as when an FDI-sending country’s primary enrollment ratio improves it sends lower levels of FDI, holding everything else constant. The latter result leads to an economically important and common policy recommendation that as nations improve their education levels, they can expect their economy to become more human-capital intensive, and either attract more FDI and/or build up their industries so that foreign investment is less important to bridge the gap that usually exists between the level of human capital in developed and developing countries’ workforces. The last statistically significant institutional variable in the fourth specification is the level of political constraints in the FDI-receiving country. Its value of -4.563 indicates that as the index score (ranging from 0 to 1) increases by 0.1 points, which 66

means that a country is becoming more politically constrained, the FDI flows to a country decrease by about 46%, ceteris paribus. The magnitude of this coefficient is particularly striking, but, to put it in context, from 1980 to 2006 there were only five instances when an OECD country experienced a change in their political constraints index of 0.1 or greater.7 This result is also opposite in sign from the coefficient on the political constraints variable in the aggregate FDI flows model; however, it could still be the case that both results are accurate. The key to interpreting this coefficient is the fact that the FDI-receiving countries are only countries that belong to the OECD; therefore, as these OECD countries that are already highly politically constrained on average become even more politically constrained, FDI-sending countries seek other destinations for their investments where they might be more likely to influence the investment regime. Consequently, combining this result with the result from the aggregate FDI model yields a policy recommendation to developed countries that maintaining a system of government with sufficient checks and balances is associated with higher levels of FDI, but, at the same time, there are diminishing marginal returns to making a system of government more constrained and it appears that most, if not all, developed countries have reached the point where the marginal return to improvement is zero or negative.

7

The countries and years during which this change took place include Greece (1990-91), Hungary (199091), Poland (1989-90), South Korea (1991-92), and Turkey (1991-92).

67

One important thing to note for Specification 4 is that the number of observations falls by about 44 percent due to a lack of data for some countries. In fact, the countries that are missing data are also likely to be developing countries, therefore, since there is no fixed rule as to which countries have data for primary enrollment and political constraints, there is a possibility that the set of countries is a nonrandom sample that could be subject to bias. Finally, the trade agreement variables yield several statistically significant coefficients, which are economically telling and practically important. The coefficient on the log of the combined GDPs of an OECD FDI-receiving country and its trade agreement partner is significant and has a value of 0.062, indicating that a one percent increase in the combined GDPs of the two countries in a trade agreement is associated with about a 0.06% increase in FDI inflows to the OECD country in the agreement, ceteris paribus. The log of the distance between the FDI-receiving OECD country and its trade agreement partner has an unanticipated positive sign. In terms of policy relevance, this result at least partially validates the strategy of developed countries, such as the U.S. over the past decade, that have pursued bilateral trade agreements. When the trade agreement variable is broken by trade agreement type, the relationship is not quite as straightforward. The value of the coefficient on the natural log of the combined GDPs of the two countries is only statistically significant for Free Trade agreements, with a value of 0.031. This value indicates that a ten percent increase in the 68

combined GDPs of the two countries in each type of agreement is associated with a 0.31 percent increase for Free Trade agreements. In a policy context, these results do not support the hypothesis that the more economically comprehensive and integrated the trade agreement is between two countries, the more likely those two countries will be to invest in one another’s country. If OECD countries can not join a more integrated trade agreement, pursuing a free trade agreement is a policy option associated with increasing foreign investment flows from the countries in which the OECD country is involved in the agreement, ceteris paribus. There is also evidence from these results that as the average distance increases between the countries involved in a free trade agreement (FTA) FDI flows to the OECD FDI-receiving country increase, holding everything else constant. Combining this result with the previous result related to countries’ GDPs in an FTA yields a substantive policy result. Developed countries that have formed or would like to form a strategic trading relationship with a non-traditional and geographically distant trading partner should strongly consider signing a free trade agreement with that country, as the evidence supports the hypothesis that it sends a signal to investors in the foreign country to increase their level of investment in the developed FDI-receiving country. The U.S. trade experience over the past decade is an example of this strategy, where FDI inflows from Jordan for example, the 100th largest trading partner with the U.S when an FTA was signed in 2000, increased from -$10 million in 1999 to $11 million in 2001. 69

Bilateral FDI Outflows from OECD Countries (Senders) to All Countries (Receivers) The economic significance for global and local associations in these models is very close in magnitude to that of the bilateral FDI inflows to OECD countries from all countries, thus, the discussion begins with institutional variables. In these model specifications, only the primary enrollment ratio in the FDI-receiving country is not statistically significant. The GATT/WTO membership dummy variable for the FDI-receiving country is significant in the first specification in which it is included with a value of -0.188, translating to a 19 percent decrease in FDI flows sent from an OECD country to the FDIreceiving country compared to an FDI-receiving country that is the same in all other characteristics but is not a member of the WTO. Again, this result is counterintuitive, but perhaps when a country joins the WTO it has more opportunities to seek new sources of investment from non-traditional source countries, and this new investment crowds out some of the foreign investment it had been receiving from certain OECD countries in the past. The policy significance of the primary enrollment and political constraints variables is similar to the model where the dependent variable is OECD country FDI inflows. However, the coefficient on the political constraints variable for the FDIreceiving country is 1.220, indicating that as the index score (ranging from 0 to 1) increases by 0.1 points, which means that a country is becoming more politically 70

constrained, the FDI flows to a country increase by about 12%, ceteris paribus. This result mirrors the result for the coefficient on the political constraints variable in the aggregate FDI model. This finding reinforces the long-term policy recommendation that countries, especially developing nations, should build checks and balances into their system of government in order to, among countless other benefits, attract foreign investment. With respect to the trade agreement variables, the coefficients on the log of the combined GDPs of an OECD FDI-sending country and its trade agreement partner and its related log of the distance between the FDI-sending OECD country and its trade agreement partner are statistically significant with the anticipated sign. When the trade agreement variable is broken down by trade agreement type, the coefficients on “Other”, FTA, and Common Market GDP are significant. There are several differences between these results and the previous model, with FDI inflows to OECD countries as the dependent variable, which are worth noting. First, the value on the coefficient the log of the combined GDPs of an OECD FDI-sending country and its “Other” trade agreement partner is 0.170, indicating that a one percent increase in this coefficient, holding everything else constant, is associated with a 0.170 percent increase in FDI flows to the FDI-receiving country. This partial relationship is even larger in magnitude than the partial relationship between the log of the combined GDPs of an OECD FDI-sending country and its Free Trade agreement partner and FDI 71

flows. Additionally, the value on the coefficient of the distance coefficient of the natural log of the distance between the country pair if it was involved in an “Other” agreement is negative. These combined results imply that, for policy purposes, countries can consider very loosely worded and vague trade-related agreements with geographically proximate OECD FDI-sending countries as a tool to increase FDI flows from that OECD country. For example, if Honduras negotiated an agreement with the United States that dedicated the countries to “improve” trade relations, Honduras could expect that U.S. investors would be more likely to invest in its country. Second, the value on the coefficient the log of the combined GDPs of an OECD FDI-sending country and its Common Market partner is -0.154, indicating that a one percent increase in this coefficient, holding everything else constant, is associated with a 0.154 percent decrease in FDI flows to the FDI-receiving country. This result is counterintuitive, but might be explained by the fact that there are only two Common Markets, the European Union (EU) and MERCOSUR, in the dataset. Of the two Common Markets, only the EU has expanded its membership over time. Consequently, perhaps this counterintuitive partial relationship with the dependent variable captures the more recent expansions in the EU from the EU-15 to the EU-25, where the new countries that joined were at significantly lower levels of economic development than the original members, and there was some sentiment amongst the old member populations that the new members would be a burden, leading to the phenomenon known in EU circles as 72

“enlargement fatigue.” Regardless of this potential explanation, future research should study this relationship carefully, perhaps treating EU countries as one bloc instead of separately. Third, the value on the coefficient of the natural log of the distance between the country pair if it was involved in a free trade agreement (FTA) is negative instead of positive. This finding indicates that, ceteris paribus, as the distance between the OECD FDI-sending country and another country increases by one percent, the level of FDI outflows from the OECD country decreases by about 0.15 percent. This result has important policy implications that when a country signs an FTA with a non-traditional, geographically distant OECD country trading partner it should not necessarily expect an increase in FDI inflows from that country. This finding is completely opposite from the previous result that indicated a positive partial association with FDI inflows to an OECD country from a country in which it was involved in an FTA.

73

Conclusions This paper attempts to build upon previous literature that studies the relationship between trade agreements and foreign direct investment flows. The paper analyzes GATT/WTO membership, human capital levels, political constraints within a country’s government, and the different relationships associated with different types of trade agreements from an aggregate FDI model and bilateral FDI models. There are several main points to take away from this paper. •

Trade Agreements and FDI are Related, but the Type of Trade Agreement Matters – The aggregate FDI models indicate that, on the whole, an increase in the combined GDPs of a country and its trade agreement partners of ten percent is associated with an increase in FDI flows to that country by about 0.005 percent, ceteris paribus. The bulk of this association likely stems from the more economically integrative trade associations, such as Customs Unions. Therefore, as an overall trade strategy outside of the international arena under the WTO, countries should consider more economically comprehensive and integrated trade pacts if attracting higher levels of FDI is a goal. The bilateral FDI models do not directly support the hypothesis that more economically comprehensive agreements are more likely to send a positive signal to investors in the foreign country involved in the trade agreement. The lack of support for this hypothesis from the more economically integrated agreements (e.g., Customs Unions and 74

Common Markets) could be a product of the relative scarcity of these types of agreements from 1980 to 2006. From a developed country perspective, offering any country a free trade agreement (especially to geographically distant and likely non-traditional trading partners) will, on average, increase FDI flows from the foreign country into the developed country. From a developing country perspective, “Other” and free trade agreements with geographically proximate developed countries are associated with receiving higher FDI inflows from those countries. In summation, the initial null hypothesis that trade agreements are not associated with FDI flows can be rejected in the aggregate and bilateral FDI models, while the null hypothesis each type of trade agreement will not be associated with FDI flows can be rejected for customs unions in the aggregate FDI models, “Other” and Common Market trade agreements when the FDIsending country is a member of the OECD, and Free Trade agreements regardless of the FDI-sending or FDI-receiving country. •

The WTO Can Create New Opportunities – Results from the aggregate FDI flows model demonstrate that joining the WTO can attract new investment partners and destinations in the form of increased FDI flows. Simultaneously, from the bilateral FDI models, joining the WTO is associated with diminishing some countries’ reliance on sending FDI to a single country in their investment strategy. Consequently, the null hypothesis that WTO membership is not 75

associated with FDI flows can be rejected for both the aggregate and bilateral FDI models. •

Human Capital and Political Constraints are Factors – In the aggregate FDI models, human capital was not associated with increased or decreased FDI flows. However, in the bilateral FDI models, higher levels of human capital are associated with either FDI inflows and/or economic self-improvements to the point that foreign investment from any one country is not as necessary to a country’s development. In terms of political constraints, there was evidence in both model types that countries with more checks and balances are more likely to attract FDI, ceteris paribus, with the important caveat that the law of diminishing marginal returns still applies and has likely reached zero or become negative for OECD nations. Therefore, the null hypothesis that the primary enrollment ratio in a country is not associated with FDI flows cannot be rejected in the aggregate FDI model, but can be rejected in the bilateral FDI models, with the exception of the FDI-receiving country when the FDI-sending country is exclusively a member of the OECD. Similarly, the null hypothesis that the level of political constraints in a country is not associated with FDI flows can be rejected in the aggregate FDI model and the bilateral FDI models, with the exception of the FDI-sending country when the FDI-receiving country is exclusively a member of the OECD.

76

•

Sound Macroeconomic Policies Matter – The global and local associations in both the aggregate and bilateral models were all generally in line with previous literature on this topic. Variables like GDP growth, an economy’s openness to trade, and inflation are all significantly associated with the level of FDI flows the country is able to attract.

•

Some Factors are Beyond Policy Prescription – From the bilateral models, variables like whether countries shared a border, were landlocked, or shared a colonial history were all significantly associated with the level of bilateral FDI flows to OECD countries and originating from OECD countries with a large magnitude.

77

References Adams, R., Dee, P., Gali, J. and McGuire, G. “The Trade and Investment Effects of Preferential Trading Arrangements — Old and New Evidence.” Productivity Commission Staff Working Paper, Canberra. May 2003. http://www.pc.gov.au/__data/assets/pdf_file/0006/60468/tiepta.pdf (accessed September 28, 2008). Ahlquist, John S. "Economic Policy, Institutions, and Capital Flows: Portfolio and Direct Investment Flows in Developing Countries." International Studies Quarterly (2006): 681-704. Andriamananjara, S. “On the size and number of regional integration arrangements: a political economy model.” World Bank, World Bank Institute. May 1999. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=170108 (accessed March 25, 2009). Balsubramanyam, V.N., Mohammed Salisu, and David Sapsford. “Foreign Direct Investment and Growth in EP and IS Countries.” The Economic Journal 106, no. 434 (January): 92-105. Bénassy-Quéré, Agnès, Coupet, Maylis, and Thierry Mayer. “Institutional Determinants of Foreign Direct Investment.” The World Economy (2007): 764-82. http://team.univ-paris1.fr/teamperso/mayer/institutions_pub.pdf (accessed September 26, 2008). Blomström, Magnus and Ari Kokko. “Regional Integration and Foreign Direct Investment.” Working Paper no. 6019, National Bureau of Economic Research (April 1997): 1-36. http://www.nber.org/papers/w6019 (accessed September 28, 2008). Blomström, Magnus, Lipsey, Robert E., and Mario Zejan. “What Explains Developing Country Growth?” In Convergence and Productivity: Gross-National Studies and Historical Evidence, edited by William Baumol, Richard Nelson, and Edward Wolff. Oxford: Oxford University Press, 1994. Blonigen, Bruce A. “A Review of the Empirical Literature on FDI Determinants.” Working Paper no. 11299, National Bureau of Economic Research (April 2005): 1-37. http://www.nber.org/papers/w11299 (accessed September 25, 2008). 78

Blonigen, Bruce A. "Firm-Specific Assets and the Link Between Exchange Rates and Foreign Direct Investment." American Economic Review 87, no. 3 (June 1997): 447-65. Blonigen, Bruce A. "Tariff-jumping antidumping duties." Journal of International Economics (2002): 31-49. Borensztein, E., J. De Gregorio, and J.W. Lee. “How Does Foreign Investment Affect Growth?” Journal of International Economics 45, no. 1 (June 1998): 115-135. Brown, Oli, Mzukisi Qobo, and Alejandra Ruiz-Dana. “Conclusions: the role of regional trade integration in conflict prevention.” In Regional Trade Integration and Conflict Resolution, edited by Shaheen Rafi Khan. http://www.idrc.ca/es/ev132679-201-1-do_topic.html# (accessed October 4, 2008). Büthe, T. and Milner, H. "The Politics of Foreign Direct Investment into Developing Countries: Increasing FDI through Policy Commitment via Trade Agreements and Investment Treaties." Paper presented at the annual meeting of the American Political Science Association, Hilton Chicago and the Palmer House Hilton, Chicago, IL: August 8, 2004. http://www.allacademic.com/meta/p59852_index.html Carkovic, Maria V and Levine, Ross. “Does Foreign Direct Investment Accelerate Economic Growth?” University of Minnesota Department of Finance Working Paper: (June 2002). Available at SSRN: http://ssrn.com/abstract=314924. Centre D’Etudes Prospectives Et D’Informations Internationales. “Geodesic Distances.” (2008). http://www.cepii.fr/anglaisgraph/bdd/distances.htm (accessed March 25, 2009). Chowdhury, Abdur and Mavrotas, George. “FDI and Growth: What Causes What?” The World Economy 29, no. 1 (January 2006): 9-19. Available at SSRN: http://ssrn.com/abstract=875102. Clemens, Michael A. "The long walk to school: Development goals in historical perspective", CGD Working Paper 37 (2004). Washington, DC: Center for Global Development. Dartmouth University. “Tuck Trade Agreements Database.” (2008). http://www.dartmouth.edu/~tradedb/prosearch.php (accessed March 25, 2009). 79

Dunning, John H. “Trade, location of economic activity and MNE: A search for an eclectic approach.” In The International Allocation of Economic Activity, edited by B. Ohlin, P. O. Hesselborn, and P.M. Wijkman. London: Macmillan, 1977. Economist Intelligence Unit. “World investment prospects to 2011: Foreign direct investment and the challenge of political risk.” Columbia Program on International Investment (2008): 1-245. Froot, Kenneth A. and Jeremy C. Stein. “Exchange Rates and Foreign Direct Investment: An Imperfect Capital Markets Approach.” The Quarterly Journal of Economics (1991): 1,191-217. Globerman, Steven and Daniel Shapiro. “National Political Infrastructure and Foreign Direct Investment.” Working Paper no. 37, Industry Canada Research Publications Program (December 2002): 1-60. http://www.ic.gc.ca/epic/site/eas-aes.nsf/vwapj/wp37e.pdf/$FILE/wp37e.pdf (accessed September 28, 2008). Henisz, Witold. “The Political Constraints Index Dataset.” (2002). http://www. management.wharton.upenn.edu/henisz/polcon/contactinfo.html (accessed March 25, 2009). Hicks, Brian. "Regional Trade Agreements and Foreign Direct Investment." Thesis, Texas Tech University, 2007. http://etd.lib.ttu.edu/theses/available/etd06212007-111642/unrestricted/hicks_brian_thesis.pdf (accessed December 8, 2008). Jackson, James K. “Foreign Direct Investment in the United States: An Economic Analysis.” Congressional Research Service. http://fpc.state.gov/documents/organization/109490.pdf (accessed February 4, 2009). Jaumotte, Florence. “Foreign Direct Investment and Regional Trade Agreements: The Market Size Effect Revisited.” Working Paper no. 04-206. International Monetary Fund (November 2004): 1-31. http://www.imf.org/external/pubs/ft/wp/2004/wp04206.pdf (accessed September 26, 2008).

80

Kennedy, Tim. "Algeria set to join WTO, sign US free trade agreement." Bilaterals.org (December 5, 2004). http://www.bilaterals.org/article.php3?id_article=1016 (accessed December 4, 2008). Kolstad, Ivar, and Line Tøndel. “Social Development and Foreign Direct Investments in Developing Countries.” Development Studies and Human Rights, Chr. Michelsen Institute (2002). http://www.cmi.no/publications/file/?797=social-developmentand-foreign-direct-investments (accessed March 25, 2009). Krishna, P. “Regionalism and multilateralism: A political economy approach.” Quarterly Journal of Economics 113, no. 1 (1998): 227–51. Krueger, Anne O. “Are preferential trading agreements trade-liberalizing or protectionist?” Journal of Economic Perspectives 13, no. 4 (1999): 105–124. Kumar, Anil. “Does Foreign Direct Investment Help Emerging Economies?” Federal Reserve Bank of Dallas. Economic Letter-Insights from the Federal Reserve Bank of Dallas 2, no. 1 (January 2007). http://www.dallasfed.org/research/eclett/2007/el0701.html (accessed December 2, 2008). Lederman, Daniel, William Maloney, and Luis Serven. “Lessons from NAFTA for Latin America and Caribbean Countries: A Summary of Research Findings.” (Washington, DC: World Bank, 2005). http://www.wilsoncenter.org/news/docs/Lessons_from_NAFTA.pdf (accessed March 25, 2009). Levy, P. “A political-economic analysis of free-trade agreements.” American Economic Review 87, no. 4 (1997): 506–19. Li, Quan, and Adam Resnick. "Reversal of Fortunes: Democratic Institutions and Foreign Direct Investment Inflows to Developing Countries." International Organization, 57 (Winter 2003): 175-211. Lim, Ewe-Ghee. “Determinants of, and the Relation Between, Foreign Direct Investment and Growth: A Summary of the Recent Literature.” IMF Working Paper no. 01/175. http://www.imf.org/external/pubs/ft/wp/2001/wp01175.pdf (accessed March 25, 2009).

81

MacDermott, Raymond. “Regional trade agreement and foreign direct investment.” North American Journal of Economics and Finance 18 (2007): 107-16. ScienceDirect, Georgetown University (accessed September 28, 2008). McGill University. “Preferential Trade Agreements Database.” (2008). http://ptas.mcgill.ca/ (accessed March 25, 2009). Medvedev, Denis. “Beyond Trade: The Impact of Preferential Trade Agreements on Foreign Direct Investment Inflows.” Working Paper no. 4065. Policy Research, World Bank (April 2006): 1-39. Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=944158 (accessed September 25, 2008). Medvedev, Denis. “Preferential Trade Agreements and their Role in World Trade.” Working Paper no. 4038. Policy Research, World Bank (October 2006): 1-87. Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=938031 (accessed September 25, 2008). Moosa, Imad A. Foreign Direct Investment: Theory, Evidence and Practice. New York: Palgrave Macmillan, 2002. Nigh, D. “Political Events and the Foreign Direct Investment Decision: an Empirical Examination.” Managerial and Decision Economics 7 (1986): 99-106. Park, Innwon and Soonchan Park. “Reform-Creating Regional Trade Agreements and Foreign Direct Investment: Applications for East Asia.” Working Paper Series. The International Centre for the Study of East Asian Development, Kitakyushu (February 2007): 1-35. http://www.icsead.or.jp/7publication/workingpp/wp2007/2007-01.pdf (accessed September 28, 2008). Saggi, Kamal. “Trade, Foreign Direct Investment, and International Technology Transfer: A Survey.” World Bank, Development Research Group Working Paper (November 30, 1999). Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=630721 (accessed March 25, 2009). Shin, Kwanho & Yunjong Wang. "Exchange Rate Uncertainty and Free Trade Agreement between Japan and Korea." Trade Working Papers 359, East Asian Bureau of Economic Research (2003). 82

SourceOECD. “International Direct Investment Statistics.” Available through Georgetown University Library (2008). Teorell, Jan, Sören Holmberg & Bo Rothstein. The Quality of Government Dataset, version 15May08. University of Gothenburg: The Quality of Government Institute (2008). http://www.qog.pol.gu.se (accessed March 25, 2009). te Velde, Dirk Willem and Miatta Fahnbuulleh. “Investment Related Provisions in Regional Trade Agreements.” Overseas Development Institute. London (2003): 1-85. http://www.odi.org.uk/iedg/projects/inv%20prov%206.pdf (accessed September 26, 2008). UNCTAD. “World Investment Report: Trends and Determinants.” New York (1998). http://www.unctad.org/Templates/WebFlyer.asp?intItemID=2426&lang=1 (accessed March 25, 2009). UNCTAD. “World Investment Report: Transnational Corporations and the Infrastructure Challenge. United Nations Conference on Trade and Development.” New York (2008). http://www.unctad.org/Templates/WebFlyer.asp?intItemID=4629&lang=1 (accessed December 2, 2008). United States Trade Representative. “NAFTA Policy Brief: NAFTA Benefits.” Washington, DC (October 2007). http://www.ustr.gov/assets/trade_agreements/regional/nafta/fact_sheets/asset_upl oad_file366_13495.pdf (accessed December 4, 2008). Wooldridge, J.M. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press, 2002. World Bank. “WDI Online.” Washington, DC: World Bank Group. Available through Georgetown University Library (2008). WTO. “The Doha Declaration Explained.” http://www.wto.org/english/tratop_e/dda_e/dohaexplained_e.htm#investment (accessed March 23, 2009).

83

Zanatta, Mariana, Ionara Costa, and Sergey Filippov. “Foreign Direct Investment: Key Issues for Promotion Agencies.” United Nations University Issue Brief (2006). http://www.merit.unu.edu/publications/pb/unu_pb_2006_10.pdf (December 2, 2008). Zissimos, B. and Vines, D. “Is the WTO’s Article XXIV a free trade barrier?” CSGR Working Paper no. 49/00, Centre for the Study of Globalisation and Regionalisation, University of Warwick (2000). Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=229823 (accessed March 25, 2009).

84