The Impact of Free Trade Agreements on Foreign Direct Investment: Controlling for Endogeneity through a Dynamic Model Specification

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1 The Impact of Free Trade Agreements on Foreign Direct Investment: Controlling for Endogeneity through a Dynamic Model Specification Cristina Lira* Junsoo Lee Byung Ki Lee Robert Reed February 15, 2010 Abstract In recent years, international economists have devoted a great deal of attention to understanding the determinants of Foreign Direct Investment (FDI) across countries. At the same time, many countries have entered into Free Trade Agreements (FTA). Given their consequences for growth and welfare, it is important to determine how participation in an FTA affects the desire of firms to extend capital to foreign markets. There are theoretical arguments that support a positive (complementary) relationship between FTA and FDI between countries or a negative (substitution) relationship. Empirical studies have also failed to provide any clear conclusions. This paper finds that existing research is mixed because previous work treats participation in a FTA as an exogenous variable. Using a panel of countries for the years , this paper effectively controls for participation in a FTA when analyzing the impact on FDI. Moreover, the lengthy time series also us to examine the dynamic nature of the relationship between FTA and FDI. The possibility of feedback effects can also be studied. Overall, we find that failing to address these econometric issues yields inaccurate conclusions regarding the impact of FTA on FDI. While simple OLS estimation generally finds that FTAs promote FDI, our analysis shows that FDI is either unrelated to participation in a FTA or is negatively affected. JEL classification: F13, C52 Keywords: FDI, FTA, gravity equation, endogeneity, dynamic panel model Cristina Lira (Corresponding author), Department of Economics, Finance and Legal Studies, University of Alabama, Tuscaloosa, AL 35486, , clira@cba.ua.edu; Junsoo Lee, Department of Economics, Finance and Legal Studies, University of Alabama, Tuscaloosa, AL 35486, , jlee@cba.ua.edu; Byung Ki Lee, Korea Economic Research Institute, Hana Daetoo Security Bldg 27-3, Seoul, Korea, , lbk@keri.org; and Robert Reed, Department of Economics, Finance and Legal Studies, University of Alabama, Tuscaloosa, AL 35486, , 1

2 1. Introduction In recent years, there has been much attention devoted to studying the implications of Free Trade Agreements (FTA) for capital flows (FDI) between countries. Will trade agreements, by relieving trade barriers, cause FDI to decrease? Or, do trade agreements serve as a proxy for policies designed to promote economic activity more generally between countries? Understanding the connections between FDI and trade agreements is important for designing policies to promote living standards across countries. Promoting FTA or FDI is often seen as a strategy to obtain different outcomes in terms of domestic growth and welfare. In the study of the relationship between FTA and FDI there are two main arguments. First, according to the standard tariff-jumping argument, with a decrease in tariff barriers it will be easier to trade than to implement FDI. In such cases, tariff jumping FDI implemented as a substitute of trade through FTA can lead to a division of labor that does not reflect the comparative advantages of the countries. However, the effect of FTA likely depends on the motivation for FDI to occur. Some authors suggest that FDI diversion is possible when FDI from a country to countries that do not belong to the region decreases when the source country enter the FTA. Second, the participation in a FTA could be a signal from the host country to attract FDI. Signing an FTA is a commitment of the host country not to change the domestic law in a way that will allow reaping greater benefits from the investment at the expenses of the multinational enterprises. According to this view FDI and FTA are complements because the participation in the agreement will signal the stability of the 2

3 legal environment where FDI are potentially implemented, and therefore it will boost them. FTA could affect FDI because they are expressions of the strategic behavior between home firm and host countries. The outcomes of this behavior can involve the political economy of the countries and not only their trading policies. Therefore, there are theoretical arguments that support a positive relationship (complementary) between FTA and FDI but others that suggest a negative link (substitution). As such, the question of what is the overall effect of FTA on FDI is an empirical matter. However, the analysis of this relationship is complicated by a number of econometric issues. First of all, there is an issue of the potential endogeneity of FTA in association with FDI. The issue of endogeneity can play an important role when the dummy variable denoting the presence of FTA is used to estimate the effects on FDI. FTA is not randomly assigned and it is not exogenously given since the choice of implementing FDI is adopted as a strategy of the firms that observe the presence or not of FTA. In general, simultaneity is easily understood as FDI and FTA could each be explained by some additional common factor or factors. Therefore, using FTA as an explanatory variable for FDI without controlling for endogenous effects could produce biased estimates. Recently, Baier and Bergstrand (2007) convincingly argue that trade policy is not an exogenous variable while many previous studies in international trade typically assume this. The authors show that the effect of FTA on trade flows would be underestimated considerably if endogeneity is not adjusted properly. They emphasized the endogeneity bias caused by ignoring the unobserved bilateral fixed effects that exist between two trading partners. Specifically, they point out the fact that countries likely 3

4 select endogenously into FTAs perhaps for the reasons explained by the unobservable factors that are correlated with the level of trade. However, as we explain in more details in section 3, unobservable heterogeneity will likely bias estimates upward in the relationship between FTA and FDI. Second, a great deal of research stresses that FDI activity between the foreign firm and the host government is a dynamic process. This happens because past experience of FDI could affect the current amount of FDI due to strategic behavior by the host government, and it is logical to expect that capital flows to a host country may increase over time. In addition, the effect of FTA on FDI can appear after some delay. In this manner, it is hard to separate policies adopted by host governments from the dynamic investment decisions of a foreign firm. Therefore, a dynamic specification has been favored in some of recent papers. For example, Cheng and Kwan (2000) noted that there is a strong self-reinforcing effect of FDI on itself. Additionally, Egger and Merlo (2007) identify a sluggish adjustment pattern of FDI stocks. Nonetheless, the relationship between FDI and FTA has not been examined fully in a dynamic specification. Third, conventional approaches using instrumental variables and control-functions to correct for contemporaneous endogeneity will often yield inconclusive and less reliable results. These approaches require instrumental variables that are not easy to find. Also the variables proposed in the literature about FTA and trade, related to the institutional environment, are not good instruments because they are highly correlated with FTA and the dependent variable (Baier and Bergstrand, 2007). We employ alternative variables that better satisfy the exclusion restriction. 4

5 In this paper, we analyze if FDI and FTA are complements or substitute addressing the problem of the endogeneity of FTA in a dynamic model specification. We adopt the perspective of the source country and we use data about outward FDI 1 in a gravity model. We use a panel data set on the bilateral FDI of 20 OECD countries towards 50 OECD and non-oecd countries. We have actual records of FDI for the years Our dataset allows us to exploit cross sectional and time series dimensions because it includes a large number of source and host countries over a long time span. We start with the standard empirical model used for the analysis of the gravity equation. We include the past value of FDI and the forward values of FTA in pooled OLS, fixed effect and first differenced (FD) models to control for a dynamic relationship and the possibility of a feedback effect. Our results show consistently that previous analysis produced upward biased estimation of the effect of FTA on FDI. The coefficients estimated with methodologies that consider both endogeneity and the possibility of a dynamic relationship are lower than the estimates from pooled OLS and other non-dynamic panel models. This correspond to our results where we show that when unobserved bilateral fixed effects are not controlled for, the estimates of the effects of FTAs on FDI tend to be positive and significant. But, these values decrease significantly or become negative when unobserved bilateral fixed effects are controlled for. Our new results seem robust in various model specifications and different estimation methods. 1 The data about inward FDI are more commonly used in the analysis of the attractiveness of the countries that would like to attract FDI and then reflect the perspective of the host country. 5

6 The main result of the paper is that the presence of FTA seems not to have a large correlation with the flow of FDI. This can be explained by the fact that with FTA, and therefore without trade barriers, it is easier for firms to trade than to implement FDI. The organization of the paper is as follows: Section 2 presents a review of the literature about the relationship among FDI, trade and FTA. Section 3 explains the econometric issues that arise from the study of this relationship. Section 4 describes the data, the empirical methodology we are using, and the results of our analysis. Section 5 concludes. 2. Trade, FDI and FTA Trade of goods, together with flow of capital, is one of the forms of economic activity between countries. For this reason, the relationship between the movement of goods and capital becomes important in order to understand the effect of FTA on FDI. FDI has been historically considered to be flows of capital that substitute the movement of goods realized with trade (Mundell, 1957). FDI were thought to be implemented mainly in order to avoid tariff barriers and to exploit the difference in factor prices (horizontal FDI) 2. Thus, as long as FDI is associated with tariff-jumping intent, the formation of FTA will discourage FDI. Since with FTAs there is a reduction of tariffs and trade costs 3, trade is more convenient than FDI. However, FDI could be also activated in order to take advantage of the different skill endowments and then to minimize production costs (vertical FDI). When firms want to produce goods in the labor-abundant countries, FDI takes a form of single-plant 2 For a review of the literature about horizontal and vertical FDI, as well as more recent classification, see Blonigen et al. (2007) 3 We are not considering explicitly the FTAs that include provisions about investment. About this topic, refer to Leshier and Miroudout (2006) and te Velde and Bezemer (2004). 6

7 vertical activity and this takes place more frequently between countries with different factor endowments. In this case, FDI and trade are complements and trade barriers tend to discourage both FDI and trade. Then, it is expected that a reduction of trade barriers by forming a FTA will tend to encourage vertical FDI. As such, FDI and FTA are also complements. Some authors suggest that FDI diversion is possible when FDI from a country to countries that do not belong to the region decreases when the source country enters the FTA. In analogy with trade diversion, the change can represent a loss in efficiency if the destination of the FDI is moved to a partner in the agreement only on the base of the partnership. However, FDI can introduce more advanced technology in the host country and then upgrade the quality of the goods exported by this country; see Levi Yeyati et al., (2003) for similar arguments. Also the regional 4 extent of FDI is important to analyze the relationship between FTA and FDI. As noted in Caves (1996), trade with countries that do not belong to the same FTA is restricted and then FDI towards these countries is an alternative tool to source a foreign market. However, in the same region vertical FDI will benefit from the cheaper availability of production factors. Trade rules that lower trade costs will decrease the cost of production factors and intermediate goods. This will encourage the creation of international production networks 5 and vertical FDI. 4 FDI can be divided in intraregional and extra-regional according to the fact that the host country belongs or not to the same FTA. The lower tariffs in the region create a wider market for the members within the region: therefore, member countries can be less interested in extra-region markets and there could be a reduction in outward FDI. However, extra-region countries could be interested in the large market created by the agreement and contribute to the increase in inward FDI. 5 In the same host country, the effect that FTA has on FDI could be different in the sectors of the economy. This happens because different sectors use more or less intensively the factors that are 7

8 In summary, the lower tariffs in the region can reduce the motivation for horizontal FDI in favor of trade, if other trade costs, as transportation costs, do not discourage trade. On the other side, the difference in endowment within the region is striking in determining the effect of FTA on intraregional vertical FDI 6. However, the data of FDI between two countries will consist of both horizontal and vertical FDI. Therefore, the question of the effects of FTA on FDI should be examined empirically. At a minimum, to the extent that FDI occurs between developed countries, we may presume that more horizontal FDI might have occurred. At the same time, to the extent that FDI occurs between developed countries and developing countries, we may also presume that more of vertical FDI might have occurred. Thus, we examine the issue using the sub-sample of the data. Nonetheless, these results can be best viewed as robust checks. 3. Econometric Issues and Estimation Strategies Many empirical papers do not consider some econometric issues that can lead to biased or inconsistent estimates. We consider some of them in turn. Unobserved heterogeneity and Endogeneity of FTA Standard cross-sectional or pooling analyses do not solve for the problem of unobserved heterogeneity given by country specific factors historically and geographically time invariant. Not considering heterogeneity but using the ordinary least square model will produce biased estimates and then panel estimation techniques have to involved in the terms of the agreement. Skill differences between home and host countries are therefore a factor that interacts with FTA in promoting FDI or discouraging them. 6 In addition, trade rules can generate an uneven distribution of FDI in the region because the FDI in the same region will be concentrated in the countries with the strongest advantage by location and not evenly in all the partners of the region (Jaumotte, 2004; Venables, 1999). 8

9 be used. It is also possible that the unobserved heterogeneity is correlated with the regressor. In this case even exploiting the panel dimension of the data with a random effect model is not sufficient to obtain unbiased estimates. It seems important to recognize the importance of bilateral unobserved fixed effects in addition to country specific fixed effects, already widely used. In fact, ignoring those yields a crucial source of endogeneity. The point is that bilateral trade, FDI and FTA occur naturally between two countries and there can be historical and political factors that affect trade policy. As such, time varying variables in the equation may not fully explain why FDI, trade and FTA occur among specific countries while they do not occur among other countries. As previously noted, the choice of FTA may not be exogenous and may not be randomly assigned. This is obvious considering that participation in a FTA is voluntary. In addition, the determinants of this decision could be unobservable and they could be the same factors that affect the level of FDI. The endogeneity of FTA has also been pointed out in the analysis of the relationship between FTA and trade. In fact, the same factors that affect trade can have a high explanatory power for the probability of signing a FTA (Baier and Bergrstrand, 2004). However, only recently international trade economists have begun to pay attention to this important problem; see Baier and Bergstrand (2007). More importantly, we believe that the direction of bias induced by unobserved factors in the relationship between FTA and FDI is the opposite of bias that can be caused by the same unobservable factors in the relationship between FTA and trade flow. This outcome of a negative relationship between FDI and FTA can be explained by following a similar argument of Baier and Bergstrand (2007), while the direction of the effect is 9

10 opposite of the relationship between FTA and trade. Suppose that policy-related barriers are present between two countries, but are not observable to econometricians. Actually, trade partners often seem natural and there can be many time invariant factors that cannot by explained fully by the variables included in the model. In our analysis, we attempt to control the effects of many important variables in addition to those in the gravity model. The unobservable factors not explained in this FDI equation tend to induce (rather than reduce) FDI and may be positively correlated with the incentive to pursue an FTA. The factors may well reflect implicit domestic regulations and trade barriers. These factors will increase the chance of forming an FTA in two trading partners and can make firms seek to invest more to overcome the hurdle of trade barriers. Thus, the error term of the FDI equation is positively correlated with the intensity of domestic regulations and is also positively correlated with FTA. On the Use of IV estimation Another way to address the problem of endogeneity of FDI in association with FTA is the use of instrumental variables. This is the method most often suggested by the studies on FDI and FTA that acknowledge the potential endogeneity. However, it is extremely difficult to select good IVs. In fact, the variables usually chosen as instruments are measures of institutional and political conditions, or more in general factors that can compromise trade environment. The problem with these variables is that they can be viewed as determinants of both FDI and FTA (Baier and Bergstrand, 2007) while, according to conventional exclusion requirements, valid instruments should affect only FTA and they should not be correlated with FDI. Given this, the so-called exclusion restrictions are hardly maintained. 10

11 In this regard, we examine the case where variables representing peer effects that lead to FTAs can serve as instruments. Specifically, the number of FTAs formed by each of the countries and the number of FTAs in the same regions are expected to be correlated with country i s FTAs but not with FDI; see Mansfield (1998) and Buthe and Milner (2008). Regarding the exclusion restriction requirement, these variables are expected to be less critical than the other possible instruments that denote institutional and political conditions. Other approaches to address the problem of endogenous effects are in the empirical literature about the effect of FTA on trade. For example, Baier and Bergstrand (2007) suggest that the best method to evaluate the effect of FTA on trade is estimating a gravity equation with differenced panel data rather than cross sectional data with instrumental variables. In particular, the authors state that first differenced panel data provides better results than fixed effects. In fact, first differencing outperforms fixed effect estimation when the time span is large and it is likely that there is correlation over time of the unobserved heterogeneity. However, this approach gives unbiased estimates only if new membership in the agreement is uncorrelated with the time invariant error term. Otherwise, the result will suffer from a bias like in the OLS model (Wooldridge, 2002). However, one may adopt IV estimation using first differenced data. In such cases, past values or changes of exogenous variables and the dependent variables can serve as instruments in either static or dynamic model settings. We will explore this possibility when we perform various estimation methods 7. 7 Another option suggested in the literature about the effect of FTA on trade is to use differencein-difference methodology (for example Egger et al., 2008). This could be a potential estimation 11

12 Dynamic effects In the analysis it is important to consider also that past experience of FDI could affect the current amount of FDI and that the effect of FTA on FDI is not always contemporaneous. This observation introduces a dynamic dimension in the relationship between FTA and FDI. Thomas and Worrall (1994) show with a game-theoretic model that the host countries do not want to expropriate today the profit of multinational enterprises because doing this they will not have investments in the future. A self-enforcing agreement is signed in order to signal the willingness not to expropriate. The game theoretic framework and the dynamic setting are used also for the analysis of tax incentives to induce FDI. For example, Raff and Srinivasan (1998) underline that providing tax incentives attract more FDI than when the firms have to jump the tariff wall because of the higher amount of information about the local environment that the incentives provide. The foreign firms do not have complete information about the local business environment and the provision of tax incentives is a signal from the government, which has more information, about the existing conditions. According to Cheng and Kwan (2000), the observed stock of FDI in a country is generated by a self-reinforcing effect that moves the stock to an equilibrium level, without the interaction to any other determinant of FDI, and the change in the determinants that moves the equilibrium to a new level. Many explanations are offered for this effect: most of them are based on the idea that the competition generated by FDI procedure because it allows to overcome the endogeneity issue and to obtain consistent estimates because it allows controlling for all time invariant unobserved effect. Some cautions are suggested in order to avoid the problem of correlation in the data (Egger et al., 2008; Bertrand et al., 2004). To the best of our knowledge few studies apply this procedure to the analysis of FDI (Jang, 2007, Petroulas, 2007). 12

13 will create a more efficient industry and growth and these will affect future FDI (Te Velde and Bezemer, 2004). The dynamic effect is usually found to be positive even if some authors find different signs in the different phases of the interaction (for example, Thomas and Worrall, 1994; Neary, 2001). If the relationship is dynamic, the usual static panel estimates can be inconsistent, depending on the length of the time period, and a dynamic specification should be considered when considering both the effect of FDI on itself and the effect of FTA on FDI. In particular, this will give more precise results about the nature of the relationship between FTA and FDI because it will allow separating the short run and the long run relationships 8. It is important to recognize the importance of controlling for both the dynamic persistence effect and the unobserved heterogeneity effect since an estimator allowing for only one of these factors can be biased. For example, Angrist and Pischke (2009, p. 246) show that estimates of a positive treatment effect will tend to be large when a dynamic specification is proper but one mistakenly uses fixed effects. One the other hand, if fixed effects are present but one mistakenly estimate an equation with a lagged dependent variable without fixed effects, estimates of a positive treatment effect will tend to be too small. Thus, it seems important to control for both of these factors at the same time. In this paper, we take this point into account in our various estimation methods. Feedback effect A more complicated matter in this analysis is the possibility of a feedback effect between FDI and FTA: current or past FDI affect future FTA or current or future FTA 8 Egger (2001) uses a dynamic model specification but to analyze the relationship between trade and FDI; Egger and Merlo (2007) employs a dynamic model specification in the study of the effect of Bilateral Investment Treaties on FDI. 13

14 can be explained by past FDIs. This implies that the assumption of strict exogeneity will not hold. Thus, even in the absence of contemporaneous endogeneity, there can be an issue of a feedback effect implying that FTA can be associated with past errors in the FDI equation. For example, the membership in FTA could be influenced by FDI previously implemented thus creating a feedback effect. In fact, the nature of the FDI implemented and the success of the economic activity between countries can affect the decisions of the host countries in shaping their trading policies. Obviously, the static version of the treatment effect model does not guarantee consistent estimates. Moreover, it seems important to examine the possibility of a feedback effect from FDI on FTA in a dynamic model specification, although we did not find studies that acknowledge the possibility of a feedback effect between FDI and FTA. In fact, the presence of well established FDI can prevent the formation of FTAs if the main goal of the agreements is to support trading policies. The implementation of FDI in a country is a way to enter the market and make it more protective, through increased exports, with respect to other future potential competitors (Blonigen and Ohno, 1998). However, this does not necessarily imply that the past experience of FDI will lead to a higher probability of signing a FTA. Actually, the activity between the countries can continue in the form of capital flows without involving trade and tariff reduction policies. On the contrary, FDI can be the first step for a broader integration between two or more countries and then their implementation can boost the signing of a FTA. We will examine carefully the possibility of a feedback effect. Control variables and Model Specification 14

15 Perhaps, the gravity equation is the most used empirical model in the empirical literature on trade 9 and it has been frequently adopted from the analysis of FDI. The simplest formulation of the gravity model states that bilateral FDI depends on the GDPs of the participating countries and on the distance between them. The use of the gravity model for the case of FDI is somewhat ad-hoc as noted by Stein and Daude (2004). Nonetheless, given the inter-relationship among FDI, FTA and trade, it would be logical to utilize the variables included in the gravity model. As such, we also examined the case where the GDP variables are substituted for other related variables, but our main estimation results are not changed. Following this framework there have been many modifications in order to analyze a more complete set of factors that can explain FDI. In the specification have been introduced measures of the institutional and political climate 10, like the level of bureaucracy and the level of corporate tax, which characterize the security of trade and indicators of the macroeconomic stability (Levi Yeyati et al., 2003). These attractiveness variables make more likely the movement of capital since they motivate the investment or support its success. Recently, FTA has been added among the determinants of FDI and their presence could be seen as one of the attractiveness variables. In fact, as previously discussed, joining an FTA signals the willingness of the host country not to change the domestic law to obtain more benefits from the investments. 9 Originally the gravity model was only an empirical specification; its theoretical foundations have been derived after its extensive use in literature; see Levi Yeyati et al. (2003) for references to theoretical foundations. 10 As noted by Burche and Milner (2008), most of the studies refer to domestic political variables, while variables about the international political environment are not often included. 15

16 Following the knowledge-capital model 11 (Carr et al., 2001) we include in the gravity equation measures of the gap in skilled and unskilled labor. In fact, according to the model, the relative abundance of skilled and unskilled labor will guide the multinational firm in choosing the location of its different activities. In particular, the assumptions of the model state that knowledge based and knowledge generating activities can be geographically separated from production and that the knowledge-intensive activities are more efficiently located where skilled labor is less expensive. This will give a rationale for the vertical segmentation of production. This assumption also states that knowledge intensive activities can be used by several production facilities at the same time. This motivates horizontal investments and the production of the same goods in different facilities. One potential issue in applying the gravity model is the possible endogeneity of GDP. Baier and Bergstrand (2007) discuss reasons to ignore the potential endogeneity of incomes in the equation for trade, but this does not preclude potential endogeneity in the equation for FDI. We also examine the case where trade variables, export or import, are included as regressors. These trade variables can be endogenous in the equation for FDI. Nonetheless, this potential issue can be dealt with when IV estimation is used in a dynamic model based on first differenced data. Another issue in the estimation of the gravity model is the use of flow of FDI instead of stocks. While in the original formulation of the gravity equation the dependent variable is flow, it is possible to see examples of both in the literature. In particular some authors state that in panel data models is better to use stocks. This is motivated by the 11 The knowledge-capital model is the other main reference for the empirical analysis of international capital flows. In contrast with the gravity equation, this model has a strong theoretical foundation that suggests the variables to include in the empirical analysis. 16

17 fact that in most of the specifications the first difference of the variables are used and that the theoretical models suggest conclusion of comparative statics in terms of stock. The difference in flows, which will be used in panel data models, would be only an approximate measure of the change in stocks and then it will be more difficult to test theoretical hypothesis (Egger and Merlo 2007). In addition, stocks are less volatile than flows (Enders et al., 2006). Since FDI can be reversible, stock are considered a better measure of the inflow of investments than the increment in FDI stocks, which is the flow (Stein and Daude, 2003). Some authors state that the persistence effect is larger when stock is used. In our analysis we use stock of FDI as main dependent variable but we run some specifications with flows of FDI in order to have additional robustness checks for our results. 4. Data and Empirical Estimation Results In this paper we adopt a gravity model with some variables from the knowledgecapital model to analyze a panel of the bilateral flows of FDI of 20 OECD countries for the years Since we are interested in the implication of FDI as strategy of the source country we use the information about outward FDI. We use the information for the stock of FDI from OECD International Direct Investment (2008) 12. The main independent variable, a dummy for the presence of FTA, is collected from WTO (2008). A positive sign of the coefficient of this variable tells us that there is a complementary 12 Most of the empirical analyses are based on macroeconomic series of FDI while few use plantlevel micro data, subject to smaller measurement errors (Petroulas, 2007). In addition, considering specifically the relationship between FDI and the trade generated by FTA micro level data are more useful in understanding the nature of the relationship. In fact, there can be events, like financial crises, that affect both trade and FDI since both are linked through the balance of payments: the macroeconomic data will show complementarity even if the positive correlation is given only by the common determinant (Paez, 2008). 17

18 relationship between FTA and FDI while a negative sign suggests that FTA and FDI are alternative policies of internationalization. Other independent variables are the GDP of the home and host country, as indicator of the size of the market, and the difference in per capita GDP. These variables are computed from the PRS Group. According to the idea that horizontal FDI increases with the size of the market, a positive sign of the coefficient of these variable will signal the presence of horizontal FDI. We are not able to distinguish between horizontal and vertical FDI from our dataset but this information about the nature of FDI will be valuable in the discussion of the nature of the relationship (complementary/substitution) between FTA and FDI. We also include measures for the gap in skilled and unskilled capital. The information about capital and labor are from the World Development Indicators and the measures of the gaps are computed following Helpman (1987). Given that FDI are motivated by the exploitation of comparative advantages, we expect that the coefficients for the gaps have a positive sign. Our empirical specification contains the measure of the difference in openness between the two countries. This variable is derived scaling the size of import and export with respect to GDP. We include also indicators of the institutional and political climate and specifically the difference in the level of bureaucracy and the composite country risk index compiled by the PRS Group. The countries are more likely to be connected by FDI if the level of bureaucracy is similar and if the country risk index is low because the multinational firms will look for a country with a relative low country risk index that can provide a stable environment for business. 18

19 A last set of variables includes the distance between the pair of countries as well as the presence of a common language, of a common border and the past experience as a colony: these variables are collected from CPEII and we expect a positive sign for the coefficient of these variables since they show similarity between the countries involved and then that it is easier to communicate and operate. A variable that measure physical distance is introduced, following the standard specification of the gravity equation. In the literature about trade, a negative correlation between distance and FDI is expected, according to the fact that the distance is an obstacle for trade. However in the case of FDI a higher distance could be the motivation to implement FDI instead of trade and then we can expect a positive sign. At the same time, higher distance could prevent FDI because it is more difficult to check how the operations in the host country are performing. Finally, we are aware of the criticism about the relevance of physical distance: some authors suggest that other measures of distance are better proxies 13 of the difficulties in international exchange of goods (Brainard, 1997). Using the information about the stock of FDI we derive the flow of FDI and we get a sample of 9,745 observations. In assessing the importance of FTA on FDI, the pairs of countries that entered an FTA during the observation period are an interesting subset to study. The pairs that have entered an FTA form a sample of 2,358 observations. Considering the subset of FDI flows among OECD countries, there are 5,449 observations while the observations for the flow of FDI between OECD and non- OECD 13 In addition to this, physical distance does not vary over time and it cannot be distinguished from other time-invariant variables in a panel analysis. 19

20 countries are 4,296. The complete descriptive statistics about the dependent and independent variables are reported in the Data Appendix. OLS and Panel FE Estimation Most conclusions about the sign of the relationship between FTA and FDI are based on estimations of the gravity equation that used pooled data. The estimating equation is FDI ijt = + d fta ijt + X ijt + e ijt (1) where subscripts i and j indicate source and host country respectively and t indicate year; where fta ijt = 1 for the pair of two countries that have established an FTA at year t, and 0 otherwise; and where X ijt contains control variables including the variables of the gravity model and others. We first examine preliminary results using OLS estimation. Table 1 provides results obtained using the same methodology for our sample. The dependent variable is denoted as lnfdi_s for logged FDI stocks or lnfdi for logged FDI flows. The estimations in column 1 show that the coefficient for FTA is significant and positive. This result is maintained also when we modify the set of explicative variables. In particular, column 2 reports the estimates for the specification that use alternative measures for GDP like the similarity in GDP. The results are robust to the inclusion or exclusion of time dummies that control for time fixed effects. We report the results with time fixed effects, which could account for changes in FDI stocks caused by unpredicted events. We run the same specification using flows of FDI instead of stocks as dependent variable. In Table 1, column 3 we report the estimates for this case. The results are very similar to the previous both for significance, signs and magnitude of the coefficients. This preliminary result would 20

21 support a positive correlation between FTA and FDI suggesting a complementary relationship between them. There are, however, some limitations to this pooling analysis. First of all, we wish to take in account the bilateral unobserved heterogeneity in the intercept term and its correlation with the regressors. This is an important source of endogeneity relating to FTA. To address this problem we perform a panel analysis according to the following estimation equation FDI ijt = ij + d fta ijt + X ijt + e ijt (2) where ij denotes the unobservable bilateral heterogeneity effect, associated with the pairs of countries in our sample. Table 1 column 4 shows the results of the estimation. In this case we have that the coefficient for FTA is significant when we use the stock of FDI as dependent variable. We obtain quite different results. The negative sign of the coefficient of FTA provides evidence of substitution between FTA and FDI, although the coefficient is not significant at the conventional 5% level. Adding time dummies and changing the set of explicative variables does not modify this result. Clearly, a positive correlation between FTA and FDI is hardly supported. This result is more reliable than the estimates obtained with pooled OLS because the endogeneity bias caused by ignoring unobservable bilateral heterogeneity is mitigated by the estimation procedure. We will see below that when we control for more factors in our estimations, the coefficient of FTA becomes highly significant and negative. As explained in section 3, there is a positive correlation between the error term in the FDI equation and the intensity of domestic regulations. The error term is also positively correlated with FTA. In such cases, following a standard econometric argument we can expect that the effect of FTA will tend to be overestimated. It is clear that panel data 21

22 estimation methods correcting for bilateral fixed effects can mitigate endogeneity bias induced by ignoring unobservable bilateral heterogeneity and we show explicitly that this is the case 14. Panel IV Estimation We next address potential endogeneity by using instrumental variables. However, we employ a set of instruments for FTA different from the variables most often used. Typical instrumental variables are measures of political and institutional characteristics of the country. These variables are highly correlated also with the dependent variable and therefore they do not satisfy the exclusion restrictions and they are not good instruments. Our regression includes as instrumental variables the number of the FTA joined by the country in a given year (n_fta_i), the number of FTA joined by the host country in a given year (n_fta_j) and the number of FTA joined by the source country in a given year and in the same geographical region (n_fta_region_i). These variables are found to be good predictors of FTA but to have a low correlation with FDI. In fact, the analysis of correlation reveals that the correlation between the variable for FTA and the instruments is 0.41, 0.60 and 0.17 for n_fta_i, n_fta_j, and n_fta_region_i respectively, while the correlation of the instruments with the stock of FDI is 0.02, 0.09, The panel 2SLS estimates from the regressions with these instrumental variables are in Table 2. In column 1 there are the results for the specification with stock of FDI as dependent variable. As in the previous estimates, the sign is negative but not significant. 14 The estimates are somewhat different when the dependent variable is the flow of FDI instead of stocks of FDI (Table 1, column 6): the coefficient of FTA is not significant and its sign is not stable if the set of explanatory variables is changed. However, these results do not yet adjust for other important econometric issues. 22

23 The coefficient for FTA is not significant when the flow of FDI is used (column 2) for this reason its negative sign gives only weak evidence on the nature of the relationship. Given that FTA is an endogenous dummy variable, we also consider a two step IV procedure where the predicted probability from a reduced form probit model is used as an additional instrument in the usual 2SLS regression, in addition to the other set of instruments. 15 The results in columns 3 and 4 from using the 2 step IV estimator are not much different from those from IV estimation. Another suggestion to deal with the potential endogeneity given by unobserved heterogeneity is to use first differenced data. Specifically, we take the first difference of the model in equation (2). This procedure will remove the individual effect, but we need to take into account the endogeneity of FTA. The two sources of endogeneity are addressed running a specification in first difference and including the instrumental variables used in the previous estimates. Table 2, column 5, provides the estimates of this model. The coefficient of our main explanatory variable FTA is significant and it has a negative sign: this again suggests a relationship of substitution between FDI and FTA. When the bias given by the endogeneity is mitigated, the absolute value of the coefficient of FTA is larger than in the estimation of the bilateral panel fixed effect. Until now the fact that relationship between FTA and FDI could be dynamic has not been considered. To examine if the dynamic specification is necessary, we adopt a formal test using the results in Table 2. For this, we obtain the residual from each of the IV estimations, and re-estimate the model by adding the lagged residuals. It appears clear 15 In this sense, this 2SLS estimator is also referred to as a two-step estimator; see Wooldridge (2002). The predicted probability from the probit model is given as the cdf of the standard normal distribution. The set of instruments would be included in the probit model to satisfy the exclusion restriction. These estimates are free of the problem of a forbidden regression when the cdf is used as a regressor. 23

24 that a static specification can be rejected. Feedback Effect in a Dynamic Model Specification To take in account the potential sluggish adjustment of FDI we include in our panel specification the lag for FDI. FDI ijt = ij + ρfdi ijt-1 + d FTA ijt + X ijt + e ijt (3) The estimates in Table 3, column 1 show that the coefficient for the lagged dependent variable is highly significant and therefore suggests that a dynamic specification should be considered. This evidence is supported also by a model with different explanatory variables (not shown) and by a specification where flows of FDI are used as dependent variable instead of stocks (column 2). As discussed in section 3, not only past FDI can affect current FDI but is also possible that there is a feedback effect implying that past participation in FTA influences the current level of FDI. In order to test the possibility of a feedback effect, we can examine the coefficients of the forward (future) variables for FTA when they are added as regressors for this effect. We have also examined the cases where the lagged values are additionally included, but this specification does not add more useful information. The coefficients of the forward variables of FTA are significant, as reported in Table 3. This suggests that strict exogeneity fails because the existence of future FTA is correlated with the current stock of FDI, implying that Cov(FTA ijs, e ijt ) 0 for s > 0. Running the same specification for the flow of FDI we have a very similar result (columns 2 and 4). This result holds when we change the set of explanatory variables (columns 5 and 6). To produce a consistent estimate of the dynamic relationship between FDI and FTA we first consider a first difference- instrumental variable (FD-IV) model following 24

25 the Anderson-Hsiao (1982) procedure, which uses the lag of the level dependent variable as an instrument for the first difference of the lagged dependent variable. Table 4, column 1, shows the result for this estimation. The main results are not changed when two lags are added. Column 2 reports the estimates for the same model with the inclusion of the peer effect instrumental variables. The coefficient of FTA is significant and negative, suggesting again a substitution relationship between FTA and FDI. The result does not change when one further lag of the dependent variable is added in the model (not shown). Table 4, columns 3 and 4 provide the estimates for the same model, except when the flow of FDI is used as the dependent variable. However in this specification the coefficient of FTA is negative but not significant. The main results are not changed when we consider an alternative model specification in columns 5 and 6. One can argue that the GMM (Generalized Method of Moments) estimator of Arellano-Bond s (1991) will be more efficient than the Anderson-Hsiao estimator. This happens because the Anderson-Hsiao procedure does not exploit all available lags and does not consider the structure of the errors. Arellano-Bond GMM estimation method, in addition to use first differenced data, exploits additional instruments obtained from the orthogonality conditions between the errors and the lagged values of the dependent variable. There are other estimation methods that extend their work; for example, the system GMM estimator of Arellano and Bover (1995) can be also considered. On the contrary, it is also argued recently that using too many orthogonality conditions can lead to erroneous biased results; for instance, see Roodman (2009) for the perils of using too many instruments in dynamic panel models. Thus, we also examine the sensitivity of using different sets of orthogonality conditions. Moreover, we examine cases where we 25

26 additionally include peer instrumental variables to the model, as done in Tables 3 and 4, to address the contemporaneous simultaneity as well as the feedback effect of FTA. The results from the estimation of the model with GMM are reported in Table 5. In all cases, we report the bias corrected standard errors of Windmeijer (2005). Column 1 shows the estimates for the specification with a maximum of 7 lags for instruments for the lagged first difference of the dependent variable. The coefficient for FTA is negative but not significant. Note that this result does not account for the feedback effect of FTA. Column 2 considers the feedback effect of FTA. It provides the estimates when 5 lags of the lagged variables of FTA are used as instruments in addition to the peer instrumental variables. In this case, FTA is treated as weakly exogenous. Note that the coefficient of FTA is now significant. Column 3 provides the same results as in Column 2 except that bilateral GDP (bilgdp) is additionally treated as weakly exogenous and 5 lags of the lagged variables of FTA are used as additional instruments. The coefficient of FTA is again significant. Running the same models with flow of FDI as dependent variable we have weaker evidence of the negative association between FDI and FTA (columns 4, 5, and 6). Robustness and the Results using Sub-samples We also consider other estimation strategies. We examine the sensitivity of our results by using less or more lags for instruments for all weakly exogenous variables which include the lagged dependent variable, FTA and the bilateral GDP. We also consider different model specifications with different sets of regressors, and different estimation methods including the system GMM estimators of Arellano and Bover (1995). For this method, the level equation is jointly estimated along with the differenced equation. All of these results are omitted to save space, but they show that our main 26

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