Effects of Hub-and-Spoke Free Trade Agreements on Trade: Panel Data Analysis

Alba, Joseph D.; Hur, Jung; Park, Donghyun Working Paper Effects of Hub-and-Spoke Free Trade Agreements on Trade: Panel Data Analysis ADB Economics Working Paper Series, No. 127 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Alba, Joseph D.; Hur, Jung; Park, Donghyun (2008) : Effects of Hub-and-Spoke Free Trade Agreements on Trade: Panel Data Analysis, ADB Economics Working Paper Series, No. 127, Asian Development Bank (ADB), Manila, https://hdl.handle.net/11540/1781 This Version is available at: https://hdl.handle.net/10419/109332 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Alba, Jung Hur, and Donghyun Park No. 127 | October 2008 ADB Economics Working Paper Series No. 127 Effects of Hub-and-Spoke Free Trade Agreements on Trade: Panel Data Analysis Joseph D. Alba, Jung Hur, and Donghyun Park October 2008 Joseph D. Alba is Associate Professor, Nanyang Technological University; Jung Hur is Assistant Professor, National University of Singapore; and Donghyun Park is Senior Economist, Economics and Research Department, Asian Development Bank. Asian Development Bank 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org/economics ©2008 by Asian Development Bank October 2008 ISSN 1655-5252 Publication Stock No.: The views expressed in this paper are those of the author(s) and do not necessarily reflect the views or policies of the Asian Development Bank. The ADB Economics Working Paper Series is a forum for stimulating discussion and eliciting feedback on ongoing and recently completed research and policy studies undertaken by the Asian Development Bank (ADB) staff, consultants, or resource persons. The series deals with key economic and development problems, particularly those facing the Asia and Pacific region; as well as conceptual, analytical, or methodological issues relating to project/program economic analysis, and statistical data and measurement. The series aims to enhance the knowledge on Asia’s development and policy challenges; strengthen analytical rigor and quality of ADB’s country partnership strategies, and its subregional and country operations; and improve the quality and availability of statistical data and development indicators for monitoring development effectiveness. The ADB Economics Working Paper Series is a quick-disseminating, informal publication whose titles could subsequently be revised for publication as articles in professional journals or chapters in books. The series is maintained by the Economics and Research Department. Contents Abstract v I. Introduction. Introduction 1 II. Definition and Evidence of FTA Hubs and Spokes 3 A. Definition of FTA Hub and SpokeA. Definition of FTA Hub and Spoke 3 B. Evidence of FTA Hubs and Spokes 4 III. Data and Empirical Framework 6 IV. Empirical Results 11 V. Concluding Observations 13 Appendix A: List of Regional Trade Agreements Notified to WTO, 1958–2005 (132 Agreements) 15 Appendix B: List of Economies (N=99) 17 Appendix C: Additional Regressors in Table 4 18 References 19 Abstract Overlapping free trade agreements (FTAs) have given rise to hub-and-spoke FTAs that may promote trade by giving an export advantage to the FTA hub country. We empirically investigate the effect of hub-and-spoke FTAs on trade using panel data consisting of 99 countries and covering the period 1960–1999. Our empirical analysis of the panel data yields three notable findings. First, FTAs have a significant and positive impact on trade. Second, hub-and-spoke FTAs increase trade above and beyond FTAs, and thus reinforce the trade-boosting effects of FTAs. Third, our results imply an annual growth rate of 4.9% in bilateral trade and hence a doubling of trade after 14½ years between FTA partners. Our results indicate that the hub-and-spoke nature of FTAs has a positive effect on trade, in addition to the direct, trade-liberalizing effect of FTAs. Therefore, in a world of overlapping FTAs, a more accurate empirical analysis of the relationship between FTAs and trade calls for taking into account the hub-and-spoke characteristic of FTAs. Effects of Hub-and-Spoke Free Trade Agreements on Trade: Panel Data Analysis | 7 We supplement Rose’s data set with our data for FTAs and FTA hubs. As noted earlier, our primary source of data for these variables is the Regional Trade Agreements Notified to the GATT/WTO and in Force by Date of Entry into Force, available at the WTO website and reproduced in Appendix A. We construct an FTA variable and two FTA hub variables from all RTAs notified to the WTO between 1960 and 1999. Merging Rose’s data set with our data set for FTAs and FTA hubs leaves us with a balanced panel data set consisting of 99 countries (see Appendix B) and running from 1960 to 1999.7 As Baier and Bergstrand (2007) point out, FTAs are typically phased in over 5–10 years and thus will not become fully effective before this time period. Therefore, following Baier and Bergstrand, we exclude from our sample the post-2000 period that saw a surge of new FTAs.8 We now define the FTA variable and the two FTA hub variables. FTAij=1 for two countries i and j if they have an FTA with each other and FTAij=0 otherwise. If FTAij=1, HUBij=1 for country i if country i is an FTA hub with respect to country j and HUBij=0 otherwise. The volume of country i’s exports to country j will depend on whether the two countries have an FTA with each other, and, if so, whether country i is an FTA hub with respect to country j. In terms of our notation, FTA HUB E E E ij ij ij ij ij 11 0 0 2 1 0 ⇒→ →      →        Eij, or exports from country i to country j, can be expressed as E FTA HUB E HUB E FTA E ij ij ij ij ij ij ij ij = + −   + −    2 1 0 1 1( ) (1) where Eij0, Eij1 and Eij2 are functions of Xi, or a vector of control variables for country i. E X ij i i i i 2 2 2 = + + α β ε ; E X ij i i i i 1 1 1 = + + α β ε ; E X ij i i i i 0 0 0 = + + α β ε (2) The volume of bilateral trade between country i and country j is the sum of Eij and Eji, which, making use of equations (1) and (2) above, can be expressed as 7 Rose’s data set has bilateral trade for some countries with only one observation. These observations are dropped in FD regressions but not for OLS or FE regressions. We create a balanced panel so we can have comparable results for different regressions. A balanced panel with a long time series also allows us to better address the problem of serial correlation. 8 Including the post-2000 FTAs, which are likely to be less than fully effective due to the gradual nature of FTA-based trade liberalization, will impart an upward bias to the estimated effect of FTA on trade, especially in light of the rapid growth of FTAs in the post-2000 period. 8 | ADB Economics Working Paper Series No. 127 E E X X FTA FTAHUB FTAHUB FTA ij ji i i j j ij ij ji ij + = + + + + + + α β β µ µ µ ε 0 0 1 2 (11 1 0 0 2 1 2 1 0 i j i j ij i i ji j j i FTAHUB FTAHUB+ − − + − + − + + ε ε ε ε ε ε ε ε ε ) ( ) ( ) 00 j (3) where FTAHUBij = FTAij´HUBij and FTAHUBji = FTAij´HUBji (note FTAij = FTAji); a0 = a i0+a j0; 0 = a i1+a j1−a i0-a j0; 1 = ai2−a i1 and 2 = a j2 − a j1. We are interested in estimating the value of 0, 1 and 2. A positive estimated value of 0 implies that an FTA has a positive effect on bilateral trade between country i and country j. If 0>0, this implies that (ai1+aj1) > (ai0+aj0). That is, the sum of country i’s exports to country j and country j’s exports to country i are greater if they have an FTA with each other than if they do not. By definition, the sum of exports is bilateral trade, so this is equivalent to saying that bilateral trade is larger with an FTA than without an FTA. A positive estimated value of 1 or 2 or both implies that the overlapping of FTAs has a further positive effect on the trade of FTA hub countries. However, we must distinguish between two possible scenarios. First, only one of the two estimates is positive, i.e., (1>0, 2=0) or (1=0, 2>0). This implies that ai2>ai1 or aj2>aj1 and only one of the two FTA partners is a hub country. Second, both estimates are positive so that 1>0 and 2>0. This implies that ai2>ai1 and aj2>aj1, and both FTA partners are hub countries. As pointed out earlier, it is theoretically possible for two countries to be each other’s hub and spoke at the same time if both belong to more than two FTAs. In either case, regardless of whether one or both of the FTA partners is a hub, bilateral trade is larger than if neither is a hub. That is, trade between two FTA partners is larger if they are in a hub-and-spoke relationship than if they are not. Our empirically testable specification of the gravity model is lnT X FTA FTAHUB FTAHUB ij t ij t ij t ij t ji t ij = + + + + + α β µ µ µ ε 0 0 1 2 (4) Tijt is the total trade volume—the sum of Eij and Eji—between country i and country j at time t. The vector Xij refers to the 43 explanatory variables for country i and j in Rose’s data set. Those variables are listed in Appendix C and include five time-variant variables. One of the variables is a dummy binary variable that takes on the value of 1 if both countries are members of the same RTA, e.g., EU, and 0 otherwise. This is important because we want to separate out the FTA hub effect from the RTA membership effect.9 We follow Baier and Bergstrand (2007) in including up to six lags of FTAijt in the estimation of equation (4).10 The lags capture an institutional feature of FTAs, i.e., 9 It was earlier pointed out in Section IIB that members of RTAs, especially the EU, are prominent FTA hubs. 10 We also obtained the regression results that include up to 10 lags of FTA. However, the lags of FTA after 6 years are Effects of Hub-and-Spoke Free Trade Agreements on Trade: Panel Data Analysis | 9 they are typically phased in over a period of 5–10 years; as well as the nature of FTAs’ economic effects, i.e., terms of trade changes associated with FTAs tend to have lagged effects on trade volumes. We also generalize the specification of equation (4) by including time dummy variables. The first-differenced form of equation (4) can be expressed as: ∆ ∆ ∆ ∆ ∆ ∆lnT X FTA FTAHUB FTAHUB t ij t ij t ij t ij t ji t ij = + + + + β µ µ µ ε 0 1 2 (5) Notice that the term Xt ij in (5) includes only the five time-variant explanatory variables from Rose’s data set while the term Xij in (4) includes all 43 explanatory variables from the same data set. Incidentally, the fixed effects model accounts for time-invariant variables so that its estimation also requires only the five time-variant variables. As noted earlier, the biggest econometric criticism of the empirical literature on the relationship between FTAs and trade is that FTA variables are assumed to be exogenous rather than endogenous. To formally test for the exogeneity of FTAs, we perform the heteroskedasticity-robust C-test for exogeneity developed by Baum, Schaffer, and Stillman (2003). Table 3 below reports the results, which show that for pooled OLS regressions, the C-test rejects the null hypotheses that FTAijt and FTAHUBijt are exogenous at the 1% significance level.11 In contrast, for the FE and FD regressions, the C-tests cannot reject the null hypotheses that all three FTA-related variables (FTAijt, FTAHUBijt and FTAHUBjit) are exogenous even at the 10% significance level. Our C-test results thus confirm Baier and Bergstrand’s contention that the problem of endogeneity can be addressed by using panel data and FE/FD regressions. Both FE and FD regressions assume that the errors in the regressions are serially uncorrelated. If the errors are serially correlated, the FE and FD estimators may be inefficient and inconsistent. There is a risk of serial correlation since bilateral trade levels in earlier years may affect current bilateral trade levels. We use the test for serial correlation outlined by Wooldridge (2002). Table 3 reports the results of the test, which involves running heteroskedasticity-robust OLS, FE and FD regressions on the residuals, and the lagged residuals. The results indicate serial correlation in the pooled OLS and FE regressions but not in the FD regressions.12 We correct for serial correlation in pooled OLS and FE regressions by using the Prais-Winsten (1954) transformation. We also test for strict exogeneity since its violation may also result in inefficient and inconsistent FE and FD estimators. For this purpose, we use a test put forth by not significant. The results for regressions that include up to 10 lags are available upon request from the authors. 11 In conducting the C-test of exogeneity, we have to assume excluded instruments correlated to FTA or FTAHUB but orthogonal to the error terms. However, as Baier and Bergstrand mention, it is difficult to find such instruments. For the C tests of exogeneity, we use as instruments the lagged log values of per capita trade and lagged log values of the sum of per capita real GDP. 12 The notes in Table 3 provide a more in-depth discussion of the test and results. 10 | ADB Economics Working Paper Series No. 127 Wooldridge (2002). For FE model, the test involves running a FE regression on the dependent variable, the regressors and the lead (t+1) values of the subset of regressors. For the FD model, the test involves running a regression on the FD dependent variable, FD regressors and a subset of regressors in levels. Based on the results, which are reported in Table 3, we can reject the null of strict exogeneity for the FE model but cannot do so for the FD model.13 Therefore, the FD model, which does not suffer from serial correlation and does not violate strict exogeneity, is the most robust among the three models. Nevertheless, the estimates are broadly similar for the pooled OLS, FE, and FD regressions. This implies that endogeneity in pooled OLS regressions and violation of strict exogeneity in FE regressions does not seriously bias the results. Table 3: Specification Tests Variable olS Estimates FE Estimates FD Estimates Statistic P-Value Statistic P-Value Statistic P-Value C-Test for Endogeneity FTAijt8.521 0.004 1.135 0.287 1.836 0.175 FTAHUBijt0.671 0.412 1.352 0.245 0.052 0.820 FTAHUBjit16.231 0.0001 0.928 0.335 2.224 0.136 Test for Serial Correlation ρ0.898 (0.002) 0.000 0.743 (0.003) 0.000 −0.243 (0.004) 0.000 Test for Strict Exogeneity γ– – 0.271 (0.032) 0.000 −0.003 (0.008) 0.687 OLS = ordinary least squares, FE = fixed effect, FD = fixed difference, FTA = free trade area. Note: As developed by Baum, Schaffer, and Stillman (2003), the C-statistic is a test statistic for testing endogeneity of FTAjit, FTAHUBijt, and FTAHUBjit. The test has a null that a regressor is exogenous and an alternative that it is endogenous. The C-statistic has a chi-square distribution and is robust to heteroskedasticity. Woodridge (2002) outlines a test for serial correlation for OLS, FE estimates, and FD estimates. The test involves running heteroskedasticity-robust OLS, FE, and FD regressions on the residuals and the lagged residuals. Where ρ is the coefficient of the lagged residuals, the null hypothesis is ρ = 0 for no serial correlation under OLS and alternative hypothesis of serial correlation is ρ ≠ 0 in OLS regression. For FE regression, the null hypothesis of no serial correlation is ρ = -1/(T-1) and the alternative hypothesis of serial correlation is ρ > 0. For FD regressions, the null hypothesis of no serial real correlation is ρ = −0.5 and the alternative hypothesis of serial correlation is ρ > 0. Wooldridge also specifies a regression-based tests for strict exogeneity in FE and FD models. For the FE model, the test involves running a fixed effect regression on the dependent variable, the regressors, and the lead of the subset of regressors (t+1). We consider only the FTAijt+1 for the test. With γ as the coefficient of FTAij t+1, the null of strict exogeneity is γ = 0 against the alternative of violation of strict exogeneity of γ ≠ 0. For the FD model, the test involves running a regression on the FD dependent variable, FD regressors, and a subset of regressors in levels. We consider only FTAijt. With γ as the coefficient of FTAijt, the null of strict exogeneity is γ = 0 against the alternative of violation of strict exogeneity ofγ ≠ 0. Values in parenthesis are robust standard errors. 13 Please refer to the notes for Table 3 for a more in-depth discussion of the test and results. Effects of Hub-and-Spoke Free Trade Agreements on Trade: Panel Data Analysis | 11 IV. Empirical Results Table 4 reports our results for the pooled OLS, FE, and FD regressions. The pooled OLS and FE regressions have been corrected for serial correlation by the Prais-Winsten transformation. We do not report the results for the 43 control variables in Rose (2004) but they are available from authors upon request. In general, our results for those variables are quite close to Rose’s results, and most of the coefficient estimates have the expected signs and are significant. For example, the effect of sharing a common border on bilateral trade was positive and highly significant as was the effect of both trading partners being Asian countries. Membership in RTAs also had a positive, highly significant effect on trade.14 On the other hand, the effect of distance between the trading partners was negative and highly significant, as was the effect of both partners being least developed countries. The fact that our results for the 43 control variables are largely consistent with economic intuition gives us some confidence that our empirical analysis will be able to isolate and identify the effects of FTAs and their hub-and-spoke nature on trade flows. For our purposes, the most relevant coefficient estimates are those of the FTA variable and the two FTA hub variables, so we will focus upon those variables in our discussion of the results of the three regressions in Table 4. For the pooled OLS regressions, we include all of Rose’s 43 explanatory variables but we only include the five time-variant variables for the FE and FD regressions. The pooled OLS results show that FTAij has a significant and positive impact on bilateral trade after 2 years. That is, countries that have an FTA with each other trade more with each other than countries having no FTA with each other. The coefficient estimate of FTAHUBji is 0.076 and has a p-value of 6.0% while the coefficient estimate of FTAHUBij is insignificant. The pooled OLS results thus lend some support to a positive effect of hub-and-spoke FTAs on trade but, as noted earlier, those results suffer from the endogeneity of the three FTA-related variables. The results of the FE regressions, which do not suffer from endogenous FTA-related variables, indicate that FTAij has a significant and positive impact on bilateral trade. The average treatment effect of FTA, which refers to the notion that bilateral trade will differ based on whether or not the two countries share an FTA, is 0.083 after 5 years. The coefficient estimate of FTAHUBji is 0.108 and has a p-value of 3.2% while the coefficient estimate of FTAHUBij is insignificant. The total average treatment effect, or the sum of the FTA effect and the hub-and-spoke FTA effect, is 0.191. The FE results thus lend strong support to a positive effect of hub-and-spoke FTAs on trade but, as noted earlier, the FE model is a first order autoregressive process that may violate the assumption of strict exogeneity. Tests for strict exogeneity confirm that the assumption is violated, which implies that the FE estimators may be inefficient and inconsistent. 14 As noted earlier, RTA members, especially EU members, figure prominently among FTA hubs. As such, we want to separate out RTA membership effects from FTA hub effects. 12 | ADB Economics Working Paper Series No. 127 Table 4: Estimation Results for Heteroskedasticity-Robust OLS, Fixed Effect, and FirstDifferenced Models (dependent variable: ln(ltrade)ij t) olS Estimation using the Prais-Winsten Transformation FE Estimation using PraisWinsten Transformation FD Estimation Regressor Coefficient Robust standard error P-value Coefficient Robust standard error P-value Coefficient Robust standard error P-value FTAij t FTAij t-1 FTAij t-2 FTAij t-3 FTAij t-4 FTAij t-5 FTAij t-6 FTAHUBij t FTAHUBji t Sum ln(rgdp)jit −0.021 0.046 0.644 -0.050 0.065 0.441 0.009 0.042 0.821 0.012 0.019 0.517 0.013 0.032 0.680 0.000 0.018 0.986 0.048*** 0.017 0.004 0.047 0.033 0.158 0.040** 0.016 0.011 −0.088*** 0.023 0.000 −0.020 0.034 0.557 −0.002 0.017 0.895 0.051*** 0.015 0.001 0.029 0.035 0.395 0.041*** 0.014 0.004 0.074*** 0.024 0.002 0.083** 0.040 0.039 0.070*** 0.024 0.003 0.066*** 0.019 0.001 0.066 0.039 0.096 0.056*** 0.018 0.002 −0.002 0.045 0.959 0.033 0.067 0.621 −0.004 0.041 0.921 0.076* 0.041 0.060 0.108** 0.050 0.032 0.075* 0.040 0.061 0.497*** 0.012 0.000 0.243** 0.011 0.000 0.171*** 0.014 0.000 Additional regressors 43 4 4 Time dummies Yes Yes Yes No. of obs. 56576 56576 54912 F-statistic – 85.93 0.000 47.16 0.000 ATE 0.168 0.215 ***, **, and * indicate 1%, 5%, and 10% levels of significance, respectively. OLS = ordinary least squares, FE = fixed effect, FD = fixed difference, FTA = free trade area, ATE = average treatment effect. Note: ln(trade)ijt is the ln of country i and j’s total trade with each other; FTAijt is dummy variable that is 1 if country i and country j have a free trade agreement (FTA) and 0 otherwise; FTAHUBijt (FTAHUBjit) a dummy variable that is 1 if country i (j) becomes a hub when it enters into an FTA with country j (i) and 0 otherwise; sum ln(rgdp) is the sum of the log real GDP of country i and country j. ρ is the coefficient of the lagged residuals to test for serial correction. It is calculated by running regressions on the residuals with the lag of the residuals. The residuals for the fixed effects and first differenced models are derived from regressions of time-demeaned and FD variables, respectively. The additional regressors are described in Rose (2004) and Appendix C. The results of the additional regressors are mostly significant with the correct signs. These results are not reported because of space limitation but are available from the authors upon request. The Prais-Winsten (1954) transformation adjusts for serial correlation. ATE is the average treatment effect with at least 5% level of significance. We earlier saw that the FD regressions do not suffer from endogeneity, serial correlation, and violations of strict exogeneity. As such, the FD estimators are efficient and consistent, and the FD results are therefore likely to be the more robust and reliable than the pooled OLS or FE results. The FD results indicate that the average treatment effect of FTAs at the 5% significance level, or the sum of FTAij coefficients significant at the 5% significance level is 0.207. This represents an annual growth rate of bilateral trade of about 3.5%.15 The coefficient estimate of FTAHUBji is 0.075 and has a p-value of 6.1% while the coefficient estimate of FTAHUBij is insignificant. This suggests that a hub-and15 The ATE of e0.207 implies that the trade will be increasing by 123% over the 6 years. So the annual growth rate of trade is r=3.5%, which is calculated from (1+r)6=1.23. Effects of Hub-and-Spoke Free Trade Agreements on Trade: Panel Data Analysis | 13 spoke FTA has a moderately significant positive impact on trade. If we incorporate the hub-and-spoke nature of FTAs, the average treatment effect of FTAs rises further to 0.282. This represents an annual growth rate of trade of about 4.9% and a doubling of trade volume after 14½ years. Overall, our empirical analysis based on pooled OLS, FE and FD regressions yields two main findings. First, FTAs have a positive and significant impact on bilateral trade between FTA members in all three regressions. Our results thus confirm the presence of average treatment effects for FTAs; i.e., whether two countries have an FTA or not matters for the volume of bilateral trade. Furthermore, the positive and significant effect seems to materialize not immediately but with a time lag. Second, the hub and spoke nature of FTAs appears to reinforce and augment the significantly positive effect of FTAs on trade. It is noteworthy that the estimated size of the hub-and-spoke effect is quite similar across the three regressions: 0.075 for FD, 0.108 for FE, and 0.076 for pooled OLS. This gives us some confidence about the robustness of our estimated hub-andspoke effect. Interestingly and significantly, if we take into account both the direct effect of FTAs and the additional hub-and-spoke effect, the growth rate of trade implied by our FD results is 4.9%, which is quite close to the actual growth rate of 5.1% between FTA members computed from our data. V. Concluding Observations Although the concept of hub-and-spoke trade systems is not new to the trade literature, what has been lacking in the literature is a systematic empirical analysis of their effects. We hope that our paper helps to address this significant shortcoming in the literature. More specifically, we apply the concept of hubs and spokes to FTAs and use a panel data set comprising 99 countries and covering 40 years (1960–1999) to empirically examine the effect of FTA hubs and spokes on trade. Our point of departure is an increasingly prominent stylized fact of international trade in the real world, namely the overlapping of FTAs, which give rise to hub-and-spoke FTAs. Intuitively, an FTA hub belonging to two FTAs—A and B—enjoy a competitive advantage in exporting its goods vis-à-vis FTA spokes, which belong to only one of the two FTAs. The hub has a price advantage vis-àvis A-only countries in the B market and price advantage vis-à-vis B-only countries in the A market. To the extent that this advantage results in higher exports and hence trade, we can expect the hub-and-spoke feature of overlapping FTAs to increase trade above and beyond the direct, trade-liberalizing effect of FTAs. Indeed one of our two main empirical findings is that the hub-and-spoke nature of FTAs in a world of overlapping FTAs does indeed have a positive and significant effect on bilateral trade among FTA members. More precisely, our results imply an average annual growth 14 | ADB Economics Working Paper Series No. 127 rate of trade of 4.9% between FTA members and hence a doubling of bilateral trade after 14½ years. This growth rate of 4.9% implied by our regression results is remarkably close to the actual growth rate of 5.1% we computed from our data, which lends credibility to the robustness of our results. The positive and significant effect of hub-and-spoke FTAs reinforces our other main empirical finding, namely a positive and significant effect of FTAs on trade. Our finding of a positive relationship between FTAs and trade reconfirms the results of Baier and Bergstrand (2007), which do not account for hub-and-spoke effects. Their results imply a 7% annual growth rate of bilateral trade between FTA members and a doubling of trade after 10 years. However, the actual growth rate of trade we computed from their data was only 4.3%, significantly below the growth rate implied by their regression results. At a broader level, both our paper and Baier and Bergstrand represent efforts to improve and refine the empirical analysis of the relationship between FTAs and trade. In fact, we use the methodology developed by Baier and Bergstrand to address the econometric issue of endogenous FTA-related variables. In this paper, we propose to further improve the measurement of FTA’s trade effects by accounting for a characteristic of FTAs hitherto neglected by the empirical literature. More specifically, we argue that in a world of overlapping FTAs, a more accurate estimation of the effect of FTAs on trade requires taking into account the hub-and-spoke nature of FTAs. Furthermore, the proliferation of FTAs is likely to further increase the empirical relevance of hub-and-spoke FTAs in the future. Our empirical evidence provides some support for our argument that the empirical analysis of FTAs would benefit from explicitly recognizing those effects. Given the large and growing role of FTAs in international trade, it is of utmost importance to measure their impact as accurately as possible. This suggests there is plenty of scope for useful future research. For one, the empirical literature fails to incorporate rules of origin (RoO). These rules are an essential part of FTAs and define the conditions under which the importing country will view a product as originating in an FTA partner. RoO entail costs, e.g., a Mexican firm’s costs of certifying the Mexican origins of its exports to the US under NAFTA, which introduce a protectionist bias. Inactive FTAs is another potential issue for future research. For example, an FTA may exist in name only if firms forego the FTA-based preferential treatment and act as if they were from outside the FTA area. Including inactive FTAs in the empirical analysis distorts the estimation of an FTA’s trade effects. However, operationalizing RoO and inactive FTAs for empirical purposes will be far from straightforward. Effects of Hub-and-Spoke Free Trade Agreements on Trade: Panel Data Analysis | 15 Appendix A List of Regional Trade Agreements Notified to WTO, 1958–2005 (132 Agreements) 1958: European Community 1960: European Free Trade Association 1961: Central American Common Market 1970: EFTA accession of Iceland 1971: EC–Overseas Countries and Territories 1973: EC–Switzerland and Liechtenstein; EC accession of Denmark, Ireland and United Kingdom; EC–Iceland; EC–Norway; Caribbean Community and Common Market 1976: EC–Algeria 1977: Agreement on Trade and Commercial Relations between the Government of Australia and the Government of Papua New Guinea; EC–Syria 1981: EC accession of Greece 1983: Closer Trade Relations Trade Agreement 1985: United States–Israel 1986: EC Accession of Portugal and Spain 1991: EC–Andorra: Southern Common Market 1992: EFTA–Turkey 1993: EFTA–Israel; Armenia–Russian Federation; Kyrgyz Republic–Russian Federation; EC–Romania; EFTA–Romania; Faroe Islands–Norway; Faroe Islands–Iceland; EFTA–Bulgaria; EC–Bulgaria 1994: North American Free Trade Agreement; Georgia–Russian Federation 1995: Romania–Moldova; EC accession of Austria, Finland and Sweden; Faroe Islands–Switzerland; Kyrgyz Republic–Armenia; Kyrgyz Republic–Kazakhstan; Armenia–Moldova 1996: EC–Turkey; Georgia–Ukraine; Armenia–Turkmenistan; Georgia–Azerbaijan; Kyrgyz Republic–Moldova; Armenia–Ukraine 1997: EC–Faroe Islands; Canada–Israel; Turkey–Israel; EC–Palestinian Authority; Canada–Chile; Eurasian Economic Community; Croatia–Former Yugoslav Republic of Macedonia 1998: Kyrgyz Republic–Ukraine; Romania–Turkey; EC–Tunisia; Kyrgyz Republic– Uzbekistan; Mexico–Nicaragua; Georgia–Armenia 1999: Bulgaria–Turkey; Central European Free Trade Agreement accession of Bulgaria; EFTA–Palestinian Authority; Georgia–Kazakhstan; Chile–Mexico; EFTA–Morocco 2000: Georgia–Turkmenistan; EC–South Africa; Bulgaria–FYROM; EC–Morocco; EC–Israel; Israel–Mexico; EC–Mexico; Southern African Development Community; Turkey–FYROM 2001: Croatia–Bosnia and Herzegovina; New Zealand–Singapore; EFTA–FYROM; EC–FYROM; Romania–Israel; EFTA–Mexico; India–Sri Lanka; United States– Jordan; Armenia–Kazakhstan 2002: Bulgaria–Israel; EFTA–Jordan; EFTA–Croatia; Chile–Costa Rica; EC–Croatia; EC–Jordan; Chile–El Salvador; Albania–FYROM; FYROM–Bosnia and Herzegovina; Canada–Costa Rica; Japan–Singapore 16 | ADB Economics Working Paper Series No. 127 2003: EFTA–Singapore; EC–Chile; CEFTA accession of Croatia; EC–Lebanon; Panama– El Salvador; Croatia–Albania; Turkey–Bosnia and Herzegovina; Turkey–Croatia; Singapore–Australia; Albania–Bulgaria; Albania–UNMIK (Kosovo); Romania–Bosnia and Herzegovina 2004: Romania–FYROM; Albania–Romania; PRC–Macao, China; PRC–Hong Kong, China; United States–Singapore; United State–Chile; Republic of Korea–Chile; Moldova–Bosnia and Herzegovina; European Union Enlargement; Bulgaria–Serbia and Montenegro; EC–Egypt; Croatia–Serbia and Montenegro; Romania–Serbia and Montenegro; Moldova–Serbia and Montenegro; Albania–Serbia and Montenegro; Moldova–Croatia; Albania–Moldova; Bulgaria–Bosnia and Herzegovina; Moldova– FYROM; Moldova–Bulgaria; Albania–Bosnia and Herzegovina; EFTA–Chile 2005: Thailand–Australia; US–Australia; Japan–Mexico; Turkey–PLO; EFTA–Tunisia; Thailand–New Zealand; Turkey–Tunisia CACM = Central American Common Market, CARICOM = Caribbean Community and Common Market, CEFTA = Central European Free Trade Agreement, CER = Closer Trade Relations Trade Agreement, EAEC = Eurasian Economic Community, EC = European Community, EFTA = European Free Trade Association, FYROM = Former Yugoslav Republic of Macedonia, MERCOSUR = EC–Andorra: Southern Common Market, NAFTA = North American Free Trade Agreement, OCTs = EC–Overseas Countries and Territories, PATCRA = Agreement on Trade and Commercial Relations between the Government of Australia and the Government of Papua New Guinea, SADC = Southern African Development Community.