Some Empirical Facts About International Trade Flows

Transcription

1 Some Empirical Facts About International Trade Flows Mark N. Harris Department of Econometrics and Business Statistics, Monash University László Kónya School of Economics and Finance, La Trobe University László Mátyás Department of Economics, Central European University December 2009 Abstract The last decade has seen a proliferation of empirical models in the trade literature. Focus has ranged from the effect of particular explanatory variables to improved econometric techniques. However, there appears to be a lack of analyses on large up-to-date international trade datasets aiming at describing the key features, or stylized facts, of observed bilateral trade flows. Uncovering them is crucial as any empirical econometric model should reflect the basic properties of the data generating process. On the basis of a new dataset we find that bilateral trade, despite being often unbalanced, tends to be reciprocal and persistent, and that the extensive margin of trade must not be disregarded. Moreover, bilateral trade flows are probably best modeled as a mixed panel of stationary and non-stationary processes. The stationary versus non-stationary separation of these flows, although not random, does not appear to be related to some evident common characteristics of the trading partners. JEL Classification Numbers: C29, F19, Key Words: international trade, bilateral trade data, gravity model Corresponding author; [email protected]; Address: School of Economics and Finance, La Trobe University, Victoria, 3086, Australia.

2 1 Introduction The last decade has witnessed a multitude of papers concerned with empirically modelling the volume of international trade. This course of research tends to focus on two main issues: the econometric specification of the models (like, e.g., the role of GATT/WTO in Rose, 2004; Subramanian and Wei, 2007; and Tomz et al., 2007) and the estimation problems related to these specifications (see e.g., Mátyás, 1997, 1998; Egger, 2000; Anderson and van Wincoop, 2003; and Helpman et al., 2008). However, there appears to be a lack of analyses on large upto-date trade datasets with the aim of describing the key features of observed international trade flows. This knowledge though should form the basis of any econometric analysis as these features of the data generating process must be reflected in the empirical models. This paper aspires to fill in this gap. The analysis of a new international trade data set reveals six stylized facts which can be summed up as follows. The lack of trade between country pairs tends to be reciprocal (i) and persistent (ii). During the sample period the scope of both the potential and active bilateral trading relationships widened considerably, underlying the importance of the extensive margin of trade (iii). Positive bilateral trade, despite being often unbalanced, also tends to be reciprocal (iv) and persistent (v). Although various panel data unit root tests suggest that a significant fraction of the export and import series is stationary, this fraction is probably neither too small nor too large, so international trade is in fact a mixed panel of stationary and non-stationary processes (vi). 2 A New International Trade Dataset Most empirical studies published recently on bilateral trade flows between several countries over a number of time periods had been based on two unbalanced international trade panel data sets compiled from secondary sources by Feenstra et al. (1997) and Rose (2004). Feenstra et al. (1997) relied on two databases: the World Trade Database (WTDB) of Statistics Canada, which contains bilateral trade flows of goods for 183 countries over , classified according to the Standard International Trade Classification (SITC, Revision 2); and the Compatible Trade and Production (COMTAP) database of the OECD, which reports manufacturing production in 22 OECD countries and bilateral trade flows of manufactured goods between these countries and all their trading partners (185 countries altogether) over , classified according to the International Standard Industrial Classification (ISIC, Revision 2). 1 Roses s (2004) dataset contains aggregate bilateral merchandise trade flows for 175 countries over , obtained from the IMF s Direction of Trade Statistics (DOT). Since in principle each directed flow is reported twice, once as an export and once as an import, and the corresponding values are usually different for various reasons, there are potentially two trade matrices. 2 In order to avoid this ambiguity, the WTDB benchmarks each country s total exports to the total imports by the world from that country as reported in the DOT, while Rose (2004) uses average bilateral trade flows calculated as the arithmetic mean of real merchandise exports and imports in both directions, i.e., of potentially four different measures. On the other 1 The trade flows were originally on the SITC classification, but they had been converted to ISIC. 2 The discrepancies are partly due to the fact that exports are usually recorded as f.o.b. (free on board) and imports as c.i.f. (cost, insurance and freight) values, but they might also originate from the different qualities of the exporting and importing countries trade statistics and from the uncertainty of the origin of certain import and the destination of certain export flows. 2

3 hand, the COMTAP makes no attempt to resolve the differences between the reported export and import flows but includes both. This paper considers a new dataset covering 180 countries over 46 years, from 1960 to It contains separate export and import data. Nominal trade flows in US$ were collected from the IMF s DOT CD-ROM and then deflated by the US CPI (all items, city average, 2000 = 100) obtained from the IMF s International Financial Statistics (IFS) CD-ROM; 3 yielding real exports and imports of country i to/from country j in year t (REXPORT ijt, f.o.b. and RIMPORT ijt, c.i.f., both in 2000 US$), respectively. 3 The Stylized Facts Here we report some basic features of our data set. In particular, we are interested in the relationship between zero and positive trade flows and in the relationship between relatively small and large trade flows. We consider reciprocity and persistence by first comparing the corresponding trades flowing in opposite directions and then the corresponding lagged and current flows. Finally, we study the time-series properties of the data. 3.1 Missing and Zero Values Given our timeframe and the number of countries, there are potentially 1,482,120 threedimensional, i.e. ijt, cases. However, not all 180 countries existed throughout the whole sample period. For this and various other reasons we ended up with 947,846 cases, out of which we have 275,901 missing REXPORT and 274,158 missing RIMPORT observations. There are also 278,273 zero REXPORT and 257,402 zero RIMPORT observations, which are either genuine zeros, or are rounded figures and represent nominal flows below half a million US$. This leaves us with 393,672 positive REXPORT and 416,286 positive RIMPORT observations. Unfortunately, the division between zero and missing trade flows is rather blurred in the existing international trade data banks and there is no agreement in the literature how to handle them. For example, Helpman et al. (2008) treat zero trade observations as missing values, while Felbermayr and Kohler (2006) replace missing observations with zero trade flows. We, however, consider the zero and missing trade flows as conceptually different. A zero means that for the given year there was no positive export/import flow recorded between the two countries or that the nominal value of exports/imports was less than half a million US$. As regards the missing values, we accept the lack of information and do not assume that the missing trade flows are equal to zero. 3.2 Zero versus Positive Trade Flows In order to check whether zero/positive flows in one direction are independent of zero/positive flows in the opposite direction, we perform chi-square analyses. The results are displayed in Tables 1 and 2. Table 1: Chi-Square Test on Export Flows from Country i to Country j and from Country j to Country i 3 In both cases we used the December 2006 edition. 3

4 REXPORT ijt = 0 REXPORT ijt > 0 REXPORT jit = 0 REXPORT jit > (26.6%) (13.4%) (40.0%) (9.2%) (50.8%) (60.0%) (35.8%) (64.2%) Pearson χ *** Cramer s V Note: *** indicates significance at the 1% level. Table 2: Chi-Square Test on Import Flows to Country i from Country j and to Country j from Country i RIMPORT ijt = 0 RIMPORT ijt > 0 RIMPORT jit = 0 RIMPORT jit > (22.7%) (10.1%) (32.8%) (13.5%) (53.7%) (67.2%) (36.2%) (63.8%) Pearson χ *** Cramer s V Note: *** indicates significance at the 1% level. The chi-square tests and Cramer s V statistics 4 indicate reciprocity of zero trade flows (Fact 1); that is whether in any given year country i (j) exports to or imports from country j (i) is dependent of whether country j (i) exports to or imports from country i (j). In particular, from Table 1, given that country i does not export to country j, the probability (relative frequency) that country j does not export to country i either is (and given that country j does not export to country i, the probability that country i does not export to country j either is 0.665). Likewise, from Table 2, the probability of country j not importing from country i, when country i does not import from country j, is about (the probability of country i not importing from country j, when country j does not import from country i, is about 0.693). Consequently, if country i (j) does not export to or import from country j (i), it is much more likely that j (i) does not export to or import from i (j) either. We perform similar analyses on the current and lagged trade flows to find out whether zero/positive flows in year t-1 are (in)dependent of zero/positive flows in year t. The results in Tables 3 and 4 imply persistence of no-trade (Fact 2); that is whether country i exports to or imports from country j in year t is dependent of whether it did so in year t-1. In particular, from Table 3, the probability that country i does not export to country j in year t if it did not do so in year t-1 is (and the probability that country i did not export to country j in year t-1 if it does not do so in year t either is 0.887). Likewise, from Table 4, the probability that country i does not 4 Cramer's V is based on the chi-square statistic and it measures the strength of association or dependence between two nominal variables in a contingency table. 4

5 import from country j in year t if it did not do so in year t-1 is (and probability that country i did not import from country j in year t-1 if it does not do so in year t is 0.871). Hence, it is safe to say that if a country did not export to or import from another country in year t-1, then it is more likely not to do so in year t either. Nevertheless, there is still 11-16% chance that from one year to the next a zero bilateral export or import flow between two countries becomes positive, or vice versa, suggesting that the extensive margin of trade must not be disregarded. Table 3: Chi-Square Test on Export Flows from Country i to Country j in Years t and t-1 REXPORT ij,t-1 = 0 REXPORT ij,t-1 > 0 REXPORT ijt = 0 REXPORT ijt > (36.0%) (4.6%) (40.6%) (5.8%) (53.6%) (59.4%) (41.8%) % Pearson χ *** Cramer s V Note: *** indicates significance at the 1% level. Table 4: Chi-Square Test on Import Flows to Country i from Country j in Years t and t-1 RIMPORT it,j-1 = 0 RIMPORT ij,t-1 > 0 RIMPORT ijt = 0 RIMPORT ijt > (32.5%) (4.8%) (37.3%) (6.0%) (56.7%) (62.7%) (38.5%) (61.5%) Pearson χ *** Cramer s V Note: *** indicates significance at the 1% level. This conclusion is reinforced by Figures 1 and 2. Figure 1 shows that the total number of export and the total number of import flows recorded in our data set increased from about 6 thousand in 1960 to about 25 thousand by 2000 and then dropped to about 23 thousand in On the other hand, Figure 2 illustrates the evolution of the proportions of zero export and import flows. Apart from some fluctuations, the relative frequency of zero bilateral trade flows decreased steadily from about 72-74% in 1960 to 37-40% in Hence, during our sample period the scope of both the potential and active bilateral trading relationships widened considerably, underlying the importance of the extensive margin of trade (Fact 3). 5

6 Figure 1: Number of Bilateral Trade Flows (Zero and Positive) 30,000 25,000 20,000 15,000 10,000 5, REXPORT RIMPORT Figure 2: Proportions of Zero Trade Flows 70% 60% 50% 40% 30% 20% 10% 0% REXPORT = 0 RIMPORT = 0 6

7 3.3 Small versus Large Trade Flows Next, we consider the intensive margin of trade and study the year to year changes and the inertia of positive trade flows. Figures 3 and 4 illustrate the frequency distributions of the firstdifferences of the logarithms, i.e. the growth rates, of the export and import flows. Both distributions appear to be fairly symmetrical around and 0.061, respectively, but the Jarque-Bera tests (not shown here) still reject normality. If we consider a logarithmic change below -2 or above +2, i.e. more than thirty times the mean, as exceptionally large, then about eight percent of all year to year changes can be classified as extreme falls or jumps. Figure 3: Year to Year Changes of Export Flows 160, , , ,000 80,000 60,000 40,000 20, Figure 4: Year to Year Changes in Import Flows 200, , ,000 80,000 40,

8 We perform chi-square analyses to see whether the relative magnitudes of the trade flows in one direction are related to the relative magnitudes of the corresponding trade flows in the opposite direction. In order to simplify the task, we classify positive trade flows as small (S) or large (L), and for any given country pair we define small and large export and import flows as being below and above the sample mean of REXPORT and RIMPORT, respectively, in the given relation. The results in Tables 5 and 6 support volume reciprocity (Fact 4); that is whether in any given year country i s exports to or imports from country j is small/large is dependent of whether country j s exports to or imports from country i is small/large. In particular, from Table 5, given that the export flow of country i to country j is small, the probability that the export flow of country j to country i is also small is (and given that the export flow of country j to country i is small, the probability that the export flow of country i to country j is small too is 0.713). From Table 6, the probability that the import flow of country j from country i is small when the import flow of country i from country j is small is about (and the probability that the import flow of country i from country j is small when the import flow of country j from country is small too is about 0.723). Consequently, the relative magnitudes of country i s exports to or imports from country j are related to the relative magnitudes of country j s exports to or imports from country i. However, judging by the small Cramer s V statistics, these relations are rather weak. Table 5: Chi-Square Tests on Small and Large Export Flows from Country i to Country j and from Country j to Country i REXPORT ijt = S REXPORT ijt = L REXPORT jit = S REXPORT jit = L (51.8%) (20.9%) (72.7%) (19.0%) (8.3%) (27.3%) (70.8) (29.2%) Pearson χ *** Cramer s V Note: *** indicates significance at the 1% level. Table 6: Chi-Square Tests on Small and Large Import Flows from Country i to Country j and from Country j to Country i RIMPORT ijt = S RIMPORT ijt = L RIMPORT jit = S RIMPORT jit = L (50.7%) (19.5%) (70.2%) (21.3%) (8.5%) (29.8%) (72.0%) (28.0%) Pearson χ *** Cramer s V Note: *** indicates significance at the 1% level. 8

9 The results in Tables 7 and 8 indicate volume persistence (Fact 5); that is whether there is a small or large trade flow between countries i and j in year t is dependent of the size of the trade flow between them in year t-1. From Table 7, the probability of observing a small export flow from country i to country j in year t when the same flow a year before was small too is (and the probability of a small export flow from country i to country j in year t-1, given that the same flow the following year is small too is 0.893). Finally, from Table 8, the probability that the import flow of country j from country i is small in year t, given that it was small in year t-1 too, is (and the probability that the import flow of country i from country j is small in year t-1 if it is also small in year t is about 0.889). Therefore, between any two countries, a small (large) trade flow is likely to be followed by a similarly small (large) trade flow. Table 7: Chi-Square Tests on Positive Export Flows from Country i to Country j in Years t and t-1 REXPORT ij,t-1 = S REXPORT ij,t-1 = L REXPORT jit = S REXPORT jit = L (64.2%) (7.6%) (71.8%) (9.2%) (19.0%) (2.2%) (73.4%) (26.6%) Pearson χ *** Cramer s V Note: *** indicates significance at the 1% level. Table 8: Chi-Square Tests on Positive Import Flows to Country i from Country j in Years t and t-1 RIMPORT it,j-1 = S RIMPORT ij,t-1 = L RIMPORT ijt = S RIMPORT ijt = L (63.3%) (7.9%) (71.2%) (9.4%) (19.4%) (28.8%) (72.7%) (27.3%) Pearson χ *** Cramer s V Note: *** indicates significance at the 1% level. 3.4 The Dynamics of Flows It is important to be aware of the time series properties of trade data sets. As seen earlier, there is strong evidence of trade persistence, which implies a likely dynamic data generating process. Although it is well documented in the literature that in the presence of stochastic trends the commonly used inferential procedures can be misleading, empirical models are usually 9

10 estimated from panel data by tacitly assuming that bilateral trade flows and the quantitative explanatory variables are all stationary. To see how sensible this practice is we perform five heterogeneous panel unit root tests on the logarithms of REXPORT and RIMPORT, allowing for individual autoregressive parameters, intercepts and linear trends. These tests are panel data variants of the augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. 5 Due to the increased sample size they have higher power against the unit root null hypothesis than the corresponding univariate tests, though some of the alleged power gains can be in fact size distortion. We start with three first generation panel unit root tests: the Im, Pesaran and Shin (IPS, 2003) Z t-bar test, and the Fisher-type augmented Dickey-Fuller (FADF) and Phillips-Perron (FPP) tests advocated by Maddala and Wu (MW, 1999). These procedures are based on separate ADF or PP tests applied to the individual series generated by the cross sections, i.e. by the directed ij dyads in our case. The IPS test combines the individual ADF statistics while the Fisher-type tests combine the p-values from the individual tests. They all assume, however, that the panel members are independently distributed cross-sectionally; an assumption likely violated because of the implicit accounting restrictions on the bilateral trade flow data and to common international factors like oil price and the business cycles of the major economies (Fidrmuc, 2009). For this reason, we also apply two second generation panel unit root tests which allow for cross-section dependence. The first, proposed by Pesaran (P, 2007), is a generalization of IPS and is based on standard ADF regressions augmented with the cross-section averages of the lagged levels and first differences of the individual series (CADF). The second, advocated by Demetrescu et al. (2006) is a panel data application of the modified weighted inverse normal (MWIN) method of Hartung (H, 1999), which accounts for cross-section dependence by estimating the mean correlation between the probits of the individual ADF statistics. Since in empirical models the dependent variable is usually the logarithm of some measure of trade flow, we perform the panel unit root tests on the logs of REXPORT and RIMPORT. It is important to mention though that the results do not necessarily carry over to the levels of these variables because the time series properties can be sensitive to the logarithmic transformation. For example, if a variable, say x t, is integrated of order one then its first difference is stationary and the expected value of the period-to-period changes is constant. On the other hand, if ln(x t ) is integrated of order one then the logarithmic change is stationary and thus the expected value of the growth rate is constant. These two conclusions, however, are clearly incompatible if {x t } has a tendency to increase or decrease. 6 First, we consider all available bilateral trade flows and then only the strictly positive bilateral trade flows. 7 The results are summarized in the second columns of Tables In each case all five tests reject the null hypothesis that every panel member has a unit root, even at the one percent significance level, in favor of the alternative hypothesis that a significant but unknown fraction of the data series is stationary. 5 See e.g. Breitung and Pesaran (2008) about these and other panel unit root tests in general. 6 About unit root testing and non-linear transformations see e.g. Franses and McAleer (1998). 7 In order to ensure that the logarithmic transformation is applicable on all observations, we increase each available trade flow by one. 10

11 Table 9: Panel Unit Root Tests on the Logs of All Export Flows Test Test Statistic Proportion of individual rejections at the 5% level 10% level IPS - Z t-bar *** MW - FADF *** MW - FPP *** P - CADF *** H - MWIN *** Note: a) IPS - Z: Z t-bar test of Im, Pesaran and Shin (2003); MW - FADF: Fisher ADF test of Maddala and Wu (1999); MW - FPP: Fisher PP test of Maddala and Wu (1999); P - CADF: Cross-sectionally augmented ADF test of Pesaran (2007); H - MWIN: Modified weighted inverse normal method of Hartung (1999). b) In the IPS - Z, MW - FADF and P - CADF tests we allowed for at most 5 lags and determined the individual lag structures by minimizing the Schwartz criterion. The MW - FPP test was based on the Bartlett kernel. In the H - MWIN test we used the same κ parameter values than Hartung (1999), but report only the most conservative test statistic. For the IPS - Z and P - CADF tests simulated critical values are available only for at most 100 and 200 panel members, respectively. c) Proportion of individual rejections refers to the proportion of individual ADF, PP and CADF tests that reject the unit-root null hypothesis at the 5 and 10% significance level. d) *** and ** indicate significance at the 1% and 5% levels, respectively. Table 10: Panel Unit Root Tests on the Logs of Positive Export Flows Proportion of individual Test Test Statistic rejections at the 5% level 10% level IPS - Z t-bar *** MW - FADF *** MW - FPP *** P - CADF *** H - MWIN *** Note: See Table 9. 11

12 Table 11: Panel Unit Root Tests on the Logs of All Import Flows Proportion of individual Test Test Statistic rejections at the 5% level 10% level IPS - Z t-bar *** MW - FADF *** MW - FPP *** P - CADF *** H - MWIN *** Note: See Table 9. Table 12: Panel Unit Root Tests on the Logs of Positive Import Flows Proportion of individual Test Test Statistic rejections at the 5% level 10% level IPS - Z t-bar *** MW - FADF *** MW - FPP *** P - CADF *** H - MWIN *** Note: See Table 9. The panel unit root tests, however, do not provide information about the actual fractions of nonstationary and stationary series and it might happen that the rejection of the null hypothesis is due to just a small number of stationary panel members. For this reason, in Tables 9-12, we also report the proportions of individual ADF, PP and CADF tests that reject the unit-root null hypothesis. Depending on the variable and on the significance level, this fraction is somewhere between 28% and 75%. Although this range is fairly wide, it is clear that the panel unit-root test outcomes are not determined by a few outliers. In fact the high power of the panel unit root tests, as the other side of the coin, can lead to heavy empirical size distortion. Nevertheless, the trade panel series are certainly neither entirely stationary nor totally non-stationary, and should not be considered as such, not even for the sake of simplicity. Consequently, international trade flows are best modeled as a mixed panel of stationary and non-stationary processes (Fact 6). The mixed nature of international trade flows raises the question whether the stationary nonstationary division of the data is completely random or is related to some common characteristic of the trading partners, such as, for example, GATT/WTO membership. To answer this question we perform chi-square analysis between a variable which indicates whether for a given bilateral trade flow the individual ADF test rejects the unit root null hypothesis at the five percent significance level and another variable which shows whether the trading partners were at the 12

13 same time members of the GATT/WTO during half of the sample period or longer. The results, summarized in Tables 13-16, indicate that there is a strongly significant but rather weak relationship between these variables. We ran similar tests replacing joint GATT/WTO membership with other grouping variables. Namely, we categorized the country pairs according to OECD membership, whether the trading partners have a bilateral trade agreement, or belong to the same trading block, monetary union. We also considered country size measured by land size, average population and real GDP during the sample period, etc. Using any of these measurements, we classified a country large if it is above the median of all countries, and compared pairs of large or small countries to the others. For the sake of brevity we do not report these results in details, just summarize the main findings. 8 Table 13: Chi-Square Test on the Stationarity of the Logs of All Export Flows and Joint GATT/WTO Membership of the Trading Partners Both One or None Stationary Non-Stationary 3973 (27.3%) 3938 (27.0%) 7911 (54.3%) 3520 (24.1%) 3148 (21.6%) 6668 (45.7%) 7493 (51.4%) 7086 (48.6%) Pearson χ *** Cramer s V Note: a) Both: both countries were OECD members at the same time during half of the sample period or longer; One or None: only one country or none was OECD member during half of the sample period or longer. b) *** indicates significance at the 1% level. Table 14: Chi-Square Test on the Stationarity of the Logs of Positive Export Flows and Joint GATT/WTO Membership of the Trading Partners Both One or None Stationary Non-Stationary 1470 (12.4%) 1979 (16.7%) 3449 (29.1%) 4259 (36.0%) 4131 (34.9%) 8390 (70.9%) 5729 (48.4%) 6110 (51.6%) Pearson χ *** Cramer s V Note: See Table Of course, the details are available from the corresponding author on request. 13

14 Table 15: Chi-Square Test on the Stationarity of the Logs of All Import Flows and Joint GATT/WTO Membership of the Trading Partners Both One or None Stationary Non-Stationary 4142 (27.6%) 4159 (27.7%) 8301 (55.3%) 3625 (24.1%) 3104 (20.6%) 6729 (44.7%) 7767 (51.7%) 7263 (48.3%) Pearson χ *** Cramer s V Note: See Table 13. Table 16: Chi-Square Test on the Stationarity of the Logs of Positive Import Flows and Joint GATT/WTO Membership of the Trading Partners Both One or None Stationary Non-Stationary 1629 (13.1%) 2125 (17.0%) 3754 (30.1%) 4472 (35.8%) 4260 (34.1%) 8732 (69.9%) 6101 (48.9%) 6385 (51.1%) Pearson χ *** Cramer s V Note: See Table 13. Most importantly, the chi-square tests detect significant relationships whenever the zero trade flows are excluded and in most cases when all trade flows are included in the analysis. The significant relationships are relatively stronger when only the positive trade flows are considered and also for larger countries, but in general they are rather weak. 9 In summary, the stationary non-stationary separation of the bilateral trade flows is certainly not completely random, however, this partition is rather blurred and there is not a single clear cut factor behind it. 4 Conclusion Our analysis of a new international trade data set demonstrated that bilateral trade, despite being often unbalanced, tends to be reciprocal and persistent. This is true for the trade/no trade dichotomy, as well as for the volume of trade. We also highlighted the importance of the extensive margin of trade. Further we found that bilateral trade flows are in fact generated by a mixture of stationary and non-stationary processes. The stationary versus non-stationary 9 The strongest relationship is between positive exports and imports being stationary non-stationary and simultaneous OECD membership. In these cases Cramer s V is for positive exports and for positive imports. 14

15 separation of these flows is definitely not random, yet this partition does not appear to be related to some common characteristic of the trading partners. These results suggest that international trade is probably best modeled with double-hurdle type models, whereby the first hurdle is the trade/no trade dichotomy and the second hurdle is the stationary/non-stationary separation for the positive trade flows. The specifications must also take into account the persistent and reciprocal nature of trade. References Anderson, J.E. and van Wincoop, E. (2003): Gravity with Gravitas: A Solution to the Border Puzzle, American Economic Review, vol. 93, pp Breitung, J. (2000): The Local Power of Some Unit Root Tests for Panel Data, in Baltagi, B. ed., Advances in Econometrics, vol. 15: Nonstationary Panels, Panel Cointegration, and Dynamic Panels, JAI Press, Amsterdam, pp Breitung, J. and Pesaran, M.H. (2008): Unit Roots and Cointegration in Panels, in Mátyás, L. and Sevestre, P. eds., Advanced Studies in Theoretical and Applied Econometrics vol. 46: The Econometrics of Panel Data - Fundamentals and Recent Developments in Theory and Practice, 3 rd ed., Springer-Verlag, pp Demetrescu, M., Hassler, U., and Tarcolea, A.-I. (2006): Combining Significance of Correlated Statistics with Application to Panel Data, Oxford Bulletin of Economics and Statistics, vol. 68, pp Egger, P. (2000): A Note on the Proper Econometric Specification of the Gravity Equation, Economics Letters, vol. 66, pp Feenstra, R.C., Lipsey, R.E., Bowen, H.P. (1997): World Trade Flows, , with Production and Tariff Data, NBER Working Paper No Felbemayr, G.J. and Kohler, W. (2006): Exploring the Intensive and Extensive Margins of World Trade, Review of World Economics, vol. 142, pp Fidrmuc, J. (2009): Gravity Models in Integrated Panels, Empirical Economics, vol. 37, pp Franses, H.P. and McAleer, M. (1998): Testing for Unit Roots and Non-Linear Transformations, Journal of Time Series Analysis, vol. 19, pp Hartung, J. (1999): A Note on Combining Dependent Tests of Significance, Biometrical Journal, vol. 41, pp Helpman, E., Melitz, M., and Rubinstein, Y. (2008): Estimating Trade Flows: Trading Partners and Trading Volumes, Quarterly Journal of Economics, vol. 123, pp Im, K. S., Pesaran, M. H., and Shin, Y. (2003): Testing for Unit Roots in Heterogeneous Panels, Journal of Econometrics, vol. 115, pp

16 Levin, A., Lin, C. F., and Chu, C. (2002): Unit Root Tests in Panel Data: Asymptotic and Finite- Sample Properties, Journal of Econometrics, vol. 108, pp Maddala, G. S. and Wu, S. (1999): A Comparative Study of Unit Root Tests with Panel Data and A New Simple Test, Oxford Bulletin of Economics and Statistics, vol. 61, pp Mátyás, L. (1997): Proper Econometric Specification of the Gravity Model, The World Economy, vol. 20, pp Mátyás, L. (1998): The Gravity Model: Some Econometric Considerations, The World Economy, vol. 21, pp Pesaran, M.H. (2007): A Simple Panel Unit Root Test in the Presence Of Cross-Section Dependence, Journal of Applied Econometrics, vol. 22, pp Rose, A.K. (2004): Do We Really Know That the WTO Increases Trade? American Economic Review, vol. 94, pp Subramanian, A. and Wei, S.-J. (2007): The WTO Promotes Trade, Strongly but Unevenly, Journal of International Economics, vol. 72, pp Tomz, M., Goldstein, J.L., and Rivers, D. (2007): Do We Really Know that the WTO Increases Trade? Comment, American Economic Review, vol. 97, pp