A further factor in panel data spanning more than two periods is potential autocorrelation. Wooldridge (2002) suggests first differenced estimators may be preferable to the pair-specific dummies. An alternative is to employ the estimator developed by Baltagi & Wu (1999). This allows one to account for autocorrelation in the error term by specifying a first-order autoregressive process for the error terms, which we do here.<sup>16</sup> A potential rationalization of autocorrelation is lags in the implementation of agreements after signing and in the trade response to the implementation. For this reason, like Baier & Bergstrand (2007), we run specifications with lagged values of the FTAs.

Like most datasets spanning a big range of countries, about 20% of our observations are dropped due to zero or missing trade flows. If the factors driving the recording of positive trade flows also drive the size of those trade flows, there can be bias in the estimates. This can be problematic if the proportion of missing observations is large and, in our application, if we think the bias would affect one type of agreement more than another. Like Baier & Bergstrand (2007), we do not take explicit account of the missing observations and at the same time acknowledge that the use of a reliable method that does so would be a welcome addition to this body of research.<sup>17</sup>

# **5. Results**

This section presents regression output (Table 2) together with comparative statics (Table 3). The first column of Table 2 replicates the result from Baier & Bergstrand (2007: table 5, column 1), which shows a significant positive coefficient on the FTA dummy. The remaining columns distinguish between agreements signed between Northern countries (NN), between Southern countries (SS) and between Northern and Southern countries (NS). In column 2, all three dummies are significant. In other words, regardless of whether the agreement is signed between two developed countries, two developing countries or a developed and developing country, it is associated with a rise in bilateral trade.

The SS coefficient is the highest, implying the signing of such an agreement would raise bilateral trade by _e_<sup>0.528</sup> –1=70%. This provides evidence that trade increases from FTAs also apply to those involving Southern countries. This important result will be robust across all of our specifications. The SS coefficient is slightly larger than the NN coefficient but much larger than the NS coefficient, which implies trade would rise by _e_<sup>0.32</sup> –1=38%. The results are almost identical when we impose the theoretically motivated restriction of unity on GDP in column 3, which we do by dividing trade by the product of importer and exporter GDP.

The first 3 rows of Table 3 provide the comparative statics associated with column 2 of Table 2.<sup>18</sup> Row 1 has no MR adjustment. The next two rows adjust each coefficient using Table 1. As we anticipated, the

> 16 We prefer the Baltagi & Wu (1999) approach to first differencing because it is computationally more convenient, allows explicit modelling of autocorrelation and is in principle less susceptible to downward bias from measurement error.

> 17 A number of approaches, including the estimator developed by Santos Silva & Tenreyro (2006) attempt to deal with this source of bias. A number of applications of this estimator, particularly with panel data, appear to use the method for gauging the robustness of signs to more traditional methods. In many cases, no attempt is made to interpret the actual magnitude and the coefficients seem to be very sensitive to the specification. For our particular application, we experienced problems with convergence that we speculate are due to the large number of dummies included in our specification.

> 18 Baier & Bergstrand (2007: footnote 11) only consider the no MR case, refer to this as the “treatment effect” and reserve the term “comparative statics” for cases that do account for MR.

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