Table 3 also presents the difference between the SS and NS effects together with the standard error of the difference and the p-value. The very first row indicates that the p-value of the difference between NS and SS coefficients is 0.06 in the no MR case, and the difference is statistically significant at the 5% level after accounting for MR. In the 4-country case, a SS agreement would raise trade by 68%. This is double the effect of a NS agreement, which would raise trade by 33%. The subsequent 3 rows of Table 3 are based on column 3 of Table 2; they produce slightly larger and more significant differences between the SS and NS agreements.

The next regression we present is the Baltagi & Wu (1999) estimator designed to account for potential autocorrelation in the errors. Column 4 of Table 2 leaves the NS coefficient almost unchanged but reduces the other two.<sup>19</sup> While all three coefficients are still positive and significant, they are now similar in size. The applicable comparison in Table 3 confirms that these are not statistically significantly different even after accounting for MR. This tempers the claim that NS agreements have bigger effects than SS agreements. We report that the LBI test statistic for autocorrelation in an unbalanced panel is close to 2, which suggests that autocorrelation is not a material problem (Baltagi & Wu, ibid), so the standard fixed effects estimates in columns 1 and 2 may be more appropriate (Wooldridge, 2002).

As an alternative, column 5 includes values of the FTAs for the preceding 5-year period. All six lagged FTA coefficients are significant, with the lagged terms involving Northern countries being higher than the contemporaneous terms. This suggests agreements with Northern countries take longer to realise trade gains. The last three rows of Table 3 present the long run comparative static effects calculated by summing the contemporaneous and lagged coefficients.

Ignoring MR, we see that the difference between the SS and NS effects is larger than when we had no lags (0.261>0.209 for example). However, this is smaller relative to the overall effect. The standard error of the difference is now greater because we are comparing four coefficients and not just two, so the p- value is 0.10. Once we adjust for MR, however, the difference is amplified such that the implied increase is significantly different at 5% for the 4-country case. Accounting for lags also produces generally higher effects than the static specifications, which is consistent with Baier & Bergstrand (2007). For instance, the 4-country example implies effects of NS and SS agreements of 53% and 107%.

As further checks, Table 2 contains two more regressions. We include a second lag in column 6. While the NN dummy is significant even at the second lag, the others are not and do not materially affect the other coefficients. Further, as a check against reverse causality, column 7 includes a specification with future terms for the FTAs, which are not significant.

We noted that NN agreements are also associated with increases in trade. There is also tentative evidence that the Northern countries would see bigger rises in trade if they signed agreements with other Northern countries rather than with Southern countries. We report that the differences in effects are significant at the 10% level in the no lag specification and at the 5% level in the 2-lag specification, but insignificant in the 1-lag specification. Furthermore, as indicated by the multipliers in Table 1, accounting for MR would reduce the difference. For example, using the estimates in column 2 and allowing for MR in the 4-country case, the difference is only 0.096 and the p-value for the difference is

> 19 The finding of generally lower coefficients when addressing autocorrelation is consistent with Baier & Bergstrand (2007), who do so by first differencing. Applying the Baltagi & Wu (1999) estimator to a specification with only one FTA dummy, our coefficient of 0.35 is similar to their first-differenced estimate of 0.31.

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