converges to the maximum likelihood estimator (MLE) in models without serial correlation, the same does not hold when the disturbances are assumed to follow an AR(1) process. Therefore, we elect to use the simpler three-step methodology; in any case, for the model in equation (2) the differences between the results generated by the two estimators are marginal and do not affect hypothesis testing or inference.<sup>50</sup> Similarly, allowing the AR(1) coefficients to vary across countries or constraining them to be the same does not appreciably change the results, and we therefore opt for the more efficient method of imposing a common autoregressive parameter for all cross-sections.

Table <u>4: FGLS estimation results: net FDI infows</u>

|**Variable**|**Coefci**|**ent**<br>**Std. error**|
|---|---|---|
|GDP|0.899<sup>_∗∗_</sup>|0.017|
|OPEN|0.941<sup>_∗∗_</sup><br>|0.083|
|GDPGRO|0.256<sup>_†_</sup><br>|0.142|
|CPIGRO|-0.008<sup>_†_</sup>|0.005|
|RERGRO|-0.336<sup>_∗_</sup><br>|0.146|
|WLDGRO|-0.677<sup>_†_</sup>|0.391|
|WLDFDIadj|0.373<sup>_∗∗_</sup>|0.039|
|PTAGDP|0.060<sup>_∗∗_</sup>|0.018|
|DISTA|-0.155<sup>_∗∗_</sup>|0.058|
|N||1541|
|Log-likelihood<br>_χ_<sup>2</sup>||-1716.811<br>3146.643|
|Signifcance levels :<br>_Note:_ The depende<br>natural logarithms.|_†_ 10%<br>nt variable<br>|_∗_5%<br>_∗∗_1%<br>is expressed in|

The FGLS estimates of equation (2) are shown in Table 4. Because the REER data are not available for all sample countries and periods, the number of observations shrinks to 1541—representing an unbalanced panel of 87 countries between 1980 and 2004. This also limits the coverage of PTAs in our sample to 180 agreements, down from the original 196. The Wald test (summarized by the _χ_<sup>2</sup> statistic in the last row of the table) is significant at the 1 percent level and shows that the independent variables are significant as a group. The individual coefficient estimates are all significant at the ten percent level or below and imply reasonable elasticity values. The estimated elasticity of net FDI inflows with respect to market size (GDP) is slightly below unity (but significantly different from it) and is broadly consistent with estimates in the previous literature. For example, Kolstad and Tondel (2002) obtain an elasticity estimate of slightly higher than one using gross FDI per capita as a dependent variable, while Coughlin and Segev (2000) estimate a similar elasticity with gross FDI data for China.<sup>51</sup> No such comparisons can be drawn for the semi-elasticity of trade openness, since the exact definition of this variable varies widely across studies. GDP growth has a positive effect on net FDI inflows while the impact of inflation is negative but relatively small and only borderline significant ( _p_ = 0 _._ 099). The estimated coefficients on the global variables—world

> 50The standard errors are somewhat larger for the three-step estimator, therefore presenting the three-step results allows us to err on the side of caution.

> 51Other studies, such as Asiedu (2002) and Addison and Heshmati (2003), impose a unitary elasticity by moving GDP to the left-hand side. Although our estimated elasticity of net FDI inflows with respect to GDP is significantly different from unity ( _χ_<sup>2</sup> =34.09, _p_ = 0 _._ 00), the absolute value of the coefficient is reasonably close to one.

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