(2003) and Adams et al. (2003), both of whom find a significant negative impact of distance on outward FDI stocks). However, even in the context of net FDI inflows per country, one might expect that more FDI is likely to take place between neighboring members of a preferential agreement. On the other hand, to the extent that trade volumes are negatively related to distance and trade and FDI may be substitutes, we could see a positive relationship between distance and net FDI inflows. Therefore, while the expected sign of this variable is not clear and likely depends on the purpose of investment (i.e. whether FDI is market-seeking, tariff-jumping, etc.), in either case it could be an important determinant of net FDI inflows between PTA partners.

_Multicollinearity_ . A high level of correlation among regressors is quite common in time-series data. However, collinearity among the model’s independent variables inflates standard errors and results in overfitting, potentially over-estimating the effects of some collinear variables and underestimating the effects of others. There are few formal tests for multicollinearity, and the applicability of existing ones (such as VIF) to panel data is not entirely clear. Nonetheless, simply regressing one of the collinear variables on the other (and controlling for fixed effects in panel data) is often enough to determine the severity of the problem. We have already mentioned multicollinearity issues while discussing the potential correlation between global growth and global FDI, where we concluded that the contribution of one variable to explaining changes in the other is minimal. Other potential multicollinearity problems in the model described by equation (1) include correlation between the level of GDP and openness, as well as GDP and relative GNI per capita.

One of the reasons GDP and openness may be collinear is that smaller countries tend to have larger trade-to-GDP ratios. However, the within-panel (time-series) correlation, which is much more important for panel analysis using fixed effects, is very low. In an LSDV regression of openness on GDP the overall _R_<sup>2</sup> is just under three percent, while the within _R_<sup>2</sup> is around one percent.<sup>40</sup> Thus, although the two variables are negatively correlated, as indicated by a significant _t_ -statistic, the variation in one variable explains only a small fraction of variation in the other, and therefore multicollinearity is unlikely to pose a serious problem for estimation of partial effects of GDP and openness on net FDI inflows.

Unfortunately, the same conclusion cannot be drawn for GDP and relative GNI per capita. While Lederman et al. (2005) claim that the latter is indicative of real labor costs, it is quite likely that both variables may capture the effects of market size.<sup>41</sup> In fact, when regressing one variable on the other (and using fixed effects) we find a significantly positive slope coefficient and an _R_<sup>2</sup> of almost 16 percent.<sup>42</sup> Therefore, the relatively high correlation between these two variables is likely to cause estimation problems. This is evident from the results of Table 11 in Appendix B, where the relative GNI variable switches signs when we move from one estimation approach to another. While there are approaches that provide consistent estimates in the presence of multicollinearity (e.g. principal components), these have usually not been generalized to panel settings and do not control for other problems such as heteroscedasticity and autocorrelation. In such instances, the more common approach has been to drop the problematic variable or, if the theory demands its inclusion, to estimate the model as is and note that standard errors are likely to be inflated and coefficients unstable. Since equation (1) already includes a control for market size, we do not need to add another measure of size to the model. Since relative GNI is intended to capture differences in relative labor costs, it would be preferable to use real wage data in its

> 40See Appendix B for the estimation results.

> 41See, for example, an earlier reference to the discussion of market size in Chakrabarti (2001), who includes GNI per capita in the list of variables that reflect it.

> 42See Appendix B for the estimation results.

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