place. However, this is problematic for several reasons: data on wages is quite scarce, reliability and cross-country comparability are highly questionable, and the level of aggregation is not clear (i.e. whether one should focus on skilled wages vs. overall, urban vs. economywide, etc.). Therefore, the best approach seems to be to drop the relative GNI per capita and rely on the fixed effects to capture some of the cross-country variation in wage levels.

_Endogeneity_ . In the course of the previous discussion, we have already mentioned potential endogeneity problems with GDP, openness, GDP growth, and global FDI.<sup>43</sup> Endogeneity, or twoway causality, presents problems not only for inference but also for estimation, since the right hand side variable is potentially correlated with the variation in the dependent variable that is relegated to the error term. Thus, the _Cov_ [ _ε,_ **X** ] = 0 assumption is violated, and consistent estimation requires obtaining instrument(s) that are uncorrelated with the error term. In fact, both of the commonly used endogeneity tests—the Durbin-Wu-Hausman test and the C-statistic (the difference-in-Sargan statistic)—rely on comparing regression results before and after applying the instruments. In order to test a subset of orthogonality conditions (i.e. the exogeneity of one or more instruments), both tests contrast the efficient, but potentially inconsistent estimates before the IV procedure with the consistent, but possibly inefficient IV estimates. If the pre-IV estimates are found to be inconsistent, the variables in question are deemed endogenous and instrumentation is warranted.

A common problem for this type of analysis is coming up with a set of instruments that simultaneously exhibit a high correlation with the endogenous variable(s) and no correlation with the error term. In some instances, the instrument choice is obvious. For example, consider the global FDI variable, which is problematic because it is determined by a combination of left-hand side values for all the sample countries. While it is possible to instrument world FDI with a sufficient number of its own lags, a more direct approach is to remove the endogeneity by subtracting each country’s net FDI inflows from the global value. In this case, the remainder will still control for global FDI patterns but the relationship with the error term in each panel will be removed.

The choice of instruments is not so obvious for GDP and the trade-to-GDP ratio. We have already mentioned that tariff levels are not an attractive measure of openness due to data availability and the ambigious relationship between the level of tariffs and the outward orientation of the economy.<sup>44</sup> Various openness indices, such as those constructed by Sachs and Warner (1995), have been widely criticized for capturing a whole range of policies not directly related to an economy’s outward orientation. Finally, instrumenting the trade-to-GDP ratio with its own lags is problematic in annual data because this variable does not vary much through time. It is partly due to these issues that a number of FDI studies have treated the openness variable as exogenous, despite the potential two-way causality between trade and FDI (see, for example, Asiedu, 2002; Kolstad and Tondel, 2002; Addison and Heshmati, 2003).

In contrast to endogeneity tests, which require the user to pick a set of instruments _a priori_ , Addison and Heshmati (2003) suggest the use of the Granger causality concept in order to establish the direction of causality between the model variables.<sup>45</sup> We follow this approach and regress the

> 43Note that we have already established that endogeneity is unlikely to be a problem for the GDP growth variable, and we therefore will not address it in the following discussion.

> 44This is largely due to the presence of non-tariff barriers, which are highly distortionary yet difficult to quantify. In addition, there is no agreement in the literature on whether simple or trade-weighted average tariffs are the preferred vehicle: simple averages attach too much weight to tariff peaks and valleys, while weighted averages ignore highly protected sectors.

> 45This approach is a nice alternative because endogeneity tests do not actually test for endogeneity of the variables but rather for exogeneity of the instruments. In addition, the implementation of the available endogeneity tests in the presence of serial correlation is not clear.

25