potential internal instruments. The use of too many instruments produces more statistically significant results, but increases the risk of ―over fitting‖ the model. This trade-off is addressed by restricting the number of instruments as proposed by Roodman (2006); i.e., one instrument is created for each variable and lag distance rather than for each time period, variable, and lag distance. Lastly, the time series depth does not provide a sensible number of observations for conducting panel unit root tests (Baltagi, 2005). Nevertheless, the system GMM estimation performs well even in the presence of near random walk processes (Stata Press, 2007).

In line with the elaborated analytical framework, the baseline growth equation takes the following dynamic specification: 1+ 2 _-_ + 1 ln + 2 + 3 + 4 + 5 + 6 + 7 * + 8 + _t_ + (1) where _i_ denotes the countries under investigation for which data is available during the observed period ( _i_ = 1, ..., 90), _t_ stands for the four-year non-overlapping intervals ( _t_ = 9 and refers to the non-overlapping intervals: 1973-1976; 1977-1980; ...; 2005-2008; however, as was already noted, transition countries are covered only in the 1997-2008 period), _gri,t_ is the average growth of real GDP per capita (measured in purchasing power parity terms in the Penn world tables 6.3), _fci,t_ represents alternative measures of a country’s reliance on foreign capital, and the _CG dummy_ represents an indicator variable for membership into three different EU-related groups (EU10, EU candidates and EU neighborhood). Also, _α1_ is the common intercept, _α2_ is the coefficient on the lagged dependent variable, _βi_ denotes the slope coefficients of the vector of explanatory variables (Table 1), _t_ stands for the period-specific effects (time dummies), and _vi,t_ is the stochastic disturbance term, assumed to be independently and identically distributed. The inclusion of the period effects is to account for common shocks and cross-sectional interdependence. The _vi,t_ ’s are four calendar years apart (i.e., four-year spans) and are less likely to be serially correlated than in an annual data setup. In order to address the problems of potential endogeneity and instrument proliferation, data for the explanatory variables enter the regression in the year preceding the four-year non-overlapping interval. As mentioned earlier, exceptions are the different indicators of international financial integration, the gross national saving and the gross capital formation, which are treated as endogenous variables and enter the regression as contemporaneous averages in each four-year period.

The relationship between foreign capital and growth is estimated through two different specifications: (1) the current account balance as a summary measure of the net amount of international capital inflows (the flow perspective), and (2) the country’s net foreign asset (NFA) position as an indicator of the net external wealth and the degree of international risk sharing (the stock perspective). The model is also augmented by intercept and slope dummy variables (interaction terms) for three European sub-groups to account for potentially different growth effects in the reliance on foreign capital. This is one of the extensions we pursue relative to earlier studies in an attempt to identify differences across emerging European countries. Specifically, as suggested above, three separate dummy variables for the new EU member states, the EU candidate and potential candidate countries, and the EU neighborhood countries—see 10