As expected, our PCA Index of infrastructure stock has a large and positive effect on growth and all variables have the expected signs and, with the exception of human capital, are significant (Table 1). The estimates show that: (a) there is evidence of conditional convergence for real output per worker, due to the negative coefficient; (b) economic growth is enhanced by a faster accumulation of human capital and infrastructure stock as well as higher institutional quality; (c) growth is adversely affected by higher inflation and heavier government burden. The estimates do not show a robust relationship between trade openness and growth, as in Calderon (2009).<sup>31</sup> We apply several tests to confirm the consistency of the GMM estimator. Similar to Calderon (2009), we rely primarily on internal instruments within the dynamic model of panel data framework. The Sargan test of overidentifying restrictions tests the overall validity of the instruments by analyzing the sample analog of the moment conditions used in the estimation process; failure to reject the null hypothesis that the conditions hold gives support to the model. The second test is the serial correlation test of the error term. In the GMM-system specification, we test whether the differenced error term (that is, the residual of the regression in differences) shows second-order serial correlation. First-order serial correlation of the differenced error term is expected, given the presence of the lagged dependent variables among the regressors. Second-order serial correlation of the differenced residual indicates that the original error term is serially correlated and follows a moving average process of order two or higher. This would render the proposed instruments invalid (and would call for higher-order lags to be used as instruments). Again, failure to reject the null lends support to the model.

# Table 1. Growth: panel regression analysis - GMM

_Dependent variable: Annual growth in real GDP per capita Sample: 31 countries, 2010–2014 (unbalanced panel) Estimation: GMM-IV system estimation_

|_Variable_||
|---|---|
|_Infrastructure Stock_|_0.041***_|
|_(first principal component of stocks)_|_(0.015)_|
|_Control Variables_||
|_GDP per capita_|_-0.292***_|
|_(last period, logs)_|_(0.037)_|
|_Trade openness_|_-0.094***_|
|_(trade volume as % of GDP, logs)_|_(0.026)_|
|_Human capital_|_0.254_|
|_(Penn World Table - human capital index, logs)_|_(0.169)_|
|_Institutional quality_|_0.011*_|
|_(WGI for government effectiveness, logs)_|_(0.006)_|

31 For example, there is a sizable heterogeneity in the extent to which growth responds to trade liberalization (Wacziarg and Welch, 2003; Chang, Kaltani, and Loayza, 2005). Greenaway et al. (1998) suggested that trade liberalization negatively affects growth in the short run. Trade share in GDP has negative impact on growth for heavily regulated economies (Bolaky and Freund, 2004). Michaely et al. (1991) and Panagariya (2004) provided case studies indicating that in an unstable macroeconomic environment and with an overvalued exchange rate, the effect of trade liberalization is likely to be reversed. Wacziarg and Welch (2003) also reported that restrictive macroeconomic policies can explain why some countries did not experience higher growth following trade liberalization. They stressed that currency overvaluation can undo trade liberalization. Finally, results may also depend on measures of trade openness (Ciccone, 2008).

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