observe that the IV estimates of EXPY are now significant and higher than those of the OLS estimates. Looking at the bottom of Table 8, the under and weak identification tests are rejected. But, just as in the full sample case, the overidentification tests are rejected which again casts doubts on the instrument set.

Second, to overcome the small sample problem and have a better insight on the effect of EXPY on growth for different income-level economies we include dummies indicating the type of economy and these interacted with EXPY. The coefficients of the interactions tell us the specific effect of EXPY on growth given the economy’s level of income relative to high-income economies. The results of these estimations are shown in Table 9. In the OLS estimations, the biggest and most significant positive effect of EXPY is on growth in lower-middle-income economies, followed by the effect in lower-income economies. The point estimates for the lower-income economies are actually half the ones of the lower-middle-income economies, after controlling for institutional quality, financial depth and export diversification. The effect of EXPY on growth in upper-middleincome economies appears to be insignificant.

In columns (6) to (10) of Table 9 we present the IV estimates for this exercise. The estimates are far more imprecise and in most cases they have increase dramatically. Looking at the specification test, in one hand, we cannot reject the null hypothesis of weak identification and, in the other hand, we reject the overidentification test. Finally, the weak-instrument-robust inference tells us that EXPY is significant at 5 and 10 percent level.

# **Panel Level Analysis**

In view of the fact that the relationship between EXPY and growth is more likely to be one of long term and aiming at obtaining more efficient and consistent estimates, we present in what follows estimation results using panel data. We construct 5-year, 8-year and 10-year panels spanning from 1962 to 2000 using Feenstra et al. (2005). In the next set of tables we present results of OLS, IV, OLS-Fixed Effects and GMM estimations for the full sample as well as for the sample excluding high-income countries. All estimations include human capital and initial GDP per capita as additional covariates. The OLS estimates although informative, are likely to be biased since they do not take into account the potential endogeneity of EXPY nor the unobserved time-invariant country characteristics that might affect growth. We can correct the first problem by making use of IV estimators (2SLS) where the same variables as in the cross-section analysis would serve to instrument EXPY. But by only using this technique, however, we do not solve the problem of unobserved time-invariant characteristics. On the other hand, we can estimate a Fixed Effects model to correct for the second problem but we still need to correct for the potential endogeneity of EXPY. Also, we cannot estimate an IV model with fixed effects using the variables employed earlier as instruments since these are practically time invariant and therefore they get differenced out under this estimation approach. Given that finding more suitable instruments represents an extremely difficult task, we apply the System-GMM technique suggested originally in Arellano and Bover (1995) and fully developed in Blundell and Bond (1998). The advantage of this technique, designed to estimate dynamic panel data models, is that addresses both problems. It consists of setting a system of equations in differences and in levels and using lagged levels and the first lag of the first-differences of the endogenous variables as instruments in these equations respectively in addition to allowing for fixed effects. This approach has become very popular in growth-related empirical work, examples are Caselli (1996), Levine et al (2000), Loayza et al (2004), HHR(2006), among others.

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