the log of domestic credit as percentage of GDP as proxy for financial depth, and an index of export diversification<sup>6.</sup> Table 7 shows OLS and Instrumental Variables estimates. As can be observed from the OLS estimates presented in columns (1) to (5), EXPY has a significantly positive effect on growth that is robust to the inclusion of other covariates. However, notice that the magnitude of the effect varies between 0.035 and 0.043, being invariant only to the inclusion of the Rule of Law Index. Regarding the effect of other covariates on growth, the initial level of GDP per capita is always negative and significant. The human capital variable and the Rule of Law Index have a robust positive and significant effect on growth as expected. Domestic credit and export diversification are insignificant in all specifications<sup>7</sup> .

The last five columns of Table 7 show IV estimates where log of population and log of land area were used as instruments<sup>8</sup> . We choose these instruments following HHR (2006) who argue that specializations patterns depend on idiosyncratic elements and fundamentals like human capital and labor force size. HHR base their excludability restriction in the fact that there has not been found yet any empirical evidence of scale effects in growth models<sup>9</sup> , then arguing that population size and land area, as proxies of country size, are plausible instruments for EXPY<sup>10</sup> .

By looking at the IV estimations in Table 7 one can observe that, as is generally the case, the standard errors are much bigger (almost double) compared to the OLS estimations, although the EXPY point estimates are relatively close to the previous estimates. Notice, however, that the effects of the other covariates continue to be robust (except for GDP per capita in some specifications). The specification tests presented at the bottom of the table are useful to shed light on understanding these results. The overidentification test fails in practically all cases. Moreover, the identification tests are rejected at only 5 and 10 percent of significance level. Suspecting weak instruments problem we run the Stock-Wright S-statistic to do weak-instrument-robust inference and find that EXPY is significant at 5 and 10 percent level in most cases<sup>11</sup> .

Given that the EXPY might affect growth differently depending on the level of income of the economies we run two sets of experiments. First, as shown in Table 8, we run the same specifications as before but excluding from the sample high-income economies according to The World Bank classification. The OLS estimates on the restricted sample are very similar to the ones encountered in Table 7. Yet, the coefficients of EXPY are smaller in some specifications. Also, the estimates are in general less precise, possibly due to the smaller sample size. On the other hand,

> 6 The diversification index is defined as 1-Herfindahl Index following Hwang (2006).  The source for GDP per capita and domestic credit as percentage of GDP is the Penn World Tables.  For the average years of schooling we used the Barro-Lee dataset.  See Barro and Lee (1994,2000)

> 7 Credit is significant only when schooling is not included. Other specifications and test are available upon request from the authors.

> 8 These variables were extracted from the Penn World Tables.

> 9 For this purpose HRR cite Rose, Andrew K. “Size Really Doesn’t Matter: In Search for a National Scale Effect”, NBER Working Paper No. 12191, April 2006.

> 10 HHR show a regression showing the correlation between EXPY and the instrument set and find land area and population to be significant.  In our estimations, though, land is only slightly significant in the first stage of the specification presented in column (6).  We run a test for the redundancy of this variable and fail to reject it in all specifications.  Also, we run the IV regressions excluding land from the instrument set and got very similar results, except that the point estimates of both EXPY and GDP per capita were very small and the latter was no longer significant.

> 11 We obtained the same results when running the Anderson-Rubin test with the exception of column (6) in Table 10, which rejects the null at 1% of significance level.

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