Table 2 shows the posterior inclusion probability, the posterior mean and the posterior standard error for the parameters corresponding to the 19 variables of our data set with time variation when we apply the BACE-SDM and BMA-FLS approaches in a panel data context. These results are based on the whole sample, that is, 73 countries for the period 1960-2000. The main conclusion from the table is that, in addition to initial GDP, there are several covariates which appear robustly related to economic growth. However, we defer our main conclusions to Table 4 below.

In Tables 3 and 5, we follow the methodology employed by Ciccone and Jarocinski (2005). Employing the same sample period for both sources of income data<sup>11</sup> , we can assess the sensitivity to changes in data source of the results in terms of posterior inclusion probability and posterior mean. The measures shown are self-explanatory: the results are considerably less sensitive to di¤erences in income data source than found in the previous literature, at least for the comparison between World Bank and Penn World Table income data<sup>12</sup> . In order to further explore this issue, we redo the sensivity analysis using the BACE-SDM approach without considering the panel structure of the data. By doing this, the only di¤erence vis-a-vis Ciccone and Jarocinsky (2005) is the number of regressors considered in the exercise. While they consider 67 potential explanatory variables, we consider 34. Looking at the results, presented in Table 6, we can see that the sensivity with K = 34 is much smaller than with K = 67. Therefore, we conclude that the number of potential explanatory variables under consideration is critical for the sensivity of the results to changes in the source of international income data used. Concretely, the fewer the regressors, the smaller the sensivity.

Results when applying the BAMLE Approach with PWT 6.2 income data for the whole period are summarized in Table 4. Addionally to initial GDP, a fair number of regressors could be considered as robust determinants of economic growth accordingly to the Bayesian robustness check used in the approach. The most conclusive evidence is for investment price, distance to major world cities and political rights. All three regressors a¤ect growth with the expected sign: in particular, as found by Easterly (1993), a low level of distorsions in the economy (i.e. lower investment price) would promote economic growth. A better geographic situation, (i.e. a better access to international markets) is also an important growth enhancing factor as argued by Krugman (1991). Finally, in contrast to Barro (1991) but in line with Sala-i-Martin (1997), a higher level of democracy (i.e. a lower value of the variable measuring restrictions on political rights) is found to be positively related to higher growth rates. On the other hand, since their posterior inclusion probability is higher than their prior inclusion probability, many other variables such as demographic indicators, a measure of trade openness, the dummy for landlocked countries, the investment share, the civil liberties index and the government share can be considered as robust determinants of economic growth. Finally, there is one regressor, life expectancy, that poses a puzzle. In

> 11Note that WDI 2005 income data only covers the period 1975-2000.

> 12See Ciccone and Jarocinski (2005) for more details on the cross-country context.

16