(BMA) methodology to a panel data framework. The use of panel data in empirical growth regressions has many advantages with respect to typical cross-country regressions. First of all, the prospects for reliable generalizations in cross-country growth regressions are often constrained by the limited number of countries available, therefore, the use of within-country variation to multiply the number of observations is a natural response to this constraint. On the other hand, the use of panel data methods allows to solve the inconsistency of empirical estimates which typically arises with omitted country speci...c e¤ects which, if not uncorrelated with other regressors, lead to a misspeci...cation of the underlying dynamic structure, or with endogenous variables which may be incorrectly treated as exogenous. Since the seminal work of Islam (1995), a lot of studies such as Caselli, Esquivel and Lefort (1996) have employed panel data models with country speci...c e¤ects in empirical growth regressions.

In our case, to simultaneously address both omitted variable bias and issues of endogeneity, we employ a Maximum Likelihood estimator which is able to use the within variation across time and also the between variation across countries.

Against this background, the paper presents a novel approach, Bayesian Averaging of Maximum Likelihood Estimates (BAMLE), which is easy to interpret and easy to apply since it only requires the elicitation of one hyper-parameter, the expected model size, m. Moreover, the impact of di¤erent prior assumptions about m is minimal with the prior structure employed. Our methodology is similar to the BACE approach by SDM, but given the use of a maximum likelihood estimator, BAMLE is more ‡exible and it can be applied to a broader range of situations. In fact, under the assumption of spherical disturbances, BACE can be considered a particular case of BAMLE.

On the other hand, empirical results indicate that the sensivity of the list of robust growth determinants emerging from our approach to the choice of alternative sources of international income data is considerably smaller than found in the previous literature. The reason is that the number of potential regressors we include in our dataset is much smaller than the number considered in previous studies. Therefore, we conclude that the sensitivity of the results to variations in the source of international income data found by Ciccone and Jarocinski (2005) is also present when we consider country speci...c e¤ects. However, given our results, we can also conclude that the fewer the regressors the smaller the sensitivity. For the purposes of robustness, this suggests that the set of candidate variables should avoid inclusion of multiple proxies for the same theoretical e¤ect.

The remainder of the paper is organized as follows. Section 2 describes the BMA methodology and extends to the panel data case the prior structures proposed by SDM and FLS. Section 3 constructs the likelihood function, describes the use of the BIC approximation in the BMA context, and introduces the prior assumptions employed for implementation of the BAMLE approach. In Section 4 we brie‡y describe the data set. The empirical results employing two di¤erent sources for international income data (World Development Indicators 2005 -WDI 2005- and Penn World Table 6.2 -PWT 6.2-) are presented in Section 5. The ...nal 3