variable and growth is ranked according to the _ex post_ inclusion probability. From the narrow list of 18 variables that are found to be significantly partially correlated with economic growth in the Sala-i-Martin et al. paper, five are selected as core explanatory variables in our econometric estimations. As examined below, the inclusion of these determinants (initial GDP per capita, educational attainment, population growth, relative price of investment goods, and trade openness) has been strongly advocated by growth theories. The list of determinants of economic growth used is by no means exhaustive and the criticism by Brock and Durlauff (2001) on the arbitrariness and ―open-endedness‖ of growth specifications remains. While the lack of a fully fledged growth theory can only partly justify possible omitted variables, it is important to provide a theoretical justification for those that are selected. To this end, the relationship between the determinants used in this paper and the rates of medium-term economic growth is as follows.

The initial level of GDP per capita tests ( _GDPPC_ ) the proposition of the absolute (unconditional) beta ( _β_ ) convergence hypothesis. According to neoclassical growth theory (e.g., Solow, 1956), when structural differences between countries are accounted for, the low-income countries tend to display higher rates of growth than the developed economies. The validity of the hypothesis is tested by examining the influence of initial period real GDP per capita on the average growth rate of the real GDP per capita in purchasing power parity terms (PPP) during the following four-year period. The relevant empirical literature produces consistent evidence in favour of this relationship. Given the theoretical guidance and the robust empirical findings, the expected sign of the coefficient on this variable is negative.

The educational attainment of the adult population ( _edu_ or the average years of schooling) is a far-from-perfect proxy for a country’s human capital but, given the lack of alternative education indicators, this measure plays a satisfactory role in growth regressions. The indicator does not capture the changes either in the quality of education or in the education system capacity to meet the skill composition of labor demand. We use the Barro and Lee (2010) educational attainment international dataset and refer to population above 25 years of age. An additional limitation of the measure of average years of schooling is that it ―assumes unrealistically that a year of education adds a constant quantity of human capital, whether undertaken by a primary pupil or a college student‖ (Barro and Lee, 2001, pp. 558). Nevertheless, the expected sign is positive since human capital formation is expected to exhibit growth-conducive effects.

The relationship between population growth ( _popgr_ ) and growth continues to raise controversies among development economists. ―Population pessimists‖ argue that population growth inhibits development since greater population requires additional production capital per worker and involves public costs for raising children (e.g., Johnson and Lee, 1987). The mainstream neoclassical growth models predict that population growth has the same effect as depreciation: the increased supply of labor reduces the capital stock per unit of labor. According to this class of models, the steady state level of output will, all other things equal, increase if the rate of population growth decelerates. On the other hand, the camp of ―population optimists‖ conjectures that population growth in the long run brings ―improvement of productivity through both the contribution of new ideas and also the learning-by-doing consequent upon increased production volume‖ (Simon, 1987, p. 168). In particular, the endogenous growth literature (e.g., Lucas, 1990; and Romer, 1990 and 1994) emphasizes that the non-rivalry of new ideas accompanied by population growth are strong engines of economic growth. The proponents of 8