Our main goal is to ascertain the importance of human capital as a facilitator of technological diffusion in a sample of seven Mediterranean countries (Algeria, Cyprus, Israel, Egypt, Syria, Tunisia, and Turkey) for the period 1960-2000. First, we estimate the technological progress growth rate and the technological gap between each country in our sample and the technological leader (the USA), following the methodology of Benhabib and Spiegel (2002). We then address the issue of the importance of technology diffusion for the TFP growth rate through the Nelson and Phelps (1966) hypothesis - the potential speed of technology diffusion is inversely related to the degree of technological backwardness of the follower country and its ability to absorb new technologies will depend positively on its human capital level. The non-linear specification of the TFP growth rate proposed by Benhabib and Spiegel (2002) is estimated to control for the type of technological diffusion: logistic or exponential. The empirical analysis is applied to two samples: a smaller one consisting of the above-mentioned countries, and a larger one that includes some European countries. First, we studied the unit root characteristic of the TFP growth rate series using unit root panel tests. The results obtained allowed the use of traditional econometric methods for both equations. For the first equation estimations were performed using the NLLS estimation procedure, as it is a non-linear equation. The second equation, was estimated using OLS with robust errors, the fixed effects model and the random effects model, as it is a linear equation. The empirical importance of human capital in fostering technological diffusion is also addressed through the FDI channel, by which technology is transferred from the leader to the followers. The host economy needs a sufficient level of human capital in order to apply the technology of the leader, i.e., the stock of human capital of the follower country limits its absorptive capability. We also analyse the role of human capital as a facilitator of the diffusion of a particular type of technology, ICT, where there is a role for different educational levels. In both cases we take Lee (2000) as the basic framework for our estimations. Finally, the last part of the paper discusses the importance of technological shocks to the process of technological diffusion. The speed of technological diffusion, and consequently the evolution of cross-country differences in GDP growth rates and levels, depend, to a large extent, on exogenous shocks. We propose to model technological shocks for each of the seven countries in our sample in a simple VAR model with four variables: their TFP growth rate, the logarithm of GDP per capita, the logarithm of investment per capita, and the logarithm of the stock of human capital.
