Estimating the effect of technological factors from samples affected by collinearity: a data-weighted entropy approach

Measuring the effect of technological activities on productivity growth is an issue that attracted much attention in recent works on empirical econometric studies. Specifically, in the field of regional economics, several attempts have been made in order to quantify the contribution of R&D to labor productivity growth at a regional scale, considering both the internal R&D and the effects obtained by geographical spillovers. The results obtained, however, are characterized by a huge variability and in many cases there is no empirical evidence of positive contributions of R&D activities to productivity growth. Our argument is that this can be a consequence of dealing with samples’ affect by a high level of collinearity. This paper proposes the use of the data-weighted prior (DWP) estimator suggested by Golan (J Econom 101:165–193, 2001). The main advantage of this estimator is that it discriminates between relevant and irrelevant regressors better than other estimators when dealing with highly collinear samples. We evaluate the performance of the DWP estimator by Monte Carlo simulations and illustrate how it works by means of a real-world example.