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We estimate the speed of income convergence for a sample of 196 European NUTS 2 regions over the period 1985-1999. So far there is no direct estimator available for dynamic panels with strong spatial dependencies. We propose a two-step procedure, which involves first spatial filtering of the variables to remove the spatial correlation, and application of standard GMM estimators for dynamic panels in a second step. Our results show that ignorance of the spatial correlation leads to potentially misleading results. Applying a system GMM estimator on the filtered variables, we obtain a speed of convergence of seven per cent.

This paper assesses the strength of productivity spillovers nonparametrically in a data-set of 12 industries and 231 NUTS2 regions in 17 European Union member countries between 1992 and 2006. It devotes particular attention to measuring catching up through spillovers depending on the technology gap of a unit to the industry leader and the local human capital endowment. We find evidence of a non-(log-)linear relationship between the technology gap to the leader as well as human capital and growth. Spillovers are strongest for units with a small technology gap to the leader and with abundant human capital.

We estimate the speed of income convergence for a sample of 196 European NUTS 2 regions over the period 1985-1999. So far there is no direct estimator available for dynamic panels with strong spatial dependencies. We propose a two-step procedure, which involves first spatial filtering of the variables to remove the spatial correlation, and application of standard GMM estimators for dynamic panels in a second step. Our results show that ignorance of the spatial correlation leads to potentially misleading results. Applying a system GMM estimator on the filtered variables, we obtain a speed of convergence of 6.9 per cent and a capital elasticity of 0.43.

This paper investigates whether preference-based (empirical) power indices differ significantly from their preference-free (theoretical) counterparts. Drawing on the most comprehensive sample of EU Council votes to date (1993-2011), we use item-response models to estimate the EU27 member states' preferences (ideal points) in a one-dimensional policy space. Their posterior distributions are then used for the calculation of empirical versions of the Banzhaf and the Shapley-Shubik index, invoking the concepts of connected coalitions and bloc voting. Our ideal point estimates indicate significant differences between member states' preferences, which often translate into significant differences between empirical and theoretical power under individual voting. However, the formation of voting blocs appears to offset differences in countries' ideal points as the bloc size grows. This result does not hold up for the Shapley-Shubik index, whose empirical variants differ from the theoretical one, both under individual and bloc voting. [Copyright Elsevier B.V.]