The objective of this study is to identify knowledge spillovers that spread across regions in Europe and vary in magnitude for different industries. The study uses a panel of 203 NUTS-2 regions covering the 15 pre-2004 EU-member-states to estimate the impact over the period 1998-2003, and distinguish between five major industries. The study implements a fixed effects panel data regression model with spatial autocorrelation to estimate effects using patent applications as a measure of R&D output to capture the contribution of R&D (direct and spilled-over) to regional productivity at the industry level. The results suggest that interregional knowledge spillovers and their productivity effects are to a substantial degree geographically localised and this finding is consistent with the localisation hypothesis of knowledge spillovers. There is a substantial amount of heterogeneity across industries with evidence that two industries (electronics, and chemical industries) produce interregional knowledge spillovers that have positive and highly significant productivity effects. The study, moreover, confirms the importance of spatial autoregressive disturbance in the fixed effects model for measuring the TFP impact of interregional knowledge spillovers at the industry level. (authors' abstract)

This paper directs interest on country-specific labour market discrimination Roma may suffer in South East Europe. The study lies in the tradition of statistical Blinder-Oaxaca decomposition analysis. We use microdata from UNDP's 2004 survey of Roma minorities, and apply a Bayesian approach, proposed by Keith and LeSage (2004), for the decomposition analysis of wage differentials. This approach is based on a robust Bayesian heteroscedastic linear regression model in conjunction with Markov Chain Monte Carlo (MCMC) estimation. The results obtained indicate the presence of labour market discrimination in Albania and Kosovo, but point to its absence in Bulgaria, Croatia, and Serbia. (authors' abstract)

This report describes the aim, scope, underlying literature and results of the research project "The Nexus between the Financial and the Real Sector". We studied the contribution of the financial sector as a whole and its individual segments (bank credits, the issuance of bonds and shares) to real economic growth in EU Member and Candidate Countries, the United States and Japan. We supplement existing approaches with the inclusion of the bond market and of foreign direct investment in the banking sector, wherein for the first time, we provide empirical evidence for slightly positive effects thereof. Methodically, we extend previous research by the production-function approach and document the importance of the market microstructure. We recommend to include liberalisation and integration effects, the bond and insurance sector, and effects of foreign bank entry and investment into future research on the Finance-Growth-Nexus. (author's abstract) / Series: EI Working Papers / Europainstitut

We employ income projection models based on human capital dynamics in order to assess quantitatively the role that educational improvements are expected to play as a driver of future income convergence in Europe. We concentrate on income convergence dynamics between emerging economies in Central and Eastern Europe and Western European countries during the next 50 years. Our results indicate that improvements in human capital contribute significantly to the income convergence potential of European emerging economies. Using realistic scenarios, we quantify the effect that future human capital investments paths are expected to have in terms of speeding up the income convergence process in the region. The income projection exercise shows that the returns to investing in education in terms of income convergence in Europe could be sizeable, although it may take relatively long for the poorer economies of the region to rip the growth benefits. (authors' abstract) / Series: Department of Economics Working Paper Series

This paper considers the problem of model uncertainty associated with variable selection and specification of the spatial weight matrix in spatial growth regression models in general and growth regression models based on the matrix exponential spatial specification in particular. A natural solution, supported by formal probabilistic reasoning, is the use of Bayesian model averaging which assigns probabilities on the model space and deals with model uncertainty by mixing over models, using the posterior model probabilities as weights. This paper proposes to adopt Bayesian information criterion model weights since they have computational advantages over fully Bayesian model weights. The approach is illustrated for both identifying model covariates and unveiling spatial structures present in pan-European growth data. (authors' abstract) / Series: Department of Economics Working Paper Series