This study introduces a new firm-level dataset containing over two million projects co-funded by the European Union´s (EU) structural and Cohesion funds in 25 EU member states in the multi-annual financial framework 2007-2013. Information on individual beneficiary firms and institutions published by regional authorities is linked with business data from Bureau van Dijk's ORBIS database. Moreover, we show how modern text mining techniques can be used to categorise EU funded projects into fifteen thematic categories proposed by the European Commission. A first analysis of the dataset reveals substantial heterogeneity of beneficiaries and projects across and within countries. While in the majority of lagging regions the largest project expenditure is dedicated to transportation and energy infrastructure, in most other regions the major part is assigned to innovation and technological development as well as business (including SME) support. In an econometric analysis we control for project and firm characteristics and find that the highest single project values are associated with older beneficiary firms that are larger in size. Furthermore, the projects with topmost expenditure are carried out in Dutch and British regions. / Series: Department of Economics Working Paper Series

In this paper we put forward a Bayesian Model Averaging method aimed at performing inference under model uncertainty in the presence of potential spatial autocorrelation. The method uses spatial filtering in order to account for uncertainty in spatial linkages. Our procedure is applied to a dataset of income per capita growth and 50 potential determinants for 255 NUTS-2 European regions. We show that ignoring uncertainty in the type of spatial weight matrix can have an important effect on the estimates of the parameters attached to the model covariates. After integrating out the uncertainty implied by the choice of regressors and spatial links, human capital investments and transitional dynamics related to income convergence appear as the most robust determinants of growth at the regional level in Europe. Our results imply that a quantitatively important part of the income convergence process in Europe is influenced by spatially correlated growth spillovers.

Club-convergence analysis provides a more realistic and detailed picture about regional income growth than traditional convergence analysis. This paper presents a spatial econometric framework for club-convergence testing that relates the concept of club-convergence to the notion of spatial heterogeneity. The study provides evidence for the club-convergence hypothesis in cross-regional growth dynamics from a pan-European perspective. The conclusions are threefold. First, we reject the standard Barro-style regression model which underlies most empirical work on regional income convergence in favour of a two regime [club] alternative in which different regional economies obey different linear regressions when grouped by means of Getis and Ord's local clustering technique. Second, the results point to a heterogeneous pattern in the pan-European convergence process. Heterogeneity appears in both the convergence rate and the steady-state level. But, third, the study also reveals that spatial error dependence introduces an important bias in our perception of the club-convergence and shows that neglect of this bias would give rise to misleading conclusions.

Relatedness, industrial branching and technological cohesion in US metropolitan areas, Regional Studies. Work by evolutionary economic geographers on the role of industry relatedness for regional economic development is extended into a number of methodological and empirical directions. First, relatedness is measured as the intensity of inputoutput linkages between industries. Second, this measure is employed to examine industry evolution in 360 US metropolitan areas. Third, an employment-weighted measure of metropolitan technological cohesion is developed. The results confirm that technological relatedness is positively related to metropolitan industry portfolio membership and industry entry and negatively related to industry exit. The decomposition of technological cohesion indicates that the selection of related incumbent industries complements industry entry and exit as the main drivers of change in metropolitan technological cohesion.

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. / Series: EI Working Papers / Europainstitut

This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries. In addition, a correct interpretation of the spatial regression parameter estimates that takes into account the simultaneous feed- back nature of the spatial autoregressive model is provided. Our findings indicate that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates. (authors' abstract)

This paper suggests an empirical framework for analysing income distribution dynamics and cross-region convergence in the European Union of 27 member states, 1995- 2003. The framework lies in the research tradition that allows the state income space to be continuous, puts emphasis on both shape and intra-distribution dynamics and uses stochastic kernels for studying transition dynamics and implied long-run behaviour. In this paper stochastic kernels are described by conditional density functions, estimated by a product kernel estimator of conditional density and represented by means of novel visualisation tools. The technique of spatial filtering is used to account for spatial effects, in order to avoid misguided inferences and interpretations caused by the presence of spatial autocorrelation in the income distributions. The results reveal a slow catching-up of the poorest regions and a process of polarisation, with a small group of very rich regions shifting away from the rest of the cross-section. This is well evidenced by both, the unfiltered and the filtered ergodic density view. Differences exist in detail, and these emphasise the importance to properly deal with the spatial autocorrelation problem. (authors' abstract)

This paper presents a theoretical growth model that extends the Mankiw-Romer-Weil [MRW] model by accounting for technological interdependence among regional economies. Interdependence is assumed to work through spatial externalities caused by disembodied knowledge diffusion. The transition from theory to econometrics leads to a reduced-form empirical spatial Durbin model specification that explains the variation in regional levels of per worker output at steady state. A system of 198 regions across 22 European countries over the period from 1995 to 2004 is used to empirically test the model. Testing is performed by assessing the importance of cross-region technological interdependence, and measuring direct and indirect (spillover) effects of the MRW determinants on regional output. (author's abstract)

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity. (authors' abstract)

This paper presents a theoretical growth model that extends the Mankiw-Romer-Weil [MRW] model by accounting for technological interdependence among regional economies. Interdependence is assumed to work through spatial externalities caused by disembodied knowledge diffusion. The transition from theory to econometrics leads to a reduced-form empirical spatial Durbin model specification that explains the variation in regional levels of per worker output at steady state. A system of 198 regions across 22 European countries over the period from 1995 to 2004 is used to empirically test the model. Testing is performed by assessing the importance of cross-region technological interdependence, and measuring direct and indirect (spillover) effects of the MRW determinants on regional output. (author's abstract)