The claim that economic crisis matters for well-being seems intuitive; supporting evidence, however, remains elusive. The present study aims to examine the individual and contextual determinants of well-being across regions in Europe during the 2007-2008 economic crisis. This study contributes to the existing research on the determinants of well-being in three ways. First, while most studies explain the determinants of well-being in the context of non-crisis, this study examines the determinants during a period of crisis. Second, while most research on well-being focuses on cross-national comparisons of well-being, this study investigates variations at both the regional and national levels. Third, while most studies use either individual or aggregate analyses to examine the determinants of well-being, this study uses multilevel models. This study uses datasets that combine individual, regional and country level data. Individual data is taken from the 2008 European Values Study (EVS) and the 2004-2010 European Social Survey (ESS). Regional level data comes from Eurostat and Euroboundarymaps, while country level data comes from the Inglehart Index, UNU-WIDER and Esping-Andersen categorisation on welfare states. To analyse the data, this study uses various multilevel models including multivariate multilevel model, multilevel simultaneous equations model and spatial dependence multilevel model. The main findings show that during the crisis under consideration, well-being is associated not only with individual determinants, but also with regional and national determinants. Results suggest that happiness and health are positively correlated at individual, regional and national levels. In terms of social capital, this study shows the reciprocal relationship between association membership and trust. Frequent Internet use at the time of crisis is positively associated with well-being. Finally, the findings suggest that, by means of unobserved factors, well-being is spatially correlated with the well-being of those neighbouring regions.

This study suggests a two-step approach to identifying and interpreting regional convergence clubs in Europe. The first step involves identifying the number and composition of clubs using a space-time panel data model for annual income growth rates in conjunction with Bayesian model comparison methods. A second step uses a Bayesian space-time panel data model to assess how changes in the initial endowments of variables (that explain growth) impact regional income levels over time. These dynamic trajectories of changes in regional income levels over time allow us to draw inferences regarding the timing and magnitude of regional income responses to changes in the initial conditions for the clubs that have been identified in the first step. This is in contrast to conventional practice that involves setting the number of clubs ex ante, selecting the composition of the potential convergence clubs according to some a priori criterion (such as initial per capita income thresholds for example), and using cross-sectional growth regressions for estimation and interpretation purposes. (authors' abstract)

This paper concentrates on the central link between productivity and knowledge capital, and shifts attention from firms and industries to regions. The objective is to measure knowledge elasticity effects within a regional Cobb- Douglas production function framework, with an emphasis on knowledge spillovers. The analysis uses a panel of 203 European regions to estimate the effects over the period 1997-2002. The dependent variable is total factor productivity (TFP). We use a region-level relative TFP index as an approximation to the true TFP measure. This index describes how efficiently each region transforms physical capital and labour into outputs. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of interregional knowledge spillovers. We use patents to measure knowledge capital. Patent stocks are constructed such that patents applied at the European Patent Office in one year add to the stock in the following and then depreciate throughout the patents effective life according to a rate of knowledge obsolescence. A random effects panel data spatial error model is advocated and implemented for analyzing the productivity effects. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and adding an important dimension to the discussion, showing that knowledge spillover effects increase with geographic proximity. (authors' abstract)

La thèse traite le sujet de la formation du capital humain en Europe au niveau régional et ses implications sur la croissance économique. Elle est caractérisée par son approche à la fois régionale, à long terme et européenne. A cet effet, je me réfère à la Théorie de la Croissance Unifiée et à la Nouvelle Economie Géographique qui sont les contributions théoriques les plus importantes dans le domaine et je construis une nouvelle grande base de données sur la formation du capital humain et d’autres facteurs à partir de nombreuses sources diverses. Pour les analyses empiriques, des méthodes spatiales et SIG ont été employées en plus des modèles économétriques standards. Ainsi, la thèse explore la formation du capital humain dans les régions du continent européen entre 1790 et 2010. Par ailleurs, elle souligne la relation entre les indicateurs du capital humain ainsi que les déterminants du capital humain et les implications du capital humain sur la croissance économique à long terme. / This thesis highlights the formation of human capital in the European regions and its implications for economic growth. It is characterised by its combined regional, long-term and European approach. To this end, I refer to Unified Growth Theory and New Economic Geography as the most important recent theoretical contributions and construct an unparalleled new and large database on regional human capital and other economic factors from numerous diverse sources. For the empirical analysis, spatial and GIS methods are employed in addition to standard econometric models. In this way, the thesis explores human capital formation in the regions of the European continent between 1790 and 2010. Moreover, it underlines the relationship between human capital proxies, the determinants of human capital and the long-run impact of human capital on economic growth.

In this paper we present an econometric framework aimed at obtaining projections of income growth in Europe at the regional level. We account for model uncertainty in terms of the choice of explanatory variables, as well as the nature of the spatial spillovers of output growth and human capital investment. Building on recent advances in Bayesian model averaging, we construct projected trajectories of income and human capital simultaneously, while integrating out the effects of other covariates. This approach allows us to assess the potential contribution of future educational attainment to economic growth and income convergence among European regions over the next decades. Our findings suggest that income convergence dynamics and human capital act as important drivers of income growth for the decades to come. In addition we find that the relative return of improving educational attainment levels in terms of economic growth appears to be higher in peripheral European regions. (authors' abstract) / Series: Department of Economics Working Paper Series

This paper adopts a spatial econometric perspective to analyse regional convergence of per capita income in Europe in 1995 to 2000 and, moreover, relaxes the assumption of a single steady-state growth path which appears to be out of tune with reality of empirical dynamics. The two-club spatial error convergence model with groupwise heteroskedasticity is found to be most appropriate for the data at hand. Two empirical key findings are worthwhile to note. The first is that the data provide much support for unconditional ß-convergence in Europe. The second is that the usual convergence conclusions hold. But they do so for reasons that are not revealed by the classical test equation that is typical in mainstream economics literature. (authors' abstract)

This paper investigates the impact of knowledge capital stocks on total factor productivity through the lens of the knowledge capital model proposed by Griliches (1979), augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on total factor productivity (TFP) in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen and Diewert (1982). This index describes how efficiently each region transforms physical capital and labour into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy regional knowledge capital stocks for N=203 regions over the 1997- 2002 time period. In estimating the effects we implement a spatial panel data model that controls for the spatial autocorrelation due to neighbouring regions and the individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and add an important spatial dimension to the discussion, by showing that productivity effects of knowledge spillovers increase with geographic proximity. (authors' abstract)

Using a New Economic Geography (NEG) model, this study estimates the relationship between regional per capita income and the market accessibility of regions. This accessibility cannot be observed directly, so it has to be constructed. We follow a two-step-procedure as suggested by Redding and Venables (2004) and use results of a gravity-type model to infer \real market potential". To this end, we make use of a novel dataset of bi-regional trade ows between (and within) 254 European NUTS-2 regions (for 26 European countries excluding Bulgaria and Romania) for the year 2010. In a second step we test the hypothesis that access to domestic as well as to large foreign markets increases factor incomes. We find evidence that supports this hypothesis on a regional level. This also holds when we control for other potential income determinants. In order for the estimates to be unbiased, we additionally take the spatial structure of the data into account. Our findings indicate that, although the specification derived from theory should be able to capture some spatial spillovers, additionally controlling for spatial autocorrelation in the residuals is necessary to fit the European data. (authors' abstract) / Series: Department of Economics Working Paper Series

Despite years of deepening economic integration among the states and regions of the European Union, empirical research remains inconclusive about speed of convergence across regions, if not its existence. This thesis provides novel evidence on convergence in the EU while focusing on development at regional level after the Great Recession. It uses recently developed log t convergence test by Phillips and Sul (2007). Our findings speak against the convergence in level of income per capita among the European regions and give us five clubs of regions converging in their income growth rates instead. Investigating further the geographical distribution of the convergence clubs, we confirm high inequality within the member states and find large continuous area of high convergence clubs in the urbanized part of Western Europe. Furthermore, we investigated the determinants of convergence club membership using Logistic Regression. The main explanatory variable of interest were Business Services (BS), a dynamic sector of the economy with presumably strong positive effect on regional innovative potential. We found positive effect of BS on membership in higher convergence clubs. Yet, this effect seems to diminish for the very highest club.

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.