In this paper we analyse regional growth in Central and Eastern Europe in the second half of the 1990s, a period in which pronounced disparities between a group of catching-up regions and another group of falling back regions appeared. We aim to identify the factors behind a dynamic growth performance and the weaknesses leading to poor growth and thus investigate the role of (foreign) investment, education and innovation as well as geographical factors in a model of economic growth. The key relationships proposed by this model are then estimated with empirical data for the period 1995-2000 using spatial econometric tools. We find that foreign direct investment was paramount for regional growth in that period. EU border regions and capital areas clearly outperformed others. Further, regional growth clusters have appeared. Surprisingly, the high level of secondary education in Eastern European regions played no role with growth. Higher education, in contrast served to facilitate technology transfer. (author's abstract) / Series: EI Working Papers / Europainstitut

Spatial interaction models of the gravity type are used in conjunction with sample data on flows between origin and destination locations to analyse international and interregional trade, commodity, migration and commuting patterns. The focus is on the classical log-normal model version and spatial econometric extensions that have recently appeared in the literature. These new models replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for spatial dependence in flow magnitudes. The paper also discusses problems that arise in applied practice when estimating (log-normal) spatial interaction models. (authors' abstract)

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)

The paper sheds some light on the issue of geographically mediated knowledge spillovers from university research activities to regional knowledge production in high tech industries in Austria. Knowledge spillovers occur because knowledge created by university is typically not contained within that institution, and thereby creates value for others. The conceptual framework for analysing geographic spillovers of university research on regional knowledge production is derived from Griliches (1979). It is assumed that knowledge production in the high tech sectors essentially depends on two major sources of knowledge: the university research that represents the potential pool of knowledge spillovers and R&D performed by the high tech sectors themselves. Knowledge is measured in terms of patents, university research and R&D in terms of expenditures. We refine the standard knowledge production function by modelling research spillovers as a spatially discounted external stock of knowledge. This enables us to capture regional and interregional spillovers. Using district-level data and employing spatial econometric tools evidence is found of university research spillovers that transcend the geographic scale of the political district in Austria. It is shown that geographic boundedness of the spillovers is linked to a decay effect. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

The aim of this study is to analyze regional disparities and to test the convergence hypothesis across the provinces of Turkey. The study also attempts to analyze the spatial spillovers in the growth process of the provinces. The analyses cover the 1987-2001 period. Two alternative methodologies are used in the analyses. First, the methodology of &amp / #946 / -convergence based on cross-sectional regressions is used and effects of spatial dependence are analyzed using spatial econometric techniques. Second, Markov chain analysis is used and spatial dependence is integrated using spatial Markov chains. Results of both methodologies signal nonexistence of convergence and existence of spatial spillovers in the growth process of provinces.

Le fait que les activités d'innovation soient très concentrées dans l'espace, et en particulier dans les villes, a motivé un effort de recherche important pour comprendre les dynamiques spatiales de l'innovation. Si les flux de connaissances sont largement reconnus comme un facteur déterminant de la géographie de l’innovation, les mécanismes par lesquels ils se diffusent dans l'espace demeurent méconnus. En particulier, la littérature souligne l'importance de la mobilité des travailleurs qualifiés et des réseaux de collaboration scientifique. Cette thèse examine le rôle de la mobilité qualifiée dans la diffusion des connaissances, ainsi que la distribution spatiale des activités d’innovation qui en résulte. Pour répondre à cette question, cette thèse procède en trois étapes. Le premier chapitre fournit un cadre conceptuel articulant trois courants de littérature dont la nouvelle économie géographique, les modèles de croissance endogène, ainsi que les contributions empiriques sur la géographie de l’innovation. Une des conclusions principales de ce chapitre est que les modèles alliant économie géographique et croissance endogène fournissent un cadre théorique pertinent, puisqu’ils reconnaissent le rôle de la mobilité qualifiée et des externalités de connaissances dans la répartition des activités d’innovation. Cependant, leurs conclusions demeurent incomplètes pour au moins deux raisons.Premièrement, les dynamiques de migration sont très simplistes, et l’introduction de travailleurs avec des caractéristiques et des préférences de localisation hétérogènes altère le mécanisme cumulatif à la base de l’agglomération. Le second chapitre analyse les trajectoires de mobilité des inventeurs entre les villes Européennes, ainsi que leur dimension spatiale. En utilisant ces résultats, un modèle de gravité avec filtres spatiaux est utilisé pour estimer formellement comment le marché du travail, les réseaux de collaborations ainsi que les aménités, influencent les flux de mobilité des inventeurs.Deuxièmement, ces modèles ne considèrent pas la mobilité qualifiées comme un mécanisme de diffusion des connaissances. La littérature empirique a établi que les individus qualifiés influencent les flux de connaissances par leur mobilité professionnelle, ainsi que par les réseaux de collaboration et la capacité d’absorption qui en résulte. Le troisième chapitre estime un modèle spatial de Durbin pour étudier ces trois mécanismes dans un cadre unifié. L'hypothèse sous-jacente est que la mobilité et les réseaux donnent accès aux connaissances externes, mais la proportion de ces connaissances utilisée pour l'innovation dépend de la capacité d'absorption.Ces résultats ont de nombreuses implications pour la géographie de l'innovation. Alors que la mobilité de long terme devrait constituer une force d'agglomération importante, le développement des mobilités de court terme ou circulaires constituent une force de dispersion. L'importance relative de ces deux effets demeure incertaine, car les choix de localisation sont hétérogènes, de sorte que les trajectoires de mobilité varient considérablement. Ces éléments pourraient fournir une explication partielle aux écarts de croissance entre les zones urbaines, et dans une perspective plus dynamique, si cette différence tend à s’accroître ou à se résorber au cours du temps. / The fact that innovative activity is remarkably concentrated in space, and in particular in cities, has motivated an important research effort to understand the spatial dimension of innovation, and the underlying mechanisms at work. While the literature has established the importance knowledge flows for location of innovation, the mechanisms through which they diffuse in space remain largely understudied. In particular, studies have insisted on the importance of skilled workers' mobility and the networked nature of knowledge production for innovation. Building on these considerations, this thesis investigates the role of skilled mobility in the diffusion of knowledge, and the resulting distribution of innovative activity. To answer this question, the thesis proceeds in three steps. The first chapter sets the conceptual framework and surveys the related literature. One of the main conclusion of this review is that some new economic geography and growth models provide a useful theoretical framework, because they recognize the importance of skilled mobility and knowledge externalities for the distribution of innovation. However, they fail to provide a reasonable answer to our research question for at least two reasons. First, the migration dynamics are very simplistic, and introducing heterogeneity in workers' characteristics and location preferences alters the cumulative mechanism of agglomeration. The second chapter provides a descriptive analysis on the patterns of inventors' mobility across urban areas, and their spatial dimension. Using these results, a spatial filtering gravity model is used to analyse formally how employment opportunities, professional networks and urban amenities, influence inventors' mobility flows. Second, these models do not consider workers' role in the diffusion of knowledge. The literature has established that skilled individuals influence the diffusion of knowledge by moving across organisation, creating network relationships and building absorptive capacities. The third chapter implements a spatial Durbin model to study these three mechanisms in an integrated framework. It is assumed that that mobility and networks provide access to knowledge, but the proportion of accessible knowledge used for innovation depends on absorptive capacity. These results have implications for the geography of innovation. While long-term mobility acts as a strong agglomeration force, the development of short-term, circular patterns of mobility should give rise to dispersion. The relative importance of these two effects is uncertain, because workers have different propensities and motivation to move, so that mobility patterns differ considerably. This should help explaining the persistence of long-run growth differentials among urban areas, and in a more dynamic setting, whether these gaps tend to widen or fall over time.

Esta dissertação apresenta um estudo sobre o mercado imobiliário de Porto Alegre que tem como objetivo verificar a formação de preços dos mercados de venda e de locação. Este objetivo é cumprido de duas formas: a) analisando como o tempo que um imóvel está no mercado afeta seu preço e b) comparando como diferentes modelos econométricos preveem os preços de imóveis da capital gaúcha. Para o primeiro tópico, encontram-se evidências de que os proprietários reduzem, em média, 9% o valor de locação para atrair clientes à medida que o tempo passa. Isto não se concretiza da mesma forma no mercado de venda, uma vez que há uma valorização à medida que o imóvel está no mercado. Para o segundo tópico, foram estimados cinco modelos (ingênuo, MQO, SAR, SEM e combinação) para averiguar a performance de previsão fora da amostra dos preços de imóveis. Embora as estatísticas descritivas dos erros não permitam análises conclusivas a respeito dos modelos, a estimação do model confidence set de Hansen et al. (2011) mostra o modelo ingênuo como a melhor série de previsões para os mercados de venda e de locação. / This thesis presents a study on the real estate market of Porto Alegre in order to verify the price formation in both sales and rental markets. This objective is accomplished in two ways: a) analyzing how time on market of a property impacts its price and b) comparing how different econometric models predict real estate prices. For the first topic, I show evidence that owners reduce on average 9% of rental value of available homes in order to attract customers as time on market goes by. This pattern is not present in the sales market, in which an appreciation occurs as time passes, indicating an inflation compensation. For the second topic, five models (naive, OLS, SAR, SEM and a combination) were estimated to determine out-of-sample forecasting accuracy. Although traditional error measures do not allow to choose the best forecasting series, the model confidence set procedure of Hansen et al. (2011) shows the naïve model as being the best forecasting model.

abstract: Gerrymandering is a central problem for many representative democracies. Formally, gerrymandering is the manipulation of spatial boundaries to provide political advantage to a particular group (Warf, 2006). The term often refers to political district design, where the boundaries of political districts are “unnaturally” manipulated by redistricting officials to generate durable advantages for one group or party. Since free and fair elections are possibly the critical part of representative democracy, it is important for this cresting tide to have scientifically validated tools. This dissertation supports a current wave of reform by developing a general inferential technique to “localize” inferential bias measures, generating a new type of district-level score. The new method relies on the statistical intuition behind jackknife methods to construct relative local indicators. I find that existing statewide indicators of partisan bias can be localized using this technique, providing an estimate of how strongly a district impacts statewide partisan bias over an entire decade. When compared to measures of shape compactness (a common gerrymandering detection statistic), I find that weirdly-shaped districts have no consistent relationship with impact in many states during the 2000 and 2010 redistricting plan. To ensure that this work is valid, I examine existing seats-votes modeling strategies and develop a novel method for constructing seats-votes curves. I find that, while the empirical structure of electoral swing shows significant spatial dependence (even in the face of spatial heterogeneity), existing seats-votes specifications are more robust than anticipated to spatial dependence. Centrally, this dissertation contributes to the much larger social aim to resist electoral manipulation: that individuals & organizations suffer no undue burden on political access from partisan gerrymandering. / Dissertation/Thesis / Doctoral Dissertation Geography 2017

This paper focuses on Austrian outbound foreign direct investment (FDI, measured by sales of Austrian affiliates abroad) in Europe over the period 2009-2013, using a spatial Durbin panel data model specification with fixed effects, and a spatial weight matrix based on the first-order contiguity relationship of the countries and normalised by its largest eigenvalue. Third-country effects essentially enter the empirical analysis in two major ways: first, by the endogenous spatial lag on FDI (measured by FDI into markets nearby the host country), and, second, by including an exogenous market potential variable that measures the size of markets nearby the FDI host country in terms of gross domestic product. The question whether the empirical result is compatible with horizontal, vertical, export-platform or complex vertical FDI then depends on the sign and significance levels of both the coefficient of the spatial lag on FDI and the direct impact estimate of the market potential variable. The paper yields robust results that provide significant empirical evidence for horizontal FDI as the main driver of Austrian outbound FDI in Europe. This result is strengthened by the indirect impact estimate of the market potential variable indicating that spatial spillovers do not matter. (authors' abstract) / Series: Working Papers in Regional Science