In recent years there is a growing interest in determining the impact of inequality on economic growth. Theoretical papers as well as empirical applications have, however, produced controversial results. Although there is a considerable part of the literature that considers inequality detrimental to growth, more recent studies have challenged this result and found a positive effect of inequality on growth. In this paper, we provide a contribution to the empirical puzzle by using meta‐analysis to systematically describe, identify and analyse the variation in outcomes of empirical studies. We find that estimation methods, data quality and sample coverage systematically affect the results. The results point out that it will be particularly useful to increasingly focus research on determining the impact of income inequality on economic growth using single‐country data at the regional level, or a relatively homogeneous set of countries with adequate controls for country‐wide differences in economic, social and institutional characteristics.

This article adds to the empirical evidence on the impact of agglomeration externalities on regional growth along three main dimensions. On the basis of data on 259 Europe NUTS2 (Nomenclature of Territorial Units for Statistics) regions and 15 NACE (Nomenclature statistique des Activités économiques dans la Communauté Européenne) 1.1 2-digit industries for the period 1990–2007, we show that agglomeration externalities are stronger in technology-intensive industries, also after controlling for sorting; that specialization externalities are stronger for low density regions, while diversity matters more for denser urban areas; and, finally, that Jacobs externalities comprise a pure diversification effect (related variety) and a portfolio effect (unrelated variety), although evidence of positive effects on regional growth is only found for the latter. An additional contribution of this article is to extend the analysis on the basis of a full geographical coverage of European NUTS2 regions, with the aim to generalize the empirical identification of the impacts of specialization and diversification externalities with respect to the existing literature. Our results are robust to a rich set of consistency checks, including the use of spatial autoregressive models with autoregressive disturbances, used to assess to what extent the effects of agglomeration externalities are localized

The aim of this study is to empirically examine regional resilience by assessing economic growth patterns in two distinct groups of regions across the European Union in the aftermath of the 2008 economic and financial crisis. In an effort to consider the regions as interconnected economic areas and account for spillover effects, the model incorporates complex spatial effects that consider both spatial heterogeneity and spatial dependence. The analysis follows a step‐wise approach. First, spatial heterogeneity is assessed by employing Exploratory Spatial Data Analysis, which identifies two distinct spatial regimes, a core and a periphery, based on their initial level of economic development. A Spatial Durbin Model is then employed to estimate the determinants of regional resilience and growth in both regimes, including potential spillover effects. Results indicate that while both spatial regimes experience processes of economic convergence, recent determinants of growth, as well as spillover dynamics, differ across the two. In the core regime, better institutions, higher shares of investment, and an economy specialized in higher value‐added sectors significantly spur domestic growth, with investment also inducing positive spillover effects to neighbouring regions. In the peripheral regime, low shares of lower‐secondary educational attainment and high shares of tertiary educational attainment have a significant positive effect on domestic growth, with higher shares of tertiary educational attainment also inducing positive spillover effects. Moreover, technological readiness is also identified as an important factor in the peripheral regime with positive spillover effects. Upon the bedrock of these findings, initial policy proposals are offered.

No abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractFinding proper policy instruments to promote productivity growth features prominently on the Lisbon agenda and is central in many national as well as European policy debates. In view of the increased mobility of high-skilled workers in Europe, ongoing globalization and increased interregional and international co-operation, location patterns of innovative activity may be subject to drastic changes. A proper understanding of location patterns of innovative outputs can enhance the effectiveness and efficiency of national and European innovation policies. Building on the literature on the knowledge production function the aim of this paper is to explain the observed differences in the production of innovative output across European regions. Our main research question is whether geographical proximity and social capital are important vehicles of knowledge transmission for the production of innovative output in Europe. Several other variables are used to control for structural differences across European regions. We find support for the hypothesis that both social capital and geographical proximity are important factors in explaining the differences in the production of innovative output across European regions.

International audienceTraditionally the analysis of the spatial distribution of economic activity on a discrete space is based on measures that suffer from a series of important drawbacks. This paper proposes a methodological advance in two respects. First, we extend the analysis to take spatial dependence explicitly into account. Second, we control for differences in the size distribution of firms between territorial units. These problems have so far only been treated separately in the literature. Using data for Italy, we apply exploratory spatial data analysis to identify sectoral location patterns in both the manufacturing industry as well as in the business services. We find that large differences prevail in the geographical concentration of production across sectors. The results of the exploratory spatial data analysis reveal the existence of well-defined clusters of economic activities

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractFinding proper policy instruments to promote productivity growth features prominently on the Lisbon agenda and is central in many national as well as European policy debates. In view of the increased mobility of high-skilled workers in Europe, ongoing globalization and increased interregional and international co-operation, location patterns of innovative activity may be subject to drastic changes. A proper understanding of location patterns of innovative outputs can enhance the effectiveness and efficiency of national and European innovation policies. Building on the literature on the knowledge production function the aim of this paper is to explain the observed differences in the production of innovative output across European regions. Our main research question is whether geographical proximity and social capital are important vehicles of knowledge transmission for the production of innovative output in Europe. Several other variables are used to control for structural differences across European regions. We find support for the hypothesis that both social capital and geographical proximity are important factors in explaining the differences in the production of innovative output across European regions.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractExisting indices measuring the spatial distribution of economic activity such as the Krugman Specialisation Index, the Hirschmann-Herfindahl index and the Ellison-Glaeser index typically do not take into account the spatial structure of the data. In this paper, we first consider traditional measures of geographical concentration, and subsequently extend the analysis to take spatial dependence into account. Using data for Italy for the years 1991 and 2001, we apply exploratory spatial data analysis to identify sectoral location patterns in both the manufacturing industry as well as in services. We find that large differences prevail in the geographical concentration of production across sectors. The results of the exploratory spatial data analysis reveal the existence of welldefined clusters of economic activities. JEL codes:C12, R12, R30