This paper studies the link between knowledge, innovation, and growth in European regions using nonparametric methods. Our findings suggest that knowledge inputs and the share of innovative firms have a heterogeneous and nonlinear relationship with growth. This evidence has been exploited to examine the consequences of alternative policies using a counterfactual estimation setup, the results of which imply that increasing the formal knowledge base may be optimal in most regions. Less knowledge and innovation intensive regions will also benefit from a higher innovation potential and from a trustworthy and entrepreneurial economic environment.
This paper analyzes the persistence of the shock caused by the American Civil War on the relative city size distribution of the United States. Our …ndings suggest that the e¤ects of this shock were permanent, which sharply contrasts with previous results regarding World War II for Japanese and German cities. It should be taken into account that the con ‡ict considered in this paper took place at an earlier stage of the industrialization and urbanization processes. Moreover, our results are determined by the fact that the battles were fought in the open …eld, not in urban areas. Some related evidence regarding the presence of a 'safe harbour e¤ect'is reported.
This paper analyses the determinants of regional economic growth in the European Union adopting a non-parametric approach. Although the local-linear kernel estimator applied does not explicitly take into account the spatial dimension of the data, it is found to be consistent in our context. In addition, the geographically weighted regression turns out to be less efficient. We obtain evidence of a non-linear relationship between regional growth and its determinants in the form of parameter heterogeneity and threshold effects. These non-linearities mainly affect the initial productivity of labour, the human capital endowment and, as a novelty, the level of infrastructures.JEL classification: C14, C20, O18, R11
This paper introduces model uncertainty into the empirical study of the determinants of terrorism at country level. This is done by adopting a Bayesian model averaging approach and accounting for the over-dispersed count data nature of terrorist attacks. Both a broad measure of terrorism and incidents per capita have been analyzed. Our results suggest that, among the set of regressors considered, those reflecting labor market conditions and economic prospects tend to receive high posterior inclusion probabilities. These findings are robust to changes in the model specification and sample composition and are not meaningfully affected by the generalized linear regression model applied. Evidence of a geographically heterogeneous relationship between terrorism and its determinants is also provided. Abbreviation: BMA-Bayesian Model Averaging; GLM-Generalized Linear Models ARTICLE HISTORY Terrorism determinants; model uncertainty; Bayesian model averaging; generalized linear models JEL CLASSIFICATION C11; C25; C52; D74; H56. MotivationThere has been an upsurge in the empirical analysis of the socioeconomic determinants of terrorism at country level after the 9/11 attacks. Related studies explore alternative information sources and dimensions of terrorist activity, consider different potential determinants of terrorism and sample periods, and apply a wide array of estimation methods. This implies that, although it is important to unveil the main causes of terrorism to deal with this scourge and mitigate its substantial and multidimensional costs, there is no clear consensus on the origins of terror in the literature. As an example, Krieger and Meierrieks (2011) provide an overview and critical discussion of the early evidence about the sources and targets of transnational terrorism, reaching no conclusive results.A theoretical foundation for the hypothesis that economic grievances generate terrorism, based on the rational-choice theory and without dismissing noneconomic factors, can be found in Meierrieks (2014). This author suggests that the lack of empirical consensus on the causes of terrorism has to do with its heterogeneity and that its link with the economy needs to be further investigated. Morris and LaFree (2016) also review the quantitative studies about country-level correlates of terrorism, with an emphasis on economic, political and demographic variables. These authors highlight the significant influence exerted by economic development and inequality, democracy, failed states and physical integrity rights. In a related work, Kis-Katos, Liebert, and Schulze (2011) find that the roots of domestic and transnational terrorism are similar. In particular,
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