Knowledge spillovers: On the impact of genetic distance and data revisions

This paper assesses the robustness of the empirical results in Ertur and Koch (2007), who develop a model, which accounts for technological interdependence among countries through spatial externalities. The original version models interdependence via an interaction matrix based on geographic distance. In contrast, in this paper, data on genetic distance, defined as the time since two populations have shared a common ancestor, from Spolaore and Wacziarg (2009) is used to construct the interaction matrix. It is found that, whereas in the original model indirect spillovers from capital investment were insignificant, using genetic distance, these spillovers now have a significant effect on steady-state income per worker. However, the version of the model with an interaction matrix based on genetic distance implies an implausibly large capital share of income. In addition, the model is subjected to a further series of robustness checks. The original version relies on data from Penn World Table (PWT) Version 6.1. More recent versions are currently available, and the data has been extensively revised (Johnson et al., 2013). It is shown that results are in general not robust across different versions of the PWT. Furthermore, the estimation results are highly sensitive both to the measure used to model interaction between countries (genetic or geographic distance) and to the specific functional form on which the weights in the interaction matrix are based

Schumpeterian growth with technological interdependence: An application to US states

In this paper, the Schumpeterian growth model developed by Ertur and Koch (2011) that includes spatial interactions between units of observation working via R&D spillovers is presented in detail. The implications of this model and three additional growth models with and without spatial interaction that are nested within this framework are tested for the US states econometrically. It is found that investments in R&D have a positive impact on steady-state income per worker in the Schumpeterian growth model without complex interaction between states, but this effect is absent in the model proposed by Ertur and Koch (2011), even though the estimate for the coefficient measuring interconnectedness between regions is positive and significant. This latter result is robust to alternative specifications of the interaction matrix

Spatial Interaction and Economic Growth

Spatial interaction is a central characteristic of economic activity. This thesis aims at providing new insights into the role of integration in the agglomeration-growth nexus and argues that integration is a multidimensional concept that pins down the impact of institutions to a spatial dimension. Also, the impact of genetic distance and data revisions on knowledge spillovers is discussed. Finally, knowledge spillovers are analyzed by applying a multi-region endogenous growth model to the US

Integration as a spatial institution: Implications for agglomeration and growth

In this article we analyze the interdependent issues of urbanization, growth, and globalization by presenting key empirical facts and relevant underlying economic theories on each. We look more closely, but without providing a detailed formal analysis, at a model by Baldwin and Forslid (2000) that combines a seminal model from the endogenous growth literature (Romer 1990) with one from the new economic geography literature (Krugman 1991). In the analysis the significance of a sophisticated consideration of the concept of integration is pointed out. We investigate the issue of scale, scale economies, and density and the important role integration plays in these considerations as well. We especially argue that future research should more precisely focus on integration as a dynamic concept that does not only affect agglomeration and growth, but which is itself the endogenous outcome of various interdependencies and which complements the institutional settings of the territories that are linked to each other

Human metabolic individuality in biomedical and pharmaceutical research.

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research