Intra-firm and extra-firm linkages in the knowledge economy: the case of the emerging mega-city region of Munich

With the aim of identifying emerging patterns of spatial development and the driving forces behind the associated process, in this article we draw together two threads of interlinked phenomena. First, we look at how multi-location firms from the knowledge economy develop their intra-firm networks internationally. Second, we establish the partners with which these firms have working relationships along individual chains of value, and in which these extra-firm linkages are located. We start from a conceptual background that combines the location behaviour of firms with a value chain approach. We analyse the two main pillars of the knowledge economy – advanced producer services (APS) and high-tech firms. A case study carried out in the greater Munich area provides the empirical basis and draws on quantitative and qualitative research methods. The results provide evidence that the greater Munich area can be regarded simultaneously as a hierarchically organized polycentric mega-city region and high-grade localized system of value chains.     

Biases and Uncertainty in Climate Projections

Abstract. We study statistical procedures to quantify uncertainty in multivariate climate projections based on several deterministic climate models. We introduce two different assumptions – called constant bias and constant relation respectively – for extrapolating the substantial additive and multiplicative biases present during the control period to the scenario period. There are also strong indications that the biases in the scenario period are different from the extrapolations from the control period. Including such changes in the statistical models leads to an identifiability problem that we solve in a frequentist analysis using a zero sum side condition and in a Bayesian analysis using informative priors. The Bayesian analysis provides estimates of the uncertainty in the parameter estimates and takes this uncertainty into account for the predictive distributions. We illustrate the method by analysing projections of seasonal temperature and precipitation in the Alpine region from five regional climate models in the PRUDENCE project.