Regions in the global knowledge economy

Two bodies of literature converge to explain regions in the global knowledge economy and to identify the factors that lead to competitiveness and innovation of a local economic system. The first section of this statement summarizes the progress in regional studies from a purely locational approach to the focus on clusters and industrial districts. The second part shows how advances in the economics of innovation lead to a renewed and different interest to regions and local systems of innovation. The third section concludes showing how the two trends of the literature just mentioned are instrumental to explain regions in a context where competition becomes global and increasingly based on knowledge goods and services. The focus on the “glocal” exchange of outputs of the knowledge economy is useful to explain the factors behind the rise and fall of new centers of production and growth. In this statement glocalization is defined as the phenomenon that leads to the competition, on a global market, of products and services whose successful development from the conceptualization of an idea to the actual commercial application requires enabling factors (such as institutions, entrepreneurship, knowledge, skills…) that are embedded in a specific local environment. The study of this phenomenon justifies the convergence of regional economics and the economics of innovation. The goal of this statement is to present the literature which might be used in two classes on regional development in the knowledge economy and glocalization of production, that could be taught in a planning, business or public policy department.

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring a large integration step to impute over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multitype branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for hematopoiesis and transposable element evolution.Comment: 18 pages, 4 figures, 2 table

Locally adaptive smoothing with Markov random fields and shrinkage priors

We present a locally adaptive nonparametric curve fitting method that operates within a fully Bayesian framework. This method uses shrinkage priors to induce sparsity in order-k differences in the latent trend function, providing a combination of local adaptation and global control. Using a scale mixture of normals representation of shrinkage priors, we make explicit connections between our method and kth order Gaussian Markov random field smoothing. We call the resulting processes shrinkage prior Markov random fields (SPMRFs). We use Hamiltonian Monte Carlo to approximate the posterior distribution of model parameters because this method provides superior performance in the presence of the high dimensionality and strong parameter correlations exhibited by our models. We compare the performance of three prior formulations using simulated data and find the horseshoe prior provides the best compromise between bias and precision. We apply SPMRF models to two benchmark data examples frequently used to test nonparametric methods. We find that this method is flexible enough to accommodate a variety of data generating models and offers the adaptive properties and computational tractability to make it a useful addition to the Bayesian nonparametric toolbox.Comment: 38 pages, to appear in Bayesian Analysi

Why is strategic R&D (still) homebound in a globalized industry? The case of leading firms in wireless telecom

This paper looks at internationalization of R&D in the wireless telecommunications industry. We compare the international distribution of strategic R&D activities related to the development of wireless standards to other (non standard related) projects. While there is evidence that leading companies in this industry are sourcing globally their know how, still more strategic R&D projects remain homebound. This finding is further elaborated through conversations with R&D and IP managers at Ericsson, Motorola, Nokia, and Qualcomm. These semi-structured interviews suggested that a closer look at the internationalization of R&D investment requires scholars to consider maturation and decentralization of R&D and R&D management.