Firms in Scottish High Technology Clusters: software, life sciences, microelectronics, optoelectronics and digital media – preliminary evidence and analysis on firm size, growth and optimality

This paper reports on: (a) new primary source evidence on; and (b) statistical and econometric analysis of high technology clusters in Scotland. It focuses on the following sectors: software, life sciences, microelectronics, optoelectronics, and digital media. Evidence on a postal and e-mailed questionnaire is presented and discussed under the headings of: performance, resources, collaboration & cooperation, embeddedness, and innovation. The sampled firms are characterised as being small (viz. micro-firms and SMEs), knowledge intensive (largely graduate staff), research intensive (mean spend on R&D GBP 842k), and internationalised (mainly selling to markets beyond Europe). Preliminary statistical evidence is presented on Gibrat’s Law (independence of growth and size) and the Schumpeterian Hypothesis (scale economies in R&D). Estimates suggest a short-run equilibrium size of just 100 employees, but a long-run equilibrium size of 1000 employees. Further, to achieve the Schumpeterian effect (of marked scale economies in R&D), estimates suggest that firms have to grow to very much larger sizes of beyond 3,000 employees. We argue that the principal way of achieving the latter scale may need to be by takeovers and mergers, rather than by internally driven growth.

The Significance of Identifying Industrial clusters The Case of Scotland

Industrial clustering policy is now an integral part of economic development planning in most advanced economies. However, there have been concerns in some quarters over the ability of an industrial cluster-based development strategy to deliver its promised economic benefits and this has been increasingly been blamed on the failure by governments to identify industrial clusters. In a study published in 2001, the DTI identified clusters across the UK based on the comparative scale and significance of industrial sectors. The study identified thirteen industrial clusters in Scotland. However the clusters identified are not a homogeneous set and they seem to vary in terms of their geographic concentration within Scotland. This paper examines the spatial distribution of industries within Scotland, thereby identifying more localised clusters. The study follows as closely as possible the DTI methodology which was used to identify such concentrations of economic activity with particular attention directed towards the thirteen clusters identified by the DTI. The paper concludes with some remarks of the general problem of identifying the existence of industrial clusters.

Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables

Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have long run steady states which are subject to breaks. Furthermore, the nature of the break process is often unknown. In this paper, we draw on methods from the Bayesian clustering literature to develop an econometric methodology which: i) finds groups of variables which have the same number of breaks; and ii) determines the nature of the break process within each group. We present an application involving a five-variate steady-state VAR.