Local Knowledge, Systems and Policy GID4206

Basics

Full course description

Local economic systems do not innovate in the same way. They do not react and contribute in the same way to global challenges, technological change and globalisation. Within cities, industries, clusters and regions, technical change and innovation is governed through the interactions of various agents with different capabilities (e.g. individuals, firms, universities, policy makers, institutions). To understand innovation and innovation policy, we must understand how these various agents (as producers and users of knowledge) interact and develop their capabilities. New ideas about knowledge imply that knowledge transfer is not as easy as was once thought. The diffusion of a new idea or capability is a complex process.  Not all knowledge can be codified, and the non-codified knowledge, which is by nature difficult to transmit, is extremely important for both innovation and diffusion. This implies that much knowledge creation and diffusion is geographically localised and policy must take this into account.

In the first week of this course, students will review local system approaches and examine how new ideas about knowledge and innovation affect our understanding of the processes of innovation and of innovation policy. The focus is on the systemic features of innovation and how they play out in a local context. In the second week, we concentrate on taxonomies of innovation and knowledge flows. In some regions knowledge transmission across buyer-supplier links in value-chains are central, in other, science-based systems, innovation builds on scientific advances in university research. In some regions knowledge creation is key, in others knowledge absorption or adoption is central to the region’s progress.  Some regions are dominated by high tech, others by low tech, but innovation occurs in both. These axes all enter any taxonomy of regional innovation. One specific question we address is the role of universities in local development. Universities can be a source of novel knowledge and innovation, or a source of highly skilled labour. The role a university plays depends on what kind of local innovation is driving the region.  In the third week, we concentrate on policies that attempt to build strong links among local agents in innovation chains or value chains (e.g. cluster policies, smart specialisation). For example, concerns about creating a critical mass of knowledge workers have led many regions to create “science parks”, or “technopoles”, hoping to generate new industries, or to become the next Silicon Valley. But less dramatic policies also exist. In the fourth week, we analyse the contents of successful policy. The main policy issue is how to create interactions among local knowledge actors that contribute to the performance of local economies. What can we learn from success stories from different local systems? For example, Italian industrial districts have often been considered paradoxical: small, relatively isolated regions in Italy, apparently poorly connected to the outside world, but they are world leaders in their fields. How does this happen, and could it be reproduced? Will smart cities be the new locus and focus of specialisation and growth? Has globalisation removed local effects or can local policy makers still influence their economies?

This course overall looks at how local agents interact in the innovation ecosystem. The goal is to analyse and understand how these local interactions and interventions contribute to the performance of the local economies, and to prepare policy recommendations for specific regional development strategies.

Course objectives

• Describe in detail the factors that feature in a local or regional innovation system;
• Understand policy considerations relevant to regional policy-makers.
• Be familiar with policy measures both actual and potential aimed at nurturing a regional innovation system;
• Understand the roles of different actors in the system.
• Generate ideas relevant to innovation policy design and improvement.
• Be comfortable with basic social network analysis using R and igraph software.

Prerequisites

None

Recommended reading

Course reader.