GVC/City REDI Seminar Series: Juan Mateos-Garcia

Room 110 University House
Wednesday 16 November 2016 (12:00-13:00)

This event is part of the GVC/City REDI Seminar Series.

Speaker: Juan Mateos-Garcia (NESTA)

About the speaker

Juan Mateos-Garcia is the Head of Innovation Mapping in Policy and Research. His job is to use new data sources and analytical methods to improve innovation policy and practice. He is particularly interested in how new technologies and industries emerge, on the way in which ideas spread across networks, and on the processes through which, as a society, we can manage this process of continuous change for the benefit of all.

Technically, Juan Mateos-Garcia is interested in the potential of machine learning and network science as tools to understand our complex economy, and of reproducibility as a way of building trust around new data sources and methods, making them more suitable for policy application. I use Python and R. He is currently leading Arloesiadur, a project to build a data analytics platform to inform innovation policy in Wales.


Complex places for complex times: An analysis of the complexity of local economies in the UK

Juan Mateos-Garcia and James Gardiner, Nesta

A growing body of literature suggests that complexity - a measure of the industrial diversification of an economy - is associated to better economic outcomes along a range of dimensions, from GDP growth to inequality. A reason for this is that complex economies contain more capabilities that can be combined in unique ways to generate hard to imitate products and services; this diversity also makes local economies more resilient. Most of this research has focused on countries as its unit of analysis, and used export data to generate its measures of complexity. - yet decades of research in economic geography and regional science tell us that there are often significant sub-national differences in industrial specialisation and outcomes. This paper sets out to account for this by using industrial clustering data to measure the complexity of local economies inside the UK. This analysis is potentially policy relevant: Several studies have linked the discontent leading to the results of the European Referendum to regional economic divides in the UK, and the same seems to apply to the advance of Donald Trump in the USA. Understanding what industrial configurations drive local economic growth is a big question for policymakers. 

Our measures of economic complexity are based on local industrial profiles in a set of 64 'related' sets of industries which we have identified using an algorithm recently developed by Delgado and colleagues. Perhaps as expected, we find that local authorities in London and the South tend to be more economically complex, and that urban areas are more complex than rural areas. The presence of knowledge intensive and creative sectors tends to be more strongly associated to higher economic complexity, while primary sectors, leisure and some types of manufacturing are associated to lower levels of complexity. We find that our measure of complexity is significantly associated to higher local average earnings even after controlling for other local factors and urbanisation. We also find that more economically complex areas strongly tended to vote remain in the European Union in the June Referendum, which is consistent with a higher degree of satisfaction with an economic status quo based on openness and competition.

We conclude by looking at the link between local economic complexity and experimental indicators of informal networking based on a data set extracted from Meetup.com, a web platform used to organise events. In doing this, we seek to understand that are the 'mechanisms' of complexity: do highly complex areas display higher levels of informal networking conducive to urbanisation economies and knowledge spillovers, or is complexity a structural (and not necessarily relational) feature of local economies? We find higher levels of informal networking in more economically complex areas, as well as higher levels of networking between communities operating in different industrial and technological domains (as compared to communities operating in the same industries). An interpretation of this finding is that the co-location of a diverse set of industries generates opportunities for networking, resulting in flows of information and talent that generates innovation and further enhances complexity. We expect that activating this positive feedback loop may be a significant challenge for less complex local economies, and consider the implications for policy.

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