By Alex Bishop and Juan Mateos-Garcia
Imagine you are a policymaker tasked with developing an industrial strategy for your region. Where do you look for evidence to understand your specialisation profile? Or perhaps you own a business specialising in the manufacture of medical devices connected to the internet of things, and you’ve just secured funding allowing you to scale-up and relocate. Where should you move in order to access the knowledge and markets your business needs to thrive? Currently, you may turn to official labour market statistics which give, for example, the number of businesses and people employed within each sector of the economy. This may help you identify regions with activity in the sectors you need; however, the latest data available is over a year old (business surveys are time-consuming and costly) but you need to make a decision based on the local economy today. How do you make a future-proof industrial strategy if you don’t know what’s happening now? Furthermore, the Standard Industrial Classification (SIC) for the UK dates back to 2007 and doesn’t contain concepts such as the internet of things – where does the business owner locate to? London seems like a safe bet?
What decision makers need is a framework or measure that is predictive rather than reactive, and access to more timely and less aggregate data. An article we recently published in the National Institute Economic Review explores one way forward by studying the link between Economic Complexity (a metric capturing the state of an industrial ecosystem) of UK regions and the number of businesses within those regions engaging in “emergent” activities calculated by extracting descriptions and locations of businesses from their websites using data mining – more timely and less aggregate data – and using Natural Language Processing (NLP) techniques.
Economic Complexity refers to the diversity and sophistication of the productive capabilities present in a location. If a location, whether it be a region or country, displays a comparative advantage in the supply of complex products then it must possess some hard to imitate capabilities enabling it to capture more market share. A location’s future specialisation trajectory is shaped by its economic complexity via the ‘Principle of relatedness’ – regions tend to enter activities for which they already have relevant knowledge and capabilities. The Economic Complexity Index (ECI) and Fitness provide quantitative measures of the complexity of a location taking into account these definitions.
Complex economies not only enter existing activities for which they already have relevant knowledge and capabilities but also develop novel (what we term ‘emergent’) technologies and industries that recombine local capabilities, knowledge and skills in novel ways. As mentioned above, the aggregate nature of official labour market statistics means that the link between complexity and the emergence of novel capabilities remains understudied. By using business website descriptions from Glass, a startup collecting web data about businesses, and NLP (the application of computational techniques to the analysis and synthesis of natural language) we can identify which businesses are likely to be engaging in these emergent technologies and thus get a much more granular view of a region’s economy.
Our analysis begins to shed light on many aspects of complexity and emergence in the UK. For example, we find that economic complexity is strongly positively correlated with the levels of emergent activity and economic outcomes within a Travel To Work Area; however, we find some Travel To Work Areas outside of the South England with high levels of complexity and emergence but not superior economic outcomes perhaps due to missing key resources required to generate impacts, such as access to finance or markets – an area of high policy relevance. Another policy implication is that the development of economic complexity relies on specialisation in digital ‘vanguard’ sectors which cluster in a few locations; however, policymakers seeking to develop their local economies should recognise that such specialisation is risky, that is, unlikely to generate benefits in the short term and susceptible to shocks.
In conclusion, the fusion of complexity economics and novel data sources – such as the Glass web data – provide a more timely, forward-thinking, and granular picture of regional economies enabling the development of tools to aid decision makers in understanding the specialisation profiles and potential future trajectories of local economies.
A full version of this paper is available here: https://www.niesr.ac.uk/publications/exploring-link-between-economic-complexity-and-emergent-economic-activities
This paper was presented at the ESCoE Conference on Economic Measurement.
ESCoE blogs are published to further debate. Any views expressed are solely those of the author(s) and so cannot be taken to represent those of the ESCoE, its partner institutions or the Office for National Statistics.