52 This looks at the structural economic characteristics of different regions and provides a way of summarising and ranking them. The particular way it does so is by measuring their “economic complexity”, following the work of Hidalgo and Hausmann (2009) in the context of international trade. Without going into the technicalities, the complexity index can loosely be thought of as weighing the two structural characteristics of an economic area which theory would suggest are crucial for economic success. The first is the degree of specialism or comparative advantage in a particular product or industry: the greater this specialism, the greater an area’s complexity. The second is the degree of diversity in this specialism across products and industries: the greater this diversity, the greater an area’s complexity.53  Though the construction of an Economic Complexity Index (ECI) is itself complex, the principles underlying it are not. An economic area exhibits complexity when it has a diverse set of highly-specialised industries. An  52  Mealy and Coyle (2019). This description sets out a stylised interpretation of ECIs as they were originally conceived by Hidalgo and Hausmann (2009). Recent work by Mealy, Farmer and Teytelboyn (2019), however, has re-assessed the interpretation of ECIs and found that diversity is mathematically orthogonal to ECIs. In this new interpretation, ECIs instead provide a useful way to understand place-based similarly in industrial specialisations. 53  38  All speeches are available online at www.bankofengland.co.uk/speeches  38  area lacking complexity has few or no industries or products which are specialised.

Importantly, it improves the decisions they make, nationally and locally, governments and citizens. Weather is by no means a unique example. Micro-level data, digital twins and massive-scale simulations are being used to better understand all manner of other systems at all manner of resolutions – from the sub-atomic scale when seeking the Higgs-Boson, to the global scale when modelling flows across oceans, the world wide web and galaxies. All have benefitted from drawing on mass micro-level data, crowd-  sourced, centrally-stitched, micro-to-macro. Until recently, fewer such high-resolution data existed when it came to tracking flows of goods and services, people and money through our economy. That is changing. These data increasingly exist in companies, whose management systems capture activity in close to real time. They exist through now-ubiquitous  sensor, camera and satellite technology. And they exist in governments, local and central, in the form of administrative data on people, goods and money flows. Increasingly, these data are being put to use to understand the economy.40 In the UK, the Office for National Statistics recently started publishing a range of high frequency economic indicators, based on such sources as administrative data on VAT returns and geospatial data on traffic flows. The Bank has made increasing use of these sorts of data in its own real-time assessment of the economy. Let me give a couple of examples. One topic attracting attention recently has been the economic consequences of a disruptive Brexit.