. the same data could be used to analyse the “ effective ” working population in other uk cities, perhaps helping explain their under - performance. it could also be used to answer industrial strategy questions, such as what impact new transport routes have on the effective working population of the uk ’ s major cities and hence on their incomes through agglomeration effects. imagine a transport strategy whose aim was to raise the effective working population of those uk cities currently operating below the line. using chart 22, doing so would raise uk gdp per head by around 10 % or around $ 4, 100 ( £3, 100 ) in today ’ s money. those annual benefits could be compared with the one - off costs of investment to determine the desirability of such a policy, as part of the uk ’ s industrial strategy. my second example draws inspiration from recent work at the university of cambridge by penny mealy, including by my colleague on the industrial strategy council diane coyle. 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 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. all speeches are available online at www. bankofeng

their bis central bankers ’ speeches credit assessment of firms or for sectoral analyses. for this reason, industry representatives have started to voice concerns about the collection of statistics which are insufficiently comprehensive and that cannot be used for detailed market analysis. 4. what are the challenges and the costs of using micro data? the above examples may suggest that the advent of micro data has moved us into a brave new world in which we have better information, better analyses, and a mutually beneficial exchange of information. but any cost - benefit analysis must, of course, also address the challenges and the costs that are associated with the use of micro data. among those, data confidentiality and the costs of upgrading and maintaining it systems feature prominently. a ) data confidentiality and data protection the traditional “ aggregate ” statistics that central banks use and produce generally do not raise data confidentiality issues. whenever a sufficient number of observations ( typically : three ) have been aggregated, the behavior of individual firms, households and banks cannot be tracked from specific data points. micro data, by contrast, almost always raise confidentiality issues that may differ substantially across jurisdictions. for the united states, information about the profitability and the balance sheets of commercial banks has been available for many years in the “ call reports ” that are published by the federal reserve bank of chicago. these data have been used for many important studies on the us banking industry. it would not be possible to publish such data in many other jurisdictions. “ feedback loops ” to the industry or other users – which would allow the behavior of individual firms or competitors to be identified – are not permitted. perhaps even more importantly, micro data cannot be merged across jurisdictions, which complicates comparisons of analytical work for different countries. in order to overcome this obstacle, new analytical and research networks have emerged that allow information and research results to be exchanged across countries without sharing the underlying ( confidential ) data. one example is the international banking research network. established in 2012, the ibrn brings together researchers from more than 20 central banks around the world – in addition to international organizations such as the bis, the ecb and the imf – in order to analyze current issues pertaining to international banking. the different country teams use comparable, country - level data on cross - border banking activities and common methodological tools to analyze issues such as the transmission of liquidity risk, spillovers of ( macro ) prudential policies, or the cross - border effects of monetary policy. as the studies use