tabellini and trebbi ( 2017 ). for example, alesina, tabellini and trebbi ( 2017 ), carlino and defina ( 1996 ), cumming ( 2018 ), dow and montagnoli ( 2007 ) and fratantoni and schuh ( 2003 ). krugman ( 1991 ) and krugman and venables ( 1996 ). izraeli and murphy ( 2003 ). all speeches are available online at www. bankofengland. co. uk / speeches ( c ) factor mobility a third sector of factors influencing the choice of optimal policy area concerns the degree of factor mobility. easy movement of factors between regions can smooth - out differences between regions. for example, people moving from low to high unemployment areas can smooth out regional differences in incomes. in this way, factor mobility can serve as a risk - sharing device between areas, even without policy intervention. the other side of this coin is that, people and money do not always flow in this direction. factor mobility can sometimes increase, rather than smooth out, differences between regions. for example, people or firms may move to areas which offer strong existing incomes and jobs. if so, this will tend to amplify, not ameliorate, regional differences. that is the theory. the optimal policy strategy involves balancing these factors, based on the structural characteristics of an economy, its degree of diversity versus specialism and the flows of factors within it. a digital twin of the economy, of the type outlined earlier, would embody these factors and flows and would provide a quantitative test - bed for making policy choices. in the absence of such a model, let me discuss two examples where existing data and analytical tools could be used to inform policy choices. in line with theory, the first looks at flows of factors within a region, the second at structural economic characteristics across regions. these data and techniques can be used to shed light on policies which might boost economic growth, locally or nationally, in line with the uk government ’ s industrial strategy. 49 my first example draws inspiration from recent work by tom forth at the open data institute in leeds, on behalf of the productivity insights network ( pin ) here at the university. 50 this uses data on flows of people to assess local growth prospects. the story starts with the observation that many uk cities appear to punch below their demographic weight in income terms. put differently, a number of uk cities do not

appear to benefit as much as their international counterparts from agglomeration effects. to demonstrate, chart 22 plots ( log ) city population size against gdp per head in 563 cities in advanced economies around the world, including a number of uk cities. it also shows a line of best fit. this is upward - sloping and statistically significant, consistent with agglomeration effects. moving from a city the size of oxford ( population 535, 000 ) to one double its size such as sheffield ( around 1. 2 million ) should be department for business, energy and industrial strategy ( 2017 ). in a personal capacity, i chair the government ’ s industrial strategy council which evaluates its progress. forth ( 2019 ). all speeches are available online at www. bankofengland. co. uk / speeches expected, on average, to boost gdp per head from £27, 300 per head to £28, 100 per head, or around 2. 6 %. 51 chart 22 : city population and gdp per head sources : oecd metropolitan areas population and bank calculations. notes : gdp per capita in us $ in 2015 and log scale used for metropolitan area population. in fact, gdp per head in sheffield is around 44 % lower than in oxford. this is not just a north - south divide. income per head in sheffield is little different than in doncaster just down the road ( population 305, 000 ). data set underlying chart 22 refers to gdp per capita in us $ in 2015. numbers which convert to sterling in the text are based around average $ / £ bilateral exchange rate of 1. 34 in 2018. all speeches are available online at www. bankofengland. co. uk / speeches and it is not just sheffield. many large uk cities sit below the line : belfast, glasgow, leeds, manchester, cardiff, birmingham, as well as sheffield. agglomeration multipliers seem to be consistently smaller in a number of uk cities. why? forth ’ s work suggests transport networks are a large part of the explanation. by analysing commuting times into birmingham at different times of the day, he provide a measure of the “ effective ” working population. at peak times, the effective working population of birmingham is perhaps 50 % smaller than its measured population due to poor transport infrastructure. this helps explain why birmingham punches below its demographic weight. this is a terrific example of how high - resolution data can be used to understand local patterns of economic growth