Does how we define cities change our understanding of them?
There are many ways of defining cities, but the one used should be selected for economic rather than political reasons.
There are many ways of defining cities, but the one used should be selected for economic rather than political reasons.
Defining a city or large town is not as straightforward as it may sound. And it’s something that there are any different opinions about – Centre for Cities gets more correspondence on this issue than anything else that we do. The nature of the debate tends to focus on the actual boundaries used, rather than asking if it actually makes a difference to the data on a place’s performance. And it is the latter that is the more interesting question.
The four most common ones are:
Long time followers of the work of Centre for Cities will know that we use the PUA definition by default. In our view when doing economic research this gives the most consistent definition of cities, making comparisons as fair as possible. It is by no means perfect, but we think it is the best of the options available. It is also worth noting though that the definition depends in part on the issue being looked at. If the question is of productivity, for example, then looking at workplaces within the built up footprint of a city is the most appropriate. But if instead the query is about the labour market, then it may well be that the TTWA is most appropriate (noting that TTWAs involve trade offs in their definitions – in practice there can be substantial overlap between them).
Crucially here, the choice of geography should have a good argument behind it as to why that geography is being used. Over the years at the root of questions about definitions are concerns that the data on a given definition doesn’t fit a story that an individual or organisation wants to tell, for example, a city doesn’t perform well on a particular indicator, or that it is politically salient to change a boundary rather than it necessarily making economic sense.
This will depend on the indicator. In terms of the size of different places, it obviously does – there are many more people living in the built up footprint of Manchester (the PUA definition) than in Manchester local authority. A complaint we often get is that Manchester is much larger than Liverpool or Leeds because more local authorities are used in its definition. This of course though is determined by its built up footprint – the number of local authorities that cut through it is an outcome, not a driver of the defintion.
When accounting for size though and looking at shares of all residents or all jobs, it isn’t always so clear. An important metric in the levelling up debate, for example, is productivity. When looking across three separate geographies, there is little variation in outcomes on this measure (see Table 1 below). The three largest cities after London underperform on all three definitions. And the ranking between them doesn’t change either.
Table 1: Productivity does not vary by city definition for Birmingham, Glasgow and Manchester
| City | Output per worker, 2018 (£) | ||
| Core local authority | Primary urban area | Approximate travel to work area | |
| Birmingham | 61,385 | 60,0695 | 64,246 |
| Glasgow | 54,046 | 54,306 | 55,608 |
| Manchester | 61,384 | 58070 | 61,105 |
Source: ONS
We should be dissatisfied though that these definitions don’t show greater variation, rather than relieved that there is no difference. Because the core local authority is home to the city centre of each city, and city centres are especially attractive to high productivity services businesses, the core local authority should be the most productive of these different definitions. But on this measure the opposite is true in Birmingham and Glasgow, and there is barely any difference in Manchester.
This serves to further underline the underperformance of this group of cities. We should expect that cities are more productive than non-urban areas. And because of the particular role that city centres play in the national economy, we should expect different parts of a city to perform differently too.
And this reminds us of a bigger point – that it’s not just the definition that is used that is important, but also the framework in which the data and definitions are analysed within. The default position within much spatial analysis is that everywhere should be the same. For some indicators this is true. But for others, such as productivity, it is not.
It’s right to question definitions of cities and to be flexible where appropriate. But when doing economic analysis, changes to those definitions should be done for economic reasons rather than ‘small p’ political ones.
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