Group items tagged

Self-employed people in the North and Midlands are more likely to be in insecure, lower-paid roles at high risk from economic shocks

Cities in the Greater South East are more likely to be able to shift to working from home

The jobs that could be more easily done from home – such as consultants or finance – are concentrated in cities in the Greater South East (see the figure below). Assuming some sectors could completely shift to home working if necessary, up to one in two workers in London could shift to working from home. Meanwhile in Reading, Aldershot and Edinburgh over 40 per cent of workers could too.

There are three key things to note in this: Looking between late-February and mid-March, we see that the drop-off in footfall happened earlier and was much sharper in London than the other cities. Looking between early-April and mid-June, we see that the small and the medium-sized cities experienced less of a decline than London and the other large cities, and they also started to recover from this earlier. Looking between mid-June (when non-essential retail reopened) and Saturday 4 July, we see that while the trajectory is upwards everywhere, the small and the medium-sized cities have seen a much sharper climb back up towards normal.

There are three things potentially playing into this: City centres of large cities tend to have less residential and industrial space and are often concentrations of office jobs, which have been and still are being done from home. This limits how many workers are in the city centre compared to before.  Larger cities, especially London are more reliant on public transport than their smaller counterparts. With public transport still limited in both capacity and use for public health reasons, it is now harder for people to travel into the centres of these cities. Due to their size, larger cities have more options for going to the pub or shopping beyond the centre and it may be that this has further reduced footfall in the city centre.

This and our other analysis on the topic suggests that we are unlikely to see large-scale changes in footfall and a ‘return to the normal’ in the city centres of the largest cities until office workers are welcome to and do return. 

The current epidemic is a classic application of what economists call “radical uncertainty” (most recently explored by John Kay and Mervyn King in their brilliant book of that title, which came out last month): in a world that has inevitably become too complex to be adequately captured in models, a world of both “known unknowns” and “unknown unknowns”, the most sensible response to the question “what should we do?” is “I don’t know”. At the onset of this crisis, we could not put probabilities on which forms of social distancing would best limit its spread because we’d never done it before. We didn’t know how people would alter their behaviour in response to the appeal to “save the NHS”. We didn’t even know whether reducing the spread was desirable: perhaps fewer deaths now would come at the cost of more next winter. And these were just the known unknowns. With a disruption as big as this, unknown unknowns are also lurking. We have no experience of the material and economic repercussions from shutdowns of this nature and their aftermath in a modern economy, and no meaningful way of assigning probabilities; nor of how people’s behaviour will evolve.

What the modellers should have said, right from the beginning, was that it was vital to establish two fundamental parameters: the incidence and the rate of contagion, both of which require mass testing, and without which mortality rates are impossible to decipher and hence sensible policy impossible to implement. It is frankly astounding that four months into this new virus such tests are only now being instigated.

. Shifting responsibilities down the system not only enables rapid scale-up, it has a further huge advantage: the power of decision is closer to the coalface of practitioner experience. We learn not just from accumulating and analysing codifiable knowledge – the domain of the expert. We learn by doing, or by trying to do things that we can’t do and that force us to experiment. A decentralized system learns from a litany of failed experiments running in parallel, and so it learns fast: teams copy other teams that have hit on something that works well enough to get the job done.