An uneven recovery so far appears to be “levelling down” UK’s local areas
It is clear that, at the individual level, COVID-19 has led to further polarisation: individuals with lower incomes or poorer health have suffered the most (see 8 February here). However, many have taken this to mean that, geographically too, the lowest-income areas have taken the largest hit.
This is true to some extent, but only at a very granular level. For example, within types of geographies (e.g., among different cities, or comparing different towns), historically less-well-off parliamentary constituencies had the highest claimant count figures in January (see 24 February here). With higher levels of aggregation, the opposite is true: places that used to be highest-earning, on average, have had more of their economic activity disrupted by the pandemic (see 1 March here). [To get a sense of scale, an average parliamentary constituency has about 100,000 inhabitans; the average ITL2 region (used in the 2nd link) has around 1.6 million inhabitants.]
How is this possible? It is a mathematical feature of averages that they can be heavily influenced by even a fairly small number of very high earners. This tends to characterise the UK’s “most affluent” places quite well. As I have mentioned previously, geographical areas with higher average incomes tend to also have the highest income inequalities (see e.g., data visualisation here). This means that high-income areas are also home to many, many lower-earning individuals whose jobs have been vulnerable to COVID-19 related social distancing restrictions. The pandemic has been a large enough shock such that, even if better-off households haven’t see much impact, indicators of economic trouble — such as claimant count, furloughs, and lack of wage increases — have been more visible in these more affluent areas.
Unfortunately, official statistics at detailed geographical granularity lag behind real time significantly. [For example, furlough data for May will be published on 1st of July. Claimant count for June will be published in mid-July. As far as I know, output (GDP or GVA) by sub-region (e.g., ITL3 or local authority) will not be available for another year or two; the latest at the moment is for 2019. For income — e.g., gross disposable household income — the latest sub-regional data is from 2018.]
The good news is that there are more timely, geographically granular indicators that are published with only a few days’ delay and correlate very well with official statistics. I have recently looked into Google’s COVID-19 Community Mobility Reports, which provide information about people movements for each of the 151 unitary authorities or counties in the UK. And people movements — into retail and recreation facilities, workplaces, public transport hubs, and so on — are highly indicative of what is going on in the economy. It goes without saying that such metrics do not replace official statistics; but they give a glance into what is going on.
For today’s chart, I have chosen to look at the mobility index for “workplaces” (on the Y-axis) because (out of various combinations I tried out) it seems to be the best predictor of furlough levels; which, in turn, seem to be the best predictor of national-level monthly GDP over the pandemic period. I have aggregated the data up to the same 24 geography types I used in my previous blog on “levelling up”, to see if — as far as we can tell — we are seeing more of a K-shaped recovery (with hardest hit places recovering slowest, and others bouncing back faster); and whether the COVID-19 crisis is, on average, creating wider geographic disparities (at this level of aggregation).
Three things stand out. First, the most affluent local areas pre-pandemic are recovering the slowest so far. London and its communting zones are a particular case in point, at the bottom right hand corner of the chart. However, the overall national pattern also seems to be broadly one of “levelling down”. [Note: a huge caveat here. I have not done any quantitative analysis to gauge how big a dent this might make in more affluent areas’ medium-term fortunes. It is quite possible that even a fairly large “levelling down” effect in the short term will not ultimately change the broad ranking of where places are relative to each other on economic metrics like GDP and income per head. Certainly, historically, such rankings have hardly moved.]
Second, yes, we are seeing a bit of a K-shaped recovery when it comes to types of geographies, too. [For what this K-shaped recovery looks like for different sectors, see the data visualisation here.] While I haven’t charted it here, given that more affluent places had already been hit hardest, and that, as shown in the chart, they are recovering slowest, it seems that the “levelling down” phenomenon is going to take some time to play out. In the meanwhile, there would seem to be limited momentum for previously affluent places to “bounce back”. [There are a vast number of reasons for this — including the homeworking trend, which is much more prevalent in places with a higher proportion of managerial and professional jobs.]
Third, even among the less-well-off areas, “cities” are doing worse than “towns”. The former includes places in the “Ethnically diverse metropolitan living”, “Industrial multi-ethnic”, and “Larger towns and cities” categories such as Birmingham, Manchester and Glasgow. They all sit well below the regression line, indicating that relative to other places with a similar starting-point level of income, workplace mobility has recovered a lot slower than elsewhere. Now, you could argue this is because such places also have a higher share of people that can, and might want to, work remotely, and this is no doubt the case. But that leaves the centres of these cities, and the lower-paid, more physically-present occupations (such as baristas, bar tenders, barbers, or beaticians) also lacking in customers.
The so called “left behind” places (e.g., “Mining legacy”, “Manufacturing legacy” and “Service economy”), in contrast, while historically poorer, seem now to be recovering somewhat faster. Some of this may be a seasonal effect, such as travel and tourism picking up domestically in places like Blackpool. But much of it is probably in the nature of employment in these places: the majority of jobs are not in very highly paid, and certainly not growing sectors, such as professional services; but neither are they in very low-pay sectors, such as hospitality, that are vulnerable to COVID-19 related restrictions.
So what? Well, I conclude what I always conclude when it comes to “levelling up” or regional disparities. It’s complex. It’s difficult. But, most obviously, there is no single answer. Every place is different, has its strengths and weaknesses, has a different historical legacy (and these things are incredibly path dependent), and therefore needs a different set of actions to improve the prospects of its people. And because the drivers of prosperity interact in virtuous and vicious cycles (see e.g., #16 here), it would seem that any strategies to revitalise places — whether previously “left behind” or “affluent” but now hit by the pandemic — needs to be designed from the bottom up.