Last week we used our sales data to compare the real time effect of COVID-19 on the economies of left behind places[1] to a matched sample of more affluent areas. We found that, although affluent areas are experiencing more volatility (larger % drops in sales compared to the same week last year), in the longer term, we are concerned that left behind places will be less able to survive the COVID-19 economic fallout. As a result, we are launching the Left Behind Corona Shock Tracker below, to align with the work Tortoise has done on towns, but focusing on Left Behind communities specifically. Data will be updated weekly for the next 3 weeks.

Looking at the data, we can explain some differences by considering the context and the data we are using – for example, in Little Hulton we think there is an Amazon depot, which is why there could be such a large increase in sales for that left behind ward. But for others, we need your help in explaining what the data is telling us. Do we think it’s influenced by predominant economic sector, spending behaviours, transport availability, or other socio-economic factors in these communities?

Please contact us via Twitter or email and we will share your explanations and thoughts with our updated Left Behind Corona Shock tracker release next week.  

Data Notes


[1] OCSI and Local Trust,’ Left Behind? Understanding communities on the edge’, 2019 (access @ https://localtrust.org.uk/wp-content/uploads/2019/08/local_trust_ocsi_left_behind_research_august_2019.pdf)

[2] A note on the data: we started with all 207 left behind wards, the sales data was available for 190 of them. We then removed those wards where grocery and non-grocery sales were less than £1000 a week, in order to control for big percentage swings due to small sales values. And we had the above 161 wards remaining.


Social Economy Data Lab

The Social Economy Data Lab team

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