Economists are a funny bunch. Whilst the rest of us might spend our Friday evenings winding down after a hard week’s work, economists can often be found late into the evening fiddling around with new datasets or producing scatter plot graphs comparing Gross Value Added (GVA) in Glasgow and Edinburgh, or showing productivity in the Cornish Manufacturing sector. Well, at least that’s what our economist, Duncan Brown, sometimes does, but then again it should be noted that he is also an avid Leicester City supporter, and so perhaps its unfair to assume that this is a trait that all economists share!
But whatever our reservations about his choice of football team and occasional Friday evening activities, we have to admit that what Duncan produces in those moments is often extremely interesting. Lots of stuff about topical things like automation and migrant labour, and even — we kid you not — an experimental map including every garden centre in the country.
We’ve decided it would be unfair to withhold Duncan’s twilight labours from the wider world any longer, and so beginning today we bring you Data Dabbling — a fortnightly peek at some of the really cool stuff he’s been working on.
For this first piece, Duncan has created two interactive charts, made up from a synthetic area which includes Leeds, Stroud, Melton, Swindon and Norwich, to look at the trade off between productivity and jobs. The first chart shows seven headline industry groups, with the width of each block being the number of jobs in the sector, and the height of each block being GVA per job (a measure of labour productivity):
What the chart helps to show is the way that GVA is made up of a few jobs in high productivity industries (for example, Agriculture, mining and utilities), with a much larger number of jobs in lower productivity industries (for instance, Retail services and logistics).
We can look at the same phenomenon in a different way, this time using the 49 economic industry clusters we have previously identified. The chart below uses jobs on the x-axis and GVA per job on the y-axis to identify where the trade-offs exist. For instance, high up on the left is oil and gas, which has super-high productivity but only 500 jobs (oil and gas always scores very highly when measured in labour productivity terms, because it’s a capital-intensive industry involving lots of money being spent on extraction rights and the drilling and mining equipment, with only a few people actually doing the work). Meanwhile, down near the bottom, on the right-hand side are health and care, commercial services and food and beverage, which all have a lot of jobs, but which are labour-intensive and have extremely low productivity.
The challenge is to move industries up and to the right overall, adding to both jobs and productivity in order to have the biggest impact on economic growth.
We hope this first instalment of Data Dabbling has been interesting, and that you’ll look out for the next one in two weeks time. If you’d like to find out about how we can help you understand jobs and productivity in your area, get in touch.