In what order should we return to work?

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Continuing the theme of occupational risk analysis, here are some more ways to slice and dice the data.
If you had to restart the economy how would you go about it?

If a client came to me and asked “which departments should I start up, given that I want to do it as safely as possible?” then I’d start by asking for some data: how much does each department contribute (and what is their size) and how risky is their operation.

Then, by slicing and dicing the data accordingly, we look for opportunities which have least risk but best return. All things being equal (which they are not ⚠), finding these kind of opportunities which balance the priorities in the most optimal way usually means a bigger “return” on a small action.

This particular ONS occupational data doesn’t contain size of contribution to the economy (I’m sure somewhere there will be a dataset that could be joined with it), so for now I will illustrate the point with the sector size, as measured by number of workers. And actually, it’s not an unreasonable view: irrespective of their contribution to the economy, the more workers go back to work, the more a sense of some kind of “normality” will pervade for those workers and their families.

If we arrange our data on axes as follows, measuring sector size and risk score, then we would naturally target occupations towards the upper right as large-employers-with-low-risk. This region of the chart is not densely populated, but ultimately working leftwards from the right, anything towards the top (the biggest, greenest marks) would be a reasonable next candidate.

Now, I bet you’re already making some interesting observations from that chart alone. And probably raising some objections to how useful it is. Bear with me.

Let’s look at the next question is raises: if I imagine a number of sectors do return to work, what impact does that have on the economy?

Again, we cannot measure economic contribution in this data set but let’s simply look at the number of workers. If a basic policy was to ask the least-risk workers to return to work first (based on lowest ranking of proximity to others) then we could start at the left of this chart and work rightwards occupation-by-occupation. In doing so, the line charts the the cumulative number of workers that would now be back at work.

It’s interesting we can see a pretty sharp rise in numbers in the first half of the line: there are only a small number of workers with really low proximity risk, but actually there’s a steep rise in sector size after that such that about 50% of the workforce are actually in the lowest half for the proximity risk.
If you wanted to develop a targeted strategy for return to work, you could work along this line and ask these occupations to return to work (if indeed they stopped).
It also begs another interesting question: how far along this line do you need to go to make a significant difference to the functioning of the economy? E.g. if 50% of workers are working at more-or-less normal capacity, how functional is the economy? (is it operating at 10%? 50%? 90%?). We can’t answer that question with this data of course but the answer would be very informative.

Now, of course, this chart is a gross simplification. All of this is not that straightforward, as there are several compounding factors with the #covid19 lockdown situation:

  • key workers are already at work to keep critical national functions running and indeed some of these workers are highest risk

  • many of the lower risk workers who, by definition, have least contact with other people, are actually still working because they can work remotely

  • some occupations which theoretically could restart rely on a market that is not available due to lockdown of movement and change in spending behaviour

So, to improve this analysis, one would need to be able to both remove workers who are already working from the picture and consider what else needs to be in place for some businesses to restart.

Moreover, there is considerable interlock between some occupations

  • some people are prevented from working because they require “childcare” (I’m including school in that loose definition)

  • some people are needed to work regardless because their function is so critical to public health or infrastructure

So, in fact, one would need to map out those interlocks and understand them to really identify which are the optimum occupations that could resume.

Data-led decisions

This would not be necessarily a trivial exercise. But either way, I’m hoping to demonstrate that these types of decisions can be data-led. That actually good data should allow us to make good decisions, even if - or perhaps especially if - they are difficult ones.

In closing, here’s a final (somewhat sizeable) chart which lists all the professions in the data set by, sorted by least proximity to other people (position in list & length of bar represents proximity level). The colour represents the combined risk score based on proximity and frequency of exposure to other people.
Hopefully this is sufficiently readable to find your own occupation (or its nearest equivalent).