What?

How to close the gap between the top-down quant data we use for sector analysis (GVA, jobs, companies house etc) and deeper sector knowledge that exists in regional networks / often just in people's heads.

Why? Sanity-check economic claims (they might be off, or totally wrong); flesh out what the data suggests is happening e.g. make more nuanced sector picture; shape what questions we want our sector work to answer; generally co-create a deeper, data-informed but networked picture of the regional economy.

What should we be considering if we want to make a sustainable, useful, networked thing that could do this 'closing gap' work linking quant findings to regional expert / ground level knowledge? What kind of thing could it be?

The answer might just be: keep it a very loose network, keep a list of people. But something a bit more structured might increase its chances of being useful and longer-lasting?

What I especially like about this: it can only be done at regional scale. National data will often miss vital bits of the picture; combining data with expert regional knowledge = better.

Connected things

I'm increasingly concerned that regional economic data has patches where it can be actively misleading. Oftentimes, it's all we've got, and we have to make the best of that.

But if we could build better regional insight networks, what else might that change? Can we allow ourselves to move away from the rankitis that flows quite naturally from the spuriously accurate economic data we get?

Compare to Econ error rates (that link includes a quick look at Y&H GVA sector error via the ABS; code in link). If we knew those error rates, we'd be floating in a much murkier space relative to other places. How much do those comparisons drive how we think about development? What other thinking modes might emerge if we had better insight networks ourselves?

The risk: losing that national picture. But if that picture isn't entirely reliable anyway...?