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Tom Forth @thomasforth
, 9 tweets, 3 min read Read on Twitter
Economists tracking the impact of Brexit created a really interesting technique, that I'd never have thought of, and that I really like.

They took the other countries in the G7 and said "what combination of these economies was the UK economy most like, up to 2016?"
It's like a recipe, for example,
The UK = 1/5 of Canada, 2/5 of France, 1/10 of Germany, 1/10 of Japan, 1/5 of USA, 0 of Italy.
The UK economy is really like France, a bit like Canada and the USA, hardly like Japan and Germany, nothing like Italy.
An original paper on this is "The Economic Consequences of the Brexit Vote". They create a "doppelganger" UK, then compare how it performed compared to the real UK since 2016. It's such a good paper. centreformacroeconomics.ac.uk/Discussion-Pap…
So, I've been thinking.... we can use this "doppelganger" approach to estimate the impact of all kinds of things.

And the "Solver" add-in for Excel makes it really easy.

And so... … because I am somewhat unusual... … I present SimWales!
I've taken the GVA/head data for all the English regions, and created a SimWales -- which is a weighted combination of those regions' economy up to 2011 (when the Welsh voted to *really* get devolution going).

Wales is about 4/9 West Midlands + 3/9 North East + 1/9 London.
The Solver add-in for Excel is so good. I used to write optimisation problems like this in Python, or Matlab, or C# and it was a pain to set up. In Excel it's so simple by comparison.
Anyway, here's the result... both Wales and SimWales have grown by 15% since 2011. No significant difference.
I done Scotland too. 2011 when the SNP became the majority government. Pleasingly Scotland's economy is a linear combination of only two English regions. It's half London, half North East England. It has also not significantly overperformed its doppelganger. Just 1% more growth.
If we had UK regional economic data back to, say, 1987, we could do a good doppelganger evaluation of Scottish devolution. Twenty years of data to create a doppelganger from, then twenty years since then to see if it has made any difference.
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