Lots of energy is currently being spent debating whether an increase in government spending (or, per Joshua’s recent post, taxes) increases GDP or decreases unemployment, and if so by how much.
Given the amount of debate, I think it’s not unreasonable to conclude that approaches to this question taken by macroeconomists haven’t exactly yielded an answer everyone can believe in. (A nice overview of the basic methods in use is here.)
So, maybe it’s time to think outside the macro box.
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If you ask a reduced-form applied microeconomist like myself whether more government spending decreases unemployment, we have two initial instincts:(1) do some experimenting with an entire economy (perhaps not completely impossible, but not quite there yet) or (2) find a situation where one part of a country gets more $ spent on it than another part for reasons unrelated to their current or future unemployment rates, and compare the two.
A nice approach would be to look at changes in spending caused by purely political factors. So if, for instance, a lot of money is poured into swing electorates just before an election, you could look at whether unemployment fell more in that area than others.
This is tough to do in the US – there’s surprisingly little evidence of the feds there directing resources on political grounds in ways that affect aggregate spending statistics. Because of that, going the extra step and figuring out whether unemployment is affected by such spending is very dodgy.
Perhaps people who’ve looked at pork barrel spending in Australia or Canada might consider extending their work to look at the effects on local unemployment rates? India might offer even more interesting possibilities.
There are some problems with this approach – especially dealing with local spillover effects and dynamics – but I don’t think they’re insurmountable. It would also be nice in trying to figure out whether it’s worthwhile for governments to spend money trying to support depressed areas, or whether any such spending just leaks out of the targeted communities.