Friday, November 18, 2011
Posted by dkuehn at 1:42 PM
Brad DeLong linked to my earlier comment on the county-level multiplier estimates, arguing that what they provide is a good lower bound estimate. To a certain extent I agree with him. I would actually use this sort of argument a lot in project meetings at the Urban Institute. You are never going to completely rid yourself of endogeneity and bias, but what you really worry about is bias that overstates your conclusions. There were several instances where there were still estimation bias issues we couldn't deal with, but they biased the results towards a null result. I would always note that this wasn't a bad thing. We want to produce some insights, and if the only biases we have make those insights more conservative that can actually help convince people that there's something there.
So a lower bound estimate of 1.5 should help getting the point across that the fiscal multiplier is very real.
I am on board with that... mostly. Bias is still bad though, and you really need to try to think it through. What happens to these multiplier estimates when resources are fully employed? In that case, the results would be biased away from a null finding. Fiscal infusions draw resources away from comparison counties giving the impression of a larger multiplier when actually the fiscal infusion made no real contribution - and likely made the economy less efficient.
That worries me that we have underestimates during recessions and overestimates during normal times. Brad's point is a good one, it just makes me nervous.
Andrew Bossie also has comments on the last post that are worth sharing. First, he writes:
"There is a level I agree with you to this. In which case, metropolitan areas are a good robustness check."
I'm not entirely sure of what he has in mind here - hopefully he can elaborate in the comments here. One robustness check (well, really just a different specification) that I was thinking of was using some sort of propensity score matching approach to match counties in completely different regions of the country to each other on the basis of pre-recessionary characteristics. That way you're looking at two areas (really, a big collection of pairs of areas) which are comparable, but which are not going to lose demand to each other. I'm not sure if this would really work, though. After all, a "low fiscal stimulus" area is likely to be near another high fiscal stimulus area, and its ex-post performance is going to be impacted by the fiscal infusion in that neighboring area - and that fiscal infusion is definitely going to be correlated with your treatment area. Ultimately, I have no idea how big these biases are - and that's part of the problem. And I wouldn't know how to go about estimating how big the bias is. I suppose one way to estimate it would simply be to compare it to something like Barro and Redlick's work: how do national multiplier estimates from military procurement compare to state and county-level multiplier estimates from military procurement? Does that sound like a good approach to people?
Andrew goes on:
"In the united states, particularly "historically speaking" loanable funds markets have been HIGHLY localized becuase of unit banking. Even in the new banking environment there are still something like 8000 local banks."
This, I think, is the wrong way of looking at it. Certainly there's wide variability in loanable funds markets, and I think I said this in my original post. The point is, the main impact that fiscal policy has on the loanable funds market would not vary across localities. That's quite different from saying there is no variation across localities. It's not that there's no observed variation - it's that there's no reason to expect the variation in these county-level markets to be correlated with the impact of national fiscal policy in the cross section.
These sorts of models difference out all the variation common to a single area over time, as well as the variation common to both areas. Indeed - that's why we like these sorts of models so much. They get rid of a lot of the heavy lifting when it comes to holding things constant. You'll still have variation between local loanable funds markets to work with, but that will not include the most important variation for macroeconomic considerations: variations at the national and international level.