Sunday, May 24, 2015

Beating dead horses

The econ twittersphere has erupted in response to a provocative (but very blog-like) essay by the great Paul Romer, published in the Papers & Proceedings at AER. Romer is annoyed that certain old freshwater econ guys use math in an annoying way. Romer follows up here; Tony Yates has some thoughts here. Noah Smith, always up for a good bashing of said old guys, opines here, making the same points he usually makes (to wit: those silly freshwater guys just build models in which government can't be good, physicists and engineers have physics and engineering models that perform better empirically, here are some examples of silly things predicted by some DSGE models, etc.).

In recent years, guys like Noah have made very clear that they don't like bare bones RBC and that they don't like certain old guys in macroeconomics. But during these years, while the blogosphere has obsessed over this stuff, macroeconomists have been doing a ton of interesting work for which the blog debate is an uninteresting sideshow.

I've said this before and I will say it again: Whatever one might think of the contributions of the certain old guys to macroeconomics, the field has moved lightyears beyond that stuff. Nobody is using bare-bones RBC. The "freshwater vs saltwater" distinction is a redundant taxonomy--as best I can tell, it's really about Calvo pricing vs. flexible price models, while the sticky price assumption is just one of hundreds of ways that people add frictions to the RBC model. If you use the water-based terms instead of just describing specific frictions, you're just facilitating mood affiliation.

Few, if any, of the people writing models with flexible prices (but other frictions) would say that nominal frictions don't matter. It's just that nominal stickiness is one among many ways in which the real world deviates from bare-bones RBC, and every model must assume away something, and sometimes nominal stickiness is that something for good reason (meanwhile, a lot of good Calvo pricing papers ignore important financial sector frictions, not to mention heterogeneity and tons of other stuff, and that's ok). Sure, you can always find an absurd element of any model, as Noah does with relish in his post. But we're stuck with a world in which no model can explain everything, and in any case a paper that's good at some things and bad at others is an opportunity for another paper that's good at a few more things and bad at slightly fewer things. That's the nature of the discipline. It will always be easy to make the discipline look silly to outsiders who haven't confronted the magnitude of the problems we face.

Let me also say this: if there is anyone out there who criticizes the absurd oversimplification that is the representative agent model* but also criticizes mathiness, here's a newsflash: deviations from rep agent require hard math and/or nasty computation. The Mian and Sufi critique requires models in which agents differ at least along a wealth distribution. The Geanakoplos stuff requires hard math. So be careful how you use the term "mathiness" (I think Romer is using it in a reasonably precise way, and a lot of people are misinterpreting him and using it too broadly). More realistic models are going to require harder math, though I agree completely with both Romer and Roger Farmer that adding more math isn't always productive.

So while the blogosphere keeps restating 1970s fights, practicing macroeconomists are doing a lot of really interesting research that makes the freshwater/saltwater taxonomy irrelevant or at least useless. Bashing caricatures of the economics profession is a great way to get followers and sell books, but it doesn't advance the discipline.

UPDATE: This post is more snarky than I intended or am comfortable with. I think Noah and I are actually closer on this than it would seem from this text and the comments below. My basic point is that I hope people do not use the blogosphere as a sufficient statistic for what is going on in modern macroeconomics.

*I actually think rep agent is remarkably useful, particularly compared to how much it costs.

Saturday, May 9, 2015

Mobilizing and upgrading idle, depreciated capital

Here's a Bloomberg article:

Real estate buyers seeking money to renovate and flip U.S. houses are getting help from some of the world’s biggest investment firms. 
Colony Capital Inc., Blackstone Group LP and Cerberus Capital Management are among the companies that have started making bridge loans to investors who buy homes to sell them quickly for a profit. 

The title of the article (likely not chosen by the author) is heavy on mood affiliation: "House flippers are back together with Wall St. What could possibly go wrong?"

Lately I've watched a few episodes of Flip or Flop, an HGTV show (on Netflix) that follows a couple who flip houses for a living. The show takes a fair amount of artistic license (producers: you can't portray your stars as living on the financial edge if they drive a custom Escalade), but it is a nice illustration of what flipping can do for the economy.

The houses are typically in pretty bad shape. Many of them were foreclosures. They have been sitting empty for some time. In some cases, the previous residents stole things or poured concrete down drains. Generally they are unlivable (well, by modern American middle class standards). The flippers buy the houses, do very nice renovations on them, then put them on the market within a month or two.

This is really good! Idle capital is a waste. Houses with concrete in their drains don't do us any good. Depreciation is bad. These people are making large additions to the US capital stock, so it's efficient to allocate resources to them (with all the usual caveats about overborrowing externalities, potential policy distortions, etc.). The economy needs this stuff; hence:

Home flippers are benefiting from rising prices, limited new construction and a shortage of inventory on the market.

And it makes sense for big, risk-neutral firms to play this game (see my brief conversation with Lucas Goodman about this).

There's also this:

The new lenders are focused on more experienced investors, many of whom have have established companies, rather than the amateurs that proliferated during the housing boom a decade ago. Today’s flippers are more sophisticated after the crash weeded out most of the weaker investors, Lewis said.

Friday, May 1, 2015

Why manufacturing?

From Dietz Vollrath:

One of my continuing questions about research in economic growth is why it insists on remaining so focused on manufacturing to the exclusion of the other 70-95% of economic activity in most economies. 

He mentions work by Chad Syverson and others. It's true--much of the productivity literature focuses on manufacturing.

Why do we persist in focusing on this particular subset of industries, sectors, and firms? I think one of the main reasons is that our data collection is skewed towards manufacturing, and we end up with a “lamppost” problem.

That is almost certainly the main reason. I guarantee that the people using the microdata would love to be able to carefully study productivity outside of manufacturing. Vollrath describes how industry code schemes are heavily tilted toward detail in manufacturing. Read his post; it's very instructive. (And hey--he's not the only one who blogs industry codes!). I think the switch from SIC to NAICS was a huge improvement on this, though the problem hasn't disappeared.

It's not just about industry codes. The big microdata sources are typically limited to employment information for businesses outside of manufacturing, i.e., there's no capital. And the lack of coverage has persistent consequences. Recent improvements to the data often aren't easy to roll back to earlier years, which means it's hard to study the time series.

The other issue, though, is that even our primary productivity concept--TFP--is really designed for a manufacturing world. The Census Bureau actually has survey microdata for firms in retail and services. But how do we measure capital in those industries? What other inputs are important? This is a much bigger problem than simple lack of coverage. (Obviously, there is a literature).

The further you get from producing widgets with machines, the harder it is to map the TFP concept to the real world. And that's just at the firm (or establishment) level! Facing this challenge in the microdata colors one's views of the TFP concept at the macro level. There is no clean mapping from micro to macro; go down this rabbit hole if you won't take my word for it. Aggregate TFP isn't actually a thing, even if it's still a useful fiction.