Friday, May 24, 2013

Monetary policy doesn't require borrowing, and macro is hard

From Nick Rowe:
Monetary policy does not work by increasing actual borrowing. That is not the causal channel of the monetary policy transmission mechanism. Monetary policy works by increasing spending, not borrowing. And one person's spending is another person's income, so people in aggregate do not need to borrow more in order to spend more. Their increased spending finances itself.
This is model specific, of course. Read the whole post, or at least section #1. This is a very nice example of the need for macro analysts to avoid the temptation to simply aggregate micro intuition. Feedback and resource constraints matter. General equilibrium matters. Says Rowe,
Yep. Macro is hard. You can't just sit back and think "how would I react if my rate of interest fell?" You have to think about how my reactions would affect others, and how their reactions would affect me, and so on.


Wednesday, May 15, 2013

More on housing and startups

Image source


People are starting to notice the epic collapse of startup activity of recent years. Via Arnold Kling, see this great note by Glenn Reynolds and a reader suggesting that the decline in housing collateral could be a large factor.

I think so too, as I've discussed before. I'm working on a paper that I hope will shed some light on this question. A few things to keep in mind:

1. Part of the decline is secular; I noted this here. This has coincided with a more general secular decline in business dynamism, and we still don't know what's driving it. The startup problem seems to matter for the broader dynamism decline, though. It's difficult to disentangle the secular component from the Great Recession component.

2. I note here that (a) national house price indices and home equity peak about the same time as startup activity (2006, before the "recession" started) and (b) residential investment peaks about that time as well despite other investment series peaking at least a year later. That's far from a smoking gun, but it is suggestive.

3. More formal empirical evidence for this link is emerging. I discussed one paper here (this one exploits the famous Saiz housing supply elasticity instrument). Another paper, this one exploiting time series and regional variation, obtains similar results. Both of these papers cast doubt on the notion that the Mian and Sufi channel (the household balance sheet channel) is sufficient for understanding the full consequences of house prices (in part because the two papers I mentioned find effects in tradeables in addition to nontradeables).

4. The full details of a housing collateral/startup channel require some unpacking. For example: you could build a really simple model with frictionless housing markets and housing collateral that would not give you a clear house price/startup relationship. To see this, suppose housing and nondurable consumption enter into utility as Cobb-Douglas, so expenditure shares are fixed. Then a house price decline just causes people to buy more houses. You need something more; lots of housing market frictions (which is reasonable) or a simultaneous decline in loan-to-value ratios will probably do it.

5. It would be nice to be able to quantitatively compare the consequences of the main channels through which housing collapse smashed the economy. These include this housing startup channel; the Mian and Sufi consumption channel; the standard residential investment/construction industry channel; and the bank balance sheet channel. Someone should write a paper about this... (working on it).

6. Figuring out the cause of the startup collapse is important since startups account for almost all net job creation.


Crazy prices on Deep Space Nine

Image source

I broadly agree with Matthew Yglesias about Star Trek. But his post reminded me of a complicated aspect of the series: its economy. Particularly when watching Deep Space Nine, it can be difficult to reconcile the apparent plenty provided by replicator technology with the "profits" obsession of the Ferengi. The Star Trek universe has a currency--typically gold-pressed latinum--and it appears to have value and uses even in an environment with little scarcity.

Also, some of the prices don't make any sense.

Memory Alpha provides this discussion of the currency with examples of prices mentioned during the series. I'm going to convert some of these prices to dollar values by using a very lucky mention--wages. "Quark pays his Bajoran employees one slip of latinum a day during the Cardassian Occupation." We can probably assume that these are low-skilled wages, and we can use what we know about low-skill wages in dollars to build exchange rates.

We're also given a set of conversion rates between latinum denominations: 1 bar = 20 strips = 2000 slips.

I don't know what sort of labor market is supposed to have existed during the Cardassian Occupation; maybe all employers had monopsony power in labor markets, or maybe labor was scarce. I'll try three different specifications:
  • Low wage conversion assumption: $1/hour or $8/day
  • Minimum wage conversion assumption: $7.25/hour or $58/day
  • High wage conversion assumption: $20/hour or $160/day
Of course, they may not be working 8-hour days, but I think these specifications cover reasonable scenarios. Now consider a few of the mentions of latinum and how they convert to dollars:


Slips Low wage conversion ($) Minimum wage conversion ($) High wage conversion ($)
Crate of root beer 10 80 580 1,600
Pajamas 300 2,400 17,400 48,000
Cadet's uniform 500 4,000 29,000 80,000
Dress 1,700 13,600 98,600 272,000
Wreckage of a ship 6,000 48,000 348,000 960,000
Nog's life savings 10,000 80,000 580,000 1,600,000
Quark's wager on Sisko vs. Q fight 10,000 80,000 580,000 1,600,000
A day's revenue at Quark's 10,000 80,000 580,000 1,600,000
Morica Bilby, shipping consultant, weekly wages 10,000 80,000 580,000 1,600,000
Someone's bar tab at Quark's 44,000 352,000 2,552,000 7,040,000
2,000 tons of Kohlanese barley 378,000 3,024,000 21,924,000 60,480,000
Quark's evacuation stash 1,200,000 9,600,000 69,600,000 192,000,000
Offer to buy Quark's bar 10,000,000 80,000,000 580,000,000 1,600,000,000

These are some pretty startling numbers. A cadet's uniform costs between $4,000 and $80,000. A dress is between $13,600 and $272,000. Quark is a very wealthy man (or else has a serious gambling problem); he wagers half a million dollars on a fight and keeps tens of millions in cash under his bed for emergencies.

I would say that these prices are pretty inconsistent. Note that this observation does not depend on my dollar conversion choices; just look at the "Slips" column and observe that pajamas cost 300 days of wages in the food service industry. Maybe the post-scarcity economy leads to strange preferences and relative prices. Or maybe the writers didn't think very hard about latinum mentions.

It almost makes Star Trek seem unrealistic!

Monday, May 13, 2013

Employment services and misclassification

Lately there has been some talk about temporary help services (see here and here). This industry, and the industry of employment services more generally, is interesting not only for its potential business cycle implications but also for its economic measurement implications.

In Census and BLS data, "employment services" is an industry category (4-digit NAICS 5613) that includes job placement services, temp agencies, and other services that allow businesses to outsource HR and other tasks. We have seen some interesting activity in employment services during the last 20 years (click for larger image):


Here I've plotted "employment services" employment as a share of "professional and business services" employment (red line). Observe that this ratio has risen by almost 5 percentage points since 1990. Since readers may know that services generally have made huge gains in employment during this time, I also provide "employment services" employment as a share of total private nonfarm employment (blue line).

What interests me is the fact that a lot of employees in this industry are misclassified by industry codes. People on the payrolls of temp agencies could actually be working in any industry. This may become a measurement problem if employment services resume their gains of recent decades; to the extent that these workers are misclassified, US data overstate the number of workers in these narrow services industries and understate the number of workers for the industries in which temporary employees are working.

Consider an example. If I have a manufacturing plant with a big HR department, but I decide to close the HR shop and pay an HR services firm to do that work, very little has actually changed in the industry composition of the US economy--but the data will record that the manufacturing sector shrank and the services sector grew.

Consider another example. Suppose a change occurs among retailers that makes them want to fill existing jobs with temporary, rather than permanent, workers, and they do this by contracting with temp agencies. Again, the actual industry composition of employment hasn't changed, but the data will indicate a smaller retail sector and a larger services sector.

Something to keep in mind for those watching the evolution of the US industry composition.