Wednesday, February 27, 2013

And this is "wonkery"

Here's a tweet from Ezra Klein:

So now you're thinking: Klein must be linking to a representative survey of economists in which a large majority endorse the minimum wage without reservation! Or maybe he's linking to a thorough literature review that surveys the relevant literature and argues compellingly in favor of studies that find that the minimum wage raises welfare on net!

If you were thinking that, you'd be disappointed. Here are the survey results Klein is using (click for larger images):

So on the first question, 58 percent of economists surveyed are either uncertain, have no opinion, or believe that raising the minimum wage to $9/hour would make it harder for low-skilled workers to find employment. On the second question, 46 percent of economists surveyed are uncertain, have no opinion, or believe that raising the minimum wage to $9 and indexing it to inflation is an undesirable policy, compared with 47% who think it's desirable (not everyone opined).

So the survey can't even find a majority of economists to endorse minimum wage as the questions are posed here, but Ezra Klein claims that "economists think the minimum wage is worth it." It looks to me like the topic is fraught with uncertainty, but a few bloggers (like Klein and Mike Konczal) want you to think that it's a clear-cut issue. Lest you think that these survey results are relatively conclusive for the tough field of economics, take a look at how the survey has turned out for a few other recent issues (1 2 3 4 5).

Economists are capable of coming to reasonable consensus; they haven't done so on the minimum wage.

UPDATE, 2/28/2013: Klein is still making his claim that "economists think the minimum wage is worth it," this time on the popular "Wonkbook" story aggregator. What I find most amusing is that Klein has pioneered the idea of journalist-as-wonk, where a "wonk" is someone who, ostensibly, likes and understands the use of data in analysis of policy. The charitable reading of these IGM-based claims about the minimum wage is that Klein is in too much of a hurry to actually read a graph. Given his known political preferences, though, a cynic might think he's being disingenuous. In any case, this kind of sloppy use of data does not reflect well on the "wonk" brand.

Tuesday, February 26, 2013

Housing is a durable asset

Edward Leamer wrote this no later than 2009, probably before:

The worst way to enter a recession is with a heavy debt load premised on overly optimistic ideas about future growth of sales and earnings. It is doubly bad if that debt is collateralized with durable assets and inventories, whose prices tend to be very soft in recessions, if you can find buyers at all.

This is from page 123 of this book, which I consistently find to be one of the most useful reference books on my shelf, and the italics were added by me.

Monday, February 25, 2013

Job flows, industry composition, and the changing US economy

Net job numbers hide a lot of underlying "churning" or reallocation. It would be interesting to divide this reallocation into (a) the part that is necessary for, or aligned with, net job numbers and (b) "excess reallocation"--job flows that occur in excess of the net figure. In other words, how much shrinking and growing do establishments do that isn't necessary for net aggregate employment growth?

Predictably, this has been done already (skip this paragraph if it looks boring). This must-read paper describes a measure of "excess reallocation," originally defined (I think) by Davis, Haltiwanger, and Schuh (1996). Simply put, the measure is job creation (employment growth at expanding establishments) plus job destruction (employment reduction at shrinking establishments) minus net employment growth (JC + JD - |JC-JD|, where JD is recorded as a positive number). To get a rate, divide this by employment. This is the excess reallocation rate, and it gives us an idea of what portion of jobs are moving around between establishments in a given year.

Excess reallocation rates vary widely across industries--some industries are more volatile than others in terms of job flows. Figure 1 plots annual excess reallocation rates for a few selected sectors* (click for larger image). "FIRE" stands for finance, insurance, and real estate.

Figure 1

Note the following: Construction has typically had the highest rate of excess reallocation. Between March 1979 and March 1980, about half of construction jobs were reallocated--in excess of the net change in construction employment. Manufacturing is consistently the least volatile sector, by a nontrivial margin. This suggests that establishment size and job matches are relatively stable in manufacturing when compared with construction (or any other sector). Presumably there are a lot of factors that determine how volatile a sector's employment is; you can probably imagine some of them. Some sectors have seen declines in these rates, with construction falling by about 20 percent of employment over the last 30 years (but that still leaves almost a third of construction jobs being reallocated). Reallocation rates vary less across industries now than they did 30 years ago--are we seeing convergence? There appear to be both cyclical and secular forces at work.

People preparing to enter the job market may want to keep these job volatility data in mind.

When looking at sector-level data, it's good to be aware of how large a role each sector plays in the national job economy. Figure 2 plots sector employment as a percent of overall private/nonfarm employment for the same sectors (click for larger image). I include only even years to keep the graph readable.

Figure 2

Most readers are probably not surprised by much on this chart. Manufacturing's employment share has steadily declined, while the share of employees working in the services sector has dramatically increased from a quarter to nearly half of the private/nonfarm economy. The other sectors haven't changed a lot (but keep in mind that this is just employment--a chart of sector share of output would look different).

Note two facts from the charts above: First, services experiences higher employment volatility than manufacturing. Second, the share of employment in services has grown dramatically while the share of employment in manufacturing has declined. Is it safe to assume, then, that overall economywide employment volatility has grown? No. Figure 3 plots excess reallocation for the entire economy (click for larger image).

Figure 3

There is a secular decline in excess reallocation rates for the broad economy, and this trend is acting in the opposite direction from the secular trend in industry composition (i.e., if industry composition had stayed constant, reallocation rates may have fallen even more). It turns out that looking at simple job creation and destruction rates reveals a similar trend. In other data sources, the trend can be seen going back even further in time. These trends have already been documented in the academic literature and other places (e.g., here or here, with citations). It's difficult to explain the trend by appealing to composition effects (though changes in the firm age distribution help). It's a bit of a puzzle, and it's the subject of some of my current research (with coauthors who know far more about it than I do).

Broadly speaking, job market dynamism has been declining. The amount of churning that goes on behind net employment growth has been falling. It's not clear whether this is a good thing or a bad thing--reallocation imposes costs on workers (and firms), but it is also one of the key mechanisms behind productivity growth. Without knowing what's driving the trend, we can't know whether it has any policy implications.

*The BDS uses SIC sector definitions rather than the newer NAICS standard; and in general these categories don't line up exactly with the industry classes used in the popular payroll surveys (i.e., CES). I am not sure of the method used to roll SIC codes forward past 2002. Also, my understanding is that the FIRE category may be somewhat less reliable prior to the early 1990s. If you want to become utterly cynical about economic data, ask someone who compiles and releases survey or administrative data how they feel about industry codes.

Monday, February 18, 2013

Two economies of young firms

In a previous post, I looked at data on job creation by startups and showed that, in any given year, startups create more jobs on net than all other firms combined. The result that startups are key to job creation is now well documented, here and elsewhere. Figure 1 is the chart I used (click for larger image):

Figure 1

Last week, the Atlanta Fed's macroblog ran a post reviewing some recent research that highlights the need to be careful when aggregating:

Over [2006], expanding firms more than 10 years old added a whopping 11 million jobs--about three times as many jobs as created by new firms. Of course, some older firms were downsizing or closing--contracting mature firms destroyed an estimated 10 million jobs.

Those who read my post on gross job flows will not be surprised by these numbers. Net jobs figures hide a huge amount of churning. It would be a big mistake to assume that older firms are uniformly shedding jobs or doing nothing.

In light of these data on startups and older firms, I thought it would be interesting to look at what happens to young firms immediately after their first year. Figure 2 shows job creation (number of jobs added by expanding establishments) and job destruction (number of jobs eliminated by shrinking establishments) for 1-year-old firms--that is, firms that were startups in the previous year.

Figure 2

So some new firms immediately begin shedding workers while others keep expanding. Though startups are creating 2 to 4 million jobs per year, in their second year of existence some of them are responsible for destroying almost a quarter of those jobs. Luckily, other firms of the same age keep expanding and about make up for it--though it's interesting that 1-year-old firms have been net job destroyers since 2001.

Figure 3

Figure 3 shows the same data for 2-year-old firms. By this age, firms have become net job destroyers (in every year except 2000). Figure 3 shows 3-year-old firms.

Figure 4

It doesn't take long for each startup cohort's surge of job creation to disappear. This isn't a big surprise--the high failure rate of startups is not a secret. In some sense, the data almost paint a picture of two economies. In one, successful startups enter and steadily create jobs, becoming somewhat permanent. In the other, firms enter, struggle, and shrink or collapse within a few years, constantly contributing to labor market reallocation but providing little in terms of stable labor demand. A nice discussion of this is provided by this BDS briefing:

Young firms have higher employment growth rates, if they survive, than older firms. . . . However, younger firms experience much more employment loss due to establishment exit, nearly 20 percent at very young firms, than do larger firms. 
The pattern for young firms is thus one of "up or out" with very rapid net growth for survivors balanced by a very high exit rate. . . . Lumping together all firms of the same age is clearly misleading, given this "up or out" dynamic. Young firms obviously are doing both better and worse than more mature firms in terms of growth and survival.

So while it's still true that startups are crucial for net job creation, their involvement in the job market is highly volatile and impermanent.

Tuesday, February 5, 2013

Is there a link between housing and startups?

In a previous post I described the puzzling collapse of startup activity prior to the Great Recession. This has been well documented by these authors and others. I mentioned that one possible explanation is the one offered by Moscarini and Posten-Vinay (based on the stylized fact that startups tend to be small). But there may be other things going on as well.

What's interesting about this decline in startup activity is that not only did it precede the broader recession, but it also coincided with the housing collapse. Consider Figure 1 (click for larger image), which uses annual data:

Figure 1

Figure 1 plots startup activity in terms of the number of startup firms and the number of jobs created by startups, against the left axis, and the Case Shiller house price index and home equity against the right axis. Everything is normalized by year 2000 levels. The timing is interesting, particularly if we think Case Shiller reports reality with a lag.

This pattern holds when considering shares instead of quantities; consider Figure 2 (click for larger image), which also uses annual data:

Figure 2

This figure is similar, with startup activity on the left axis and housing stuff on the right axis, but this time I've used job creation from startups as a component of the economywide job creation rate, and startups as a percent of all firms.* Again, it appears that housing wealth and startup activity are moving together.

It turns out that some more formal analysis confirms the result that there exists a relationship between house prices and startup activity, see here.

And just to remind readers that the recession started after the decline in startup activity, consider Figure 3 (click for larger image), which uses quarterly data:

Figure 3

Observe that residential investment dives about a year prior to other investment series. So there seems to be a puzzle here--why did startup activity begin declining before the broader economy slowed down, and does the answer have anything to do with housing? We don't know. It is likely that housing plays an important collateral role for many entrepreneurs, so a collapse in housing values could tighten credit conditions for young firms. This is one of the topics of my current PhD research.

By the way, here are some other posts in which I use the incomparable Business Dynamics Statistics:

*Ignore FHFA as one of the data sources listed for Figures 1 and 2; that series looks similar, but I took it out of the plots for congestion reasons, and I'm too lazy to fix it now.