Arnold Kling  

Momentum in Employment: Why it Matters

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The other day, I verified Ed Leamer's finding that there is momentum in payroll employment. This is an extremely important finding, as I will explain below.

Just as a teaser, I think it strongly supports Tyler Cowen's ZMP story, but it poses problems for the New Keynesian model and its relatives.

Suppose we are talking about the growth rate of GDP or the growth rate of employment. Broadly speaking, there are three ways that a macroeconomic variable can behave:

1. Mean reversion--if it was high last period, it tends to be low this period.
2. Random walk--it does not care what it did last period.
3. Momentum--if it was high last period, it tends to be high this period.

This is a short-horizon description. If our time horizon is decades, then all interesting macro variables are mean-reverting.

Ed Leamer's Macroeconomic Patterns and Stories has a brief chapter 6 that discusses persistence and momentum. He writes,


when you read in the newspaper that GDP growth was the very high number, 5.6, forget it. It doesn't matter. There is no persistence. But if you read that the unemployment rate jumped up by 0.5% points, that's really important. That jump in unemployment is not going away any time soon.

Leamer explains the momentum in unemployment as follows:

Firms do not hire or fire all in a single quarter. They make an employment plan and phase it in over several quarters.

That story is lame, for at least three reasons.

1. I am not sure it's true. I think firms tend to make employment adjustments, particularly downward adjustments, really quickly. You do not want to have people hanging around waiting for the axe to fall. You lay off people as quickly as possible, so that you can move on. In any case, somebody could look at the JOLTS data and see whether it's true.

2. Even if the story is true for one firm, it does not explain the aggregate data. If firm X is in a hiring mode and firm Y is in a firing mode, they cancel each other out. If firms are independent, the law of large numbers should take any momentum out of the net hiring number. On the other hand, if firms' hiring plans are highly correlated with one another, then that is the story.

3. The story does not explain why GDP growth and employment growth have such different time series properties. GDP growth is close to a random walk, and employment growth has strong momentum.

The story I would tell is that there are clusters of firms that interact with one another. In an expanding cluster, growth of one firm leads to growth in others. In the 1920's, as more people were employed in building automobiles, there were bound to be more people employed at gas stations. In a contracting cluster, declines in some firms lead to declines in others. As you get fewer horse-and-buggy drivers, you get fewer horse trainers, fewer horseshoe makers, and fewer manure sweepers.

I would expect to see momentum in these sorts of clusters. Not every firm gets the expansionary or contractionary signal at once. The signals take a while to propagate.

For example, in a classic inventory cycle, cars might pile up on dealer lots. The car manufacturers have layoffs and cut production, and this leads to an inventory pile-up at the plants that manufacture spark plugs, so they have layoffs and cut production, and this leads to an inventory pile-up at the companies that supply the materials to make spark plugs, and so on.

What about the momentum in employment growth relative to GDP growth? We do know that GDP and employment tend to move together, but evidently the relationship is not one-to-one. Instead, it seems that well after GDP growth slows down, the net gains in employment stay high for a while. (Remember that the gross flows into and out of employment are on the order of 4 million a month, while the net gain or loss is only about 150,000 a month.) Similarly, after GDP growth turns up, it takes a while for hiring to pick up.

I think that this cries out for a story of Zero Marginal Product. At the end of the boom, it takes firms a while to realize that they have a problem with ZMP workers. Forty years ago, auto inventories would pile up before manufacturers realized it, and meanwhile their production workers were producing cars that they could not sell. Today, in the Garett Jones economy, most workers are ZMP in the short run, but firms take the short-run view only when they are in financial difficulty.

In 2000, folks suddenly came to terms with the ZMP of workers at many Dotcom darlings. In 2008, we came to terms with the ZMP of many mortgage securities traders and home builders.

I think that the momentum in employment casts doubt on stories that explain employment in terms of real wages. Those stories imply a lot of mean reversion. In fact, the big "contribution" of the New Keynesian model to the older macro models has been to force the modelers to impose strong mean reversion on real wages and employment in order to get past the "microfoundations" police. But the data instead tell us that there is persistence.

I think that the momentum in employment could be consistent with the "general disequilibrium" version of Keynesian economics. (Barro-Grossman, for those who remember. Or Clower.) I think it could be consistent with the PSST model of Austrian economics that I have been pushing. Clusters of interdependent firms are an example of a pattern of sustainable specialization and trade.

I suspect that the JOLTS data could be exploited to learn more about the sources of momentum in employment. I would think that my hypothesis about interdependent clusters ought to be investigated.


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COMMENTS (5 to date)
Daniel Kuehn writes:

A few thoughts -

1. I agree with you that employment isn't always going to be smooth, particularly when layoffs are necessary, but just because you have a mass layoff doesn't mean it's smooth relative to the relevant counterfactual. In a particularly harsh inventory cycle, all workers may be ZMP. You will see a mass layoff, but you may not see a layoff of all workers - which implies relative persistence despite a large shock. I don't think your post necessarily disagrees with this, but I just wanted to make that point in Leamer's favor. Which leads me to -

2. This persistence story seems to mesh well with the idea of "labor as investment", but that also seems to cause problems for the ZMP story. A lot of the time when Cowen talks about ZMP it sounds like he's talking about short-term ZMP, like the ZMP that might emerge from a rough inventory cycle. But would firms really care about this? If labor is an investment, then what they're really thinking of is the NPV of ZMP, in which case there would be considerably less ZMP as a response to a business cycle (you'd only really expect to see it in cases of technological obsolescence). In other words - "labor as investment" (1.) confirms the idea of persistence, and (2.) makes ZMP more of a secular concern than a cyclical one.

3. Its true I'm young and unread, but I'm a little confused on how this hurts the New Keynesian story. New Keynesians seem to hang a lot on sticky prices and rigidity - far more than earlier Keynesians. This persistence understanding would seem to bolster a lot of those concerns. And "labor as investment" would seem to bolster specific New Keynesian models like efficiency wage models, right? I don't see how this necessarily conflicts with New Keynesianism.

4. I do like how at the end you acknowledge the variety of theories that this jives with. One of my biggest beefs with macroeconomics is that it acts like all these "thoeries" are at war with each other. In reality, they all describe various processes that are operating to a certain extent. The question isn't "which is right?". They all capture an important element of the economy. The question is "what mix of these processes is influencing the economy right now?"/

Ironman writes:

Arnold writes:

That story is lame, for at least three reasons.

1. I am not sure it's true. I think firms tend to make employment adjustments, particularly downward adjustments, really quickly. You do not want to have people hanging around waiting for the axe to fall. You lay off people as quickly as possible, so that you can move on. In any case, somebody could look at the JOLTS data and see whether it's true.

You might use JOLTS data to see it, but you can also look at the trends in weekly first time unemployment insurance claims and see if it's true. To jump ahead in the plot, in the most recent cycle, there has been a two-to-three week lag from economic event driving employee retention decisions to when they affect first-time unemployment claim filings. That lag would coincide with typical weekly or biweekly pay cycles, with employers delaying the implementation of their decision to lay off employees until the beginning of their next payroll cycle.

2. Even if the story is true for one firm, it does not explain the aggregate data. If firm X is in a hiring mode and firm Y is in a firing mode, they cancel each other out. If firms are independent, the law of large numbers should take any momentum out of the net hiring number. On the other hand, if firms' hiring plans are highly correlated with one another, then that is the story.

This is dead on target. Going back to that first-time unemployment claims data - we identified two unique discrepancies among the several trends that have been established since 2005, both of which represented significant deviations from those trends (as it happens, both were the result of government interventions.) Neither were sustained, as firms' employee retention plans resumed following the same track they were on previously. If it were not on target, then we would see new trends following different trajectories.

3. The story does not explain why GDP growth and employment growth have such different time series properties. GDP growth is close to a random walk, and employment growth has strong momentum.

I think this gets more into the idea of organizational capital. Having worked in a variety of manufacturing operations, I can confirm that a good-sized portion of a firms' workforce is dedicated to things other than supporting existing production.

When production is reduced in response to lower demand, the employees most at risk of being laid off are those who are most separated from that existing production. Work slows down for those who remain, but can ramp up quickly when demand for the products being produced rises.

The production rates can be volatile as they quickly respond to changes in demand, which accounts in part for why GDP appears to follow a random walk. However, it is not until higher production rates are sustained that they will affect firms' hiring decisions, given the high transaction costs associated with that activity.
(On a side note: If you want to potentially explain why economic recoveries have become progressively more jobless over the years, you might want to look at the trend in these transaction costs.)

Altogether, I would argue that this explains a good part of why you see such a disconnect between GDP growth and employment growth.

Dave writes:

These "interdependent clusters" that you speak of already have a term in economics: complementary goods and services.

Dave writes:

Actually, I suppose suppliers would be in the clusters too.

Monkey Daddy writes:

Far from posing problems to the Keynesians (New or otherwise), the story you would tell fits the narrative well.

The "zero marginal product" ZMP worker is actually a "zero value marginal product" ZVMP worker. What has changed is not the marginal product, but rather the value of the marginal product -- which has fallen to a decline in price, due to a decline in demand.

The horse-and-buggy example is an extremely inapt analogy for the current economic downturn. Is there some new supply of some new product that is displacing existing substitutes? Perhaps people are so busy on Facebook that they no longer need transportation, leading to a decline in transportation demand? No, the problems the current economy faces are not that of new jetpack supply diminishing demand for automobiles (and their corresponding supply requirements).

ZVMP workers in the tech sector ca. 2001 lost their value because of declining bubbilicious prices specific to their sector, not because their marginal ability to churn out banner ads declined. The difference between houses built in the 1990s and the 2000s is not the difference between horse-and-buggy and automobiles. There is no recalculation due to creative destruction; instead, there is a general lack of demand, therefore lower prices, therefore lower quantity supplied (and labor demanded). The decline in demand (and prices, and VMP) is not specific to a sector, but instead is general.

[Broken link fixed--Econlib Ed.]

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