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February 26, 2014
The Pattern of Job Creation and Destruction by Firm Age and Size
A recent Wall Street Journal blog post caught our attention. In particular, the following claim:
It’s not size that matters—at least when it comes to job creation. The age of the company is a bigger factor.
The following chart shows the average job-creation rate of expanding firms and the average job-destruction rates of shrinking firms from 1987 to 2011, broken out by various age and size categories:
In the chart, the colors represent age categories, and the sizes of the dot represent size categories. So, for example, the biggest blue dot in the far northeast quadrant shows the average rate of job creation and destruction for firms that are very young and very large. The tiny blue dot in the far east region of the chart represents the average rate of job creation and destruction for firms that are very young and very small. If an age-size dot is above the 45-degree line, then average net job creation of that firm size-age combination is positive—that is, more jobs are created than destroyed at those firms. (Note that the chart excludes firms less than one year old because, by definition in the data, they can have only job creation.)
The chart shows two things. First, the rate of job creation and destruction tends to decline with firm age. Younger firms of all sizes tend to have higher job-creation (and job-destruction) rates than their older counterparts. That is, the blue dots tend to lie above the green dots, and the green dots tend to be above the orange dots.
The second feature is that the rate of job creation at larger firms of all ages tends to exceed the rate of job destruction, whereas small firms tend to destroy more jobs than they create, on net. That is, the larger dots tend to lie above the 45-degree line, but the smaller dots are below the 45-degree line.
As pointed out in the WSJ blog post and by others (see, for example, work by the Kauffman Foundation here and here), once you control for firm size, firm age is the more important factor when measuring the rate of job creation. However, young firms are more dynamic in general, with rapid net growth balanced against a very high failure rate. (See this paper by John Haltiwanger for more on this up-or-out dynamic.) Apart from new firms, it seems that the combination of youth (between one and ten years old) and size (more than 250 employees) has tended to yield the highest rate of net job creation.
By John Robertson, a vice president and senior economist in the Atlanta Fed’s research department, and
Ellyn Terry, a senior economic analyst in the Atlanta Fed's research department
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February 21, 2014
What Is the Stance of Monetary Policy?
Will the Federal Open Market Committee's (FOMC) current large-scale asset purchase program, so-called QE3, continue to melt away as spring arrives? The release of the minutes from the January meeting of the FOMC, along with commentary from various participants in that meeting (noted in rapid succession here, here, and here, for example) have left the distinct impression that the answer is most probably yes.
The anticipated winding down of asset purchases almost inevitably invokes a habit of language concerning what it all means for the stance of monetary policy. From the New York Times, for example, we have this (emphasis added):
When Federal Reserve officials last met at the end of January, they were surprised by the strength of the economy, cheered by the optimism of consumers and convinced they should continue to dismantle the Fed's economic stimulus campaign, according to an account the Fed released Wednesday.
The sentiment expressed in that highlighted passage is front and center at the G-20 meetings, currently taking place in Australia (again, emphasis added):
Setting the scene for this weekend's Group of 20 meetings, Australian Treasurer Joe Hockey's main challenge was to avoid appearing partial in the escalating blame-game between the U.S. and developing countries over the recent exodus of capital from emerging markets….
Emerging market countries like India and Brazil have blamed the wide-scale selloff in local stocks, bonds and currencies on the Federal Reserve's plan to exit gradually from monetary-stimulus policies, which last year began sending investors into a panic.
Here's the thing. It is not at all clear that winding down asset purchases means an exit from or dismantling of monetary stimulus, gradual or otherwise. In Atlanta Fed President Dennis Lockhart's words yesterday:
In our public remarks over much of last year, my colleagues and I stressed a couple of very important messages. First, even with the phase-out of asset purchases, the basic stance of policy remains highly accommodative. To translate, the Committee intends to keep interest rates very low. The second message was that the QE program and the Fed's policy interest-rate target are two separate tools of policy. Consequently, we can wind down the asset purchases—a program that was meant to provide temporary, supplemental "oomph" to the low interest-rate policy—and preserve the accommodative positioning of policy appropriate for the reality of our economic situation.
But those are not just words. Several months back, Jim Hamilton publicized the work of Cynthia Wu and Dora Xia (former and current students of his), who have developed a method of using term structure data to infer the "shadow," or implicit, monetary policy rate. (Follow-up posts appeared thereafter at Econbrowser—here and here—and from the crew here at macroblog.)
Just recently, the Wu-Xia data has been updated, giving us a first glance at the post-taper shadow policy rate (see the chart):
Both Treasury yields and the shadow policy rate did in fact spike last June following then-Chairman Ben Bernanke's post-FOMC press conference, wherein he signaled that the asset-purchase taper was indeed on the table. But he also made the point that bringing down the QE pillar of the Fed's policy mix is decidedly not the same thing as bringing monetary stimulus to an end, a message that was subsequently emphasized by Fed officials many times, in many forums.
Though financial market participants may have been convinced that the taper meant tightening initially, it does appear that communications and forward guidance have done the trick of more than reversing that initial impression. At the very least, the Wu-Xia calculations are consistent with that interpretation.
By Dave Altig, executive vice president and research director at the Atlanta Fed
February 21, 2014 | Permalink
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February 11, 2014
A Second Look at the Employment-to-Population Ratio
This analysis is a companion piece to my Atlanta Fed colleague John Robertson's recent macroblog post. John's blog highlighted some findings of a recent New York Fed study by Samuel Kapon and Joseph Tracy on the employment-to-population (E/P) ratio. Their work has received considerable attention in the media and blogosphere (for example, here, here, and here). Kapon and Tracy's final chart (reproduced below) has received particular scrutiny.
The blue line represents the authors' estimate of the demographically adjusted E/P ratio purged of business-cycle effects. This line can be thought of as "trend." The chart shows that as of November 2013, the E/P ratio was only –0.7 percentage point below trend. Was the "gap" between actual and trend E/P really this small?
Attempting to answer this question requires digging into the details of Kapon and Tracy's method for estimating trend. One key excerpt is the following:
To overlay our demographically adjusted E/P ratio with the actual E/P ratio, we need to adopt a normalization… [W]e adopt the normalization that over the thirty-one years in our data sample [1982–2013] any business-cycle deviations between the actual and the adjusted E/P ratios will average to zero.
This methodology seems reasonable since one might typically expect business cycle effects to average out over 30 years. However, the 1982–2013 sample period is somewhat unusual in that the unemployment rate was elevated at both the starting and ending points.
The chart below shows estimates of three labor market gaps derived from the Congressional Budget Office's (CBO) estimates—released on February 4, 2014—of the potential labor force and the long-term natural rate of unemployment. (This rate is often referred to as the nonaccelerating inflation rate of unemployment, or NAIRU, and refers to the level of unemployment below which inflation rises.)
On average, the trend E/P ratio is below the actual rate by 0.86 percentage point. If one were to normalize the Kapon and Tracy E/P trend so that its average value was equal to CBO's trend, then the November 2013 E/P gap is about 1.5 percentage points. Whether or not the CBO estimate is the right benchmark is a matter of taste. CBO's recent estimate of NAIRU in the fourth quarter of 2013—5.5 percent—is lower than the 6 percent median estimate from the Survey of Professional Forecasters in the third quarter of 2013.
A second, more subtle issue in the Kapon and Tracy analysis is their treatment of cohorts:
We divide these individuals into 280 different cohorts defined by each individual's decade of birth, sex, race/ethnicity, and educational attainment. We assume that individuals within a specific cohort have similar career employment rate profiles. We use the 10.2 million observations [of CPS microdata] to estimate these 280 career employment rate profiles.
A well-known 2006 Brookings paper by Stephanie Aaronson and other Fed economists modeled trend labor force participation rate(LFPR) using birth-year cohorts. With estimates of trend LFPR and NAIRU, we can back out a trend E/P ratio. The chart below, adapted from Aaronson et al., plots age-group LFPRs against birth year.
We see that successive birth-year cohorts born between 1925 and 1950 had steadily increasing labor force attachment. Attachment for more recently born cohorts has leveled off and even declined slightly. People born in the 1990s have very low labor force attachment by historical standards. The inclusion of the "1990s—decade of birth" dummy variable in the Kapon and Tracy research probably implies that their model is interpreting much of this decline as structural. However, an alternative interpretation is that the decline is cyclical, because persons born after 1990 have been in an environment of high unemployment for most of their short working lives.
To gauge the sensitivity of trend or structural LFPR to how the youngest cohorts are treated, I used a stripped-down version of a model similar to Aaronson et al. Monthly LFPRs are modeled as a function of age, sex, birth date, and the CBO's estimate of the output gap during the January 1981 to January 2014 period. Time series published by the U.S. Bureau of Labor Statistics for 30 different age-sex cells are used so that the regression has 11,550 observations. Structural LFPR is constructed with the fitted values of the regression with a value of 0 percent for the output gap at all points in time. The trend E/P ratio is then backed out with the CBO's estimate of NAIRU.
The model is run with two different assumptions: First, following the approach of Aaronson et al., people born after 1986 have the same birth-year cohort effects as those born in December 1986. Second, no constraints are placed on birth-year cohort effects. Trend values of LFPR and E/P (taking on board the CBO's NAIRU) are plotted in the two charts below:
The January 2014 E/P gap with unconstrained cohort effects, as in Kapon and Tracy, is –1.0 percent, well below the –1.7 percent gap in the model with constrained cohort effects. Ultimately, both models are still very consistent with Kapon and Tracy's bottom line:
It is important to control for changing demographic factors when looking at the behavior of the E/P ratio over time. This step is particularly important today when these demographic factors are exerting downward pressure on the actual E/P rate, suggesting that the recent lack of improvement in the E/P ratio does not imply a lack of progress in the labor market. The adjusted E/P rate corroborates the basic picture from the unemployment rate that the labor market has been recovering over the past few years, but that it still has a ways to go to reach a full recovery.
By Pat Higgins, senior economist in the Atlanta Fed's research department
February 11, 2014 | Permalink
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February 06, 2014
A Prime-Aged Look at the Employment-to-Population Ratio
Trying to interpret changes in labor utilization measures such as the employment-to-population ratio is complicated by the fact that they do not refer to the same set of people over time. The age composition of the population is changing, and behavior can vary across and within age cohorts.
This issue is illustrated in a recent New York Fed study of the employment-to-population ratio by Samuel Kapon and Joseph Tracy. This ratio nosedived during the recent recession by about 4 percentage points and has barely budged since.
This measure of labor utilization is the clear laggard on any labor market recovery dashboard. But the authors show that it is not so clear that the employment-to-population ratio is really so far from where it should be, once you control for the fact the employment rates tend to be lower for younger and older people and that the age composition within the population has shifted over time. This idea is similar to the one used to estimate the trend labor force participation rate in this Chicago Fed study by Daniel Aaronson, Jonathan Davis, and Luojia Hu. The issue of controlling for dominant demographic trends is one of the reasons we at the Atlanta Fed decided not to feature either the overall employment-to-population ratio or the overall labor force participation rate in our Labor Market Spider Chart.
A simple, and admittedly crude, alternative to computing the demographically adjusted employment-to-population ratio trend is to look at a segment of the population that is on a relatively flat part of the employment (or participation) rate curve. A common standard for this is the so-called prime-aged population (people aged 25 to 54). These individuals are less likely to be making retirement decisions than older individuals and are less likely to be making schooling decisions than younger people. Of course, this approach doesn't control for within-cohort factors like educational differences.
So what do we find? The prime-aged employment-to-population ratio declined almost 5 percentage points between the end of 2007 and 2009 (versus 4 percentage points overall) and since then has recovered about 25 percent of that decline. Using the end of 2007 as reference, the Kapon and Tracy trend estimate has declined about 1.7 percentage points, which implies the overall employment-to-population ratio, by not continuing to decline, has improved by about 40 percent.
Then what does the analysis say about labor utilization in the wake of the recession? Once demographic factors are controlled for, both aforementioned measures indicate that labor-resource utilization has improved relative to trend. In fact, as Kapon and Tracy note, the relative improvement would be even greater if you believed that employment was above trend before the recession.
By John Robertson, a vice president and senior economist in the Atlanta Fed's research department
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