June 02, 2014
How Discouraged Are the Marginally Attached?
Of the many statistical barometers of the U.S. economy that we monitor here at the Atlanta Fed, there are few that we await more eagerly than the monthly report on employment conditions. The May 2014 edition arrives this week and, like many others, we will be more interested in the underlying details than in the headline job growth or unemployment numbers.
One of those underlying details—the state of the pool of “discouraged” workers (or, maybe more precisely, potential workers)—garnered special attention lately in the wake of the relatively dramatic decline in the ranks of the official labor force, a decline depicted in the April employment survey from the U.S. Bureau of Labor Statistics. That attention included some notable commentary from Federal Reserve officials.
Federal Reserve Bank of New York President William Dudley, for example, recently suggested that a sizeable part of the decline in labor force participation since 2007 can be tied to discouraged workers exiting the workforce. This suggestion follows related comments from Federal Reserve Chair Janet Yellen in her press conference following the March meeting of the Federal Open Market Committee:
So I have talked in the past about indicators I like to watch or I think that are relevant in assessing the labor market. In addition to the standard unemployment rate, I certainly look at broader measures of unemployment… Of course, I watch discouraged and marginally attached workers… it may be that as the economy begins to strengthen, we could see labor force participation flatten out for a time as discouraged workers start moving back into the labor market. And so that's something I'm watching closely.
What may not be fully appreciated by those not steeped in the details of the employment statistics is that discouraged workers are actually a subset of “marginally attached” workers. Among the marginally attached—individuals who have actively sought employment within the most recent 12-month period but not during the most recent month—are indeed those who report that they are out of the labor force because they are discouraged. But the marginally attached also include those who have not recently sought work because of family responsibilities, school attendance, poor health, or other reasons.
In fact, most of the marginally attached are not classified (via self-reporting) as discouraged (see the chart):
At the St. Louis Fed, B. Ravikumar and Lin Shao recently published a report containing some detailed analysis of discouraged workers and their relationship to the labor force and the unemployment rate. As Ravikumar and Shao note,
Since discouraged workers are not actively searching for a job, they are considered nonparticipants in the labor market—that is, they are neither counted as unemployed nor included in the labor force.
More importantly, the authors point out that they tend to reenter the labor force at relatively high rates:
Since December 2007, on average, roughly 40 percent of discouraged workers reenter the labor force every month.
Therefore, it seems appropriate to count some fraction of the jobless population designated as discouraged (and out of the labor force) as among the officially unemployed.
We believe this logic should be extended to the entire group of marginally attached. As we've pointed out in the past, the marginally attached group as a whole also has a roughly 40 percent transition rate into the labor force. Even though more of the marginally attached are discouraged today than before the recession, the changing distribution has not affected the overall transition rate of the marginally attached into the labor force.
In fact, in terms of the propensity to flow into employment or officially measured unemployment, there is little to distinguish the discouraged from those who are marginally attached but who have other reasons for not recently seeking a job (see the chart):
What we take from these data is that, as a first pass, when we are talking about discouraged workers' attachment to the labor market, we are talking more generally about the marginally attached. And vice versa. Any differences in the demographic characteristics between discouraged and nondiscouraged marginally attached workers do not seem to materially affect their relative labor market attachment and ability to find work.
Sometimes labels matter. But in the case of discouraged marginally attached workers versus the nondiscouraged marginally attached workers—not so much.
By Dave Altig, executive vice president and research director,
John Robertson, a vice president and senior economist, and
Ellyn Terry, a senior economic analyst, all of the Atlanta Fed's research department
May 20, 2014
Where Do Young Firms Get Financing? Evidence from the Atlanta Fed Small Business Survey
During last week's "National Small Business Week," Janet Yellen delivered a speech titled "Small Business and the Recovery," in which she outlined how the Fed's low-interest-rate policies have helped small businesses.
By putting downward pressure on interest rates, the Fed is trying to make financial conditions more accommodative—supporting asset values and lower borrowing costs for households and businesses and thus encouraging the spending that spurs job creation and a stronger recovery.
In general, I think most small businesses in search of financing would agree with the "rising tide lifts all boats" hypothesis. When times are good, strong demand for goods and services helps provide a solid cash flow, which makes small businesses more attractive to lenders. At the same time, rising equity and housing prices support collateral used to secure financing.
Reduced economic uncertainty and strong income growth can help those in search of equity financing, as investors become more willing and able to open their pocketbooks. But even when the economy is strong, there is a business segment that's had an especially difficult time getting financing. And as we've highlighted in the past, this is also the segment that has had the highest potential to contribute to job growth—namely, young businesses.
Why is it hard for young firms to find credit or financing more generally? At least two reasons come to mind: First, lenders tend to have a rearview-mirror approach for assessing commercial creditworthiness. But a young business has little track record to speak of. Moreover, lenders have good reason to be cautious about a very young firm: half of all young firms don't make it past the fifth year. The second reason is that young businesses typically ask for relatively small amounts of money. (See the survey results in the Credit Demand section under Financing Conditions.) But the fixed cost of the detailed credit analysis (underwriting) of a loan can make lenders decide that it is not worth their while to engage with these young firms.
While difficult, obtaining financing is not impossible. Over the past two years, half of small firms under six years old that participated in our survey (latest results available) were able to obtain at least some of the financing requested over all their applications. This 50-percent figure for young firms strongly contrasts with the 78 percent of more mature small firms that found at least some credit. Nonetheless, some young firms manage to find some credit.
This leads to two questions:
- What types of financing sources are young firms using?
- How are the available financing options changing?
To answer the first question, we pooled all of the financing applications submitted by small firms in our semiannual survey over the past two years and examined how likely they were to apply for financing and be approved across a variety of financing products.
Applications and approvals
While most mature firms (more than five years old) seek—and receive—financing from banks, young firms have about as many approved applications for credit cards, vendor or trade credit, or financing from friends or family as they do for bank credit.
The chart below shows that about two-thirds of applications on behalf of mature firms were for commercial loans and lines of credit at banks and about 60 percent of those applications were at least partially approved. In comparison, fewer than half of applications by young firms were for a commercial bank loan or line of credit, fewer than a third of which were approved. Further, about half of the applications by mature firms were met in full compared to less than one-fifth of applications by young firms.
In the survey, we also ask what type of bank the firm applied to (large national bank, regional bank, or community bank). It turns out this distinction matters little for the young firms in our sample—the vast majority are denied regardless of the size of the bank. However, after the five-year mark, approval is highest for firms applying at the smallest banks and lowest for large national banks. For example, firms that are 10 years or older that applied at a community bank, on average, received most of the amount requested, and those applying at large national banks received only some of the amount requested.
Half of young firms and about one-fifth of mature firms in the survey reported receiving none of the credit requested over all their applications. How are firms that don't receive credit affected? According to a 2013 New York Fed small business credit survey, 42 percent of firms that were unsuccessful at obtaining credit said it limited their business expansion, 16 percent said they were unable to complete an existing order, and 16 percent indicated that it prevented hiring.
This leads to the next couple of questions: How are the available options for young firms changing? Is the market evolving in ways that can better facilitate lending to young firms?
When thinking about the places where young firms seem to be the most successful in obtaining credit, equity investments or loans from friends and family ranked the highest according to the Atlanta Fed survey, but this source is not highly used (see the first chart). Is the low usage rate a function of having only so many "friends and family" to ask? If it is, then perhaps alternative approaches such as crowdfunding could be a viable way for young businesses seeking small amounts of funds to broaden their financing options. Interestingly, crowdfunding serves not just as a means to raise funds, but also as a way to reach more customers and potential business partners.
A variety of types of new lending sources, including crowdfunding, were featured at the New York Fed's Small Business Summit ("Filling the Gaps") last week. One major theme of the summit was that credit providers are increasingly using technology to decrease the credit search costs for the borrower and lower the underwriting costs of the lender. And when it comes to matching borrowers with lenders, there does appear to be room for improvement. The New York Fed's small business credit survey, for example, showed that small firms looking for credit spent an average of 26 hours searching during the first half of 2013. Some of the financial services presented at the summit used electronic financial records and relevant business data, including business characteristics and credit scores to better match lenders and borrowers. Another theme to come out of the summit was the importance of transparency and education about the lending process. This was considered to be especially important at a time when the small business lending landscape is changing rapidly.
The full results of the Atlanta Fed's Q1 2014 Small Business Survey are available on the website.
By Ellyn Terry, an economic policy analysis specialist in the Atlanta Fed's research department
May 16, 2014
Which Flavor of QE?
Yesterday's report on consumer price inflation from the U.S. Bureau of Labor Statistics moved the needle a bit on inflation trends—but just a bit. Meanwhile, the European Central Bank appears to be locked and loaded to blast away at its own (low) inflation concerns. From the Wall Street Journal:
The European Central Bank is ready to loosen monetary policy further to prevent the euro zone from succumbing to an extended period of low inflation, its vice president said on Thursday.
"We are determined to act swiftly if required and don't rule out further monetary policy easing," ECB Vice President Vitor Constancio said in a speech in Berlin.
One of the favorite further measures is apparently charging financial institutions for funds deposited with the central bank:
On Wednesday, the ECB's top economist, Peter Praet, in an interview with German newspaper Die Zeit, said the central bank is preparing a number of measures to counter low inflation. He mentioned a negative rate on deposits as a possible option in combination with other measures.
I don't presume to know enough about financial institutions in Europe to weigh in on the likely effectiveness of such an approach. I do know that we have found reasons to believe that there are limits to such a tool in the U.S. context, as the New York Fed's Ken Garbade and Jamie McAndrews pointed out a couple of years back.
In part, the desire to think about an option such as negative interest rates on deposits appears to be driven by considerable skepticism about deploying more quantitative easing, or QE.
A drawback, in my view, of general discussions about the wisdom and effectiveness of large-scale asset purchase programs is that these policies come in many flavors. My belief, in fact, is that the Fed versions of QE1, QE2, and QE3 can be thought of as three quite different programs, useful to address three quite distinct challenges. You can flip through the slide deck of a presentation I gave last week at a conference sponsored by the Global Interdependence Center, but here is the essence of my argument:
- QE1, as emphasized by former Fed Chair Ben Bernanke, was first and foremost credit policy. It was implemented when credit markets were still in a state of relative disarray and, arguably, segmented to some significant degree. Unlike credit policy, the focus of traditional or pure QE "is the quantity of bank reserves" (to use the Bernanke language). Although QE1 per se involved asset purchases in excess of $1.7 trillion, the Fed's balance sheet rose by less than $300 billion during the program's span. The reason, of course, is that the open-market purchases associated with QE1 largely just replaced expiring lending from the emergency-based facilities in place through most of 2008. In effect, with QE1 the Fed replaced one type of credit policy with another.
- QE2, in contrast, looks to me like pure, traditional quantitative easing. It was a good old-fashioned Treasury-only asset purchase program, and the monetary base effectively increased in lockstep with the size of the program. Importantly, the salient concern of the moment was a clear deterioration of market-based inflation expectations and—particularly worrisome to us at the Atlanta Fed—rising beliefs that outright deflation might be in the cards. In retrospect, old-fashioned QE appears to have worked to address the old-fashioned problem of influencing inflation expectations. In fact, the turnaround in expectations can be clearly traced to the Bernanke comments at the August 2010 Kansas City Fed Economic Symposium, indicating that the Federal Open Market Committee (FOMC) was ready and willing pull the QE tool out of the kit. That was an early lesson in the power of forward guidance, which brings us to...
- ...QE3. I think it is a bit early to draw conclusions about the ultimate impact of QE3. I think you can contend that the Fed's latest large-scale asset purchase program has not had a large independent effect on interest rates or economic activity while still believing that QE3 has played an important role in supporting the economic recovery. These two, seemingly contradictory, opinions echo an argument suggested by Mike Woodford at the Kansas City Fed's Jackson Hole conference in 2012: QE3 was important as a signaling device in early stages of the deployment of the FOMC's primary tool, forward guidance regarding the period of exceptionally low interest rates. I would in fact argue that the winding down of QE3 makes all the more sense when seen through the lens of a forward guidance tool that has matured to the point of no longer requiring the credibility "booster shot" of words put to action via QE.
All of this is to argue that QE, as practiced, is not a single policy, effective in all variants in all circumstances, which means that the U.S. experience of the past might not apply to another time, let alone another place. But as I review the record of the past seven years, I see evidence that pure QE worked pretty well precisely when the central concern was managing inflation expectations (and, hence, I would say, inflation itself).
By Dave Altig, executive vice president and research director of the Atlanta Fed
May 13, 2014
Today’s news brings another indication that low inflation rates in the euro area have the attention of the European Central Bank. From the Wall Street Journal (Update: via MarketWatch):
Germany's central bank is willing to back an array of stimulus measures from the European Central Bank next month, including a negative rate on bank deposits and purchases of packaged bank loans if needed to keep inflation from staying too low, a person familiar with the matter said...
This marks the clearest signal yet that the Bundesbank, which has for years been defined by its conservative opposition to the ECB's emergency measures to combat the euro zone's debt crisis, is fully engaged in the fight against super-low inflation in the euro zone using monetary policy tools...
Notably, these tools apparently do not include Fed-style quantitative easing:
But the Bundesbank's backing has limits. It remains resistant to large-scale purchases of public and private debt, known as quantitative easing, the person said. The Bundesbank has discussed this option internally but has concluded that with government and corporate bond yields already quite low in Europe, the purchases wouldn't do much good and could instead create financial stability risks.
Should we conclude that there is now a global conclusion about the value and wisdom of large-scale asset purchases, a.k.a. QE? We certainly have quite a bit of experience with large-scale purchases now. But I think it is also fair to say that that experience has yet to yield firm consensus.
You probably don’t need much convincing that QE consensus remains elusive. But just in case, I invite you to consider the panel discussion we titled “Greasing the Skids: Was Quantitative Easing Needed to Unstick Markets? Or Has it Merely Sped Us toward the Next Crisis?” The discussion was organized for last month’s 2014 edition of the annual Atlanta Fed Financial Markets Conference.
Opinions among the panelists were, shall we say, diverse. You can view the entire session via this link. But if you don’t have an hour and 40 minutes to spare, here is the (less than) ten-minute highlight reel, wherein Carnegie Mellon Professor Allan Meltzer opines that Fed QE has become “a foolish program,” Jeffries LLC Chief Market Strategist David Zervos declares himself an unabashed “lover of QE,” and Federal Reserve Governor Jeremy Stein weighs in on some of the financial stability questions associated with very accommodative policy:
You probably detected some differences of opinion there. If that, however, didn’t satisfy your craving for unfiltered debate, click on through to this link to hear Professor Meltzer and Mr. Zervos consider some of Governor Stein’s comments on monitoring debt markets, regulatory approaches to pursuing financial stability objectives, and the efficacy of capital requirements for banks.
By Dave Altig, executive vice president and research director of the Atlanta Fed.
May 09, 2014
How Has Disability Affected Labor Force Participation?
You might be unaware that May is Disability Insurance Awareness Month. We weren’t aware of it until recently, but the issue of disability—as a reason for nonparticipation in the labor market—has been very much on our minds as of late. As we noted in a previous macroblog post, from the fourth quarter of 2007 through the end of 2013, the number of people claiming to be out of the labor force for reasons of illness or disability increased almost 3 million (or 23 percent). The previous post also noted that the incidence of reported nonparticipation as a result of disability/illness is concentrated (unsurprisingly) in the age group from about 51 to 60.
In the past, we have examined the effects of the aging U.S. population on the labor force participation rate (LFPR). However, we have not yet specifically considered how much the aging of the population alone is responsible for the aforementioned increase in disability as a reason for dropping out of the labor force.
The following chart depicts over time the percent (by age group) reporting disability or illness as a reason for not participating in the labor force. Each line represents a different year, with the darkest line being 2013. The chart reveals a long-term trend of rising disability or illness as a reason for labor force nonparticipation for almost every age group.
The chart also shows that disability or illness is cited most often among people 51 to 65 years old—the current age of a large segment of the baby boomer cohort. In fact, the proportion of people in this age group increased from 20 percent in 2003 to 25 percent in 2013.
How much can the change in demographics during the past decade explain the rise in disability or illness as a reason for not participating in the labor market? The answer seems to be: Not a lot.
Following an approach you may have seen in this post, we break down into three components the change in the portion of people not participating in the labor force due to disability or illness. One component measures the change resulting from shifts within age groups (the within effect). Another component measures changes due to population shifts across age groups (the between effect). A third component allows for correlation across the two effects (a covariance term). Here’s what you get:
To recap, only about one fifth of the decline in labor force participation as a result of reported illness or disability can be attributed to the population aging per se. A full three quarters appears to be associated with some sort of behavioral change.
What is the source of this behavioral change? Our experiment can’t say. But given that those who drop out of the labor force for reasons of disability/illness tend not to return, it would be worth finding out. Here is one perspective on the issue.
You can find even more on this topic via the Human Capital Compendium.
By Dave Altig, research director and executive vice president at the Atlanta Fed, and
Ellyn Terry, a senior economic analyst in the Atlanta Fed's research department
April 28, 2014
New Data Sources: A Conversation with Google's Hal Varian
New Data Sources: A Conversation with Google's Hal Varian
In recent years, there has been an explosion of new data coming from places like Google, Facebook, and Twitter. Economists and central bankers have begun to realize that these data may provide valuable insights into the economy that inform and improve the decisions made by policy makers.
As chief economist at Google and emeritus professor at UC Berkeley, Hal Varian is uniquely qualified to discuss the issues surrounding these new data sources. Last week he was kind enough to take some time out of his schedule to answer a few questions about these data, the benefits of using them, and their limitations.
Mark Curtis: You've argued that new data sources from Google can improve our ability to "nowcast." Can you describe what this means and how the exorbitant amount of data that Google collects can be used to better understand the present?
Hal Varian: The simplest definition of "nowcasting" is "contemporaneous forecasting," though I do agree with David Hendry that this definition is probably too simple. Over the past decade or so, firms have spent billions of dollars to set up real-time data warehouses that track business metrics on a daily level. These metrics could include retail sales (like Wal-Mart and Target), package delivery (UPS and FedEx), credit card expenditure (MasterCard's SpendingPulse), employment (Intuit's small business employment index), and many other economically relevant measures. We have worked primarily with Google data, because it's what we have available, but there are lots of other sources.
Curtis: The ability to "nowcast" is also crucially important to the Fed. In his December press conference, former Fed Chairman Ben Bernanke stated that the Fed may have been slow to acknowledge the crisis in part due to deficient real-time information. Do you believe that new data sources such as Google search data might be able to improve the Fed's understanding of where the economy is and where it is going?
Varian: Yes, I think that this is definitely a possibility. The real-time data sources mentioned above are a good starting point. Google data seems to be helpful in getting real-time estimates of initial claims for unemployment benefits, housing sales, and loan modification, among other things.
Curtis: Janet Yellen stated in her first press conference as Fed Chair that the Fed should use other labor market indicators beyond the unemployment rate when measuring the health of labor markets. (The Atlanta Fed publishes a labor market spider chart incorporating a variety of indicators.) Are there particular indicators that Google produces that could be useful in this regard?
Varian: Absolutely. Queries related to job search seem to be indicative of labor market activity. Interestingly, queries having to do with killing time also seem to be correlated with unemployment measures!
Curtis: What are the downsides or potential pitfalls of using these types of new data sources?
Varian: First, the real measures—like credit card spending—are probably more indicative of actual outcomes than search data. Search is about intention, and spending is about transactions. Second, there can be feedback from news media and the like that may distort the intention measures. A headline story about a jump in unemployment can stimulate a lot of "unemployment rate" searches, so you have to be careful about how you interpret the data. Third, we've only had one recession since Google has been available, and it was pretty clearly a financially driven recession. But there are other kinds of recessions having to do with supply shocks, like energy prices, or monetary policy, as in the early 1980s. So we need to be careful about generalizing too broadly from this one example.
Curtis: Given the predominance of new data coming from Google, Twitter, and Facebook, do you think that this will limit, or even make obsolete, the role of traditional government statistical agencies such as Census Bureau and the Bureau of Labor Statistics in the future? If not, do you believe there is the potential for collaboration between these agencies and companies such as Google?
Varian: The government statistical agencies are the gold standard for data collection. It is likely that real-time data can be helpful in providing leading indicators for the standard metrics, and supplementing them in various ways, but I think it is highly unlikely that they will replace them. I hope that the private and public sector can work together in fruitful ways to exploit new sources of real-time data in ways that are mutually beneficial.
Curtis: A few years ago, former Fed Chairman Bernanke challenged researchers when he said, "Do we need new measures of expectations or new surveys? Information on the price expectations of businesses—who are, after all, the price setters in the first instance—as well as information on nominal wage expectations is particularly scarce." Do data from Google have the potential to fill this need?
Varian: We have a new product called Google Consumer Surveys that can be used to survey a broad audience of consumers. We don't have ways to go after specific audiences such as business managers or workers looking for jobs. But I wouldn't rule that out in the future.
Curtis: MIT recently introduced a big-data measure of inflation called the Billion Prices Project. Can you see a big future in big data as a measure of inflation?
Varian: Yes, I think so. I know there are also projects looking at supermarket scanner data and the like. One difficulty with online data is that it leaves out gasoline, electricity, housing, large consumer durables, and other categories of consumption. On the other hand, it is quite good for discretionary consumer spending. So I think that online price surveys will enable inexpensive ways to gather certain sorts of price data, but it certainly won't replace existing methods.
By Mark Curtis, a visiting scholar in the Atlanta Fed's research department
April 17, 2014
Using State-Level Data to Estimate How Labor Market Slack Affects Wages
At a recent speech in Miami, Atlanta Fed President Dennis Lockhart had this to say:
Wage growth by most measures has been very low. I take this as a signal of labor market weakness, and in turn a signal of a lack of significant upward unit cost pressure on inflation.
This macroblog post examines whether the data support this assertion (answer: yes) and whether wage inflation is more sensitive to some measures of labor underutilization than other measures (answer: apparently, yes). San Francisco Fed President John Williams touched on the latter topic in a recent speech (emphasis mine):
We generally look at the overall unemployment rate as a good yardstick of labor market slack and inflation pressures. However, its usefulness may be compromised today by the extraordinary number of long-term unemployed—defined as those out of the workforce for six months or longer... Standard models of inflation typically do not distinguish between the short- and long-term unemployed, because they're assumed to affect wage and price inflation in the same way. However, recent research suggests that the level of long-term unemployment may not influence inflation pressures to the same degree as short-term unemployment.
And Fed Chair Janet Yellen said this at her March 19 press conference:
With respect to the issue of short-term unemployment and its being more relevant for inflation and a better measure of the labor market, I've seen research along those lines. I think it would be tremendously premature to adopt any notion that says that that is an accurate read on either how inflation is determined or what constitutes slack in the labor market.
The research to which President Williams refers are papers by economists Robert Gordon and Mark Watson, respectively. (For further evidence, see this draft by Princeton economists Alan Krueger, Judd Cramer and David Cho.)
The analysis here builds on this research by broadening the measures of labor underutilization beyond the short-term and long-term unemployment rates that add up to the standard unemployment rate called U-3. The U-5 underutilization measure includes both conventional unemployment and "marginally attached workers" who are not in the labor force but who want a job and have actively looked in the past year. The difference between U-5 and U-3 is a very close proxy for the number of marginally attached relative to the size of the labor force.
U-6 encompasses U-5 as well as those who work less than 35 hours for an economic reason. The difference between U-6 and U-5 is a very close proxy for the share of "part-time for economic reason" workers in the labor force. These nonoverlapping measures of labor underutilization rates are all shown in the chart below.
The series are highly correlated, making it difficult to isolate the impact of any particular labor underutilization rate on wage inflation (e.g., "How much will wage inflation change if the short-term unemployment rate rises 1.0 percentage point, holding all of the underutilization measures in the above figure constant?").
We follow the approach of Staiger, Stock, and Watson (2001) by using state-level data to relate wage inflation to unemployment in a so-called "wage-Phillips curve." Because the 2007–09 recession hit some states harder than others, we can use the cross-sectional variation in outcomes across states to arrive at more precise estimates of the separate impacts of the labor underutilization measures on wage inflation (see the chart).
Five-year state-level wage inflation rates for 2008–13, using monthly Current Population Survey(CPS) microdata, are shown on the vertical axis. The CPS microdata are also used to construct all of the labor underutilization measures. Each circle represents an individual state (red for long-term unemployment and blue for short-term unemployment), and each circle's area is proportional to the state's population share. Two noteworthy states are pointed out for illustration. North Dakota has had lower unemployment and (much) higher wage inflation than the other states (presumably because of its energy boom). And California has had higher unemployment and (somewhat) lower wage inflation than average. Even after excluding North Dakota, we see a clear negative relationship between wage inflation and underutilization measured with either short-term or long-term unemployment.
Because short-term and long-term unemployment are highly correlated (also apparent in the above plot), one can't tell visually if one underutilization measure is more important for wage inflation than the other. To make this assessment, we need to estimate a regression. The regression—which also includes both U-5 minus U-3 and U-6 minus U-5—adjusts wages for changes in the composition of the workforce. This composition adjustment, also made by Staiger, Stock and Watson (2001), controls for the fact that lower-skilled workers tend to be laid off at a disproportionately higher rate during recessions, thereby putting upward pressure on wages. The regression also weights observations by population shares.
The regression estimates imply that short-term unemployment is the most important determinant of wage inflation while U-6 minus U-5—the proxy for "part-time for economic reason" workers—also has a statistically significant impact. The other two labor underutilization measures do not affect wage inflation statistically different from zero. Rather than provide regression coefficients, we decompose observed U.S. wage inflation for 1995–2013 into contributions from the labor underutilization measures, workforce composition changes, and everything else (see the chart).
Both short-term unemployment and workers who are part-time for economic reasons have pushed down wage inflation. But the "part-time for economic reason" impact has become relatively more important recently because of the stubbornly slow decline in undesired part-time employment.
By Pat Higgins, a senior economist in the Atlanta Fed's research department
April 10, 2014
Reasons for the Decline in Prime-Age Labor Force Participation
As a follow up to this post on recent trends in labor force participation, we look specifically at the prime-age group of 25- to 54-year-olds. The participation decisions of this age cohort are less affected by the aging population and the longer-term trend toward lower participation of youths because of rising school enrollment rates. In that sense, they give us a cleaner window on responses of participation to changing business cycle conditions.
The labor force participation rate of the prime-age group fell from 83 percent just before the Great Recession to 81 percent in 2013. The participation rate of prime-age males has been trending down since the 1960s. The participation rate of women, which had been rising for most of the post-World War II period, appears to have plateaued in the 1990s and has more recently shared the declining pattern of participation for prime-age men. But the decline in participation for both groups appears to have accelerated between 2007 and 2013 (see chart 1).
We look at the various reasons people cite for not participating in the labor force from the monthly Current Population Survey. These reasons give us some insight into the impact of changes in employment conditions since 2007 on labor force participation. The data on those not in the official labor force can be broken into two broad categories: those who say they don't currently want a job and those who say they do want a job but don't satisfy the active search criteria for being in the official labor force. Of the prime-age population not in the labor force, most say they don't currently want a job. At the end of 2007, about 15 percent of 25- to 54-year-olds said they didn't want a job, and slightly fewer than 2 percent said they did want a job. By the end of 2013, the don't-want-a-job share had reached nearly 17 percent, and the want-a-job share had risen to slightly above 2 percent (see chart 2).
Prime-Age Nonparticipation: Currently Want a Job
Most of the rise in the share of the prime-age population in the want-a-job category is due to so-called marginally attached individuals—they are available and want a job, have looked for a job in the past year, but haven't looked in the past four weeks—especially those who say they are not currently looking because they have become discouraged about job-finding prospects (see the blue and orange lines of chart 3). In 2013, there were about 1.1 million prime-age marginally attached individuals compared to 0.7 million in 2007, and the prime-age marginally attached accounted for about half of all marginally attached in the population.
The marginally attached are aptly named in the sense that they have a reasonably high propensity to reenter the labor force—more than 40 percent are in the labor force in the next month and more than 50 percent are in the labor force 12 months later (see chart 4). This macroblog post discusses what the relative stability in the flow rate from marginally attached to the labor force means for thinking about the amount of slack labor resources in the economy.
Prime-Age Nonparticipation: Currently Don't Want a Job
As chart 2 makes evident, the vast majority of the rise in prime-age nonparticipation since 2009 is due to the increase in those saying they do not currently want a job. The largest contributors to the increase are individuals who say they are too ill or disabled to work or who are in school or training (see the orange and blues lines in chart 5).
Those who say they don't want a job because they are disabled have a relatively low propensity to subsequently (re)enter the labor force. So if the trend of rising disability persists, it will put further downward pressure on prime-age participation. Those who say they don't currently want a job because they are in school or training have a much greater likelihood of (re)entering the labor force, although this tendency has declined slightly since 2007 (see chart 6).
Note that the number of people in the Current Population Survey citing disability as the reason for not currently wanting a job is not the same as either the number of people applying for or receiving social security disability insurance. However, a similar trend has been evident in overall disability insurance applications and enrollments (see here).
Some of the rise in the share of prime-age individuals who say they don't want a job could be linked to erosion of skills resulting from prolonged unemployment or permanent changes in the composition of demand (a different mix of skills and job descriptions). It is likely that the rise in share of prime-age individuals not currently wanting a job because they are in school or in training is partly a response to the perception of inadequate skills. The increase in recent years is evident across all ages until about age 50 but is especially strong among the youngest prime-age individuals (see chart 7).
But lack of required skills is not the only plausible explanation for the rise in the share of prime-age individuals who say they don't currently want a job. For instance, the increased incidence of disability is partly due to changes in the age distribution within the prime-age category. The share of the prime-age population between 50 and 54 years old—the tail of the baby boomer cohort—has increased significantly (see chart 8).
This increase is important because the incidence of reported disability within the prime-age population increases with age and has become more common in recent years, especially for those older than 45 (see chart 9).
The health of the labor market clearly affects the decision of prime-age individuals to enroll in school or training, apply for disability insurance, or stay home and take care of family. Discouragement over job prospects rose during the Great Recession, causing many unemployed people to drop out of the labor force. The rise in the number of prime-age marginally attached workers reflects this trend and can account for some of the decline in participation between 2007 and 2009.
But most of the postrecession rise in prime-age nonparticipation is from the people who say they don't currently want a job. How much does that increase reflect trends established well before the recession, and how much can be attributed to the recession and slow recovery? It's hard to say with much certainty. For example, participation by prime-age men has been on a secular decline for decades, but the pace accelerated after 2007—see here for more discussion.
Undoubtedly, some people will reenter the labor market as it strengthens further, especially those who left to undertake additional training. But for others, the prospect of not finding a satisfactory job will cause them to continue to stay out of the labor market. The increased incidence of disability reported among prime-age individuals suggests permanent detachment from the labor market and will put continued downward pressure on participation if the trend continues. The Bureau of Labor Statistics projects that the prime-age participation rate will stabilize around its 2013 level. Given all the contradictory factors in play, we think this projection should have a pretty wide confidence interval around it.
Note: All data shown are 12-month moving averages to emphasize persistent shifts in trends.
By Melinda Pitts, director, Center for Human Capital Studies,
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
April 08, 2014
A Closer Look at Post-2007 Labor Force Participation Trends
The rate of labor force participation (the share of the civilian noninstitutionalized population aged 16 and older in the labor force) has declined significantly since 2007. To what extent were the Great Recession and tepid recovery responsible?
In this post and one that will follow, we offer a series of charts using data from the Current Population Survey to explore some of the possible reasons behind the 2007–13 drop in participation. This first post describes the impact of the changing-age composition of the population and changes in labor force participation within specific age cohorts—see Calculated Risk posts here and here for a related treatment, and also this recent BLS study. The next post will look at the issue of potential cyclical impacts on participation by examining the behavior of the prime-age population.
Putting the decline in context
After rising from the mid-1960s through 1990, the overall labor force participation rate was relatively stable between 1990 and 2007. But participation has declined sharply since 2007. By 2013, participation was at the lowest level since 1978 (see chart 1).
For men, the longer-term declining trend of participation accelerated after 2007. For women, after having been relatively stable since the late 1990s, participation began to decline after 2009. The decline for both males and females since 2009 was similar (see chart 2).
The impact of retirement
One of the most important features of labor force participation is that it varies considerably over the life cycle: the rate of participation is low among young individuals, peaks during the prime-age years of 25 to 54, and then declines (see chart 3). So a change in the age distribution of the population can result in a significant change in overall labor force participation.
The age distribution of the population has been shifting outward for some time. This is a result of the so-called baby boomer generation—that is, people born between 1946 and 1964 (see chart 4). The oldest baby boomers turned 62 in 2008 and became eligible for Social Security retirement benefits.
At the same time the age distribution of the population has shifted out, the rate of retirement of older Americans has been declining. Retirement rates have generally been drifting down since the early 2000s (see chart 5). The decline in age-specific retirement rates has resulted in rising age-specific labor force participation rates. For example, from 1999 to 2013, the share of 62-year-old retirees declined from 38 percent to 28 percent. The BLS projects that this trend will continue at a similar pace in coming years (see table 3 of the BLS report).
Although the decline in the propensity to retire has put some upward pressure on overall labor force participation, that effect is dominated by the sheer increase in the number of people reaching retirement age. The net result has been a steep rise in the share of the population saying they are not in the labor force because they are retired (see chart 6).
Participation by age group
Individuals aged 16–24
The labor force participation rate for young individuals (between 16 and 24 years old) has been generally declining since the late 1990s. After slowing in the mid-2000s, the decline accelerated again during the Great Recession. However, participation has been relatively stable since 2009 (see chart 7). Nonetheless, the BLS projects that the participation rate for 16- to 24-year-olds will decline further, albeit at a slower pace than it declined between 2000 and 2009, and will fall a little below 50 percent by 2022.
The change in participation among young people can be attributed almost entirely to enrollment rates in education programs (see here) and lower labor force participation among enrollees (see chart 8). The change in the share of 16- to 24-year-olds who say they don't currently want a job because they are in school closely matches the change in labor force participation for the entire cohort.
Individuals aged 25–54 (prime age)
Generally, people aged 25 to 54 are the group most likely to be participating in the labor market (see chart 3). These so-called prime-age individuals are less likely to be making retirement decisions than older individuals, and less likely to be enrolled in schooling or training than younger individuals.
However, the prime-age labor force participation rate declined considerably between 2007 and 2013, and at a much faster pace than had been seen in the years prior to the recession (see chart 9). Reflective of the overall gender-specific participation differences seen in chart 2, the decline in prime-age female participation did not take hold until after 2009, and since 2009 the decline in both prime-age male and female participation has been quite similar. Nevertheless, the BLS projects that prime-age participation will stabilize in coming years and prime-age participation in 2022 will be close to its 2013 level.
The BLS projects that participation by age group will look like this in 2022 relative to 2013 (see chart 10).
Participation by youths is projected to continue to fall. The participation of older workers is projected to increase, but it will remain significantly lower than that of the prime-age group. Combined with an age distribution that has also continued to shift outward (see chart 11), the overall participation rate is expected to decline over the next several years from its 2013 level of around 63.3 percent. From the BLS study:
A combination of demographic, structural, and cyclical factors has affected the overall labor force participation rate, as well as the participation rates of specific groups, in the past. BLS projects that, as has been the case for the last 10 years or so, these factors will exert downward pressure on the overall labor force participation rate over the 2012–2022 period and the rate will gradually decline further, to 61.6 percent in 2022.
However, an important assumption in the BLS projection is that the post-2007 decline in prime-age participation will not persist. Indeed, the data for the first quarter of 2014 does suggest that some stabilization has occurred.
But separating what is trend from what is cyclical is challenging. The rapid pace of the decline in participation among the prime-age population between 2007 and 2013 is somewhat puzzling. Could this decline reflect a temporary cyclical effect or something more permanent? A follow-up blog will explore this question in more detail using the micro data from the Current Population Survey.
Note: All data shown are 12-month moving averages to emphasize persistent shifts in trends.
Update: The authors acknowledge a debt to Tomaz Cajner and Bruce Fallick for their influence on some of this material. We regret inadvertently omitting this acknowledgement in the original post.
By Melinda Pitts, director, Center for Human Capital Studies,
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
March 18, 2014
Human Capital Topics Now Searchable
A little more than a week ago, all eyes were on the February Employment Situation report released by the U.S. Bureau of Labor Statistics. The Establishment Survey surprised on the upside: nonfarm payrolls rose 175,000 in February, and payrolls were revised upward for December and January. The Household Survey indicated that the unemployment rate edged up slightly to 6.7 percent in February from 6.6 percent the prior month, and the labor force participation rate held steady at 63.0 percent.
These are some of the facts on the table as the Federal Open Market Committee meets today and tomorrow and, judging from recent comments from the folks who will be at that meeting, those facts (and more like them) will be very much front of mind.
These days, multiple tools are available to assist both casual and expert observers in navigating the rich and sometimes baffling story of labor markets in the post-Great Recession world. Just last week, you could find a new "Guide for the Perplexed" on labor market slack in The New York Times and an interactive feature on the "Eight Different Faces of the Labor Market" at the New York Fed's Liberty Street Economics blog. And that's not to mention the most recent update of the Atlanta Fed’s own 13-headed Labor Market Spider Chart.
All of these contributions reflect a great deal of effort to understand the story of what's happening in labor markets. As part of that effort, our colleagues across the Federal Reserve System have been taking deeper dives into employment statistics and reaching out into their communities to get a better understanding of labor force dynamics and workforce development issues. This research can be found on the various Reserve Bank and Board websites.
To facilitate access to that work, the Atlanta Fed's Center for Human Capital Studies has worked to bring those resources together in the Federal Reserve Human Capital Compendium (HCC). We are pleased to announce that we have recently enhanced the HCC so you can perform simple or advanced searches that allow you to research whatever facet of that research strikes your fancy (see the figure):
We encourage you to take your own deeper dive into the latest research across the Federal Reserve System by browsing the HCC or searching out those labor topics that have piqued your interest lately.
By Whitney Mancuso, a senior economic analyst in the Atlanta Fed's research department