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.
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
March 07, 2014
Thinking About Progress in the Labor Market
Today's employment report for the month of February maybe took a bit of drama out of one key question going into the next meeting of the Federal Open Market Committee (FOMC): What will happen to the FOMC's policy language when the economy hits or passes the 6.5 percent unemployment rate threshold for considering policy-rate liftoff? With the unemployment rate for February checking it at 6.7 percent, a breach of the threshold clearly won't have happened when the Committee meets in a little less than two weeks.
I say "maybe took a bit of drama out" because I'm not sure there was much drama left. All you had to do was listen to the Fed talkers yesterday to know that. This is from the highlights summary of a speech yesterday by Charles Plosser, president of the Philadelphia Fed...
President Plosser believes the Federal Open Market Committee has to revamp its current forward guidance regarding the future federal funds rate path because the 6.5 percent unemployment threshold has become irrelevant.
... and this from a Wall Street Journal interview with William Dudley, president of the New York Fed:
Mr. Dudley, in a Wall Street Journal interview, also said the Fed's 6.5% unemployment rate threshold for considering increases in short-term interest rates is "obsolete" and he would advocate scrapping it at the Fed's next meeting March 18–19.
From our shop, Atlanta Fed president Dennis Lockhart echoed those sentiments in a speech at Georgetown University:
Given that measured unemployment is so close to 6.5 percent, the time is approaching for a refreshed explanation of how unemployment or broader employment conditions are to be factored into a liftoff decision.
That statement doesn't mean we in Atlanta are disregarding the unemployment rate altogether. We have for some time been describing the broader net we have cast in fishing for labor market clues. One important aspect of that broader perspective is captured in the so-called U-6 measure of unemployment, about which President Lockhart's speech gives a quick tutorial:
The data used to construct the unemployment rate come from a survey of households conducted by the Census Bureau for the Bureau of Labor Statistics. To be counted as a participant in the labor force, a respondent must give rather specific qualifying answers to questions in the survey...
Those who are available, have looked for work in the past year, but have not recently looked for work are labeled "marginally attached." They are not in the official labor force, so they are not officially unemployed. You might say they are a "shadow labor force"...
One measure that counts the marginally attached in the pool of the unemployed is U-6.
U-6 also includes working people who identify themselves as working "part time for economic reasons." These are people who want to work full time (defined as 35 hours or more) but are able only to get fewer than 35 hours of work.
The "shadow labor force" comment is based on these observations. First, in President Lockhart's words:
The makeup of the class of marginally attached workers is quite fluid. About 40 percent of the marginally attached in any given month join the official labor force in the subsequent month.
There is no new story there. The frequency with which people move from marginally attached to in the labor force has been stable for quite a while (see the chart):
President Lockhart's second observation regarding the marginally attached is more important:
But only about 10 percent of those who move into the labor force find a job right away. In effect, they went from unofficially unemployed to officially unemployed.
The chart below depicts this observation:
Relative to before the Great Recession, the frequency with which people transitioned from marginally attached to employment has fallen by about 5 percentage points.
That decline is related to this conclusion (again from President Lockhart):
Here's my point: what U-6 captures matters. Measures such as marginally attached and part time for economic reasons became elevated in the recession and have not come down materially. Said differently, broader measures of unemployment like U-6 suggest that a significant level of slack remains in our employment markets.
It is not that we have failed to see progress in the U-6 measure of labor market slack. In fact, since the end of the recession, the U-6 unemployment rate has declined about in tandem with the standard official unemployment rate (designated U-3 by the U.S. Bureau of Labor Statistics; see the chart):
What is the case is that we have failed to undo the outsized run-up in the marginally attached and people working part-time for economic reasons that occurred during the recession (see the chart):
One interpretation of these observations is that the relative increase in U-6 represents structural changes that cannot be fixed by policies aimed at stimulating spending. But we are drawn to the fact, described above, that the marginally attached are flowing into the labor market at the same pace as before the recession, but they are finding jobs at a much slower pace, making us hesitant to fully embrace a structural interpretation.
Or, as our boss said yesterday:
As a policymaker, I am concerned about the unemployed in the official labor force, but I am also concerned about the unemployed in the shadow labor force. To get close to full employment, as I think of it, would involve substantial absorption of this shadow labor force. I do not think we're near that point yet. This is one of the reasons I support continuing with a highly accommodative policy and deferring liftoff for a while longer.
But if you are looking for some good news, here it is: Though the official unemployment rate has been essentially flat for the past three months, the broader U-6 measure that we are monitoring closely has fallen by half a percentage point. More of that, and we will really be getting somewhere.
By Dave Altig, research director and executive vice president at the Atlanta Fed
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
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 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 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