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November 24, 2014
And the Winner Is...Full-Time Jobs!
Each month, the U.S. Census Bureau for the U.S. Bureau of Labor Statistics (BLS) surveys about 60,000 households and asks people 15 years and older whether they are employed and, if so, if they are working full-time or part-time. The BLS defines full-time employment as working at least 35 hours per week. This survey, referred to as both the Current Population Survey and the Household Survey, is what produces the monthly unemployment rate, labor force participation rate, and other statistics related to activities and characteristics of the U.S. population.
For many months after the official end of the Great Recession in June 2009, the Household Survey produced less-than-happy news about the labor market. The unemployment rate didn't start to decline until October 2009, and nonfarm payroll job growth didn't emerge confidently from negative territory until October 2010. Now that the unemployment rate has fallen to 5.8 percent—much faster than most would have expected even a year ago—the attention has turned to the quality, rather than quantity, of jobs. This scrutiny is driven by a stubbornly high rate of people employed part-time "for economic reasons" (PTER). These are folks who are working part-time but would like a full-time job. Several of my colleagues here at the Atlanta Fed have looked at this phenomenon from many angles (here, here, here, here, and here).
The elevated share of PTER has left some to conclude that, yes, the economy is creating a significant number of jobs (an average of more than 228,000 nonfarm payroll jobs each month in 2014), but these are low-quality, part-time jobs. Several headlines have popped up over the past year or so claiming that "...most new jobs have been part-time since Obamacare became law," "Most 2013 job growth is in part-time work," "75 Percent Of Jobs Created This Year  Were Part-Time," "Part-time jobs account for 97% of 2013 job growth," and as recently as July of this year, "...Jobs Report Is Great for Part-time Workers, Not So Much for Full-Time."
However, a more careful look at the postrecession data illustrates that since October 2010, with the exception of four months (November 2010 and May–July 2011), the growth in the number of people employed full-time has dominated growth in the number of people employed part-time. Of the additional 8.2 million people employed since October 2010, 7.8 million (95 percent) are employed full-time (see the charts).
The pair of charts illustrates the contribution of the growth in part-time and full-time jobs to the year-over-year change in total employment between January 2000 and October 2014. By zooming in, we can see the same thing from October 2010 (when payroll job growth entered consistently positive territory) to October 2014. Job growth from one month to the next, even using seasonally adjusted data, is very volatile.
To get a better idea of the underlying stable trends in the data, it is useful to compare outcomes in the same month from one year to the next, which is the comparison that the charts make. The black line depicts the change in the number of people employed each month compared to the number employed in the same month the previous year. The green bars show the change in the number of full-time employed, and the purple bars show the change in the number of part-time employed.
During the Great Recession (until about October 2010), the growth in part-time employment clearly exceeded growth in full-time employment, which was deep in negative territory. The current high level of PTER employment is likely to reflect this extended period of time in which growth in part-time employment exceeded that of full-time employment. But in every month since August 2011, the increase in the number of full-time employed from the year before has far exceeded the increase in the number of part-time employed. This phenomenon includes all of the months of 2013, in spite of what some of the headlines above would have you believe.
So, in the post-Great Recession era, the growth in full-employment is, without a doubt, way out ahead.
Author's note: The data used in this post, which are the same data used to generate the headlines linked above, reflect either full-time or part-time employment (total hours of work at least or less than 35 per week, respectively). They do not necessarily reflect employment in a single job.
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October 15, 2014
What's behind Declining Labor Force Participation? Test Your Hypothesis with Our New Data Tool
The share of people (age 16 and over) participating in the labor market—that is, either working or looking for work—declined significantly during the recession. As many researchers have noted (see our list of supplemental reading under "More Information"), there is clearly a cyclical component to the decline. When labor market opportunities dry up, it influences decisions to pursue activities other than work, such as schooling, taking care of family, or retiring. However, much of the decrease in the overall labor force participation rate (LFPR) could be the result of the continuation of longer-term behavioral and demographic trends.
While the U.S. Bureau of Labor Statistics (BLS) produces aggregate statistics on LFPR by various demographic measures, the published data tables don't detail the reasons people give for not participating in the labor market. However, we have cut and coded the micro monthly data from BLS's Current Population Survey so that you can explore your own questions.
For example: Are millennials less likely to participate in the labor market than earlier cohorts? Are people retiring sooner? Are women less likely to stay at home than in the past? You can answer these and other types of questions on our new Labor Force Participation Dynamics page (click on "Interact and Download Data").
In addition to allowing you to create your own charts and download the chart data, the website also guides you through some of the major factors we found that contributed to the decline in LFPR from 2007 to mid-2014, as well as a picture of the trend in those factors before the recession began.
The chart below (also in the Executive Summary) provides an overview of the major factors that we noted in our analysis of the data. Each bar shows the contribution to the 3 percentage point change in the overall LFPR from 2007 to mid-2014.
What the chart doesn't show is whether the trends were occurring before the Great Recession. For a deeper dive into any of the factors in the chart, see the "Long-Term Behavioral and Demographic Trends" section. The most influential factor has been the changing distribution of the population (see "Aging Population"). The fact that a greater portion of Americans are retirement age now than in 2007 accounts for about 1.7 percentage points of the decline. At the same time, older Americans are more likely to be working than in the past, a trend that has been putting upward pressure on LFPR for some time. All else being equal, if those older than 60 were just as likely to retire as they were in 2007, LFPR would be about 1.0 percentage point lower than it is today.
Other factors bringing down the overall LFPR include an increased incidence of people saying they are unable to work as a result of disability or illness (click on "Health Problems"), increased school attendance among the young (click on "Rising Education"), and decreased participation among individuals 25–54—the age group with the greatest attachment to the labor force (click on "Focus on Prime Working-Age Individuals").
These are the factors we found to be the most significant drivers of changes in LFPR, but you can also explore many other questions with these data. Check out the interactive data tools and read our take on the data and let us know what you think.
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September 29, 2014
On Bogs and Dots
Consider this scenario. You travel out of town to meet up with an old friend. Your hotel is walking distance to the appointed meeting place, across a large grassy field with which you are unfamiliar.
With good conditions, the walk is about 30 minutes but, to you, the quality of the terrain is not so certain. Though nobody seems to be able to tell you for sure, you believe that there is a 50-50 chance that the field is a bog, intermittently dotted with somewhat treacherous swampy traps. Though you believe you can reach your destination in about 30 minutes, the better part of wisdom is to go it slow. You accordingly allot double the time for traversing the field to your destination.
During your travels, of course, you will learn something about the nature of the field, and this discovery may alter your calculation about your arrival time. If you discover that you are indeed crossing a bog, you will correspondingly slow your gait and increase the estimated time to the other side. Or you may find that you are in fact on quite solid ground and consequently move up your estimated arrival time. Knowing all of this, you tell your friend to keep his cellphone on, as your final meeting time is going to be data dependent.
Which brings us to the infamous “dots,” ably described by several of our colleagues writing on the New York Fed’s Liberty Street Economics blog:
In January 2012, the FOMC began reporting participants’ FFR [federal funds rate] projections in the Summary of Economic Projections (SEP). Market participants colloquially refer to these projections as “the dots” (see the second chart on page 3 of the September 2014 SEP for an example). In particular, the dispersion of the dots represents disagreement among FOMC [Federal Open Market Committee] members about the future path of the policy rate.
The Liberty Street discussion focuses on why the policy rate paths differ among FOMC participants and across a central tendency of the SEPs and market participants. Quite correctly, in my view, the blog post’s authors draw attention to differences of opinion about the likely course of future economic conditions:
The most apparent reason is that each participant can have a different assessment of economic conditions that might call for different prescriptions for current and future monetary policy.
The Liberty Street post is a good piece, and I endorse every word of it. But there is another type of dispersion in the dots that seems to be the source of some confusion. This question, for example, is from Howard Schneider of Reuters, posed at the press conference held by Chair Yellen following the last FOMC meeting:
So if you would help us, I mean, square the circle a little bit—because having kept the guidance the same, having referred to significant underutilization of labor, having actually pushed GDP projections down a little bit, yet the rate path gets steeper and seems to be consolidating higher—so if it’s data dependent, what accounts for the faster projections on rate increases if the data aren’t moving in that direction?
The Chair’s response emphasized the modest nature of the changes, and how they might reflect modest improvements in certain aspects of the data. That response is certainly correct, but there is another point worth emphasizing: It is completely possible, and completely coherent, for the same individual to submit a “dot” with an earlier (or later) liftoff date of the policy rate, or a steeper (or flatter) path of the rate after liftoff, even though their submitted forecasts for GDP growth, inflation, and the unemployment rate have not changed at all.
This claim goes beyond the mere possibility that GDP, inflation, and unemployment (as officially defined) may not be sufficiently complete summaries of the economic conditions a policymaker might be concerned with.
The explanation lies in the metaphor of the bog. The estimated time of arrival to a destination—policy liftoff, for example—depends critically on the certainty with which the policymaker can assess the economic landscape. An adjustment to policy can, and should, proceed more quickly if the ground underfoot feels relatively solid. But if the terrain remains unfamiliar, and the possibility of falling into the swamp can’t be ruled out with any degree of confidence...well, a wise person moves just a bit more slowly.
Of course, as noted, once you begin to travel across the field and gain confidence that you are actually on terra firma, you can pick up the pace and adjust the estimated time of arrival accordingly.
To put all of this a bit more formally, an individual FOMC participant’s “reaction function”—the implicit rule that connects policy decisions to economic conditions—may not depend on just the numbers that that individual writes down for inflation, unemployment, or whatever. It might well—and in the case of our thinking here at the Atlanta Fed, it does—depend on the confidence with which those numbers are held.
For us, anyway, that confidence is growing. Don’t take that from me. Take it from Atlanta Fed President Lockhart, who said in a recent speech:
I'll close with this thought: there are always risks around a projection of any path forward. There is always considerable uncertainty. Given what I see today, I'm pretty confident in a medium-term outlook of continued moderate growth around 3 percent per annum accompanied by a substantial closing of the employment and inflation gaps. In general, I'm more confident today than a year ago.
Viewed in this light, the puzzle of moving dots without moving point estimates for economic conditions really shouldn’t be much of a puzzle at all.
By Dave Altig, executive vice president and research director of the Atlanta Fed
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September 15, 2014
The Changing State of States' Economies
Timely data on the economic health of individual states recently came from the U.S. Bureau of Economic Analysis (BEA). The new quarterly state-level gross domestic product (GDP) series begins in 2005 and runs through the fourth quarter of 2013. The map below offers a look at how states have fared since 2005 relative to the economic performance of the nation as a whole.
It’s interesting to see the map depict an uneven expansion between the second quarter of 2005 and the peak of the cycle in the fourth quarter of 2007. By the fourth quarter of 2008, most parts of the country were experiencing declines in GDP.
The U.S. economy hit a trough during the second quarter of 2009, according to the National Bureau of Economic Research, but 20 states and the District of Columbia recovered more quickly than the rest. The continued progress is easy to see, as is the far-reaching impact of the tsunami that hit Japan on March 11, 2011, which disrupted economic activity in many U.S. states. By the fourth quarter of 2013, only two states—Mississippi and Minnesota—experienced negative GDP.
The map shows that not all states are growing even when overall GDP is growing, and not all states are shrinking even when overall GDP is shrinking. But if we want to know more about which states are driving the change in overall GDP growth, then the geographic size of the state might not be so important.
Depicting states scaled to the size of their respective economies provides another perspective, because it’s the relative size of a state’s economy that matters when considering the contribution of state-level GDP growth to the national economy. The following chart uses bubbles (sized by the size of the state’s economy) to depict changes in states’ real GDP from the second quarter of 2005 through the fourth quarter of 2013.
This chart shows how the economies of larger states such as California, New York, Texas, Florida, and Illinois have an outsize influence on the national economy, despite some having a smaller geographic footprint. (Conversely, changes in the relatively small economy of a geographically large state like Montana have a correspondingly small impact on changes in the national economy.)
Overall GDP is now well above its prerecession peak. But have all states also fully recovered their GDP losses? The chart below depicts the cumulative GDP growth in each state from the end of 2007 to the end of 2013. The size of the circle represents the magnitude of the change in the level of real GDP between the end of 2007 and 2013. Most states have fully recovered in terms of GDP. (North Dakota’s spectacular growth stands out, thanks to its boom in the oil and gas industry.) However, Florida, Nevada, Connecticut, Arizona, New Jersey, and Michigan had not returned to their prerecession spending levels as of the end of 2013. For Florida, Nevada, and Arizona, the depth of the collapse in those states’ booming housing sectors is almost certainly responsible for the relative shortfall in performance since 2007.
The next release of the state-level GDP data, scheduled for September 26, will provide insight into the relative performance of state economies during the first quarter of 2014 at a time when overall GDP shrank by more than 2 percent (annualized rate). Some analysts have suggested that weather disruptions were a leading cause for that decline. The state-level GDP data will help tell the story.
By Whitney Mancuso, a senior economic analyst in the the Atlanta Fed's research department
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August 25, 2014
What Kind of Job for Part-Time Pat?
As anyone who follows macroblog knows, we have been devoting a lot of attention recently to the issue of people working part-time for economic reasons (PTER), which means people who want full-time work but have not yet been able to find it. As of July 2014, the number of people working PTER stood at around 7.5 million. This level is down from a peak of almost 9 million in 2011 but is still more than 3 million higher than before the Great Recession. That doesn’t mean they won’t ever find full-time work in the future, but their chances are a lot lower than in the past.
Consider Pat, for example. Pat was working PTER at some point during a given year and was also employed 12 months later. At the later date, Pat is either working full-time, still working PTER, or is working part-time but is OK with it (which means Pat is part-time for noneconomic reasons). How much luck has Pat had in finding full-time work?
As the chart below shows, there is a reasonable chance that after a year, Pat is happily working full-time. But it has become much less likely than it was before the recession. In 2007, an average of 61 percent of the 2006 Pats transitioned into full-time work. The situation got a lot worse during the recession, and has not improved. In 2013, only 49 percent of the 2012 cohort of Pats had found a full-time job. The decline in finding full-time work is largely accounted for by the rise in the share of Pats who are stuck working PTER. In 2007, 18 percent of the Pats were still PTER after a year, rising to around 30 percent by 2011, where it has essentially remained.
Now, our hypothetical Pats are a pretty heterogeneous bunch. For example, they are different ages, different genders, different educational backgrounds, and in different industries. Do such differences matter when it comes to the chances of Pat finding a full-time job? For example, let’s look at Pats working in goods-producing industries versus services-producing ones. In goods-producing industries, the chance is greater that Pat will find full-time work (more jobs in goods-producing industries are full-time), and there is a bit more of a recovery in full-time job finding for goods-producing industries than for services-producing ones. But overall, the dynamics are similar across the broad industry types, as the charts below show:
As another example, the next four charts show the average 12-month full-time and PTER job-finding rates for all of our hypothetical Pats by gender and education. The full-time/PTER finding rates display broadly similar patterns across gender and education, albeit at different levels. (The same holds true across age groups but is not shown.)
People who find themselves working part-time involuntarily are having more difficulty getting full-time work than in the past, even if they stay employed. But it doesn’t seem that much of this can be attributed to any particular demographic or industry characteristic of the worker. The phenomenon is pretty widespread, suggesting that the problem is a general shortage of full-time jobs rather than a change in the characteristics of workers looking for full-time jobs.
By John Robertson, a vice president and senior economist, and
Ellyn Terry, an economic policy analysis specialist, both of the Atlanta Fed's research department
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August 08, 2014
To say that last week was somewhat eventful on the macroeconomic data front is probably an exercise in understatement. Relevant numbers on GDP growth (past and present), employment and unemployment, and consumer price inflation came in quick succession.
These data provide some of the context for our local Federal Open Market Committee participant’s comments this week (for example, in the Wall Street Journal’s Real Time Economics blog, with similar remarks made in an interview on CNBC’s Closing Bell). From that Real Time Economics blog post:
Although the economy is clearly growing at a respectable rate, Federal Reserve Bank of Atlanta President Dennis Lockhart said Wednesday it is premature to start planning an early exit from the central bank’s ultra-easy policy stance.
“I’m not ruling out” the idea the Fed may need to raise short-term interest rates earlier than many now expect, Mr. Lockhart said in an interview with The Wall Street Journal. But, at the same time, “I’m a little bit cautious” about the policy outlook, and still expect that when the first interest rate hike comes, it will likely happen somewhere in the second half of next year.
“I remain one who is looking for further validation that we are on a track that is going to make the path to our mandate objectives pretty irreversible,” Mr. Lockhart said. “It’s premature, even with the good numbers that have come in...to draw the conclusion that we are clearly on that positive path,” he said.
Why so “cautious”? Here’s the Atlanta Fed staff’s take on the state of things, starting with GDP:
With the annual benchmark revision in hand, 2013 looks like the real deal, the year that the early bet on an acceleration of growth to the 3 percent range finally panned out. Notably, fiscal drag (following the late-2012 budget deal), which had been our go-to explanation of why GDP appeared to have fallen short of expectations once again, looks much less consequential on revision.
Is 2014 on track for a repeat (or, more specifically, comparable performance looking through the collection of special factors that weighed on the first quarter)? The second-quarter bounce of real GDP growth to near 4 percent seems encouraging, but we are not yet overly impressed. Final sales—a number that looks through the temporary contribution of changes in inventories—clocked in at a less-than-eye-popping 2.3 percent annual rate.
Furthermore, given the significant surprise in the first-quarter final GDP report when the medical-expenditure-soaked Quarterly Services Survey was finally folded in, we’re inclined to be pretty careful about over-interpreting the second quarter this early. It’s way too early for a victory dance.
Regarding labor markets, here is our favorite type of snapshot, courtesy of the Atlanta Fed’s Labor Market Spider Chart:
There is a lot to like in that picture. Leading indicators, payroll employment, vacancies posted by employers, and small business confidence are fully recovered relative to their levels at the end of the Great Recession.
On the less positive side, the numbers of people who are marginally attached or who are working part-time while desiring full-time hours remain elevated, and the overall job-finding rate is still well below prerecession levels. Even so, these indicators are noticeably better than they were at this time last year.
That year-over-year improvement is an important observation: the period from mid-2012 to mid-2013 showed little progress in the broader measures of labor-market performance that we place in the resource “utilization” category. During the past year, these broad measures have improved at the same relative pace as the standard unemployment statistic.
We have been contending for some time that part-time for economic reasons (PTER) is an important factor in understanding ongoing sluggishness in wage growth, and we are not yet seeing anything much in the way of meaningful wage pressures:
There was, to be sure, a second-quarter spike in the employment cost index (ECI) measure of labor compensation growth, but that increase followed a sharp dip in the first quarter. Maybe the most recent ECI reading is telling us something that hourly earnings are not, but that still seems like a big maybe. Outside of some specific sectors and occupations (in manufacturing, for example), there is not much evidence of accelerating wage pressure in either the data or in anecdotes we get from our District contacts. We continue to believe that wage growth is most consistent with the view that that labor market slack persists, and underlying inflationary pressures (from wage costs, at least) are at bay.
Clearly, it’s dubious to claim that wages help much in the way of making forward predictions on inflation (as shown, for example, in work from the Chicago Fed, confirming earlier research from our colleagues at the Cleveland Fed). And in any event, we are inclined to agree that the inflation outlook has, in fact, firmed up. At this time last year, it was hard to argue that the inflation trend was moving in the direction of the Committee’s objective (let alone that it was not actually declining).
But here again, a declaration that the risks have clearly shifted in the direction of overshooting the FOMC’s inflation goals seems wildly premature. Transitory factors have clearly elevated recent statistics. The year-over-year inflation rate is still only 1.5 percent, and by most cuts of the data, the trend still looks as close to that level as to 2 percent.
We do expect measured inflation trends to continue to move in the direction of 2 percent, but sustained performance toward that objective is still more conjecture than fact. (By the way, if you are bothered by the appeal to a measure of core personal consumption expenditures in that chart above, I direct you to this piece.)
All of this is by way of explaining why we here in Atlanta are “a little bit cautious” about joining any chorus singing from the we’re-moving-on-up songbook. Paraphrasing from President Lockhart’s comments this week, the first steps to policy normalization don’t have to wait until the year-over-year inflation rate is consistently at 2 percent, or until all of the slack in the labor market is eliminated. But it is probably prudent to be fairly convinced that progress to those ends is unlikely to be reversed.
We may be getting there. We’re just not quite there yet.
By Dave Altig, executive vice president and research director of the Atlanta Fed
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August 05, 2014
What’s Driving the Part-Time Labor Market? Results from an Atlanta Fed Survey
A subtle shift appears to be emerging in the public discussion of part-time employment in the United States. In monetary policy circles, elevated levels of part-time employment have generally been taken as a signal of lingering weakness in the labor market. (See, for example, here and here.) In this view, the rise in the use of part-time workers is a response to weak economic conditions, and the rate of part-time utilization will return to something approaching the prerecession average as firms respond to strengthening demand by increasing the hours of some of part-time staff who want more hours (thus reducing the number and share of part-time workers who would like full-time work) and by creating more full time jobs for those who want them (thus reducing the share of involuntary part-time workers).
But some labor market observers interpret the recent rise in the share of part-time jobs as more structural in nature—and hence less likely to be remedied by demand-inducing strategies such as monetary stimulus. If the arithmetic of having full-time or part-time workers has changed (for example, we frequently hear about increased compensation costs resulting from health care changes associated with the Affordable Care Act), then employers might lean more on part-time workers, at least while they can. Employers might be more able to do so while there is an ample supply of unemployed people and fewer full-time job opportunities, or if technology has made it sufficiently easy to manage workers’ hours. Virginia Postrel at BloombergView recently wrote an essay about how technology is helping firms better manage part-time employees. From that essay:
For many part-time workers in the post-crash economy, life has become like endless jury duty. Scheduling software now lets employers constantly optimize who’s working, better balancing labor costs and likely demand.
Perhaps the “demand” aspect of that passage refers to the level of overall spending in the economy (a point made in another BloombergView piece that Postrel’s column cites). But there is an undeniable technological slant to this story—one that is not so obviously about the condition of the economy. And based on recent legislative proposals out of Congress, some lawmakers seem to see an issue that is likely to persist beyond the current business cycle.
So is our issue insufficient demand, about which monetary policy can arguably do something, or is it a change in the nature of work in the United States, which is arguably impervious to the effects of changes in monetary policy?
Both of these questions seem valid, and reasonable perspectives support both of them (see, for example, here and here). So as we try to sort this out, we turned to the Atlanta Fed’s Regional Economic Information Network of business contacts and went to the source: employers themselves.
First, though, let’s review a few facts. During the recession, full-time employment fell substantially while the number working part-time actually increased. Today, there are about 12 percent more people working part-time than before the recession and about 2 percent fewer people working full-time hours. As the chart below shows, this slow rebound in full-time employment—and the sustained level of part-time employment—has resulted in a greater share of employed working part-time: 19 percent of employed people are working fewer than 35 hours compared with 17 percent of all employed before the recession began.
To delve more deeply into these facts, we collected the responses of 339 firms with at least 20 employees to two questions: “Compared to before the recession, is your current mixture of part-time and full-time employees different? Do you think your current mixture will change over the next couple of years?” The responses (presented in the chart below) are weighted by national firm size and industry distributions.
About two-thirds of firms indicated their mixture of full-time and part-time employees was not currently different than before the recession began. One quarter of firms said they currently have a higher share of part-time employees, and 8 percent have a smaller share. Looking forward, 31 percent believe their workforce will possess a greater share of part-time workers in two years than it does now.
What did employers cite as the reason for the increase in part-time employment? Firms that currently have a higher share of part-time employees gave about equal weighting to cyclical and structural factors, as the chart below indicates. Most chose the options “Full-time employee compensation costs have increased relative to those of part time employees” and “Business conditions (sales) are not yet strong enough to justify converting part-time jobs to full-time” as either somewhat important or very important. These firms saw the other options—“Technology has made it easier to manage part-time employees” and “More job candidates are willing to take part-time jobs”—as less important.
The next chart shows that structural factors are on the minds of employers, especially among firms who haven’t yet increased their share of part-time employees. Expectations of increases in the compensation cost of full-time employees relative to part-time workers were cited as the most important factor for all firms, but the difference in the relative importance among expected compensation costs and other factors was greater among firms that have not yet increased their part-time share of employment. Expected weak sales and future ample supply of people willing to work part-time were also seen as somewhat important factors for many firms.
Do firms anticipate a return to their prerecession mix of part-time and full-time employment? Although we didn’t ask this question directly, the next chart constructs an answer based on their responses to our other two questions.
Compared with prerecession levels, 34 percent of firms indicated they expect the share of part-time employees in their firm to be higher in two years. This segment includes the vast majority (90 percent) of the 25 percent of firms who already have a higher share now than before the recession and 12 percent of other firms who currently have the same share but anticipate increases during the next two years. Surprisingly, only about 2 percent of firms currently have a higher share of part-time workers and anticipate decreases over the next two years (they are represented in the above chart in the “no change” category).
To sum up, the results have something for people on either side of the cyclical-versus-structural debate. Weak business conditions and the increase in the relative cost of full-time employees have been about equally important drivers of the increase in the use of part-time employees thus far. Thinking about the future, firms mostly cite an expected rise in the relative cost of full-time workers as the reason for shifting toward more part-time employees. So while there are some clear structural forces at work, a large amount of uncertainty around the future cost of health care and the future pace of economic growth also exists. The extent to which these factors will ultimately affect the share working part-time remains to be seen.
By Ellyn Terry, an economic policy analysis specialist in the Atlanta Fed’s research department
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June 23, 2014
Torturing CPI Data until They Confess: Observations on Alternative Measures of Inflation (Part 1)
On May 30, the Federal Reserve Bank of Cleveland generously allowed me some time to speak at their conference on Inflation, Monetary Policy, and the Public. The purpose of my remarks was to describe the motivations and methods behind some of the alternative measures of the inflation experience that my coauthors and I have produced in support of monetary policy.
In this, and the following two blogs, I'll be posting a modestly edited version of that talk. A full version of my prepared remarks will be posted along with the third installment of these posts.
The ideas expressed in these blogs and the related speech are my own, and do not necessarily reflect the views of the Federal Reserve Banks of Atlanta or Cleveland.
Part 1: The median CPI and other trimmed-mean estimators
A useful place to begin this conversation, I think, is with the following chart, which shows the monthly change in the Consumer Price Index (CPI) (through April).
The monthly CPI often swings between a negative reading and a reading in excess of 5 percent. In fact, in only about one-third of the readings over the past 16 years was the monthly, annualized seasonally adjusted CPI within a percentage point of 2 percent, which is the FOMC's longer-term inflation target. (Officially, the FOMC's target is based on the Personal Consumption Expenditures price index, but these and related observations hold for that price index equally well.)
How should the central bank think about its price-stability mandate within the context of these large monthly CPI fluctuations? For example, does April's 3.2 percent CPI increase argue that the FOMC ought to do something to beat back the inflationary threat? I don't speak for the FOMC, but I doubt it. More likely, there were some unusual price movements within the CPI's market basket that can explain why the April CPI increase isn't likely to persist. But the presumption that one can distinguish the price movements we should pay attention to from those that we should ignore is a risky business.
The Economist retells a conversation with Stephen Roach, who in the 1970s worked for the Federal Reserve under Chairman Arthur Burns. Roach remembers that when oil prices surged around 1973, Burns asked Federal Reserve Board economists to strip those prices out of the CPI "to get a less distorted measure. When food prices then rose sharply, they stripped those out too—followed by used cars, children's toys, jewellery, housing and so on, until around half of the CPI basket was excluded because it was supposedly 'distorted'" by forces outside the control of the central bank. The story goes on to say that, at least in part because of these actions, the Fed failed to spot the breadth of the inflationary threat of the 1970s.
I have a similar story. I remember a morning in 1991 at a meeting of the Federal Reserve Bank of Cleveland's board of directors. I was welcomed to the lectern with, "Now it's time to see what Mike is going to throw out of the CPI this month." It was an uncomfortable moment for me that had a lasting influence. It was my motivation for constructing the Cleveland Fed's median CPI.
I am a reasonably skilled reader of a monthly CPI release. And since I approached each monthly report with a pretty clear idea of what the actual rate of inflation was, it was always pretty easy for me to look across the items in the CPI market basket and identify any offending—or "distorted"—price change. Stripping these items from the price statistic revealed the truth—and confirmed that I was right all along about the actual rate of inflation.
Let me show you what I mean by way of the April CPI report. The next chart shows the annualized percentage change for each component in the CPI for that month. These are shown on the horizontal axis. The vertical axis shows the weight given to each of these price changes in the computation of the overall CPI. Taken as a whole, the CPI jumped 3.2 percent in April. But out there on the far right tail of this distribution are gasoline prices. They rose about 32 percent for the month. If you subtract out gasoline from the April CPI report, you get an increase of 2.1 percent. That's reasonably close to price stability, so we can stop there—mission accomplished.
But here's the thing: there is no such thing as a "nondistorted" price. All prices are being influenced by market forces and, once influenced, are also influencing the prices of all the other goods in the market basket.
What else is out there on the tails of the CPI price-change distribution? Lots of stuff. About 17 percent of things people buy actually declined in price in April while prices for about 13 percent of the market basket increased at rates above 5 percent.
But it's not just the tails of this distribution that are worth thinking about. Near the center of this price-change distribution is a very high proportion of things people buy. For example, price changes within the fairly narrow range of between 1.5 percent and 2.5 percent accounted for about 26 percent of the overall CPI market basket in the April report.
The April CPI report is hardly unusual. The CPI report is commonly one where we see a very wide range of price changes, commingled with an unusually large share of price increases that are very near the center of the price-change distribution. Statisticians call this a distribution with a high level of "excess kurtosis."
The following chart shows what an average monthly CPI price report looks like. The point of this chart is to convince you that the unusual distribution of price changes we saw in the April CPI report is standard fare. A very high proportion of price changes within the CPI market basket tends to remain close to the center of the distribution, and those that don't tend to be spread over a very wide range, resulting in what appear to be very elongated tails.
And this characterization of price changes is not at all special to the CPI. It characterizes every major price aggregate I have ever examined, including the retail price data for Brazil, Argentina, Mexico, Columbia, South Africa, Israel, the United Kingdom, Sweden, Canada, New Zealand, Germany, Japan, and Australia.
Why do price change distributions have peaked centers and very elongated tails? At one time, Steve Cecchetti and I speculated that the cost of unplanned price changes—called menu costs—discourage all but the most significant price adjustments. These menu costs could create a distribution of observed price changes where a large number of planned price adjustments occupy the center of the distribution, commingled with extreme, unplanned price adjustments that stretch out along its tails.
But absent a clear economic rationale for this unusual distribution, it presents a measurement problem and an immediate remedy. The problem is that these long tails tend to cause the CPI (and other weighted averages of prices) to fluctuate pretty widely from month to month, but they are, in a statistical sense, tethered to that large proportion of price changes that lie in the center of the distribution.
So my belated response to the Cleveland board of directors was the computation of the weighted median CPI (which I first produced with Chris Pike). This statistic considers only the middle-most monthly price change in the CPI market basket, which becomes the representative aggregate price change. The median CPI is immune to the obvious analyst bias that I had been guilty of, while greatly reducing the volatility in the monthly CPI report in a way that I thought gave the Federal Reserve Bank of Cleveland a clearer reading of the central tendency of price changes.
Cecchetti and I pushed the idea to a range of trimmed-mean estimators, for which the median is simply an extreme case. Trimmed-mean estimators trim some proportion of the tails from this price-change distribution and reaggregate the interior remainder. Others extended this idea to asymmetric trims for skewed price-change distributions, as Scott Roger did for New Zealand, and to other price statistics, like the Federal Reserve Bank of Dallas's trimmed-mean PCE inflation rate.
How much one should trim from the tails isn't entirely obvious. We settled on the 16 percent trimmed mean for the CPI (that is, trimming the highest and lowest 8 percent from the tails of the CPI's price-change distribution) because this is the proportion that produced the smallest monthly volatility in the statistic while preserving the same trend as the all-items CPI.
The following chart shows the monthly pattern of the median CPI and the 16 percent trimmed-mean CPI relative to the all-items CPI. Both measures reduce the monthly volatility of the aggregate price measure by a lot—and even more so than by simply subtracting from the index the often-offending food and energy items.
But while the median CPI and the trimmed-mean estimators are often referred to as "core" inflation measures (and I am guilty of this myself), these measures are very different from the CPI excluding food and energy.
In fact, I would not characterize these trimmed-mean measures as "exclusionary" statistics at all. Unlike the CPI excluding food and energy, the median CPI and the assortment of trimmed-mean estimators do not fundamentally alter the underlying weighting structure of the CPI from month to month. As long as the CPI price change distribution is symmetrical, these estimators are designed to track along the same path as that laid out by the headline CPI. It's just that these measures are constructed so that they follow that path with much less volatility (the monthly variance in the median CPI is about 95 percent smaller than the all-items CPI and about 25 percent smaller than the CPI less food and energy).
I think of the trimmed-mean estimators and the median CPI as being more akin to seasonal adjustment than they are to the concept of core inflation. (Indeed, early on, Cecchetti and I showed that the median CPI and associated trimmed-mean estimates also did a good job of purging the data of its seasonal nature.) The median CPI and the trimmed-mean estimators are noise-reduced statistics where the underlying signal being identified is the CPI itself, not some alternative aggregation of the price data.
This is not true of the CPI excluding food and energy, nor necessarily of other so-called measures of "core" inflation. Core inflation measures alter the weights of the price statistic so that they can no longer pretend to be approximations of the cost of living. They are different constructs altogether.
The idea of "core" inflation is one of the topics of tomorrow's post.
By Mike Bryan, vice president and senior economist in the Atlanta Fed's research department
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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
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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.
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