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Date: Monday, 15 Sep 2014 21:59

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.

Photo of Whitney MancusoBy Whitney Mancuso, a senior economic analyst in the the Atlanta Fed's research department


Author: "macroblog" Tags: "Economic conditions, Economic Growth and..."
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Date: Monday, 25 Aug 2014 18:49

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.

Distribution of Employment of Workers Who Were Part-Time for Economic Reasons One Year Earlier

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:

Distribution of Employment of Workers Who Were Part-Time for Economic Reasons One Year Earlier by Industry

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.)

Distribution of Employment of Workers Who Were Part-Time for Economic Reasons One Year Earlier by Gender

Distribution of Employment of Workers Who Were Part-Time for Economic Reasons One Year Earlier by Education

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.

Photo of John RobertsonBy John Robertson, a vice president and senior economist, and

 

Photo of Ellyn TerryEllyn Terry, an economic policy analysis specialist, both of the Atlanta Fed's research department


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Date: Monday, 25 Aug 2014 16:48

As the early data on the third quarter begin to roll in, the (very tentative) conclusion is that nothing we know yet contradicts the consensus gross domestic product (GDP) forecast (from the Blue Chip panel, for example) of seasonally adjusted annualized Q3 growth in the neighborhood of 3 percent. The latest from our GDPNow model:

The GDPNow model forecast for real GDP growth (seasonally adjusted annual rate) in the third quarter of 2014 was 3.0 percent on August 19, up from 2.8 percent on August 13. The nowcast for inventory investment ticked up following the Federal Reserve's industrial production release on August 15 while the nowcast for residential investment growth increased following this morning's new residential construction release from the U.S. Census Bureau.

 

The contribution of residential investment is obviously welcome, but the inventory contribution in the industrial production release tilts in the direction of one of our concerns about growth performance in the second quarter. Specifically, too much inventory spending, too little "core" spending.

On the plus side, our projections for current-quarter investment spending have been increasing, outside of nonresidential structures. On the much less positive side, the nowcast for consumer spending has been falling off and currently looks to expand at a pace barely above 2 percent.

Weakness over the course of this recovery in the key GDP expenditure components of consumer spending and investment has been the subject of a lot of commentary, recent entries being provided by Jonathon McCarthy (on the former, at Liberty Street Economics) and Jim Hamilton (on the latter, at Econbrowser). McCarthy in particular points to less-than-robust consumption expenditure as a source of growth since the end of the recession that has been slower than hoped for:

One contributor to the subdued pace of economic growth in this expansion has been consumer spending. Even though consumption growth has been somewhat stronger in the past couple of quarters, it has still been weak in this expansion relative to previous expansions.

 

An earlier version of the McCarthy theme appeared in this post on Atif Mian and Amir Sufi's House of Debt blog:

...the primary culprit: consumption of services and non-durable goods. They are shockingly weak relative to other recoveries.

 

There is something of a chicken-and-egg conundrum in all of this discussion. Has GDP growth disappointed because consumer and business spending has been lackluster? Or has consumer and business spending been weaker than we expected because GDP growth has lagged the pace of past recoveries?

In fact, the growth rates of consumption expenditure and business fixed investment—which excludes the residential housing piece—have not been particularly unusual over the course of this recovery once you account for the pace of GDP growth.

The following charts illustrate the average contributions of consumption and investment spending as a percent of average GDP growth for the 20 quarters following six of the last seven U.S. recessions. (I have excluded the period following the 1969–70 recession because 20 quarters after that downturn include the entirety of the 1973–75 recession.)

Consumption Share of GDP Growth Consumption Share of GDP Growth

It is worth noting that these observations also apply to the components of consumption (across services, durables, and nondurables) and business fixed investment (across equipment and intellectual property and structures), as the following two charts show:

Business Fixed Investment Share of GDP Growth Business Fixed Investment Share of GDP Growth

The conclusion is that if growth in consumption and investment has been particularly tepid over the course of the recovery, it merely reflects the historically tepid growth in GDP.

Or the other way around. These charts represent nothing more than arithmetic exercises, a mechanical decomposition of GDP growth into couple of the spending components that make up to the whole. They tell us nothing about causation.

What we have is the same too-full bag of possible explanations for why GDP has not yet returned to levels that—before the financial crisis—we would have associated with "potential": too much regulation, too little lending, excessive uncertainty, not enough government-driven demand, and so on. Maybe more investment spending would cause more growth. Maybe not.

In the language of the hot topic of the moment, this ultimately takes us to the debate over secular stagnation—what does it mean, does it exist, what is its cause if it does exist? Steve Williamson provides a useful summary of the debate, which is not yet at the point of providing actual answers. And unfortunately, the answers really matter.

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Author: "macroblog" Tags: "Economic Growth and Development, GDP"
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Date: Tuesday, 19 Aug 2014 13:42

In recent macroblog posts, our colleagues Dave Altig and John Robertson have posed the questions Getting There? and Are We There Yet?, respectively. "There" in these posts refers to "full employment." Dave and John conclude that while we may be getting there, we're not there yet.

Not everyone agrees with that assessment, of course. Among the recent evidence some observers cite in defense of an approaching full-employment and growing wage pressures is the following chart. It shows a rather strong correlation between survey data from the National Federation of Independent Business (NFIB) on the proportion of firms planning to raise worker compensation over the next three months and lagged wage and salary growth (see the chart). (This recent post from the Dismal Scientist blog and this short article from the Dallas Fed also discuss this assessment.)

Businesses' Response to Tightening Job Market

OK, no people brave enough to weigh in on this issue are actually saying they know for certain where the line is that separates rising wage pressures from just more of the same. But if you are looking for a sign of impending wage pressure, the chart above certainly looks compelling. Well, except that a pretty large gap has opened up between the behavior of the NFIB survey data and the actual growth trend in compensation since 2011. We'll have more on that in a moment.

The Federal Reserve Bank of Atlanta also conducts a survey of businesses, and among the things we occasionally ask our panel is how much they expect to adjust their compensation of workers (including benefits) in the year ahead. But our survey data aren't showing the same rise in compensation expectations that we see in the NFIB survey data (see the tables).

Firm's Compensation Expectations

Of the 210 business respondents who answered the compensation question in our August survey, 81 percent expect to increase compensation over the next 12 months, compared with 4 percent who expect to reduce compensation for the next 12 months. In other words, on net, 77 percent of the businesses in our panel expect to raise compensation during the next 12 months. This share is a shade less than the proportion of firms that expected to increase compensation in May 2013.

Our survey data are not directly comparable to the NFIB since the NFIB survey asks firms about their plans during the next three months, and we ask about plans during the coming 12 months. Moreover, the NFIB surveys small businesses—roughly 75 percent of the businesses in the NFIB survey employ fewer than 20 workers, and about 60 percent employ fewer than 10.

So we cut our survey to isolate the smaller firms. The first observation we note is that as the size of the firm shrinks, so does the proportion of small firms planning to increase wages. This result isn't especially surprising since the small firms in our panel report considerably worse prevailing business conditions than do the large firms. But more to the point, we still fail to pick up a rise in expected wage pressure. On net in August, 53 percent of the firms in our panel that employ fewer than 20 workers expect to raise worker compensation during the next 12 months. That percent is down from 69 percent of similarly sized firms in May 2013.

Further, the average amount that firms expect to increase wages (2.7 percent) is also about unchanged from 15 months ago (2.8 percent), and this result is rather consistent by firm size and industry. If anything, our panel of businesses reports less expected compensation pressure in the year ahead than when we last asked them in May 2013. So no matter how we cut our panel data, we have trouble confirming the story that firms are anticipating significantly more wage pressure today than a year or so ago.

But maybe this is missing the big point of the figure that kicked off this post. Since about 2011, there appears to be a growing discrepancy between the recent trend in the NFIB survey on compensation increases and actual compensation increases. One could interpret that observation in two very different ways. The first is that the growing gap between the NFIB survey data and actual wage growth suggests pressure on compensation that will soon break loose. Perhaps. But another interpretation is that the relationship between the NFIB survey and actual wage increases has broken down recently. Correlation is different than causation, and many correlations coming from the labor market in recent years appear to be deviating from their historical norms. Isn't that the takeaway of the two earlier macroblog posts?

We're not brave enough to say that we know for certain that the economy isn't on the verge of an accelerated pace of compensation growth. But, if we were brave enough, we'd say our survey data indicate that such acceleration is unlikely.

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Date: Thursday, 14 Aug 2014 16:55

Editor’s note: This macroblog post was published yesterday with some content inadvertently omitted. Below is the complete post. We apologize for the error.

Anyone who has undertaken a long road trip with children will be familiar with the frequent “are we there yet?” chorus from the back seat. So, too, it might seem on the long post-2007 monetary policy road trip. When will the economy finally look like it is satisfying the Federal Open Market Committee’s (FOMC) dual mandate of price stability and full employment? The answer varies somewhat across the FOMC participants. The difference in perspectives on the distance still to travel is implicit in the range of implied liftoff dates for the FOMC’s short-term interest-rate tool in the Summary of Economic Projections (SEP).

So how might we go about assessing how close the economy truly is to meeting the FOMC’s objectives of price stability and full employment? In a speech on July 17, President James Bullard of the St. Louis Fed laid out a straightforward approach, as outlined in a press release accompanying the speech:

To measure the distance of the economy from the FOMC’s goals, Bullard used a simple function that depends on the distance of inflation from the FOMC’s long-run target and on the distance of the unemployment rate from its long-run average. This version puts equal weight on inflation and unemployment and is sometimes used to evaluate various policy options, Bullard explained.

We think that President Bullard’s quadratic-loss-function approach is a reasonable one. Chart 1 shows what you get using this approach, assuming a goal of year-over-year personal consumption expenditure inflation at 2 percent, and the headline U-3 measure of the unemployment rate at 5.4 percent. (As the U.S. Bureau of Labor Statistics defines unemployment, U-3 measures the total unemployed as a percent of the labor force.) This rate is about the midpoint of the central tendency of the FOMC’s longer-run estimate for unemployment from the June SEP.

Chart 1: Progress toward Objectives: U-3 Gap

Notice that the policy objective gap increased dramatically during the recession, but is currently at a low value that’s close to precrisis levels. On this basis, the economy has been on a long, uncomfortable trip but is getting pretty close to home. But other drivers of the monetary policy minivan may be assessing how far there is still to travel using an alternate road map to chart 1. For example, Atlanta Fed President Dennis Lockhart has highlighted the role of involuntary part-time work as a signal of slack that is not captured in the U-3 unemployment rate measure. Indeed, the last FOMC statement noted that

Labor market conditions improved, with the unemployment rate declining further. However, a range of labor market indicators suggests that there remains significant underutilization of labor resources.

So, although acknowledging the decline in U-3, the Committee is also suggesting that other labor market indicators may suggest somewhat greater residual slack in the labor market. For example, suppose we used the broader U-6 measure to compute the distance left to travel based on President Bullard’s formula. The U-6 unemployment measure counts individuals who are marginally attached to the labor force as unemployed and, importantly, also counts involuntarily part-time workers as unemployed. One simple way to incorporate the U-6 gap is to compute the average difference between U-6 and U-3 prior to 2007 (excluding the 2001 recession), which was 3.9 percent, and add that to the U-3 longer-run estimate of 5.4 percent, to give an estimate of the longer-run U-6 rate of 9.3 percent. Chart 2 shows what you get if you run the numbers through President Bullard’s formula using this U-6 adjustment (scaling the U-6 gap by the ratio of the U-3 and U-6 steady-state estimates to put it on a U-3 basis).

Chart 2: Progress toward Objectives: U-3 Gap versus U-6 Gap

What the chart says is that, up until about four years ago, it didn’t really matter at all what your preferred measure of labor market slack was; they told a similar story because they tracked each other pretty closely. But currently, your view of how close monetary policy is to its goals depends quite a bit on whether you are a fan of U-3 or of U-6—or of something in between. I think you can put the Atlanta Fed’s current position as being in that “in-between” camp, or at least not yet willing to tell the kids that home is just around the corner.

In an interview last week with the Wall Street Journal, President Lockhart effectively put some distance between his own view and those who see the economy as being close to full employment. The Journal’s Real Time Economics blog quoted Lockhart:

“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.

Mr. Lockhart said the current unemployment rate of 6.2% will likely continue to decline and tick under 6% by the end of the year. But, he said, there remains evidence of underlying softness in the job sector, and, he also said, while inflation shows signs of firming, it remains under the Fed’s official 2% target.

Our view is that the current monetary policy journey has made considerable progress toward its objectives. But the trip is not yet complete, and the road ahead remains potentially bumpy. In the meantime, I recommend these road-trip sing-along selections.

Photo of John RobertsonBy John Robertson, a vice president and senior economist in the Atlanta Fed’s research department


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Date: Tuesday, 12 Aug 2014 17:09

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.

Number of People Employed Full-Time and Part-Time

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.

Change in Firms' Mixture of Part-Time and Full-Time Employees

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.

Reasons for Increasing Share of Part-Time Employees since the Beginning of the Recession

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.

Reasons for Increasing or Maintaining a Higher Share of Part-Time Employees Over the Next Two Years

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.

Anticipated Change in Share Working Part-Time Two Years from Now Compared with Prerecession Share

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.

Photo of Ellyn TerryBy Ellyn Terry, an economic policy analysis specialist in the Atlanta Fed’s research department


Author: "macroblog" Tags: "Economic conditions, Employment, Labor M..."
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Date: Friday, 08 Aug 2014 17:16

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:

Atlanta Fed 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:

Total Private Earnings, year/year % change, sa

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.

'Trends' in the June Core PCE

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.

Photo of Dave AltigBy Dave Altig, executive vice president and research director of the Atlanta Fed


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Date: Friday, 01 Aug 2014 18:17

The housing market appears to have endured a particularly cruel month in June. Fairly good numbers on existing home sales provided some antidote to a second consecutive monthly decline in housing starts and a sharp decline in new home sales. But that palliative is less comforting than it might otherwise be given the fact that existing sales were still 2.3 percent below the June 2013 rate, and budding optimism diminished further with this week's unexpected drop off in the pace of pending home sales.

In her recent remarks before the Senate Committee on Banking, Housing, and Urban Affairs and before the House Committee on Financial Services, Federal Reserve Chair Janet Yellen took particular note of ongoing weakness in residential real estate:

The housing sector, however, has shown little recent progress. While this sector has recovered notably from its earlier trough, housing activity leveled off in the wake of last year's increase in mortgage rates, and readings this year have, overall, continued to be disappointing.

The statement following the conclusion of this week's meeting of the Federal Open Market Committee provided an exclamation point to Chair Yellen's commentary:

Information received since the Federal Open Market Committee met in June indicates that ... recovery in the housing sector remains slow.

The housing market was a bright spot in the economy from early 2012 to mid-2013, and there's no shortage of conjecture on why it has morphed into a source of concern. Reasonable hypotheses include reduced affordability brought on by higher mortgage rates and real estate prices, tighter lending conditions and ongoing balance sheet issues for households (think student debt), and supply constraints associated with rising construction costs and lot availability (at least in the most desirable locations, as examples here and here discuss).

In a March post in the Atlanta Fed's SouthPoint, affordability issues—specifically, interest rates and prices—constituted two of the top three explanations given by our broker and builder contacts in the Southeast for recent slower growth in the housing market. Earlier, we had examined the affordability issue in an Atlanta Fed Real Estate Research post. In it, we decomposed the affordability index that the National Association of Realtors (NAR) produces each month. We used our decomposition to show that the rebound in housing prices in 2012 served as a huge drag on affordability and, after six years of contributing to affordability, mortgage interest rates became a drag in mid-2013.

How—and why—has the affordability index changed since we last checked? The chart below provides an update through May 2014 (the latest date for which we have the data necessary for our decomposition):


On a year-over-year basis, affordability has fallen as a result of rising prices and last summer's uptick in interest rates. Still, affordability remains high by precrisis standards. And given that we have recently passed the anniversary of the first "taper talk," the impact of the interest rate component should fade if rates remain stable and thus become similar to, if not below, year-ago levels. Likewise, house price growth has decelerated and will continue to be less of a drag on affordability as measured by NAR.

It may be fair to attribute some of the recent softness in housing to affordability. But in light of the still relatively high readings of our index, it seems likely that the main culprits are one or more of the other factors discussed above.

Photo of Carl HudsonBy Carl Hudson, director of the Atlanta Fed's Center for Real Estate Analytics, and

 

Photo of Jessica DillJessica Dill, senior economic research analyst in the Atlanta Fed's research department

 


Author: "macroblog" Tags: "Housing, Real Estate"
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Date: Monday, 21 Jul 2014 20:11

We are still more than a week away from receiving the advance report for U.S. gross domestic product (GDP) from April through June. Based on what we know to date, second-quarter growth will be a large improvement over the dismal performance seen during the first three months of this year. As of today, our GDPNow model is reading an annualized second-quarter growth rate at 2.7 percent. Given that the economy declined by 2.9 percent in the first quarter, the prospects for the anticipated near-3 percent growth for 2014 as a whole look pretty dim.

The first-quarter performance was dominated, of course, by unusual circumstances that we don't expect to repeat: bad weather, a large inventory adjustment, a decline in real exports, and (especially) an unexpected decline in health services expenditures. Though those factors may mean a disappointing growth performance for the year as a whole, we will likely be willing to write the first quarter off as just one of those things if we can maintain the hoped-for 3 percent pace for the balance of the year.

Do the data support a case for optimism? We have been tracking the six-month trends in four key series that we believe to be especially important for assessing the underlying momentum in the economy: consumer spending (real personal consumption expenditures, or real PCE) excluding medical services, payroll employment, manufacturing production, and real nondefense capital goods shipments excluding aircraft.

The following charts give some sense of how things are stacking up. We will save the details for those who are interested, but the idea is to place the recent performance of each series, given its average growth rate and variability since 1990, in the context of GDP growth and its variability over that same period.

140721a


140721b


140721c


140721d


What do we learn from the foregoing charts? Three out of four of these series appear to be consistent with an underlying growth rate in the range of 3 percent. Payroll employment growth, in fact, is beginning to send signals of an even stronger pace.

Unfortunately, the series that looks the weakest relates to consumer spending. If we put any stock in some pretty basic economic theory, spending by households is likely the most forward-looking of the four measures charted above. That, to us, means a cautious attitude is the still the appropriate one. Or, to quote from a higher Atlanta Fed power:

... it will likely be hard to confirm a shift to a persistent above-trend pace of GDP growth even if the second-quarter numbers look relatively good.

This experience suggests to me that we can misread the vital signs of the economy in real time. Notwithstanding the mostly positive and encouraging character of recent data, we policymakers need to be circumspect when tempted to drop the gavel and declare the case closed. In the current situation, I feel it's advisable to accrue evidence and gain perspective. It will take some time to validate an outlook that assumes above-trend growth and associated solid gains in employment and price stability.

Photo of Dave AltigBy Dave Altig, executive vice president and research director, and

 

Photo of Pat HigginsPat Higgins, a senior economist, both in the Atlanta Fed's research department

 


Author: "macroblog" Tags: "Data Releases, Economic Growth and Devel..."
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Date: Friday, 18 Jul 2014 22:04

With employment trends having turned solidly positive in recent months, attention has focused on the quality of the jobs created. See, for example, the different perspectives of Mortimer Zuckerman in the Wall Street Journal and Derek Thompson in the Atlantic. Zuckerman highlights the persistently elevated level of part-time employment—a legacy of the cutbacks firms made during the recession—whereas Thompson points out that most employment growth on net since the end of the recession has come in the form of full-time jobs.

In measuring labor market slack, the part-time issue boils down to how much of the elevated level of part-time employment represents underutilized labor resources. The U-6 measure of unemployment, produced by the U.S. Bureau of Labor Statistics, counts as unemployed people who say they want to and are able to work a full-time schedule but are working part-time because of slack work or business conditions, or because they could find only part-time work. These individuals are usually referred to as working part-time for economic reasons (PTER). Other part-time workers are classified as working part-time for non-economic reasons (PTNER). Policymakers have been talking a lot about U-6 recently. See for example, here and here.

The "lollipop" chart below sheds some light on the diversity of the share of employment that is PTER and PTNER across industries. The "lolly" end of the lollipop denotes the average mix of employment that is PTER and PTNER in 2013 within each industry, and the size of the lolly represents the size of the industry. The bottom of the "stem" of each lollipop is the average PTER/PTNER mix in 2007. The red square lollipop is the percent of all employment that is PTER and PTNER for the United States as a whole. (Note that the industry classification is based on the worker's main job. Part-time is defined as less than 35 hours a week.)


The primary takeaways from the chart are:

  1. The percent of the workforce that is part time varies greatly across industries (compare for example, durable goods manufacturing with restaurants).
  2. All industries have a greater share of PTNER workers than PTER workers (for example, the restaurant industry in 2013 had 32 percent of workers who said they were PTNER and about 13 percent who declared themselves as PTER).
  3. All industries had a greater share of PTER workers in 2013 than in 2007 (all the lollipops point upwards).
  4. Most industries have a lower share of PTNER workers than in the past (most of the lollipops lean to the left).
  5. Most industries have a greater share of part-time workers (PTER + PTNER) than in the past (the increase in PTER exceeds the decline in PTNER for most industries).

Another fact that is a bit harder to see from this chart is that in 2007, industries with the largest part-time workforces did not necessarily have the largest PTER workforces. In 2013, it was more common for a large part-time workforce to be associated with a large PTER workforce. In other words, the growth in part-time worker utilization in industries such as restaurants and some segments of retail has bought with it more people who are working part-time involuntarily.

So the increase in PTER since 2007 is widespread. But is that a secular trend? If it is, then the increase in the PTER share would be evident since the recession as well. The next lollipop chart presents evidence by comparing 2013 with 2012:


This chart shows a recent general improvement. In fact, 25 of the 36 industries pictured in the chart above have experienced a decline in the share of PTER, and 21 of the 36 have a smaller portion working part-time in total. Exceptions are concentrated in retail, an industry that represents a large share of employment. In total, 20 percent of people are employed in industries that experienced an increase in PTER from 2012 to 2013. So while overall there has been a fairly widespread (but modest) recent improvement in the situation, the percent of the workforce working part-time for economic reasons remains elevated compared with 2007 for all industries. Further, many people are employed in industries that are still experiencing gains in the share that is PTER.

Why has the PTER share continued to increase for some industries? Are people who normally work full-time jobs still grasping those part-time retail jobs until something else becomes available, has there been a shift in the use of part-time workers in those industries, or is there a greater demand for full-time jobs than before the recession? We'll keep digging.

Photo of John RobertsonBy John Robertson, a vice president and senior economist, and

 

Photo of Ellyn TerryEllyn Terry, a senior economic analyst, both of the Atlanta Fed's research department

 


Author: "macroblog" Tags: "Data Releases, Employment, Labor Markets..."
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Date: Thursday, 10 Jul 2014 19:26

The June 18 statement from the Federal Open Market Committee opened with this (emphasis mine):

Information received since the Federal Open Market Committee met in April indicates that growth in economic activity has rebounded in recent months.... Household spending appears to be rising moderately and business fixed investment resumed its advance, while the recovery in the housing sector remained slow. Fiscal policy is restraining economic growth, although the extent of restraint is diminishing.

I highlighted the business fixed investment (BFI) part of that passage because it contracted at an annual rate of 1.2 percent in the first quarter of 2014. Any substantial turnaround in growth in gross domestic product (GDP) from its dismal first-quarter pace would seem to require that BFI did in fact resume its advance through the second quarter.

We won't get an official read on BFI—or on real GDP growth and all of its other components—until July 30, when the U.S. Bureau of Economic Analysis (BEA) releases its advance (or first) GDP estimates for the second quarter of 2014. But that doesn't mean we are completely in the dark on what is happening in real time. We have enough data in hand to make an informed statistical guess on what that July 30 number might tell us.

The BEA's data-construction machinery for estimating GDP is laid out in considerable detail in its NIPA Handbook. Roughly 70 percent of the advance GDP release is based on source data from government agencies and other data providers that are available prior to the BEA official release. This information provides the basis for what have become known as "nowcasts" of GDP and its major subcomponents—essentially, real-time forecasts of the official numbers the BEA is likely to deliver.

Many nowcast variants are available to the public: the Wall Street Journal Economic Forecasting Survey, the Philadelphia Fed Survey of Professional Forecasters, and the CNBC Rapid Update, for example. In addition, a variety of proprietary nowcasts are available to subscribers, including Aspen Publishers' Blue Chip Publications, Macroeconomic Advisers GDP Tracking, and Moody's Analytics high-frequency model.

With this macroblog post, we introduce the Federal Reserve Bank of Atlanta's own nowcasting model, which we call GDPNow.

GDPNow will provide nowcasts of GDP and its subcomponents on a regularly updated basis. These nowcasts will be available on the pages of the Atlanta Fed's Center for Quantitative Economic Research (CQER).

A few important notes about GDPNow:

  • The GDPNow model forecasts are nonjudgmental, meaning that the forecasts are taken directly from the underlying statistical model. (These are not official forecasts of either the Atlanta Fed or its president, Dennis Lockhart.)
  • Because nowcasts are often based on both modeling and judgment, there is no reason to expect that GDPNow will agree with alternative forecasts. And we do not intend to present GDPNow as superior to those alternatives. Different approaches have their pluses and minuses. An advantage of our approach is that, because it is nonjudgmental, our methodology is easily replicable. But it is always wise to avoid reliance on a single model or source of information.
  • GDPNow forecasts are subject to error, sometimes substantial. Internally, we've regularly produced nowcasts from the GDPNow model since introducing an earlier version of it in an October 2011 macroblog post. A real-time track record for the model nowcasts just before the BEA's advance GDP release is available on the CQER GDPNow webpage, and will be updated on a regular basis to help users make informed decisions about the use of this tool.

So, with that in hand, does it appear that BFI in fact "resumed its advance" last quarter? The table below shows the current GDPNow forecasts:


We will update the nowcast five to six times each month following the releases of certain key economic indicators listed in the frequently asked questions. Look for the next GDPNow update on July 15, with the release of the retail trade and business inventory reports.

If you want to dig deeper, the GDPNow page includes downloadable charts and tables as well as numerical details including the model's nowcasts for GDP, its subcomponents, and how the subcomponent nowcasts are built up from both the underlying source data and the model parameters. This working paper supplies the model's technical documentation. We hope economy watchers find GDPNow to be a useful addition to their information sets.

Photo of Pat HigginsBy Pat Higgins, a senior economist in the Atlanta Fed's research department


Author: "macroblog" Tags: "Data Releases, Economic Growth and Devel..."
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Date: Monday, 30 Jun 2014 17:56

A recent Policy Note published by the Levy Economics Institute of Bard College shows that what we thought had been a decade of essentially flat real wages (since 2002) has actually been a decade of declining real wages. Replicating the second figure in that Policy Note, Chart 1 shows that holding experience (i.e., age) and education fixed at their levels in 1994, real wages per hour are at levels not seen since 1997. In other words, growth in experience and education within the workforce during the past decade has propped up wages.

Chart 1: Actual and Fixed Real Wages, 1994-2013

The implication for inequality of this growth in education and experience was only touched on in the Policy Note that Levy published. In this post, we investigate more fully what contribution growth in educational attainment has made to the growth in wage inequality since 1994.

The Gini coefficient is a common statistic used to measure the degree of inequality in income or wages within a population. The Gini ranges between 0 and 100, with a value of zero reflecting perfect equality and a value of 100 reflecting perfect inequality. The Gini is preferred to other, simpler indices, like the 90/10 ratio, which is simply the income in the 90th percentile divided by the income in the 10th percentile, because the Gini captures information along the entire distribution rather than merely information in the tails.

Chart 2 plots the Gini coefficient calculated for the actual real hourly wage distribution in the United States in each year between 1994 and 2013 and for the counterfactual wage distribution, holding education and/or age fixed at their 1994 levels in order to assess how much changes in age and education over the same period account for growth in wage inequality. In 2013, the Gini coefficient for the actual real wage distribution is roughly 33, meaning that if two people were drawn at random from the wage distribution, the expected difference in their wages is equal to 66 percent of the average wage in the distribution. (You can read more about interpreting the Gini coefficient.) A higher Gini implies that, first, the expected wage gap between two people has increased, holding the average wage of the distribution constant; or, second, the average wage of the distribution has decreased, holding the expected wage gap constant; or, third, some combination of these two events.

Chart 2: Wage Distribution Gini Coefficients over Time

The first message from Chart 2 is that—as has been documented numerous other places (here and here, for example)—inequality has been growing in the United States, which can be seen by the rising value of the Gini coefficient over time. The Gini coefficient’s 1.27-point rise means that between 1994 and 2013 the expected gap in wages between two randomly drawn workers has gotten two and a half (2 times 1.27, or 2.54) percentage points larger relative to the average wage in the distribution. Since the average real wage is higher in 2013 than in 1994, the implication is that the expected wage gap between two randomly drawn workers grew faster than the overall average wage grew. In other words, the tide rose, but not the same for all workers.

The second message from Chart 2 is that the aging of the workforce has contributed hardly anything to the growth in inequality over time: the Gini coefficient since 2009 for the wage distribution that holds age constant is essentially identical to the Gini coefficient for the actual wage distribution. However, the growth in education is another story.

In the absence of the growth in education during the same period, inequality would not have grown as much. The Gini coefficient for the actual real wage distribution in 2013 is 1.27 points higher than it was in 1994, whereas it's only 0.49 points higher for the wage distribution, holding education fixed. The implication is that growth in education has accounted for about 61 percent of the growth in inequality (as measured by the Gini coefficient) during this period.

Chart 3 shows the growth in education producing this result. The chart makes apparent the declines in the share of the workforce with less than a high school degree and the share with a high school degree, as is the increase in the shares of the workforce with college and graduate degrees.

Chart 3: Distribution of the Workforce across Educational Status

There is little debate about whether income inequality has been rising in the United States for some time, and more dramatically recently. The degree to which education has exacerbated inequality or has the potential to reduce inequality, however, offers a more robust debate. We intend this post to add to the evidence that growing educational attainment has contributed to rising inequality. This assertion is not meant to imply that education has been the only source of the rise in inequality or that educational attainment is undesirable. The message is that growth in educational attainment is clearly associated with growing inequality, and understanding that association will be central to the understanding the overall growth in inequality in the United States.

Photo of Jessica DillBy Julie L. Hotchkiss, a research economist and senior policy adviser at the Atlanta Fed, and

Fernando Rios-Avila, a research scholar at the Levy Economics Institute of Bard College


Author: "macroblog" Tags: "Education, Employment, Inequality, Labor..."
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Date: Thursday, 26 Jun 2014 17:19

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.

This is the last of three posts on that talk. The first post reviewed alternative inflation measures; the second looked at ways to work with the Consumer Price Index to get a clear view of inflation. The full text of the speech is available on the Atlanta Fed's events web page.

The challenge of communicating price stability

Let me close this blog series with a few observations on the criticism that measures of core inflation, and specifically the CPI excluding food and energy, disconnect the Federal Reserve from households and businesses "who know price changes when they see them." After all, don't the members of the Federal Open Market Committee (FOMC) eat food and use gas in their cars? Of course they do, and if it is the cost of living the central bank intends to control, the prices of these goods should necessarily be part of the conversation, notwithstanding their observed volatility.

In fact, in the popularly reported all-items CPI, the Bureau of Labor Statistics has already removed about 40 percent of the monthly volatility in the cost-of-living measure through its seasonal adjustment procedures. I think communicating in terms of a seasonally adjusted price index makes a lot of sense, even if nobody actually buys things at seasonally adjusted prices.

Referencing alternative measures of inflation presents some communications challenges for the central bank to be sure. It certainly would be easier if progress toward either of the Federal Reserve's mandates could be described in terms of a single, easily understood statistic. But I don't think this is feasible for price stability, or for full employment.

And with regard to our price stability mandate, I suspect the problem of public communication runs deeper than the particular statistics we cite. In 1996, Robert Shiller polled people—real people, not economists—about their perceptions of inflation. What he found was a stark difference between how economists think about the word "inflation" and how folks outside a relatively small band of academics and policymakers define inflation. Consider this question:

140626_tbl1

And here is how people responded:

140626_tbl2

Seventy-seven percent of the households in Shiller's poll picked number 2—"Inflation hurts my real buying power"—as their biggest gripe about inflation. This is a cost-of-living description. It isn't the same concept that most economists are thinking about when they consider inflation. Only 12 percent of the economists Shiller polled indicated that inflation hurt real buying power.

I wonder if, in the minds of most people, the Federal Reserve's price-stability mandate is heard as a promise to prevent things from becoming more expensive, and especially the staples of life like, well, food and gasoline. This is not what the central bank is promising to do.

What is the Federal Reserve promising to do? To the best of my knowledge, the first "workable" definition of price stability by the Federal Reserve was Paul Volcker's 1983 description that it was a condition where "decision-making should be able to proceed on the basis that 'real' and 'nominal' values are substantially the same over the planning horizon—and that planning horizons should be suitably long."

Thirty years later, the Fed gave price stability a more explicit definition when it laid down a numerical target. The FOMC describes that target thusly:

The inflation rate over the longer run is primarily determined by monetary policy, and hence the Committee has the ability to specify a longer-run goal for inflation. The Committee reaffirms its judgment that inflation at the rate of 2 percent, as measured by the annual change in the price index for personal consumption expenditures, is most consistent over the longer run with the Federal Reserve's statutory mandate.

Whether one goes back to the qualitative description of Volcker or the quantitative description in the FOMC's recent statement of principles, the thrust of the price-stability objective is broadly the same. The central bank is intent on managing the persistent, nominal trend in the price level that is determined by monetary policy. It is not intent on managing the short-run, real fluctuations that reflect changes in the cost of living.

Effectively achieving price stability in the sense of the FOMC's declaration requires that the central bank hears what it needs to from the public, and that the public in turn hears what they need to know from the central bank. And this isn't likely unless the central bank and the public engage in a dialog in a language that both can understand.

Prices are volatile, and the cost of living the public experiences ought to reflect that. But what the central bank can control over time—inflation—is obscured within these fluctuations. What my colleagues and I have attempted to do is to rearrange the price data at our disposal, and so reveal a richer perspective on the inflation experience.

We are trying to take the torture out of the inflation discussion by accurately measuring the things that the Fed needs to worry about and by seeking greater clarity in our communications about what those things mean and where we are headed. Hard conversations indeed, but necessary ones.

Photo of Mike BryanBy Mike Bryan, vice president and senior economist in the Atlanta Fed's research department

 


Author: "macroblog" Tags: "Business Cycles, Data Releases, Inflatio..."
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Date: Wednesday, 25 Jun 2014 16:46

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.

Photo of Mike BryanBy Mike Bryan, vice president and senior economist in the Atlanta Fed's research department

Author: "macroblog" Tags: "Data Releases, Economic conditions, Infl..."
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Date: Tuesday, 24 Jun 2014 21:29

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.

This is the second of three posts based on that talk. Yesterday's post considered the median CPI and other trimmed-mean measures.

Is it more expensive, or does it just cost more money? Inflation versus the cost of living

Let me make two claims that I believe are, separately, uncontroversial among economists. Jointly, however, I think they create an incongruity for how we think about and measure inflation.

The first claim is that over time, inflation is a monetary phenomenon. It is caused by too much money chasing a limited number of things to buy with that money. As such, the control of inflation is rightfully the responsibility of the institution that has monopoly control over the supply of money—the central bank.

My second claim is that the cost of living is a real concept, and changes in the cost of living will occur even in a world without money. It is a description of how difficult it is to buy a particular level of well-being. Indeed, to a first approximation, changes in the cost of living are beyond the ability of a central bank to control.

For this reason, I think it is entirely appropriate to think about whether the cost of living in New York City is rising faster or slower than in Cleveland, just as it is appropriate to ask whether the cost of living of retirees is rising faster or slower than it is for working-aged people. The folks at the Bureau of Labor Statistics produce statistics that can help us answer these and many other questions related to how expensive it is to buy the happiness embodied in any particular bundle of goods.

But I think it is inappropriate for us to think about inflation, the object of central bank control, as being different in New York than it is in Cleveland, or to think that inflation is somehow different for older citizens than it is for younger citizens. Inflation is common to all things valued by money. Yet changes in the cost of living and inflation are commonly talked about as if they are the same thing. And this creates both a communication and a measurement problem for the Federal Reserve and other central banks around the world.

Here is the essence of the problem as I see it: money is not only our medium of exchange but also our numeraire—our yardstick for measuring value. Embedded in every price change, then, are two forces. The first is real in the sense that the good is changing its price in relation to all the other prices in the market basket. It is the cost adjustment that motivates you to buy more or less of that good. The second force is purely nominal. It is a change in the numeraire caused by an imbalance in the supply and demand of the money being provided by the central bank. I think the concept of "core inflation" is all about trying to measure changes in this numeraire. But to get there, we need to first let go of any "real" notion of our price statistics. Let me explain.

As a cost-of-living approximation, the weights the Bureau of Labor Statistics (BLS) uses to construct the Consumer Price Index (CPI) are based on some broadly representative consumer expenditures. It is easy to understand that since medical care costs are more important to the typical household budget than, say, haircuts, these costs should get a greater weight in the computation of an individual's cost of living. But does inflation somehow affect medical care prices differently than haircuts? I'm open to the possibility that the answer to this question is yes. It seems to me that if monetary policy has predictable, real effects on the economy, then there will be a policy-induced disturbance in relative prices that temporarily alters the cost of living in some way.

But if inflation is a nominal experience that is independent of the cost of living, then the inflation component of medical care is the same as that in haircuts. No good or service, geographic region, or individual experiences inflation any differently than any other. Inflation is a common signal that ultimately runs through all wages and prices.

And when we open up to the idea that inflation is a nominal, not-real concept, we begin to think about the BLS's market basket in a fundamentally different way than what the BLS intends to measure.

This, I think, is the common theme that runs through all measures of "core" inflation. Can the prices the BLS collects be reorganized or reweighted in a way that makes the aggregate price statistic more informative about the inflation that the central bank hopes to control? I think the answer is yes. The CPI excluding food and energy is one very crude way. Food and energy prices are extremely volatile and certainly point to nonmonetary forces as their primary drivers.

In the early 1980s, Otto Eckstein defined core inflation as the trend growth rate of the cost of the factors of production—the cost of capital and wages. I would compare Eckstein's measure to the "inflation expectations" component that most economists (and presumably the FOMC) think "anchor" the inflation trend.

The sticky-price CPI

Brent Meyer and I have taken this idea to the CPI data. One way that prices appear to be different is in their observed "stickiness." That is, some prices tend to change frequently, while others do not. Prices that change only infrequently are likely to be more forward-looking than are those that change all the time. So we can take the CPI market basket and separate it into two groups of prices—prices that tend to be flexible and those that are "sticky" (a separation made possible by the work of Mark Bils and Peter J. Klenow).

Indeed, we find that the items in the CPI market basket that change prices frequently (about 30 percent of the CPI) are very responsive to changes in economic conditions, but do not seem to have a very forward-looking character. But the 70 percent of the market basket items that do not change prices very often—these are accounted for in the sticky-price CPI—appear to be largely immune to fluctuations in the business conditions and are better predictors of future price behavior. In other words, we think that some "inflation-expectation" component exists to varying degrees within each price. By reweighting the CPI market basket in a way that amplifies the behavior of the most forward-looking prices, the sticky-price CPI gives policymakers a perspective on the inflation experience that the headline CPI can't.

Here is what monthly changes in the sticky-price CPI look like compared to the all-items CPI and the traditional "core" CPI.


Let me describe another, more radical example of how we might think about reweighting the CPI market basket to measure inflation—a way of thinking that is very different from the expenditure-basket approach the BLS uses to measure the cost of living.

If we assume that inflation is ultimately a monetary event and, moreover, that the signal of this monetary inflation can be found in all prices, then we might use statistical techniques to help us identify that signal from a large number of price data. The famous early-20th-century economist Irving Fisher described the problem as trying to track a swarm of bees by abstracting from the individual, seemingly chaotic behavior of any particular bee.

Cecchetti and I experimented along these lines to measure a common signal running through the CPI data. The basic idea of our approach was to take the component data that the BLS supplied, make a few simple identifying assumptions, and let the data itself determine the appropriate weighting structure of the inflation estimate. The signal-extraction method we chose was a dynamic-factor index approach, and while we didn't pursue that work much further, others did, using more sophisticated and less restrictive signal-extraction methods. Perhaps most notable is the work of Ricardo Reis and Mark Watson.

To give you a flavor of the approach, consider the "first principal component" of the CPI price-change data. The first principal component of a data series is a statistical combination of the data that accounts for the largest share of their joint movement (or variance). It's a simple, statistically shared component that runs through all the price data.

This next chart shows the first principal component of the CPI price data, in relation to the headline CPI and the core CPI.


Again, this is a very different animal than what the folks at the BLS are trying to measure. In fact, the weights used to produce this particular common signal in the price data bear little similarity to the expenditure weights that make up the market baskets that most people buy. And why should they? The idea here doesn't depend on how important something is to the well-being of any individual, but rather on whether the movement in its price seems to be similar or dissimilar to the movements of all the other prices.

In the table below, I report the weights (or relative importance) of a select group of CPI components and the weights they would get on the basis of their contribution to the first principal component.

140624b

While some criticize the CPI because it over weights housing from a cost-of-living perspective, it may be these housing components that ought to be given the greatest consideration when we think about the inflation that the central bank controls. Likewise, according to this approach, restaurant costs, motor vehicle repairs, and even a few food components should be taken pretty seriously in the measurement of a common inflation signal running through the price data.

And what price movements does this approach say we ought to ignore? Well, gasoline prices for one. But movements in the prices of medical care commodities, communications equipment, and tobacco products also appear to move in ways that are largely disconnected from the common thread in prices that runs through the CPI market basket.

But this and other measures of "core" inflation are very much removed from the cost changes that people experience on a monthly basis. Does that cause a communications problem for the Federal Reserve? This will be the subject of my final post.

Photo of Mike BryanBy Mike Bryan, vice president and senior economist in the Atlanta Fed's research department

 

Author: "macroblog" Tags: "Business Cycles, Data Releases, Inflatio..."
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Date: Friday, 20 Jun 2014 20:34

Just before Wednesday's confirmation from Fed Chairwoman Janet Yellen that the Federal Open Market Committee (FOMC) does indeed still see slack in the labor market, Jon Hilsenrath and Victoria McGrane posted a Wall Street Journal article calling notice to the state of debate:

Nearly four-fifths of those who became long-term unemployed during the worst period of the downturn have since migrated to the fringes of the job market, a recent study shows, rarely seeking work, taking part-time posts or bouncing between unsteady jobs. Only one in five, according to the study, has returned to lasting full-time work since 2008.

Deliberations over the nature of the long-term unemployed are particularly lively within the Federal Reserve.... Fed officials face a conundrum: Should they keep trying to spur economic growth and hiring by holding short-term interest rates near zero, or will those low rates eventually spark inflation without helping those long out of work?

The article goes on to provide a nice summary of the ongoing back-and-forth among economists on whether the key determinant of slack in the labor market is the long-term unemployed or the short-term unemployed. Included in that summary, checking in on the side of "both," is research by Chris Smith at the Federal Reserve Board of Governors.

We are fans of Smith's work, but think that the Wall Street Journal summary buries its own lede by focusing on the long-term/short-term unemployment distinction rather than on what we think is the more important part of the story: In Hilsenrath and McGrane's words, those "taking part-time posts."

We are specifically talking about the group officially designated as part-time for economic reasons (PTER). This is the group of people in the U.S. Bureau of Labor Statistics' Household Survey who report they worked less than 35 hours in the reference week due to an economic reason such as slack work or business conditions.

We have previously noted that the long-term unemployed have been disproportionately landing in PTER jobs. We have also previously argued that PTER emerges as a key negative influence on earnings over the course of the recovery, and remains so (at least as of the end of 2013). For reference, here is a chart describing the decomposition from our previous post (which corrects a small error in the data definitions):

140620a

Our conclusion, clearly identified in the chart, was that short-term unemployment and PTER have been statistically responsible for the tepid growth in wages over the course of the recovery. What's more, as short-term unemployment has effectively returned to prerecession levels, PTER has increasingly become the dominant negative influence.

Our analysis was methodologically similar to Smith's—his work and the work represented in our previous post were both based on annual state-level microdata from the Current Population Survey, for example. They were not exactly comparable, however, because of different wage variables—Smith used the median wage while we use a composition-adjusted weighted average—and different regression controls.

Here is what we get when we impose the coefficient estimates from Smith's work into our attempt to replicate his wage definition:

140620b

Some results change. The unemployment variables, short-term or long-term, no longer show up as a drag in wage growth. The group of workers designated as "discouraged" do appear to be pulling down wage growth and in ways that are distinct from the larger group of marginally attached. (That is in contrast to arguments some of us have previously made in macroblog that looked at the propensity of the marginally attached to find employment.)

It is not unusual to see results flip around a bit in statistical work as this or that variable is changed, or as the structure of the empirical specifications is tweaked. It is a robustness issue that should always be acknowledged. But what does appear to emerge as a consistent negative influence on wage growth? PTER.

None of this means that the short-term/long-term unemployment debate is unimportant. The statistics are not strong enough for us to be ruling things out categorically. Furthermore, that debate has raised some really interesting questions, such as Glenn Rudebusch and John Williams's recent suggestion that the definition of economic slack relevant for the FOMC's employment mandate may be different from the definition appropriate to the FOMC's price stability mandate.

Our message is pretty simple and modest, but we think important. Whatever your definition of slack, it really ought to include PTER. If not, you are probably asking the wrong question.

Photo of Dave AltigBy Dave Altig, executive vice president and research director, and

 

Photo of Pat HigginsPat Higgins, a senior economist, both of the Atlanta Fed's research department


 

Author: "macroblog" Tags: "Employment, Labor Markets, Unemployment"
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Date: Monday, 09 Jun 2014 20:55

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Despite Friday´s report of a further solid increase in payroll employment, the utilization picture for the official labor force remains mixed. The rate of short-term and long-term unemployment as well as the share of the labor force working part time who want to work full time (a cohort also referred to as working part time for economic reasons, or PTER) rose during the recession.

The short-term unemployment rate has since returned to levels experienced before the recession. In contrast, longer-term unemployment and involuntary part-time work have declined, but both remain well above prerecession levels (see the chart).

Alternative Labor Utilization Measures

Some of the postrecession decline in the short-term unemployment rate has not resulted from the short-term unemployed finding a job, but rather the opposite—they failed to get a job and became longer-term unemployed. Before the recession, the number of unemployed workers who said they had been looking for a job for more than half a year accounted for about 18 percent of unemployed workers. Currently, that share is close to 36 percent.

Moreover, job finding by unemployed workers might not completely reflect a decline in the amount of slack labor resources if some want full-time work but only find part-time work (that is, are working PTER). In this post, we investigate the ability of the unemployed to become fully employed relative to their experience before the Great Recession.

The job-finding rate of unemployed workers (the share of unemployed who are employed the following month) generally decreases toward zero with the length of the unemployment spell. Job-finding rates fell for all durations of unemployment in the recession.

Since the end of the recession, job-finding rates have improved, especially for shorter-term unemployed, but remain well below prerecession levels. The overall job-finding rate stood at close to 28 percent in 2007 and was about 20 percent for the first four months of 2014. The chart below shows the job-finding rates for select years by unemployment duration:

1 Month Job Finding Rate

What about the jobs that the unemployed find? Most unemployed workers want to work full-time hours (at least 35 hours a week). In 2007, around 75 percent of job finders wanted full-time work and either got full-time work or worked PTER (the remainder worked part time for noneconomic reasons). For the first four months of 2014, the share wanting full-time work was also about 75 percent. But the portion of job finders wanting full-time work and only finding part-time work increased from about 22 percent in 2007 to almost 30 percent in 2014, and this job-finding underutilization share has become especially high for the longer-term unemployed.

The chart below displays the job-finding underutilization share for select years by unemployment duration. (You can also read further analysis of PTER dynamics by our colleagues at the Federal Reserve Board of Governors.)

Share of Job Finders

Finding a job is one thing, but finding a satisfactory job is another. Since the end of the recession, the number of unemployed has declined, thanks in part to a gradually improving rate of job finding. But the job-finding rate is still relatively low, and the ability of an unemployed job seeker who wants to work full-time to actually find full-time work remains a significant challenge.

Photo of John RobertsonJohn Robertson, a vice president and senior economist and

Photo of Ellyn TerryEllyn Terry, a senior economic analyst, both of the Atlanta Fed's research department

Author: "macroblog" Tags: "Employment, Labor Markets, Unemployment"
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Date: Monday, 02 Jun 2014 20:49

Of the many statistical barometers of the U.S. economy that we monitor here at the Atlanta Fed, there are few that we await more eagerly than the monthly report on employment conditions. The May 2014 edition arrives this week and, like many others, we will be more interested in the underlying details than in the headline job growth or unemployment numbers.

One of those underlying details—the state of the pool of “discouraged” workers (or, maybe more precisely, potential workers)—garnered special attention lately in the wake of the relatively dramatic decline in the ranks of the official labor force, a decline depicted in the April employment survey from the U.S. Bureau of Labor Statistics. That attention included some notable commentary from Federal Reserve officials.

Federal Reserve Bank of New York President William Dudley, for example, recently suggested that a sizeable part of the decline in labor force participation since 2007 can be tied to discouraged workers exiting the workforce. This suggestion follows related comments from Federal Reserve Chair Janet Yellen in her press conference following the March meeting of the Federal Open Market Committee:

So I have talked in the past about indicators I like to watch or I think that are relevant in assessing the labor market. In addition to the standard unemployment rate, I certainly look at broader measures of unemployment… Of course, I watch discouraged and marginally attached workers… it may be that as the economy begins to strengthen, we could see labor force participation flatten out for a time as discouraged workers start moving back into the labor market. And so that's something I'm watching closely.

What may not be fully appreciated by those not steeped in the details of the employment statistics is that discouraged workers are actually a subset of “marginally attached” workers. Among the marginally attached—individuals who have actively sought employment within the most recent 12-month period but not during the most recent month—are indeed those who report that they are out of the labor force because they are discouraged. But the marginally attached also include those who have not recently sought work because of family responsibilities, school attendance, poor health, or other reasons.

In fact, most of the marginally attached are not classified (via self-reporting) as discouraged (see the chart):

140602

At the St. Louis Fed, B. Ravikumar and Lin Shao recently published a report containing some detailed analysis of discouraged workers and their relationship to the labor force and the unemployment rate. As Ravikumar and Shao note,

Since discouraged workers are not actively searching for a job, they are considered nonparticipants in the labor market—that is, they are neither counted as unemployed nor included in the labor force.

More importantly, the authors point out that they tend to reenter the labor force at relatively high rates:

Since December 2007, on average, roughly 40 percent of discouraged workers reenter the labor force every month.

Therefore, it seems appropriate to count some fraction of the jobless population designated as discouraged (and out of the labor force) as among the officially unemployed.

We believe this logic should be extended to the entire group of marginally attached. As we've pointed out in the past, the marginally attached group as a whole also has a roughly 40 percent transition rate into the labor force. Even though more of the marginally attached are discouraged today than before the recession, the changing distribution has not affected the overall transition rate of the marginally attached into the labor force.

In fact, in terms of the propensity to flow into employment or officially measured unemployment, there is little to distinguish the discouraged from those who are marginally attached but who have other reasons for not recently seeking a job (see the chart):

140602b

What we take from these data is that, as a first pass, when we are talking about discouraged workers' attachment to the labor market, we are talking more generally about the marginally attached. And vice versa. Any differences in the demographic characteristics between discouraged and nondiscouraged marginally attached workers do not seem to materially affect their relative labor market attachment and ability to find work.

Sometimes labels matter. But in the case of discouraged marginally attached workers versus the nondiscouraged marginally attached workers—not so much.

Photo of Dave AltigBy Dave Altig, executive vice president and research director,

Photo of John RobertsonJohn Robertson, a vice president and senior economist, and

Photo of Ellyn TerryEllyn Terry, a senior economic analyst, all of the Atlanta Fed's research department

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Date: Tuesday, 20 May 2014 21:31

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:

  1. What types of financing sources are young firms using?
  2. 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.

Photo of Ellyn TerryBy Ellyn Terry, an economic policy analysis specialist in the Atlanta Fed's research department


Author: "macroblog" Tags: "Economic conditions, Small Business"
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Date: Friday, 16 May 2014 14:54

Yesterday's report on consumer price inflation from the U.S. Bureau of Labor Statistics moved the needle a bit on inflation trends—but just a bit. Meanwhile, the European Central Bank appears to be locked and loaded to blast away at its own (low) inflation concerns. From the Wall Street Journal:

The European Central Bank is ready to loosen monetary policy further to prevent the euro zone from succumbing to an extended period of low inflation, its vice president said on Thursday.

"We are determined to act swiftly if required and don't rule out further monetary policy easing," ECB Vice President Vitor Constancio said in a speech in Berlin.

One of the favorite further measures is apparently charging financial institutions for funds deposited with the central bank:

On Wednesday, the ECB's top economist, Peter Praet, in an interview with German newspaper Die Zeit, said the central bank is preparing a number of measures to counter low inflation. He mentioned a negative rate on deposits as a possible option in combination with other measures.

I don't presume to know enough about financial institutions in Europe to weigh in on the likely effectiveness of such an approach. I do know that we have found reasons to believe that there are limits to such a tool in the U.S. context, as the New York Fed's Ken Garbade and Jamie McAndrews pointed out a couple of years back.

In part, the desire to think about an option such as negative interest rates on deposits appears to be driven by considerable skepticism about deploying more quantitative easing, or QE.

A drawback, in my view, of general discussions about the wisdom and effectiveness of large-scale asset purchase programs is that these policies come in many flavors. My belief, in fact, is that the Fed versions of QE1, QE2, and QE3 can be thought of as three quite different programs, useful to address three quite distinct challenges. You can flip through the slide deck of a presentation I gave last week at a conference sponsored by the Global Interdependence Center, but here is the essence of my argument:

  • QE1, as emphasized by former Fed Chair Ben Bernanke, was first and foremost credit policy. It was implemented when credit markets were still in a state of relative disarray and, arguably, segmented to some significant degree. Unlike credit policy, the focus of traditional or pure QE "is the quantity of bank reserves" (to use the Bernanke language). Although QE1 per se involved asset purchases in excess of $1.7 trillion, the Fed's balance sheet rose by less than $300 billion during the program's span. The reason, of course, is that the open-market purchases associated with QE1 largely just replaced expiring lending from the emergency-based facilities in place through most of 2008. In effect, with QE1 the Fed replaced one type of credit policy with another.
  • QE2, in contrast, looks to me like pure, traditional quantitative easing. It was a good old-fashioned Treasury-only asset purchase program, and the monetary base effectively increased in lockstep with the size of the program. Importantly, the salient concern of the moment was a clear deterioration of market-based inflation expectations and—particularly worrisome to us at the Atlanta Fed—rising beliefs that outright deflation might be in the cards. In retrospect, old-fashioned QE appears to have worked to address the old-fashioned problem of influencing inflation expectations. In fact, the turnaround in expectations can be clearly traced to the Bernanke comments at the August 2010 Kansas City Fed Economic Symposium, indicating that the Federal Open Market Committee (FOMC) was ready and willing pull the QE tool out of the kit. That was an early lesson in the power of forward guidance, which brings us to...
  • ...QE3. I think it is a bit early to draw conclusions about the ultimate impact of QE3. I think you can contend that the Fed's latest large-scale asset purchase program has not had a large independent effect on interest rates or economic activity while still believing that QE3 has played an important role in supporting the economic recovery. These two, seemingly contradictory, opinions echo an argument suggested by Mike Woodford at the Kansas City Fed's Jackson Hole conference in 2012: QE3 was important as a signaling device in early stages of the deployment of the FOMC's primary tool, forward guidance regarding the period of exceptionally low interest rates. I would in fact argue that the winding down of QE3 makes all the more sense when seen through the lens of a forward guidance tool that has matured to the point of no longer requiring the credibility "booster shot" of words put to action via QE.

All of this is to argue that QE, as practiced, is not a single policy, effective in all variants in all circumstances, which means that the U.S. experience of the past might not apply to another time, let alone another place. But as I review the record of the past seven years, I see evidence that pure QE worked pretty well precisely when the central concern was managing inflation expectations (and, hence, I would say, inflation itself).

Photo of Dave AltigBy Dave Altig, executive vice president and research director of the Atlanta Fed


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