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July 21, 2014

GDP Growth: Will We Find a Higher Gear?

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

 


July 21, 2014 in Data Releases, Economic Growth and Development, Forecasts, GDP | Permalink

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July 18, 2014

Part-Time for Economic Reasons: A Cross-Industry Comparison

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

 


July 18, 2014 in Data Releases, Employment, Labor Markets, Unemployment | Permalink

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Hi,
I think one of your axes on the lollipop charts are mislabeled.

They both read % employed that are PTNER; shouldn't one of them read % employed that are PTER?

Thanks.

Posted by: Heidi | July 18, 2014 at 05:11 PM

We noticed that, and we made that fix shortly after the chart's initial posting. We appreciate your close reading of the data!

Posted by: macroblog | July 21, 2014 at 03:45 PM

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July 10, 2014

Introducing the Atlanta Fed's GDPNow Forecasting Model

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


July 10, 2014 in Data Releases, Economic Growth and Development, Forecasts, GDP | Permalink

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Is there a link or RSS find one could follow to see each update of the GDPnow data?

Posted by: Bryan Willman | July 12, 2014 at 01:28 PM

Thanks for your question about GDPNow. To receive updates, please sign up for our e-mail notifications and select the Center for Quantitative Research. The e-mail subscription page link is at www.frbatlanta.org/webscriber/user/dsp_login.cfm. If you have any additional questions, please contact us at pubs@frbatlanta.org.

For more information about GDPNow, visit www.frbatlanta.org/cqer/researchcq/gdpnow.cfm.

Posted by: macroblog | July 15, 2014 at 12:25 PM

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June 26, 2014

Torturing CPI Data until They Confess: Observations on Alternative Measures of Inflation (Part 3)

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

 


June 26, 2014 in Business Cycles, Data Releases, Inflation | Permalink

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It would seem the non-economists may also be saying that the economists low inflation is their own stagnant wage.

Sure, they may see prices rising, but they stated what they suffer is the reduction of purchasing power.

Perhaps they would be happy to see prices rising rapidly as long as their own wages outpace.

The 70s may not have been so bad for them.

Posted by: cfaman | June 27, 2014 at 10:01 AM

In addition to the issues discussed in the article, Fed policy makers typically ignore one-time prices changes, particularly those originating on the supply side of the economy -- e.g., those caused by bad weather or a foreign conflict. 

The public can't ignore those price changes, which comprise their daily reality.

Posted by: Thomas Wyrick | July 06, 2014 at 05:57 PM

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June 24, 2014

Torturing CPI Data until They Confess: Observations on Alternative Measures of Inflation (Part 2)

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

 

June 24, 2014 in Business Cycles, Data Releases, Inflation | Permalink

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Great thoughts, thanks for sharing. taking the the idea of core inflation as the movements in prices that contain information about future inflation, have you ever thought about applying partial least squares (PLS) rather than PCA for dimension reduction, and making a future value of headline inflation the Y variable in the PLS decomposition of the Y'X? then you would get weightings that reflected the information content of each price series x on future Y, rather than PCA which simply decomposes the variance within X'X

Posted by: Michael Hugman | June 25, 2014 at 11:10 AM

This is very interesting. But I wonder, is it really possible to distinguish monetary inflation from cost-of-living inflation? As you say, monetary inflation reflects an imbalance between the supply and demand for money. Where does the demand for money come from? Presumably from the level of real activity. And how do we measure real activity independent of money, if not as a level of well-being?

In fact, the measurement of quantity in terms of well-being is the explicit basis of the hedonic price adjustments that go into a significant fraction of the CPI. So at the least, if you want a pure monetary measure of inflation, shouldn't you strip those adjustments back out?

Along the same lines, you say the inflation controlled by the central should be identical in New York and Cleveland. But what if monetary policy produces identical rates of money supply growth in both cities, while different real growth rates mean that money demand is rowing faster in one place than the other?

Posted by: JW Mason | June 27, 2014 at 09:42 AM

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June 23, 2014

Torturing CPI Data until They Confess: Observations on Alternative Measures of Inflation (Part 1)

On May 30, the Federal Reserve Bank of Cleveland generously allowed me some time to speak at their conference on Inflation, Monetary Policy, and the Public. The purpose of my remarks was to describe the motivations and methods behind some of the alternative measures of the inflation experience that my coauthors and I have produced in support of monetary policy.

In this, and the following two blogs, I'll be posting a modestly edited version of that talk. A full version of my prepared remarks will be posted along with the third installment of these posts.

The ideas expressed in these blogs and the related speech are my own, and do not necessarily reflect the views of the Federal Reserve Banks of Atlanta or Cleveland.

Part 1: The median CPI and other trimmed-mean estimators

A useful place to begin this conversation, I think, is with the following chart, which shows the monthly change in the Consumer Price Index (CPI) (through April).


The monthly CPI often swings between a negative reading and a reading in excess of 5 percent. In fact, in only about one-third of the readings over the past 16 years was the monthly, annualized seasonally adjusted CPI within a percentage point of 2 percent, which is the FOMC's longer-term inflation target. (Officially, the FOMC's target is based on the Personal Consumption Expenditures price index, but these and related observations hold for that price index equally well.)

How should the central bank think about its price-stability mandate within the context of these large monthly CPI fluctuations? For example, does April's 3.2 percent CPI increase argue that the FOMC ought to do something to beat back the inflationary threat? I don't speak for the FOMC, but I doubt it. More likely, there were some unusual price movements within the CPI's market basket that can explain why the April CPI increase isn't likely to persist. But the presumption that one can distinguish the price movements we should pay attention to from those that we should ignore is a risky business.

The Economist retells a conversation with Stephen Roach, who in the 1970s worked for the Federal Reserve under Chairman Arthur Burns. Roach remembers that when oil prices surged around 1973, Burns asked Federal Reserve Board economists to strip those prices out of the CPI "to get a less distorted measure. When food prices then rose sharply, they stripped those out too—followed by used cars, children's toys, jewellery, housing and so on, until around half of the CPI basket was excluded because it was supposedly 'distorted'" by forces outside the control of the central bank. The story goes on to say that, at least in part because of these actions, the Fed failed to spot the breadth of the inflationary threat of the 1970s.

I have a similar story. I remember a morning in 1991 at a meeting of the Federal Reserve Bank of Cleveland's board of directors. I was welcomed to the lectern with, "Now it's time to see what Mike is going to throw out of the CPI this month." It was an uncomfortable moment for me that had a lasting influence. It was my motivation for constructing the Cleveland Fed's median CPI.

I am a reasonably skilled reader of a monthly CPI release. And since I approached each monthly report with a pretty clear idea of what the actual rate of inflation was, it was always pretty easy for me to look across the items in the CPI market basket and identify any offending—or "distorted"—price change. Stripping these items from the price statistic revealed the truth—and confirmed that I was right all along about the actual rate of inflation.

Let me show you what I mean by way of the April CPI report. The next chart shows the annualized percentage change for each component in the CPI for that month. These are shown on the horizontal axis. The vertical axis shows the weight given to each of these price changes in the computation of the overall CPI. Taken as a whole, the CPI jumped 3.2 percent in April. But out there on the far right tail of this distribution are gasoline prices. They rose about 32 percent for the month. If you subtract out gasoline from the April CPI report, you get an increase of 2.1 percent. That's reasonably close to price stability, so we can stop there—mission accomplished.


But here's the thing: there is no such thing as a "nondistorted" price. All prices are being influenced by market forces and, once influenced, are also influencing the prices of all the other goods in the market basket.

What else is out there on the tails of the CPI price-change distribution? Lots of stuff. About 17 percent of things people buy actually declined in price in April while prices for about 13 percent of the market basket increased at rates above 5 percent.

But it's not just the tails of this distribution that are worth thinking about. Near the center of this price-change distribution is a very high proportion of things people buy. For example, price changes within the fairly narrow range of between 1.5 percent and 2.5 percent accounted for about 26 percent of the overall CPI market basket in the April report.

The April CPI report is hardly unusual. The CPI report is commonly one where we see a very wide range of price changes, commingled with an unusually large share of price increases that are very near the center of the price-change distribution. Statisticians call this a distribution with a high level of "excess kurtosis."

The following chart shows what an average monthly CPI price report looks like. The point of this chart is to convince you that the unusual distribution of price changes we saw in the April CPI report is standard fare. A very high proportion of price changes within the CPI market basket tends to remain close to the center of the distribution, and those that don't tend to be spread over a very wide range, resulting in what appear to be very elongated tails.


And this characterization of price changes is not at all special to the CPI. It characterizes every major price aggregate I have ever examined, including the retail price data for Brazil, Argentina, Mexico, Columbia, South Africa, Israel, the United Kingdom, Sweden, Canada, New Zealand, Germany, Japan, and Australia.

Why do price change distributions have peaked centers and very elongated tails? At one time, Steve Cecchetti and I speculated that the cost of unplanned price changes—called menu costs—discourage all but the most significant price adjustments. These menu costs could create a distribution of observed price changes where a large number of planned price adjustments occupy the center of the distribution, commingled with extreme, unplanned price adjustments that stretch out along its tails.

But absent a clear economic rationale for this unusual distribution, it presents a measurement problem and an immediate remedy. The problem is that these long tails tend to cause the CPI (and other weighted averages of prices) to fluctuate pretty widely from month to month, but they are, in a statistical sense, tethered to that large proportion of price changes that lie in the center of the distribution.

So my belated response to the Cleveland board of directors was the computation of the weighted median CPI (which I first produced with Chris Pike). This statistic considers only the middle-most monthly price change in the CPI market basket, which becomes the representative aggregate price change. The median CPI is immune to the obvious analyst bias that I had been guilty of, while greatly reducing the volatility in the monthly CPI report in a way that I thought gave the Federal Reserve Bank of Cleveland a clearer reading of the central tendency of price changes.

Cecchetti and I pushed the idea to a range of trimmed-mean estimators, for which the median is simply an extreme case. Trimmed-mean estimators trim some proportion of the tails from this price-change distribution and reaggregate the interior remainder. Others extended this idea to asymmetric trims for skewed price-change distributions, as Scott Roger did for New Zealand, and to other price statistics, like the Federal Reserve Bank of Dallas's trimmed-mean PCE inflation rate.

How much one should trim from the tails isn't entirely obvious. We settled on the 16 percent trimmed mean for the CPI (that is, trimming the highest and lowest 8 percent from the tails of the CPI's price-change distribution) because this is the proportion that produced the smallest monthly volatility in the statistic while preserving the same trend as the all-items CPI.

The following chart shows the monthly pattern of the median CPI and the 16 percent trimmed-mean CPI relative to the all-items CPI. Both measures reduce the monthly volatility of the aggregate price measure by a lot—and even more so than by simply subtracting from the index the often-offending food and energy items.


But while the median CPI and the trimmed-mean estimators are often referred to as "core" inflation measures (and I am guilty of this myself), these measures are very different from the CPI excluding food and energy.

In fact, I would not characterize these trimmed-mean measures as "exclusionary" statistics at all. Unlike the CPI excluding food and energy, the median CPI and the assortment of trimmed-mean estimators do not fundamentally alter the underlying weighting structure of the CPI from month to month. As long as the CPI price change distribution is symmetrical, these estimators are designed to track along the same path as that laid out by the headline CPI. It's just that these measures are constructed so that they follow that path with much less volatility (the monthly variance in the median CPI is about 95 percent smaller than the all-items CPI and about 25 percent smaller than the CPI less food and energy).

I think of the trimmed-mean estimators and the median CPI as being more akin to seasonal adjustment than they are to the concept of core inflation. (Indeed, early on, Cecchetti and I showed that the median CPI and associated trimmed-mean estimates also did a good job of purging the data of its seasonal nature.) The median CPI and the trimmed-mean estimators are noise-reduced statistics where the underlying signal being identified is the CPI itself, not some alternative aggregation of the price data.

This is not true of the CPI excluding food and energy, nor necessarily of other so-called measures of "core" inflation. Core inflation measures alter the weights of the price statistic so that they can no longer pretend to be approximations of the cost of living. They are different constructs altogether.

The idea of "core" inflation is one of the topics of tomorrow's post.

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

June 23, 2014 in Data Releases, Economic conditions, Inflation | Permalink

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Or you aware that if you look at the NSA core CPI that over half of the annual increase normally occurs in the first quarter.

Normally, if the first quarter change in the NSA core CPI is smaller than in the prior year the annual increase will be smaller than in the prior year. The same thing holds if it is larger.

I would be happy to send you an excell file
with the data arranged to demonstrate this.

Posted by: Spencer | June 24, 2014 at 11:11 AM

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August 30, 2013

Still Waiting for Takeoff...

On Thursday, we got a revised look at the economy’s growth rate in the second quarter. While the 2.5 percent annualized rate was a significant upward revision from the preliminary estimate, it comes off a mere 1.1 percent growth rate in the first quarter. That combines for a subpar first-half growth rate of 1.8 percent. OK, it’s growth, but not as strong as one would expect for a U.S. expansion and clearly a disappointment to the many forecasters who had once (again) expected this to be the year the U.S. economy shakes itself out of the doldrums.

Now, we’re not blind optimists when it comes to the record of economic forecasts. We know well that the evidence says you shouldn’t get overly confident in your favorite economists’ prediction. Most visions of the economy’s future have proven to be blurry at best.

Still, we at the Atlanta Fed want to know how to best interpret this upward revision to the second-quarter growth estimate and how it affects our president’s baseline forecast “for a pickup in real GDP growth over the balance of 2013, with a further step-up in economic activity as we move into 2014.”

What we can say about the report is that the revised second-quarter growth estimate is a decided improvement from the first quarter and a modest bump up from the recent four-quarter growth trend (1.6 percent). And there are some positive indicators within the GDP components. For example, real exports posted a strong turnaround last quarter, presumably benefiting from Europe’s emerging from its recession. And the negative influence of government spending cuts, while still evident in the data, was much smaller than during the previous two quarters.  Oh, and business investment spending improved between the first and second quarters.

All good, but these data simply give us a better fix on where we were in the second quarter, not necessarily a good signal of where we are headed. To that we turn to our “nowcast” estimate for the third quarter based on the incoming monthly data (the evolution of which is shown in the table below).

A "nowcasting" exercise generates quarterly GDP estimates in real time. The technical details of this exercise are described here, but the idea is fairly simple. We use incoming data on 100-plus economic series to forecast 12 components of GDP for the current quarter. We then aggregate those forecasts of GDP components to get a current-quarter estimate of overall GDP growth.

We caution that unlike others, our nowcast involves no interpretation whatsoever of these data. In what is purely a statistical exercise, we let the data do all the speaking for themselves.

Given the first data point of July—the July jobs report—the nowcast for the third quarter was pretty bleak (1.1 percent). Things improved a few days later with the release of strong international trade data for June, and stepped up further with the June wholesale trade report. But the remainder of the recent data point to a third-quarter growth rate that is very close to the lackluster performance of the first half.


In his speech a few weeks ago, President Dennis Lockhart indicated what he was looking for as drivers for stronger growth in the second half of this year.

“I expect consumer activity to strengthen.”

Today’s read on real personal consumption expenditures (PCE) probably isn’t bolstering confidence in that view. Real PCE was virtually flat in July, undermining private forecasters’ expectation of a moderate gain. Our nowcast for real GDP slipped down 0.5 percentage points to 1.4 percent on the basis of this data, and pegged consumer spending at 1.7 percent for Q3—in line with Q2’s 1.8 percent gain.

“I expect business investment to accelerate somewhat.”

The July data were pretty disappointing on this score. The durable-goods numbers released a few days ago were quite weak, causing our nowcast, and those of the others we follow, to revise down the third-quarter growth estimate.

“I expect the rebound we have seen in the housing sector to continue.”

Check. Our nowcast wasn’t affected much by the housing starts data, but the existing sales numbers produced a positive boost to the estimate. Our nowcast’s estimate of residential investment growth in the third quarter is well under what we saw in the second quarter. But at 5.3 percent, the rebound looks to be continuing.

“I expect the recent improvement in exports to last.”

Unfortunately, the July trade numbers don’t get reported until next week. So we’re going to mark this one as missing in action.  But as we said earlier, that June trade number was strong enough to cause our third-quarter nowcast to be revised up a bit.

“And I expect to see an easing of the public-sector spending drag at the federal, state, and local levels.”

Again, check. The July Treasury data indicated growth in government spending overall.

So the July data are a mixed bag: some positives, some disappointments, and some missing-in-actions. But if President Lockhart were to ask us (and something tells us he just might), we’re likely to say that on the basis of the July indicators, the “pickup in real GDP growth over the balance of 2013” isn’t yet very evident in the data.

This news isn’t likely to come as a big surprise to him. Again, here’s what he said publicly two weeks ago:

When I weigh the balance of risks around the medium-term outlook I laid out, I have some concerns about the potential for ambiguous or disappointing data. I also think that it is important to be realistic about the degree to which we are likely to have clarity in the near term about the direction of the economy. Both the quantity of information and the strength of the signal conveyed by the data will likely be limited. As of September, the FOMC will have in hand one more employment report, two reports on inflation, a revision to the second-quarter GDP data, and preliminary incoming signals about growth in the third quarter. I don't expect to have enough data to be sure of my outlook.

It’s still a little early to say with any confidence we won’t eventually see a pickup this quarter, and we can hope that the incoming August numbers show a more marked improvement. All we can say at this point is that after seeing most of the July data, it still feels like we’re stuck on the tarmac.

Photo of Mike BryanBy Mike Bryan, vice president and senior economist,

Photo of Patrick HigginsPatrick Higgins, senior economist, and

Photo of Brent MeyerBrent Meyer, economist, all in the Atlanta Fed's research department


August 30, 2013 in Data Releases, Economic Growth and Development, Economics, Forecasts, GDP | Permalink

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August 16, 2013

GDP, Jobs, and Growth Accounting

The latest on productivity, from the Associated Press via USA Today:

U.S. worker productivity accelerated to a still-modest 0.9% annual pace between April and June after dropping the previous quarter.

The second-quarter gain...reversed a decline in the January-March quarter, when the Labor Department's revised numbers show productivity shrank at a 1.7% annual pace.

Labor costs rose at a 1.4% annual pace from April through June, reversing a revised 4.2% drop the previous quarter.

Productivity measures output per hour of work. Weak productivity suggests that companies may have to hire because they can't squeeze more work from their existing employees....

Productivity growth has been weaker recently, rising 1.5% in 2012 and 0.5% in 2011.

Annual productivity growth averaged 3.2% in 2009 and 3.3% in 2010. In records dating back to 1947, it's been about 2%.

Though not quite in the category of spectacular—and coming off revisions that if anything made things look weaker than previously thought—last quarter's uptick is a welcome development. Earlier this week, in a speech to the Atlanta Kiwanis club, Atlanta Fed President Dennis Lockhart laid out several scenarios with materially different implications for how the GDP and employment picture might play out over the next several years:

As a matter of arithmetic, healthy employment growth coupled with tepid GDP growth implies weak labor productivity growth. And in fact, productivity growth in recent quarters has been significantly below historical norms.

[I] believe that the recent low growth of productivity is probably just a temporary downdraft after the rather strong productivity growth when the economy emerged from recession.

If productivity growth rebounds to more typical levels, the coincidence of job gains at a pace of around 190,000 per month in recent months and GDP growth below 2 percent cannot persist. Again, it's a matter of arithmetic. Either GDP growth will rise to levels consistent with recent employment growth, or employment growth will fall to levels more consistent with the weak GDP data we've been witnessing.

I've got a working assumption on this question, and it is captured in the Atlanta Fed's baseline forecast for the second half of this year and 2014. This outlook calls for a pickup in real GDP growth over the balance of 2013, with a further step-up in economic activity as we move into 2014.

You can get a sense of this outlook by considering the output of one particular model that we use here at the Atlanta Fed. The model, which is purely statistical, gives us a view into how productivity, GDP, employment, and the unemployment rate might move together (along with other labor market variables like labor force participation and average hours worked). Here is the bottom line of an exercise that assumes GDP growth through 2015 comes in at about the central tendency of the projections from the Federal Reserve's June 2013 Summary of Economic Projections.

For this exercise, we have adjusted the 2013 growth forecast down slightly due to the weaker-than-expected growth in the first half of the year. Additionally, we have plugged in assumptions for productivity growth—1.5 percent per quarter (SAAR), the average gain over the past eight years—and nonfarm business output growth. We then let the model forecast the remaining variables, all of which are for the labor market:

130816a

The model forecasts employment gains in the neighborhood of what the economy has been generating over the past several years, and a steadily declining unemployment rate.

Now consider two "stall" scenarios in which GDP growth fails to get beyond 2.3 percent. The first of these scenarios is the one noted in the Lockhart Kiwanis speech, with productivity recovering but job growth falling off the pace:

130816b

From a policy perspective, this one may not cause too much handwringing about the appropriate course of action. The weak GDP growth is accompanied by a failure to make the type of progress on the unemployment rate that the FOMC has clearly articulated as the necessary condition for adjustments in policy rates:

[T]he Committee decided to keep the target range for the federal funds rate at 0 to 1/4 percent and currently anticipates that this exceptionally low range for the federal funds rate will be appropriate at least as long as the unemployment rate remains above 6-1/2 percent, inflation between one and two years ahead is projected to be no more than a half percentage point above the Committee's 2 percent longer-run goal, and longer-term inflation expectations continue to be well anchored.

Absent unforeseen issues with inflation, staying the course would seem to be in order.

But there is a second stall scenario in which productivity and GDP growth remain tepid, even as labor market indicators improve:

130816c

The difference in this experiment is that the expectations of those that President Lockhart referred to in his speech as the "innovation pessimists" are correct. Recent weakness in productivity growth reflects a fall in trend productivity growth. In this case, essentially identical labor market outcomes would nonetheless correspond to an economy that can't seem to hit "escape" velocity.

If it is clear that this configuration of outcomes is associated with a structural break in productivity growth, an argument against monetary policy stimulus would have some weight. After all, in most cases we don't expect the tools of monetary policy to fix structural efficiency problems.

But, alas, such clarity rarely arrives in real time. The experiments above give some sense of how difficult it can be to discover the right branch to follow on the policy decision tree.

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


August 16, 2013 in Data Releases, Employment, GDP, Labor Markets, Productivity, Unemployment | Permalink

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The fundamental challenge with income statistics is that we are being confused by the terrible impact of a completely left tailed incomes distribution prior to the crisis with a job growth that is happening at the bottom of the pyramid, which effectively means that even though the job numbers look good, its impact on the GDP growth would be minuscule.

Posted by: Procyon Mukherjee | August 17, 2013 at 07:01 AM

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August 02, 2013

What a Difference a Month Makes? Maybe Not Much

By most accounts, the July employment report released this morning was something of a disappointment, perhaps more because it fell short of expectations than for any absolute signal it sends about the state of the economy. To be sure, the 162,000 net jobs created in July were below June’s 12-month average, which itself ticked down a bit as a result of negative revisions to the May and June statistics.

“Ticked down a bit” is the operative phrase, as the average monthly jobs gain from May 2012 through June 2013 now registers at 189,000 as opposed to the 191,000 reported last month. With this month’s new data, the 12-month average gains (from June 2012 to July 2013) clock in at 190,000 jobs per month, still right on the trend that has prevailed over the past couple of years. In other words, not much has changed in the longer view of things.

Our interests here at macroblog run to the policy implications, of course. Not too surprisingly, focal points are 1) the 7 percent unemployment rate neighborhood that Chairman Bernanke has associated with Federal Open Market Committee forecasts of what will prevail around the time that the Fed’s current asset purchase program might be ending and 2) the benchmark 6 1/2 percent unemployment that the statement following this week’s FOMC meeting continued to identify as the earliest possible point at which adjustments to the Committee’s interest rate target will be considered.

Following last month’s employment report I offered up calculations from the Atlanta Fed’s Jobs Calculator™ regarding the dates at which these unemployment thresholds might be reached, under the assumptions that jobs gains average 191,000 per month going forward, the participation rate remains constant at the reported June level, and there will be no change in the relationship between employment statistics from the payroll (or establishment) survey (whence comes the headline jobs number) and the employment statistics from the household survey (statistics used to calculate the unemployment rate). All of these figures change month to month, so it may be useful to update that exercise with current statistics (with last month’s calculations noted parenthetically):

Job growth and unemployment rates as of July 2013 (June 2013) employment reports


Not much change there. In fact, the unemployment rates in these calculations fall a little faster than last month’s calculations suggested, in part due to the ancillary assumptions on participation rates and the payroll-employment /household-employment ratio.

In the spirit of pessimism—an economist’s university-given right—I’ll ask: what if the latest 162,000-job-gain number is closer than the trailing 12-month average to what we will experience going forward? Easiest enough to explore:

Unemployment rates under the assumption of 162,000 jobs created per month going forward


I will leave it to you to decide whether the differences imply important policy distinctions.

Side note: For a broader look at labor market conditions, take a look at the Atlanta Fed’s spider chart, updated as of today’s employment report.

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


August 2, 2013 in Data Releases, Employment, Labor Markets, Monetary Policy | Permalink

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June 07, 2013

The Hiring Forecasts of Small Firms: Will the Pace of Employment Growth Pick Up?

The U.S. Bureau of Labor Statistics (BLS) announced today that the U.S. labor market added 175,000 payroll jobs in May, continuing a trend of steady but disappointingly slow employment growth. The employment recovery has been even slower among small firms. Will it pick up in the coming 12 months? Results from the Atlanta Fed's latest survey of small businesses in the Southeast suggest that employment growth among small firms will continue but not necessarily at a faster pace.

Since the recession began, changes in employment have been asymmetric across firm size. In contrast to large firms, employment at small and medium sized businesses began decreasing earlier, declined more, and, by last March, was a little further from its prerecession level. As of the first quarter of 2012, employment at firms with fewer than 500 employees was 5 percent below prerecession levels, compared to just 2 percent for firms with more than 500 employees. So why is employment at small firms not recovering as quickly as employment at large firms? Is it poised to accelerate and perhaps catch up?

Employment to Firm Size; Indexed to Q1-2008=1

While the Business Employment Dynamics data series from the BLS only go through first-quarter 2012 (chart 1), we can use our semi-annual survey of small business in the Southeast to find out a little more about the experiences of small firms through first-quarter 2013 as well look at their forecasts through the first quarter of 2014. Four-hundred-seventy-eight firms across the industry and age spectrum participated in the first-quarter 2013 survey, which was conducted during the first three weeks in April. Although the survey is not a random sample, the results are weighted to make them more representative of a national distribution.

When asked about changes in employment over the period Q1 2012 to Q1 2013, employer firms on net said there was almost no change. Slightly more than 40 percent of firms said they had not altered employment levels. The remainder of the responses were distributed pretty evenly between "expansion" and "contraction". As you can see in chart 2, the distribution of firms creating jobs was almost a mirror image of the distribution of firms shedding jobs in terms of the magnitude of change.

Changes in Size of Workforce--Q1 2012 to Q1 2013

In addition to asking about changes during the past 12 months, the survey probed small firms about their expectations for the coming 12 months. Using the power of our panel data set, we can compare the expectations of firms that took the survey exactly one year ago with their actual hiring activity during that time period to determine how accurately firms predict what the future holds and whether these hiring plans are indeed good forecasts of future activity.

As it turns out, the 184 firms participating in both surveys came pretty close to meeting their hiring expectations. However, they did tend to overestimate the extent to which employment would increase (or underestimate the extent to which it would decrease), regardless of how well firms were performing at the time they made their forecast (see chart 3). For example, firms that had recently experienced reductions in their workforce expected the greatest positive change in the pace of hiring, and in fact went on to report the highest actual change during this period. Firms that had not changed their employment levels recently or had changed them by up to 10 percent expected very little growth—on average, they achieved just slightly less than expected. Regardless of how well the firm had recently performed (in terms of employment growth in the previous period), the degree to which hiring increased or downsizing decreased was less pronounced than anticipated.

Actual Pace of Hiring and Expected Pace of Hiring

Small firms are reasonably good at predicting the direction and relative magnitude of their employment growth, but on average tend to overestimate. For this reason, it might be useful to examine changes in the hiring expectations index (as opposed to changes in the pace of employment growth) when trying to understand how the forecast of firms participating in the survey might translate into actual employment growth of small firms in the Southeast.

Chart 4 shows the hiring index of firms across four broad industry groups. In the first quarter of 2013, the index for hiring in the coming 12 months was essentially unchanged from the Q3 2012 survey, and significantly below that of the Q1 2012 survey. The only industry whose employment forecast was notably positive was the construction and real estate industry. Firms in that category have been steadily increasing their hiring forecasts since the third quarter of 2011.

MHiring Expectations Diffusion Index

The fact that hiring expectations did not improve in the first-quarter survey leads to another, perhaps more important question: Why didn't they?

One contributing factor that could be having a particularly large impact on hiring expectations is rocky sales. Firms may be less willing to hire if they are uncertain about the future or if they do not expect consistent sales growth. Indeed, by looking at the experiences of firms in the past 12 months, we can tell that there is a clear correlation between rising sales and rising employment. As chart 5 shows, half of employer firms reported a recent rise in sales, and the more sales had risen, the more likely firms were to have increased their workforce.

Change in Sales

A couple of questions that arise from chart 5 are: What about the firms that recently experienced sales growth but didn't hire? Are they planning to hire in the coming 12 months? About one-third of firms say "yes". One driving factor in that decision appears to be sustained sales growth; another is reduced uncertainty. As chart 6 makes apparent, the sales expectations of firms in this group is higher on average for the one-third of firms that say they do plan to hire in the coming 12 months than for the two-thirds who do not. All the firms in the hiring group also expect sales growth to continue, with the most common response being greater than 10 percent growth. In contrast, while 77 percent of firms in the not-hiring group anticipate sustained sales growth, the group’s most common response was lower than that of the hiring group: 1 percent to 5 percent.

Q1 2013 Sales Forecasts of Firms

Another factor that may be related to hiring is reduced uncertainty. Employer firms experiencing sales growth in the past 12 months are more likely to anticipate hiring if they perceive a decrease in uncertainty compared to six months ago. Seventy percent of firms that had a recent increase in sales and decreased uncertainty concerns relative to six months ago anticipate hiring in the coming year. In contrast, 46 percent of those who had experienced a recent increase in sales but also perceived heightened uncertainty anticipate hiring.

For now, the results suggest that uncertainty and rocky sales growth are negatively affecting the hiring plans of small firms and, unfortunately, that small firms are not likely to increase their rate of hiring in the next 12 months. However, if uncertainty eases and sales growth continues, small firms will likely revisit their hiring plans and the pace of hiring just might improve.

By Ellyn Terry, a senior economic analyst in the Atlanta Fed's research department

June 7, 2013 in Data Releases, Employment, Labor Markets, Small Business | Permalink

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While expected "sales" may be a hender in hiring for small firms, the "Affordable Healthcare Act" is having a far more dramatic effect than anyone will admit. Why, is a mystery.

Posted by: Tom Damson | June 10, 2013 at 03:39 PM

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