The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.
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April 18, 2018
Hitting a Cyclical High: The Wage Growth Premium from Changing Jobs
The Atlanta Fed's Wage Growth Tracker rose 3.3 percent in March. While this increase is up from 2.9 percent in February, the 12-month average remained at 3.2 percent, a bit lower than the 3.5 percent average we observed a year earlier. The absence of upward momentum in the overall Tracker may be a signal that the labor market still has some head room, as suggested by participants at the last Federal Open market Committee (FOMC) meeting, who noted this in the meeting:
Regarding wage growth at the national level, several participants noted a modest increase, but most still described the pace of wage gains as moderate; a few participants cited this fact as suggesting that there was room for the labor market to strengthen somewhat further.
Although wages haven't been rising faster for the median individual, they have been for those who switch jobs. This distinction is important because the wage growth of job-switchers tends to be a better cyclical indicator than overall wage growth. In particular, the median wage growth of people who change industry or occupation tends to rise more rapidly as the labor market tightens. To illustrate, the orange line in the following chart shows the median 12-month wage growth for workers in the Wage Growth Tracker data who change industry (across manufacturing, construction, retail, etc.), and the green line depicts the wage growth of those who remained in the same industry.
As the chart indicates, changing industry when unemployment is high tends to result in a wage growth penalty relative to those who remain employed in the same industry. But when the unemployment rate is low, voluntary quits rise and workers who change industries tend to experience higher wage growth than those who stay.
Currently, the wage growth premium associated with switching employment to a different industry is around 1.5 percentage points and growing. For those who are tempted to infer that the softness in the Wage Growth Tracker might signal an impending labor market slowdown, the wage growth performance for those changing jobs suggests the opposite: the labor market is continuing to gradually tighten.
February 28, 2018
Weighting the Wage Growth Tracker
The Atlanta Fed's Wage Growth Tracker (WGT) has shown its usefulness as an indicator of labor market conditions, producing a better-fitting Phillips curve than other measures of wage growth. So we were understandably surprised to see the WGT decline from 3.5 percent in 2016 to 3.2 percent in 2017, even as the unemployment rate moved lower from 4.9 to 4.4 percent.
This unexpected disconnect between the WGT and the unemployment rate naturally led us to wonder if it was a consequence of the way the WGT is constructed. Essentially, the WGT is the median of an unweighted sample of individual wage growth observations. This sample is quite large, but it does not perfectly represent the population of wage and salary earners.
Importantly, the WGT sample has too few young workers, because young workers are much more likely to be in and out of employment and hence less likely to have a wage observation in both the current and prior years. To examine the effect of this underrepresentation, we recomputed median wage growth after weighting the WGT sample to be consistent with the distribution of demographic and job characteristics of the workforce in each year. It turns out that this adjustment is important when the labor market is tight.
During periods of low unemployment, young people who stay employed tend to experience larger proportionate wage bumps than older workers. In 2017, for example, the weighted median is 40 basis points higher than the unweighted version. However, both the unweighted version (the gray line in the chart below) and the weighted version of the WGT (the blue line) declined by a similar amount from 2016 to 2017. The decline in the weighted median is also statistically significant (the p-value for the test is 0.07, indicating that the observed difference is unlikely to be due to chance).
Another issue that could affect comparisons of wage growth over time is the changing demographic characteristics of the workforce. In particular, we know that workers' wage growth tends to slow as they approach retirement age, and the fraction of older workers has increased markedly in recent years. To examine this trend, we re-computed the weighted median, but fixed the demographic and job characteristics of the workforce so they would look as they did in 1997.
Our 1997-fixed version shows that median wage growth in recent years would be a bit higher if not for the aging of the workforce (the dashed orange line in the chart below). Moreover, this demographic shift appears to explain some of the slowing in median wage growth from 2016 to 2017. Whereas the 1997-fixed median also slows over the year, the difference is not statistically significant (a test of the null hypothesis of no change in the 1997-fixed weighted median between 2016 and 2017 yielded a p-value of 0.38).
Long story short, our analysis suggests that median wage growth of the population of wage and salary earners is currently higher than the WGT would indicate, reflecting the strong wage gains young workers experience in a tight labor market. Moreover, the increasing share of older workers is acting to restrain median wage growth. Although the decline in median wage growth from 2016 to 2017 appears to be partly the result of the aging workforce, there still may be more to it than just that, and so we will continue to monitor the WGT and related measures closely in 2018 for signs of a pickup. We also want to note that with the release of the February wage data in mid-March, we will make a monthly version of the weighted WGT available.
January 17, 2018
What Businesses Said about Tax Reform
Many folks are wondering what impact the Tax Cuts and Jobs Act—which was introduced in the House on November 2, 2017, and signed into law a few days before Christmas—will have on the U.S. economy. Well, in a recent speech, Atlanta Fed president Raphael Bostic had this to say: "I'm marking in a positive, but modest, boost to my near-term GDP [gross domestic product] growth profile for the coming year."
Why the measured approach? That might be our fault. As part of President Bostic's research team, we've been curious about the potential impact of this legislation for a while now, especially on how firms were responding to expected policy changes. Back in November 2016 (the week of the election, actually), we started asking firms in our Sixth District Business Inflation Expectations (BIE) survey how optimistic they were (on a 0–100 scale) about the prospects for the U.S. economy and their own firm's financial prospects. We've repeated this special question in three subsequent surveys. For a cleaner, apples-to-apples approach, the charts below show only the results for firms that responded in each survey (though the overall picture is very similar).
As the charts show, firms have become more optimistic about the prospects for the U.S. economy since November 2016, but not since February 2017, and we didn't detect much of a difference in December 2017, after the details of the tax plan became clearer. But optimism is a vague concept and may not necessarily translate into actions that firms could take that would boost overall GDP—namely, increasing capital investment and hiring.
In November, we had two surveys in the field—our BIE survey (undertaken at the beginning of the month) and a national survey conducted jointly by the Atlanta Fed, Nick Bloom of Stanford University, and Steven Davis of the University of Chicago. (That survey was in the field November 13–24.) In both of these surveys, we asked firms how the pending legislation would affect their capital expenditure plans for 2018. In the BIE survey, we also asked how tax reform would affect hiring plans.
The upshot? The typical firm isn't planning on a whole lot of additional capital spending or hiring.
In our national survey, roughly two-thirds of respondents indicated that the tax reform hasn't enticed them into changing their investment plans for 2018, as the following chart shows.
The chart below also makes apparent that small firms (fewer than 100 employees) are more likely to significantly ramp up capital investment in 2018 than midsize and larger firms.
For our regional BIE survey, the capital investment results were similar (you can see them here). And as for hiring, the typical firm doesn't appear to be changing its plans. Interestingly, here too, smaller firms were more likely to say they'd ramp up hiring. Among larger firms (more than 100 employees), nearly 70 percent indicated that they'd leave their hiring plans unchanged.
One interpretation of these survey results is that the potential for a sharp acceleration in GDP growth is limited. And that's also how President Bostic described things in his January 8 speech: "For now, I am treating a more substantial breakout of tax-reform-related growth as an upside risk to my outlook."
September 07, 2017
What Is the "Right" Policy Rate?
What is the right monetary policy rate? The Cleveland Fed, via Michael Derby in the Wall Street Journal, provides one answer—or rather, one set of answers:
The various flavors of monetary policy rules now out there offer formulas that suggest an ideal setting for policy based on economic variables. The best known of these is the Taylor Rule, named for Stanford University's John Taylor, its author. Economists have produced numerous variations on the Taylor Rule that don't always offer a similar story...
There is no agreement in the research literature on a single "best" rule, and different rules can sometimes generate very different values for the federal funds rate, both for the present and for the future, the Cleveland Fed said. Looking across multiple economic forecasts helps to capture some of the uncertainty surrounding the economic outlook and, by extension, monetary policy prospects.
Agreed, and this is the philosophy behind both the Cleveland Fed's calculations based on Seven Simple Monetary Policy Rules and our own Taylor Rule Utility. These two tools complement one another nicely: Cleveland's version emphasizes forecasts for the federal funds rate over different rules and Atlanta's utility focuses on the current setting of the rate over a (different, but overlapping) set of rules for a variety of the key variables that appear in the Taylor Rule (namely, the resource gap, the inflation gap, and the "neutral" policy rate). We update the Taylor Rule Utility twice a month after Consumer Price Index and Personal Income and Outlays reports and use a variety of survey- and model-based nowcasts to fill in yet-to-be released source data for the latest quarter.
We're introducing an enhancement to our Taylor Rule utility page, a "heatmap" that allows the construction of a color-coded view of Taylor Rule prescriptions (relative to a selected benchmark) for five different measures of the resource gap and five different measures of the neutral policy rate. We find the heatmap is a useful way to quickly compare the actual fed funds rate with current prescriptions for the rate from a relatively large number of rules.
In constructing the heatmap, users have options on measuring the inflation gap and setting the value of the "smoothing parameter" in the policy rule, as well establishing the weight placed on the resource gap and the benchmark against which the policy rule is compared. (The inflation gap is the difference between actual inflation and the Federal Open Market Committee's 2 percent longer-term objective. The smoothing parameter is the degree to which the rule is inertial, meaning that it puts weight on maintaining the fed funds rate at its previous value.)
For example, assume we (a) measure inflation using the four-quarter change in the core personal consumption expenditures price index; (b) put a weight of 1 on the resource gap (that is, specify the rule so that a percentage point change in the resource gap implies a 1 percentage point change in the rule's prescribed rate); and (c) specify that the policy rule is not inertial (that is, it places no weight on last period's policy rate). Below is the heatmap corresponding to this policy rule specification, comparing the rules prescription to the current midpoint of the fed funds rate target range:
We should note that all of the terms in the heatmap are described in detail in the "Overview of Data" and "Detailed Description of Data" tabs on the Taylor Rule Utility page. In short, U-3 (the standard unemployment rate) and U-6 are measures of labor underutilization defined here. We introduced ZPOP, the utilization-to-population ratio, in this macroblog post. "Emp-Pop" is the employment-population ratio. The natural (real) interest rate is denoted by r*. The abbreviations for the last three row labels denote estimates of r* from Kathryn Holston, Thomas Laubach, and John C. Williams, Thomas Laubach and John C. Williams, and Thomas Lubik and Christian Matthes.
The color coding (described on the webpage) should be somewhat intuitive. Shades of red mean the midpoint of the current policy rate range is at least 25 basis points above the rule prescription, shades of green mean that the midpoint is more than 25 basis points below the prescription, and shades of white mean the midpoint is within 25 basis points of the rule.
The heatmap above has "variations on the Taylor Rule that don't always offer a similar story" because the colors range from a shade of red to shades of green. But certain themes do emerge. If, for example, you believe that the neutral real rate of interest is quite low (the Laubach-Williams and Lubik-Mathes estimates in the bottom two rows are −0.22 and −0.06) your belief about the magnitude of the resource gap would be critical to determining whether this particular rule suggests that the policy rate is already too high, has a bit more room to increase, or is just about right. On the other hand, if you are an adherent of the original Taylor Rule and its assumption that a long-run neutral rate of 2 percent (the top row of the chart) is the right way to think about policy, there isn't much ambiguity to the conclusion that the current rate is well below what the rule indicates.
"[D]ifferent rules can sometimes generate very different values for the federal funds rate, both for the present and for the future." Indeed.
July 11, 2017
Another Look at the Wage Growth Tracker's Cyclicality
Though Friday's employment report showed that payroll employment rose by a robust 222,000 jobs in June—much higher than most forecasts—enthusiasm for the news was tempered somewhat by average hourly wages coming in below expectations. Is the (ongoing) relatively tepid pace of wage growth a cause for concern? Perhaps, but the ups and downs of average wages over the course of the business cycle—the pattern of expansion-recession-expansion that typifies modern economies—are a bit more complicated than they may seem.
The year-over-the-year growth in the average wage level that we see in the official employment conditions report is influenced by wages paid to people who were employed either today or a year earlier. That is, the wages of those who remained employed (EE) as well as those who entered employment (NE) and those who exited employment (EN). Because the individuals in these groups may command different wages on average—due to experience, for example—the usual wage growth measures confound the effects of changes in the average wage of people with particular types of year-over-year employment histories. In that sense, the usual wage growth statistic may not exactly be comparing apples to apples.
Research by, for example, Solon, Barsky, and Parker 1992 and Daly and Hobjin 2016 explores the effect of the changing composition of workers over time using microdata on individuals with known employment histories. They show that people who enter and exit employment have a lower average wage than those who stay employed over the year and that the net exit/entry flow increases when the labor market is weak—more people leave employment, and fewer people enter it. As a result, the disproportionate increase in the net flow of workers with a lower-than-average wage serves to boost the overall average wage level during recessions.
One approach to making a more apples-to-apples comparison of average wages over time is to strip out the effect that comes from the change in the share of workers who stay employed and who entered or exited employment. Technically speaking, the composition-adjusted wage growth series is determined by adding the change in average log hourly wage within the EE group and the same change within the EN/NE group, while holding constant the respective average population shares in each group. The chart below illustrates the result of this adjustment.
I should note that the change in the average wage uses data only for people who have a known employment status a year earlier, which results in a wage growth series that is somewhat higher than the change in the average wage of all employed people, some of whom have an unknown employment history.
As the chart shows, relative to the adjusted series (the green line), growth in overall average wages (the orange line) stayed up longer during the last recession, then fell by less, and was slower to adjust to improving labor market conditions (falling unemployment) after the recession ended. The correlation between the overall growth in average wages and the inverse of the unemployment rate is 0.72, and this correlation rises to 0.79 using the adjusted wage growth series.
An alternative approach to making a more apples-to-apples comparison of average wages is to ignore the entry/exit margin and only look at people who are employed both today and a year earlier (EE). The Wage Growth Tracker (computed here as the difference in average log hourly wage) does that for the subset of EE people who have an actual wage record in both periods (no earnings information is collected for self-employed workers in the Current Population Survey). The following chart compares this version of the Wage Growth Tracker with the growth in overall average wages.
The Atlanta Fed's Wage Growth Tracker uses the median change in wages rather than the average change, but it displays very similar dynamics.
As the chart shows, the growth in average wages for those who remain in wage and salary jobs (the red line) is a bit smoother than growth in overall average wages (the orange line) and moves more in sync with the inverse of the unemployment rate (the correlation is 0.85). However, its level is quite a bit higher than growth in overall average wages. This disparity is because the average wage for those entering employment is less than for those exiting, so the change in average wages along the entry/exit margin is always negative.
But enough math—let's put this all together. If you want a measure of wage growth that reflects relative labor market strength, then looking at wage growth after controlling for entry/exit composition effects is probably a good idea. The Wage Growth Tracker seems to do that job reasonably well. However, the Wage Growth Tracker almost certainly overstates the growth in per hour wage costs that employers are facing. Most importantly, it ignores the employment exit/entry margin. Hence, one should avoid interpreting the Wage Growth Tracker as a direct measure of growth in labor costs—a point also discussed in this recent Atlanta Fed podcast episode . The next reading from the Wage Growth Tracker will be available when the Census Bureau releases the Current Population Survey microdata, usually within a couple of weeks of the national employment report. Given that the unemployment rate has remained relatively low recently, I would expect the Wage Growth Tracker to stay at a relatively high level. Check back here then and we'll see what we learn.
February 05, 2016
Introducing the Refined Labor Market Spider Chart
In January 2013, Atlanta Fed research director Dave Altig introduced the Atlanta Fed's labor market spider chart in a macroblog post.
In a follow-up post that June, Atlanta Fed colleague Melinda Pitts and I introduced a dedicated page for the spider chart located at the Center for Human Capital Studies (CHCS) webpage. It shows the distribution of 13 labor market indicators relative to their readings just before the 2007–09 recession (December 2007) and the trough of the labor market following that recession (December 2009). The substantial improvement in the labor market during the past three years is quite evident in the spider chart below.
As of December 2012, none of the indicators had yet reached their prerecession levels, and some had a long way to go. Now, many of these indicators are near their prerecession values—and some have blown by them.
To make the spider chart more relevant in an environment with considerably less labor market slack than three years ago, we are introducing a modified version, which you can see here. Below is an example of a chart I created using the menu-bars on the spider chart's web page:
In this chart, I plot the May 2004 and November 2015 percentile ranks of labor market indicators relative to their distributions since March 1994. As with the previous spider chart, indicators such as the unemployment rate, where larger values indicate more labor market slack, have been multiplied by –1. The innermost and outermost rings represent the minimum and maximum values of the variables from March 1994 to January 2016. The three dashed gray rings in between are the 25th, 50th, and 75th percentiles of the distributions. For example, the November 2015 value of 12-month average hourly earnings growth (2.26 percent) is the 23rd percentile of its distribution. This means that 23 percent of the other monthly observations on hourly earnings growth since March 1994 are lower than it is.
I chose May 2004 and November 2015 because they had the last employment situation reports before "liftoffs" of the federal funds rate. November 2015 appears to be stronger than May 2004 for some indicators (job openings, unemployment rate, and initial claims) and weaker for others (hires rate, work part-time for economic reasons, and the 12-month growth rate of the Employment Cost Index).
The average percentile ranks of the variables for these two months are similar, as the chart below depicts:
Also shown in the chart is the Kansas City Fed's Level of Activity Labor Market Conditions Indicator. It is a sum of 24 not equally weighted labor market indicators, standardized over the period from 1992 to the present. In spite of its methodological and source-data differences with the average percentile rank measure plotted above, it tracks quite closely, especially since 2004. However, as shown in the spider chart that I referred to above, there is quite a bit of variation within the indicators that may provide additional information to our analysis of the average trends.
We made a number of other changes to the spider chart to ensure it reflects current labor market issues. These changes are documented in the FAQs and "Indicators" sections of the new spider chart page. Of particular note, users can choose not only the years for which they wish to track information, but also the period of reference that provides the basis of the spider chart. The payroll employment variable is now the three-month average change rather than a level. Temporary help services employment has been dropped, and two measures of 12-month compensation growth and the employment-population ratio (EPOP) for "prime-age workers" (25 to 54 years) have been added.
Some care should be taken when comparing recent labor market data values with those 10 or more years ago as structural changes in the labor market might imply that a "normal" value today is different than a "normal" value in, say, 2004. The variable choices for the refined spider chart were made to mitigate this problem to some extent. For example, we use the prime-age EPOP as a crude adjustment for population aging, putting downward pressure on the labor force participation rate and EPOP over the past 10 years (roughly 2 percentage points). This doesn't entirely resolve the comparability issue since, within the prime-age population, the self-reporting rate of illness or disability as a reason for not wanting a job has increased about 1.5 percentage points since 1998 (see the macroblog posts here and here and the CHCS Labor Force Participation Dynamics webpage). If this increase in disability reporting is partly structural—and a Brookings study by Fed economist Stephanie Aaronson and others concludes it is—some of the decline in the prime-age EPOP since the late 1990s may not be a result of a weaker labor market per se.
Other variables in the spider chart may have had structural changes as well. For example, a study by San Francisco Fed economists Rob Valleta and Catherine van der List concludes that structural factors explain just under half of the rise in the share of workers employed part-time for economic reasons over the 2006 to 2013 period.
To partially account for structural changes in trends, we allow the user to select one of 11 time periods over which the distributions are calculated. The default period is March 1994 to present, which is what was used in the example above, but users can choose a window as short as five years where, presumably, structural changes are less important. A trade-off with using a short window is that a "normal" value may not produce a result close to the median. For example, the median unemployment rate is 5.6 percent since March 1994 and 7.3 percent since February 2011. The latter value is much farther away from the most recent estimates of the natural rate of unemployment from the Congressional Budget Office and the Survey of Professional Forecasters (both 5.0 percent).
In our June 2013 macroblog post introducing the spider chart, we wrote that we would reevaluate our tools and determine a more appropriate way to monitor the labor market when "the labor market has turned a corner into expansion." The new spider chart is our response to the stronger labor market. We hope users find the tool useful.
November 24, 2014
And the Winner Is...Full-Time Jobs!
Each month, the U.S. Census Bureau for the U.S. Bureau of Labor Statistics (BLS) surveys about 60,000 households and asks people 15 years and older whether they are employed and, if so, if they are working full-time or part-time. The BLS defines full-time employment as working at least 35 hours per week. This survey, referred to as both the Current Population Survey and the Household Survey, is what produces the monthly unemployment rate, labor force participation rate, and other statistics related to activities and characteristics of the U.S. population.
For many months after the official end of the Great Recession in June 2009, the Household Survey produced less-than-happy news about the labor market. The unemployment rate didn't start to decline until October 2009, and nonfarm payroll job growth didn't emerge confidently from negative territory until October 2010. Now that the unemployment rate has fallen to 5.8 percent—much faster than most would have expected even a year ago—the attention has turned to the quality, rather than quantity, of jobs. This scrutiny is driven by a stubbornly high rate of people employed part-time "for economic reasons" (PTER). These are folks who are working part-time but would like a full-time job. Several of my colleagues here at the Atlanta Fed have looked at this phenomenon from many angles (here, here, here, here, and here).
The elevated share of PTER has left some to conclude that, yes, the economy is creating a significant number of jobs (an average of more than 228,000 nonfarm payroll jobs each month in 2014), but these are low-quality, part-time jobs. Several headlines have popped up over the past year or so claiming that "...most new jobs have been part-time since Obamacare became law," "Most 2013 job growth is in part-time work," "75 Percent Of Jobs Created This Year  Were Part-Time," "Part-time jobs account for 97% of 2013 job growth," and as recently as July of this year, "...Jobs Report Is Great for Part-time Workers, Not So Much for Full-Time."
However, a more careful look at the postrecession data illustrates that since October 2010, with the exception of four months (November 2010 and May–July 2011), the growth in the number of people employed full-time has dominated growth in the number of people employed part-time. Of the additional 8.2 million people employed since October 2010, 7.8 million (95 percent) are employed full-time (see the charts).
The pair of charts illustrates the contribution of the growth in part-time and full-time jobs to the year-over-year change in total employment between January 2000 and October 2014. By zooming in, we can see the same thing from October 2010 (when payroll job growth entered consistently positive territory) to October 2014. Job growth from one month to the next, even using seasonally adjusted data, is very volatile.
To get a better idea of the underlying stable trends in the data, it is useful to compare outcomes in the same month from one year to the next, which is the comparison that the charts make. The black line depicts the change in the number of people employed each month compared to the number employed in the same month the previous year. The green bars show the change in the number of full-time employed, and the purple bars show the change in the number of part-time employed.
During the Great Recession (until about October 2010), the growth in part-time employment clearly exceeded growth in full-time employment, which was deep in negative territory. The current high level of PTER employment is likely to reflect this extended period of time in which growth in part-time employment exceeded that of full-time employment. But in every month since August 2011, the increase in the number of full-time employed from the year before has far exceeded the increase in the number of part-time employed. This phenomenon includes all of the months of 2013, in spite of what some of the headlines above would have you believe.
So, in the post-Great Recession era, the growth in full-employment is, without a doubt, way out ahead.
Author's note: The data used in this post, which are the same data used to generate the headlines linked above, reflect either full-time or part-time employment (total hours of work at least or less than 35 per week, respectively). They do not necessarily reflect employment in a single job.
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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.
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.
By Dave Altig, executive vice president and research director, and
Pat Higgins, a senior economist, both in the Atlanta Fed's research department
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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:
- The percent of the workforce that is part time varies greatly across industries (compare for example, durable goods manufacturing with restaurants).
- 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).
- All industries had a greater share of PTER workers in 2013 than in 2007 (all the lollipops point upwards).
- Most industries have a lower share of PTNER workers than in the past (most of the lollipops lean to the left).
- 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.
By John Robertson, a vice president and senior economist, and
Ellyn Terry, a senior economic analyst, both of the Atlanta Fed's research department
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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.
By Pat Higgins, a senior economist in the Atlanta Fed's research department
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- Thoughts on a Long-Run Monetary Policy Framework, Part 4: Flexible Price-Level Targeting in the Big Picture
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- Thoughts on a Long-Run Monetary Policy Framework, Part 2: The Principle of Bounded Nominal Uncertainty
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- What Are Businesses Saying about Tax Reform Now?
- A First Look at Employment
- Weighting the Wage Growth Tracker
- GDPNow's Forecast: Why Did It Spike Recently?
- How Low Is the Unemployment Rate, Really?
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