The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.
- BLS Handbook of Methods
- Bureau of Economic Analysis
- Bureau of Labor Statistics
- Congressional Budget Office
- Economic Data - FRED® II, St. Louis Fed
- Office of Management and Budget
- Statistics: Releases and Historical Data, Board of Governors
- U.S. Census Bureau Economic Programs
- White House Economic Statistics Briefing Room
October 05, 2015
Labor Report Silver Lining? ZPOP Ratio Continued to Rise in September
We have received several requests for an update of our ZPOP ratio statistic to incorporate September's data. We have also been asked whether the ZPOP ratio can be constructed from labor force data from the U.S. Bureau of Labor Statistics (BLS).
The ZPOP ratio is an estimate of the share of the civilian population aged 16 years and over whose labor market status is what they say they currently want (assuming that people who work full-time want to do so). A rising ZPOP ratio is consistent with a strengthening labor market. We constructed the ZPOP ratio from the microdata in the BLS's Current Population Survey, but we can also construct a very close approximation from the BLS's Labor Force Statistics data. Here's how (using data that are not seasonally adjusted):
- Take total employment, and add those not in the labor force who do not currently want a job. Then subtract those who were at work from one to 34 hours for economic reasons. The ZPOP ratio is this figure as a percentage of the civilian population 16 years and over.
The following chart shows the history of the resulting ZPOP ratio over 20 years, seasonally adjusted.
Unlike the headline U-3 unemployment rate, which remained unchanged from August to September, the seasonally adjusted ZPOP ratio improved slightly (from 92.0 to 92.1 percent). Relative to an estimated 230,000 increase in the population over the month, the improvement in the ZPOP ratio was the result of an increase in the number of people who said they do not currently want a job and a decline in involuntary part-time employment in excess of the decline in total employment.
Finally, the chart below shows the performance of the seasonally adjusted ZPOP ratio relative to the comparable employment-to-population (EPOP ratio) and the EPOP ratio for those aged 25–54. The relatively greater recovery in the ZPOP ratio since 2009 is primarily because the EPOP ratios do not adjust for the share of the population who say they do not currently want a job.
September 22, 2015
The ZPOP Ratio: A Simple Take on a Complicated Labor Market
In her press conference following the latest FOMC meeting, Federal Open Market Committee (FOMC) Chair Janet Yellen emphasized that she still sees cyclical weakness in the labor market, even as the headline unemployment rate has moved close to FOMC participants' median estimate of its longer-run normal level.
She also noted that FOMC participants look at many different indicators of labor utilization, because the headline unemployment rate (commonly known as the U-3 rate) is overstating the health of the labor market. One alternative measure that has received some attention is the employment-to-population (EPOP) ratio. However, a well-recognized problem with the EPOP ratio is that because it defines utilization as employment, trends in demographic and behavioral labor force participation can affect it.
This problem is partially addressed by looking at the EPOP ratio for the prime-age population, or by making adjustments for demographic changes as suggested by Kapon and Tracy at the New York Fed and further analyzed by our Atlanta Fed colleague Pat Higgins. Here, we propose an alternative approach that uses a broader definition of utilization that makes it less affected by labor supply trends.
The Current Population Survey does not ask the question "are your labor services being fully utilized?" Therefore, we have to use our judgment to classify someone as fully utilized. The figure below shows the choices we make. We assume that everyone who says they are working fewer hours than they want is underutilized (the red boxes). This includes those in the labor force but unemployed, those not in the labor force but wanting a job, and those working part-time but wanting full-time hours (similar to the treatment of underutilization in the broad U-6 unemployment rate measure).
Everyone working full-time, working part-time for a noneconomic reason, and those who say they don't want a job are considered fully utilized (the green boxes). Of course, this takes the "don't want a job" classification at face value. For example, someone who is retired is counted as fully utilized, irrespective of the (unknown) reason they chose to retire.
As shown in the Chart 1 below, the share of the population 16 years or older that is fully utilized—what we call the utilization-to-population (ZPOP) ratio—is currently about 1.5 percentage points below its prerecession level, after having fallen by 6 percentage points during the recession.
Notice that because the ZPOP ratio treats those who are not employed and don't want a job as fully utilized, it is less affected by demographic and behavioral trends in labor force participation than the EPOP ratio. (You can learn more on our website about how demographic and behavioral trends are affecting labor force participation.) When compared with the EPOP ratio, the ZPOP ratio paints a somewhat rosier picture of labor market conditions (see chart 2).
In sum, the utilization-to-population (ZPOP) ratio is the share of the working-age population that is working full time, is voluntarily working part-time, or doesn't want to work any hours. According to this measure, about 91 percent of the working-age population is considered fully utilized. The remaining 9 percent are "underutilized" and are a roughly even mixture of the unemployed, those not in the labor force but wanting to work, and those working part-time but wanting full-time hours.
The headline U-3 unemployment rate is very close to its prerecession level but is thought to overstate the health of the labor market. At the same time, we think that the EPOP ratio overstates the amount of remaining labor market slack. The ZPOP ratio is in the middle; approaching its prerecession level but still with some way to go.
September 01, 2015
Should I Stay or Should I Go Now?
A recent article by Jason Faberman and Alejandro Justiniano at the Chicago Fed shows that there is a strong relationship between quit rates—as a proxy for the pace of job switching—and wage growth. Movements in the quit rate and wage growth are both procyclical. A tighter (weaker) labor market implies workers are more (less) likely to find better employment matches, and employers are more (less) willing to offer higher wages to attract new workers and retain existing workers.
To get some idea of the different wage outcomes of job switching versus job staying, we can use microdata underlying the Atlanta Fed's Wage Growth Tracker from the Current Population Survey. The following chart plots the quarterly private-sector quit rate (orange line) from the Job Openings and Labor Turnover Survey using Davis, Faberman, and Haltiwanger (published in 2012 in the Journal of Monetary Economics) estimates before 2001. Also shown is the median year-over-year wage growth of private-sector wage and salary earners who switched jobs (blue line) or stayed in the same job (green line). Job stayers are approximated by the restriction that they are in the same broad industry and occupation as 12 months earlier and have been with the same employer for each of the last four months. Job switchers do not satisfy these restrictions but were employed in the current month and 12 months earlier.
The correlation between the quit rate and median wage growth is strongly positive and is slightly higher for job switchers (0.91) than for job stayers (0.88). In most periods, the median wage growth of job switchers is higher than for job stayers. This difference is consistent with the notion that job switching tends to involve moving to a better-paying job. However, during periods when the quit rate is slowing, median wage growth slows for both job stayers and switchers (reflecting the correlation between quits and wages), and the wage-growth premium from job switching tends to vanish.
Since the end of the last recession, the quit rate has been rising and a wage-growth premium for job switching has emerged again. Interestingly, during the last year, the wage growth of job stayers appears to have strengthened as well, consistent with a general tightening of the labor market.
August 21, 2015
No Wage Change?
Even when prevailing market wages are lower, businesses can find it difficult to reduce wages for their current employees. This phenomenon, often referred to as "downward nominal wage rigidity," can result in rising average wages for incumbent workers despite high unemployment levels. Some economic models predict that a period of subdued wage growth can follow, even as the labor market recovers—a kind of delayed wage-adjustment effect.
In her 2014 Jackson Hole speech, Fed Chair Janet Yellen suggested this effect may explain sluggish growth in average wages in recent years, despite significant declines in the rate of unemployment.
This macroblog post looks at evidence of wage rigidity, particularly a spike in the frequency of zero wage changes relative to wage declines. A comparison is made between hourly and weekly wages and between incumbent workers (job stayers) and those who have changed employers (job switchers).
Chart 1 shows the fractions of job stayers reporting the same or a lower hourly or weekly wage than 12 months earlier. These measures are constructed from the Current Population Survey microdata in the Atlanta Fed's Wage Growth Tracker. They include workers who are paid hourly (accounting for about 60 percent of all wage and salary earners). The measures exclude those who usually receive overtime and other supplemental pay and those with imputed or top-coded (redacted) wages. Weekly wage is defined as the hourly wage times the usual number of hours per week worked at that rate. The data are aggregated to an annual frequency (except for 2015, where the first six months of the year are covered).
Job stayers cannot be exactly identified in the data and are approximated by those who are in the same occupation and industry as they were 12 months earlier and the same job as they were in the prior month. Consistent with other studies (see, for example, the work of our colleagues at the San Francisco Fed), we find that the incidence of unchanged hourly wages among job stayers is substantial (although some of this is probably the result of rounding errors in self-reported wages). The measured share of unchanged hourly wages rose disproportionately between 2008 and 2010, and it has remained elevated since. Zero hourly wage changes (the green line in chart 1) have become almost as common as declines in hourly wages (the blue line in chart 1).
Chart 1 also suggests that weekly wages for job stayers show a pattern over time broadly similar to hourly wages. But the fraction of unchanged weekly wages (the purple line in chart 1) is lower. Each year, about 60 percent of those with no change in their hourly wage had no change in their weekly wage (or hours) either. Also, there are relatively more declines in weekly wages (the orange line in chart 1) than in hourly wages—mostly the result of reduced hours worked. On average, a reduction in weekly wages is associated with a four-hour decline in hours worked per week. About 90 percent of those with lower hourly wages also had lower weekly wages, and 20 percent of those with no change in their hourly wage had a lower weekly wage (working fewer hours).
If job stayers show a relatively high incidence of no wage change, we might expect a different story for job switchers, since they are establishing a new wage contract with a new employer. Chart 2 shows the fraction of job switchers reporting the same or a lower hourly or weekly wage than 12 months earlier. Job switchers are approximated by workers who are in a different industry than a year earlier.
Not surprisingly, a smaller share of workers experience no change in their hourly or weekly wage when switching jobs. But the pattern of zero wage change for job switchers over time is generally similar to that of job stayers. It is also true that a decline in hourly and weekly wages is more likely for job switchers than for job stayers, with a significant temporary spike in the relative frequency of wage declines for job switchers during the last recession.
Taken at face value, this analysis suggests the presence of some amount of wage rigidity. Also, rigidity increases during recessions and has remained quite elevated since the end of the last recession—especially for job stayers. The question then becomes whether this phenomenon has important macroeconomic consequences. A prediction of most models in which wage stickiness has allocative effects is that it causes firms to increase layoffs when faced with a decline in aggregate demand. Interestingly, during the last recession—when wage stickiness appears to have increased substantially—the rate of layoffs was not unusually high relative to earlier recessions. What was atypical was the size of the decline in the rate of job creation, and this decline contributed to unusually long unemployment spells. As noted by Elsby, Shin, and Solon (2014), it is not clear that an increase in wage rigidity would constrain the hiring of new workers more than it constrains the retention of existing workers.
On the other hand, persistently high wage rigidity in the wake of the Great Recession is consistent with the relatively sluggish pace of wage increases seen in most measures of aggregate wage growth via the "bending" of the short-run Phillips curve (as described by Daly and Hobijn (2014)). Interestingly, the Atlanta Fed's Wage Growth Tracker is an exception. It has indicated somewhat stronger wage growth during the last year than other measures. It will be interesting to see if that trend continues in coming months.
July 15, 2015
Have Changing Job and Worker Characteristics Restrained Wage Growth?
In the wake of the Great Recession, nominal wage growth has been subdued. But it is unclear how much of this relatively low wage growth reflects protracted weakness in the labor market versus other factors, such as changes in the composition of the workforce and jobs over time. Wage growth tends to vary across personal and job characteristics, so it stands to reason that changes in the composition of the workforce, alongside demographic and work characteristics, could be an important explanation of overall movements in wage growth.
In this post, we explore the impact of the changing mixture of worker characteristics (by age and education) and types of jobs (by industry and occupation) on the Atlanta's Fed Wage Growth Tracker. We find that composition effects do not account for the low median wage growth experienced in recent years. Holding worker and job characteristics fixed at their 1997 shares raises the median wage growth in 2014 by only about 0.2 percentage point. Our results are consistent with the analysis in a previous macroblog post, which found that changing industry-employment shares could not explain much of the sluggish growth in the average hourly earnings data from the payroll survey.
Median wage growth, composition change by worker characteristics
In terms of demographics, we consider two features: a worker's age and education. As shown in this earlier macroblog post, younger workers tend to experience higher median wage growth than do older workers. Although older workers tend to be paid more based on experience, they are also more likely to be near the top of the wage distribution for their job, so the median older worker experiences less wage growth. The difference is quite large. In 2014, the median wage growth of workers over age 54 was around 1.2 percentage points lower than the overall median.
A person's education can also affect his or her wage growth. Workers with a high school diploma or less tend to have lower median wage growth. In 2014, the median wage growth of less-educated workers was about 0.1 percentage point lower than the overall median, reflecting that these workers are more likely to be earning minimum wage, which does not change very frequently.
In addition, the employment shares by age and education have changed over time. The proportion of workers in the Atlanta Fed's Wage Growth Tracker data who are over age 54 has more than doubled from 12 percent in 1997 to 25 percent in 2014. During the same period, the share of workers without a college degree has declined from 63 percent to 49 percent (see the charts).
Wage growth, composition change by job characteristics
In terms of job characteristics, we consider two features: the worker's industry (where they work) and their occupation (what they do). Before 2011, workers in service-producing industries experienced slightly higher (about 0.1 percentage point) median wage growth than all workers. But since then, the trends have flipped. In recent years, median wage growth of individuals working in service-producing industries has been slightly below the median wage growth of all workers.
Nonetheless, workers in professional occupations such as managerial, legal, scientific, and engineering jobs tend to experience relatively higher median wage growth. In 2014, the median wage growth of workers in these professional jobs was 0.2 percentage point higher than the median wage growth for all workers.
The share of workers in service-producing industries and in professional jobs has increased moderately over time. In 1997, 77 percent of workers in the data were employed in service-producing industries. In 2014, the share had increased to 82 percent. During the same period, the share of workers in professional occupations rose from 36 percent to 41 percent.
Composition effects on median wage growth
Individually, an aging workforce is putting downward pressure on wage growth, whereas rising education levels are adding upward pressure. The rising share of workers in professional occupations is also pushing wages up somewhat, although the impact of the rising share of workers in service-producing industries is ambiguous. But how large are these effects when combined?
To get an idea, we conducted two counterfactual experiments. First, we held fixed the age and education distributions at their 1997 levels (the first year in our Wage Growth Tracker data). Second, we held fixed the age, education, industry, and occupation characteristics at their 1997 levels. We used three age groups (16–24, 25–54, and 55-plus years of age), two education groups (college degree and no college degree), two industry groups (service- or goods-producing industries), and two occupation groups (professional and other occupations).
The blue line in the next chart is the median wage growth over time with no adjustments for changes in composition. For example, for 2014, the chart shows median wage growth of workers in the data set with earnings in January 2014 and January 2013, February 2014 and February 2013, etc. This depiction is the Atlanta Fed Wage Growth Tracker, but at an annual frequency. The other two lines show the results of the experiment: demographically adjusted (green) and both demographically and job adjusted (orange).
These experiments suggest that—for our data set, at least—the impact on the median of the wage growth distribution from shifts in the composition of the workforce and jobs over time has increased in recent years, but the impact is not especially large. For example, the unadjusted median wage growth for 2014 is 2.5 percent. Holding fixed all four characteristics at their 1997 levels would have raised this by only 0.2 percentage point. Shifting worker and job characteristics are not a primary explanation of low median wage growth since 2009.
June 19, 2015
Will the Elevated Share of Part-Time Workers Last?
There seems to be mounting evidence that at least part of the elevated share of part-time employment in the economy is here to stay. We have some insights to offer based on a recent survey of our business contacts.
Why are we interested? A higher part-time share of employment isn't necessarily a bad thing, if people are doing so voluntarily. Unfortunately, the elevated share is concentrated among people who would prefer to be working full-time. Using the average rate of decline over the past five years, the part-time for economic reasons (PTER) share of employment is projected to reach its prerecession average in about 10 years.
This is significantly slower than the decline in the unemployment rate, whose trajectory suggests a much sooner arrival—in around a year. The deviation raises an important policy question for measuring the amount of slack there is beyond what the unemployment rate suggests, and ultimately the extent to which policy can effectively reduce it.
What are the drivers? Data versus anecdotes
Researchers (here, here, and here) have pointed to factors such as industry shifts in the economy, changing workforce demographics, rising health care costs, and the Affordable Care Act as potentially important drivers of this shift. But we can glean only so much information from data. When a gap develops, we generally turn to our business contacts who are participating members in our Regional Economic Information Network (REIN) to fill in the missing information.
According to our contacts, the relative cost of full-time employees remains the most important reason for having a higher share of part-time employees than before the recession, which is the same response we received in last summer's survey on the same topic. Lack of strong enough sales growth to justify conversion of part-time to full-time workers came in as a close second.
The importance rating for each of the factors was notably similar to last year's survey, with one exception. Technology was rated as somewhat important, reflecting an uptick from the average response we received last year. We've certainly heard anecdotally that scheduling software has enabled firms to better manage their part-time staff, and it seems that this factor has gained in importance over the past year.
The chart below summarizes the reasons our business contacts gave in the July 2014 and the May 2015 surveys. The question was asked only of those who currently have a higher share of part-time workers than they did before the recession. The chart shows the results for all respondents, whether they responded to one or both surveys. When we limited our analysis to only those who responded to both surveys, the results were the same.
Will the elevated share persist?
The results suggest that a return to prerecession levels is unlikely to occur in the near term.
The chart below shows employers' predictions for part-time employment at their firms, relative to before the recession. About 27 percent of respondents believe that in two years, their firms will be more reliant on part-time work compared to before the recession. About 7 percent do not currently have an elevated share of part-time employees but believe they will in two years. About two-thirds believe their share of part-time will be roughly the same as before, while only 8 percent believe they will have less reliance on part-time workers compared to before the recession.
The majority of our contacts believe their share of part-time employment will normalize over the next two years, but some believe it will stay elevated. Still, 2017 does not mean the shift will be permanent. In fact, firms cited a balance of cyclical and structural factors for the higher reliance on part-time. Low sales growth and an ample supply of workers willing to take part-time jobs could both be viewed as cyclical factors that will dissipate as the economy further improves.
Meanwhile, higher compensation costs of full-time relative to part-time employees and the role of technology that enables companies to more easily manage their workforce can be considered structural factors influencing the behavior of firms. Firms that currently have a higher share of part-time employees gave about equal weight to these forces, suggesting that, as other research has found, both cyclical and structural factors are important explanations for the slow decline in the part-time share of employment.
June 08, 2015
Falling Job Tenure: It's Not Just about Millennials
The image of a worker in the 1950s is one of a man (for the most part) who plans on spending his entire career with one employer. We hear today, however, that "...long gone is the lifelong loyalty to a corporation with steadfast servitude for years on end." One report tells us that "people entering the workforce within the past few years may have more than 10 different jobs before they retire." The reason? "Millennials don't like commitments." Well, the explanation is probably not that simple, but even simply measuring trends in job tenure is also not all that straightforward.
Despite a strong impression that entire careers spent with one employer are a thing of the past, some have declared the image of job-hopping millennials a myth. (You can read some discussions at About.com, CNBC, and Marketwatch, for example.) These reports are all based on a September 2014 news release from the U.S. Bureau of Labor Statistics (BLS) stating that among every employee age group (even the youngest), median job tenure has not declined from when it was reported 10 years earlier. (Median job tenure is basically the "middle" amount of job tenure. If all workers are lined up from lowest tenure to highest tenure, the median tenure would be the amount of time the person in the middle of that line has been with his/her employer.)
Chart 1 illustrates the biennial data on job tenure reported by the BLS and interpreted by the reports mentioned above as indication that job tenure is not falling. Each line represents an age range, from 20- to 30-year-olds at the bottom (the lowest median tenure among all age groups) to 61- to 70-year-olds on the top (the age group with the highest median tenure). It sure doesn't look as though workers at each age group are staying with their jobs for shorter periods.
However, the problem with simply comparing median tenure across time by age group is that different ages at different time periods face different labor market institutions, incentives, and expectations. There are generational, or cohort, differences in what the labor market looks like and has to offer a 25-year-old born in 1923 and a 25-year-old born in 1993. In other words, each generation is represented across the age groups at different points in time.
The different colored points across age groups in chart 1 indicate the range of years the people in that particular year, in that age group, were born (and to what named generation they belong). The labor market facing a 31-to 40-year-old baby boomer in 1996 looks quite different from the labor market facing a 31-to-40-year-old Gen Xer in 2012, and the social, economic, and behavioral differences are even more dramatic the farther apart the generations become.
For example, one of the most dramatic changes facing workers has been the transformation from defined-benefit to defined-contribution retirement plans. The number of years a worker spends with an employer is no longer an investment in the employee's retirement. (William Even and David Macpherson (1996) illustrated the important link between the presence of an employer-sponsored retirement plan and worker tenure in their paper "Employer Size and Labor Turnover: The Role of Pensions.")
Additionally, the share of those 25 and over with a college degree in the United States has increased from 5 percent in 1950 to 32 percent in 2014, according to data from the U.S. Census Bureau. A more educated workforce is one with more general, or transferable, human capital, reducing the need to stay with just one employer to reap a return on one's investment in human capital. The transition of the U.S. economy from a basis in manufacturing to one based in services, supported by technology, also means employers require more general, rather than specific, human capital.
Firms have also changed the way they invest in workers, offering less on-the-job training than they used to, weakening their ties to the worker. And on top of all of this, because of near-instantaneous access to information, movies, and music brought by the digital age, younger cohorts are purported to have shorter attention spans than older cohorts (as reported here). All these factors shape the environment in which workers and employers view the value of longevity in their relationship.
To get a more accurate picture of the lifetime pattern of median job tenure and how it has changed across generations, we use the same BLS data used to produce the chart above to group workers into cohorts, or people who have similar experiences by virtue of when they were born. In other words, we rearrange the data used in chart 1 to line people up by birth year rather than by calendar year in order to illustrate (in chart 2) that median job tenure is indeed declining through the generations.
What we see in this chart—using the 20- to 30-year-olds, for example—is that the median job tenure was four years among those born in 1953 (baby boomers) when they were between 20 and 30 years old. For 20- to 30-year-olds born in 1993 (millennials), however, median job tenure is only one year. Similar—and some even more dramatic—declines occur across cohorts within each age group.
Declining job tenure is not just all about millennials having short attention spans. In fact, there is a greater (five-year) decline in median job tenure between 41- and 50-year-old "Depression babies" (born in 1933) and 41- to 50-year-old Gen Xers (born in 1973). So, just as our colleagues here at the Atlanta Fed discovered with regard to declines in first-time home mortgages, millennials aren't to blame for everything!
So what does declining job tenure mean for the U.S. labor market? From the perspective of the worker, portable retirement savings and, now, portable health insurance mean that workers confront a world of possibilities that our parents and grandparents never dreamt of. Yes, perhaps the days of predictability in one's career is a thing of the past. But so is the "eggs-in-one-basket" loss of retirement savings when your employer goes out of business as well as potentially slower career progression within a single firm.
From the economy's perspective, the flexibility of workers seeking their highest rents and the flexibility of firms to seek better matches for their needed skills mean greater productivity—not to mention growth—all around.
June 05, 2015
Atlanta Fed's Wage Growth Measure Increased Again in April
A measure of 12-month wage growth constructed here at the Atlanta Fed increased by 3.3 percent in April. This rate is up from 3.1 percent in March and at its highest level since March 2009 (see the chart).
As mentioned in an earlier macroblog post, this measure behaves broadly like the wage and salary component of the Employment Cost index (ECI). The ECI data pertain to the last month in the quarter and are published with about a four-week lag. In contrast, the Atlanta Fed measure uses individuals' hourly wage data, 12 months apart, from the Current Population Survey (CPS). The data come from publicly available CPS microdata produced by the U.S. Bureau of Labor Statistics (BLS) and are typically released two or three weeks after the monthly BLS labor report.
Timeliness is one thing, but is it useful? It turns out there is a relatively strong correlation between this wage growth measure and the employment rate (100 minus the unemployment rate) lagged by 12 months (see the chart).
At least in terms of this measure of wage growth, it seems that improvement in labor utilization is translating into rising wage growth. This development is something our boss, Atlanta Fed President Dennis Lockhart, has been looking for. We expect to be able to update this wage growth measure with the May CPS data in a few weeks.
May 01, 2015
Signs of Strengthening Wage Growth?
The average hourly earnings measure for the private sector, reported in the U.S. Bureau of Labor Statistics's Establishment Survey, increased by a meager 2.1 percent in the first quarter (year over year). This increase was barely above the 2.0 percent pace observed in the fourth quarter of last year. However, Thursday's Employment Cost Index report showed a more sizable uptick in the wage and salary growth picture. Year-over-year growth in the first quarter was 2.5 percent, up from 2.1 percent in the fourth quarter of 2014. Another wage measure that we discussed in a February macroblog post also moved notably higher in the first quarter. That measure, which is derived from earnings data in the Current Population Survey, increased from 2.8 percent in the fourth quarter of 2014 to 3.2 percent in the first quarter of this year (see the chart).
This Wall Street Journal article (subscription required) also notes that anecdotal signs suggest a turnaround in wage growth, especially among lower-wage occupations. Overall, we take the evidence to suggest some emerging momentum in wage growth. Rising wage growth is an encouraging sign and is consistent with a tightening labor market.
April 02, 2015
What Seems to Be Holding Back Labor Productivity Growth, and Why It Matters
The Atlanta Fed recently released its online Annual Report. In his video introduction to the report, President Dennis Lockhart explained that the economic growth we have experienced in recent years has been driven much more by growth in hours worked (primarily due to employment growth) than by growth in the output produced per hour worked (so-called average labor productivity). For example, over the past three years, business sector output growth averaged close to 3 percent a year. Labor productivity growth accounted for only about 0.75 percentage point of these output gains. The rest was due primarily to growth in employment.
The recent performance of labor productivity stands in stark contrast to historical experience. Business sector labor productivity growth averaged 1.4 percent over the past 10 years. This is well below the labor productivity gains of 3 percent a year experienced during the information technology productivity boom from the mid-1990s through the mid-2000s.
John Fernald and collaborators at the San Francisco Fed have decomposed labor productivity growth into some economically relevant components. The decomposition can be used to provide some insight into why labor productivity growth has been so low recently. The four factors in the decomposition are:
- Changes in the composition of the workforce (labor quality), weighted by labor's share of income
- Changes in the amount and type of capital per hour that workers have to use (capital deepening), weighted by capital's share of income
- Changes in the cyclical intensity of utilization of labor and capital resources (utilization)
- Everything else—all the drivers of labor productivity growth that are not embodied in the other factors. This component is often called total factor productivity.
The chart below displays the decomposition of labor productivity for various time periods. The bar at the far right is for the last three years (the next bar is for the past 10 years). The colored segments in each bar sum to average annual labor productivity growth for each time period.
Taken at face value, the chart suggests that a primary reason for the sluggish average labor productivity growth we have seen over the past three years is that capital spending growth has not kept up with growth in hours worked—a reduction in capital deepening. Declining capital deepening is highly unusual.
Do we think this sluggishness will persist? No. In our medium-term outlook, we at the Atlanta Fed expect that factors that have held down labor productivity growth (particularly relatively weak capital spending) will dissipate as confidence in the economy improves further and firms increase the pace of investment spending, including on various types of equipment and intellectual capital. We currently anticipate that the trend in business sector labor productivity growth will improve to a level of about 2 percent a year, midway between the current pace and the pace experienced during the 1995–2004 period of strong productivity gains. That is, we are not productivity pessimists. Time will tell, of course.
Clearly, this optimistic labor productivity outlook is not without risk. For one thing, we have been somewhat surprised that labor productivity has remained so low for so long during the economic recovery. Moreover, the first quarter data don't suggest that a turning point has occurred. Gross domestic product (GDP) in the first quarter is likely to come in on the weak side (the latest GDPNow tracking estimate here is currently signaling essentially no GDP growth in the first quarter), whereas employment growth is likely to be quite robust (for example, the ADP employment report suggested solid employment gains). As a result, we anticipate another weak reading for labor productivity in the first quarter. We are not taking this as refutation of our medium-term outlook.
Continued weakness in labor productivity would raise many important questions about the outlook for both economic growth and wage and price inflation. For example, our forecast of stronger productivity gains also implies a similarly sized pickup in hourly wage growth. To see this, note that unit labor cost (the wage bill per unit of output) is thought to be an important factor in business pricing decisions. The following chart shows a decomposition of average growth in business sector unit labor costs into the part due to nominal hourly wage growth and the part offset by labor productivity growth:
The 1975–84 period experienced high unit labor costs because labor productivity growth didn't keep up with wage growth. In contrast, the relatively low and stable average unit labor cost growth we have experienced since the 1980s has been due to wage growth largely offset by gains in labor productivity. Our forecast of stronger labor productivity growth implies faster wage growth as well. That said, a rise in wage growth absent a pickup in labor productivity growth poses an upside risk to our inflation outlook.
Of course, the data on productivity and its components are estimates. It is possible that the data are not accurately reflecting reality in real time. For example, colleagues at the Board of Governors suggest that measurement issues associated with the price of high-tech equipment may be causing business investment to be somewhat understated. That is, capital deepening may not be as weak as the current data indicate. In a follow-up blog to this one, my Atlanta Fed colleague Patrick Higgins will explore the possibility that the weak labor productivity we have recently experienced is likely to be revised away with subsequent revisions to GDP and hours data.
TrackBack URL for this entry:
Listed below are links to blogs that reference What Seems to Be Holding Back Labor Productivity Growth, and Why It Matters:
- Can Two Wrongs Make a Right?
- Are People in Middle-Wage Jobs Getting Bigger Raises?
- GDPNow and Then
- What's behind the Recent Uptick in Labor Force Participation?
- Is the Number of Stay-at-Home Dads Going Up or Down?
- Labor Force Participation: Aging Is Only Half of the Story
- Putting the MetLife Decision into an Economic Context
- The Rise of Shadow Banking in China
- Which Wage Growth Measure Best Indicates Slack in the Labor Market?
- Collateral Requirements and Nonbank Online Lenders: Evidence from the 2015 Small Business Credit Survey
- May 2016
- April 2016
- March 2016
- February 2016
- January 2016
- November 2015
- October 2015
- September 2015
- August 2015
- July 2015
- Business Cycles
- Business Inflation Expectations
- Capital and Investment
- Capital Markets
- Data Releases
- Economic conditions
- Economic Growth and Development
- Exchange Rates and the Dollar
- Fed Funds Futures
- Federal Debt and Deficits
- Federal Reserve and Monetary Policy
- Financial System
- Fiscal Policy
- Health Care
- Inflation Expectations
- Interest Rates
- Labor Markets
- Latin America/South America
- Monetary Policy
- Money Markets
- Real Estate
- Saving, Capital, and Investment
- Small Business
- Social Security
- This, That, and the Other
- Trade Deficit
- Wage Growth