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April 17, 2015
Déjà Vu All Over Again
In a recent interview, Fed Vice Chairman Stanley Fischer said, “The first quarter was poor. That seems to be a new seasonal pattern. It's been that way for about four of the last five years.”
The picture below illustrates the vice chair's sentiment. Output in the first quarter has grown at a paltry 0.6 percent during the past five years, compared to a 2.9 percent average during the remaining three quarters of the year.
What's causing this pattern? Well, it could be we just get really unlucky at the same time every year. Or, it could be a more technical problem with seasonal adjustment after the Great Recession (this paper by Jonathan Wright covers the topic using payroll data). It also seems likely that we can just blame the weather (see this Wall Street Journal blog post).
Whatever the reason for the first-quarter weakness, it appears to be happening again. Our current quarterly tracking estimate—GDPNow—has first-quarter growth hovering just above zero. As for the rest of the year, we'll have to wait and see. We of course hope it follows the postrecession pattern.
April 06, 2015
Is Measurement Error a Likely Explanation for the Lack of Productivity Growth in 2014?
Over the past three years nonfarm business sector labor productivity growth has averaged only around 0.75 percent—well below historical norms. In 2014 it was negative, as can be seen in chart 1.
The previous macroblog post by Atlanta Fed economist John Robertson looked at possible economic explanations for why the labor productivity data, taken at face value, have been relatively weak in recent years. In this post I look at the extent to which “measurement error” can account for the weakness we have seen in the data. By measurement error, I mean incomplete data and/or sampling errors that are reduced when more comprehensive data are available several years later. I do not mean the inherent difficulties in measuring productivity in sectors such as health care or information technology.
As seen in chart 1, negative four-quarter productivity growth rates have been quite infrequent in nonrecessionary periods since 1948. In S. Borağan Aruoba's 2008 Journal of Money, Credit and Banking article “Data Revisions Are Not Well Behaved,” he found that initial estimates of annual productivity growth are negatively correlated with subsequent revisions. That is, low productivity growth rates tend to be revised up while high rates tend to be revised down. This is illustrated in chart 2.
In each of the panels, points in the scatterplot represent an initial estimate of fourth-quarter over fourth-quarter productivity growth together with a revised estimate published either one or three years later. For example, the green points in each plot show estimates of productivity growth over the four quarters ending in the fourth quarter of 2011. In each plot, the x-coordinate shows the March 7, 2012, estimate of this growth rate (0.3 percent). The y-coordinate of the green dot in chart 2a shows the March 7, 2013, estimate of fourth-quarter 2011/fourth-quarter 2010 productivity growth (0.4 percent) while the y-coordinate of the green dot in chart 2b shows the March 5, 2015, estimate (0.0 percent).
In each chart, the red dashed line shows the predicted revised value of productivity growth as a function of the early estimate (using a simple linear regression). Chart 2a shows that, on average, we would expect almost no revision to the most recent estimate of four-quarter productivity growth one year later. Chart 2b, however, shows that low initial estimates of productivity growth tend to be revised up three years later while high estimates tend to be revised down. Based on this regression line, the current estimate of -0.1 percent fourth-quarter 2014/fourth-quarter 2013 productivity growth is expected to be revised up to 0.3 percent by April 2018.
The intuition for this is fairly straightforward. Low productivity growth could come about from either underestimating output growth, overestimating growth in hours worked, or a combination of the two. Which of these is most likely to occur, according to historical revisions? This is shown in chart 3, which plots the predicted revisions to four-quarter nonfarm employment growth and four-quarter nominal gross domestic product (GDP) growth conditional on two assumed values for the initial estimate of four-quarter productivity growth: 0 percent (low) and 4 percent (high).
Nominal GDP is used instead of real GDP as methodological changes to the latter (e.g., the introduction of chain-weighting starting in 1996) make an apples-to-apples comparison of pre- and post-revised values difficult. Using fourth-quarter over fourth-quarter growth rates since 1981, the diamonds on the solid lines in chart 3 show that an initial estimate of 0 percent productivity growth would, on average, be associated with a three-year upward revision of 0.39 percentage point to four-quarter nominal GDP growth and a three-year downward revision of 0.10 percentage point to four-quarter nonfarm payroll employment.
With 4 percent productivity growth, the diamonds on the dashed lines show predicted three-year revisions to nominal GDP growth and employment growth of -0.40 percentage point and 0.14 percentage point, respectively. As the chart shows, these estimates are sensitive to the sample period used to predict the revisions. Using only data since 1989 (not shown), the regression would not predict a downward revision to employment growth conditional on an initial estimate of 0 percent productivity growth. Overall, however, the plot suggests that revisions to output growth are more sensitive to initial estimates of productivity growth than revisions to payroll employment growth are. This is consistent with the sentiments expressed by Federal Reserve Vice Chairman Stanley Fischer and Atlanta Fed President Dennis Lockhart at the March 30–April 1 Financial Markets Conference that employment or unemployment data may be more reliably measured than GDP.
Nevertheless, according to charts 2 and 3, the importance of measurement error in productivity growth is fairly modest. Ex-ante, we should not expect last year's puzzlingly low productivity growth simply to be revised away.
Editor's note: Upon request, the programming code and data for charts used in this macroblog post is available from the author.
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.
March 06, 2015
Signs of Improvement in Prime-Age Labor Force Participation
This morning's job report provided further evidence of a stabilizing labor force participation (LFP) rate. After falling over 3 percentage points since 2008, LFP has been close to 62.9 percent of the population for the past seven months. Although demographics and behavioral trends explain much of the overall decline (our web page on LFP dynamics gives a full account), there is a cyclical component at work as well. In particular, the labor force attachment of "prime-age" (25 to 54 year olds) individuals to the labor force is something we're watching closely. Federal Reserve Bank of Atlanta President Dennis Lockhart noted as much in a February 6 speech:
Over the last few years, there has been a worrisome outflow of prime-age workers—especially men—from the labor force. I believe some of these people will be enticed back into formal work arrangements if the economy improves further.
There are signs that some of the prime-age individuals who had retreated to the margins of the labor market have been flowing back into the formal labor market.For one thing, LFP among prime-age individuals stopped declining 16 months ago for women and nine months ago for men. By our estimates, declining LFP in this age category accounts for about one-third of the overall decline in LFP since 2007, so 25- to 54-year-olds' decision to engage in the labor market has a big effect on the overall rate (see the chart). Even with an improving economy, however, a turnaround in LFP among prime-age individuals might not occur.
The reason an improving economy might not reverse the LFP trends is that LFP for both prime-age men and women had been on a longer-term downward trend even before the recession began, suggesting that factors other than the recession-induced decline in labor demand have been important. But the decline in the "shadow labor force"—the share of the prime-age population who say they want a job but are not technically counted as unemployed—demonstrates the cyclical nature of the labor market. For the last year and half, the share of these individuals in the labor force has been generally declining (see the chart).
Moreover, the job-finding success of the shadow labor force has improved. Although the 12-month flow into the official labor force has remained reasonably close to 50 percent, the likelihood of flowing into unemployment (as opposed to employment) rose during the recession. But during the past two years, that trend appears to be reversing (see the chart).
The ability of the prime-age shadow labor force to find work is improving at the same time that the LFP rate of the prime-age population is stabilizing. Taken together, this trend is consistent with improving job market opportunities and further absorption of the nation's slack labor resources.For a more complete analysis of long-term behavioral and demographic effects on LFP for the prime-age and non-prime-age populations, see our Labor Force Participation Dynamics web page, which now includes 2014 data.
March 05, 2015
Could Reduced Drilling Also Reduce GDP Growth?
Five or six times each month, the Atlanta Fed posts a "nowcast" of real gross domestic product (GDP) growth from the Atlanta Fed's GDPNow model. The most recent model nowcast for first-quarter real GDP growth is provided in table 1 below alongside alternative forecasts from the Philadelphia Fed's quarterly Survey of Professional Forecasters (SPF) and the CNBC/Moody's Analytics Rapid Update survey. The Atlanta Fed's nowcast of 1.2 percent growth is considerably lower than both the SPF forecast (2.7 percent) and the Rapid Update forecast (2.6 percent).
Why the discrepancy? The less frequently updated SPF forecast (now nearly a month old) has the advantage of including forecasts of major subcomponents of GDP. Comparing the subcomponent forecasts from the SPF with those from the GDPNow model reveals that no single factor explains the difference between the two GDP forecasts. The GDPNow model forecasts of the real growth rates of consumer spending, residential investment, and government spending are all somewhat weaker than the SPF forecasts. Together these subcomponents account for just under 1.0 percentage point of the 1.5 percentage point difference between the GDP growth forecasts.
Most of the remaining difference in the GDP forecasts is the result of the different forecasts for real business fixed investment (BFI) growth. The GDPNow model projects a sharp 13.5 percent falloff in nonresidential structures investment that largely offsets the reasonably strong increases in the other two subcomponents of BFI. Much of this decline is due to petroleum and natural gas well exploration; a component which accounts for almost 30 percent of nonresidential structures investment and looks like it will fall sharply this quarter. The remainder of this blog entry "drills" down into this portion of the nonresidential structures forecast (pun intended). (A related recent analysis using the GDPNow model has been done here).
A December macroblog post I coauthored with Atlanta Fed research director Dave Altig presented some statistical evidence that in the past, large declines in oil prices have had a pronounced negative effect on oil and mining investment. Chart 1 below shows that history appears to be repeating itself.
The Baker Hughes weekly series on active rotary rigs for oil and natural gas wells has plummeted from 1,929 for the week ending November 21 to 1,267 for the week ending February 27. The Baker Hughes data are the monthly source series for drilling oil and gas wells industrial production (IP) and one of the two quarterly source series for the U.S. Bureau of Economic Analysis's (BEA) estimate of drilling investment (for example, petroleum and natural gas exploration and wells). The other source series for drilling investment is footage drilled completions from the American Petroleum Institute, released about a week before the BEA publishes its initial estimate of GDP.
Chart 2 displays three of these indicators of drilling activity. The data are plotted in logarithms so that one-quarter changes approximate quarterly growth rates. The chart makes clear that the changes in each of the three series are highly correlated, suggesting that the Baker Hughes rig count can be used to forecast the other series. The Baker Hughes data end on February 27, and we can (perhaps conservatively) extrapolate it forward by assuming it remains at its last reading of 1,267 active rigs through the end of the quarter. We can then use a simple regression to forecast the February and March readings of drilling oil and gas wells IP. Another simple regression with the IP drilling series and its first-quarter forecast allows us to project first-quarter real drilling investment. The forecasts, shown as dashed lines in chart 2, imply real drilling investment will decline at an annual rate of 52 percent in the first quarter. This decline is steeper than the current GDPNow model forecast of a 36 percent decline as the latter does not account for the decline in active rotary rigs in February.
A 52 percent decline in real nonresidential investment in drilling would likely subtract about 0.5 percentage point off of first-quarter real GDP growth. However, it's important to keep in mind that a lot of first quarter source data for GDP are not yet available. In particular, almost none of the source data for the volatile net exports and inventory investment GDP subcomponents have been released. So considerable uncertainty still surrounds real GDP growth this quarter.
February 26, 2015
Are Shifts in Industry Composition Holding Back Wage Growth?
The last payroll employment report from the U.S. Bureau of Labor Statistics (BLS) included some relatively good news on wages. Private average hourly earnings rose an estimated 12 cents in January, the largest increase since June 2007. Even so, earnings were up only 2.2 percent over the last year versus average growth of 3.4 percent in 2007.
What accounts for the sluggish growth in average earnings? The average hourly earnings data for all workers is essentially the sum of the average earnings per hour within an industry weighted by that industry's share of employment. In this piece, Ed Lazear argues that a shift of the U.S. economy away from some high-paying industries to lower-paying industries may have contributed to dampened wage growth. Lazear specifically calls out the reduced share of employment in the relatively high-paying finance industry, at hospitals, and in the information sector as potential culprits. A shift in employment away from relatively high-wage jobs will put downward pressure on the growth in average wages.
To get some idea of the effect of industry composition on wages, I took the 2014 calendar year average wage for each industry group at the two-digit NAICS level and multiplied it by the share of employment in that industry in 2014 (admittedly, two-digit NAICS level of disaggregation is very coarse and masks a lot of potential shifts in job-types within industries). Summing across the industries gives an estimate of total average private hourly earnings in 2014. I then repeated the exercise, but using the 2007 industry shares of employment instead (see the chart).
Would average wages have been higher if we had the same mix of employment across industries as we had before the recession? The answer seems to be yes, but not much higher. If nothing had changed in the economy's industry employment mix since 2007, then average wages would have been about 12 cents higher.
This translates into a 16.8 percent increase in nominal wages between 2007 and 2014 versus a 16.2 percent increase if the actual industry employment shares where used, because the decline in the shares of employment in the relatively high paying industries Lazear cites has not been very large, and some higher-paying industries have seen growth. Moreover, some industries with below-average wages, such as retail trade, have experienced a decline in their share of employment as well.
February 23, 2015
Are Oil Prices "Passing Through"?
In a July 2013 macroblog post, we discussed a couple of questions we had posed to our panel of Southeast businesses to try and gauge how they respond to changes in commodity prices. At the time, we were struck by how differently firms tend to react to commodity price decreases versus increases. When materials costs jumped, respondents said they were likely to pass them on to their customers in the form of price increases. However, when raw materials prices fell, the modal response was to increase profit margins.
Now, what firms say they would do and what the market will allow aren't necessarily the same thing. But since mid-November, oil prices have plummeted by roughly 30 percent. And, as the charts below reveal, our panelists have reported sharply lower unit cost observations and much more favorable margin positions over the past three months...coincidence?
February 20, 2015
Business as Usual?
Each month, we ask a large panel of firms to compare their current sales with "normal times." In our February survey, the firms in our panel reported their sales were approaching normal. Indeed, on average, larger firms (those with 100 or more employees) tell us sales levels this month were right at normal. But smaller firms, although improving, are still lagging their larger counterparts (see the chart).
These qualitative assessments suggest a continuation of the trend we've seen in our quarterly quantitative data (these data are compiled at the end of each quarter). In December, our panel of firms reported sales levels about 2.7 percent below normal—virtually identical to the Congressional Budget Office's estimate of the output gap. Here, too, our survey data show that on average, sales of the larger firms in our panel were essentially back to normal, but smaller firms were still reporting ample slack (see the chart).
Our next quantitative assessment of slack in U.S. business is due for release on March 20.
February 17, 2015
What's (Not) Up with Wage Growth?
In recent months, there's been plenty of discussion of the surprisingly sluggish growth in hourly wages. It certainly has the attention of our boss, Atlanta Fed President Dennis Lockhart, who in a speech on February 6 noted that
The behavior of wages and prices, in contrast, remains less encouraging, and, frankly, somewhat puzzling in light of recent growth and jobs numbers.
So what's up—or not up—with wage growth? Using samples of matched worker-level wage data from the U.S. Bureau of Labor Statistics' Current Population Survey, chart 1 plots the annual time series of median 12-month growth rates in per-hour wages. Like most wage growth measures, this chart indicates that wage growth has been gradually increasing since the end of the recession, but growth remains quite a bit lower than before the recession began. Prior to the recession, the median growth rate of wages was around 4 percent a year. This growth rate declined to 1.7 percent in 2010 (as the incidence of wage freezes become much more prevalent, as shown in this research) and increased to 2.5 percent in 2014. For comparison, the chart also shows the annual growth in the Employment Cost Index's measure of wages. The trends in the two measures are broadly similar.
A previous macroblog post discussed details about the method of constructing the median wage growth data.
It's well known that wage growth varies across job characteristics such as occupation, industry, and hours worked as well as across worker characteristics such as education and age. For example, younger workers tend to experience higher hourly wage growth than older workers (even though their hourly wage tends to be lower), and part-time workers tend to have lower wage growth than full-time workers. We thought it might be interesting to look at wage growth for various job and worker characteristics. Are there any bright spots where the median growth in wages has approached prerecession levels?
The answer seems to be no, at least not for the set of characteristics we examined.
The following charts plot the annual time series of the median 12-month growth rate in the wages of workers with a given characteristic (occupation, age, etc.). Chart 2 depicts workers across three broad occupation groups: general-services jobs, production-oriented occupations discussed in our last macroblog post, and a category encompassing managerial, professional, and technical occupations (labeled “professional” in the chart).
Chart 3 shows the median year-over-year wage growth of workers employed in goods-producing versus service-producing industries.
Chart 4 shows the median growth in the wages of individuals working full-time versus those working part-time.
Chart 5 shows the median wage growth of workers with less than an associate degree and those with at least an associate degree.
Chart 6 shows the median growth in the wages of individuals between 16 and 35 years of age, those 36 to 55 years of age, and those over 55 years of age.
We can sum up our findings by saying that median wage growth is higher for some characteristics than others, and the recent trend in wage growth is generally positive across characteristics. But none of the characteristic-specific median growth rates we looked at are close to returning to prerecession levels. Lower-than-normal wage growth appears to be a very widespread feature of the labor market since the end of the recession.
February 12, 2015
Are We Becoming a Part-Time Economy?
Compared with 2007, the U.S. labor market now has about 2.5 million more people working part-time and about 2.2 million fewer people working full-time. In this sense, U.S. businesses are more reliant on part-time workers now than in the past.
But that doesn't necessarily imply we are moving toward a permanently higher share of the workforce engaged in part-time employment. As our colleague Julie Hotchkiss pointed out, almost all jobs created on net from 2010 to 2014 have been full-time. As a result, from 2009 to 2014, the part-time share of employment has declined from 21 percent to 19 percent and is about halfway back to its prerecession level.
But the decline in part-time utilization is not uniform across industries and occupations. In particular, the decline is much slower for occupations that tend to have an above-average share of people working part-time. This portion of the workforce includes general-service jobs such as food preparation, office and administrative support, janitorial services, personal care services, and sales.
The following chart compares the share of part-time employment for these general-service occupations with the share for production-type occupations (such as machine operators, fabricators, construction workers, and truck drivers).
The above chart suggests that if you talk to retailers or restaurateurs, they will say that they always relied pretty heavily on part-time workers. Their utilization increased during the recession, and it really hasn't changed much since then. But manufacturers or construction firms are more likely to say that part-time work is not that common, and although they did increase their utilization of part-time workers during the recession by quite a lot, things have been gradually returning to normal.
Why is the part-time share of employment declining more slowly in general-service occupations? The economy has been generating full-time general-service jobs at a much slower pace than in the past. Of the approximately 7.6 million full-time jobs created between 2010 and 2014, only about 17 percent have been in general-service occupations, versus about 32 percent of the 7.8 million full-time jobs created between 2003 and 2007. At the current rate of full-time job creation in general-service occupations, it would take more than 10 years for the part-time share of employment in general-service occupations to return to its prerecession average.
From the workers' perspective, a relevant question is whether these part-time utilization rates are desirable. Some people work part-time and do not currently want or are not available for full-time work (so-called part-time for noneconomic reasons, PTNER). Others are available and want full-time work but are working part-time because of slack business conditions or the unavailability of full-time jobs (so-called part-time for economic reasons, PTER). The following chart shows the share of employment in the general-service and production occupation groupings that is PTER and PTNER.
The chart indicates that most of the movement in the part-time share of employment is coming from people who want full-time work. In both cases, the share of involuntary part-time employment rose during the recession, but for general-service occupations it has been more persistent than for production jobs.
Why has the demand for full-time workers in general-service occupations been more subdued than for other jobs? As the following chart shows, wage growth for these occupations has been quite weak in the past few years, suggesting that employers have not been experiencing much tightness in the supply of workers to fill vacancies for these occupations. Presumably, then, the firms generally find it acceptable to have a greater share of part-time workers than in the past.
The overall share of the workforce employed in part-time jobs is declining and is likely to continue to decline. But the decline is not uniform across industries and occupations. Working part-time has become much more likely in general-service occupations than in the past—and a greater share of those workers are not happy about it.
By John Robertson, vice president and senior economist, and
Ellie Terry, an economic policy analysis specialist, both of the Atlanta Fed's research department
- Sales Flexing Muscle at More Firms
- All Eyes on the Consumer
- Signs of Strengthening Wage Growth?
- What the Weather Wrought
- Déjà Vu All Over Again
- Is Measurement Error a Likely Explanation for the Lack of Productivity Growth in 2014?
- What Seems to Be Holding Back Labor Productivity Growth, and Why It Matters
- Signs of Improvement in Prime-Age Labor Force Participation
- Could Reduced Drilling Also Reduce GDP Growth?
- Are Shifts in Industry Composition Holding Back Wage Growth?
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