The Atlanta Fed's macroblog provides commentary on economic topics including monetary policy, macroeconomic developments, financial issues and Southeast regional trends.
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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 27, 2015
myCPI: Getting More Personal with Inflation
Last Friday's inflation report was interesting. The consumer price index (CPI) rose only 1.2 percent in April, as falling energy and flat food prices helped to keep the overall index in check.
Does a 1.2 percent (annualized) rise in the cost of living sound about right to you? No? Well, the performance of the CPI reflects the buying habits of the average urban consumer, which is a way to say it sort of reflects the buying habits of everyone, but isn't likely to reflect the buying habits of anyone in particular.
Are you a dapper guy? Good news for you—the cost of men's suits, sport coats, and outerwear fell 4.5 percent (monthly) in April. Fitness buff? Not such good news for you—sporting goods prices jumped 0.9 percent last month. Did you spent a lot of time in the emergency room in April? Even worse news for you: the cost of hospital services rose 1.9 percent last month, their biggest jump in about 25 years! Are you a big blue monster that lives on Sesame Street? Then you had a really good month in April—cookie prices fell 2.4 percent.
OK, you get the idea: different people, different experiences with costs. And of course the folks over at the Bureau of Labor Statistics (BLS) recognize that "it is unlikely that your experience will correspond precisely with either the national indexes or the indexes for specific cities or regions." (Here are some helpful facts about the CPI.)
But that got us wondering if we could take some of the same building blocks that the BLS uses to construct the CPI and create somewhat more individualized price indexes that reflect a wider variety of price change experiences.
So we created 144 individualized market baskets that attempt to capture some of the variation that occurs across different demographic characteristics including age, income, gender, size of household, education, and whether or not you are a homeowner. (You can find greater detail on the construction of these indexes here.) The resulting indexes—we're calling this myCPI—may yield a closer approximation to your cost of living experience than one based on the apocryphal average consumer.
For example, suppose you are a single female who is over 55 years old, rents her place, has an income of more than $70,000, and didn't attend college. In April, your myCPI rose at an annualized rate of 1.4 percent, pretty close to the official CPI growth rate of 1.2 percent for the month. However, your myCPI has risen 1.1 percent over the past year, whereas the official CPI has fallen 0.2 percent.
Are you a male, under 35 years old, married, and without a college degree, but you own your home and make more than $70,000 annually? Your myCPI was virtually flat in April, and people matching your description have seen their cost of living decline by 1.0 percent over the past year.
1-month percent change (annualized rate)
Year-over-year percent change
Female, over 55, without college degree, renter, high income
Couple, less than 35 years, without college degree, homeowner, high income
Family (3+ persons); head of household 35–55 years old, homeowner, college degree, middle income
We don't know exactly what you are buying, where you shop, and what prices you are paying, so we can't know how closely your particular circumstance matches any of the 144 indexes we came up with. But within some (perhaps large) margin of error, we can construct a market basket based on the spending habits of people who fit your description in rather broadly defined terms, and we can apply the major price components of the CPI to that particular basket of things. So if you want an idea of the rise (or fall) in the cost of living for someone like yourself (and you know you do), head on over to our website, answer a few questions, and sign up. Every month we'll send you an e-mail update on your myCPI shortly after every CPI release.
May 18, 2015
Sales Flexing Muscle at More Firms
The news in this month's Business Inflation Expectations (BIE) report is that, in the aggregate, firms' unit sales levels continue to strengthen: Specifically, the survey question measures firms' perceptions of current unit sales levels relative to "normal times."
This month, 70 percent of firms indicated their sales levels are at or above what they consider normal. Last November, that share was 61 percent, and one year ago, it was only 54 percent. We typically report the aggregate results in a diffusion index (see the chart), which also shows the overall progression toward "normal times" (a value of 0).
But, typical of aggregate statistics, these results obscure the diversity of experience among sectors. Digging deeper, we found that most (but not all) of the sectors represented in our panel have shown further improvement in their sales performance relative to last November (see the chart).
Retailers and those in the real estate and rental leasing/construction sectors reported the most significant improvement since November, with retailers approaching what they consider normal sales levels. This news is likely to be most welcome to Dennis Lockhart, our boss here in Atlanta, who has put the performance of the consumer on his "must watch" list. Two industries—finance and insurance, and transportation and warehousing—reported above-normal sales levels in our recent survey.
Only the manufacturers in our panel indicated that their sales performance has deteriorated since November, and they are now reporting sales well below normal. Of course, this news shouldn't be terribly surprising given the recent softness in the manufacturing indexes from both the Institute for Supply Management and industrial production data. This information was also on the boss's watch list, as he made clear in his speech:
Well, judging from our May BIE report, manufacturers aren't seeing improvement quite yet.
The stronger dollar was likely reflected in a drag on net exports...[and] looking ahead, I expect net exports to be a modest drag on economic activity over much of the year.... It should be noted, however, that in recent weeks the dollar has stabilized and oil prices have begun to move up a little. These developments, if they stick, could dilute somewhat what would otherwise be drags on the economy in the near term. We shall see.
May 07, 2015
All Eyes on the Consumer
It appears that the first quarter may have been even worse than we thought. The CNBC rapid update—consensus estimates from a panel of forecasters—registered a decline of 0.3 percent as of yesterday.
Clearly, the year didn't start out so well, but here at the Atlanta Fed we have not yet lost faith. We are sticking to the narrative that 2015 will be another solid year of recovery.
That said, our faith is not blind and, befitting data-dependent policymakers, we need to make some call about what it will take to shake our confidence. In a speech delivered yesterday (May 6) in Baton Rouge, Louisiana, Atlanta Fed President Dennis Lockhart pointed to our current lodestar:
As I assess the possible and necessary contributors to a rebound in the second quarter and thereafter, attention has to fall on consumer spending, in my view.
Is there a case for optimism? We think so, and it is based on the assumption that the fundamentals supporting consumer spending have been stronger than the actual recent pace of expenditures. President Lockhart continues:
What's up with the consumer? It's puzzling. The fundamentals supporting consumption growth seem strong. I consider consumer fundamentals to be real personal income growth, household wealth, access to credit, and consumer confidence. Consumer confidence is, in turn, highly influenced by the broad employment outlook.
To be more precise about that sentiment, the chart below illustrates an experiment based on a simple model that incorporates President Lockhart's description of "fundamentals." To be even more precise, we ask the following question: What would we have predicted for consumer spending growth during the past four months based on the history of actual consumer spending and its relationship to income, employment (and unemployment), confidence measures, and wealth (specifically, equity prices)? We also threw inflation and oil prices into the mix for good measure.
Here's what we got:
In other words, the "fundamentals" suggest the four-month annualized growth of consumer spending should have been in excess of 4 percent, as opposed to the approximately 1.5 percent we actually saw. That is a story we don't expect to persist, and our current view of the year is that first-quarter consumer spending results are not indicative of future performance.
Consumers are, of course, a forward-looking bunch, and it is possible the recent weak spending reflects a looming reality not captured by the simple model described above. But our forecast for now is that consumers will move to the fundamentals, and not vice versa.
As President Lockhart said in Louisiana: "Stay tuned."
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 20, 2015
What the Weather Wrought
At Seeking Alpha, Joseph Calhoun responds to Friday's macroblog post, which noted that, over the course of the recovery, first-quarter gross domestic product (GDP) growth has on average been slower than the quarterly performance over the balance of the year:
... the "between-the-lines" meaning of the Atlanta post is to ignore all of this since this weakness is being portrayed as "just like last year" a statistical problem in the one measure that economists think most represents the economy.
Rest assured, we try pretty hard to not place any messages "between the lines," and the penultimate sentence of Friday's piece was meant to strike the appropriately tentative tone: "As for the rest of the year, we'll have to wait and see."
We do believe, like others, that weather was at play in the subpar performance of 2015's debut. Severe weather, in February in particular, can explain some of the first-quarter weakness, but "some" is the operative qualifier.
As the following chart illustrates, relative to a baseline forecast without weather effects—proxied with National Oceanic and Atmospheric Administration measures of heating and cooling days through March—we estimate that the severity of the winter subtracted about 0.6 percentage point from GDP growth:
Two points: First, to the extent that weather is a culprit in subpar first-quarter growth, we should see some payback in the current quarter (as, dare we say, we saw last year).
Second, we (the Atlanta Fed staff) did not begin the year projecting first-quarter growth at a mere 1.8 percent annualized (as the benchmark forecast in the experiment illustrated above implies). That rate of growth is a considerable step-down from our forecast at the beginning of the year, forced by the realities of the incoming data (as captured, for example, by GDPNow estimates). That gap leaves plenty of explaining left to do.
Observable developments can plausibly explain much of the forecast miss—mainly the initial, somewhat ambiguous, impact of energy price declines and the rapid, steep appreciation of the dollar, which has clearly been associated with a suppression of export activity. Our current view is that, as energy prices and the exchange rate stabilize, we will see a return to growth patterns that are closer to 3 percent than 1 percent.
We are not, however, selling the position that it is wise to be completely sanguine about the rest of the year. Here is the official word from Dennis Lockhart, president of the Atlanta Fed (subscription required for full citation):
I lean to a later lift-off date [for the federal funds rate target]. To the extent you want to simplify that debate to June versus September, I lean to September. I don't think, given the progress we have made, the state of the economy, and my confidence that the first quarter was an aberration, that it would be horribly damaging to go a little earlier versus later. But my preference would be to wait for more confirming evidence that we are on the track we think we are on and we expect to carry us back to inflation toward target.
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.
- Far Away Yet Close to Home: Discussing the Global Economy's Effects
- New Atlanta Fed Series Shows Wage Growth Held Steady in May
- Approaching the Promised Land? Yes and No
- Will the Elevated Share of Part-Time Workers Last?
- Falling Job Tenure: It's Not Just about Millennials
- Atlanta Fed's Wage Growth Measure Increased Again in April
- myCPI: Getting More Personal with Inflation
- Sales Flexing Muscle at More Firms
- All Eyes on the Consumer
- Signs of Strengthening Wage Growth?
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