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May 31, 2018
Learning about an ML-Driven Economy
Developments in artificial intelligence (AI) and machine learning (ML) have drawn considerable attention from both the real and financial sides of the economy. The Atlanta Fed's recent Financial Markets Conference, Machines Learning Finance: Will They Change the Game?, explored the implications of AI/ML for the financial system and public policy. The conference also included two macroeconomics-related sessions. A presentation of an academic paper, and the subsequent discussion, looked at why AI/ML has not (yet) shown up in the productivity statistics. Also, a policy panel on the implications of AI/ML developments for monetary policy was part of the conference. This post summarizes the policy panel discussion.
Vincent Reinhart, chief economist at Standish Mellon Asset Management, opened the panel discussion with the observation that developments in AI/ML could affect the performance of the overall economy in a variety of ways. For example, advancing technology could better match workers with jobs and, as a result, boost employment. On the other hand, it could also complicate job matching by forcing jobs and workers to become more specialized.
A combination of three factors is driving the recent growth in AI/ML, explained Carolyn Evans, head economist and senior data scientist at Intel Corporation: increased data availability, faster computers, and improved algorithms for analyzing the data. Like Reinhart, she noted that AI/ML could have various effects on the economy. For example, AI/ML is helping to reduce cost and boost supply. On the demand side, AI/ML is increasing the efficiency of product searches by buyers. However, as some online sellers become better than others at using AI/ML to help customers find the products they want, customer relationships may become stickier. In addition, firms may come to value interactions with customers more highly because these interactions could provide them with valuable data to use with AI/ML to better serve current and future customers. Evans raised the question of whether these developments could change the nature of pricing.
Dallas Fed president Rob Kaplan said he believes AI/ML is causing a structural change. It is not the first new technology to affect the economy, but the economic effects of this technology are more pervasive. For instance, business pricing power is already more constrained than it used to be, but even businesses that seemingly have some power currently worry that they make themselves more vulnerable to AI/ML-enabled disruption if they raise prices. Kaplan also emphasized the importance of skills training and building human capital to alleviate what he views as the inevitable loss of jobs to AI/ML.
The issue of how monetary policymakers should think about AI/ML was the focus of a presentation by Chicago Fed president Charles Evans. He observed that the "sign, magnitude, and timing" of any resulting structural change are all uncertain. This uncertainty, he said, argues against the use of fixed policy rules such as the Taylor Rule. He suggested that the Federal Reserve should instead follow an "outcome-based policy," adjusting policy based on the evolution of expected inflation and unemployment relative to the policy objectives of stable prices and full employment.
You can download the available presentations from the 2018 Financial Markets Conference web pages. The videos will be posted as they become available. Read Notes from the Vault for a summary of sessions on the strengths and weaknesses of ML, some financial regulatory and broader ethical issues, and the use of ML by investors.
February 13, 2018
GDPNow's Forecast: Why Did It Spike Recently?
If you felt whipsawed by GDPNow recently, it's understandable. On February 1, the Atlanta Fed's GDPNow model estimate of first-quarter real gross domestic product (GDP) growth surged from 4.2 percent to 5.4 percent (annualized rates) after a manufacturing report from the Institute for Supply Management. GDPNow's estimate then fell to 4.0 percent on February 2 after the employment report from the U.S. Bureau of Labor Statistics. GDPNow displayed a similar undulating pattern early in the forecast cycle for fourth-quarter GDP growth.
What accounted for these sawtooth patterns? The answer lies in the treatment of the ISM manufacturing release. To forecast the yet-to-be released monthly GDP source data apart from inventories, GDPNow uses an indicator of growth in economic activity from a statistical model called a dynamic factor model. The factor is estimated from 127 monthly macroeconomic indicators, many of which are used to estimate the Chicago Fed National Activity Index (CFNAI). Indices like these can be helpful for forecasting macroeconomic data, as demonstrated here and here.
Perhaps not surprisingly, the CFNAI and the GDPNow factor are highly correlated, as the red and blue lines in the chart below indicate. Both indices, which are normalized to have an average of 0 and a standard deviation of 1, are usually lower in recessions than expansions.
A major difference in the indices is how yet-to-be-released values are handled for months in the recent past that have reported values for some, but not all, of the source data. For example, on February 2, January 2018 values had been released for data from the ISM manufacturing and employment reports but not from the industrial production or retail sales reports. The CFNAI is released around the end of each month when about two-thirds of the 85 indicators used to construct it have reported values for the previous month. For the remaining indicators, the Chicago Fed fills in statistical model forecasts for unreported values. In contrast, the GDPNow factor is updated continuously and extended a month after each ISM manufacturing release. On the dates of the ISM releases, around 17 of the 127 indicators GDPNow uses have reported values for the previous month, with six coming from the ISM manufacturing report.
[ Enlarge ]
For months with partially missing data, GDPNow updates its factor with an approach similar to the one used in a 2008 paper by economists Domenico Giannone, Lucrezia Reichlin and David Small. That paper describes a dynamic factor model used to nowcast GDP growth similar to the one that generates the New York Fed's staff nowcast of GDP growth. In the Atlanta Fed's GDPNow factor model, the last month of ISM manufacturing data have large weights when calculating the terminal factor value right after the ISM report. These ISM weights decrease significantly after the employment report, when about 50 of the indicators have reported values for the last month of data.
In the above figure, we see that the January 2018 GDPNow factor reading was 1.37 after the February 1 ISM release, the strongest reading since 1994 and well above either its forecasted value of 0.42 prior to the ISM release or its estimated value of 0.43 after the February 2 employment release. The aforementioned rise and decline in the GDPNow forecast of first-quarter growth is largely a function of the rise and decline in the January 2018 estimates of the dynamic factor.
Although the January 2018 reading of 59.2 for the composite ISM purchasing managers index (PMI) was higher than any reading from 2005 to 2016, it was little different than either a consensus forecast from professional economists (58.8) or the forecast from a simple model (58.9) that uses the strong reading in December 2017 (59.3). Moreover, it was well above the reading the GDPNow dynamic factor model was expecting (54.5).
A possible shortcoming of the GDPNow factor model is that it does not account for the previous month's forecast errors when forecasting the 127 indicators. For example, the predicted composite ISM PMI reading of 54.4 in December 2017 was nearly 5 points lower than the actual value. For this discussion, let's adjust GDPNow's factor model to account for these forecast errors and consider a forecast evaluation period with revised current vintage data after 1999. Then, the average absolute error of the 85–90 day-ahead adjusted model forecasts of GDP growth after ISM manufacturing releases (1.40 percentage points) is lower than the average absolute forecast error on those same dates for the standard version of GDPNow (1.49 percentage points). Moreover, the forecasts using the adjusted factor model are significantly more accurate than the GDPNow forecasts, according to a standard statistical test . If we decide to incorporate adjustments to GDPNow's factor model, we will do so at an initial forecast of quarterly GDP growth and note the change here .
Would the adjustment have made a big difference in the initial first-quarter GDP forecast? The February 1 GDP growth forecast of GDPNow with the adjusted factor model was "only" 4.7 percent. Its current (February 9) forecast of first-quarter GDP growth was the same as the standard version of GDPNow: 4.0 percent. These estimates are still much higher than both the recent trend in GDP growth and the median forecast of 3.0 percent from the Philadelphia Fed's Survey of Professional Forecasters (SPF).
Most of the difference between the GDPNow and SPF forecasts of GDP growth is the result of inventories. GDPNow anticipates inventories will contribute 1.2 percentage points to first-quarter growth, and the median SPF projection implies an inventory contribution of only 0.4 percentage points. It's not unusual to see some disagreement between these inventory forecasts and it wouldn't be surprising if one—or both—of them turn out to be off the mark.
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.
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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.
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September 26, 2013
The New Normal? Slower R&D Spending
In case you need more to worry about, try this: the pace of research and development (R&D) spending has slowed. The National Science Foundation defines R&D as “creative work undertaken on a systematic basis in order to increase the stock of knowledge” and application of this knowledge toward new applications. (The Bureau of Economic Analysis (BEA) used to treat R&D as an intermediate input in current production. But the latest benchmark revision of the national accounts recorded R&D spending as business investment expenditure. See here for an interesting implication of this change.)
The following chart shows the BEA data on total real private R&D investment spending (purchased or performed on own-account) over the last 50 years, on a year-over-year percent change basis. (For a snapshot of R&D spending across states in 2007, see here.)
Notice the unusually slow pace of R&D spending in recent years. The 50-year average is 4.6 percent. The average over the last 5 years is 1.1 percent. This slower pace of spending has potentially important implications for overall productivity growth, which has also been below historic norms in recent years.
R&D spending is often cited as an important source of productivity growth within a firm, especially in terms of product innovation. But R&D is also an inherently risky endeavor, since the outcome is quite uncertain. So to the extent that economic and policy uncertainty has helped make businesses more cautious in recent years, a slow pace of R&D spending is not surprising. On top of that, the federal funding of R&D activity remains under significant budget pressure. See, for example, here.
So you can add R&D spending to the list of things that seem to be moving more slowly than normal. Or should we think of it as normal?
By John Robertson, vice president and senior economist in the Atlanta Fed’s research department
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August 16, 2013
GDP, Jobs, and Growth Accounting
The latest on productivity, from the Associated Press via USA Today:
U.S. worker productivity accelerated to a still-modest 0.9% annual pace between April and June after dropping the previous quarter.
The second-quarter gain...reversed a decline in the January-March quarter, when the Labor Department's revised numbers show productivity shrank at a 1.7% annual pace.
Labor costs rose at a 1.4% annual pace from April through June, reversing a revised 4.2% drop the previous quarter.
Productivity measures output per hour of work. Weak productivity suggests that companies may have to hire because they can't squeeze more work from their existing employees....
Productivity growth has been weaker recently, rising 1.5% in 2012 and 0.5% in 2011.
Annual productivity growth averaged 3.2% in 2009 and 3.3% in 2010. In records dating back to 1947, it's been about 2%.
Though not quite in the category of spectacular—and coming off revisions that if anything made things look weaker than previously thought—last quarter's uptick is a welcome development. Earlier this week, in a speech to the Atlanta Kiwanis club, Atlanta Fed President Dennis Lockhart laid out several scenarios with materially different implications for how the GDP and employment picture might play out over the next several years:
As a matter of arithmetic, healthy employment growth coupled with tepid GDP growth implies weak labor productivity growth. And in fact, productivity growth in recent quarters has been significantly below historical norms.
[I] believe that the recent low growth of productivity is probably just a temporary downdraft after the rather strong productivity growth when the economy emerged from recession.
If productivity growth rebounds to more typical levels, the coincidence of job gains at a pace of around 190,000 per month in recent months and GDP growth below 2 percent cannot persist. Again, it's a matter of arithmetic. Either GDP growth will rise to levels consistent with recent employment growth, or employment growth will fall to levels more consistent with the weak GDP data we've been witnessing.
I've got a working assumption on this question, and it is captured in the Atlanta Fed's baseline forecast for the second half of this year and 2014. This outlook calls for a pickup in real GDP growth over the balance of 2013, with a further step-up in economic activity as we move into 2014.
You can get a sense of this outlook by considering the output of one particular model that we use here at the Atlanta Fed. The model, which is purely statistical, gives us a view into how productivity, GDP, employment, and the unemployment rate might move together (along with other labor market variables like labor force participation and average hours worked). Here is the bottom line of an exercise that assumes GDP growth through 2015 comes in at about the central tendency of the projections from the Federal Reserve's June 2013 Summary of Economic Projections.
For this exercise, we have adjusted the 2013 growth forecast down slightly due to the weaker-than-expected growth in the first half of the year. Additionally, we have plugged in assumptions for productivity growth—1.5 percent per quarter (SAAR), the average gain over the past eight years—and nonfarm business output growth. We then let the model forecast the remaining variables, all of which are for the labor market:
The model forecasts employment gains in the neighborhood of what the economy has been generating over the past several years, and a steadily declining unemployment rate.
Now consider two "stall" scenarios in which GDP growth fails to get beyond 2.3 percent. The first of these scenarios is the one noted in the Lockhart Kiwanis speech, with productivity recovering but job growth falling off the pace:
From a policy perspective, this one may not cause too much handwringing about the appropriate course of action. The weak GDP growth is accompanied by a failure to make the type of progress on the unemployment rate that the FOMC has clearly articulated as the necessary condition for adjustments in policy rates:
[T]he Committee decided to keep the target range for the federal funds rate at 0 to 1/4 percent and currently anticipates that this exceptionally low range for the federal funds rate will be appropriate at least as long as the unemployment rate remains above 6-1/2 percent, inflation between one and two years ahead is projected to be no more than a half percentage point above the Committee's 2 percent longer-run goal, and longer-term inflation expectations continue to be well anchored.
Absent unforeseen issues with inflation, staying the course would seem to be in order.
But there is a second stall scenario in which productivity and GDP growth remain tepid, even as labor market indicators improve:
The difference in this experiment is that the expectations of those that President Lockhart referred to in his speech as the "innovation pessimists" are correct. Recent weakness in productivity growth reflects a fall in trend productivity growth. In this case, essentially identical labor market outcomes would nonetheless correspond to an economy that can't seem to hit "escape" velocity.
If it is clear that this configuration of outcomes is associated with a structural break in productivity growth, an argument against monetary policy stimulus would have some weight. After all, in most cases we don't expect the tools of monetary policy to fix structural efficiency problems.
But, alas, such clarity rarely arrives in real time. The experiments above give some sense of how difficult it can be to discover the right branch to follow on the policy decision tree.
By Dave Altig, executive vice president and research director of the Atlanta Fed
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February 01, 2013
Just in case you were inclined to drop the "dismal" from the "dismal science," Northwestern University professor Robert Gordon has been doing his best to talk you out of it. His most recent dose of glumness was offered up in a recent Wall Street Journal article that repeats an argument he has been making for a while now:
The growth of the past century wasn't built on manna from heaven. It resulted in large part from a remarkable set of inventions between 1875 and 1900...
This narrow time frame saw the introduction of running water and indoor plumbing, the greatest event in the history of female liberation, as women were freed from carrying literally tons of water each year. The telephone, phonograph, motion picture and radio also sprang into existence. The period after World War II saw another great spurt of invention, with the development of television, air conditioning, the jet plane and the interstate highway system…
Innovation continues apace today, and many of those developing and funding new technologies recoil with disbelief at my suggestion that we have left behind the era of truly important changes in our standard of living…
Gordon goes on to explain why he thinks potential growth-enhancing developments such as advances in healthcare, leaps in energy-production technologies, and 3-D printing are just not up to late-19th-century snuff in their capacity to better the lot of the average citizen. To paraphrase, your great-granddaddy's inventions beat the stuffing out of yours.
There has been a lot of commentary about Professor Gordon's body of work—just a few examples from the blogosphere include Paul Krugman, John Cochrane, Free Exchange (at The Economist), Gary Becker, and Thomas Edsall (who includes commentary from a collection of first-rate economists). Most of these posts note the current-day maladies that Gordon offers up to furrow the brow of the growth optimists. Among these are the following:
And inequality in America will continue to grow, driven by poor educational outcomes at the bottom and the rewards of globalization at the top, as American CEOs reap the benefits of multinational sales to emerging markets. From 1993 to 2008, income growth among the bottom 99% of earners was 0.5 points slower than the economy's overall growth rate.
Serious considerations, to be sure, but there is actually a chance that some of the "headwinds" that Gordon emphasizes are signs that something really big is afoot. In fact, Gordon's headwinds remind me of this passage, from a paper by economists Jeremy Greenwood and Mehmet Yorukoglu published about 15 years ago:
A simple story is told here that connects the rate of technological progress to the level of income inequality and productivity growth. The idea is this. Imagine that a leap in the state of technology occurs and that this jump is incarnated in new machines, such as information technologies. Suppose that the adoption of new technologies involves a significant cost in terms of learning and that skilled labor has an advantage at learning. Then the advance in technology will be associated with an increase in the demand for skill needed to implement it. Hence the skill premium will rise and income inequality will widen. In the early phases the new technologies may not be operated efficiently due to a dearth of experience. Productivity growth may appear to stall as the economy undertakes the (unmeasured) investment in knowledge needed to get the new technologies running closer to their full potential. The coincidence of rapid technological change, widening inequality, and a slowdown in productivity growth is not without precedence in economic history.
Greenwood and Yorukoglu go on to assess, in detail, how durable-goods prices, inequality, and productivity actually behaved in the first and second industrial revolutions. They conclude that game-changing technologies have, in history, been initially associated with falling capital prices, rising inequality, and falling productivity. Here is a representative chart, depicting the period (which was rich with technological advance) leading up to Gordon's (undeniably) golden age:
Source: "1974," Jeremy Greenwood and Mehmet Yorukoglu,
Carnegie-Rochester Conference Series on Public Policy, 46, 1997
Greenwood and Yorukoglu conclude their study with this pointed question:
Plunging prices for new technologies, a surge in wage inequality, and a slump in the advance of labor productivity - could all this be the hallmark of the dawn of an industrial revolution? Just as the steam engine shook 18th-century England, and electricity rattled 19th-century America, are information technologies now rocking the 20th-century economy?
I don't know (and nobody knows) if the dark-before-the-dawn possibility described by Greenwood and Yorukoglu is the apt analogy for where the U.S. (and global) economy sits today. (Update: Clark Nardinelli also discussed this notion.) But I will bet you there was some commentator writing in 1870 who sounded an awful lot like Professor Gordon.
By Dave Altig, executive vice president and research director of the Atlanta Fed
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March 04, 2011
Gaining perspective on the employment picture
The employment report released today indicated a moderate increase of 192,000 in nonfarm payrolls and a slight decline in the unemployment rate from 9 percent in January to 8.9 percent. While certainly an improvement over recent months, employment growth still has not reached a level needed to produce significant drops in the unemployment rate.
In a speech given yesterday, Atlanta Fed President Dennis Lockhart addressed some of the underlying issues that have potentially been holding back job growth. On the supply side, President Lockhart addressed three structural issues, including skill mismatch, house lock, and extended unemployment insurance.
"Skill mismatch exists when work skills of job seekers do not match the requirements of jobs that are available. For example, a construction worker is unlikely to have the particular skills needed in the healthcare industry."
This comment is motivated by the research of Federal Reserve economists (Valetta and Kuang and Barnichon and Figura, among others) that suggests while there is likely some evidence of skill mismatch, it's not materially different than what's been seen during past recessions.
Another possible explanation mentioned by President Lockhart for persistently high unemployment is the existence of what is sometimes referred to as "house lock."
"Currently many people owe more on their homes than their homes are worth. It's claimed that job seekers don't accept jobs available in other geographic locations because of the difficulty or cost of selling their homes."
Here too, President Lockhart says there is evidence indicating house lock is not a large contributor to the current high level of unemployment (For example, see Schulhofer-Wohl, Kaplan and Schulhofer-Wohl, and Molloy et al.)
More convincing is the argument pointing to the impact of extended unemployment insurance benefits. Research from the most recent recession and recovery—for example, see Valetta and Kuang and Aaronson et al.—suggests extended benefits have added to the unemployment rates, with estimates ranging from 0.4 percentage points to 1.7 percentage points. If that's the case, then President Lockhart says these extended benefits may be acting "as a disincentive to accept an offered job, especially if the job pays less than the one lost."
As President Lockhart indicates, however, standard skill mismatch, house lock, and unemployment insurance disincentives do not provide the full answer. So, he offers some additional factors:
"On the demand side, it's been argued that credit constraints affecting small businesses are holding back hiring. Banks are blamed for this situation and so are regulators. Getting credit at an affordable cost was a challenge during the recession. But credit conditions for established small businesses have been steadily improving for some time now. Recent surveys suggest that most small businesses are cautious about hiring more because of slow sales growth rather than lack of access to credit.
"Furthermore, a recent National Bureau of Economic Research study showed that job creation is more correlated to young businesses than the broad class of small businesses. Start-ups and young businesses are often financed in ways other than direct business loans. Difficulties getting home equity loans and other personal credit appear to have reduced formation of new businesses.
"Strong productivity growth is another much-discussed potential impediment to hiring. Stated simply, increases in productivity allow businesses to support a given level of sales with fewer people. In the longer term, rising productivity expands the economy's output, which in turn generates jobs. But in the short run, productivity investment can be the enemy of employment growth.
"Productivity growth was unusually high during the recession and in the early stages of the recovery, limiting the need for additional workers. Recently, however, productivity growth has slowed below the pace of business sales. If this trend continues, the need to hire additional workers will increase.
"Finally, in recent months, reluctance to hire has been attributed to heightened uncertainty, a common theme among my business contacts. A few weeks ago I argued that uncertainty has abated somewhat with the improving economy, the resolution of the November elections, the extension of tax cuts, and the apparent containment of the European sovereign debt crisis. I said that before Tunisia and before the fiscal struggle in Congress gathered steam. The restraining influence of uncertainty persists, to some extent."
Outside of productivity, it is difficult to measure the impact of these issues. (For example, it is difficult to survey people who did not start up a firm to determine if credit was an issue.) However, the theme of uncertainty has been a consistent factor in discussions on employment with our contacts here at the Atlanta Fed. If a simple explanation for persistent weakness in labor markets has proven elusive, there is little argument with President Lockhart's observation that "the recovery has brought little relief to the labor market."
Should today's employment release change any opinions about the strength of the labor market? In my mind, not really. There are still 7.5 million fewer jobs than at the start of the recession. There are also still over 8 million workers employed part time for economic reasons, and almost 6 million of the unemployed have been so for more than 26 weeks.
But the numbers released today did provide some additional evidence that the labor market is moving in the right direction with a level of growth consistent with at least a modest decline in unemployment. Furthermore, as consumer expenditures continue to rise, profitability increases, and the amount of uncertainty diminishes, hiring should increase. However, as President Lockhart alluded to in his speech, it will likely take time before the labor market recovery catches up to the overall economic recovery.
By Melinda Pitts
Research economist and associate policy adviser at the Atlanta Fed
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October 01, 2010
What does "structural" mean?
On Wednesday, Federal Reserve Bank of Atlanta President Dennis Lockhart summed up one of the hot policy questions of the moment this way:
"A necessary debate is jelling on the diagnosis of our economic troubles and the appropriate prescription. As I think about it, there are three lines of argument. One argument maintains there is not enough spending occurring—in economists' terms, a shortfall of aggregate demand—and that this shortfall can be reduced by further stimulus. A second argument is that the economy is undergoing deep structural adjustments in industry composition, labor markets, and household finances, especially the level of debt, and these adjustments will take considerable time to play out. Finally, it can be argued that much of the uncertainty has to be dealt with in other areas of government, and monetary policy can't do much about this kind of problem. This characterization doesn't do full justice to the complexity of the matter, but it lays out in broad strokes what questions are in play."
In some quarters, the opinion seems to be that the debate is effectively over. On the day of President Lockhart's speech, Mark Whitehouse wrote this piece in the Wall Street Journal:
"In recent months, policy makers have puzzled over the inadequate rate at which job searchers and job vacancies are coming together. By some estimates, if openings were turning into hires at the rate they typically do, the unemployment rate should be about three percentage points lower than the current 9.6%....
"A new paper, though, suggests employers themselves are at least part of the problem. The authors—Steven Davis of Chicago Booth School of Business, R. Jason Faberman of the Philadelphia Fed and John Haltiwanger of the University of Maryland—take a deep dive into Labor Department data and come up with an estimate of what they call 'recruiting intensity,' a measure of employers' vacancy-filling efforts including advertising, screening and wage offers.
"Their finding: Employers haven't been trying as hard as they usually do. Estimates provided by Mr. Davis suggest that over the three months ending July, recruiting intensity was about 12% below the average for the seven years leading up to the recession. Their lack of effort probably accounts for about a quarter of the shortfall in the hiring rate."
Paul Krugman made note of the same issue a few days earlier:
"Job openings have plunged in every major sector, while the number of workers forced into part-time employment in almost all industries has soared. Unemployment has surged in every major occupational category."
Whitehouse mentions a solution that comes from the Krugman (and many others') playbook:
"Depressing as it might seem, the finding is in some ways encouraging. It suggests that the trouble with hiring might be more a 'cyclical' function of low business confidence than a chronic, 'structural' ailment that will last for years to come."
The "low business confidence" part sounds right, but does that make the problem "cyclical"? I'm not so sure. Let's say an employer is reluctant to post a job opening because, just for example, the cost of the new employee potentially will expand by an amount that is unknowable until the details of healthcare reform legislation are clarified. Would you call that cyclical or structural? If "low confidence" reduces the search intensity of businesses, wouldn't it be reasonable to describe the resulting drop-off in job openings "structural"?
I think a reasonable answer comes down to whether the reluctance to create a job opening would be overcome by a pickup in business activity. But that may in turn depend on whether or not businesses think they can meet that demand by expanding productivity, something they have shown great aptitude for over the course of the past three years.
Maybe businesses have reached their capacity to grow through productivity gains rather than job creation. So maybe additional policy-induced demand will be enough to overcome the uncertainties that are clearly plaguing private decision makers. But I don't see that the evidence in hand so clearly tips the scales one way or the other.
By Dave Altig, senior vice president and research director at the Atlanta Fed
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July 16, 2010
A curious unemployment picture gets more curious
UPDATE: One of our eagle-eyed macroblog readers thought something was fishy-looking in the second chart of yesterday's (July 15) post. He was right—the chart was in error. This post is an updated, edited version with the erroneous chart replaced. There have also been some text revisions to better reflect the revised chart. The new text is bolded in this post.
At first blush, the second quarter statistics from the Job Openings and Labor Turnover Survey (commonly referred to as JOLTS and released Tuesday by the U.S. Bureau of Labor Statistics) suggest little has changed recently in U.S. labor markets:
"There were 3.2 million job openings on the last business day of May 2010, the U.S. Bureau of Labor Statistics reported today. The job openings rate was little changed over the month at 2.4 percent. The hires rate (3.4 percent) was little changed and the separations rate (3.1 percent) was unchanged."
Despite a slight step backward in May, the overall trend in job openings has been positive—Calculated Risk has the picture—but in a sense this fact has just deepened the puzzle of why the unemployment rate is so darn high. As we wrote in the first quarter issue of the Atlanta Fed's EconSouth:
"The disconnect between the supply of and demand for workers that is reflected in statistics such as the unemployment rate, the hiring rate, and the layoff rate can be dynamically expressed by the Beveridge curve. Named after British economist William Beveridge, the curve is a graphical representation of the relationship between unemployment (from the BLS's household survey) and job vacancies, reflected here through the JOLTS."
Since the second quarter of last year, the unemployment rate has far exceeded the level that would be predicted by the average correlation between unemployment and job vacancies over the past decade. Tuesday's report indicates that the anomaly only deepened in the first two months of the second quarter.
The dashed line in the chart above, which is estimated from the data from 2000–08, represents the predicted relationship between the number of unemployed persons in the United States and the number of job openings. That simple relationship would suggest that, given the average number of job openings in April and May, the unemployed would be expected to number about 10.4 million—not the nearly 15 million we actually saw.
Some analysts have suggested the unemployment benefits policies of the last couple of years may be responsible for abnormally high unemployment rates. Estimates generated by several researchers in the Federal Reserve—here and here, for example—suggest that extended unemployment benefits may have increased the unemployment rate by somewhere between 0.4 and 1.7 percentage points. But even if we accept those numbers and adjust the Beveridge curve by assuming that the number of unemployed would be correspondingly lower without the benefits policy, it's not clear that the puzzle is resolved:
If you tend to believe the higher end of the benefits-bias estimates, no puzzle emerges until the second quarter of 2010. And, of course, some estimates apparently deliver an even larger impact of the extended benefits policy. Let's call the question unsettled at this point.
The most tempting explanation for the seeming shift in the Beveridge curve relationship (to me, anyway) is a problem with the mismatch between skills required in the jobs that are available and skills possessed by the pool of workers available to take those jobs. The problem with this tempting explanation is that it is not so clear that the usual sort of structural shifts we might point to—for example, only nursing jobs being available to laid-off construction workers—are so obviously an explanation (an issue we explored in a previous macroblog post).
But these sorts of subplots may miss the truly big part of the story. I have noticed a recent spate of articles repeating a theme we hear anecdotally from many sources, in many industries. For example, this from a June USA Today article…
"…the [auto] industry is poised to add up to 15,000 this year and could need up to 100,000 new workers a year from 2011 through 2013.
"…Automakers need workers with more and different skills than in the past on the factory floor.… Among priorities: computer skills and the ability to work with less supervision than their predecessors. That likely means education beyond high school."
… or more recently, this one from the New York Times:
"Factory owners have been adding jobs slowly but steadily since the beginning of the year, giving a lift to the fragile economic recovery…
"Yet some of these employers complain that they cannot fill their openings.
"Plenty of people are applying for the jobs. The problem, the companies say, is a mismatch between the kind of skilled workers needed and the ranks of the unemployed."
Now I realize that a few anecdotes don't make facts, but I have been in more than a few conversations with businesspeople who have claimed that the productivity gains realized in the United States throughout the recession and early recovery reflect upgrades in business processes—bundled with a necessary upgrade in the skill set of the workers who will implement those processes. This dynamic suggests that the shift in required skills has been concentrated within individual industries and businesses, not across sectors or geographic areas that would be captured by our most straightforward measures of structural change.
The data necessary to test this proposition are not easy to come by. That challenge is unfortunate, because the return on figuring out what is beneath those Beveridge curve graphs is very high.
By Dave Altig, senior vice president and research director at the Atlanta Fed
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