NBER Macroeconomics Annual 2014: Volume 29
448
NBER Macroeconomics Annual 2014: Volume 29
448eBook
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| ISBN-13: | 9780226268873 |
|---|---|
| Publisher: | University of Chicago Press Journals |
| Publication date: | 06/02/2015 |
| Series: | National Bureau of Economic Research Macroeconomics Annual , #29 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 448 |
| File size: | 21 MB |
| Note: | This product may take a few minutes to download. |
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NBER Macroeconomics Annual Volume 29 (2015)
By Jonathan A. Parker, Michael Woodford
The University of Chicago Press
Copyright © 2015 National Bureau of Economic ResearchAll rights reserved.
ISBN: 978-0-226-26887-3
CHAPTER 1
Productivity and Potential Output before, during, and after the Great Recession
John G. Fernald
Federal Reserve Bank of San Francisco
I. Introduction
When we look back at the 1990s, from the perspective of say 2010, ... [w]e may conceivably conclude ... that, at the turn of the millennium, the American economy was experiencing a once-in-a-century acceleration of innovation.... Alternatively, that 2010 retrospective might well conclude that a good deal of what we are currently experiencing was just one of the many euphoric speculative bubbles that have dotted human history. — Federal Reserve Chairman Alan Greenspan (2000)
Disappointing productivity growth ... must be added to the list of reasons that economic growth has been slower than hoped. — Federal Reserve Chairman Ben Bernanke (2014)
The past two decades have seen the rise and fall of exceptional US productivity growth. This paper argues that labor and total factor productivity (TFP) growth slowed prior to the Great Recession. It marked a retreat from the exceptional, but temporary, information technology- fueled pace from the mid-1990s to early in the twenty-first century. This retreat implies slower output growth going forward as well as a narrower output gap than recently estimated by the Congressional Budget Office (CBO 2014a).
Industry and state data show that the pre–Great Recession productivity slowdown was in sectors that produce information technology (IT) or that use IT intensively. Sectors that were obviously unusual or "euphoric" in the first decade of the twenty-first century — including housing and finance — were not the source.
Figure 1 illustrates that the mid-1990s surge in productivity growth ended prior to the Great Recession. The surge in labor productivity growth, shown by the height of the bars, came after several decades of slower growth. But in the decade ending in 2013:Q4, growth has returned close to its 1973–1995 pace. The figure shows that the slower pace of growth in both labor productivity and TFP was similar in the four years prior to the onset of the Great Recession as in the six years since.
That the slowdown predated the Great Recession rules out causal stories from the recession itself. Theory and previous empirical literature (discussed in Section II.D) provides only limited support for the view that the Great Recession should have changed the underlying path of TFP. Figure 1 suggests no evidence that productivity was slower (or much faster) from 2007 to 2013 than in the several years before that. The evidence here complements Kahn and Rich's (2013) finding in a regime-switching model that by early 2005 — that is, well before the Great Recession — the probability reached nearly unity that the economy was in a low-growth regime.
A natural hypothesis is that the slowdown was the flip side of the mid-1990s speedup. Considerable evidence, discussed in Section III.A, links the TFP speedup to the exceptional contribution of IT — computers, communications equipment, software, and the Internet. Information technology has had a broad-based and pervasive effect through its role as a general purpose technology (GPT) that fosters complementary innovations, such as business reorganization (see Bresnahan and Trajtenberg (1995) and Helpman, ed., 1998).
Industry TFP data provide evidence in favor of the IT hypothesis versus alternatives. Notably, the euphoric "bubble" sectors of housing, finance, and natural resources do not explain the slowdown. Rather, the slowdown is in the remaining three-quarters of the economy, and is concentrated in industries that produce IT or that use IT intensively. Information technology users saw a sizable bulge in TFP growth early in the first decade of the twenty-first century, even as IT spending itself slowed. That pattern is consistent with the view that benefiting from IT takes substantial intangible organizational investments that, with a lag, raise measured productivity. By the middle of the first decade of the twenty-first century, the low-hanging fruit of IT had been plucked.
State data on gross domestic product (GDP) per worker rule out indirect channels through which the housing bubble and bust might have mattered. States differ in how much house prices ran up early in the twenty-first century and collapsed after 2006. Those differences could have influenced innovation through net-worth channels. There is little evidence that housing dynamics contributed much to the dynamics of the productivity slowdown. Rather, it is the common cross-state slowdown in IT-intensive industries that predominates.
I then turn to two implications of the mid-2000s productivity slowdown. First, a multisector neoclassical growth model implies steadystate business-sector labor-productivity growth of about 1.9%, as shown at the far right of figure 1. Prior to the Great Recession, typical estimates were notably higher. Using demographic estimates from the CBO (2014a), my benchmark estimate implies longer-term growth in GDP of about 2.1% per year. As figure 1 shows, three out of the past four decades have shown this slower pace of productivity growth. That pace, rather than the exceptional 1995–2003 pace, appears normal.
Second, by 2013, the output gap, defined as the difference between actual and a production-function measure of potential output, is narrower than estimated by the CBO (2014a). I decompose the CBO's gap into a "utilization gap" that reflects cyclical mismeasurement of TFP as well as an "hours gap." The CBO estimates that the utilization gap in 2013 was as deep as any time in history other than 1982 and 2009, and was comparable to its level in 1975. In contrast, empirical estimates from Fernald (2014; following Basu et al. 2013) suggest a small utilization gap.
Figure 2 shows two alternatives to the CBO estimates of potential, with different estimates of the utilization gap. Both use the CBO labor gap to measure deviations of hours worked from steady state. One uses actual TFP, which imposes a utilization gap of zero. When utilization eventually returns to normal — as it plausibly did prior to 2013 — this measure is appropriate. The second, labeled "Fernald," uses my utilization estimate. By 2013, the alternatives imply that about three-quarters of the 2013 shortfall of actual output from the estimated precrisis trend reflects a decline in potential output. These estimates lie well below the CBO's (2014a), which itself is well below its prerecession trend. The differences arise from the CBO's assumed path for potential TFP. In contrast to the evidence in this paper, the CBO has no mid-1990s pickup in productivity and much less of a mid-2000s slowdown.
An important caveat is that production-function measures of potential output are inherently cyclical because investment is cyclical. Slow aggregate-demand growth in the recovery has led to slow closing of the output gap. Cyclically weak investment, in turn, has contributed to slow potential growth; indeed, capital input grew at the slowest pace since World War II. Slow capital growth does not directly affect output gaps — in the CBO definition (as well as the usual dynamic stochastic general equilibrium [DSGE] definition), it affects both actual and potential output. In standard models, capacity should rebound (raising potential growth above its steady-state rate) as the economy returns toward its steady-state path.
Section II discusses "facts" about the slowdown in measured labor and total-factor productivity, and compares the experience during and since the Great Recession to previous recessions and recoveries, finding that productivity experience was comparable. Section III assesses explanations for the productivity slowdown, using industry data and (maybe) regional data. Section IV uses a multisector growth model to project medium- to long-run potential output growth. The section also discusses key uncertainties. Section V then draws on the preceding analysis to discuss current potential output and slack in the context of the general methodology followed by the Congressional Budget Office. Section VI concludes.
II. Productivity Growth before the Great Recession
Trend productivity growth slowed several years before the Great Recession.
A. The mid-2000s Slowdown in Labor-Productivity Growth
Figure 3 shows the log level of business-sector labor productivity, which rationalizes the subsamples shown in figure 1. The mid-1990s speedup in growth is clear. The literature discussed in Section III.A links that speedup to information technology (IT). The slowdown in the mid-2000s is also clear. The dates of the vertical bars are suggested by the Bai-Perron test for multiple structural change in mean growth rates for the period since 1973. I have shown the traditional new-economy 1995:Q4 start date along with a slowdown date of 2003:Q4. The breaks are statistically significant.
The Bai-Perron results complement the findings of Kahn and Rich (2007, 2013). They estimate a regime-switching model, using data on labor productivity, labor compensation, and consumption. They find that productivity switched from a high-growth to a low-growth regime around 2004. By early 2005, the probability that the economy was in a low-growth regime was close to unity.
B. Growth-Accounting Identities
Growth accounting provides further perspective on the forces underpinning the slowdown. Suppose there is a constant returns aggregate production function for output, Y:
Y = A · F(W · K (K1, K2, ...), E · L (H1, K2, ...)). (1)
Variable A is technology; K and L are observed capital and labor. Variable W is the workweek of capital and E is effort — that is, unobserved variation in the utilization of capital and labor; Ki is input of a particular type of capital — computers, say, or office buildings. Similarly, Hi is hours of work by a particular type of worker, differentiated by education, age, and other characteristics. Time subscripts are omitted.
The first-order conditions for cost-minimization imply that output elasticities for a given type of input are proportional to shares in cost. Let α be total payments to capital as a share in total costs and cji, j [member of] K, L, be the shares in the total costs of capital and labor, so that [summation]i cji = 1, j [member of] K, L,. Then the output elasticity for a given type of capital, say, is αcKi. Differentiating logarithmically (where hats are log changes) and imposing the first-order conditions yields:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (2)
Various input aggregates on the right-hand side are defined as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (3)
Growth in capital services, K, is shareweighted growth in the different types of capital goods. Similarly, growth in labor services, L, is share-weighted growth in hours for different types of workers. Total hours, H [equivalent to] H1 + H2 + ..., is the simple sum of hours worked by all types of labor, so its growth rate uses hours as weights, not cost shares. Labor quality growth, [??], is the contribution of changing worker characteristics to labor services growth beyond raw hours. Finally, [??] captures variations in capital's workweek and labor effort.
TFP growth, or the Solow residual, is output growth not explained by (observed) input growth:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (4)
The second line follows from equation (2). I will always take TFP growth to be this measured Solow residual, defined by the first line in equation (4), and refer to A as utilization-adjusted TFP.
A large literature discusses why measured TFP might not reflect technology over the business cycle. A key reason is unobserved variations in the intensity with which factors are used, [??]. Basu, Fernald, and Kimball (2006) and Basu et al. (2013) implement a theoretically based measure of utilization. Their method essentially involves rescaling variations in an observable intensity margin of (detrended) hours per worker. I return to this measure below.
From equations (2) and (4), labor productivity growth, defined as growth in output per hour, is then:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (5)
Loosely, labor productivity rises if workers have more capital or better skills (quality), or if innovation raises technology. In the short run, cyclical variations in utilization also matter.
C. Aggregate Data and Growth-Accounting Results
Both TFP and capital deepening contributed to the mid-2000s slowdown in labor-productivity growth. Specifically, figure 4 shows components of equation (5) using the quarterly growth-accounting data set described in the appendix. These data provide quarterly business-sector growth accounting variables through 2013. Variables shown are in log levels (i.e., cumulated log changes). The utilization measure applies annual estimates from Basu et al. (2013) to quarterly data. Utilization is based on variations in industry hours per worker. Using restrictions from theory, Basu and colleagues relate unobserved intensity margins of capital's workweek and labor effort to this observed intensity margin.
Panel A shows TFP and utilization-adjusted TFP. These series grew rapidly from the mid-1990s to the mid-2000s, then essentially hit a flat spot. Panel B shows capital-deepening, K/(H · LQ). In the early twenty-first century, capital-deepening growth slowed (consistent, perhaps, with the slowdown in technology growth). Panel C shows that labor quality accelerated in the Great Recession as low-skilled workers disproportionately lost jobs. Finally, panel D shows utilization itself. This series is clearly highly cyclical. By early 2011, this measure had recovered to a level close to its prerecession peaks. Indeed by the end of the sample, labor productivity (figure 3) or TFP (figure 4, panel A) appear to lie more or less on the slow-trend line from the mid-2000s.
D. Productivity Growth during the Great Recession
That the slowdown predated the Great Recession suggests it was not a result of the recession itself. Still, if productivity during the recession were unusual, that might suggest a role for the recession. For example, a few years of bad productivity luck before the recession could have been followed by the greater, and more persistent, bad luck of a severe recession. This section argues this was not the case. Rather, productivity behaved similarly to previous deep recessions: TFP and utilization fell very sharply, but recovered strongly once the recession ended.
Figure 5 shows "spider charts" comparing the Great Recession to the nine previous recessions (1953–2001). In each panel, the horizontal axis shows the number of quarters from the peak. In the Great Recession, for example, quarter 0 corresponds to 2007:Q4. The vertical axis is the percent change since the peak. I remove local trends from all data.
Panels A and B show how unusual output and hours were, with steep declines in both. For the first three quarters (through 2008:Q3), the declines in output and hours worked relative to trend were modest — at the top of the range of historical experience. After Lehman and AIG in quarter 4, output and employment fell precipitously. The trough in detrended output is about as deep as previous deep recessions, but is reached later. (In unfiltered data, the decline is deeper than previous recessions. Detrending has a larger effect in previous deep recessions when trend growth was faster.)
(Continues...)
Excerpted from NBER Macroeconomics Annual Volume 29 (2015) by Jonathan A. Parker, Michael Woodford. Copyright © 2015 National Bureau of Economic Research. Excerpted by permission of The University of Chicago Press.
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Table of Contents
Contents
Editors' Introduction Jonathan A. Parker and Michael Woodford,Abstracts,
Productivity and Potential Output before, during, and after the Great Recession John G. Fernald,
Comment Samuel Kortum and Unni Pillai,
Comment John Haltiwanger,
Discussion,
Quantifying the Lasting Harm to the US Economy from the Financial Crisis Robert E. Hall,
Comment Martin S. Eichenbaum,
Comment Narayana Kocherlakota,
Discussion,
Information Aggregation in a Dynamic Stochastic General Equilibrium Model Tarek A. Hassan and Thomas M. Mertens,
Comment Guido Lorenzoni,
Comment George-Marios Angeletos,
Discussion,
Whither News Shocks? Robert B. Barsky, Susanto Basu, and Keyoung Lee,
Comment Franck Portier,
Comment Lawrence J. Christiano,
Discussion,
Effective Monetary Policy Strategies in New Keynesian Models: A Reexamination Hess Chung, Edward Herbst, and Michael T. Kiley,
Comment Lars E. O. Svensson,
Comment Mark Gertler,
Discussion,
Labor-Market Polarization over the Business Cycle Christopher L. Foote and Richard W. Ryan,
Comment Richard Rogerson,
Comment Fatih Guvenen,
Discussion,
Costs and Benefits to Phasing out Paper Currency Kenneth Rogoff,