Why Has the Cyclicality of Productivity Changed? What Does It Mean?
Annual Review of Economics 8, October 2016, 465-496 | With Wang
U.S. labor and total factor productivity have historically been procyclical—rising in booms and falling in recessions. After the mid-1980s, however, TFP became much less procyclical with respect to hours while labor productivity turned strongly countercyclical. We find that the key empirical “fact” driving these changes is reduced variation in factor utilization—conceptually, the workweek of capital and labor effort. We discuss a range of theories that seek to explain the changes in productivity’s cyclicality. Increased flexibility, changes in the structure of the economy, and shifts in relative variances of technology and “demand” shocks appear to play key roles.
European Economic Review 88(C), April 2016, 3-20 | With Cette and Mojon
In the years since the Great Recession, many observers have highlighted the slow pace of productivity growth around the world. For the United States and Europe, we highlight that this slow pace began prior to the Great Recession. The timing thus suggests that it is important to consider factors other than just the deep crisis itself or policy changes since the crisis. For the United States, at the frontier of knowledge, there was a burst of innovation and reallocation related to the production and use of information technology in the second half of the 1990s and the early 2000s. That burst ran its course prior to the Great Recession. Continental European economies were falling back relative to that frontier at varying rates since the mid-1990s. We provide VAR and panel-data evidence that changes in real interest rates have influenced productivity dynamics in this period. In particular, the sharp decline in real interest rates that took place in Italy and Spain seem to have triggered unfavorable resource reallocations that were large enough to reduce the level of total factor productivity, consistent with recent theories and firm-level evidence.
Journal of International Money and Finance, July 2014 | With Spiegel and Swanson
We use a broad set of Chinese economic indicators and a dynamic factor model framework to estimate Chinese economic activity and inflation as latent variables. We incorporate these latent variables into a factor-augmented vector autoregression (FAVAR) to estimate the effects of Chinese monetary policy on the Chinese economy. A FAVAR approach is particularly well-suited to this analysis due to concerns about Chinese data quality, a lack of a long history for many series, and the rapid institutional and structural changes that China has undergone. We find that increases in bank reserve requirements reduce economic activity and inflation, consistent with previous studies. In contrast to much of the literature, however, we find that changes in Chinese interest rates also have substantial impacts on economic activity and inflation, while other measures of changes in credit conditions, such as shocks to M2 or lending levels, do not once other policy variables are taken into account. Overall, our results indicate that the monetary policy transmission channels in China have moved closer to those of Western market economies.
In NBER Macroeconomics Annual, ed. by Parker, Woodford, 2014
U.S. labor and total-factor productivity growth slowed prior to the Great Recession. The timing rules explanations that focus on disruptions during or since the recession, and industry and state data rule out “bubble economy” stories related to housing or finance. The slowdown is located in industries that produce information technology (IT) or that use IT intensively, consistent with a return to normal productivity growth after nearly a decade of exceptional IT-fueled gains. A calibrated growth model suggests trend productivity growth has returned close to its 1973-1995 pace. Slower underlying productivity growth implies less economic slack than recently estimated by the Congressional Budget Office. As of 2013, about ¾ of the shortfall of actual output from (overly optimistic) pre-recession trends reflects a reduction in the level of potential.
National Institute Economic Review 228: R58-R64, May 2014 | With Nechio, Daly, and Jorda
This note examines labor market performance across countries through the lens of Okun’s Law. We find that after the 1970s but prior to the global financial crisis of the 2000s, the Okun’s Law relationship between output and unemployment became more homogenous across countries. These changes presumably reflected institutional and technological changes. But, at least in the short term, the global financial crisis undid much of this convergence, in part because the affected countries adopted different labor market policies in response to the global demand shock.
American Economic Review 104(5), May 2014, 44-49 | With Jones
Modern growth theory suggests that more than 3/4 of growth since 1950 reflects rising educational attainment and research intensity. As these transition dynamics fade, U.S. economic growth is likely to slow at some point. However, the rise of China, India, and other emerging economies may allow another few decades of rapid growth in world researchers. Finally, and more speculatively, the shape of the idea production function introduces a fundamental uncertainty into the future of growth. For example, the possibility that artificial intelligence will allow machines to replace workers to some extent could lead to higher growth in the future.
American Economic Journal: Macroeconomics 3, April 2011, 29-74
We show that in a two-sector economy with heterogeneous capital subsidies and monopoly power, primal and dual measures of TFP growth can diverge from each other as well as from true technology. These distortions give rise to dynamic reallocation effects that imply technology growth needs to be measured from the bottom up rather than from the top down. Using Singapore as an example, we show how incomplete data can be used to estimate aggregate and sectoral technology growth as well as reallocation effects. Our framework can reconcile divergent TFP estimates in Singapore and can resolve other empirical puzzles regarding Asian development.
FRB St Louis Review 91(4), July 2009, 187-213 | With Basu
Potential output is an important concept in economics. Policymakers often use a one-sector neoclassical model to think about long-run growth, and they often assume that potential output is a smooth series in the short run–approximated by a medium- or long-run estimate. But in both the short and the long run, the one-sector model falls short empirically, reflecting the importance of rapid technological change in producing investment goods; and few, if any, modern macroeconomic models would imply that, at business cycle frequencies, potential output is a smooth series. Discussing these points allows the authors to discuss a range of other issues that are less well understood and where further research could be valuable.
In Price Index Concepts and Measurement, 70, ed. by E. Diewert, J. Greenlees, and C. Hulten | Chicago: University of Chicago Press for NBER, 2009. 273-320 | With Basu and Wang
This paper addresses the proper measurement of financial service output that is not priced explicitly. It shows how to impute nominal service output from financial intermediaries’ interest income, and how to construct price indices for those financial services. We present an optimizing model with financial intermediaries that provide financial services to resolve asymmetric information between borrowers and lenders. We embed these intermediaries in a dynamic, stochastic, general-equilibrium model where assets are priced competitively according to their systematic risk, as in the standard consumption- capital- asset-pricing model. In this environment, we show that it is critical to take risk into account in order to measure financial output accurately. We also show that even using a risk-adjusted reference rate does not solve all the problems associated with measuring nominal financial service output. Our model allows us to address important outstanding questions in output and productivity measurement for financial firms, such as: (1) What are the correct “reference rates” one should use in calculating bank output? (2) If reference rates need to take account of risk, does this mean that they must be ex ante rates of return? (3) What is the right price deflator for the output of financial firms? Is it just the general price index? (4) When–if ever–should we count capital gains of financial firms as part of financial service output?
Journal of Monetary Economics 54(8), November 2007, 2467-2485
Structural vector autoregressions with long-run restrictions are extraordinarily sensitive to low-frequency correlations. Recent literature finds that the estimated effects of technology shocks are sensitive to how one treats hours per capita. However, after allowing for (statistically and economically significant) trend breaks in productivity, results are much less sensitive: hours fall when technology improves. The issue is that the common high-low-high pattern of productivity growth and hours (i.e., the low-frequency correlation) inevitably leads to a positive estimated response. The trend breaks control for this correlation. This example suggests a practical need for care in using long-run restrictions.
German Economic Review 8(2), May 2007, 146-173 | With Basu
Many people point to information and communications technology (ICT) as the key for understanding the acceleration in productivity in the United States since the mid-1990s. Stories of ICT as a ‘general-purpose technology’ suggest that measured total factor productivity (TFP) should rise in ICT-using sectors (reflecting either unobserved accumulation of intangible organizational capital; spillovers; or both), but with a long lag. Contemporaneously, however, investments in ICT may be associated with lower TFP as resources are diverted to reorganization and learning. We find that U.S. industry results are consistent with general-purpose technology (GPT) stories: the acceleration after the mid-1990s was broad-based–located primarily in ICT-using industries rather than ICT-producing industries. Furthermore, industry TFP accelerations in the 2000s are positively correlated with (appropriately weighted) industry ICT capital growth in the 1990s. Indeed, as GPT stories would suggest, after controlling for past ICT investment, industry TFP accelerations are negatively correlated with increases in ICT usage in the 2000s.
American Economic Review 96(5), December 2006, 1418-1448 | With Basu and Kimball
Yes. We construct a measure of aggregate technology change, controlling for aggregation effects, varying utilization of capital and labor, nonconstant returns, and imperfect competition. On impact, when technology improves, input use and nonresidential investment fall sharply. Output changes little. With a lag of several years, inputs and investment return to normal and output rises strongly. The standard one-sector real-business-cycle model is not consistent with this evidence. The evidence is consistent, however, with simple sticky-price models, which predict the results we find: when technology improves, inputs and investment generally fall in the short run, and output itself may also fall.
contains the main aggregate data series we constructed for the paper. It also contains industry technology estimates.
– contains additional underlying industry data. These include growth rates for gross output, value added, primary inputs, total inputs, and hours per worker; and factor shares.
NBER Macroeconomics Annual, 2003 | With Basu, Oulton, and Srinivasan
We argue that unmeasured investments in intangible organizational capital associated with the role of information and communications technology (ICT) as a general purpose technology’ can explain the divergent U.S. and U.K. TFP performance after 1995. GPT stories suggest that measured TFP should rise in ICT-using sectors, perhaps with long lags. Contemporaneously, investments in ICT may in fact be associated with lower TFP as resources are diverted to reorganization and learning. In both the U.S. and U.K., we find a strong correlation between ICT use and industry TFP growth. The U.S. results, in particular, are consistent with GPT stories: the TFP acceleration was located primarily in ICT-using industries and is positively correlated with industry ICT capital growth from the 1980s and early 1990s. Indeed, as GPT stories suggest, controlling for past ICT growth, industry TFP growth appears negatively correlated with increases in ICT capital services in the late 1990s. A somewhat different picture emerges for the U.K. TFP growth does not appear correlated with lagged ICT capital growth. But TFP growth in the late 1990s is strongly and positively associated with the growth of ICT capital services, while being strongly and negatively associated with the growth of ICT investment.
Puzzles in the Chinese Stock Market
Review of Economics and Statistics, August 2002 | With Rogers
Aggregate Productivity and Aggregate Technology
European Economic Review, June 2002 | With Basu
Productivity Growth in the 1990s: Technology, Utilization, or Adjustment?
Carnegie-Rochester Series on Public Policy, December 2001 | With Basu and Shapiro
Was China the First Domino? Assessing the Links between China and the Rest of Emerging Asia
Journal of International Money and Finance, August 1999 | With Edison and Loungani
Roads to Prosperity? Assessing the Link between Public Capital and Productivity
American Economic Review, June 1999, 619-638
Returns to Scale in U.S. Manufacturing: Estimates and Implications
Journal of Political Economy, April 1997 | With Basu
Are Apparent Productive Spillovers a Figment of Specification Error?
Journal of Monetary Economics, August 1995 | With Basu