In the years since the Great Recession, many observers have highlighted the slow pace of labor and total factor productivity (TFP) growth in advanced economies. This paper focuses on the European experience, where we highlight that trend TFP growth was already low in the runup to the Global Financial Crisis (GFC). This suggests that it is important to consider factors other than just the deep crisis itself or policy changes since the crisis. After the mid-1990s, European economies stopped converging, or even began diverging, from the U.S. level of TFP. That said, in contrast to the United States, there is some macroeconomic evidence for some northern European countries that the GFC had a further adverse impact on TFP growth. Still, the challenges for economic policy look surprisingly similar to the ones discussed prior to the Great Recession, even if the policy implications seem less clear.
We account for the sources of world productivity growth, using data for more than 36 industries and 40 major economies from 1996 to 2014, explicitly taking into account changes in the misallocation of resources in labor, capital, and product markets. Productivity growth in advanced economies slowed but emerging markets grew more quickly which kept global productivity growth relatively constant until around 2010. After that, productivity growth in all major regions slowed. Much of the volatility in world productivity growth reflects shifts in the misallocation of labor across countries and industries. Using new data on PPP-based value-added measures by country and industry, we show that about a third of these shifts is due to employment growing in countries, most notably China and India, that benefit from an international cost advantage. Markups are large and rising and impact the imputed misallocation of capital. However, they have little effect on the country-industry technology contribution to global productivity.
We propose using imports, measured as reported exports of trading partners, as an alternative benchmark to gauge the accuracy of alternative Chinese indicators (including GDP) of fluctuations in economic activity. Externally-reported imports are likely to be relatively well measured, as well as free from domestic manipulation. Using principal components, we derive activity indices from a wide range of indicators and examine their fit to (trading-partner reported) imports. We choose a preferred index of eight non-GDP indicators (which we call the China Cyclical Activity Tracker, or C-CAT). Comparison with that index and others indicate that Chinese statistics have broadly become more reliable in measuring cyclical fluctuations over time. However, GDP adds little information relative to combinations of other indicators. Moreover, since 2013, Chinese GDP growth has shown little volatility around a gradually slowing trend. Other measures, including the C-CAT and imports, do not show this reduction in volatility. Since 2017, the C-CAT slowed from well above trend to close to trend. As of mid- 2019, it was giving the same cyclical signal as GDP.
What is the sustainable pace of GDP growth in the United States? A plausible point forecast is that GDP per capita will rise well under 1 percent per year in the longer run, with overall GDP growth of a little over 1-1/2 percent. The main drivers of slow growth are educational attainment and demographics. First, rising educational attainment will add less to productivity growth than it did historically. Second, because of the aging (and retirements) of baby boomers, employment will rise more slowly than population (which, in turn, is projected to rise slowly relative to history). This modest growth forecast assumes that productivity growth is relatively “normal,” if modest—in line with its pace for most of the period since 1973. An upside risk is that we see another burst of information-technology-induced productivity growth similar to what we saw from 1995 to 2004.
How reliable are China’s GDP and other data? We address this question by using trading-partner exports to China as an independent measure of its economic activity from 2000-2014. We find that the information content of Chinese GDP improves markedly after 2008. We also consider a number of plausible, non-GDP indicators of economic activity that have been identified as alternative Chinese output measures. We find that activity factors based on the first principal component of sets of indicators are substantially more informative than GDP alone. The index that best matches activity in-sample uses four indicators: electricity, rail freight, an index of raw materials supply, and retail sales. Adding GDP to this group only modestly improves in-sample performance. Moreover, out of sample, a single activity factor without GDP proves the most reliable measure of economic activity.
The manner firms respond to shocks reflects fundamental features of labor, capital, and commodity markets, as well as advances in finance and technology. Such features are integral to constructing models of the macroeconomy. In this paper we document secular shifts in the margins firms use, in aggregate, to adjust to shocks that have consequences for the economy’s cyclical behavior. These new business cycle facts on the comovement of output and its inputs are a natural complement to analyzing output and its expenditure components. Our findings shed light on the changing cyclicality of productivity in response to different shocks.
This paper describes a real-time, quarterly growth-accounting database for the U.S. business sector. The data on inputs, including capital, are used to produce a quarterly series on total factor productivity (TFP). In addition, the dataset implements an adjustment for variations in factor utilization—labor effort and the workweek of capital. The utilization adjustment follows Basu, Fernald, and Kimball (BFK, 2006). Using relative prices and input-output information, the series are also decomposed into separate TFP and utilization-adjusted TFP series for equipment investment (including consumer durables) and “consumption” (defined as business output less equipment and consumer durables).
Theory implies that the economy responds differently to technology shocks that affect the production of consumption versus investment goods. We estimate industry-level technology innovations and use the input-output tables to relax the typical assumptions in the investment-specific technical change literature—assumptions that, we find, do not hold in the data.
We find that investment-technology improvements are sharply contractionary for hours, investment, consumption, and output. Consumption-technology improvements, on the contrary, are generally expansionary. Thus, disaggregating technology shocks into consumption and investment-specific changes yields two shocks that both produce business-cycle comovement, and also explain a large fraction of annual changes in GDP and its components. Most of the responses we find are consistent with the predictions of simple two-sector models with sticky prices.
Published Articles (Refereed Journals and Volumes)
In Education, Skills, and Technical Change: Implications for Economic Growth, NBER Studies in Income and Wealth, ed. by C. Hulten and V. Ramey | NBER/University of Chicago Press, 2018 | With Bosler, Daly, and Hobijn
Over the past 15 years, labor-quality growth has been very strong–defying nearly all
earlier projections–and has added around 0.5 percentage points to an otherwise modest
U.S. productivity picture. Going forward, labor quality is likely to add considerably less
and may even be a drag on productivity growth in the medium term. Using a variety of
methods, we project that potential labor-quality growth in the longer run (7 to 10 years
out) is likely to fall in the range of 0.1 to 0.25 percent per year. In the medium term, labor-
quality growth could be lower or even negative, should employment rates of low-skilled
workers make a cyclical rebound towards pre-recession levels. The main uncertainties
in the longer run are whether the secular decline in employment of low-skilled workers
continues and whether the Great Recession pickup in educational attainment represents
the start of a new boom or is simply a transitory reaction to a poor economy.
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.
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.
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.
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.
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.
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.
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.
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.
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?
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.
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.
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.
BFK-Technology-Series.xls contains the main aggregate data series we constructed for the paper. It also contains industry technology estimates. Industry_BFK_Data.xls – 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.
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
The U.S. economy faces sizeable headwinds to keeping GDP growth even at 2% over the next decade. Demographics imply that labor force growth will be much slower than historical norms. The enormous twentieth century increase in average educational attainment is unlikely to be repeated. And the best guess for productivity growth is that it will continue to be modest—perhaps along the lines seen in the 1970s to early 1990s, or since 2004. There are no easy cures for low growth. We can hope for another wave of broadbased IT-linked innovation. But while there is enormous uncertainty, even worthwhile policy steps are unlikely to move the dial very much on their own.
After 2004, measured growth in labor productivity and total factor productivity (TFP) slowed. We find little evidence that the slowdown arises from growing mismeasurement of the gains from innovation in information-technology (IT)-related goods and services. First, mismeasurement of IT hardware is significant prior to the slowdown and because the domestic production of these products has fallen, the quantitative effect on productivity is larger in the 1995-2004 period than since, despite mismeasurement worsening for some types of IT. Hence, our adjustments make the slowdown in labor productivity worse. The effect on TFP is more muted. Second, many of the tremendous consumer benefits from smartphones, Google searches, and Facebook are, conceptually, non-market: Consumers are more productive in using their nonmarket time to produce services they value. These benefits raise consumer well-being but do not imply that market-sector production functions are shifting out more rapidly than measured. Moreover, estimated gains in non-market production are too small to compensate for the loss in overall well-being from slower market-sector productivity growth. In addition to IT, other measurement issues we can quantify (such as increasing globalization and fracking) are also quantitatively small relative to the slowdown.
What are the implications of China’s economic growth for its neighboring economies? Do the mutual benefits outweigh the costs of intensifying competition in emerging Asia? Recent research on trade between Asia and the U.S., as well as among the Asian economies, highlights the changing nature of these relationships and the attendant costs and benefits for all parties.
After the mid-1990s, labor and total factor productivity (TFP) accelerated in the United States. A growing body of research has explored the robustness of the U.S. acceleration, generally concluding that it reflects an underlying technology acceleration. This research, along with considerable anecdotal and microeconomic evidence, suggests a substantial role for information and communications technology (ICT).
In this article, we briefly discuss the results of socalled growth accounting at the aggregate level. We then look more closely at the experience since the mid- 1990s, when TFP accelerated. We look at data on which industries account for the TFP acceleration: Were the 1990s a time of rising total factor productivity growth outside of the production of ICT? Our industry data strongly support the view that a majority of the TFP acceleration reflects an acceleration outside of the production of ICT goods and software.2 Even when we focus on arguably “well-measured” sectors (Griliches 1994; Nordhaus 2002), we find a substantial TFP acceleration outside of ICT production.
This is the first paper to empirically examine whether a country’s use of an import restricting trade policy distorts a foreign country’s exports to third markets. We first develop a theoretical model of worldwide trade in which the imposition of antidumping and safeguard tariffs, or “trade remedies,” by one country causes significant distortions in world trade flows. We then empirically test this model by investigating the effect of the United States’ use of such import restrictions on Japanese exports of roughly 4800 products into 37 countries between 1992 and 2001. Our estimation yields evidence that US restrictions both deflect and depress Japanese export flows to third countries. Imposition of a US antidumping measure against Japan deflects trade, as the average antidumping duty on Japanese exports leads to a 5-7% increase in Japanese exports of the same product to the average third country market. The imposition of a US antidumping measure against a third country depresses trade, as the average US duty imposed on a third country leads to a 5-19% decrease in Japanese exports of that same product to the average third country’s market. We also document the substantial variation in trade deflection and trade depression across different importing countries and exported products.
Whatever happened to the New Economy? The good news is U.S. productivity continues to grow at a healthy pace. This article sheds light on why information and communications technology may continue to pay dividends for years to come.
A Discussion of Productivity Growth and Technology
In Technology, Growth, and the Labor Market, ed. by Ginther and Zavodny | Netherlands: Kluwer Academic Publishers, 2002
China did not succumb to the Asian crisis of 1997-99, despite two apparent sources of vulnerability: a weak financial system and increased export competition from the Asian crisis economies. This article argues that both sources of vulnerability were more apparent than real. China’s experience (especially its use of capital controls) does not offer a blueprint for other countries, because other countries would not want to replicate China’s inefficient, non-market-oriented financial system.
Anyone who follows the news, even casually, or reads product labels, is aware that the world economy has become more interdependent in recent decades. Indeed, the worldwide integration of national economies–through goods and services trade, capital flows and operational linkages among firms–has never before been as broad or as deep.
Why Is Productivity Procyclical? Why Do We Care?
In New Directions in Productivity Analysis. Studies in Income and Wealth Vol. 63, ed. by Dean, Harper, and Hulten | Chicago: University of Chicago Press, 2001 | With Basu
Growth, Reform, and the Effects of the Asian Crisis on China
China Business Review, September 1999
Why Has China Survived the Asian Crisis So Well? What Risks Remain?
In Financial Market Reform in China: Progress, Problems, and Prospects, ed. by Chen, Dietrich, and Feng | Westview Press, 1999 | With Babson