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The UK’s slow productivity growth since 2007 has been referred to as a “puzzle”, as if it were a particularly UK-specific challenge. In this paper, we highlight how the United States and northern Europe experienced very similar slowdowns. The common slowdown in productivity growth was a slowdown in total factor productivity (TFP) growth; we find little evidence that capital deepening was an important independent factor. From a conditional-convergence perspective, most of the UK slowdown follows from the slowdown at the U.S. frontier. From the mid-1980s to 2007, the UK’s relative productivity level moved closer to the level of the U.S. and northern Europe, driven by essentially complete convergence in market services TFP. In contrast, manufacturing lost ground relative to the U.S. frontier prior to 2007, and remains far below the frontier. The relative ground lost after 2007 is modest—cumulating to about 4 percentage points—and is largely attributable to somewhat unfavorable industry weights and industry-specific issues in mining, rather than a systematic UK competitiveness problem.

Information technology fueled a surge in U.S. productivity growth in the late 1990s and early 2000s. However, this rapid pace proved to be temporary, as productivity growth slowed before the Great Recession. Furthermore, looking through the effects of the economic downturn on productivity, the reduced pace of productivity gains has continued and suggests that average future output growth will likely be relatively slow.

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.

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.

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.

Financial liberalization in China has broad implications, including changing how its central bank’s monetary policy affects the nation’s economy. An estimate of Chinese economic activity and inflation based on a broad set of indicators suggests that the way policy is transmitted to China’s economy has become more like Western market economies in the past decade. Although Chinese monetary policy may actually have exacerbated its economic downturn during the global financial crisis, a move toward stimulatory policy has helped ease its slower growth more recently.

The impact of the global financial crisis on labor markets varied widely from country to country. In the United States, the unemployment rate nearly doubled from its pre-recession level. The rate rose much less in the United Kingdom and barely changed in Germany, despite larger declines in gross domestic product. Institutional and technological changes since the 1970s had previously made relationships between output and unemployment more homogeneous across countries. But the global financial crisis undid much of this convergence as countries adopted different labor market policies to adjust output.

Structural vector-autoregressions with long-run restrictions are extraordinarily sensitive to low-frequency correlations. This paper explores this sensitivity analytically and via simulations, focusing on the contentious issue of whether hours worked rise or fall when technology improves. Recent literature finds that when hours per person enter the VAR in levels, hours rise; when they enter in differences, hours fall. However, once we allow for (statistically and economically plausible) trend breaks in productivity, the treatment of hours is relatively unimportant: Hours fall sharply on impact following a technology improvement. The issue is the common high-low-high pattern of hours per capita and productivity growth since World-War II. Such low-frequency correlation almost inevitably implies a positive estimated impulse response. The trend breaks control for this correlation. In addition, the specification with breaks can easily 'explain' (or encompass) the positive estimated response when the breaks are omitted; in contrast, the no-breaks specification has more difficulty explaining the negative response when breaks are included. More generally, this example suggests a need for care in applying the long-run-restrictions approach.

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 to use in calculating bank output? In particular, should they take account of risk? (2) If reference rates need to be risk-adjusted, does it 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?