The Next Generation of the Penn World Table Feenstra, Robert C.; Inklaar, Robert; Timmer, Marcel P.
The American economic review,
10/2015, Volume:
105, Issue:
10
Journal Article
Peer reviewed
Open access
We describe the theory and practice of real GDP comparisons across countries and over time. Version 8 of the Penn World Table expands on previous versions in three respects. First, in addition to ...comparisons of living standards using components of real GDP on the expenditure side, we provide a measure of productive capacity, called real GDP on the output side. Second, growth rates are benchmarked to multiple years of cross-country price data so they are less sensitive to new benchmark data. Third, data on capital stocks and productivity are (re)introduced. Applications including the Balassa-Samuelson effect and development accounting are discussed.
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BFBNIB, CEKLJ, INZLJ, IZUM, KILJ, NMLJ, NUK, ODKLJ, PILJ, PNG, SAZU, UL, UM, UPUK, ZRSKP
This survey of business cycle synchronization in the European monetary union focuses on two issues: have business cycles become more similar, and which factors drive business cycle synchronization. ...We conclude that business cycles in the euro area have gone through periods of both convergence and divergence. Still, there is quite some evidence that during the 1990s business cycle synchronization in the euro area has increased. Higher trade intensity is found to lead to more synchronization, but the point estimates vary widely. The evidence for other factors affecting business cycle synchronization is very mixed.
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BFBNIB, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The role of physical capital is typically found to be limited in accounting for differences in GDP per worker, but this result may be because capital is customarily assumed to be a homogenous unit. ...This assumption is misleading, as different types of capital assets have different marginal products and richer countries tend to invest more in high-marginal product assets. We take this perspective to a global dataset, the Penn World Table, to improve cross-country productivity comparisons. We show that, properly measured, differences in capital input can account for a greater share of income variation, but (total factor) productivity differences remain dominant.
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The standard assumption in growth accounting is that an hour worked by a worker of given type delivers a constant quantity of labor services over time. This assumption may be violated due to vintage ...effects, which were shown to be important in the United States since the early 1980s, leading to an underestimation of the growth of labor input (Bowlus anA1d Robinson, 2012). We apply their method for identifying vintage effects to a comparison between the United States and six European countries. We find that vintage effects led to increases of labor services per hour worked by high‐skilled workers in the United States and United Kingdom and decreases in Continental European countries between 1995 and 2005. Rather than a productivity growth advantage of the US and UK, the primary difference with Continental European countries was human capital vintage effects instead.
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A country's multifactor productivity (MFP) growth, the growth of GDP that is not accounted for by growth of factor inputs, is of great interest as an indicator of living standards and technological ...progress. Yet different well-established databases show markedly different MFP growth rates for the same country and period. In this article, we show that differences in the measurement of capital input can account for one-third of the range of MFP growth rates across databases. Harmonizing a series of methodological choices for capital measurement substantially reduces variation across databases, but sizeable differences remain. This work highlights the continued relevance of these choices and can inform users who try to understand differences between databases and assess the robustness of differences in MFP growth across countries to measurement choices.
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This paper studies procyclical productivity growth at the industry level in the United States and three European countries (France, Germany and the Netherlands). Industry-specific demand-side ...instruments are used to examine the prevalence of non-constant returns to scale and unmeasured input utilization. For the aggregate US economy, unmeasured input utilization seems to explain procyclical productivity. However, this correction still leaves one in three US industries with procyclical productivity. This failure of the model can also be seen in Europe and is mostly concentrated in services industries.
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BFBNIB, FZAB, GIS, IJS, INZLJ, IZUM, KILJ, NLZOH, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
This paper reviews advanced‐economy productivity developments in recent decades. We focus primarily on the facts about, and explanations for, the mid‐2000s labor‐productivity slowdown in large ...European countries and the United States. Slower total factor productivity (TFP) growth was the proximate cause of the slowdown. This conclusion is robust to measurement challenges including the role of intangible assets, rankings of productivity levels, and data revisions. We contrast two main narratives for the stagnating TFP frontier: The shock of the Global Financial Crisis; and a common slowdown in TFP trends. Distinguishing these two empirically is hard, but the pre‐recession timing of the U.S. slowdown suggests an important role for the common‐trend explanation. We also discuss the unusual pattern of labor productivity growth since the start of the Covid‐19 pandemic. Although it is early, there is little evidence so far that the large pandemic shock has changed the slow pre‐pandemic trajectory of labor‐productivity growth.
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Investments in organization capital increase productivity of not just the investing firm but could also spillover to other firms—similar to investments in research and development. Recent evidence at ...the industry and economy level suggests such spillovers could be important. In this paper, we fail to find evidence of knowledge spillovers from organization capital that increase the productivity or market valuation of technologically similar manufacturing firms in the US. This lack of evidence at the firm-level suggests caution is in order at the more aggregate level, where spillover channels are harder to identify convincingly.
A new feature in recent versions of the Penn World Table (PWT) is data on comparative levels of total factor productivity (TFP) across countries. TFP is defined as the efficiency with which inputs ...are transformed into outputs, and differences across countries can be due to factors such as better technology or better resource allocation. Yet, surprisingly, in PWT version 10.0, a number of low-income countries have a TFP level well above that of the United States. In this article we discuss the case of Egypt in 2017. PWT then reports a productivity level that is 23 per cent higher than that of the United States despite having an income level of only one fifth of the US level. We trace this anomalous outcome to the underlying data on comparative inputs. A fully satisfactory answer to the question in the title is elusive at this point, but the analysis highlights the data challenges that affect TFP level estimates, alongside more familiar modeling and measurement challenges.
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CEKLJ, NUK, ODKLJ, UL, UM, UPUK
A new feature in recent versions of the Penn World Table (PWT) is data on comparative levels of total factor productivity (TFP) across countries. TFP is defined as the efficiency with which inputs ...are transformed into outputs, and differences across countries can be due to factors such as better technology or better resource allocation. Yet, surprisingly, in PWT version 10.0, a number of low-income countries have a TFP level well above that of the United States. In this article we discuss the case of Egypt in 2017. PWT then reports a productivity level that is 23 per cent higher than that of the United States despite having an income level of only one fifth of the US level. We trace this anomalous outcome to the underlying data on comparative inputs. A fully satisfactory answer to the question in the title is elusive at this point, but the analysis highlights the data challenges that affect TFP level estimates, alongside more familiar modeling and measurement challenges.
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CEKLJ, NUK, ODKLJ, UL, UM, UPUK
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