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Introduction and Summary 1 Variations in productivity growth have proven one of the most durable puzzles in macroeconomics. After growing rapidly for a quarter century, productivity came to a virtual halt in the early 1970s. There was no shortage of explanations for the productivity-growth slowdown. The explanations included rising energy prices, high and unpredictable inflation, rising tax rates, growing government, burdensome environmental and health regulation, declining research and development, deteriorating labor skills, depleted inventive possibilities, and societal laziness. These explanations seemed increasingly inadequate as inflation fell, tax rates were cut, regulatory burdens stabilized, government’s share output fell, research and development and patents granted grew sharply, energy prices fell back to pre-1973 levels, and a burst of invention in the new economy and other sectors fueled an investment boom in the 1990s. One of the major puzzles in the mid-1990s revolved around the inability of increasingly sophisticated and powerful computers and software to give an upward boost to productivity growth. This puzzle was expressed in the famous Solow paradox, “Computers are everywhere except in the productivity statistics.” Notwithstanding the ubiquitous computer, through thin and thick labor-productivity growth seemed to be on a stable track of slightly over 1 percent per year. However, in the late 1990s, productivity growth rebounded sharply. Over the period from 1995 to mid-2000, productivity growth in the business sector grew at a rate close to that in the pre- 1973 period. The causes of the rebound were widely debated, but at least part was clearly due to astonishing productivity growth in the “new economy” sectors of information and communications. The present study is the last of three papers devoted to developing new data and methods for measuring productivity growth and examining the extent and sources of the current productivity 1The author is grateful for comments from Ray Fair. Version is [d1284jrn.wpd] rebound. In addition to examining recent productivity behavior, the current study adds a few new features to the analysis. First, it examines the welfare-theoretic basis of productivity measures and proposes an “ideal” index of productivity growth. Second, it lays out a different way of decomposing productivity growth which divides aggregate productivity trends into factors that increase average productivity growth from the changing shares of the sizes of different sectors. Third, we develop an alternative way of measuring aggregate and industrial productivity based on industrial data built up from the income side rather than the product side of the accounts. By relying on the industrial data, we can focus on different definitions of output and get sharper estimates of the sources of productivity growth. Fourth, by working with the new industrial data, we can make more accurate adjustments for the contribution of the “new economy” than has been the case in earlier studies. Finally, this new data set allows us to create a new economic aggregate, which we call “well- measured output,” that allows us to remove those sectors where output is poorly measured or measured by inputs. Because the study is heavily methodological and data-intensive, we summarize the approach and major results in this introductory section. 1. The present study introduces a new approach to measuring industrial productivity. It develops an income-side data base, currently available to 1997–98, on labor productivity relying on data that are published by the Bureau of Economic Analysis (BEA). The data are internally consistent in that both inputs and outputs are income-side measures of value added, whereas the usual productivity measures combine expenditure-side output measures with income-side input measures. The advantage of the unified income-side measures is that they present a consistent set of industrial accounts. The disadvantages are that they are only available for the period 1977–98 and that they do not contain a set of capital accounts, so we can only examine labor productivity. 2. We have constructed a set of labor productivity measures for four different definitions of output: • GDP from the income side • BLS’s business sector output from the income side • A new measure called “well-measured output,” which includes only those sectors for which output is relatively well measured • The “new economy” as defined below 3. The constructed measures of labor productivity are reasonably consistent with the most widely studied measure, the BLS’s measure of labor productivity in the business sector. Over the period of investigation, the difference between the labor-productivity growth rate estimates of the income-side and product-side constructs is 0.05 percentage points per year. However, in the last three years, productivity from the income-side series grew more than 1 percentage point more rapidly, primarily because of the movement in the statistical discrepancy between output and income side measures. 4. There has clearly been a rebound in productivity growth since 1995. The rebound is found in all three sectoral definitions developed for this study. The labor productivity acceleration in the last three years of the period (1996–98) relative to the 1978–95 period was 1.2 percentage points for GDP, 1.8 percentage points for business sector, and 2.1 percentage points for well-measured output. 5. We have developed a new technique for decomposing changes in labor-productivity growth between different sources. This decomposition identifies a pure productivity effect (which is a fixed weighted average of the productivity growth rates of different industries); the Denison effect (which captures the effect of changing shares of employment on aggregate productivity); and the Baumol effect (which captures the interaction between the differences in productivity growth and the changing hours shares of different industries over time). Total productivity growth is the sum of these three effects. 6. Our estimates show that the pure productivity effect in recent years has been substantially above total productivity growth. For example, in the business sector for the period 1996–98, total labor-productivity growth has been 3.19 percent per year while the pure productivity effect was 3.61 percent per year. The difference was primarily due to the Baumol effect, with a rising share of hours in services accompanying slow labor-productivity growth in services. The Baumol and Denison effects are relatively small for well-measured output. 7. The first companion paper to this study provides a rigorous definition of the appropriate measure of productivity growth from a welfare-theoretic point of view. The present paper applies the ideal measure to the new data set. We find that the ideal measure is higher than other commonly used measures of labor-productivity growth in every period. The differences are relatively small in the most recent period, but they are substantial in earlier periods. On average, the ideal or welfare- theoretic measure over the 1978–98 period is about 0.2 percentage points per year higher than the other measures. 8. One key question is the contribution of the new economy to the productivity rebound. For the purpose of this study, we define the new economy as machinery, electric
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