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Measuring and Sustaining the New Economy: Report of a Workshop Appendix A Raising the Speed Limit: U.S. Economic Growth in the Information Age Dale W. Jorgenson Harvard University and Kevin J. Stiroh* Federal Reserve Bank of New York May 1, 2000 ABSTRACT This paper examines the underpinnings of the successful performance of the U.S. economy in the late 1990s. Relative to the early 1990s, output growth has accelerated by nearly two percentage points. We attribute this to rapid capital accumulation, a surge in hours worked, and faster growth of total factor productivity. The acceleration of productivity growth, driven by information technology, is the most remarkable feature of the U.S. growth resurgence. We consider the implications of these developments for the future growth of the U.S. economy. (JEL Codes: O3, O4) * Jorgenson: Department of Economics, Harvard University, email@example.com., (617) 495-0833. Stiroh: Banking Studies Function, Federal Reserve Bank of New York, firstname.lastname@example.org, (212) 720-6633. We are indebted to Mun Ho for his comments and assistance with the industry and labor data. We are also grateful to Bob Arnold of CBO for helpful comments and discussions of the CBO’s results and methods and Bruce Grimm and Dave Wasshausen of BEA for details on the BEA investment data and prices. Our thanks are due to Erwin Diewert, Robert Gordon, Steve Oliner, Dan Sichel, as well as seminar participants at the Brookings Panel on Economic Activity, the Federal Reserve Bank of New York, and the Federal Reserve Board for helpful comments and advice. Dave Fiore provided excellent research assistance. The views expressed in this paper are those of the authors only and do not necessarily reflect the views of the Federal Reserve Bank of New York or the Federal Reserve System. NOTE: This appendix was originally published in Brookings Papers on Economic Activity 1:2000 (Brookings Institution, 2000). All rights reserved.
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Measuring and Sustaining the New Economy: Report of a Workshop INTRODUCTION The continued strength and vitality of the U.S. economy continues to astonish economic forecasters.1 A consensus is now emerging that something fundamental has changed with “new economy” proponents pointing to information technology as the causal factor behind the strong performance of the U.S. economy. In this view, technology is profoundly altering the nature of business, leading to permanently higher productivity growth throughout the economy. Skeptics argue that the recent success reflects a series of favorable, but temporary, shocks. This argument is buttressed by the view that the U.S. economy behaves rather differently than envisioned by new economy advocates.2 While productivity growth, capital accumulation, and the impact of technology were topics once reserved for academic debates, the recent success of the U.S. economy has moved them into popular discussion. The purpose of this paper is to employ well-tested and familiar methods to analyze important new information made available by the recent benchmark revision of the U.S. National Income and Product Accounts (NIPA). We document the case for raising the speed limit—for upward revision of intermediate-term projections of future growth to reflect the latest data and trends. The late 1990s have been exceptional in comparison with the growth experience of the U.S. economy over the past quarter century. While growth rates in the 1990s have not yet returned to those of the golden age of the U.S. economy in the l960s, the data nonetheless clearly reveal a remarkable transformation of economic activity. Rapid declines in the prices of computers and semi-conductors are well known and carefully documented, and evidence is accumulating that similar declines are taking place in the prices of software and communications equipment. Unfortunately, the empirical record is seriously incomplete, so much remains to be done before definitive quantitative assessments can be made about the complete role of these high-tech assets. Despite the limitations of the available data, the mechanisms underlying the structural transformation of the U.S. economy are readily apparent. As an illustration, consider the increasing role that computer hardware plays as a source of economic growth.3 For the period 1959 to 1973, computer inputs contributed less 1 Labor productivity growth for the business sector averaged 2.7% for 1995-99, the four fastest annual growth rates in the 1990s, except for a temporary jump of 4.3% in 1992 as the economy exited recession (BLS (2000)). 2 Stiroh (1999) critiques alternative new economy views, Triplett (1999) examines data issues in the new economy debate, and Gordon (1999b) provides an often-cited rebuttal of the new economy thesis. 3 Our work on computers builds on the path-breaking research of Oliner and Sichel (1994, 2000) and Sichel (1997, 1999), and our own earlier results, reported in Jorgenson and Stiroh (1995, 1999, 2000) and Stiroh (1998a). Other valuable work on computers includes Haimowitz (1998), Kiley (1999), and Whelan (1999). Gordon (1999a) provides an historical perspective on the sources of U.S. economic growth and Brynjolfsson and Yang (1996) review the micro evidence on computers and productivity.
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Measuring and Sustaining the New Economy: Report of a Workshop than one-tenth of one percent to U.S. economic growth. Since 1973, however, the price of computers has fallen at historically unprecedented rates and firms and households have followed a basic principle of economics—they have substituted towards relatively cheaper inputs. Since 1995 the price decline for computers has accelerated, reaching nearly 28% per year from 1995 to 1998. In response, investment in computers has exploded and the growth contribution of computers increased more than five-fold to 0.46 percentage points per year in the late 1990s.4 Software and communications equipment, two other information technology assets, contributed an additional 0.30 percentage points per year for 1995-98. Preliminary estimates through 1999 reveal further increases in these contributions for all three high-tech assets. Next, consider the acceleration of average labor productivity (ALP) growth in the 1990s. After a 20-year slowdown dating from the early 1970s, ALP grew 2.4% per year for 1995-98, more than a percentage point faster than during 1990-95.5 A detailed decomposition shows that capital deepening, the direct consequence of price-induced substitution and rapid investment, added 0.49 percentage points to ALP growth. Faster total factor productivity (TFP) growth contributed an additional 0.63 percentage points, largely reflecting technical change in the production of computers and the resulting acceleration in the price decline of computers. Slowing labor quality growth retarded ALP growth by 0.12 percentage points, relative to the early 1990s, a result of exhaustion of the pool of available workers. Focusing more specifically on TFP growth, this was an anemic 0.34% per year for 1973-95, but accelerated to 0.99% for 1995-98. After more than twenty years of sluggish TFP growth, four of the last five years have seen growth rates near 1%. It could be argued this represents a new paradigm. According to this view, the diffusion of information technology improves business practices, generates spillovers, and raises productivity throughout the economy. If this trend is sustainable, it could revive the optimistic expectations of the 1960s and overcome the pessimism of The Age of Diminished Expectations, the title of Krugman’s (1990) influential book. A closer look at the data, however, shows that gains in TFP growth can be traced in substantial part to information technology industries, which produce computers, semi-conductors, and other high-tech gear. The evidence is equally clear that computer-using industries like finance, insurance, and real estate (FIRE) and services have continued to lag in productivity growth. Reconciliation of mas- 4 See Baily and Gordon (1988), Stiroh (1998a), Jorgenson and Stiroh (1999) and Department of Commerce (1999) for earlier discussions of relative price changes and input substitution in the high-tech areas. 5 BLS (2000) estimates for the business sector show a similar increase from 1.6% for 1990-95 to 2.6% for 1995-98. See CEA (2000, pg. 35) for a comparison of productivity growth at various points in the economic expansions of the 1960s, 1980s, and 1990s.
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Measuring and Sustaining the New Economy: Report of a Workshop sive high-tech investment and relatively slow productivity growth in service industries remains an important task for proponents of the new economy position.6 What does this imply for the future? The sustainability of growth in labor productivity is the key issue for future growth projections. For some purposes, the distinctions among capital accumulation and growth in labor quality and TFP may not matter, so long as ALP growth can be expected to continue. It is sustainable labor productivity gains, after all, that ultimately drive long-run growth and raise living standards. In this respect, the recent experience provides grounds for caution, since much depends on productivity gains in high-tech industries. Ongoing technological gains in these industries have been a direct source of improvement in TFP growth, as well as an indirect source of more rapid capital deepening. Sustainability of growth, therefore, hinges critically on the pace of technological progress in these industries. As measured by relative price changes, progress has accelerated recently, as computer prices fell 28% per year for 1995-98 compared to 15% in 1990-95. There is no guarantee, of course, of continued productivity gains and price declines of this magnitude. Nonetheless, as long as high-tech industries maintain the ability to innovate and improve their productivity at rates comparable even to their long-term averages, relative prices will fall and the virtuous circle of an investment-led expansion will continue.7 Finally, we argue that rewards from new technology accrue to the direct participants; first, to the innovating industries producing high-tech assets and, second, to the industries that restructure to implement the latest information technology. There is no evidence of spillovers from production of information technology to the industries that use this technology. Indeed, many of the industries that use information technology most intensively, like FIRE and services, show high rates of substitution of information technology for other inputs and relatively low rates of productivity growth. In part, this may reflect problems in measuring the output from these industries, but the empirical record provides little support for the “new economy” picture of spillovers cascading from information technology producers onto users of this technology.8 The paper is organized as follows. Section II describes our methodology for quantifying the sources of U.S. economic growth. We present results for the period 1959-1998, and focus on the “new economy” era of the late 1990s. Section 6 See Gullickson and Harper (1999), Jorgenson and Stiroh (2000), and Section IV, below, for industry-level analysis. 7 There is no consensus, however, that technical progress in computer and semi-conductor production is slowing. According to Fisher (2000), chip processing speed continues to increase rapidly. Moreover, the product cycle is accelerating as new processors are brought to market more quickly. 8 See Dean (1999) and Gullickson and Harper (1999) for the BLS perspective on measurement error; Triplett and Bosworth (2000) provide an overview of measuring output in the service industries.
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Measuring and Sustaining the New Economy: Report of a Workshop III explores the implications of the recent experience for future growth, comparing our results to recent estimates produced by the Congressional Budget Office, the Council of Economic Advisors, and the Office of Management and Budget. Section IV moves beyond the aggregate data and quantifies the productivity growth at the industry level. Using methodology introduced by Domar (1961), we consider the impact of information technology on aggregate productivity. Section V concludes. II. THE RECENT U.S. GROWTH EXPERIENCE The U.S. economy has undergone a remarkable transformation in recent years with growth in output, labor productivity, and total factor productivity all accelerating since the mid-1990s. This growth resurgence has led to a widening debate about sources of economic growth and changes in the structure of the economy. “New economy” proponents trace the changes to developments in information technology, especially the rapid commercialization of the Internet, that are fundamentally changing economic activity. “Old economy” advocates focus on lackluster performance during the first half of the 1990s, the increase in labor force participation and rapid decline in unemployment since 1993, and the recent investment boom. Our objective is to quantify the sources of the recent surge in U.S. economic growth, using new information made available by the benchmark revision of the U.S. National Income and Product Accounts (NIPA) released in October 1999, BEA (1999). We then consider the implications of our results for intermediate-term projections of U.S. economic growth. We give special attention to the rapid escalation in growth rates in the official projections, such as those by the Congressional Budget Office (CBO) and the Council of Economic Advisers (CEA). The CBO projections are particularly suitable for our purposes, since they are widely disseminated, well documented, and represent “best practice.” We do not focus on the issue of inflation and do not comment on potential implications for monetary policy. (a) Sources of Economic Growth Our methodology is based on the production possibility frontier introduced by Jorgenson (1966) and employed by Jorgenson and Griliches (1967). This captures substitutions among outputs of investment and consumption goods, as well inputs of capital and labor. We identify information technology (IT) with investments in computers, software, and communications equipment, as well as consumption of computer and software as outputs. The service flows from these assets are also inputs. The aggregate production function employed by Solow (1957, 1960) and, more recently by Greenwood, Hercowitz, and Krusell (1997), is an alternative to our model. In this approach a single output is expressed as a
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Measuring and Sustaining the New Economy: Report of a Workshop function of capital and labor inputs. This implicitly assumes, however, that investments in information technology are perfect substitutes for other outputs, so that relative prices do not change. Our methodology is essential in order to capture two important facts about which there is general agreement. The first is that prices of computers have declined drastically relative to the prices of other investment goods. The second is that this rate of decline has recently accelerated. In addition, estimates of investment in software, now available in the NIPA, are comparable to investment in hardware. The new data show that the price of software has fallen relative to the prices of other investment goods, but more slowly than price of hardware. We examine the estimates of software investment in some detail in order to assess the role of software in recent economic growth. Finally, we consider investment in communications equipment, which shares many of the technological features of computer hardware. i) Production Possibility Frontier Aggregate output Yt consists of investment goods It and consumption goods Ct. These outputs are produced from aggregate input Xt, consisting of capital services Kt and labor services Lt. We represent productivity as a “Hicks-neutral” augmentation At of aggregate input:9 The outputs of investment and consumption goods and the inputs of capital and labor services are themselves aggregates, each with many sub-components. Under the assumptions of competitive product and factor markets, and constant returns to scale, growth accounting gives the share-weighted growth of outputs as the sum of the share-weighted growth of inputs and growth in total factor productivity (TFP): where is investment’s average share of nominal output, is consumption’s average share of nominal output, is capital’s average share of nominal income, is labor’s average share of nominal income, 1, and ∆ refers to a first difference. Note that we reserve the term total factor productivity for the augmentation factor in Equation (1). 9 It would be a straightforward change to make technology labor-augmenting or “Harrod-neutral,” so that the production possibility frontier could be written: Y(I, C) = X(K,AL). Also, there is no need to assume that inputs and outputs are separable, but this simplifies our notation.
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Measuring and Sustaining the New Economy: Report of a Workshop Equation (2) enables us to identify the contributions of outputs as well as inputs to economic growth. For example, we can quantify the contributions of different investments, such as computers, software, and communications equipment, to the growth of output by decomposing the growth of investment among its sub-components. Similarly, we can quantify the contributions of different types of consumption, such as services from computers and software, by decomposing the growth of consumption. As shown in Jorgenson and Stiroh (1999), both computer investment and consumption of IT have made important contributions to U.S. economic growth in the 1990s. We also consider the output contributions of software and communications equipment as distinct high-tech assets. Similarly, we decompose the contribution of capital input to isolate the impact of computers, software, and communications equipment on input growth. Rearranging Equation (2) enables us to present results in terms of growth in average labor productivity (ALP), defined as yt = Yt/Ht, where Yt is output, defined as an aggregate of consumption and investment goods, and kt = Kt/Ht is the ratio of capital services to hours worked Ht: This gives the familiar allocation of ALP growth among three factors. The first is capital deepening, the growth in capital services per hour. Capital deepening makes workers more productive by providing more capital for each hour of work and raises the growth of ALP in proportion to the share of capital. The second term is the improvement in labor quality, defined as the difference between growth rates of labor input and hours worked. Reflecting the rising proportion of hours supplied by workers with higher marginal products, labor quality improvement raises ALP growth in proportion to labor’s share. The third factor is total factor productivity (TFP) growth, which increases ALP growth on a point-for-point basis. ii) Computers, software, and communications equipment We now consider the impact of investment in computers, software, and communications equipment on economic growth. For this purpose we must carefully distinguish the use of information technology and the production of information technology.10 For example, computers themselves are an output from one industry (the computer-producing industry, Commercial and Industrial Machinery), 10 Baily and Gordon (1988), Griliches (1992), Stiroh (1998a), Jorgenson and Stiroh (1999), Whelan (1999), and Oliner and Sichel (2000) discuss the impact of investment in computers from these two perspectives.
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Measuring and Sustaining the New Economy: Report of a Workshop and computing services are inputs into other industries (computer-using industries like Trade, FIRE, and Services). Massive increases in computing power, like those experienced by the U.S. economy, therefore reflect two effects on growth. First, as the production of computers improves and becomes more efficient, more computing power is being produced from the same inputs. This raises overall productivity in the computer-producing industry and contributes to TFP growth for the economy as a whole. Labor productivity also grows at both the industry and aggregate levels.11 Second, the rapid accumulation of computers leads to input growth of computing power in computer-using industries. Since labor is working with more and better computer equipment, this investment increases labor productivity. If the contributions to output are captured by the effect of capital deepening, aggregate TFP growth is unaffected. As Baily and Gordon (1988) remark, “there is no shift in the user firm’s production function (pg. 378),” and thus no gain in TFP. Increasing deployment of computers increases TFP only if there are spillovers from the production of computers to production in the computer-using industries, or if there are measurement problems associated with the new inputs. We conclude that rapid growth in computing power affects aggregate output through both TFP growth and capital deepening. Progress in the technology of computer production contributes to growth in TFP and ALP at the aggregate level. The accumulation of computing power in computer-using industries reflects the substitution of computers for other inputs and leads to growth in ALP. In the absence of spillovers this growth does not contribute to growth in TFP. The remainder of this section provides empirical estimates of the variables in Equations (1) through (3). We then employ Equations (2) and (3) to quantify the sources of growth of output and ALP for 1959-1998 and various sub-periods. (b) Output Our output data are based on the most recent benchmark revision of the NIPA.12 Real output Yt is measured in chained 1996 dollars, and PY,t is the corresponding implicit deflator. Our output concept is similar, but not identical, to one used in the Bureau of Labor Statistics (BLS) productivity program. Like BLS, we exclude the government sector, but unlike BLS we include imputations for the service flow from consumers’ durables (CD) and owner-occupied housing. These 11 Triplett (1996) points out that much of decline of computer prices reflects falling semi-conductor prices. If all inputs are correctly measured for quality change, therefore, much of the TFP gains in computer production are rightly pushed back to TFP gains in semi-conductor production since semiconductors are a major intermediate input in the production of computers. See Flamm (1993) for early estimates on semi-conductor prices. We address this further in Section IV. 12 See Appendix A for details on our source data and methodology for output estimates.
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Measuring and Sustaining the New Economy: Report of a Workshop imputations are necessary to preserve comparability between durables and housing and also enable us to capture the important impact of information technology on households. Our estimate of current dollar, private output in 1998 is $8,013B, including imputations of $740B that primarily reflect services of consumers’ durables.13 Real output growth was 3.63% for the full period, compared to 3.36% for the official GDP series. This difference reflects both our imputations and our exclusion of the government sectors in the NIPA data. Appendix Table A-1 presents the current dollar value and corresponding price index of total output and the IT assets—investment in computers Ic, investment in software Is, investment in communications equipment Im, consumption of computers and software Cc, and the imputed service flow from consumers’ computers and software, Dc. The most striking feature of these data is the enormous price decline for computer investment, 18% per year from 1960 to 1995 (Chart 1). Since 1995 this decline has accelerated to 27.6% per year. By contrast the relative price of software has been flat for much of the period and only began to fall in the late 1980s. The price of communications equipment behaves similarly to the software price, while consumption of computers and software shows declines similar to computer investment. The top panel of Table 1 summarizes the growth rates of prices and quantities for major output categories for 1990-95 and for 1995-98. In terms of current dollar output, investment in software is the largest IT asset, followed by investment in computers and communications equipment (Chart 2). While business investments in computers, software, and communications equipment are by far the largest categories, households have spent more than $20B per year on computers and software since 1995, generating a service flow of comparable magnitude. (c) Capital Stock and Capital Services This section describes our capital estimates for the U.S. economy from 1959 to 1998.14 We begin with investment data from the Bureau of Economic Analysis, estimate capital stocks using the perpetual inventory method, and aggregate capital stocks using rental prices as weights. This approach, originated by Jorgenson and Griliches (1967), is based on the identification of rental prices with marginal products of different types of capital. Our estimates of these prices incorporate differences in asset prices, service lives and depreciation rates, and the tax treatment of capital incomes.15 13 Current dollar NIPA GDP in 1998 was $8,759.9B. Our estimate of $8,013B differs due to total imputations ($740B), exclusion of general government and government enterprise sectors ($972B and $128B, respectively), and exclusion of certain retail taxes ($376B). 14 See Appendix B for details on theory, source data, and methodology for capital estimates.
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Measuring and Sustaining the New Economy: Report of a Workshop We refer to the difference between growth in capital services and capital stock as the growth in capital quality qK,t; this represents substitution towards assets with higher marginal products.16 For example, the shift toward IT increases the quality of capital, since computers, software, and communications equipment are assets with relatively high marginal products. Capital stock estimates, like those originally employed by Solow (1957), fail to account for this increase in quality. We employ a broad definition of capital, including tangible assets such as equipment and structures, as well as consumers’ durables, land, and inventories. We estimate a service flow from the installed stock of consumers’ durables, which enters our measures of both output and input. It is essential to include this service flow, since a steadily rising proportion is associated with investments in IT by the household sector. In order to capture the impact of information technology on U.S. economic growth, investments by business and household sectors as well as the services of the resulting capital stocks must be included. Our estimate of capital stock is $26T in 1997, substantially larger than the $17.3T in fixed private capital estimated by BEA (1998b). This difference reflects our inclusion of consumer’s durables, inventories, and land. Our estimates of capital stock for comparable categories of assets are quite similar to those of BEA. Our estimate of fixed private capital in 1997, for example, is $16.8T, almost the same as that of BEA. Similarly, our estimate of the stock of consumers’ durables is $2.9T, while BEA’s estimate is $2.5T. The remaining discrepancies reflect our inclusion of land and inventories. Appendix Table B-1 list the component assets and 1998 investment and stock values; Table B-2 presents the value of capital stock from 1959 to 1998, as well as asset price indices for total capital and IT assets. The stocks of IT business assets (computers, software, and communications equipment), as well as consumers’ purchases of computers and software, have grown dramatically in recent years, but remain relatively small. In 1998, combined IT assets accounted for only 3.4% of tangible capital, and 4.6% of reproducible, private assets. We now move to estimates of capital services flows, where capital stocks of individual assets are aggregated using rental prices as weights. Appendix 15 Jorgenson (1996) provides a recent discussion of our model of capital as a factor of production. BLS (1983) describes the version of this model employed in the official productivity statistics. Hulten (2000) provides a review of the specific features of this methodology for measuring capital input and the link to economic theory. 16 More precisely, growth in capital quality is defined as the difference between the growth in capital services and the growth in the average of the current and lagged stock. Appendix B provides details. We use a geometric depreciation rate for all reproducible assets, so that our estimates are not identical to the wealth estimates published by BEA (1998b).
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Measuring and Sustaining the New Economy: Report of a Workshop Table B-3 presents the current dollar service flows and corresponding price indexes for 1959-98, and the second panel of Table 1 summarizes the growth rates for prices and quantities of inputs for 1990-95 and 1995-98. There is a clear acceleration of growth of aggregate capital services from 2.8% per year for 1990-95 to 4.8% for 1995-98. This is largely due to rapid growth in services from IT equipment and software, and reverses the trend toward slower capital growth through 1995. While information technology assets are only 11.2% of the total, the service shares of these assets are much greater than the corresponding asset shares. In 1998 capital services are only 12.4% of capital stocks for tangible assets as a whole, but services are 40.0% of stocks for information technology. This reflects the rapid price declines and high depreciation rates that enter into the rental prices for information technology. Chart 3 highlights the rapid increase in the importance of IT assets, reflecting the accelerating pace of relative price declines. In the 1990s, the service price for computer hardware fell 14.2% per year, compared to an increase of 2.2% for non-information technology capital. As a direct consequence of this relative price change, computer services grew 24.1%, compared to only 3.6% for the services of non-IT capital in the 1990s. The current dollar share of services from computer hardware increased steadily and reached nearly 3.5% of all capital services in 1998 (Chart 3).17 The rapid accumulation of software, however, appears to have different origins. The price of software investment has declined much more slowly, –1.7% per year for software versus –19.5% for computer hardware for 1990 to 1998. These differences in investment prices lead to a much slower decline in service prices for software and computers, –1.6% versus –14.2%. Nonetheless, firms have been accumulating software quite rapidly, with real capital services growing 13.3% per year in the 1990s. While lower than the 24.1% growth in computers, software growth is much more rapid than growth in other forms of tangible capital. Complementarity between software and computers is one possible explanation. Firms respond to the decline in relative computer prices by accumulating computers and investing in complementary inputs like software to put the computers into operation.18 A competing explanation is that the official price indexes used to deflate software investment omit a large part of true quality improvements. This would lead to a substantial overstatement of price inflation and a corresponding understatement of real investment, capital services, and economic growth. According 17 Tevlin and Whelan (1999) provide empirical support for this explanation, reporting that computer investment is particularly sensitive to the cost of capital, so that the rapid drop in service prices can be expected to lead to large investment response. 18 An econometric model of the responsiveness of different types of capital services to own- and cross-price effects could be used to test for complementarity, but this is beyond the scope of the paper.
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Measuring and Sustaining the New Economy: Report of a Workshop TABLE B-3 Total Capital Services and High-Tech Assets Total Service Flow from Capital and CD Assets Computer Capital Service Flow Year Value Price Value Price 1959 214.7 0.32 0.00 0.00 1960 183.7 0.26 0.05 407.59 1961 192.3 0.26 0.25 602.38 1962 211.9 0.28 0.41 480.68 1963 241.7 0.30 0.56 291.73 1964 260.2 0.31 0.77 196.86 1965 289.2 0.32 1.15 169.47 1966 315.4 0.33 1.99 161.83 1967 333.8 0.33 2.13 103.65 1968 330.2 0.31 2.40 81.43 1969 349.2 0.31 2.54 63.64 1970 382.5 0.33 3.27 61.40 1971 391.4 0.32 4.83 68.40 1972 439.6 0.35 4.44 45.09 1973 517.9 0.38 4.02 30.87 1974 546.6 0.38 6.04 36.38 1975 619.2 0.42 5.36 26.49 1976 678.1 0.44 6.01 24.25 1977 742.8 0.47 6.35 19.16 1978 847.5 0.51 10.71 20.84 1979 999.1 0.57 10.45 12.30 1980 1,026.9 0.56 15.03 10.96 1981 1,221.4 0.66 15.92 7.33 1982 1,251.7 0.65 17.29 5.47 1983 1,359.1 0.71 22.77 5.06 1984 1,570.1 0.79 30.79 4.54 1985 1,660.5 0.79 33.72 3.43 1986 1,559.9 0.71 36.44 2.82 1987 1,846.6 0.80 45.07 2.76 1988 2,185.3 0.89 43.85 2.18 1989 2,243.0 0.89 47.89 1.97 1990 2,345.0 0.90 53.28 1.89 1991 2,345.8 0.88 52.65 1.69 1992 2,335.4 0.86 57.69 1.60 1993 2,377.4 0.85 62.00 1.42 1994 2,719.5 0.94 63.16 1.17 1995 2,833.4 0.94 77.77 1.11 1996 3,144.4 1.00 96.36 1.00 1997 3,466.3 1.05 103.95 0.77 1998 3,464.8 0.99 118.42 0.61 Note: Values are in billions of current dollars. Service prices are normalized to 1.0 in 1996. Total service flows include reproducible assets, consumers’ durable assets (CD), land, and inventories. All price indexes are normalized to 1.0 in 1996.
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Measuring and Sustaining the New Economy: Report of a Workshop Software Capital Service Flow Communications Capital Service Flow Computer and Software CD Service Flow Value Price Value Price Value Price 0.00 0.00 2.55 0.50 0.00 0.00 0.02 0.64 2.65 0.47 0.00 0.00 0.08 0.61 2.85 0.45 0.00 0.00 0.15 0.65 3.44 0.48 0.00 0.00 0.22 0.60 3.32 0.42 0.00 0.00 0.34 0.59 3.68 0.42 0.00 0.00 0.52 0.64 4.73 0.50 0.00 0.00 0.74 0.65 5.00 0.48 0.00 0.00 1.03 0.68 5.14 0.45 0.00 0.00 1.29 0.69 5.43 0.44 0.00 0.00 1.57 0.69 6.02 0.44 0.00 0.00 2.09 0.74 7.23 0.48 0.00 0.00 2.83 0.83 8.34 0.51 0.00 0.00 3.01 0.77 8.86 0.51 0.00 0.00 3.47 0.77 12.48 0.68 0.00 0.00 3.99 0.78 11.48 0.58 0.00 0.00 5.17 0.88 13.41 0.64 0.00 0.00 5.60 0.84 13.61 0.62 0.00 0.00 6.26 0.86 22.37 0.94 0.00 0.00 7.31 0.91 19.02 0.72 0.02 17.84 8.19 0.89 26.30 0.89 0.07 19.01 9.99 0.93 23.94 0.72 0.20 25.93 11.76 0.94 23.89 0.64 0.25 13.90 12.54 0.87 25.32 0.62 0.74 11.96 15.11 0.92 29.54 0.67 2.07 10.39 19.02 0.99 33.20 0.70 2.37 6.07 22.41 0.99 39.30 0.77 2.70 4.93 25.88 0.99 43.39 0.79 4.84 5.61 31.84 1.07 55.49 0.94 4.91 3.54 37.72 1.11 67.22 1.07 6.65 3.24 45.96 1.16 67.90 1.02 7.89 2.85 51.07 1.10 69.86 1.00 10.46 2.97 54.07 1.01 66.05 0.91 11.66 2.44 69.11 1.12 70.72 0.94 14.96 2.25 69.32 0.98 80.23 1.02 16.26 1.71 84.14 1.05 89.16 1.09 16.14 1.17 89.18 0.99 101.18 1.17 22.64 1.13 101.46 1.00 92.91 1.00 30.19 1.00 119.80 1.04 100.13 1.00 33.68 0.71 128.32 0.97 103.35 0.94 36.53 0.48
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Measuring and Sustaining the New Economy: Report of a Workshop TABLE C-1 Labor Input Labor Input Year Price Quantity Value Quality 1959 0.15 1,866.7 269.8 0.82 1960 0.15 1,877.5 289.1 0.82 1961 0.16 1,882.0 297.7 0.83 1962 0.16 1,970.7 315.3 0.86 1963 0.16 2,000.2 320.4 0.86 1964 0.17 2,051.4 346.2 0.87 1965 0.18 2,134.8 375.1 0.88 1966 0.19 2,226.9 413.7 0.89 1967 0.19 2,261.8 429.3 0.90 1968 0.21 2,318.8 480.8 0.91 1969 0.22 2,385.1 528.6 0.91 1970 0.24 2,326.6 555.6 0.90 1971 0.26 2,318.3 600.2 0.90 1972 0.28 2,395.5 662.9 0.91 1973 0.29 2,519.1 736.4 0.91 1974 0.32 2,522.2 798.8 0.91 1975 0.35 2,441.8 852.9 0.92 1976 0.38 2,525.6 964.2 0.92 1977 0.41 2,627.2 1,084.9 0.92 1978 0.44 2,783.7 1,232.4 0.93 1979 0.48 2,899.6 1,377.7 0.93 1980 0.52 2,880.8 1,498.2 0.94 1981 0.55 2,913.8 1,603.9 0.94 1982 0.60 2,853.3 1,701.6 0.94 1983 0.64 2,904.9 1,849.0 0.94 1984 0.66 3,095.5 2,040.2 0.95 1985 0.69 3,174.6 2,183.5 0.95 1986 0.75 3,192.8 2,407.1 0.95 1987 0.74 3,317.1 2,464.0 0.96 1988 0.76 3,417.2 2,579.5 0.96 1989 0.80 3,524.2 2,827.0 0.96 1990 0.84 3,560.3 3,001.9 0.97 1991 0.88 3,500.3 3,081.4 0.97 1992 0.94 3,553.4 3,337.0 0.98 1993 0.95 3,697.5 3,524.4 0.99 1994 0.96 3,806.4 3,654.6 0.99 1995 0.98 3,937.5 3,841.2 1.00 1996 1.00 4,016.8 4,016.8 1.00 1997 1.02 4,167.6 4,235.7 1.01 1998 1.06 4,283.8 4,545.7 1.01 Notes: Quantity of labor input is measured in billions of 1996 dollars; value of labor input is measured in billions of current dollars. Employment is thousands of workers, hourly compensation is in dollars, and hours worked is in millions. Price of labor input and index of labor quality are normalized to 1.0 in 1996.
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Measuring and Sustaining the New Economy: Report of a Workshop Employment Weekly Hours Hourly Compensation Hours Worked 58,209 38.0 2.3 115,167 58,853 37.7 2.5 115,403 58,551 37.4 2.6 113,996 59,681 37.5 2.7 116,348 60,166 37.5 2.7 117,413 61,307 37.4 2.9 119,111 63,124 37.4 3.0 122,794 65,480 37.1 3.3 126,465 66,476 36.8 3.4 127,021 68,063 36.5 3.7 129,194 70,076 36.4 4.0 132,553 69,799 35.8 4.3 130,021 69,671 35.8 4.6 129,574 71,802 35.8 5.0 133,554 75,255 35.7 5.3 139,655 76,474 35.0 5.7 139,345 74,575 34.6 6.3 134,324 76,925 34.6 7.0 138,488 80,033 34.6 7.5 143,918 84,439 34.5 8.1 151,359 87,561 34.5 8.8 157,077 87,788 34.1 9.6 155,500 88,902 33.9 10.2 156,558 87,600 33.6 11.1 153,163 88,638 33.9 11.9 156,049 93,176 34.0 12.4 164,870 95,410 33.9 13.0 168,175 97,001 33.5 14.2 169,246 99,924 33.7 14.1 174,894 103,021 33.6 14.3 179,891 105,471 33.7 15.3 184,974 106,562 33.6 16.1 186,106 105,278 33.2 16.9 181,951 105,399 33.2 18.3 182,200 107,917 33.5 18.8 187,898 110,888 33.6 18.9 193,891 113,707 33.7 19.3 199,341 116,083 33.6 19.8 202,655 119,127 33.8 20.3 209,108 121,934 33.7 21.3 213,951
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Measuring and Sustaining the New Economy: Report of a Workshop Chart 1: Relative Prices of Information Technology Outputs, 1960-1998 Notes: All prices indexes are relative to the output price index. CD refers to Consumer Durables. Chart 2: Output Shares of Information Technology, 1960-1998 Notes: Share of current dollar output. CD refers to Consumer Durables.
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Measuring and Sustaining the New Economy: Report of a Workshop Chart 3: Input Shares of Information Technology, 1960-1998 Notes: Share of current dollar capital and consumers' durable services. CD refers to Consumer Durables. Chart 4: Sources of U.S. Economic Growth, 1959-1998 Notes: An input's contribution is the average share-weighted, annual growth rate. CD refers to Consumer Durables. TFP refers to Total Factor Productivity.
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Measuring and Sustaining the New Economy: Report of a Workshop Chart 5: Output Contribution of Information Technology, 1959-1998 Notes: An output's contribution is the average share-weighted, annual growth rate. Chart 6: Output Contribution of Information Technology Assets, 1959-1998 Notes: An output's contribution is the average share-weighted, annual growth rate. CD refers to Consumer Durables.
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Measuring and Sustaining the New Economy: Report of a Workshop Chart 7: Input Contribution of Information Technology, 1959-1998 Notes: An input's contribution is the average share-weighted, annual growth rate. CD refers to Consumer Durables. Chart 8: Input Contribution of Information Technology, 1959-1998 Notes: An input's contribution is the average share-weighted, annual growth rate. CD refers to Consumer Durables.
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Measuring and Sustaining the New Economy: Report of a Workshop Chart 9: Sources of U.S. Labor Productivity Growth, 1959-1998 Notes: Annual contributions are defined in Equation (3) in text. TFP refers to Total Factor Productivity. Chart 10: TFP Decomposition for Alternative Deflation Cases Notes: Annual contribution of information technology is the share-weighted decline in relative prices.
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Measuring and Sustaining the New Economy: Report of a Workshop Chart 11: Industry Contributions to Aggregate Total Factor Productivity Growth, 1958-1996
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Measuring and Sustaining the New Economy: Report of a Workshop This page in the original is blank.
Representative terms from entire chapter: