Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 75
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium II RESEARCH PAPERS
OCR for page 76
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium This page intentionally left blank.
OCR for page 77
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium Accounting for Growth in the Information Age Dale W. Jorgenson Harvard University 1. THE INFORMATION AGE* 1.1. Introduction The resurgence of the American economy since 1995 has outrun all but the most optimistic expectations. Economic forecasting models have been seriously off track and growth projections have been revised repeatedly to reflect a more sanguine outlook.1 It is not surprising that the unusual combination of more rapid growth and slower inflation touched off a strenuous debate about whether improvements in America’s economic performance could be sustained. The starting point for the economic debate is the thesis that the 1990s are a * Department of Economics, Harvard University, 122 Littauer Center, Cambridge, MA 02138-3001. I am greatly indebted to Kevin Stiroh for our joint research, Jon Samuels for excellent research assistance, and Mun S. Ho for the labor data, as well as useful comments. J. Steven Landefeld, Clinton McCully, and David Wasshausen of the Bureau of Economic Analysis provided valuable data on information technology in the U.S. Tom Hale, Mike Harper, Tom Nardone and Larry Rosenblum (BLS), Kurt Kunze (BEA), Eldon Ball (ERS), Mike Dove and Scott Segerman (DMDC) also provided data for the U.S. and helpful advice. Colleagues far too numerous to mention have contributed useful suggestions. I am grateful to all of them but retain sole responsibility for any remaining deficiencies. NOTE: Tables and figures appear at the end of this paper, pp. 114-134. An earlier version of this paper was published under the title “Information Technology and the U.S. Economy” in the American Economic Review, 90:1, in March 2001. 1 See Congressional Budget Office (2000) on official forecasts and Economics and Statistics Administration (2000), p. 60, on private forecasts.
OCR for page 78
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium mirror image of the 1970s, when an unfavorable series of “supply shocks” led to stagflation—slower growth and higher inflation.2 In this view, the development of information technology (IT) is one of a series of positive, but temporary, shocks. The competing perspective is that IT has produced a fundamental change in the U.S. economy, leading to a permanent improvement in growth prospects.3 The resolution of this debate has been the “killer application” of a new framework for productivity measurement summarized in Paul Schreyer’s (2001) OECD Manual, Measuring Productivity. A consensus has emerged that the development and deployment of information technology is the foundation of the American growth resurgence. A mantra of the “new economy”—faster, better, cheaper—captures the speed of technological change and product improvement in semiconductors and the precipitous and continuing fall in semiconductor prices. The price decline has been transmitted to the prices of products that rely heavily on semiconductor technology, like computers and telecommunications equipment. This technology has also helped to reduce the cost of aircraft, automobiles, scientific instruments, and a host of other products. Swiftly falling IT prices provide powerful economic incentives for the substitution of IT equipment for other forms of capital and for labor services. The rate of the IT price decline is a key component of the cost of capital, required for assessing the impacts of rapidly growing stocks of computers, communications equipment, and software. Constant quality price indexes are essential for identifying the change in price for a given level of performance. Accurate and timely computer prices have been part of the U.S. National Income and Product Accounts (NIPA) since 1985. Unfortunately, important information gaps remain, especially on trends in prices for closely related investments, such as software and communications equipment. Capital input has been the most important source of U.S. economic growth throughout the postwar period. More rapid substitution toward information technology has given much additional weight to components of capital input with higher marginal products. The vaulting contribution of capital input since 1995 has boosted growth by close to a percentage point. The contribution of investment in IT accounts for more than half of this increase. Computers have been the predominant impetus to faster growth, but communications equipment and software have made important contributions as well. The accelerated information technology price decline signals faster productivity growth in IT-producing industries. In fact, these industries have been a rapidly rising source of aggregate productivity growth throughout the 1990s. The IT-producing industries generate less than 5 percent of gross domestic income, 2 Gordon (1998, 2000); Bosworth and Triplett (2000). 3 Greenspan (2000).
OCR for page 79
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium but have accounted for nearly half the surge in productivity growth since 1995. However, it is important to emphasize that faster productivity growth is not limited to these industries. The dramatic effects of information technology on capital and labor markets have already generated a substantial and growing economic literature, but many important issues remain to be resolved. For capital markets the relationship between equity valuations and growth prospects merits much further study. For labor markets more research is needed on investment in information technology and substitution among different types of labor. 1.2. Faster, Better, Cheaper Modern information technology begins with the invention of the transistor, a semiconductor device that acts as an electrical switch and encodes information in binary form. A binary digit or bit takes the values zero and one, corresponding to the off and on positions of a switch. The first transistor, made of the semiconductor germanium, was constructed at Bell Labs in 1947 and won the Nobel Prize in Physics in 1956 for the inventors—John Bardeen, Walter Brattain, and William Shockley.4 The next major milestone in information technology was the co-invention of the integrated circuit by Jack Kilby of Texas Instruments in 1958 and Robert Noyce of Fairchild Semiconductor in 1959. An integrated circuit consists of many, even millions, of transistors that store and manipulate data in binary form. Integrated circuits were originally developed for data storage and retrieval and semiconductor storage devices became known as memory chips.5 The first patent for the integrated circuit was granted to Noyce. This resulted in a decade of litigation over the intellectual property rights. The litigation and its outcome demonstrate the critical importance of intellectual property in the development of information technology. Kilby was awarded the Nobel Prize in Physics in 2000 for discovery of the integrated circuit; regrettably, Noyce died in 1990.6 1.2.1. Moore’s Law In 1965 Gordon Moore, then Research Director at Fairchild Semiconductor, made a prescient observation, later known as Moore’s Law.7 Plotting data on 4 On Bardeen, Brattain, and Shockley, see: http://www.nobel.se/physics/laureates/1956/. 5 Petzold (1999) provides a general reference on computers and software. 6 On Kilby, see: http://www.nobel.se/physics/laureates/2000/. On Noyce, see: Wolfe (2000), pp. 17-65. 7 Moore (1965). Ruttan (2001), pp. 316-367, provides a general reference on the economics of semiconductors and computers. On semiconductor technology, see: http://euler.berkeley.edu/~esrc/csm.
OCR for page 80
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium memory chips, he observed that each new chip contained roughly twice as many transistors as the previous chip and was released within 18-24 months of its predecessor. This implied exponential growth of chip capacity at 35-45 percent per year! Moore’s prediction, made in the infancy of the semiconductor industry, has tracked chip capacity for 35 years. He recently extrapolated this trend for at least another decade.8 In 1968 Moore and Noyce founded Intel Corporation to speed the commercialization of memory chips.9 Integrated circuits gave rise to microprocessors with functions that can be programmed by software, known as logic chips. Intel’s first general purpose microprocessor was developed for a calculator produced by Busicom, a Japanese firm. Intel retained the intellectual property rights and released the device commercially in 1971. The rapidly rising trends in the capacity of microprocessors and storage devices illustrate the exponential growth predicted by Moore’s Law. The first logic chip in 1971 had 2,300 transistors, while the Pentium 4 released on November 20, 2000, had 42 million! Over this 29 year period the number of transistors increased by 34 percent per year. The rate of productivity growth for the U.S. economy during this period was slower by two orders of magnitude. 1.2.2. Semiconductor Prices Moore’s Law captures the fact that successive generations of semiconductors are faster and better. The economics of semiconductors begins with the closely related observation that semiconductors have become cheaper at a truly staggering rate! Figure 1.1 gives semiconductor price indexes constructed by Bruce Grimm (1998) of the Bureau of Economic Analysis (BEA) and employed in the U.S. National Income and Product Accounts since 1996. These are divided between memory chips and logic chips. The underlying detail includes seven types of memory chips and two types of logic chips. Between 1974 and 1996 prices of memory chips decreased by a factor of 27,270 times or at 40.9 percent per year, while the implicit deflator for the gross domestic product (GDP) increased by almost 2.7 times or 4.6 percent per year! Prices of logic chips, available for the shorter period 1985 to 1996, decreased by a factor of 1,938 or 54.1 percent per year, while the GDP deflator increased by 1.3 times or 2.6 percent per year! Semiconductor price declines closely parallel Moore’s Law on the growth of chip capacity, setting semiconductors apart from other products. Figure 1.1 also reveals a sharp acceleration in the decline of semiconductor prices in 1994 and 1995. The microprocessor price decline leapt to more than 90 8 Moore (1997). 9 Moore (1996).
OCR for page 81
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium percent per year as the semiconductor industry shifted from a three-year product cycle to a greatly accelerated two-year cycle. This is reflected in the 2000 Update of the International Technology Road Map for Semiconductors,10 prepared by a consortium of industry associations. Ana Aizcorbe, Stephen Oliner, and Daniel Sichel (2003) have identified and analyzed break points in prices of microprocessors and storage devices. 1.2.3. Constant Quality Price Indexes The behavior of semiconductor prices is a severe test for the methods used in the official price statistics. The challenge is to separate observed price changes between changes in semiconductor performance and changes in price that hold performance constant. Achieving this objective has required a detailed understanding of the technology, the development of sophisticated measurement techniques, and the introduction of novel methods for assembling the requisite information. Ellen Dulberger (1993) introduced a “matched model” index for semiconductor prices. A matched model index combines price relatives for products with the same performance at different points of time. Dulberger presented constant quality price indexes based on index number formulas, including the Fisher (1922) ideal index used in the in the U.S. national accounts.11 The Fisher index is the geometric average of the familiar Laspeyres and Paasche indexes. Erwin Diewert (1976) defined a superlative index number as an index that exactly replicates a flexible representation of the underlying technology (or preferences). A flexible representation provides a second-order approximation to an arbitrary technology (or preference system). A. A. Konus and S. S. Byushgens (1926) first showed that the Fisher ideal index is superlative in this sense. Laspeyres and Paasche indexes are not superlative and fail to capture substitutions among products in response to price changes accurately. Grimm (1998) combined matched model techniques with hedonic methods, based on an econometric model of semiconductor prices at different points of time. A hedonic model gives the price of a semiconductor product as a function of the characteristics that determine performance, such as speed of processing and storage capacity. A constant quality price index isolates the price change by holding these characteristics of semiconductors fixed.12 10 On International Technology Roadmap for Semiconductors (2000), see: http://public.itrs.net/. 11 See Landefeld and Parker (1997). 12 Triplett (2003) has drafted a manual for the OECD on constructing constant quality price indexes for information technology and communications equipment and software.
OCR for page 82
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium Beginning in 1997, the Bureau of Labor Statistics (BLS) incorporated a matched model price index for semiconductors into the Producer Price Index (PPI) and since then the national accounts have relied on data from the PPI. Reflecting long-standing BLS policy, historical data were not revised backward. Semiconductor prices reported in the PPI prior to 1997 do not hold quality constant, failing to capture the rapid semiconductor price decline and the acceleration in 1995. 1.2.4. Computers The introduction of the Personal Computer (PC) by IBM in 1981 was a watershed event in the deployment of information technology. The sale of Intel’s 8086-8088 microprocessor to IBM in 1978 for incorporation into the PC was a major business breakthrough for Intel.13 In 1981 IBM licensed the MS-DOS operating system from the Microsoft Corporation, founded by Bill Gates and Paul Allen in 1975. The PC established an Intel/Microsoft relationship that has continued up to the present. In 1985 Microsoft released the first version of Windows, its signature operating system for the PC, giving rise to the Wintel (Windows-Intel) nomenclature for this ongoing collaboration. Mainframe computers, as well as PC’s, have come to rely heavily on logic chips for central processing and memory chips for main memory. However, semiconductors account for less than half of computer costs and computer prices have fallen much less rapidly than semiconductor prices. Precise measures of computer prices that hold product quality constant were introduced into the NIPA in 1985 and the PPI during the 1990s. The national accounts now rely on PPI data, but historical data on computers from the PPI, like the PPI data on semiconductors, do not hold quality constant. Gregory Chow (1967) pioneered the use of hedonic techniques for constructing a constant quality index of computer prices in research conducted at IBM. Chow documented price declines at more than twenty percent per year during 1960-1965, providing an initial glimpse of the remarkable behavior of computer prices. In 1985 the Bureau of Economic Analysis incorporated constant quality price indexes for computers and peripheral equipment constructed by IBM into the NIPA. Triplett’s (1986) discussion of the economic interpretation of these indexes brought the rapid decline of computer prices to the attention of a very broad audience. The BEA-IBM constant quality price index for computers provoked a heated exchange between BEA and Edward Denison (1989), one of the founders of national accounting methodology in the 1950s and head of the national accounts at BEA from 1979 to 1982. Denison sharply attacked the BEA-IBM methodology 13 See Moore (1996).
OCR for page 83
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium and argued vigorously against the introduction of constant quality price indexes into the national accounts.14 Allan Young (1989), then Director of BEA, reiterated BEA’s rationale for introducing constant quality price indexes. Dulberger (1989) presented a more detailed report on her research on the prices of computer processors for the BEA-IBM project. Speed of processing and main memory played central roles in her model. Triplett (1989, 2003) has provided exhaustive surveys of research on hedonic price indexes for computers. Gordon (1989, 1990) gave an alternative model of computer prices and identified computers and communications equipment, along with commercial aircraft, as assets with the highest rates of price decline. Figure 1.2 gives BEA’s constant quality index of prices of computers and peripheral equipment and its components, including mainframes, PCs, storage devices, other peripheral equipment, and terminals. The decline in computer prices follows the behavior of semiconductor prices presented in Figure 1.1, but in much attenuated form. The 1995 acceleration in the computer price decline parallels the acceleration in the semiconductor price decline that resulted from the changeover from a three-year product cycle to a two-year cycle in 1995. 1.2.5. Communications Equipment and Software Communications technology is crucial for the rapid development and diffusion of the Internet, perhaps the most striking manifestation of information technology in the American economy.15 Kenneth Flamm (1989) was the first to compare the behavior of computer prices and the prices of communications equipment. He concluded that the communications equipment prices fell only a little more slowly than computer prices. Gordon (1990) compared Flamm’s results with the official price indexes, revealing substantial bias in the official indexes. Communications equipment is an important market for semiconductors, but constant quality price indexes cover only a portion of this equipment. Switching and terminal equipment rely heavily on semiconductor technology, so that product development reflects improvements in semiconductors. Grimm’s (1997) constant quality price index for digital telephone switching equipment, given in Figure 1.3, was incorporated into the national accounts in 1996. The output of communications services in the NIPA also incorporates a constant quality price index for cellular phones. Much communications investment takes the form of the transmission gear, connecting data, voice, and video terminals to switching equipment. Technolo- 14 Denison cited his 1957 paper, “Theoretical Aspects of Quality Change, Capital Consumption, and Net Capital Formation,” as the definitive statement of the traditional BEA position. 15 General references on the economics of the Internet are Choi and Whinston (2000) and Hall (2002). On Internet indicators see: http://www.internetindicators.com/.
OCR for page 84
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium gies such as fiber optics, microwave broadcasting, and communications satellites have progressed at rates that outrun even the dramatic pace of semiconductor development. An example is dense wavelength division multiplexing (DWDM), a technology that sends multiple signals over an optical fiber simultaneously. Installation of DWDM equipment, beginning in 1997, has doubled the transmission capacity of fiber optic cables every 6-12 months.16 Mark Doms (2004) has provided comprehensive price indexes for terminals, switching gear, and transmission equipment. These have been incorporated into the Federal Reserve’s Index of Industrial Production, as described by Carol Corrado (2003), but are not yet included in the U.S. National Income and Product Accounts. The analysis of the impact of information technology on the U.S. economy described below is based on the national accounts and remains incomplete. Both software and hardware are essential for information technology and this is reflected in the large volume of software expenditures. The eleventh comprehensive revision of the national accounts, released by BEA on October 27, 1999, re-classified computer software as investment.17 Before this important advance, business expenditures on software were treated as current outlays, while personal and government expenditures were treated as purchases of nondurable goods. Software investment is growing rapidly and is now much more important than investment in computer hardware. Parker and Grimm (2000) describe the new estimates of investment in software. BEA distinguishes among three types of software—prepackaged, custom, and own-account software. Prepackaged software is sold or licensed in standardized form and is delivered in packages or electronic files downloaded from the Internet. Custom software is tailored to the specific application of the user and is delivered along with analysis, design, and programming services required for customization. Own-account software consists of software created for a specific application. However, only price indexes for prepackaged software hold performance constant. Parker and Grimm (2000) present a constant quality price index for prepackaged software, given in Figure 1.3. This combines a hedonic model of prices for business applications software and a matched model index for spreadsheet and word processing programs developed by Oliner and Sichel (1994). Prepackaged software prices decline at more than ten percent per year over the period 1962-1998. Since 1998 the BEA has relied on a matched model price index for all prepackaged software from the PPI; prior to 1998 the PPI data do not hold quality constant. 16 Rashad (2000) characterizes this as the “demise” of Moore’s Law. Hecht (1999) describes DWDM technology and provides a general reference on fiber optics. 17 Moulton (2000) describes the 11th comprehensive revision of NIPA and the 1999 update.
OCR for page 85
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium BEA’s prices for own-account and custom software are based on programmer wage rates. This implicitly assumes no change in the productivity of computer programmers, even with growing investment in hardware and software to support the creation of new software. Custom and own-account software prices are a weighted average of prepackaged software prices and programmer wage rates with arbitrary weights of 75 percent for programmer wage rates and 25 percent for prepackaged software. These price indexes do not hold the software performance constant and present a distorted picture of software prices, as well as software output and investment. 1.2.6. Research Opportunities The official price indexes for computers and semiconductors provide the paradigm for economic measurement. These indexes capture the steady decline in IT prices and the recent acceleration in this decline. The official price indexes for central office switching equipment and prepackaged software also hold quality constant. BEA and BLS, the leading statistical agencies in price research, have carried out much of the best work in this area. However, a critical role has been played by price research at IBM, long the dominant firm in information technology.18 It is important to emphasize that information technology is not limited to applications of semiconductors. Switching and terminal equipment for voice, data, and video communications have come to rely on semiconductor technology and the empirical evidence on prices of this equipment reflects this fact. Transmission gear employs technologies with rates of progress that far outstrip those of semiconductors. This important gap in our official price statistics has been filled by constant quality price indexes for all types of communications equipment constructed by Doms (2004), but these indexes have not been incorporated into the national accounts. Investment in software is more important than investment in hardware. This was essentially invisible until BEA introduced new measures of prepackaged, custom, and own-account software investment into the national accounts in 1999. This is a crucial step in understanding the role of information technology in the American economy. Unfortunately, software prices are a statistical blind spot with only prices of prepackaged software adequately represented in the official system of price statistics. The daunting challenge that lies ahead is to construct constant quality price indexes for custom and own-account software. 18 See Chandler (2000), Table 1.1, p. 26.
OCR for page 124
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium TABLE 2.5 Labor Services Labor Services Year Price Quantity Value Quality Employment Weekly Hours Hourly Compensation Hours Worked 1948 0.06 2,324.8 150.1 0.73 61,536 40.6 1.2 129,846 1949 0.07 2,262.8 165.5 0.73 60,437 40.2 1.3 126,384 1950 0.08 2,350.6 181.3 0.75 62,424 39.8 1.4 129,201 1951 0.08 2,531.5 210.7 0.76 66,169 39.7 1.5 136,433 1952 0.09 2,598.2 222.5 0.77 67,407 39.2 1.6 137,525 1953 0.09 2,653.0 238.5 0.79 68,471 38.8 1.7 138,134 1954 0.09 2,588.7 240.7 0.79 66,843 38.4 1.8 133,612 1955 0.09 2,675.7 252.7 0.80 68,367 38.7 1.8 137,594 1956 0.10 2,738.0 272.4 0.80 69,968 38.4 1.9 139,758 1957 0.11 2,740.9 293.0 0.81 70,262 37.9 2.1 138,543 1958 0.12 2,671.8 307.4 0.82 68,578 37.6 2.3 134,068 1959 0.11 2,762.8 316.9 0.82 70,149 37.8 2.3 137,800 1960 0.12 2,806.6 341.7 0.83 71,128 37.6 2.5 139,150 1961 0.12 2,843.4 352.1 0.84 71,183 37.4 2.5 138,493 1962 0.13 2,944.4 374.1 0.85 72,673 37.4 2.6 141,258 1963 0.13 2,982.3 382.7 0.86 73,413 37.3 2.7 142,414 1964 0.13 3,055.7 412.0 0.86 74,990 37.2 2.8 144,920 1965 0.14 3,149.7 448.1 0.86 77,239 37.2 3.0 149,378 1966 0.15 3,278.8 494.8 0.87 80,802 36.8 3.2 154,795 1967 0.16 3,327.2 518.9 0.87 82,645 36.3 3.3 156,016 1968 0.17 3,405.4 582.6 0.88 84,733 36.0 3.7 158,604 1969 0.18 3,491.1 641.4 0.88 87,071 35.9 3.9 162,414 1970 0.20 3,439.2 683.1 0.88 86,867 35.3 4.3 159,644 1971 0.22 3,439.5 740.7 0.89 86,715 35.2 4.7 158,943 1972 0.23 3,528.8 813.3 0.89 88,838 35.3 5.0 162,890 1973 0.25 3,672.4 903.9 0.89 92,542 35.2 5.3 169,329
OCR for page 125
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium 1974 0.27 3,660.9 979.2 0.89 94,121 34.5 5.8 168,800 1975 0.29 3,606.4 1,055.2 0.90 92,575 34.2 6.4 164,460 1976 0.32 3,708.0 1,182.6 0.90 94,922 34.2 7.0 168,722 1977 0.34 3,829.8 1,321.1 0.90 98,202 34.1 7.6 174,265 1978 0.37 3,994.9 1,496.8 0.90 102,931 34.0 8.2 181,976 1979 0.40 4,122.6 1,660.4 0.90 106,463 33.9 8.9 187,589 1980 0.44 4,105.6 1,809.7 0.90 107,061 33.4 9.7 186,202 1981 0.47 4,147.7 1,934.6 0.91 108,050 33.3 10.4 186,887 1982 0.50 4,110.2 2,056.5 0.92 106,749 33.1 11.2 183,599 1983 0.54 4,172.3 2,234.7 0.92 107,810 33.2 12.0 186,175 1984 0.56 4,417.4 2,458.3 0.93 112,604 33.3 12.6 195,221 1985 0.58 4,531.7 2,646.2 0.93 115,201 33.3 13.3 199,424 1986 0.64 4,567.5 2,904.1 0.93 117,158 33.0 14.4 200,998 1987 0.64 4,736.5 3,017.3 0.94 120,456 33.1 14.6 207,119 1988 0.65 4,888.8 3,173.3 0.94 123,916 33.0 14.9 212,882 1989 0.68 5,051.3 3,452.4 0.95 126,743 33.2 15.8 218,811 1990 0.71 5,137.6 3,673.2 0.96 128,290 33.0 16.7 220,475 1991 0.75 5,086.7 3,806.3 0.96 127,022 32.7 17.6 216,281 1992 0.80 5,105.9 4,087.4 0.97 127,100 32.8 18.8 216,873 1993 0.82 5,267.6 4,323.8 0.97 129,556 32.9 19.5 221,699 1994 0.83 5,418.2 4,472.4 0.98 132,459 33.0 19.7 227,345 1995 0.84 5,573.2 4,661.5 0.98 135,297 33.1 20.0 232,675 1996 0.86 5,683.6 4,878.5 0.99 137,571 33.0 20.7 235,859 1997 0.89 5,843.3 5,186.5 0.99 140,432 33.2 21.4 242,242 1998 0.92 6,020.8 5,519.5 0.99 143,557 33.3 22.2 248,610 1999 0.96 6,152.1 5,908.2 1.00 146,468 33.3 23.3 253,276 2000 1.00 6,268.5 6,268.5 1.00 149,364 33.1 24.4 257,048 2001 1.05 6,250.6 6,537.4 1.00 149,020 32.9 25.6 255,054 2002 1.06 6,188.7 6,576.2 1.01 147,721 32.9 26.1 252,399 NOTES: Value is in billions of current dollars. Quantity is in billions of 2000 dollars. Price and quality are normalized to one in 2000. Employment is in thousands of workers. Weekly hours is hours per worker, divided by 52. Hourly compensation is in current dollars. Hours worked are in mil lions of hours.
OCR for page 126
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium TABLE 2.6 Sources of Gross Domestic Product Growth 1948-2002 1948-1973 1973-1989 1989-1995 1995-2002 Outputs Gross Domestic Product 3.46 3.99 2.97 2.43 3.59 Contribution of Information Technology 0.28 0.12 0.34 0.37 0.64 Computers 0.13 0.03 0.18 0.15 0.34 Software 0.07 0.02 0.08 0.15 0.19 Communications Equipment 0.08 0.07 0.08 0.08 0.11 Contribution of Non-Information Technology 3.18 3.87 2.63 2.05 2.95 Contribution of Non-Information Technology Investment 0.69 1.04 0.45 0.21 0.41 Contribution of Non-Information Technology Consumption 2.49 2.82 2.18 1.85 2.55 Inputs Gross Domestic Income 2.79 2.99 2.68 2.17 2.88 Contribution of Information Technology Capital Services 0.36 0.15 0.38 0.49 0.93 Computers 0.17 0.04 0.20 0.22 0.52 Software 0.08 0.02 0.07 0.16 0.23 Communications Equipment 0.11 0.09 0.11 0.10 0.18 Contribution of Non-Information Technology Capital Services 1.39 1.79 1.15 0.71 1.07 Contribution of Labor Services 1.05 1.04 1.15 0.98 0.88 Total Factor Productivity 0.67 1.00 0.29 0.26 0.71 NOTES: Average annual percentage rates of growth. The contribution of an output or input is the rate of growth, multiplied by the value share.
OCR for page 127
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium TABLE 2.7 Sources of Average Labor Productivity Growth 1948-2002 1948-1973 1973-1989 1989-1995 1995-2002 Gross Domestic Product 3.46 3.99 2.97 2.43 3.59 Hours Worked 1.23 1.06 1.60 1.02 1.16 Average Labor Productivity 2.23 2.93 1.36 1.40 2.43 Contribution of Capital Deepening 1.23 1.49 0.85 0.78 1.52 Information Technology 0.33 0.14 0.34 0.44 0.88 Non-Information Technology 0.90 1.35 0.51 0.34 0.64 Contribution of Labor Quality 0.33 0.43 0.23 0.36 0.20 Total Factor Productivity 0.67 1.00 0.29 0.26 0.71 Information Technology 0.17 0.05 0.20 0.23 0.47 Non-Information Technology 0.50 0.95 0.09 0.03 0.24 Addendum Labor Input 1.81 1.83 1.99 1.64 1.50 Labor Quality 0.58 0.77 0.39 0.61 0.33 Capital Input 4.13 4.49 3.67 2.92 4.92 Capital Stock 3.29 4.13 2.77 1.93 2.66 Capital Quality 0.84 0.36 0.90 0.99 2.27 NOTES: Average annual percentage rates of growth. Contributions are defined in Equation (3) of the text.
OCR for page 128
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium TABLE 2.8 Sources of Total Factor Productivity Growth 1948-2002 1948-1973 1973-1989 1989-1995 1995-2002 Total Factor Productivity Growth 0.67 1.00 0.29 0.26 0.71 Contributions to TFP Growth Information Technology 0.17 0.05 0.20 0.23 0.47 Computers 0.10 0.02 0.13 0.13 0.33 Software 0.02 0.00 0.03 0.06 0.06 Communications Equipment 0.04 0.03 0.05 0.04 0.08 Non-Information Technology 0.50 0.95 0.09 0.03 0.24 Relative Price Changes Information Technology –6.72 –4.1 –8.5 –7.4 –11.7 Computers –22.50 –22.0 –21.5 –15.1 –33.1 Software –4.87 –5.1 –5.1 –5.3 –3.4 Communications Equipment –3.79 –2.9 –4.1 –3.8 –6.3 Non-Information Technology –0.51 –1.0 –0.1 0.0 –0.3 Average Nominal Shares Information Technology 2.03 1.00 2.35 3.04 4.10 Computers 0.46 0.10 0.64 0.83 1.00 Software 0.53 0.07 0.49 1.13 1.78 Communications Equipment 1.04 0.83 1.22 1.09 1.33 Non-Information Technology 97.97 99.00 97.65 96.96 95.90 NOTES: Average annual rates of growth. Prices are relative to the price of gross domestic income. Contributions are relative pr ice changes, weighted by average nominal output shares.
OCR for page 129
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium FIGURE 1.1 Relative Prices of Computers and Semiconductors, 1959-2002. NOTE: All price indexes are divided by the output price index. FIGURE 1.2 Relative Prices of Computers, Communications, and Software, 1948-2002. NOTE: All price indexes are divided by the output price index.
OCR for page 130
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium FIGURE 1.3 Relative Prices of Computers, Central Office Switching Equipment, and Prepackaged Software, 1959-2002. NOTE: All price indexes are divided by the output price index. FIGURE 2.1 Output Shares of Information Technology by Type, 1948-2002. NOTE: Share of current dollar gross domestic product.
OCR for page 131
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium FIGURE 2.2 Input Shares of Information Technology by Type, 1948-2002. NOTE: Share of current dollar gross domestic income. FIGURE 2.3 Output Contribution of Information Technology. NOTE: Output contributions are the average annual growth rates, weighted by the output shares.
OCR for page 132
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium FIGURE 2.4 Output Contribution of Information Technology by Type. NOTE: Output contributions are the average annual growth rates, weighted by the output shares. FIGURE 2.5 Capital Input Contribution of Information Technology. NOTE: Input contributions are the average annual growth rates, weighted by the income shares.
OCR for page 133
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium FIGURE 2.6 Capital Input Contribution of Information Technology by Type. NOTE: Input contributions are the average annual growth rates, weighted by the income shares. FIGURE 2.7 Contributions of Information Technology to Total Factor Productivity Growth. NOTE: Contributions are average annual relative price changes, weighted by average nominal output shares from Table 2.8.
OCR for page 134
Productivity and Cyclicality in Semiconductors: Trends, Implications, and Questions - Report of a Symposium FIGURE 2.8 Sources of Gross Domestic Product Growth. FIGURE 2.9 Sources of Average Labor Productivity Growth.
Representative terms from entire chapter: