J. Bradford DeLong
delong@econ.berkeley.edu
http://www.j-bradford-delong.net/

March 2002

paper draft


The Boom of the 1990s:

In the second half of the 1990s American productivity picked itself up off the ground to which it had fallen at the early-1970s start of the productivity slowdown. Between the beginning of 1995 and the semi-official NBER business cycle peak in March 2001, U.S. nonfarm-business output per person-hour worked grew at an annual rate of 2.80 percent per year. (Extending the sample through the 2001 recession to the likely trough point of 2001:4, the late-1990s growth rate is 2.69 percent per year.) Between the beginning of 1995 and the semi-official NBER business cycle peak in March 2001, U.S. real GDP grew at a pace of 4.21 percent per year.

The causes of the productivity slowdown of the 1973-1995 or so period remain disappointingly mysterious. Baily (2002) calls the growth-accounting literature on the slowdown "large but inconclusive." No single factor provides a convincing and coherent explanation, and the residual position that a large number of growth-retarding factors suddenly happened to hit at once is unlikely. By contrast, nearly all agree on the causes of the productivity speed-up of 1995-2001: it is the result of the extraordinary wave of technological innovation in computer and communications equipment–solid-state electronics and photonics. Robert Gordon (2002) writes that cyclical factors account for "0.40" percentage points of the growth acceleration, and that the rest is fully accounted for by information technology–an "0.30 [percentage] point acceleration [from] MFP growth in computer and computer-related semiconductor manufacturing" and a "capital-deepening effect of faster growth in computer capital… in the aggregate economy accounts [for] 0.60 percentage points of the acceleration." Kevin Stiroh (2001) writes that "all of the direct contribution to the post-1995 productivity acceleration can be traced to the industries that either produce [information technology capital goods] or use [information technology capital goods] most intensively, with no net contribution from other industries… relatively isolated from the [information technology] revolution." Oliner and Sichel (2000) write that "the rapid capital deepening related to information technology capital accounted for nearly half of this increase" in labor productivity growth, with a powerful "additional growth contribution… com[ing] through efficiency improvement in the production of computing equipment." Jorgenson, Ho, and Stiroh (2001) reach the same conclusions about the importance of information technology capital-deepening and increased efficiency in the production of computing and communications equipment as major drivers of the productivity growth acceleration, and they go on to forecast that labor productivity growth will be as high in the next decade as it has been in the past half-decade.

Compare our use of information technology today with our predecessors' use of information technology half a century ago. The decade of the 1950s saw electronic computers largely replace mechanical and electromechanical calculators and sorters as the world's automated calculating devices. By the end of the 1950s there were roughly 2000 installed computers in the world: machines like Remington Rand UNIVACs, IBM 702s, or DEC PDP-1s. The processing power of these machines averaged perhaps 10,000 machine instructions per second.

Today, talking rough orders of magnitude only, there are perhaps 300 million active computers in the world with processing power averaging several hundred million instructions per second. Two thousand computers times ten thousand instructions per second is twenty million. three hundred million computers times, say, three hundred million instructions/second is ninety quadrillion--a four-billion-fold increase in the world's raw automated computational power in forty years, an average annual rate of growth of 56 percent per year.

Such a sustained rate of productivity improvement at such a pace is unprecedented in our history. Moreover, there is every reason to believe that this pace of productivity growth in the leading sectors will continue for decades. More than a generation ago Intel Corporation co-founder Gordon Moore noticed what has become Moore's Law--that improvements in semiconductor fabrication allow manufacturers to double the density of transistors on a chip every eighteen months. The scale of investment needed to make Moore's Law hold has grown exponentially along with the density of transistors and circuits, but Moore's Law has continued to hold, and engineers see no immediate barriers that will bring the process of improvement to a halt anytime soon.

As the computer revolution proceeded, nominal spending on information technology capital rose from about one percent of GDP in 1960 to about two percent of GDP by 1980 to about three percent of GDP by 1990 to between five and six percent of GDP by 2000. All throughout this time, Moore’s Law–the rule of thumb enunciated by Intel cofounder Gordon Moore that every twelve to eighteen months saw a doubling of the density of transistors that his and other companies could put onto a silicon wafer–meant that the real price of information technology capital was falling as well. As the nominal spending share of GDP spent on information technology capital grew at a rate of 5 percent per year, the price of data processing–and in recent decades data communications–equipment fell at a rate of between 10 and 15 percent per year as well. At chain-weighted real values constructed using 1996 as a base year, real investment in information technology equipment and software was an amount equal to 1.7 percent of real GDP in 1987. By 2000 it was an amount equal to 6.8 percent of real GDP.

Source: National Income and Product Accounts

 


What Will the Future Bring?

Will the decade of the 2000s be more like the late 1990s, or more like the 1980s as far as growth in productivity and living standards is concerned? The smart way to bet is that the 2000s will be much more like the fast-growing late 1990s than like the 1980s. The extraordinary pace of invention and innovation in the information technology sector has generated real price declines of between ten and twenty percent per year in information processing and communications equipment for nearly forty years so far. There are no technological reasons for this pace of productivity increase in these leading sectors to decline over the next decade or so. In the consensus analysis, creased total factor productivity in the information technology capital goods-producing sector coupled with extraordinary real capital deepening as the quantity of real investment in information technology capital bought by a dollar of nominal savings grows have together driven the productivity growth acceleration of the later 1990s. It may indeed be the case that a unit of real investment in computer or communications equipment "earned the same rate of return" as any other unit of real investment, as Robert Gordon (2002) puts it. But the extraordinary cost declines had made a unit of real investment in computer or communications equipment absurdly cheap, hence the quantity of real investment and thus capital deepening in information-technology capital absurdly large.

Continued declines in the prices of information technology capital mean that a constant nominal flow of savings channeled to such investments will bring more and more real investment. As long as information technology capital earns the same rate of return as other capital, then labor productivity growth should continue very high. The social return to information technology investment would have to suddenly and discontinuously drop to nearly zero, or the share of nominal investment spending devoted to information technology capital would have to collapse, or both, for labor productivity growth in the next decade to reverse itself and return to its late 1970s or 1980s levels.

Moreover, additional considerations tend to strengthen, not weaken, forecasts of productivity growth over the next decade. It is very difficult to argue that the speculative excesses of the 1990s boom produced substantial upward distortions in the measured growth of potential output. The natural approach that economists to model investment spending in detail–the approach used by Basu, Fernald, and Shapiro (2001)–tells us that times of rapid increase in real investment are times when "adjustment costs" are unusually high, and thus times when actual productivity growth undershoots the long-run sustainable trend. Both a look back at past economic revolutions driven by technologies that were in their day analogous to the computer in their effects and a more deeper look forward into the likely determinants of productivity growth suggest a bright future.

The pace of technological progress in the leading sectors driving the "new economy" is very rapid indeed, and will continue to be very rapid for the foreseeable future. The computers, switches, cables, and programs that are the products of today's leading sectors are what Bresnehan and Trajtenberg (1985) call "general-purpose technologies," hence demand for them is likely to be extremely elastic. Rapid technological progress brings rapidly falling prices. Rapidly falling prices in the contest of extremely elastic demand will produce rapidly-growing expenditure shares. And the economic salience of a leading sector--its contribution to productivity growth--is the product of the rate at which the cost of its output declines and the share of the products it makes in total demand. Thus unless Moore's Law ceases to hold or the marginal usefulness of computers and communications equipment rapidly declines, the economic salience of the data processing and data communications sectors will not shrink.

Moreover, revious industrial revolutions driven by general purpose technologies have seen an initial wave of adoption followed by rapid total factor productivity growth in industries that use these new technologies as businesses and workers learn by using. So far this has not been true of our current wave of growth. As Robert Gordon (2002) has pointed out at every opportunity, there has been little if any acceleration of total factor productivity growth outside of the making of high-tech equipment itself: the boosts to labor productivity look very much like what one would expect from capital deepening alone, not what one would expect from the fact that the new forms of capital allow more efficient organizations.

Paul David (1991) at least has argued that a very large chunk of the long-run impact of technological revolutions does emerge only when people have a chance to thoroughly learn the characteristics of the new technology and to reconfigure economic activity to take advantage of it. In David’s view, it took nearly half a century before the American economy had acquired enough experience with electric motors to begin to use them to their full potential. By his reckoning, we today are only halfway through the process of economic learning needed for us to even begin to envision what computers will be truly useful for.

Moreover, as Crafts (2000) argues, the striking thing is not that there was a "Solow paradox" of slow productivity growth associated with computerization, but that people did not expect the economic impact to start slow and gather force over time. As he writes, "in the early phases of general purpose technologies their impact on growth is modest." It has to be modest: "the new varieties of capital have only a small weight relative to the economy as a whole." But if they are truly general-purpose technologies, their weight will grow.

 


Possible Interruptions?

Could any factors interrupt a relatively bright forecast for productivity growth over the next decade? There are three possibilities: The first is the end of the era of technological revolution–the end of the era of declining prices of information technology capital. The second is a steep fall in the share of total nominal expenditure devoted to information technology capital. And the third is a steep fall in the social marginal product of investment in information technology–or, rather, a fall in the product of the social return on investment and the capital-output ratio. The important thing to focus on in forecasting the future is that none of these have happened: In 1991-1995 semiconductor production was half a percent of nonfarm business output; in 1996-2000 semiconductor production averaged 0.9 percent of nonfarm business output. Nominal spending on information technology capital rose from about one percent of GDP in 1960 to about two percent of GDP by 1980 to about three percent of GDP by 1990 to between five and six percent of GDP by 2000. Computer and semiconductor prices declined at 15-20 percent per year from 1991-1995 and at 25-35 percent per year from 1996-2000.

 


The Usefulness of Computers

However, whether nominal expenditure shares will continue to rise in the end hinges on how useful data processing and data communications products turn out to be. What will be the elasticity of demand for high-technology goods as their prices continue to drop? The greater is the number of different uses found for high-tech products as their prices decline, the larger will be the income and price elasticities of demand--and thus the stronger will be the forces pushing the expenditure share up, not down, as technological advance continues. All of the history of the electronics sector suggests that these elasticities are high, nor low. Each successive generation of falling prices appears to produce new uses for computers and communications equipment at an astonishing rate.

The first, very expensive, computers were seen as good at performing complicated and lengthy sets of arithmetic operations. The first leading-edge applications of large-scale electronic computing power were military: the burst of innovation during World War II that produced the first one-of-a-kind hand-tooled electronic computers was totally funded by the war effort. The coming of the Korean War won IBM its first contract to actually deliver a computer: the million-dollar Defense Calculator. The military demand in the 1950s and the 1960s by projects such as Whirlwind and SAGE [Semi Automatic Ground Environment]--a strategic air defense system--both filled the assembly lines of computer manufacturers and trained the generation of engineers that designed and built.

The first leading-edge civilian economic applications of large--for the time, the 1950s--amounts of computer power came from government agencies like the Census and from industries like insurance and finance which performed lengthy sets of calculations as they processed large amounts of paper. The first UNIVAC computer was bought by the Census Bureau. The second and third orders came from A.C. Nielson Market Research and the Prudential Insurance Company. This second, slightly cheaper, generation was of computers was used not to make sophisticated calculations, but to make the extremely simple calculations needed by the Census, and by the human resource departments of large corporations. The Census Bureau used computers to replace their electro-mechanical tabulating machines. Businesses used computers to do the payroll, report-generating, and record-analyzing tasks that their own electro-mechanical calculators had previously performed.

The still next generation of computers--exemplified by the IBM 360 series--were used to stuff data into and pull data out of databases in real time--airline reservations processing systems, insurance systems, inventory control. It became clear that the computer was good for much more than performing repetitive calculations at high speed. The computer was much more than a calculator, however large and however fast. It was also an organizer. American Airlines used computers to create its SABRE automated reservations system, which cost as much as a dozen airplanes. The insurance industry automated its back office sorting and classifying.

Subsequent uses have included computer-aided product design, applied to everything from airplanes designed without wind-tunnels to pharmaceuticals designed at the molecular level for particular applications. In this area and in other applications, the major function of the computer is not as a calculator, a tabulator, or a database manager, but is instead as a what-if machine. The computer creates models of what-if: would happen if the airplane, the molecule, the business, or the document were to be built up in a particular way. It thus enables an amount and a degree of experimentation in the virtual world that would be prohibitively expensive in resources and time in the real world.

The value of this use as a what-if machine took most computer scientists and computer manufacturers by surprise. None of the engineers designing software for the IBM 360 series, none of the parents of Berkeley UNIX, nobody before Dan Bricklin programmed Visicalc had any idea of the utility of a spreadsheet program. Yet the invention of the spreadsheet marked the spread of computers into the office as a what-if machine. Indeed, the computerization of Americas white-collar offices in the 1980s was largely driven by the spreadsheet program's utility--first Visicalc, then Lotus 1-2-3, and finally Microsoft Excel.

For one example of the importance of a computer as a what-if machine, consider that today's complex designs for new semiconductors would be simply impossible without automated design tools. The process has come full circle. Progress in computing depends upon Moore's law; and the progress in semiconductors that makes possible the continued march of Moore's law depends upon progress in computers and software.

As increasing computer power has enabled their use in real-time control, the domain has expanded further as lead users have figured out new applications. Production and distribution processes have been and are being transformed. Moreover, it is not just robotic auto painting or assembly that have become possible, but scanner-based retail quick-turn supply chains and robot-guided hip surgery as well.

In the most recent years the evolution of the computer and its uses has continued. It has branched along two quite different paths. First, computers have burrowed inside conventional products as they have become embedded systems. Second, computers have connected outside to create what we call the world wide web: a distributed global database of information all accessible through the single global network. Paralleling the revolution in data processing capacity has been a similar revolution in data communications capacity. There is no sign that the domain of potential uses has been exhausted.

One would have to be pessimistic indeed to forecast that all these trends are about to come to an end in the next few years. One way to put it is that modern semiconductor-based electronics technologies fit Bresnahan and Trajtenberg's (1995) definition of a "general purpose technology"--one useful not just for one narrow class but for an extremely wide variety of production processes, one for which each decline in price appears to bring forth new uses, one that can spark off a long-lasting major economic transformation. There is room for computerization to grow on the intensive margin, as computer use saturates potential markets like office work and email. But there is also room to grow on the extensive margin, as microprocessors are used for tasks like controlling hotel room doors or changing the burn mix of a household furnace that few two decades ago would have thought conceivable.