NIST

Economics Analysis Office

Gregory Tassey

September 2009

Annotated Bibliography of 

Technology’s Impacts on Economic Growth

Economic studies over several decades have consistently shown a strong impact of R&D 

investment on both productivity and output growth, with a number of these studies also 

implying significant underinvestment. 

Macroeconomic Studies 

 “Growth accounting” studies have estimated that technology accounts for more than 

one-half of economic (GDP) growth in all OECD countries except Canada (Boskin, 

M. and L. Lau, 2000).

 The average productivity advantage of the United States over OECD countries as a 

group accounts for three quarters of the per capita income difference (McGuckin 

and van Ark, 2002).

 Changes in technology are the only source of permanent increases in productivity 

(Basu, Fernald, and Shapiro, 2001). 

 For the period 1995–99, the combination of innovation and capital deepening 

(acquisition of technology through capital investment) accounted for two-thirds of 

productivity growth (Oliner, S. and D. Sichel, 2000).

 The acceleration in productivity growth in the last half of the 1990s was due 

entirely to investments in information technology (Basu et al., 2001 and Jorgenson, 

2001).

 While three-quarters of US industries contributed to the acceleration in economic 

growth in the late 1990s, the four IT-producing industries were responsible for a 
2

quarter of that acceleration while only accounting for 3 percent of the GDP

(Jorgenson, 2005).

 Technologically stagnant sectors experience slow productivity growth and, 

therefore, above average cost and price increases; the rising prices actually increase 

these sectors’ measured share of nominal GDP, thereby lowering national

productivity growth (Baumol, 1967; Nordhaus, 2006).

 The rate of return from R&D is about four times that from physical capital, 

implying that R&D investment should be increased by a factor of four (Jones, C. 

and J. Williams, 1998, 2000)

 BEA’s long-awaited satellite R&D account for the first time provides an explicit 

estimate of "R&D's" contribution to economic growth (under the current system of 

national accounts, most of GDP growth is attributed to labor and capital plus the 

unexplained residual). BEA’s estimate is 6.5 percent over the 1995-2002 period, up 

from the longer-term 40-year average of 4.5 percent (by comparison, the 40-year 

average contribution of buildings and factories is only 2 percent). However, this 

estimate of R&D’s contribution is much lower than the growth accounting studies. 

In addition to methodological differences (the BEA approach is potentially much 

more accurate), the huge difference appears due, in part at least, to the fact that the 

growth accounting approach approximates the total impact of technology on the 

economy. In contrast, this initial BEA estimate is only for the impact of R&D 

investment on the industry in which the R&D is conducted. Thus, for example, BEA 

is only estimating the impact of computer industry R&D on the computer industry 

and does not include an estimate of this investment's impact on all the industries that 

use computers. Over time, BEA will expand the scope of R&D impact assessment.

Microeconomic Studies—Private R&D 

 Analyses of industry-level impacts of R&D indicate levels of impact similar to the 

macroeconomic analyses: about one-half of output growth and three-quarters of 

productivity growth are attributable to R&D investment (Griliches, 1995). 

 Studies of return on investment from R&D have shown a social (aggregate internal)

rate of return to R&D of between 50–100 percent, which is about double the 

estimates of private rates of return (Nadiri, 1993, Popper, 1995). It is important to 

note that these studies used the internal rate-of-return method, adapted from

corporate finance where the R&D is a narrowly defined (project-level) time series. 

Other studies have used the production-function approach where the elasticity of 

R&D investment with respect to productivity growth is assessed (Hall, 1996).

 Research has indicated significant variation across industries in the impact of R&D. 

This supports the R&D management literature, which identifies numerous factors 

affecting the productivity of R&D. However, no evidence has been produced to 

indicate diminishing returns from increased R&D across the range of R&D 
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intensities found in manufacturing industries (Cameron, 1999). Thus, no support 

exists for the argument that some industries need less R&D than others. 

 The rate of return on basic science is about three times that of applied R&D, which, 

in turn, is about double the rate of return on physical capital (Griliches, 1995)

Note: because an important phase of R&D between basic science and 

applied R&D, namely generic or fundamental technology research, is not 

part of the NSF data collection scheme, companies are forced to allocate 

those funds (such as their budgets for central corporate research to basic 

science. Most of this research is not basic science but, rather, 

generic/fundamental technology research (e.g. Bell Labs proof of concept 

for the transistor). Thus, references, such as this one to industry basic 

science, really means the long-term, high-risk generic technology research 

in central corporate labs

 High-tech industries are not only producing the higher rates of productivity growth 

but exhibit the highest wages and salaries. Specifically, Bureau of Labor Statistics 

(BLS) analysis shows that the median earnings in all but one of the 71 BLS 

technology-oriented occupations exceeded the median for all workers in 2004. In six 

high-tech occupations, earnings exceeded three times the median. In 34 more, 

earnings were twice the median; and, in another 17, earnings exceeded the median 

by 50 to 99 percent (Hecker, 2005).

Note: Most economic studies underestimate the contribution of R&D to 

productivity and output growth for two major reasons: (1) Much R&D 

(especially in the United States) is directed toward social goals, the output 

of which is difficult to measure (national defense, environmental quality, 

energy independence, space exploration, health care); (2) Measurement of 

the output in R&D-based industries (based on inaccurate price indexes) 

does not allow for quality improvements due to R&D (Griliches, 1995).

Microeconomic Studies—Government R&D 

 Differences in the impacts of industry and government R&D are blurred because (1) 

most economic analyses of this topic used data from the period when much 

government R&D was mission-oriented (as opposed to the objective of stimulating 

economic growth); (2) the data were too aggregated; and (3) economists have 

incorrectly modeled government R&D as a substitute rather than a complement to 

industry R&D (Hall, 1996).

 However, recent studies of at least one type of government research have shown 

even higher net economic benefits (at least for the types of government R&D 

studied) than for most of private R&D. 21 NIST economic impact studies 

conducted over the past 12 years of technical infrastructure supplied to industry to 

increase R&D, production, and market transaction productivities have delivered an 
4

average benefit-cost ratio of 40:1.

 Most academic research is funded by government, so the economic impact of this 

research is a reflection on government funding policies. Mansfield’s (1990) wellknown paper indicated that about one-tenth of the new products and processes 

commercialized during 1975–1985 could not have been developed (at least not without 

substantial delay) without recent academic research. 

 Most economic studies suffer from treating R&D and hence economic benefits in 

homogeneous (“black-box”) terms. This weakness prohibits assessing the 

determinants of important (“radical” or “breakthrough”) innovations that have far 

greater economic impact. The few studies cited above that focus on basic or 

academic research provide some insight into differential rates of return. An even 

fewer number of studies have examined the impacts of breakthrough vs. incremental 

innovations. In one such study of 11 case studies of radical innovation efforts within 

major corporations, a team from Rensselaer Polytechnic Institute concluded that 

“the life cycle of a discontinuous innovation project is profoundly different from a 

continuous improvement project.” The 11 projects studied exhibited several of the 

categories of market failure described in the next section. In eight of the 11 case 

studies, the researchers found that government was a major source of funds (Rice et 

al, 1998). That such radical innovations are more profitable was shown in a study by 

Kim and Morbougne (1997). They surveyed R&D-intensive companies in the 

United States and Europe to obtain sales and profits data on investments in 

incremental improvements based on the current generation of technology (product 

line extensions) and in new products based on new emerging generic technologies. 

For the average firm responding to the survey, product-line extensions dominate 

both in terms of number and sales. This result is hardly surprising, as companies 

focus most of their resources on extracting value from their current technology 

portfolios. However, a majority of profits were found to be attributable to the 

relatively few “discontinuous” innovations (i.e., innovations based on radically new 

generic technologies).

Anecdotes

(1) While automobiles’ miles per gallon have improved 40 percent since 1978, and 

replacing a 1978 incandescent bulb with today’s compact fluorescent bulb 

improves the lumens per watt by 339 percent, the improvement in computer 

systems’ instructions per second per watt since 1978 has increased 2,857,000 

percent.1

(2) “Consider a few milestones from Bell Labs: Fax transmission, long-distance 

television transmission, photovoltaic solar cells, the transistor, the UNIX 

operating system, and cellular telephony. Each of these innovations laid the 

groundwork for vibrant new industries. The transistor alone is the building block 

for computers, consumer electronics, telecom systems, high-tech medical devices, 

 

1 Technology CEO Council, “A Smarter Shade of Green – How Innovative Technologies are 

Saving Energy, Time, and Money,” 2008. 
5

and much more. Likewise, DARPA's creation of the Internet (as ARPAnet) in 

1969 and Xerox PARC's development of Ethernet and the graphical user interface 

(GUI) set the stage for the PC revolution. These basic research breakthroughs 

unleashed cycles of applied innovation that created entirely new sectors of our 

economy.” “The transistor alone is the building block for computers, consumer 

electronics, telecom systems, high-tech medical devices, and much more.”

2

(3) IT has spawned a new class of companies and capabilities:

The first web browser was released commercially in 1994. In March of 2009, 

there were 14 billion web searches.

The first text message was sent in 1992. Today, the number of text messages 

sent every day exceeds the population of the planet.

Skype launched computer-to-computer communications in 2003, registering 

445 million users by first quarter 2009 and logging in 24 billion call minutes. 

The company was acquired in 2006 for $2.3 billion.

Twitter was launched in 2006. Now there are more than 32 million users.3

(4) The most powerful codes are no longer in strings of ones and zeroes, but in four 

letters: A, T, C and G. This is the programming language of life. Different 

combinations of those four letters—DNA-bases—describe every life form on earth. 

The ability to understand and manage them will revolutionize the competitive 

landscape in every sector, from medical to agricultural.

In the medical arena, the ability to treat disease by turning genes on and off may 

make some of today’s medical therapies—amputations, consumption of toxic 

chemicals and irradiation—look as primitive as the practice of bloodletting to release 

evil spirits.

With genomics, medicine can become predictive, preventive and personalized. And 

the industry will look considerably different when information technology—capturing 

the DNA profile for every individual—is as important, if not more so, as identifying 

chemical compounds with useful properties in treating disease. Decoding the first 

human genome took 10 years and $3 billion dollars. The ability to produce a personal 

genetic code for about $5,000 in an hour or two is not far off in the future.4

References

 

2 Adrian Slywotsky (2009), “How Science Can Create Millions of New Jobs,” Business Week

(http://www.businessweek.com/magazine/content/09_36/b4145036678131.htm). August.

3

http://shifthappens.wikispaces.com/Various+Versions+of+the+Presentations. Aslo, van Opstal (2009)

4 Van Opstal (2009).
6

Basu, S., J. Fernald, and M. Shapiro (2001), “Productivity Growth in the 1990s: Technology, 

Utilization, or Adjustment” (NBER Working Paper 8359). Cambridge, MA: National Bureau 

of Economic Research.

Baumol, William (1967), “Macroeconomics of Unbalanced Growth: The Anatomy of Urban 

Crisis,” American Economic Review 57 (June): 418–420.

Boskin, M. and L. Lau (2000), “Generalized Solow-Neutral Technical Progress and Postwar 

Economic Growth,” National Bureau of Economic Research Working Paper Series (NBER 

Working Paper No. W8023).

Cameron (1998), “Innovation and Growth: A Survey of Empirical Evidence”, Nuffield College, 

Oxford, UK (http://www.hicks.nuff.ox.ac.uk/users/cameron/research/gpapers.html).

Cameron, Gavin (1999), “R&D and Growth at the Industry Level,” Nuffield College, Oxford, 

U.K. (http://hicks.nuff.ox.ac.uk/users/cameron/research/gpapers.html).

Griliches, Zvi (1995), “R&D and Productivity: Econometric Results and Measurement Issues” in 

Paul Stoneman, ed., Handbook of the Economics of Innovation and Technological Change. 

Cambridge, MA: Blackwell.

Hall, Bronwyn (1996), “The Private and Social Returns to Research and Development,” in B. 

Smith and C. Barfield, (eds.), Technology, R&D, and the Economy. Washington, DC: 

Brookings Institution and the American Enterprise Institute: 140–183.

Hecker, Daniel (2005), “High-Technology Employment: A NAICS-Based Update”, Monthly Labor 

Review (July).

Jones, C. and J. Williams (1998), “Measuring the Social Returns to R&D,” Quarterly Journal of 

Economics 113 (November): 1119–1135.

Jones, C. and J. Williams (2000), “Too Much of a Good Thing?: The Economics of Investment in 

R&D,” Journal of Economic Growth 5 (March): 65–85. 

Jorgenson, Dale (2001), “U.S. Economic Growth In the Information Age”, Issues in Science and 

Technology (Fall): 42–50.

Jorgenson, Dale (2005), “Moore’s Law and the Emergence of the New Economy,”

Semiconductor Industry Association 2005 Annual Report.

Kim, W. Chan and Renée Mauborgne (1997), ‘Value Innovation: The Strategic Logic of High 

Growth’, Harvard Business Review 75:1 (January-February): 102–112.

Mansfield, Edwin (1990), “Academic Research and Industrial Innovation,” Research Policy 20: 

1–12.

Nordhaus, William (2006), “Baumol’s Disease: A Macroeconomic Perspective,” NBER Working 

Paper Series (Working Paper 12218: http://www.nber.org/papers/w12218). 

McGuckin, R. and B. van Ark (2002), Productivity, Employment, and Income in the World’s 

Economies. New York: The Conference Board.

Nadiri, M. I. (1993), “Innovations and Technological Spillovers,” NBER Working Paper No. 

4423. New York: National Bureau of Economic Research (August).

OECD (2000), A New Economy?: The Changing Role of Innovation and Information Technology 

in Growth. Paris: Directorate for Science, Technology and Innovation.

Oliner, S. and D. Sichel (2000), The Resurgence of Growth in the Late 1990s: Is Information 

Technology the Story? Washington, DC: Federal Reserve Board.

Popper, Steven (1995), Economic Approaches to Measuring the Performance and Benefits of 

Fundamental Science. Washington, DC: RAND.

Rice, M., G. O’Conner, L. Peters, and J. Morone (1998), ‘Managing Discontinuous Innovation’, 

Research–Technology Management 41:3 (May–June): 52–58.

Tassey (2004), “Policy Issues for R&D Investment in a Knowledge-Based Economy,” Journal of 

Technology Transfer 29:2 (April 2004), 153–185.
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Tassey, Gregory (2005a), “Underinvestment in Public Good Technologies” in F. M. Scherer and 

A. N. Link, eds., Essays in Honor of Edwin Mansfield, special issue of the Journal of 

Technology Transfer 30: 89–113. 

Tassey, Gregory (2005b), “The Disaggregated Technology Production Function: A New Model 

of University and Corporate Research,” in A. N. Link and D. Siegel, eds., University-Based 

Technology Initiatives, special issue of Research Policy 34: 287–303.

Tassey, Gregory (2007), The Technology Imperative. Northampton, MA and Cheltenham, UK: 

Edward Elgar.

Tassey, Gregory (2008), “Modeling and Measuring the Roles of Technology Infrastructure,”

Economics of Innovation and New Technology 17(7): 617–631.