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MODELING AND MEASURING THE ECONOMIC ROLES OF TECHNOLOGY 

INFRASTRUCTURE 

Gregory Tassey a 

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MODELING AND MEASURING THE ECONOMIC ROLES

OF TECHNOLOGY INFRASTRUCTURE

GREGORY TASSEY∗

National Institute of Standards and Technology, Gaithersburg, MD, USA

(Received 28 March 2007; Revised in final form 29 August 2007)

Designing and managing an economy’s technology infrastructure requires both accurate economic models and data 

to drive them. Previous models treat technology as a homogeneous entity, thereby precluding assessing investment 

barriers affecting infrastructure elements. The model presented overcomes this deficiency by disaggregating the 

knowledge production function into key elements of the typical industrial technology based on the distinctly different 

investment incentives associated with each element. Without such a model, the economist’s ability to assess important 

market failures associated with investment in the major technology elements, including those with infrastructure 

(public-good) characteristics, is compromised. Unfortunately, even with the correct knowledge production function, 

the required data are difficult to collect. This forces government agencies, which fund a majority of technology 

infrastructure research, to use second-best approaches for economic analyses. The second half of this paper therefore 

presents an analytical framework that can be driven by more accessible data and provide reasonable impact assessments 

until better data become available. 

Keywords: Technology infrastructure; Technological change; Innovation; Knowledge creation; Knowledge production function; R&D productivity; R&D policy; Program impact assessment 

JEL Classification: O3; O2 

The first part of this paper presents a disaggregated or ‘multi-element’ model of technological 

change. Such a model allows examination of the roles and impacts of the major elements 

of technology, each of which is distinguished by a different degree and type of public-good 

content. This distinction implies unique investment behavior with respect to each element with 

consequent public-policy implications. The second part draws upon the considerable experience of the US National Institute of Standards and Technology in designing and conducting 

practical approaches to estimate the economic benefits from public and private investment in 

these quasi-public-good technology elements. 

1 DISAGGREGATING THE KNOWLEDGE PRODUCTION FUNCTION

The typical industrial technology is composed of three elements: the generic technology base 

(also, technology platform); supporting infratechnologies; and proprietary market applications 

∗ E-mail: tassey@nist.gov 

ISSN 1043-8599 print; ISSN 1476-8364 online © 2008 Taylor & Francis 

DOI: 10.1080/10438590701785439 
618 G. TASSEY 

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(innovations and subsequent improvements). The first two have public-good content and therefore, embody infrastructure characteristics. These critical quasi-public technology goods are 

supplied by a combination of firm-specific assets and sources external to the innovating unit 

of the firm, such as central corporate research labs, government labs, and increasingly, universities. The fundamental relationships among these elements require a technology production 

function that captures the interactive nature of the two quasi-public-good elements with each 

other and with private-sector investments in the third element, proprietary technologies. Most 

important, each element responds to different sets of investment incentives (Tassey, 2005a). 

The failure to disaggregate the technology variable based on the distinctly different character of each element and its associated unique investment incentives has limited economists’ 

ability to explain R&D investment behavior and the subsequent relationships with economic 

growth. Both macroeconomic and microeconomic growth models have made technology an 

endogenous explanatory variable. However, the vast majority of this literature has treated 

technology and the process that creates it, R&D, as homogeneous entities. Only a few efforts 

have attempted even a partial disaggregation, and those have been limited to separating scientific research from technology research. In other words, the technology variable remains 

aggregated. 

This failure has also inhibited government technology investment policies by prohibiting 

assessments of the distinctly different incentives associated with each of these three elements. 

This policy analysis problem is becoming more severe for several reasons: (1) corporate 

laboratories have reduced their share of national spending on the quasi-public elements, in 

particular, early-phase research on new, radical technologies; (2) in many countries, such 

as the USA, government spending on such research has been erratic and skewed toward a 

few technologies tied to specific social objectives; and (3) universities in many economies 

are assuming a larger role in such early-phase technology research, with implications for 

intellectual property (IP) and research portfolio management. 

1.1 The Three Elements of Industrial Technology

The enabling role of generic technologies for the development of market applications (innovations) has been discussed qualitatively (Link and Tassey, 1987; Nelson, 1992; Tassey, 1997, 

2007).1 Dosi (1982, 1988) defines a ‘technology paradigm’ as a ‘pattern’ of solutions to 

selected techno-economic problems based on highly selected principles derived from the natural sciences. Such ‘highly selected principles’ form a generic technology base from which 

market applications are drawn. A generic technology provides in essence a ‘proof of concept’ 

that reduces technical risk sufficiently to enable applied R&D investments to be rationalized.2 

Infratechnologies are the other quasi-public technology element. They include research 

tools (measurement and test methods), scientific and engineering data, the technical basis 

for interface standards, quality control techniques, etc. Collectively, they constitute a diverse 

1 A generic technology is not the same thing as a ‘general purpose technology’ as defined by Bresnahan and 

Trajtenberg (1995). The latter refers to a technology with multiple market applications (i.e., market economies of 

scope exist), a distinctly different concept from the generic base from which a particular set of technology applications 

is developed. 2 The classic example of a generic product technology is Bell labs’ proof in the late 1940s and early 1950s of 

the concept that the principles of solid state physics can be used to construct a semiconductor switch or amplifier, 

resulting in the creation of the transistor (Nelson, 1962). One of the best examples of a generic systems technology 

is the Internet. As a system (the communications network), technological advances were first required in its major 

underlying network technologies, such as queuing theory, packet switching, and routing. Demonstration of such in the 

1960s led to prototype networks in the 1970s (ARPANET) and 1980s (NSFNET), which eventually led to the Internet 

(National Research Council, 1999). Occasionally, a generic technology can take the form of a ‘method of inventing’. 

Examples are methods for manufacturing hybrid corn seeds and research methods for developing nanotechnologies 

(Darby and Zucker, 2003). 
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technical infrastructure, various types of which are applied at each stage of economic activity. 

Infratechnologies often are implemented as industry standards (Tassey, 1997, 2000).3 

Both generic technology and infratechnology elements are drawn upon by competing firms 

to create proprietary technology. However, although attainment of partial property rights is 

possible, spillovers and other sources of market failure are prominent. In fact, widespread 

use of generic technologies is desirable from a public-policy perspective because the more 

firms draw upon a technology platform, the larger the number and variety of innovations 

produced. When infratechnologies are adopted as the technical basis for standards, uniform 

as well as widespread use is mandatory. These characteristics result in various degrees of 

underinvestment across technologies and over each technology’s life cycle. Consequently, 

every industrialized nation provides funds to leverage generic technology and infratechnology 

research and subsequent assimilation by domestic industries. Such funding policies constitute 

recognition of the public-good content, even though identifying and measuring this content 

remains difficult conceptually and empirically. 

1.2 The Multi-element Knowledge Production Function

The microeconomics literature has partially recognized the need for a disaggregated technology framework to address these phenomena but has not progressed beyond a dichotomous 

model in which technology is separated into scientific and technological stocks of knowledge. In such models, scientific information is appropriately characterized as a pure public 

good (Nelson, 1959) with external (to the industry) sources of supply. However, in such models, technological knowledge is implicitly assumed to be a purely private good, even while 

acknowledging the existence of spillovers.4 

The following disaggregated knowledge production function separately specifies the key 

public and private technology elements and thereby allows the explicit representation of the 

critical elements of an industrial technology, specifically generic technologies and infratechnologies. Such an investment-based model of innovation allows assessment of the productivity 

of private-sector applied R&D, as determined by both private and public-sector expenditures 

that precede or concurrently support it.5 

As a point of departure for explicitly separating the proprietary technology element from 

the quasi-public-good elements, the following generalized model is used: 

Qi = S · F (KNj ,KEi, φj , X) (1) 

where Q is a firm’s output of technology-based goods and services. KN represents the 

non-excludable (and hence public-good) portion of the industry’s generic technology and 

is assumed equally available to all firms in the industry. X is a set of factors that affect 

output/performance in addition to the public and private technology elements. φ represents 

the innovation infrastructure of the industry, which consists of a set of infratechnologies and 

associated standards, as well as other infrastructure elements such as the availability of risk 

capital, IP laws, technical support for entrepreneurs, etc. This infrastructure affects the efficiency of production and commercialization. S is the science upon which the industry’s generic 

3 Note that infratechnologies are part of an industry’s technology base in contrast to what are referred to as 

‘infrastructure technologies.’ The latter are produced by industries whose primary role is to provide an economic 

infrastructure function for other industries (electricity, transportation, and communications). 4 A number of studies have attempted to empirically test this general specification by separately including basic 

research and applied R&D variables in a modified production framework (Mansfield, 1980, 1991; Link, 1981; 

Griliches, 1986; Jaffe, 1989; Leyden and Link, 1991; Toole, 1999). 5 See Tassey (2005b) for a comprehensive treatment of this model, including comparisons with endogenous growth 

theory and alternative output/performance functions. 
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technology is based. Because, the vast majority of science is developed outside the industry by 

universities and government research institutes and because major breakthroughs in science 

occur infrequently, the science base is considered to be externally determined and constant 

and therefore is entered in the model as a shift parameter. 

KEi is a firm’s stock of excludable (proprietary) knowledge that is used to create new 

products and services, i.e., innovations. At any point in time, a firm’s proprietary technical 

knowledge creation is equivalent to the growth in KEi, represented by 

�KEi = δiREi

λ, (2) 

where RE is applied R&D expenditures targeted at developing innovations, λ is a scale 

parameter, and δ is a firm’s R&D productivity factor.6 

The productivity factor is represented by 

−KNj /REi δi = ηj e (3) 

An important point from a policy perspective is the negative sign on KN. It implies a 

hurdle for investment in innovations, specifically an initial technical-risk barrier that must be 

overcome before substantial private investment in RE will be forthcoming. The negative sign 

may seem to be counter intuitive because generic technology does in fact enable the conduct 

of applied R&D, which in turn produces innovations. However, it is a barrier to applied R&D 

in the sense that: (1) it must be available for innovation effort to occur; and (2) on average, 

the greater the potential of a new technology, the greater the required advance in early-phase 

proof-of-concept research, i.e., the greater the initial barrier to innovative effort posed by the 

needed investment in the generic technology. 

ηj is an efficiency parameter that represents the portion of an industry’s technical infrastructure that supports knowledge production. This infrastructure is the collective effect of 

an industry’s (or supply chain’s) infratechnologies and associated standards that affect R&D 

efficiency. For example, the development and characterization of biomarkers and the ability to 

detect and interpret them in the human body greatly increases the productivity of biotechnology 

R&D. Similarly, the ability to accurately image biological activity and transmit the results for 

analysis also increases R&D efficiency. In general, the availability of such techniques increases 

potential economic benefits from inventive activity and thereby provides incentives to create 

proprietary technical knowledge. Such technical infrastructure only changes occasionally (i.e., 

slower than proprietary technologies). Moreover, because of their large public-good content, 

they often become industry standards, which themselves are only changed periodically. They, 

therefore, can be considered constant relative to the firm’s R&D investment aimed at invention and then innovation (RE in Eq. (3)).7 Thus, ηj is assumed to be a process constant over 

a technology life cycle in industry j . 

The above model implies that industries based on radically new technologies require larger 

initial generic technology research expenditures. They will therefore experience lower rates 

of technical knowledge production for a given level of private R&D expenditures for some 

time. This phenomenon helps explain the S-shaped growth curve that characterizes the typical 

6 KE is assumed to be largely determined by KN, so the rate of growth is d/dt(KE). To the extent the existing stock 

of proprietary knowledge influences the growth of KE over the technology’s life cycle, the rate of growth is more 

appropriately 1/KE d/dt (KE). Further, some models assume that the rate of growth of KE is equivalent to the rate 

of innovation. However, Eq. (1) shows that this is not the case, which further complicates public policy. 7 Critical measurement methods, interface specifications, etc. are typically required to be in place before substantial 

R&D can be conducted efficiently, but once adopted as standards, they tend to remain unchanged for extended periods 

of time. 
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FIGURE 1 Risk reduction in the R&D cycle. 

technology life cycle. In particular, a ‘risk spike’ is created by the need for investment early 

in the R&D cycle in a technology platform (generic technology) that enables subsequent 

innovation; that is, its existence blocks private investment in innovation early in the life cycle 

(Tassey, 2005a, b, 2007).8 In this early phase of the technology life cycle when the generic 

technology is immature, initial attempts at innovation through applied R&D typically fail 

miserably. 

The exponential function in Eq. (3) is, in effect, a measure of the risk faced by investors 

at different points in the R&D cycle. When the targeted technological advance is large, as is 

the case for a radically new technology, the risk is also high that expenditures for developing 

innovations (through expanding KE) will fail. That is, the hump or risk spike in Figure 1 

will be larger than for investment in new but less advanced technologies (for example, a 

next-generation generic technology, as opposed to one based on new scientific principles). 

In all cases, investment in expanding the generic technology base is required to overcome 

the risk spike, RS, and allow private investments in KE to proceed. Such a risk profile explains 

why rates of innovation based on emerging technologies can languish for years, even decades. 

However, once the risk spike is overcome, private investment in R&D can reduce private risk, 

RP, to levels that permit commercialization.9 

The extreme case is no generic technology (KN = 0). Under this condition, applied R&D 

has very low productivity and will likely not be attempted. Growth in the stock of technical 

knowledge and hence the rate of innovation is then determined by δ = η. This case could 

be called the ‘natural rate of innovation’ because it is driven solely by the general economic 

environment included in η. Such inventions fall into ‘Pasteur’s quadrant’; that is, inventions 

8 The large size of total risk and subsequent investment barrier created at this point also has been referred to as the 

‘valley of death.’ 9 More radical technologies present higher risk spikes. However, once such spikes are overcome, commercialization 

risk actually can be reduced to a greater extent than is the case for less radical technologies because the superior 

performance attributes enhance market penetration (Tassey, 2005b; 2007). 
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that occur through trial-and-error or ‘inspiration’ processes.10 This source of invention is 

increasingly rare for today’s science-driven and complex technologies. 

Substituting Eq. (3) into (2) gives the technology production function: 

−KNj /REi

REλ �KEi = ηj e i . (4) 

Equation (4) shows that the growth rate of technical knowledge is negatively related to the 

magnitude of initial technical and market risk associated with prospective investments in ‘killer 

apps’. Thus, the efficiency parameter, ηj , is a critical factor in knowledge production because 

it can help to compensate for the risks that companies face when deciding to commit to new 

technologies and/or markets. 

1.3 Investment Implications of the Model

For corporate R&D decision making, the amount of generic technology, KN, and the quality 

of the infratechnologies and standards available to an industry directly determine the adequacy 

of an industry’s technical infrastructure. The efficacy of this infrastructure directly affects the 

technical and market risk associated with R&D project selection, i.e, with RE expenditures. 

The requirement for firms to estimate both technical risk associated with market-driven 

attributes and market risk associated with variations in expected market demand is especially 

critical in the early phases of the R&D cycle. The impact is to retard private investment in 

the generic technology research that produces KN. Similarly, the unavailability of sufficient 

technical efficiency, η, contributes to this risk spike and, in fact, risk over the entire R&D 

cycle. Increasing η through better and more timely standardization improves the efficiency of 

research by defining and measuring interactions of specific performance attributes with the 

overall product technology and with complementary products in a technology system (Tassey, 

2005a, b). 

Because both KN and η are widely and commonly used, their inadequacy in effect creates 

‘public risk’. Thus, all technology-based economies subsidize generic technology and infratechnology research (the latter providing the technical basis for standards).11 If the risk spike 

is overcome by subsidizing KN and η, then private investment, RE, can sufficiently reduce 

aggregate (technical and market) risk to enable commercialization of new technology. 

Note that the i firms in industry j draw upon the same industry-level infratechnology 

endowment. This is particularly the case the greater the extent of standardization. To an 

extent, the non-excludable generic technology endowment KNj available to each firm in an 

industry is also assumed to be approximately identical to the industry endowment because, by 

definition, the non-excludable character of this technology element and its role as a platform 

for innovations within the industry leads to both approximately equal access and common use 

by all firms in the industry. 

1.4 Qualifications to the Model

The model is complicated by the quasi-public-good nature of generic technologies (and 

infratechnologies to a lesser extent), which means that some degree of property rights can 

10 See Stokes (1997). The term ‘Pasteur’s quadrant’ refers to Louis Pasteur’s invention of the vaccine, which 

preceded subsequent discovery of some new principles of microbiology. More recently, packet switching – the basis 

for computer networks including the Internet – evolved to a significant degree ahead of network theory (National 

Research Council, 1999). 11 With respect to support of generic technology research, DoD/DARPA, NIH, and NIST/ATP are examples in 

the USA, while the framework program is the major example in the European Union. Infratechnology research is 

supported by national research institutes, such as the NIST. 
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be attained and maintained by individual firms. Thus, both government and industry fund 

generic technology research and both private firms and universities patent generic technologies. 

Companies also develop infratechnologies to varying degrees (some of which contribute to 

industry standards). Thus, at the R&D stage, KN and η represent the non-proprietary segments 

of these two technology elements, with the proprietary portions embodied in KE.12 

Therefore, the quasi-public-good character of these two technology elements and the consequent need to assimilate them from external sources means that endowments may not be 

identical across firms, especially in the early portion of a technology’s life cycle. This situation can lead to at least temporary competitive advantages both among firms within domestic 

industries and across competing industries in the global economy. Beyond the early phase 

of the technology life cycle (i.e., movement up the S-shaped performance/cost curve), competitive advantage is increasingly influenced, not only by efficiency in producing KE, but 

also by various infratechnologies that affect the production and market development stages of 

economic growth (Tassey, 2007). 

2	 THE RATIONALE FOR GOVERNMENT SUPPORT OF TECHNOLOGY

INFRASTRUCTURE

Policy analysis requires a framework to identify and characterize R&D underinvestment phenomena. If industrial technologies were homogeneous entities (i.e., the so-called ‘black box’ 

model prevails), the traditional knowledge production function and output/performance models would be sufficient to inform policy makers. However for the reasons stated here and 

in previous papers, the typical industrial technology must be disaggregated into the several 

major elements implied by Eq. (4). The existence of distinctly different investment barriers 

is the key construct in determining government R&D support roles and is the rationale for 

the disaggregated model. Two of the elements have significant public-good content and hence 

have the characteristics of technical infrastructure. 

Following the model developed in the previous section, this disaggregation is shown in 

Figure 2. The shading indicates the degree of public good content in each of the major elements 

of the typical industrial technology. The technology box is derived from an underlying science 

base (a pure public good). The existence of the three distinct elements comprising industrial 

technologies defies the notion that technologies are ‘black boxes’. Instead, the three technology 

elements shown arise from different sources in response to distinctly different investment 

incentives and research processes. It is the differences in investment incentives that create the 

need for disaggregation that then drives policy analysis. 

Specifically, an industrial technology is based on a set of fundamental or generic concepts. 

Although examples can be found of technologies emerging before significant proof of concept, an industry’s generic technology increasingly must evolve (basic concepts demonstrated, 

prototypes developed and tested) before industry is willing to commit significant funds to the 

more applied R&D required for market applications of the technology. This linearity in the 

R&D cycle occurs for two reasons. First, modern technologies are increasingly dependent on 

prior scientific advances. Second, the associated increase in technological complexity means 

that proving the overall technology concept is essential to enable the much larger subsequent 

applied R&D that results in a stream of innovations. 

Generic technologies are not widely recognized as an important element of industrial 

technology and they are not perceived as a type of technical infrastructure. However, such 

12 As indicated in footnote 6, KE could be included as an explanatory variable in the knowledge production function 

and would include any proprietary segments of KN and η. 
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FIGURE 2 Technology-based industry model. 

technology platforms have definite infrastructure characteristics. First, they are subject to 

substantial spillovers due to the tacit character of the knowledge created. Second, they also 

typically exhibit economies of scope, frequently well beyond the strategic scope of most firms. 

Both factors lead to underinvestment (Tassey, 2005a). 

In Figure 2, the arrows convey the linear character of progressive knowledge application 

from basic science to generic technology development to proprietary products, processes, 

and services. Further, the diagram indicates that this evolutionary process (which is more 

complicated than shown because of feedback loops) is facilitated and in many cases made 

possible by a set of infratechnologies (included in η in Eq. (4)). As previously indicated, these 

tools include measurement methods for R&D and production control, technical support for 

interfaces between components of systems technologies, scientific and engineering databases, 

techniques such as quality assurance procedures, and test methods for facilitating marketplace 

transactions of complex technology-based products. They are ubiquitous in technology-based 

industries and often exert their impacts the form of industry standards. This technology element 

suffers from extensive spillovers. In fact, spillovers in the form of widespread use of standards 

are actually essential if this form of technical infrastructure is to be effective. 

Which quasi-public-good technology element is the target of the government research 

program/project determines the analytical and data collection approaches to strategic planning 

and retrospective impact assessment. Assuming the target has been determined by underinvestment analysis (i.e., the policy rationale has been determined), the analyst will choose an 

analytical framework with the appropriate set of metrics. Doing so will allow accurate determination of the nature of the prospective/retrospective technical outputs from the research, the 

specific outcome (economic impact) metrics to be estimated/measured, the relevant types of 

qualitative analyses of the impact, and summary economic role assessments that will provide 

feedback/justification to government managers and other stakeholders (in particular, industry 

and government). 

3 ALTERNATIVE METHODS FOR ECONOMIC IMPACT ASSESSMENT

Pressures to conduct systematic strategic planning for and retrospective impact assessments of 

research projects and programs are of relatively recent vintage, so most government agencies 
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have not acquired the internal capability to select appropriate models and impact metrics and 

then to develop the necessary data sources or to find contractor support with the appropriate 

economic assessment skills. Moreover, R&D agencies are for the most part managed by technically trained people who are unfamiliar with economic assessment tools and have difficulty 

in understanding the imperative for such analysis or who are uncomfortable with the use and 

interpretation of information produced by a distinctly different discipline. And, while some 

universities have curricula that include impact assessment techniques, little of it is designed 

for government research program evaluation. 

Thus, without an understanding and acceptance of the appropriate economic models, inadequate and inappropriate data are collected. Ideally, economists and policy analysts would 

like to estimate a fully specified performance function of the form represented by Eq. (1). 

Doing so requires the estimation of a number of functions, including a technology production 

function such as Eq. (4). The latter is the focus for R&D policy analysis because it shows 

the relationships among potential investment targets (KN, η, and RE) and the subsequent output of technical knowledge with innovation potential, KE.13 Unfortunately, the quantity and 

quality of data required to drive the multi-element technology production function are not yet 

available. 

The implication is that the policy analyst must look for a second best approach until better 

data are made available. An alternative frequently used by many government policy groups 

is to simply collect descriptive statistics through surveys. Examples would be the number 

of companies that: (1) changed their investment behavior (say, increased generic technology 

research) in response to an R&D subsidy; (2) adopted an infratechnology from a government 

laboratory and experienced a production productivity improvement; or (3) achieved a commercialization objective in a shorter period of time due to increased R&D efficiency resulting from 

some combination of government-supported generic technology and infratechnology research 

and an integrated innovation infrastructure. Unfortunately, descriptive statistics only provide 

general qualitative indicators of impact and therefore do not provide rankings or cause-andeffect information. Moreover, because of their lack of specificity, the results are frequently 

misinterpreted. That is, the efficiency or relative effectiveness of the specific applications of 

a policy instrument (direct funding of R&D, tax incentives, etc.) and ultimately the general 

effectiveness of each instrument for different types of market failures cannot be determined 

with reasonable accuracy. 

A second alternative is to use metrics that provide quantitative measures of an S&T policy’s 

economic impact but for which data are more easily obtained than is the case for the parametric 

statistics associated with production and performance functions. Such ‘compromise’ metrics 

are found in corporate finance and, while not as potentially robust as parametric statistics, 

they are compatible with the project or program orientation of government R&D subsidies. 

If used properly and combined with qualitative assessments, they can provide policy makers 

with substantial information useful for managing such programs. 

4 ANALYTICAL TOOLS

The following describes a set of policy analysis tools that National Institute of Standards and 

Technology (NIST) has developed, which: (1) enable practical qualitative and quantitative 

13 Economists have focused on production functions that combine labor and capital inputs with technology assumed 

either to be determined outside the industry being studied or sufficiently constant to allow inclusion in the function 

as a shift parameter. Totally ignored is the fact that a marketing function exists, the output of which combines with 

the technology created to determine performance in technology-based markets (Tassey, 2005b). 
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assessments of the economic impacts of ongoing or completed technology infrastructure 

programs (retrospective studies); and (2) enable identification of new technologies and economic sectors that may potentially be targeted for support in the future (strategic planning 

studies). The NIST studies have focused on the two quasi-public-good technology elements: 

infratechnology research conducted in NIST’s laboratories and generic technology research 

supported by the Institute’s advanced technology program (ATP).14 

Selection of a framework for economic analysis of R&D projects and programs is confounded by the fact that the output of this investment does not have an explicit market (in 

contrast to a good or service). Moreover, the results of R&D are neither comparable across 

projects nor countable (Griliches, 1977). Because of such constraints, it is generally not feasible to directly estimate a knowledge production function. Therefore, selection of an analytical 

framework for assessing impacts of specific R&D projects frequently is determined by data 

availability, which results in one of the two above alternative approaches being chosen to 

approximate Eq. (1). 

In applying the second methodology, the analyst would like to construct a time series of 

costs and economic outcomes for the affected industries that include a period before government intervention. At some point in the time series, a government-funded project (R&D and 

technology transfer or technical information dissemination) occurs and the subsequent portion 

of the time series reflects the technical and economic impacts of the intervention which affect 

one or more of the three stages of economic activity (R&D, production, commercialization). 

The ability to effectively apply this approach depends significantly on the nature of the 

R&D project, as well as available data. Generic technologies are typically developed early 

in a technology’s life cycle and hence little technology investment data are generated prior 

to government intervention. That is, frequently no historical time series exists to allow specification of the intervention. In fact, a major government role in most industrialized nations 

is to promote early life-cycle (generic) technology research through support policies such as 

NIST’s ATP or Europe’s framework program. In contrast, because certain types of infratechnologies are needed in the middle of a technology life cycle, an increased potential exists for 

obtaining data on economic activity prior to the government intervention. 

However, data on economic activity ‘before’ the intervention is frequently unattainable for 

either type of government project. Obviously, these data are generated farther back in time 

than subsequent post-intervention data. Therefore, unless a real-time data collection program 

is implemented, sources of data degenerate and eventually disappear over time. Consequently, 

the longer the optimal time series the lower the quality of data obtainable in the ‘before’period, 

if it is obtainable at all. Even when an intervention can be clearly defined in the middle of a 

technology life cycle, the feasibility of collecting accurate data farther back in time than about 

6 years is low in most technology-based industries.15 

Because of availability of data and other difficulties frequently preclude the construction 

of a time series of economic trends before government intervention, the analyst must often 

use a ‘counterfactual’ technique to estimate the differential impacts of the government R&D 

project.16 In the application of such a technique, industry respondents are asked a series of 

‘what if’ questions focusing on the implications of additional costs incurred by industry if 

the government project did not exist. This approach works well when the government project 

14 The economic impact assessment methodology described below is discussed in greater detail in Tassey (2003). 15 In fact, discussions with managers in some industries put a limit of 3 years on collections of some types of data 

due to the dynamic character of their industries (mergers, acquisitions, exits, labor mobility). 16 A frequently cited early application of the counterfactual technique is Fogel’s [1962] study of ‘social savings’ 

from the emergence of railroads in the USA. Although much social research involves implicit counterfactuals, Fogel 

is recognized by economic historians as the first researcher to explicitly state a counterfactual as the basis for analysis. 
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FIGURE 3 Organization of metrics by technology life cycle: NIST’s ATP. 

either is initiated beyond the early phases of the current technology life cycle so that some 

experience without the government contribution exists, or the project is an intervention in a 

life cycle that has similarities with related technologies, thereby allowing the respondents to 

extrapolate from prior experience. 

The counterfactual approach has been used extensively by NIST in assessing the economic impacts of its infratechnology research programs. In many cases, a new infratechnology 

replaces less efficient forms used in the current or previous technology life cycles. Experience 

with the less efficient infrastructure being replaced or knowledge of similar infrastructure from 

past life cycles provides industry respondents with an ability to estimate the increased costs 

that would be incurred if the new infrastructure were not available. 

While this approach may sound similar to the time series intervention, the counterfactual 

approach is a ‘second best’ solution to characterizing costs in the period before interventions 

because annual cost data cannot be estimated or data collection is judged to be too difficult. 

As a substitute, the counterfactual approach obtains a rough estimate of annual costs in the 

pre-intervention period.17 

NIST’s ATP has also used the counterfactual approach to assess the impacts of its generic 

technology funding on corporate R&D investment decisions. ATP economic studies use the 

same counterfactual technique described above for a sample of funded projects. The program 

also collects descriptive statistics for all projects together through broad surveys of all grant 

applicants as well as grant awardees. Here counterfactuals would be no R&D project, a smaller 

or less ambitious project, or a time delay in funding the same project. Whatever metrics are chosen, they must be changed over the course of the technology life cycle, as indicated in Figure 3. 

In effect, such impact estimation techniques do not explicitly measure �KE, �KN or η. 

Instead, inputs (investments in these variables) are related to outcomes (Q in Eq. (1)). 

17 If the impact of a single project is being assessed, an unambiguous time series of costs may be available from 

budget records. However, program-level impact assessment typically entails several projects, some of which may have 

multiple objectives. Moreover, industry assimilation costs, if significant, must also be estimated. Thus, retrospective 

time series specification will usually be less than ideal. 
628	 G. TASSEY 

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5 SELECTION OF METRICS

This step is critical because it drives survey design and eventual impact estimates. Unfortunately, it is frequently mishandled. The general approach requires decisions about the scope 

and heterogeneity of the technology to be studied, what to include with respect to subsequent 

categories of investment necessary to achieve commercialization, and how to account for the 

‘cost’ of scrapping the existing technology. 

Historically, government-funded R&D and subsequent government procurement in areas 

with social objectives such as national defense and energy independence have jump-started new 

industries or at least significantly expanded embryonic ones. Digital computers and network 

communications are examples. The degree to which government R&D programs facilitate the 

formation of new companies and an effective industry structure will determine the efficiency 

with which a social objective (such as better health care) is attained. Thus, useful impact 

assessment in virtually all cases will require economic impact metrics, which must be modified 

over the technology life cycle if ultimate economic impacts are to be estimated (see Figure 3). 

In selecting such metrics, the structure and coverage of benefits and costs is particularly 

important for the ultimate estimation procedure. One of the initial decisions focuses on the 

desirability of establishing and including a baseline of net benefits from an existing technology. 

For example, in studies of social rates of return from private-sector innovations, Mansfield 

et al. (1977) argued that benefits (profits) to imitators should be added to benefits accruing to 

the innovating firm and that benefits lost to competitors supplying the old technology should 

be subtracted. Further, unsuccessful R&D by competing firms should be added to total costs. 

These issues are mitigated somewhat for quasi-public-goods such as infratechnologies. In 

many (but not all) cases, infratechnologies and associated industry standards are introduced 

at points in the technology life cycle where markets already exist. In such cases, the existing 

product structure is not replaced; rather, measurement of the performance of some attribute 

of the product or an attribute that provides an interface with other products is standardized. 

The resulting productivity increase can be measured as an incremental gain in an existing 

production process, which is, in effect, equivalent to Mansfield et al.’s requirement to net out 

the residual value of obsolete technology.18 

For new generic technologies that replace older generations as technology platforms for 

innovation efforts, the issue of subtracting benefits lost requires more attention. Even here, for 

prospective studies, at least a capital budgeting approach would only require estimating rates 

of return over the study period for both the new and defender technologies from the point in 

time of the analysis and making an investment decision accordingly (that is, R&D and other 

initial investments associated with the defender technology are regarded as sunk costs and 

ignored in the calculation). For retrospective studies, one also can rationalize ignoring the 

defender technology. What really counts is the rate of return realized by the technology under 

study relative to an appropriate hurdle rate. 

5.1 Input (Cost) Metrics

All costs, private and public, should be included. Some cost data may have to be disaggregated 

and a portion assigned to the project under study. Specific cost categories are: 

•	 Direct and indirect government research program costs. Research labor, production labor 

(for prototypes and other transfer artifacts such as standard reference materials), overhead, 

equipment, and technology transfer/outreach. 

18 If assimilating the new infratechnology results in the purchase of new equipment, for example, writing off the 

old equipment could be viewed as constituting a ‘cost’. 
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•	 Industry research program costs. Research labor, equipment and overhead (for independent 

or joint research projects), ‘pull’ (technology assimilation) costs, including fees paid to 

government or universities for technology transfer, and related services. 

•	 Industry commercialization costs. Applied R&D investment, IP acquisition, production 

scale-up, market research, and workforce training costs. 

5.2 Output (Technical Knowledge) Metrics

Conducting economic impact studies of government research requires the selection of performance variables that can be directly attributed to the government funded or conducted research 

project and that can be related to subsequent economic impacts (outcomes). Examples of output 

measures frequently identified are: 

•	 contributions to underlying science; 

•	 generic technology or infratechnologies developed; and 

•	 IP produced and its dissemination resulting from the research project, including patents 

or licenses in the case of generic technology and the technical basis for and adoption of 

standards in the case of infratechnologies. 

5.3 Outcome (Economic Impact) Metrics

Selection of specific outcome metrics depends on a number of factors, including the type of 

R&D targeted by the project being studied (in particular, generic technology vs. infratechnology) and the objectives of the broader research program of which the project is a part (which 

may include industry structure and industry growth objectives). Categories of outcome metrics 

frequently estimated include impacts on 

•	 post-project assimilation/use by industry of generic technology or infratechnology; 

•	 post-project applied R&D investment by industry; 

•	 post-project increase in venture capital; 

•	 market access created and subsequent market entry decisions; 

•	 reductions in industry R&D cycle times (time to commercialization); 

•	 productivity increases (R&D or production process); 

•	 market penetration of new technology (sales and/or profits generated); 

•	 product quality; 

•	 increase in product and system reliability; and 

•	 reduction in transaction costs (equity in trade, performance verification). 

Effective use of these metrics in assessments of the economic impact of technology infrastructure projects requires the selection of quantitative measures. Because of the demanding data 

requirements for estimating Eqs. (3) and (4), the analyst will have to rely on corporate finance 

measures: net present value, benefit-cost ratio, and internal (social) rate of return. Adequate 

data typically can be collected from government project records (costs) and industry surveys 

(benefits) to enable estimation of these measures, but each has a unique set of strengths and 

weaknesses. The analyst should therefore estimate all three measures.19 

19 See Tassey (2003) for a detailed discussion of the use of each measure and their collective utility for economic 

impact assessment. 
630 G. TASSEY 

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6 SUMMARY

Technology infrastructure is a multifaceted and complex part of every industrial technology. 

Its two basic elements, generic technologies and infratechnologies, have different but profound impacts on the technology life cycle, and therefore on innovation and technology-based 

economic growth. The model presented disaggregates the traditional knowledge production 

function, thereby allowing analysis of the two categories of technology infrastructure and their 

combined effect on private-sector investments in applied R&D. 

The quasi-public-good character of generic technologies and infratechnologies means that 

both industry and government will fund portions the R&D for these technology elements, 

which means that the assessment of private-sector investment behavior is complex. For policy 

analysis purposes, data and impact assessment methods must be adapted to both the appropriate 

economic models and the feasibility of data collection. Unfortunately, both prospective studies 

for strategic planning and retrospective studies for program impact evaluation must make 

compromises with respect to the metrics selected because of data quality issues. Nevertheless, 

empirical analyses to date indicate that technology infrastructure has a substantial enabling 

effect on private-sector R&D investment decisions and performance. 

Acknowledgements

The author is indebted to Gary Anderson, Daniel Josell and an anonymous referee for 

constructive comments regarding characterization of several elements of the model. 

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