May 2003

Technology Adoption

Indicators Applied to the

ATP Flow-Control

Machining Project

ADVANCED TECHNOLOGY HNOLOGY PROGRAM

NISTIR 6888

National Institute of Standards and Technology • Technology Administration • U.S. Department of Commerce

cylinder head combustion chamber
Cover image: Processed cylinder head combustion chamber, courtesy of Extrude Hone 

Corporation (www.extrudehone.com). 
NISTIR 6888 

U.S. Department of Commerce 

Technology Administration 

National Institute of Standards and 

Technology

Office of Applied Economics 

Building and Fire Research Laboratory

Gaithersburg, Maryland 20899 

Technology Adoption Indicators Applied to the 

ATP Flow-Control Machining Project

Hayden Brown 

Office of Applied Economics 

Building and Fire Research Laboratory 

Mark Ehlen

Infrastructure Surety Group 

Sandia National Laboratories

May 2003 

Prepared for: 

Economic Assessment Office 

Advanced Technology Program 

National Institute of Standards and Technology 

Gaithersburg, MD 20899 

U.S. DEPARTMENT OF COMMERCE 

Donald L. Evans, Secretary 

TECHNOLOGY ADMINISTRATION 

Phillip J. Bond, Under Secretary of Commerce for Technology 

NATIONAL INSTITUTE FOR STANDARDS AND TECHNOLOGY 

Arden L. Bement, Jr., Director
ii

Abstract 

This report introduces technology adoption indicators (TAIs) that can be used to assess whether 

particular industries are likely to adopt new technologies. The TAIs are several measures of 

industry concentration, number of patents, number of research joint ventures, public policy, and 

history of technology adoption. Each TAI is supported by economic theory and confirmed by 

empirical studies. The TAIs are then applied to an analysis of whether a particular ATP-funded 

flow-control machining (FCM) technology might be adopted by two industries: lawnmower 

engine manufacturers and the airplane engine manufacturers. According to broadly defined TAI 

measures, based on Standard Industrial Classification (SIC) and the North American Industry 

Classification System (NAICS), lawnmower manufacturers are more likely than airplane engine 

manufacturers to adopt new technology. Using precise, narrowly defined data specific to the 

lawnmower engine industry, we confirmed that new technology adoption by lawnmower 

manufacturers is likely. In this case study, the regulatory environment—specifically, the 

environment created by Environmental Protection Agency (EPA) regulation—was the most 

important factor influencing the likelihood of the lawnmower engine industry adopting the FCM 

technology. 

New EPA regulations require emissions-improving changes to most lawnmower engines 

currently available in the marketplace, and the FCM technology is a cost-competitive alternative 

to conventional emissions-improving technologies. We compare the cost of the FCM technology 

with the cost of more conventional technology for reducing engine emissions in four lawnmower 

engine market segments: small side-valve (SV) engines, small overhead-valve (OHV) engines, 

large side-valve engines, and large overhead-valve engines. For large SV engines, the cost 

advantage of the FCM technology is significant. For small SV engines, the FCM technology has 

a modest cost advantage. 

We then use a dynamic macroeconomic model to compare the national economic impacts of the 

FCM technology cost and the conventional technology cost for each market segment. Either 

would reduce gross domestic product (GDP); however, for large SV engines, the FCM 

technology has less of a negative impact on GDP, personal income, and employment, compared 

with conventional technologies. The advantage of the FCM technology in this market is 

substantial. For example, using the FCM technology instead of conventional emission-lowering 

technology on large SV engines would result in savings of $982 million in GDP over the analysis 

period. For small SV engines, the use of the FCM technology in place of conventional 

technology would result in savings of $261 million in GDP over the analysis period. 

Key words: Advanced Technology Program (ATP); benefit cost; program evaluation; 

technology adoption 
iii

Executive Summary 

Introduction 

In its 1995 General Competition, the Advanced Technology Program funded the Flow-Control 

Machining Project, a four-year $7.9 million research joint venture involving Extrude Hone 

Corporation, a small company in Irwin, PA, General Motors, the University of Pittsburgh, and 

the University of Nebraska at Lincoln to develop two new automated finishing processes and 

make them cost effective for large-production manufacturing. The Flow-Control Machining 

processes advance the state-of-the-art in manufacturing finishing by allowing manufacturers to 

more effectively fabricate parts for their intended functional performance. It uses neural-network 

algorithms, process control methods, and new abrading techniques to develop manufacturing 

processes that are economically compatible with high-volume, relatively low-value engines. 

This report presents a case study of the economic impact of applying flow-control machining 

technology—first developed for the automobile industry—to the lawnmower industry. A 

Technology Adoption Indicator methodology is used to provide a structure for analysis of the 

adoption of technology by an industry. These indicators assist in the analysis of the potential for 

adoption of the Flow-Control Machining technology by the lawnmower industry. 

The TAI framework has other applications for ATP. First, it can be used to identify promising 

case studies for project evaluation. Second, it offers a consistent and effective methodology for 

conducting case studies. Third, it can assist in evaluating the business plans of ATP proposers. 

Fourth, it can be used to advise ATP awardees on which industries are more likely to be potential 

adopters of their technologies. 

The Technology Adoption Indicators: Development and Application to the Case Study 

A significant amount of economic research shows that some industries adopt new technologies 

faster than others. For example, economic studies of industry R&D projects based on the 

structure-conduct-performance model have found that industry characteristics such as the 

number and size distribution of firms and quality of competition affect rates of technology 

adoption. Since the model is widely used for organizing and analyzing industry characteristics 

and behavior, we apply it to develop a set of indicators that assess the likelihood of technology 

adoption. 

According to the structure-conduct-performance model, market structure affects market conduct, 

which in turn affects market performance. Industry characteristics, such as the numbers of sellers 

and buyers, affect selling and buying conduct, such as production, research and innovation, 

pricing behavior, advertising, investment, and legal tactics. Market conduct, in turn, affects the 

overall performance of the industry—that is, price and production levels. Public policy (such as 

taxes, subsidies, and regulations) affects both industry market structure and conduct. In the long 

run, all these conduct and performance activities affect the market structure. 

Market structure is evidenced by industry concentration ratio measures such as four- and eightfirm concentration ratios and the Herfindahl-Hirshman Index. Industries characterized by mid-
iv

sized, competitive firms tend to adopt technology more than industries characterized by either 

many small firms by a few, very large firms (oligopolies). In short, economic research suggests 

there is an optimal range of industry concentration in which technology adoption is most likely. 

The numbers of patents and research joint ventures provide indicators of market conduct and the 

supply of technology. Through regulation, public policy affects market conduct by serving as a 

driver of technology adoption. 

In this study, we are not only concerned about the adoption of technology in general, but with the 

adoption of a specific technology. All else being constant, the fewer the number of competing 

technologies, the greater the likelihood of any one technology innovation being adopted. 

Specifically, if there is a great demand for technological innovation but few sources of 

technological innovation, then new innovations will be rarer and will have a greater chance of 

adoption. The lawnmower industry has fewer patents and research joint ventures than the 

airplane engine industry while having higher technology demand. Therefore, the likelihood for 

adoption of the FCM technology by the lawnmower industry is judged to be higher than the 

airplane engine industry. 

In the case of the lawnmower industry, a specific public policy, in the form of an impending 

EPA regulation, is a key factor in likely market conduct. Pending EPA regulations aim to reduce 

small-engine emissions by 59% by 2007. No pending regulations affecting technology adoption 

by aircraft engine manufacturers have been identified. 

Economic Case Study on the Impact of the Adoption of FCM in the Lawnmower Industry 

An economic case study was performed of the impact of adoption of Flow-Control Machinery 

technology in the lawnmower engine application. This required first investigating the economic 

feasibility of lawnmower engine manufacturers using the FCM technology to meet the new EPA 

regulations for different types of engines. Then, for engines where adoption was economically 

feasible, a macroeconomic projection was made of the broader impact on U.S. economic output. 

Engineering tests performed by Extrude Hone, the lead company in the ATP-funded research 

joint venture and inventor of the Flow-Control Machining technology, confirmed that the 

technology can be used to meet EPA’s Phase 2 emissions requirements for 2007. Extrude Hone 

engineers also provided detailed data on the costs of applying the Flow-Control Machining 

technology to the full range of lawnmower engines. 

An analysis of the total costs to the industry of the Extrude Hone FCM process was performed 

for each of four engine market segments and compared with the costs of meeting the EPA Phase 

2 regulations with conventional technology, as estimated by the EPA. 
v

The results are as follows: 

• For “large, side-valve engines,” the FCM technology is the lower-cost option relative to 

the conventional technology both initially and over time because both the fixed costs and 

the ongoing variable costs are lower than for the conventional technology. 

• For “small, side-valve engines,” the Flow-Control Machining technology is estimated to 

be significantly less expensive than the conventional technology in 2007 but slightly 

more expensive in later years. In particular, fixed costs are concentrated in that initial 

year of use, and the Flow-Control Machining technology has lower fixed costs than 

conventional technology. On the other hand, the Flow-Control Machining technology has 

slightly higher variable costs that make it less attractive over time. 

• For “small, overhead-valve engines” and for “large, overhead-valve engines,” the FlowControl Machining technology is more expensive in terms of both fixed and variable 

costs than the conventional technology. 

The Regional Economic Models, Inc. (REMI) macroeconomic model was used to simulate the 

total national impact of adoption of the Flow-Control Machining technology for large and small 

side-valve engines. The macroeconomic model computes the total effect over time on the 

economy resulting from a change to a component of the economy. The model is based on 

economic theory, input-output (I/O) accounting, and econometrically estimated, time-dependent 

relationships between components of the economy. 

The model captures the following effects across the economy: An increase in production costs in 

the lawnmower industry—by adopting either the Flow-Control Machining technology or 

conventional technology—will cause an increase in the selling price of lawnmowers. This in turn 

will cause a decrease in the number of units sold. As production costs rise, firms that seek to 

maintain current profit levels must increase their selling prices and/or reduce other production 

costs, including the costs of labor and capital (machinery). Decreased sales and cutting costs will 

reduce employment in the lawnmower industry and in industries that supply parts and services to 

it, thereby reducing aggregate income and the broad set of purchases this income supports (such 

as for purchasing cars, homes, services, and travel). 

A comparison of the combined macroeconomic effects across the economy of Flow-Control 

Machining technology versus conventional technology shows the following: 

• For both small, side-valve engines and large, side-valve engines, adoption of FlowControl Machining technology is less costly to GDP, employment, and income in 

meeting the EPA regulations compared with adoption of conventional technology. 

• For small, side-valve engines, there is a savings of $261 million in GDP, and $244 

million in personal income over the three years 2007 to 2009 from using Flow-Control 

Machining technology. 
vi

• For large, side-valve engines, there is a savings of $982 million in GDP and $878 million 

in personal income over the five years 2003 to 2007. For these engines, the Flow-Control 

Machining technology saves 93% of GDP, employment, and personal income that would 

be lost using conventional technologies to address the EPA regulations. 

Conclusion 

The case study analysis performed clearly demonstrates the economic advantage of the FCM 

technology over conventional technologies for small and large side-valve lawnmower engines. 

The Technology Adoption Indicators methodology provides a useful framework for assessing 

whether a particular industry will adopt new technologies, and further assists in the investigation 

of industry characteristics affecting technology adoption. In this case study, EPA regulation was 

the decisive factor indicating the likelihood of adoption of the FCM technology. 

There are widespread differences across U.S. industries in the size and distribution of firms, in 

patents, in research joint ventures, and in the use of new technologies. Increased understanding 

of the relationships between these variables will help ATP assess the likelihood of adoption and 

the potential economic impact of the proposed projects. 
vii

Acknowledgements

Thanks are due to Larry Rhodes, Ralph Resnick, Hillary Gilmore, and Dan Maas of Extrude 

Hone Corporation, who described engine design and the new manufacturing and finishing 

processes being developed by the ATP-funded project. We also want to thank Extrude Hone 

Corporation for providing the cover image of a processed cylinder head combustion chamber. 

Thanks are also given to Harold Marshall, Jeanne Powell, Stephanie Shipp, Prasad Gupte, 

Jean-Louis Staudenmann, Chuck Bartholomew, and Elissa Sobolewski for their comments and 

suggestions. Thanks to Wally Jones for data organization and graphing. Finally, special thanks 

are given to Stephen Weber for the numerous discussions upon which this analysis was based 

and the excellent editorial advice and support which brought the report to its final form. 

Disclaimer: Certain trade names or company products are mentioned in the text to specify 

adequately the experimental procedure and equipment used. In no case does such identification 

imply recommendation or endorsement by the National Institute of Standards and Technology, 

nor does it imply that the equipment is the best available for the purpose. 

ix

Contents 

Abstract........................................................................................................................................... ii 

Executive Summary.......................................................................................................................iii 

Acknowledgements....................................................................................................................... vii 

Glossary ........................................................................................................................................ xii 

1. Introduction................................................................................................................................. 1 

2. Industry Characteristics Affecting Technology Adoption.......................................................... 3 

2.1 Market Structure ....................................................................................................................5 

2.1.1 Industry Concentration................................................................................................... 5 

2.1.2 Other Market Structure Measures.................................................................................. 7 

2.2 Market Conduct .....................................................................................................................8 

2.2.1 Patent Counts and Technology Adoption ...................................................................... 8 

2.2.2 Research Joint Ventures (RJVs), Innovation, and Technology Adoption ..................... 9 

2.2.3 Other Industry Conduct Measures ............................................................................... 11 

2.3 Public Policy ........................................................................................................................11 

2.4 Historical Patterns of Technology Adoption .......................................................................12 

2.5 Summary of TAI Measures Used ........................................................................................12 

3. Selecting a Case Study Using Technology Adoption Indicators (TAIs) .................................. 13 

3.1 Industry Concentration.........................................................................................................14 

3.2 Patent Counts .......................................................................................................................23 

3.3 Research Joint Venture Indicator.........................................................................................26

3.4 Public Policy ........................................................................................................................27 

3.5 Historical Adoption Trends..................................................................................................28 

3.6 Conclusion ...........................................................................................................................28 

4. Case Study of the FCM Technology Applied to the Lawnmower Manufacturing Industry .... 34 

4.1 Fixed and Variable Costs of Meeting Phase 2 Regulations Using Conventional 

Technology Compared to FCM Technology.......................................................................35 

4.2 Total Cost per Year of Using Conventional Technology Compared to FCM 

Technology..........................................................................................................................38 

4.3. Introduction to the REMI Policy Insight Macroeconomic Impact Model..........................43 

4.4. Macroeconomic Impact of the Costs of Conventional Technology Compared to FCM 

Technology, in Market Segments A and C .........................................................................44 

5. Summary and Conclusions ....................................................................................................... 47 

5.1 Development and Interpretation of the TAIs from the SCP Model.....................................47 

5.2 Economic Analysis of the FCM Technology.......................................................................48 

5.3 Application of TAIs .............................................................................................................48 
x

References..................................................................................................................................... 51 

List of Appendices 

Appendix A: EPA Phase 1 and Phase 2 Regulations on Nonroad Nonhandheld Small Engines. 55 

Appendix B: Patents Issued by SIC Code..................................................................................... 57 

Appendix C: Research Joint Ventures by SIC Code .................................................................... 61 

Appendix D: REMI Forecast Detail ............................................................................................. 64

List of Figures 

Figure 1. The Structure-Conduct-Performance Model ................................................................... 4 

Figure 2. Four-Firm Concentration Ratios of Home Lawn and Garden Equipment and Aircraft 

Engines and Engine Parts...................................................................................................... 15 

Figure 3. Eight-Firm Concentration Ratios of Home Lawn and Garden Equipment and Aircraft 

Engines and Engine Parts...................................................................................................... 16 

Figure 4. Herfindahl-Hirschman Indices of Home Lawn and Garden Equipment and Aircraft 

Engines and Engine Parts...................................................................................................... 17 

Figure 5. Lawnmower Industry CR4, 1990 to 2000 ..................................................................... 19 

Figure 6. Lawnmower Industry CR8, 1990 to 2000 ..................................................................... 21 

Figure 7. Lawnmower Industry HHI, 1990 to 2000 ..................................................................... 22 

Figure 8. Farm/Garden Machinery and Aircraft Patents Issued 1996 to 2000 ............................. 24 

Figure 9. Lawnmower-Related Patents Issued 1996–2001........................................................... 25 

Figure 10. Number of RJV Filings, Whole and Fractional........................................................... 27 

Figure 11. Side-Valve and Overhead-Valve Market Penetration, 1990 to 2000 .......................... 31 

Figure 12. Market A: Class I SV Costs......................................................................................... 41 

Figure 13. Market B: Class I OHV Costs ..................................................................................... 41 

Figure 14. Market C: Class II SV Costs ....................................................................................... 41

Figure 15. Market D: Class II OHV Costs.................................................................................... 42 

Figure 16. Savings in GDP from Using the FCM Technology Instead of the EPA-assumed 

Conventional Technology to Meet Phase 2 Regulations, in Market Segments A and C...... 45 

Figure 17. Savings in Personal Income from Using the FCM Technology Instead of the EPAassumed Conventional Technology to Meet Phase 2 Regulations, in Market Segments A 

and C ..................................................................................................................................... 45 

Figure 16. Thousands of Patents Issued (Whole Count) for 1996 to 2000, by SIC Code ............ 58 

Figure 17. Thousands of Patents Issued (Fractional Count) for 1996 to 2000, by SIC Code....... 61 
xi

Figure 18. Research Joint Venture Filings by SIC Code.............................................................. 61 

Figure 19. Market A (Class I SV): Estimated Reductions in GDP............................................... 64 

Figure 20. Market A (Class I SV): Estimated Reductions in Employment.................................. 64 

Figure 21. Market A (Class I SV): Estimated Reductions in Personal Income............................ 64 

Figure 22. Market C (Class II SV): Estimated Reductions in GDP.............................................. 66 

Figure 23. Market C (Class II SV): Estimated Reductions in Employment................................. 67 

Figure 24. Market C (Class II SV): Estimated Reductions in Personal Income........................... 67 

List of Tables 

Table 1. Summary of Findings Using Broadly Defined TAIs...................................................... 29 

Table 2. Summary of Findings Using Narrowly Defined Data on Lawnmower Manufacturers.. 29 

Table 3. Emissions Limits under Phase 2 EPA Regulations ........................................................ 34 

Table 4. Unit Costs of Meeting Phase 2 Standards Using Conventional Technology, in 2002 

Dollars................................................................................................................................... 36 

Table 5. Unit Costs of Meeting Phase 2 Standards Using FCM Technology, in 2002 Dollars.... 37 

Table 6. Total Industry Cost of Meeting Phase 2 Standards Using Conventional Technology 

Compared to FCM Technology in Class I, in Thousands of 2002 Dollars........................... 39 

Table 7. Total Industry Cost of Meeting Phase 2 Standards Using Conventional Technology 

Compared to FCM Technology in Class II, in Thousands of 2002 Dollars ......................... 39 

Table 8. Definitions of Engine Class I and II ............................................................................... 55 

Table 9. Phase 2 (1999) Emissions Regulations for Engine Class I............................................. 56 

Table 10. Phase 2 (1999) Emissions Regulations for Engine Class II.......................................... 56 
xii

Glossary

ATP — Advanced Technology Program — A partnership between government and private 

industry to conduct high-risk research to develop enabling technologies that promise 

significant commercial payoffs and widespread benefits for the economy. 

(www.atp.nist.gov) 

BEA — Bureau of Economic Analysis (www.bea.gov). 

cc — Cubic centimeters — Industry standard abbreviation used in lieu of cm3

 when referring to 

engine displacement. 

CO — Carbon monoxide — Colorless, odorless, and poisonous byproduct of combustion. 

CORE — Collaborative Research database — A database of research joint ventures listed in the 

Federal Register, maintained by Dr. Albert Link of the University of North Carolina, 

with a grant from the National Science Foundation. 

CR4 — Four-firm concentration ratio — In an identified industry, the market share held by the 

top four firms. 

CR8 — Eight-firm concentration ratio — In an identified industry, the market share held by the 

top eight firms. 

DOJ — U.S. Department of Justice (www.usdoj.gov). 

EPA — U.S. Environmental Protection Agency (www.epa.gov). 

FCM — Flow-control machining — Two finishing processes which increase the functional 

precision of cast-metal parts which carry fluids in interior passageways. FCM’s first 

targeted application was to increase airflow in automobile engines, where the increase in 

functional precision can be used to increase engine horsepower, increase fuel efficiency, 

reduce emissions, and reduce the cost of engines. 

FTC — Federal Trade Commission (www.ftc.gov). 

GDP — Gross Domestic Product — The output of goods and services produced by labor and 

property located in the United States. 

HC — Hydrocarbons — Byproducts of combustion, such as NMHC, that lead to ozone 

formation. 

HP — Horsepower — Nonmetric measurement of power (see kilowatts, kW). 
xiii

HHI — Herfindahl-Hirschman Index — In an identified industry, the sum of the squared 

percentage market shares of all the firms. 

IPO — Initial public offering — The first market issuance of stock in a company. 

kW — kilowatt — Metric measure of power, such as the power of an engine. 

NAICS — North American Industry Classification System — Industry classification system, 

from 2 to 6 digits, that replaces SIC beginning in 1997. Provides consistent framework 

for collection, analysis, and dissemination of industrial statistics used by government 

policy analysts, by academics and researchers, by the business community, and by the 

public. Follows principle of aggregation that producing units that use similar production 

processes should be grouped together. Reflects changes in technology and growth and 

diversification of services in recent decades. (http://www.census.gov/naics) 

NMHC — Nonmethane hydrocarbons — Byproducts of combustion that lead to ozone 

formation. 

NOx — Nitrogen oxides — Byproducts of combustion that contribute to acid rain production. 

OELink — Original Equipment Link — Database of small engine manufacturers and their 

products compiled and maintained by Power Systems Research, of St. Paul Minnesota 

(www.powersys.com). 

OHV — Overhead-valve — An Otto-cycle, four-stroke engine in which the intake and exhaust 

valves are located above the combustion chamber within the cylinder head. Such engines 

are sometimes referred to as “valve-in-head” engines (Federal Register). 

PM — Particulate matter — Solid pollutant in engine exhaust. 

PPI — Progressive Policy Institute — (www.ppionline.org). 

R&D — Research and development. 

REMI — Regional Economic Models, Inc. — Amherst, MA developer of Policy Insight, 

macroeconomic forecasting software (www.remi.com). 

RJV — Research joint venture — Any of a number of different cooperative business structures 

in which several firms (sometimes involving government agencies, such as ATP) pool 

resources in order to spur new research and development. 

SCP — Structure-conduct-performance — A conceptual model commonly used in Industrial 

Organization, relating market structure to market conduct and market performance. 

SIC — Standard Industrial Classification — Numerical coding system, from 2 to 4 digits, for 

systematically classifying industries (replaced by NAICS beginning in 1997). 
xiv

SSIE — Small spark-ignition engines — Engines such as those used in lawnmowers. 

SV — Side-valve — An Otto-cycle, four-stroke engine in which the intake and exhaust valves 

are located to the side of the cylinder, not within the cylinder head. Such engines are 

sometimes referred to as “L-head” engines (Federal Register). 

TAI — Technology Adoption Indicators — A set of indicators that measure the likelihood of 

technology adoption derived from publicly available, broadly defined, industry-level data 

sets covering the manufacturing sector. 

USPTO — U.S. Patent and Trademark Office (www.uspto.gov). 

USPCS — U.S. patent classification system — A coding system used by the USPTO to classify 

patents, generally by the major function or operation of the patent. 
1

1. Introduction

The Flow-Control Machining (FCM) Project is an ATP-funded research joint venture (RJV). Its 

partners are Extrude Hone Corporation (a small company in Irwin, PA, and inventor of the FCM 

technology), Ford Motor Company, General Motors, the University of Pittsburgh, and the 

University of Nebraska at Lincoln. The project resulted in the development of two finishing 

processes (the FCM technology) which increase the functional precision of cast-metal parts that 

carry fluids in interior passageways. Its first targeted application was increased airflow in 

automobile engines, analyzed in Estimated Economic Impacts of the Advanced Technology 

Program’s Flow-Control Machining Project: Early Applications in the Automobile Industry

(Ehlen 1999). 

This report develops a set of technology adoption indicators (TAIs) and uses them to select and 

analyze possible spillover applications of the FCM technology, originally developed for the 

automotive industry. To guide this and future case studies, a TAI-based methodology was 

developed that provides a structure for the analysis of the adoption of applications by an 

industry. Although used to study application adoption here, the TAI framework has other 

applications for ATP. First, it can be used to identify promising case studies for project 

evaluation. Second, it offers a consistent and effective methodology for conducting case studies. 

Third, it can assist in evaluating business plans of ATP proposers. Fourth, it can be used to 

advise ATP awardees on which industries are more likely potential adopters of their 

technologies. 

TAIs are defined based on the structure-conduct-performance (SCP) model widely used in 

economic analyses of industries (Scherer and Ross 1990). This model, supported by theoretical 

and empirical economic research, indicates that the structure of an industry affects its conduct 

and performance. For example, barriers to entry (structure) result in certain product-pricing 

behavior (conduct) that affects the ultimate efficiency and equity of the marketplace 

(performance). 

We used the SCP model to identify industry characteristics that are related to technology 

adoption and to organize those characteristics. After identifying industry characteristics that 

influence technology adoption, an exhaustive search was undertaken for measures of those 

characteristics. The measures that are presented in this report had to meet several criteria. 

Industry structure measures had to be quantitative, publicly available, comprehensive, 

theoretically related to the motivation to adopt technology, and have empirical evidence in the 

literature to support the connection to technology demand. By “comprehensive,” we mean that 

there are individual measures for every industry, at a minimum, broadly defined at the 6-digit 

North American Industry Classification System (NAICS) or 4-digit Standard Industrial 

Classification (SIC) level. (Narrowly defined data can often be found for a precisely defined 

industry, but unlike the broadly defined data, it is not available for other industries at the same 

level of resolution.) Industry conduct measures in this report also had to meet the same criteria 

and had to be theoretically related to the opportunity to adopt technology. 

This report brings together the development of the TAI methodology with its application in a 

case study of the potential for adoption of the FCM technology for nonautomotive industries. 
2

The selection of the case study is described using the broadly defined TAI measures. Once the 

selection has been made, more detailed industry-specific information is developed to supplement 

the broadly defined data. Individual case studies require narrowly defined data unique to the 

industry of each case study. 

TAIs show that, of two preselected case study candidates, lawnmower manufacturers are more 

likely than aircraft manufacturers to adopt the flow-control machining technology spillover from 

the automotive industry. 

On the demand side, the broadly defined market concentration indicators show a preference for 

the home lawn and garden industry (which includes the lawnmower manufacturers) over the 

aircraft engine industry. Narrowly defined data show that, according to the four- and eight-firm 

concentration ratios, the lawnmower industry is too highly concentrated to adopt technology. 

However, the HHI supported technology adoption by the lawnmower industry. The broadly 

defined patent TAI supported the selection of either the lawnmower industry or the airplane 

engine industry. The broadly defined research joint venture TAI showed that while both 

industries formed a significant number of RJVs, the airplane engine industry was somewhat 

more likely to adopt technology. The narrowly defined TAI data on patents, public policy, and 

historical technology adoption show that EPA regulations are likely to encourage the lawnmower 

manufacturers to adopt new engine technology. 

Our analysis builds on the work and results described in a previous ATP report, Estimated 

Economic Impacts of the Advanced Technology Program’s Flow-Control Machining Project: 

Early Applications in the Automobile Industry (Ehlen 1999). That earlier report presented a 

detailed analysis of the structure, conduct, and performance of the market for automobiles. 

Ehlen’s analysis showed that, due to stringent government regulations on automobile fuel 

economy, the automobile industry is a likely adopter of new fuel efficiency technologies such as 

the ATP flow-control machining technology; even small, short-run applications of the 

technology to large automobiles could result in significant increases in automobile sales and 

employment. Ehlen’s report, however, does not describe more generic metrics or approaches for 

estimating the likelihood of technology adoptions in other industries; nor does it address 

spillover applications outside the automotive industry. 

Section 2 of this report describes our examination of the business and economics literature and 

our selection of candidate measures of technology adoption. The measurement, data sources, and 

evaluation of each are explained. Section 3 describes the application of the broadly defined 

indicators developed in Section 2 to the case-study candidates. Additional, narrowly defined data 

unique to the selected case study industry are introduced using the TAI methodology under the 

same SCP framework. Section 4 first discusses the four market segments to which the FCM 

technology could apply and develops separate estimates of the costs of complying with a recent 

EPA regulation using conventional or FCM technology. Section 4 concludes by explaining the 

macroeconomic modeling and presenting the results of the economic impact analysis of 

implementing the FCM technology versus conventional means of achieving compliance with the 

regulation. Section 5 summarizes the report and provides a discussion of future directions for this 

research. 
3

2. Industry Characteristics Affecting Technology Adoption

This section presents the results of our research to develop a set of indicators that measure the 

likelihood of technology adoption. These indicators are subsequently used in our analysis of a 

specific ATP-funded technology, which was first developed for the automobile industry. The 

indicators developed in the report are derived from publicly available, broadly defined, industrylevel data sets covering the manufacturing sector. 

A significant amount of economic research has found that some industries adopt new 

technologies faster than others. For example, economic industrial organization studies based on 

the structure-conduct-performance (SCP) model have found that industry characteristics such as 

the number and size distribution of firms and quality of competition affect rates of technology 

adoption. Since the SCP model is widely used for organizing and analyzing industry 

characteristics and behavior, we apply it to develop a set of indicators that assess the likelihood 

of technology adoption. 

According to the SCP model (Figure 1), market structure affects market conduct, which in turn 

affects market performance. Industry characteristics such as raw materials, technology, business 

attitudes, product substitutes, the ability of new firms to enter the market, and cyclical and 

seasonal demand determine the structure of industry, and in particular the numbers of sellers and 

buyers. This structure of sellers and buyers affects selling and buying conduct, such as 

production and pricing strategies, research and innovation, pricing behavior, advertising, 

investment, and legal tactics. Market conduct, in turn, affects the overall performance of the 

industry—that is, price and production levels. Public policy (such as taxes, subsidies, and 

regulations) affects both industry market structure and conduct. In the long run, these conduct 

and performance activities affect the market structure. While the conclusions drawn from early 

SCP models generally apply to all industries, recent SCP studies of specific industries augment 

the research with data specific to those industries.1

Using the SCP model, different industry characteristics can be logically organized and analyzed 

according to their effects on technology adoption. A TAI is developed based on an industry 

characteristic with broadly defined data (available at 4-digit SIC or 6-digit NAICS and lower 

resolutions) that meet the criteria laid out in Section 1 of this report. The characteristics and 

developed indicators are grouped under market structure (Section 2.1), conduct (Section 2.2), 

public policy (Section 2.3), and history (Section 2.4). The discussion of each measure includes 

both a description of the theoretical rationale for its use in assessing the likelihood of adoption 

and recently published research on the measure. The sections also address proposed measures 

that were candidates for inclusion as TAIs. 

 

1

 This expansion of structure-conduct-performance analysis to include detailed industry-specific data has been 

termed “new industrial organization.” For a description, see Mansfield (1977). 
4

Figure 1. The Structure-Conduct-Performance Model 

Source: Scherer and Ross (1990), p. 5. 

Production and allocative efficiency

Progress

Full employment

Equity

Basic Conditions

Demand

Price elasticity

Substitutes

Rate of growth

Cyclical and

seasonal character

Purchase method

Marketing type

Supply

Raw materials

Technology

Unionization

Product durability

Value/weight

Business attitudes

Legal framework

Number of sellers and

buyers

Product differentiation

Barriers to entry

Cost structures

Vertical integration

Diversification

Pricing behavior

Product strategy and advertising

Research and innovation

Plant investment

Legal tactics

Performance

Market Structure

Conduct

Public Policy

Taxes and subsidies

International trade rules

Regulation

Price controls

Antitrust

Information provision
5

The broadly defined comprehensive industry data are used to demonstrate the industry selection 

process in this case study. Using narrowly defined specific industry data, we were able to more 

accurately calculate and apply the TAI measures in the selected industry. 

Section 3 applies the broadly defined TAI data to two case-study candidates as a screening 

mechanism. The narrowly defined TAI data are developed and applied to only the selected case 

study application. Industry concentration and patent measures are recalculated using narrowly 

defined industry data. Narrowly defined data on TAIs that are unique to the chosen case study, 

such as the history of adoption and public policy, are also utilized. 

2.1 Market Structure 

An exhaustive search of the business and economics literature generated an abundance of 

indicators connecting industry structure to technology adoption. According to our established 

criteria, industry structure measures developed in this report had to be quantitative, be publicly 

available, measure each industry in the manufacturing sector individually, be theoretically 

related to the motivation to adopt technology, and have empirical evidence in the literature to 

support the connection to technology demand. For example, we identified concentration 

measures as most useful because they are available at the 6-digit NAICS industry level and are 

related to technology adoption. Other measures of industry structure that have a close theoretical 

relationship with technology adoption would require the collection and development of new data 

for each industry and are thus not developed in this report. 

2.1.1 Industry Concentration 

Concentration—the number of sellers—is one definition of competition, and affects conduct.2

 

Examples of conduct are pricing behavior, R&D, plant investment, and technology innovation. 

Industry concentration therefore affects the demand for technology innovation and adoption and 

therefore the likelihood of technology adoption. 

Industry concentration measures, as the name suggests, are used to benchmark industries by the 

market share (percentage of sales) held by the largest firms. Economic theory and empirical 

research suggest that highly concentrated industries, i.e., those in which most of the market is 

held by few firms (or by a single firm), face little pressure to adopt new technologies, thereby 

slowing innovation.3

 On the other hand, low concentration is not an optimal environment to 

encourage innovation. As Scherer and Ross (1990, p. 637) explain: “Up to a point, increased 

fragmentation stimulates more rapid and intense support of R&D. . . . But when the number of 

firms becomes so large that no individual firm can appropriate quasi-rents sufficient to cover its 

R&D costs, innovation can be slowed or even brought to a halt.” Scherer and Ross note that 

industries characterized by mid-sized, competitive firms tend to adopt technology more than 

industries characterized by either many small firms by a few, very large firms (oligopolies). 

Small firms in highly atomistic industries do not have the capital or opportunity to adopt new 

 

2

 The concentration of buyers is also important, but in practice this is impossible to find out for all industries. 3

 For example, see Shepherd (1987). A focus on market concentration leaves out the effect of entry and exit 

conditions. The inclusion of such factors is explained in section 2.2.1. 
6

technology, and severe oligopolies and monopolists face little pressure to adopt. In short, 

economic research suggests there is an optimal range of industry concentration in which 

technology adoption is most likely.4

There are two commonly used measures of industry concentration: the n-firm concentration ratio 

(CR), where n is the number of the largest firms included in the measure, and the HerfindahlHirschman Index (HHI). 

The n-firm concentration ratio is the sum of the percentage market shares held by the n largest 

firms, or 

∑=

=

n

i

i CRn s

1

. ( 1 )

where n is the number of firms (e.g. 4 or 8) and si is the market share of firm i. The market share 

is measured as a percentage of sales. The CR4 (n=4) is the sum of the market shares of the 

largest four firms, and the CR8 (n=8) is the sum of the percentage market shares of the largest 

eight firms. An industry with exactly four firms has a CR4 of 100; while an industry with 10 

equally sized firms has a CR4 of 40. 

The HHI is defined as the sum of the squared percentage market shares of all firms in the 

industry, or 

∑=

=

N

i

i HHI s

1

2 , ( 2 )

where N is the total number of firms in the industry and si is the market share of firm i. The HHI 

measure approaches 0 as all the firms in an industry approach zero market shares (theoretically 

perfect competition). The HHI has a maximum value of 10,000 when there is only a single 

firm—i.e., a monopolist. For a theoretical industry with 10 equally sized firms, the HHI would 

be 1,000 (= 10 x 102

). 

The HHI is a more comprehensive and revealing measure of industry concentration. Because it 

uses the square of market share, and includes the share of every firm, it is able to show 

differences in concentration between industries even when the CR4 measures (or CR8 measures) 

are identical. For example, industry A consists of eight firms with the following concentrations: 

65, 5, 5, 5, 5, 5, 5, 5. The CR4 is 80 and the HHI is 4,400. Industry B consists of eight firms with 

the following concentrations: 20, 20, 20, 20, 5, 5, 5, 5. The CR4 is 80 but the HHI is only 1,700. 

The HHI, unlike the CR4, captures the fact that Industry B is less concentrated. 

 

4

 In case studies, adding industry-specific information on the impact of concentration will improve the interpretation 

of the effect of concentration on the adoption of technology. For example, technology adoption should occur at 

lower concentrations in emerging industries driven by innovation (such as life-cycle work most suited to 

manufacturing). This is discussed in section 2.1.2. 
7

According to Shepherd (1987), a CR4 between 40 and 60 identifies an industry with firm 

concentrations optimal for competitive behavior conducive to adopting new technologies. 

Scherer and Ross (1990) identify the bounds for the CR4 as 45 and 60. Scherer and Ross also 

report findings that the optimal CR8 for competitive industry is 70 (which roughly corresponds 

to a CR4 of 50 in the U.S. economy). We use a range of 10 points on either side of the optimal 

CR8 value as the range of values optimal for technology adoption. 

The Federal Trade Commission (FTC) uses both the CR and the HHI to assess the extent to 

which a proposed merger will affect competition in that industry.5

 According to the U.S. 

Department of Justice (DOJ), a market with an HHI less than 1,000 is considered 

unconcentrated, between 1,000 and 1,800 moderately concentrated, and over 1,800 highly 

concentrated.6

 The DOJ is likely to challenge mergers that increase the HHI more than 100 

points when the HHI index is greater than 1,800. A middle range—in which challenges depend 

on the increase in the HHI—occurs when the HHI is between 1,000 and 1,800. The FTC is 

unlikely to challenge mergers when the HHI is below 1,000. We use the DOJ definition of a 

moderately concentrated market (HHI between 1,000 and 1,800) to approximate the optimal 

industry concentration for technology adoption. 

To plot and compare U.S. industry concentrations, we used the U.S. Census Bureau’s 1997 

Economic Census data on four (CR4) and eight (CR8) concentration ratios and the HHI index, 

for each six-digit NAICS code manufacturing industry. The CR4, CR8, and HHI values of the 

lawnmower-engine industry and the airplane engine industry are analyzed in Section 3. 

2.1.2 Other Market Structure Measures 

Industry concentration measures may be improved upon as indicators of technology adoption. 

First, they may be improved by more narrowly delineating the relevant industry. That is, by 

defining the industry more precisely than the six-digit NAICS level, more detailed economic 

information about technology adoption can be obtained. Analysis of some industries and markets 

may require a high level of resolution. Second, while concentration ratios are moderately 

correlated with technology adoption, measures with higher correlations could be developed. For 

example, entry and exit conditions—the ability of new firms to enter and leave the relevant 

industry—influence technology adoption even in highly concentrated industries. If entry is easy, 

even firms in highly concentrated industries are likely to adopt technology as a defense against 

new entrants. 

New measures can be collected or constructed specifically for the industry under analysis. For 

example, according to Atkinson and Court (1998) at the Progressive Policy Institute (PPI), the 

relative number of new, fast-growing entrepreneurial companies in an industry is correlated with 

innovation and adoption of new technologies. The PPI researchers defined fast-growing 

companies as “companies with sales growth of at least 20% per year for four straight years,” and 

notes that the number of initial public offerings (IPOs) reflects the increase in the number of fast-

 

5

 Dr. Michael McFalls (1997) of the FTC provides an in-depth discussion of the ways in which the FTC assesses 

market power during an analysis of joint ventures. 

6

 Horizontal Merger Guidelines issued by the U.S. Department of Justice and the Federal Trade Commission, section 

1.51 on the DOJ internet site www.usdoj.gov/atr/public/guidelines/horiz_book/15.html 
8

growing entrepreneurial companies. “Economic churn”—the replacement of old firms by new, 

more efficient firms—is also correlated with technology adoption. The U.S. Census Bureau 

provides a single measure of industry births and deaths for the U.S. manufacturing sector as a 

whole but not for individual industries. These measures—fast-growing companies, number of 

IPOs, and establishment births and deaths—could provide additional information about 

technology adoption, but require further data development to apply to specific industries. 

2.2 Market Conduct 

Industry conduct measures developed for this report had to be quantitative, be publicly available, 

measure each industry in the manufacturing sector individually, be theoretically related to the 

opportunity to adopt technology, and have empirical evidence in the literature to support the 

connection to technology adoption. The TAIs on market conduct could be thought of as 

addressing the supply of technology innovation. 

Measures of industry conduct include both direct and indirect measures of technology adoption. 

For example, technology adoption itself is market conduct. Other market conduct measures also 

have an impact on technology adoption. Of these, several measures, such as the number of RJVs 

and the number of patents, currently have broadly defined industry data sets available at the 4-

digit SIC and higher resolutions. It is possible to improve upon these measures by increasing 

their resolution and by developing data for new measures. 

2.2.1 Patent Counts and Technology Adoption 

The mechanism by which the number of patents influences technology adoption is less 

theoretically founded than the connections of technology adoption with concentration ratios and 

HHI indices. In this report, we interpret the number of patents as an indicator of the supply of 

technology available to an industry, and as influencing market conduct through the amount of 

technology available to the industry.7

Griliches, Hall, and Pakes (1991) studied the relationships among R&D, patenting activity, and 

market value. Research continues on ways to use patent data to explain innovation. Recent 

research on “hot” patents and patent clustering has strengthened their empirical usefulness and 

could be incorporated into these indicators if broadly defined industry data become available 

(Breitzman 2001). (The duration of the payment of patent maintenance fees may relate to patent 

importance, and could be incorporated into patent indicators in the future.) The number of 

patents issued for potential application in an industry can be used as an indicator of technology 

adoption in that industry or in other “user” industries. The increase in innovation happens 

through a “supply-push” model of technology adoption, in which more patents result in the 

possibility of greater technology advances, as in the case of RJVs. 

The mechanism by which the number of patents influences technology adoption is less 

theoretically founded than the connections of technology adoption with concentration ratios and 

HHI indices. Consequently, the interpretation of the number of patents is unclear. In this report, 

 

7

 While the overall number of patents is an important indicator of technology supply, this measure does not address 

the marginal value of an additional patent. 
9

we use the total number of patents granted over the last five years as an indication of the amount 

of new technology available.8 

The output of patents, publications, citations, and other technology developments can be 

measured using data from the U.S. Patent and Trademark Office (USPTO).9

 The translation 

from the US Patenting Classification System (USPCS) to the SIC code system is straightforward, 

allowing industry-specific patent counts, though not at a high resolution. For the two industries 

under evaluation, patent data are available at the 3-digit SIC level. For any specific industry, it is 

possible to hone the patent-count measure to finer detail and greater accuracy by searching out 

patents directly applicable to that industry. The total number of patents as reported by the 

USPTO in SIC codes 13 to 39 from 1996 to 2000 are depicted in Appendix B. 

The Patenting Trends database (USPTO 2001) provides two types of patent counts. The “whole” 

counting method matches the USPCS to all relevant SIC codes as explained in the Patenting 

Trends documentation: 

The USPCS to SIC Concordance assigns USPCS patent subclasses to all (up to seven) identified SIC-based 

product fields to which they are pertinent. In each of the ‘Whole Counts’ product field profiles, a patent is 

counted if the patent’s ‘original’ USPCS subclass is matched, via concordance, to that product field. In the 

‘Whole Counts’ profiles, for example, if a patent has ‘original’ classification in a USPCS subclass which is 

matched to 3 unique SIC-based product fields, that patent would be counted once in each of the three 

associated ‘Whole Counts’ profiles. (USPTO 2001) 

The “fractional” counting method divides the USPCS patents equally among all the matched SIC 

fields. In other words, the patent found in three product fields would generate one-third of a 

patent in each SIC field. This has the effect of diluting the weight of broadly applicable patents 

and increasing the weight of patents dedicated to a single industry. 

We use the number of patents reported in the USPTO database over the last five years to indicate 

the supply of innovation available to an industry. All else being equal, an industry with fewer 

patents is more attractive to developers and sellers of new technology. A developer of new 

technology would prefer to target an industry with higher demand for technology and fewer 

patents rather than an industry with less demand for technology and more patents. 

2.2.2 Research Joint Ventures (RJVs), Innovation, and Technology Adoption 

As was the case with patents, the mechanism by which the number of RJVs influences 

technology adoption is less theoretically founded than the connections of technology adoption 

with concentration ratios and HHI indices. Similarly, we interpret the number of RJVs as 

indicating the supply of technology available to an industry. 

RJVs can increase the overall R&D in an industry in two ways. First, under certain conditions, 

economic theory shows that RJVs increase the level of research and development in an industry, 

thereby increasing the “supply” of new technology. These conditions are that the combined 

 

8

 In this report, the last five years of patents are combined to create the total number of patents available. Other ways 

of measuring patents include the number of highly cited patents and the rate of patenting. 

9

 U.S. Patent and Trademark Office, Patenting Trends in the United States 2000. CD-ROM. December 2001. 
10

returns to R&D exceed the private returns of the joint venture members (Stenbacka and Tombak 

1997). Under these conditions, the absence of a joint venture is a “market failure,” wherein the 

optimal level of R&D is not attained by the market. RJVs that address suboptimal levels of R&D 

in competitive markets are often justified on this basis.10 Second, the overall R&D in an industry 

can be increased when a legal restraint (such as anti-trust legislation) that hinders the formation 

of RJVs is lifted for certain RJVs that survive regulatory scrutiny. We use the survival of 

regulatory scrutiny as an indicator of RJVs that increase the overall level of R&D in an industry. 

The Progressive Policy Institute (PPI) provided empirical evidence that collaboration and 

networks (such as RJVs) create more technology and innovation.11 Causation could also run in 

the opposite direction—that is, industries characterized by innovation and technology adoption 

are conducive to RJV formation.12 Therefore, applying the theoretical and empirical 

observations that RJVs are conducive to the creation of new technology, we are able to use the 

number of RJVs in an industry as an indicator of technology adoption in that industry. The 

increase in innovation happens through a “supply-push” model of technology adoption, in which 

more and greater technology advances developed by the RJVs become so compelling that they 

attract firms to adopt them. 

To measure this relationship between R&D and innovation, the nonprofit organization Council 

on Competitiveness has developed the Innovation Index, which measures the relationship 

between industry R&D (as measured by patents) and employment in research and development, 

expenditures on research and development, percentage of R&D expenditures funded by private 

industry, and percentage of R&D performed by universities. Unfortunately, the Innovation Index 

is available only at the national level and not for individual industries. Estimates of the 

Innovation Index at a national level indicate a strong connection between R&D activities and 

innovation (patents). 

The Collaborative Research (CORE) database13 collects data on RJVs from Federal Register

filings.14 Parties involved in RJVs who wish to gain protection under the National Cooperative 

Research Act and the National Cooperative Research and Production Act must file public notice 

of the RJV in the Federal Register. The CORE database consists of firms that have announced 

their intentions in the Federal Register, which indicates a belief that the RJV is likely to be one 

that would otherwise not be permitted in the market, and one that would contribute positively to 

the current level of R&D because it has survived government scrutiny. The number of RJVs 

from the SIC codes 20 to 39, as reported in the CORE database, are depicted in Appendix C. 

 

10 The assumptions made in a study—about the marketplace and the nature of the joint venture companies—are 

often the deciding factor in determining whether RJVs result in more or less R&D. 

11 The Progressive Policy Institute (PPI) is a non-profit think tank that has researched the characteristics of the “new 

economy,” one of which is rapid technological innovation. 

12 Robert P. Lynch (1989) lists several industry conditions that are conducive to JV formation. One important 

condition is “rapid changes occur in technology,” indicating technology adoption). 

13 The CORE database is funded by the National Science Foundation (NSF) and maintained by Dr. Albert Link of 

the University of North Carolina. 

14 Free internet access to the Federal Register is available at www.access.gpo.gov/nara. From this location, notices 

appearing in the Federal Register may be searched. 
11

We use the number of RJVs reported in the CORE database as an indicator of the supply of 

technology available to an industry. All else being equal, an industry with fewer RJVs is more 

attractive to developers and sellers of new technology. A developer of new technology would 

prefer to target an industry with higher demand for technology and fewer RJVs rather than an 

industry with less demand for technology and more patents. 

2.2.3 Other Industry Conduct Measures 

In addition to RJVs and patents, there are other statistics that measure key industry 

characteristics relating to innovation and technology adoption. For example, empirical research 

of stock market reaction to RJV formation indicates that the formation of certain types of 

RJVs—such as that between a large and a small company—result in appreciation of stock 

prices.15 Koh and Venkatraman (1991), McConnell and Nantell (1985), and Mohanram and 

Nanda (1995) demonstrate that the market reaction depends on the type of RJV and the 

characteristics of the involved companies. Empirical studies have addressed different RJV 

configurations and the impacts on R&D. For example, a RJV between a large and a small 

company combines the advantages of each firm size in technological innovation (Rothwell 

1989). Technology adoption information obtained from RJV data could therefore be improved 

by controlling for the RJV configuration. 

The amount of royalty revenues from technology licensing was used by Degnan (1999) as a 

proxy for the success of innovative activities resulting from U.S. R&D efforts. Data on U.S. 

royalty income were collected and compared to the national economic growth attributable to 

technological advances. Degnan found a strong correlation between R&D expenditures and 

innovation and economic prosperity. No industry-level R&D data about royalty revenues were 

found for inclusion as indicators of technology adoption. 

2.3 Public Policy 

As illustrated in Figure 1, public policy influences industry conduct via taxes, subsidies, 

regulations, and price controls. For example, a government subsidy would increase the 

production of the subsidized goods, whereas taxes would reduce the production of taxed goods. 

Regulations that raise the cost of the finished product act like taxes. Regulations that permit lesscostly production alternatives would be similar to subsidies. 

In the context of the supply-and-demand framework used in this report, public policy can act 

either as a demand or as a supply factor for technology innovation. Some policies (such as the 

limited suspension of antitrust regulations) can encourage the development of new technology 

supply. Other policies, such as specific regulations, tend to increase the demand for new 

technology. For example, air bag requirements in automobiles have required the implementation 

and refinement of sensing systems. 

 

15 Stock prices increase based on an expectation of a successful joint venture leading to greater profit per share 

and/or increased dividends. Successful joint ventures produce new technology innovation and could, given procompetition RJVs, also produce demand for technological innovation. 
12

Regulation, as a public policy option, fits within the SCP model. It specifically affects structure 

and conduct. Regulations affect every industry, but each regulation varies in impact. There is no 

standardized, quantitative way to measure regulation or other aspects of public policy across 

industries. In a case study, specific interpretations of applicable public policies are required. The 

impact of regulation on this case study is discussed in Section 3. 

2.4 Historical Patterns of Technology Adoption 

The SCP model also takes into account industry-specific historical trends in market structure, as 

well as conduct, performance, and public policy. Industry-specific SCP trends can be used to 

assess the likelihood of technology adoption. In addition, the history of past technology adoption, 

as defined in an industry, provides valuable information about the likelihood of technology 

adoption in that industry in the future. There is no standardized way to account for past 

technology adoption with a broadly defined industry measure. In a case study, specific 

interpretations of what constitutes technology adoption, and time-trend data on those observable 

characteristics, are required. The impact of the history of technology adoption on this case study 

is discussed in Section 3.5 

2.5 Summary of TAI Measures Used 

In this report, the HHI and four- and eight-firm concentration ratios (CR4 and CR8) are used as 

indicators of demand for technology adoption. The number of patents and RJVs are used as 

indicators of supply of technology adoption. Public policy, through regulation, is another driver 

of technology adoption in this report. An examination of the history of technology adoption adds 

empirical observations to the analysis. Section 3 describes the application of the available data 

on these TAIs to the case-study candidates. 
13

3. Selecting a Case Study Using Technology Adoption Indicators (TAIs)

The primary mission of the National Institute of Standards and Technology (NIST) is to promote 

U.S. economic growth by working with industry to develop and apply technology, 

measurements, and standards. The Advanced Technology Program (ATP) at NIST supports this 

mission by providing cost share awards to industry to develop high-risk, “enabling” technologies 

which can ultimately increase economic growth, the quality of jobs, and the quality of life that 

comes from such growth. 

ATP projects are designed to be partnerships between government and industry. Individual 

awards are made to single firms or joint ventures of firms to produce technologies that enable the 

development of new products, processes, and services across diverse application areas. 

Universities, state and federal laboratories, and other nonprofit institutions also participate in the 

projects as members of joint ventures and as subcontractors. Awards are made based on rigorous 

peer-reviewed competitions designed to select those proposals best qualified in terms of their 

cutting-edge technological ideas and potential for national economic benefits. Emphasis is placed 

on the difference that ATP funding will make. Awards have specific cost-share rules: for 

example, joint-venture participants pay more than half of the total project costs. Single-company 

awardees pay all indirect project costs and may also cover some of the direct costs. Each award 

has a set of specific goals and completion dates. 

The Flow-Control Machining (FCM) Project is an ATP joint venture, its partners being Extrude 

Hone Corporation, Ford Motor Company, General Motors, the University of Pittsburgh, and the 

University of Nebraska at Lincoln. The four-year project began in 1996 and its total funding was 

$7.9 million; $4.0 million was provided by Extrude Hone, Ford, and GM, and the remainder was 

provided by ATP. The project goal was to develop two finishing processes that increase the 

functional precision of cast-metal parts that carry fluids in interior passageways. Its first targeted 

application was increased airflow in automobile engines, where the increase in functional 

precision can be used to increase engine horsepower, increase fuel efficiency, reduce emissions, 

and reduce the cost of engines. While these two processes were initially aimed at the automobile 

industry, they also have broad application in other, non-automotive sectors. If diffused into a 

wide array of industries, such as aerospace, manufacturing tooling, and medical, the impact 

could be quite large. 

The particular capabilities of the FCM technology allow it to be applied in a number of industries 

with significant societal benefit. For example, using the processes in personal watercraft could 

significantly reduce noise and pollution.16 Using the FCM processes in lawnmower engines and 

other powered lawn and garden equipment could significantly decrease emissions and increase 

fuel efficiency and horsepower. Extrude Hone also indicated that airplane engine manufacturers 

could also be a potential adopter of the FCM technology. Using the processes in aircraft engine 

cooling systems could significantly reduce engine weight and therefore overall aircraft weight. 

Of the many small-engine applications (including all-terrain vehicles and personal watercraft) 

that Extrude Hone was interested in, the company recommended the lawnmower engine 

 

16 Recent tests indicate that FCM can decrease personal watercraft (PWC) emissions by 50% while increasing power 

by 16%. Extrude Hone Corporation, “News Bulletin,” October 13, 2000. 
14

application for this case study. A new EPA regulation, described in Section 3.4, was identified as 

a compelling driver of the adoption of the FCM technology in small engine applications. 

Interested manufacturers and Extrude Hone cooperated to test the FCM technology in 

lawnmower engines. Extrude Hone also indicated that airplane engine manufacturers could also 

be a potential adopter of the FCM technology, but was not pursuing that application. 

Based on the interest shown by Extrude Hone and lawnmower engine manufacturers, as well as 

the economic implications of the use of the FCM technology in such a basic, widely used device, 

we selected the lawnmower engine application for an in-depth case study. We selected the 

airplane engine application as a comparison to the lawnmower application because the 

extremely high value and relatively low number of production units in airplane engine 

manufacturing provided a contrast to lawnmower engines. 

In this chapter, the two applications—lawnmower engines and aircraft engines—illustrate the use 

of the TAI system as a screening mechanism to identify promising industries for technology 

adoption. This analysis results in the identification of lawnmower engines as the more likely 

adopter of the FCM technology. In the case study presented in the next chapter, we proceed with 

the quantitative analysis of the FCM technology in lawnmowers, based on our discussions with 

Extrude Hone and on the impact of a new EPA regulation, and supported by the TAI analysis. 

In this chapter we illustrate the application of the TAIs, including the public policy (regulation) 

TAI.17 We use the TAIs developed in Section 2 to guide us in selecting the best application for a 

prospective case study from these choices. Section 3.1 addresses the concentration ratios and 

HHI index, section 3.2 joint ventures, section 3.3 the number of patents, section 3.4 public policy 

and regulation, and section 3.5 historical adoption trends. In the sections on industry 

concentration, joint ventures, and patents, we address the broadly defined measures (4-digit SIC 

and 6-digit NAICS industries) followed by the narrowly defined measures (specific product 

market data). The broadly defined comprehensive industry data are used to demonstrate the use 

of TAIs in comparing industries. Using narrowly defined specific industry data, we were able to 

more accurately calculate and apply the TAI measures for the selected industry. The impact of 

public policy and regulation on the industry selected for the case study is analyzed using 

industry-specific information. 

3.1 Industry Concentration 

Concentration ratio and HHI data were obtained from a table (accessed online) in the 1997 

Economic Census, Manufacturing Subject Series (U.S. Bureau of the Census 1997): “Share of 

Value of Shipments Accounted for by the 4, 8, 20, and 50 Largest Companies in Each of the 3-, 

4-, 5-, and 6-digit NAICS Industries: 1997.” The four-firm concentration ratios (CR4) for all 6-

digit (NAICS) manufacturing industries are shown in Appendix D. Figure 2 presents data on the 

CR4, Figure 3 on the CR8, and Figure 4 on the HHI in the two 6-digit industry groups that 

include lawnmower firms and aircraft engine firms. 

 

17 The industry-specific measures were developed after all the broadly defined measures had been considered and a 

decision made on which application to choose for a case study. Once the target industry—lawnmowers—had been 

selected, additional narrowly defined data were developed. For presentation purposes, we appended the specific, 

narrowly defined measures immediately after the discussion of the broadly defined measures. 
15

For the CR4, we use the bounds of 40 and 60 to identify an industry with market concentrations 

optimal for technology adoption (following the analysis of the optimal bounds identified in 

Chapter 2). For the CR8, we use a range of 60 to 80 to define the optimal market concentration 

for technology adoption. The HHI may be interpreted in a similar fashion. We use the range of 

1,000 to 1,800 as the optimal range for technology adoption. 

Figure 2. Four-Firm Concentration Ratios of Home Lawn and Garden Equipment and 

Aircraft Engines and Engine Parts

Source: U.S. Bureau of the Census (1997). 

0

20

40

60

80

100

Home Lawn & Garden Equip. Aircraft Engines & Engine Parts

Industry

CR4

64

optimal 

range 

77
16

Figure 3. Eight-Firm Concentration Ratios of Home Lawn and Garden Equipment and 

Aircraft Engines and Engine Parts

Source: U.S. Bureau of the Census (1997). 

0

20

40

60

80

100

Home Lawn & Garden Equip. Aircraft Engines & Engine Parts

Industry

CR8

82 80 optimal 

range 
17

Figure 4. Herfindahl-Hirschman Indices of Home Lawn and Garden Equipment and 

Aircraft Engines and Engine Parts

Source: U.S. Bureau of the Census (1997). 

As shown in Figure 2, the CR4 (64) for Home Lawn and Garden Equipment is above 60, the 

upper limit for optimal technology adoption. The industry is close to, though not quite within, the 

optimal CR4 range for technology adoption. The CR4 (77) result for Aircraft Engines and 

Engine Parts indicates the structure is not optimal because the top four companies hold too much 

market share to spur technology adoption. The CR8 for Home Lawn and Garden Equipment 

(80), shown in Figure 3, is just barely within the optimal range of 60 to 80. However, the CR8 

(82) for Aircraft Engines and Engine Parts is so close to the Home Lawn and Garden Equipment 

CR8 as to make any distinction impossible. 

As shown in Figure 4, the Home Lawn and Garden Equipment industry HHI is 1,172, which is 

within the optimal range. The HHI statistic, because it includes all the firms in an industry, is a 

more comprehensive and revealing measure of industry concentration. Even though the CR8 for 

the two industries is similar, the HHI reveals that the Home Lawn and Garden Equipment 

industry is less concentrated than the Aircraft Engine and Engine Parts industry. We conclude 

that this industry consists of a large number of small firms that are large enough to have the 

resources and motivations to adopt new technology. In contrast, the industry that includes 

manufacturers of aircraft engines greatly exceeds the upper bound with an HHI statistic of 2,058. 

This signifies that the industry is more highly concentrated than is optimal for the adoption of 

new technology. 

0

500

1,000

1,500

2,000

2,500

Home Lawn & Garden Equip. Aircraft Engines & Engine Parts

Industry

HHI

1,172

optimal 

range 

2,058
18

We applied the same measures (CR4, CR8, HHI) to narrowly defined CR4, CR8, and HHI data 

developed for the lawnmower industry itself.18 Specialized data on the lawnmower market were 

obtained from the Power Systems Research OELink database (Power Systems Research 2001). 

These data allowed the creation of CR4, CR8, and HHI exclusively for lawnmower 

manufacturers. Figures 5 and 6 show the CR4 and CR8 concentration ratios developed from the 

OELink data. Figure 7 shows the HHI developed from the OELink data. 

Constructed from the narrowly defined industry data, the CR4 and CR8 concentration indices 

both exceed the optimal ranges for technology adoption, throughout the entire time range. The 

more comprehensive and revealing HHI index, however, remains well within the optimal range 

for technology adoption. The HHI, while exhibiting a sharp spike mid-decade, ended at 1,666 in 

the year 2000, which is well within the optimal 1,000 to 1,800 range. 

The concentration indices presented in Figures 5 to 7 are likely to be overestimates of the true 

indices for two reasons. First, the OELink data define the relevant marketplace narrowly. The 

inclusion of companies that could but are currently not producing lawnmowers lowers all the 

concentration indices. Other companies that use or produce small engines include all-terrain 

vehicles, personal watercraft, outboard motors, small motorcycles and mopeds, and 

snowmobiles. Overestimation of concentration indices also results because both imports of 

finished lawnmowers with U.S. engines and U.S.-assembled lawnmowers with non-U.S.-

manufactured engines are not counted. 

We conclude that, on the whole, the concentration ratio measures provide stronger support for 

adoption by the lawnmower industry than the aircraft engine industry. The comprehensive HHI 

measure indicates a strong preference for the lawnmower industry. 

 

18 Once the target industry—lawnmowers—had been selected (based on all the broadly defined TAIs), additional 

narrowly defined data were developed. In order to present these additional measures in the most appropriate 

location, we appended the specific, narrowly defined measures immediately after the discussion of the broadly 

defined measures. The specific measures were developed after all the broadly defined measures had been considered 

and a decision made on which application to choose for a case study. 
19

Figure 5. Lawnmower Industry CR4, 1990 to 2000

Source: Power Systems Research (2001). 

71.6

75.0

77.9

78.6

79.9

77.8

79.6

76.3

76.8

76.1 76.2

70

72

74

76

78

80

82

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

CR4

Lawnmower manufacturers

All values exceed 

optimal range 

of 40-60. 
20

Figure 6. Lawnmower Industry CR8, 1990 to 2000 

Source: Power Systems Research (2001).

92.0

90.8

95.8 96.0

95.7

98.0

98.6

98.3

98.6 98.6 98.7

90

91

92

93

94

95

96

97

98

99

100

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

CR8

Lawnmower manufacturers

All values exceed 

optimal range 

of 60-80. 
21

Figure 7. Lawnmower Industry HHI, 1990 to 2000

Source: Power Systems Research (2001).

1533

1605

1711 1713

1789

1715

1673

1684

1662 1666

1794

1500

1550

1600

1650

1700

1750

1800

1850

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

HHI

Lawnmower manufacturers

maximum value 

in 1000-1800 

optimal range 
22

3.2 Patent Counts 

This section examines patent data to identify whether the two industries show a proclivity to 

supply technology. We describe the patent data source and the application of the patent TAI to 

both lawnmower and airplane engine manufacturers. Then the patent indicator is analyzed within 

the Structure-Conduct-Performance (SCP) framework, and the likelihoods of technology 

adoption by the two different manufacturers are compared. 

The analysis of the number of patents in an industry is used to indicate the “supply” of 

innovation available to that industry. In this study, we are not only concerned about the adoption 

of technology in general, but with the adoption of a specific technology. All else being constant, 

the fewer the number of competing technologies, the greater the likelihood of any one 

technology innovation being adopted. Specifically, if there is a great demand for technological 

innovation but few sources of technological innovation, then a new innovation has a greater 

chance of adoption than if there were numerous innovations. 

The mechanism by which the number of patents influences technology adoption is less 

theoretically founded than the connections of technology adoption with concentration ratios and 

HHI indices. Consequently, the best method for measuring the precise number and timing of 

patents is unclear, as is the interpretation of the selected measure. In this report, we use the total 

number of patents granted over the last five years.19 Patent count data were obtained from a U.S. 

Patent and Trademark Office publication, Patenting Trends in the United States 2000 (USTPO 

2001). The USPTO has created a concordance between the U.S. Patent Classification System 

(USPCS) and a limited number of SIC codes. For the two industries under evaluation, patent data 

are available at the 3-digit SIC level. 

Lawnmower engines are part of SIC code 3524, which is available at the 3-digit resolution (352) 

in the Patenting Trend database. Aircraft coolant systems are part of SIC code 3724, which is 

available at the 3-digit resolution (372) in the Patenting Trends database. Data from the last five 

years were summed to arrive at the total number of patents in the last five years (1996 to 2000, 

inclusive). This was done to indicate the current “supply” of relatively recent innovations in the 

most closely affiliated industry. 

Figure 8 shows the whole and fractional patent counts for Farm and Garden Machinery (SIC 

352), and Aircraft and Parts (SIC 372). The whole and fractional counting methods were 

described in Chapter 2 (section 2.2.1). The Farm and Garden Machinery industry has only twothirds as many patents as the Aircraft and Parts industry, under the whole patent counting 

method. Under the fractional counting method, both industries have a similar number of patents. 

Appendix B displays the whole and fractional patent counts in the manufacturing sector 

industries with SIC codes 30 to 39. Both lawnmower and aircraft engine manufacturers are in 

industries with a moderate level of patent activity. A substantial number of industries have worse 

performance. 

 

19 The Census data do not indicate the length of patent maintenance during the 5 year summary. 
23

This indicator reveals a preference for the lawnmower case study candidate using the whole 

patent counting method, and supports the selection of the lawnmower industry. Under the 

fractional patent counting method, it does not show a clear difference between the two 

candidates, and therefore does not lend support to either. 

Figure 8. Farm/Garden Machinery and Aircraft Patents Issued 1996 to 2000 

Source: USTPO (2001). 

The USPTO provides guidelines for manually constructing patent counts at a higher resolution. 

These guidelines involve searching for text that matches the selected product or industry in the 

patenting system Internet database. After collecting all relevant patent classification numbers, the 

number of patents in each of these categories can be collected. 

A multi-year survey period reflects the supply of innovations available for “supply-push” 

adoption in a particular industry. Narrowly defined data on patents related to lawnmowers were 

obtained from the USPTO database and then sorted into two categories: patents relating to 

lawnmower engines and patents relating to lawnmowers in general.20 Both types of patents are 

included because some lawnmower manufacturers make their own engines. The results over the 

past 6-year period are shown in Figure 9. 

 

20 USTPO Web site, www.uspto.gov/patft/index.html 

 11,084

 5,409

16,441

 5,894

0

5,000

10,000

15,000

20,000

Whole Fractional

Industry

Total Number of Patents (1996-2000)

Farm & Garden Machinery & Equip. (352) Aircraft & Parts (372)
24

Figure 9. Lawnmower-Related Patents Issued 1996–2001 

Source: USTPO Web site. 

Figure 9 shows that over the last six years, lawnmower-related patents increased from 24 to 35 

per year, then declined to 28. The number of engine-related patents increased from 3 to 6 per 

year. From 1996 to 1998, 11 patents were issued. The next three years, 1999 to 2001, saw 19 

patents issued. Reasons for this increase could include new emissions regulations, which are 

discussed in following sections. The figures for 1999 to 2001 indicate a recent increase in the 

technology “supply” available to lawnmower engine manufacturers. That increase supports the 

observation that suppliers of new technology target applications toward industries with high 

technology demand and low supply. In this case, Extrude Hone (which developed the FCM 

technology for automotive applications) could apply the FCM technology in an industry (such as 

the lawnmower industry) with a demand for improved engine technology. This is an example of 

a new “crossover” application for a previously developed technology. 

3.3 Research Joint Venture Indicator 

The analysis of the number of RJVs in an industry is used to indicate the “supply” of innovation 

available to that industry. In this study we are concerned with the possible adoption of a specific 

 6

 24

 34

 28

 3 3

 5

 7 6

 35

31

 31

0

5

10

15

20

25

30

35

40

45

1996 1997 1998 1999 2000 2001

Year Issued

Number of Patents

Lawnmower engine-related All lawnmower-related 
25

technology (the FCM technology). All else being constant, the fewer the number of RJVs, the 

fewer the number of resulting technologies. It follows that fewer competing technologies 

enhance the likelihood of any one technology innovation being adopted. Specifically, given 

demand for technology, if there are few innovations, then a new innovation has a greater chance 

of adoption than if there were numerous innovations. 

This section describes the source of the RJV data and the application of the RJV as an indicator 

of technology innovation supply to both lawnmower and airplane engine manufacturers. Then 

the likelihoods of technology adoption by the two different industries are compared. 

RJV data were obtained from the Collaborative Research (CORE) database.21 This database 

includes the SIC code of each RJV filing in the Federal Register, by date.22 Up to two SIC 

codes (at the 2-digit level) are available for each joint venture. We constructed a whole count and 

a fractional count, following the procedure outlined in the section on patents. A whole count 

adds up each occurrence of an SIC code—i.e., a RJV reporting two SIC codes would be counted 

once in each classification. A fractional count adds only one-half to each SIC code when the RJV 

reports two classifications. Appendix C shows RJV formation for all manufacturing industries at 

the 2-digit SIC level. 

Lawnmower engines are included under SIC 35, “Machinery, Except Electrical.” Aircraft 

engines are included under SIC 37, “Transportation Equipment.” As shown in Figure 10, the 

RJV indicator reveals that SIC 35 (50 and 42) has substantially fewer RJVs than SIC 37 (131 and 

116), using the whole and fractional RJV counting methods, respectively. 

Given highly aggregated data at the 2-digit level, this information may not be directly applicable 

to narrow industries, such as lawnmower engines. Nevertheless, this TAI reveals that the 

industry that includes lawnmowers is comparatively underserved by RJVs. The RJV indicator 

therefore supports the selection of the lawnmower industry. More narrowly defined TAI data on 

RJV formation by lawnmower manufacturers could not be found, but future case studies may be 

able to utilize narrowly defined RJV data. 

 

21 Collaborative Research (CORE) database, 2001. The CORE database is funded by the National Science 

Foundation (NSF) and maintained by Dr. Albert Link of the University of North Carolina. 

22 The current CORE database contains 8,185 records, of which 763 report SIC codes. Unfortunately, identification 

of the target SIC code is not required by the Federal Register. The coding of each filing was done manually after the 

fact. 
26

Figure 10. Number of RJV Filings, Whole and Fractional

Source: Collaborative Research (CORE) Database, 2001 

3.4 Public Policy 

Comparing government regulation across industries presents conceptual complexities. Public 

policy, such as government regulation, tends to have unique impacts and effects on industries. 

The impact of a regulation depends on, among other things, the type of regulation and its 

severity. Some regulations mandate changes in a currently marketed product, and they differ in 

the degree of change required in the product; other regulations effectively prohibit certain 

product innovations. Perhaps due to these conceptual hurdles, there is currently no broad attempt 

to measure regulation across industries. Regulation effects need to be examined on an industryby-industry basis. 

In this study, we found that the lawnmower manufacturers were constrained by pending 

Environmental Protection Agency (EPA) regulations intended to reduce small-engine emissions 

by 59% by 2007. The EPA Phase 1 and Phase 2 regulations are described in detail in Appendix 

A. The EPA expects that its regulations will cause all remaining side-valve (SV) engines used in 

lawnmowers to be converted to overhead-valve (OHV) engines. Some existing OHV engines 

used in lawnmowers will also require improvements to meet the standards. No pending 

regulations affecting technology adoption by aircraft engine manufacturers were identified. 

 50

 42

131

116

0

20

40

60

80

100

120

140

Whole Fractional

Industry

Number of RJV Filings

Machinery, Except Electrical (35) Transportation Equipment (37)
27

In the above case, the EPA regulations will, by their design, strongly encourage small-engine 

manufacturers to make engine innovations and to adopt new emissions-reducing technologies, 

but it may not be clear in some cases what level of effort is required by the firms to meet the 

regulations. This illustrates the difficulties encountered when trying to make cross-industry 

comparisons of the effects of regulations on adoption. Many regulations, such as the above 

regulations on lawnmower emissions, contain text listing explicit goals mandated (e.g., number 

of lawnmowers with lowered emissions), providing the needed data for more detailed intraindustry analysis of the regulation’s effect on technology adoption. 

3.5 Historical Adoption Trends 

The SCP model takes into account historical, industry-specific trends for all TAI measures, and 

includes the history of past technology adoption. There is no standardized way to account for 

past technology adoption across industries. Each analysis requires both specific interpretations of 

what constitutes technology adoption and time-trend data on those observable characteristics. 

In the case of lawnmowers, EPA Phase 1 emissions regulations in effect as of 1997 led to the 

replacement of some SV engine configurations with cleaner OHV engine configurations. The 

market penetration of the more sophisticated engine technology—such as OHV engine 

configurations—may be used to proxy technology adoption.23 The Power Systems Research 

OELink database (Power Systems Research 2001) provides data from 1992 to 1999 on engine 

kW rating, stroke, valve cam, valves per cylinder, cycle, cylinders, displacement, configuration, 

and torque, all of which are subject to technological improvement. We use these data to analyze 

technology adoption trends for the lawnmower engine industry. 

Figure 11, using yearly production data from Power Systems Research, shows the decrease in SV 

engines and increase in OHV engines, and indicates that lawnmower manufacturers adopted 

OHV engine technology coincident with Phase 1 emissions regulations. It is likely that Phase 2 

EPA regulations will have a similar effect, increasing the adoption of new engine technology. 

3.6 Conclusion 

On the whole, TAIs based on the structure-conduct-performance model show that lawnmower 

manufacturers are more likely than aircraft manufacturers to adopt the FCM technology spillover 

from the automotive industry. The broadly defined TAIs are summarized in Table 1, and the 

narrowly defined, industry-specific TAIs are summarized in Table 2. 

 

23 The technology adoption is likely due to new EPA regulations. 
28

Table 1. Summary of Findings Using Broadly Defined TAIs 

TAI Measure Lawnmower Engines Aircraft Engines 

CR4 (Appendix D) and CR8 Not optimal size for adoption Not optimal size for adoption 

 HHI Optimal size for adoption Not optimal size for adoption 

Patents Moderate level of patents 

indicates less competition; 

FCM adoption more likely 

High level of patents indicates 

more competition; FCM 

adoption less likely 

RJVs Moderate level of RJVs 

indicates less competition; 

FCM adoption more likely 

High level of RJVs indicates 

more competition; FCM 

adoption less likely 

Public policy/regulations No broad measure No broad measure 

History of technology 

adoption 

No broad measure No broad measure 

Table 2. Summary of Findings Using Narrowly Defined Data on Lawnmower 

Manufacturers

Measure Lawnmower Engines 

CR4 and CR8 Too concentrated to adopt 

HHI Optimal size for adoption 

RJVs No specific data 

Patents Recent slight increase indicates some 

adoption, probably due to regulation 

Public policy/regulations High likelihood of adoption due to major 

new regulation 

History of technology 

adoption 

Indicates some adoption, probably due to 

regulation 

Overall, the TAI analysis suggests that the likelihood of adoption by the lawnmower industry is 

strong. The broadly defined market concentration indicators show a clear preference for the 

lawnmower manufacturing application over aircraft engine application. The narrowly defined 

lawnmower industry data show that, according to the four- and eight-firm concentration ratios, 

the lawnmower industry is too highly concentrated to adopt technology. However, the 

Herfindahl-Hirschman Index, which is a more comprehensive measure than the CR4 and CR8, 

supports technology adoption by the lawnmower industry. The broadly defined patent TAI 

supports the selection of either the lawnmower industry or the airplane engine industry. The 

broadly defined research joint venture TAI shows that the lawnmower industry was underserved 

by RJV formation, compared to the airplane industry, suggesting an opening for an external, new 

technology. The narrowly defined TAI data on patents, public policy, and historical technology 

adoption show that Environmental Protection Agency regulations are likely to encourage the 

lawnmower manufacturers to adopt new engine technology.24 

 

24 Regulations can drive an industry to adopt technology not previously targeted to that industry. The need to meet 

the emissions regulation required an evaluation of all engine technology innovations, including those originally 

targeted at automobile engines. 
29

Figure 11. Side-Valve and Overhead-Valve Market Penetration, 1990 to 2000

Source: Power Systems Research (2001). 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Percentage of Market

Side valve (SV) Overhead valve (OHV)

Implementation of Phase 1 

EPA regulation (1997) 

31

4. Case Study of the FCM Technology Applied to the Lawnmower 

Manufacturing Industry

Over 15 million gas-powered lawn and garden mowers, trimmers, and other tools are sold each 

year, about half of which are walk-behind and riding grass mowers. This equipment produces 

significant amounts of hydrocarbons (HC), including nonmethane hydrocarbons (NMHC),25

which lead to ozone, the principal component of smog; oxides of nitrogen (NOx), which 

contribute to the production of acid rain; and carbon monoxide (CO), a colorless, odorless, and 

poisonous gas which affects infants and people with respiratory and heart problems. 

Since 1990, the EPA has been attempting to decrease emissions from lawnmowers and other 

equipment that use small spark-ignition engines (SSIEs). Phase 1 regulations were implemented 

in 1997. The Phase 2 emissions limits are shown in Table 3. (Phase 1 emissions limits are not 

directly comparable to Phase 2 limits due to a change in testing methodology.) 

Table 3. Emissions Limits under Phase 2 EPA Regulations

Emission Limit (g/kW·h) Engine 

Class Emission Type 2001 2002 2003 2004 2005 and 

later 

HC + NOx - - 16.1 16.1 16.1 

I NMHC + NOx - - 14.8 14.8 14.8 A

CO - - 610 610 610 

HC + NOx 18.0 16.6 15.0 13.6 12.1 

II NMHC + NOx 16.7 15.3 14.0 12.7 11.3 

CO 610 610 610 610 610 

A Class I engine families initially produced on or after August 1, 2003, must meet the Phase II regulations 

when introduced. Preexisting engine families must meet Phase 2 regulations by August 1, 2007. 

Source: Code of Federal Regulation.

The Extrude Hone Corporation identified lawnmower engines as an application of the FCM 

technology. The company conducted a series of tests of FCM technology on a stock OHV engine 

from a major manufacturer of lawn and garden equipment to assess whether FCM technology 

would bring a conventional engine into compliance with the EPA requirements for 2007. This 

engine has a 24 mm carburetor and is marketed as an 8.2 kW (11 horsepower, HP) lawn and 

garden engine. Three-way comparisons were made between the original engine, an engine that 

applied FCM to the carburetor (to enhance surface attributes, not to increase the size), and to an 

engine that applied FCM to the carburetor, intake pipe, and cylinder head. Extrude Hone’s 

engineering tests confirmed that the FCM technology can meet Phase 2 emissions requirements. 

 

25 Small spark-ignition engines produce about 10% of U.S. mobile source HC emissions. 
32

Extrude Hone engineers also provided detailed data on the costs of applying the FCM technology 

to the full range of lawnmower engines. These tests show FCM could reduce emissions 

competitively with the conventional technologies evaluated by the EPA. 

In this section we investigate the economic feasibility of lawnmower engine manufacturers using 

the FCM technology to meet the new EPA regulations. We did this by computing and comparing 

cost data. Then we present the results of a comparative economic impact analysis of this new 

technology versus the conventional technologies evaluated by EPA, by using the cost data in a 

dynamic macroeconomic model of the U.S. economy. 

4.1 Fixed and Variable Costs of Meeting Phase 2 Regulations Using Conventional 

Technology Compared to FCM Technology 

The FCM technology is a newly available option for decreasing emissions to Phase 2 levels. To 

assess its feasibility and potential benefit over other options, we compare the cost of using FCM 

to the cost of using conventional technology. The EPA’s analysis of costs of bringing 

conventional equipment in compliance, using incremental improvements in existing technology, 

are reported in Phase 2: Emissions Standards for New Nonroad Nonhandheld Spark-Ignition 

Engines At or Below 19 Kilowatts (EPA 1999). Cost analyses from this report are then compared 

with estimates prepared by Extrude Hone engineers of the cost of implementing the new FCM 

technology in existing conventional equipment. 

The lawnmower engine industry consists of four market segments, each of which we subjected to 

a separate comparative analysis. The market segments are defined by two engine sizes and two 

engine technologies. Engine size is measured by cylinder displacement volume measured in 

cubic centimeters (cc). 26 Small engines, those below 225 cc of displacement, are called “Class 

I.” Larger engines, those with displacement greater than or equal to 225 cc, are called “Class II.” 

The two engine technologies are side-valve (SV), and overhead-valve (OHV). Side valve engines 

are defined in the Code of Federal Regulations (GPO 2000) as a “. . . four-stroke engine in 

which the intake and exhaust valves are located to the side of the cylinder, not within the 

cylinder head . . .” Overhead-valve engines are defined in the Code of Federal Regulations as a 

“. . . four-stroke engine in which the intake and exhaust valves are located above the combustion 

chamber within the cylinder head. . . .” 

The EPA report indicated that engine manufacturers would likely meet the Phase 2 emissions 

standards largely by changing their engine technology in one of two ways. First, Class I and II 

SV engines would be replaced by improved OHV engines.27 Second, existing Class I and II 

OHV engines would be improved through better piston rings, intake, and combustion. The EPA 

analysis provided cost estimates of SV and OHV changes and the numbers of engines and engine 

families to which they would be applied. 

The four market segments are referred to in this report as segments A, B, C, and D. Market 

segment A consists of Class I SV engines. In the EPA analysis, to meet Phase 2 using 

 

26 Throughout this report, the abbreviation “cc” is used to denote cubic centimeters (cm3

). The use of “cc” is 

standard industry practice when measuring the displacement of engines, and is therefore followed in this report. 27 Vaporizing carburetion could be used to improve side-valve engine technology, but it has found limited use. 
33

conventional technology, the SV engine would be converted to an OHV engine.28 Market 

segment B consists of Class I OHV engines. In the EPA analysis, to meet Phase 2 using 

conventional technology, the OHV engine would be improved. While there are many 

conventional technologies that may be used to improve the OHV engine, the EPA report bases its 

analysis on “piston and piston ring improvements,” and “improved combustion and intake 

system.” Market segment C consists of Class II SV engines. In the EPA analysis, to meet Phase 2 

using conventional technology, the SV engine would be converted to an OHV engine. Market 

segment D consists of Class II OHV engines. To meet Phase 2 using conventional technology, 

the EPA assumes that the combustion and intake system, pistons, and piston rings would be 

incrementally improved. For our analysis, the FCM technology is compared with the 

conventional technology. 

The EPA report assumes that, within a market segment, there is no variation in the application of 

technology to meet the EPA Phase 2 regulation across the segment. In other words, all affected 

SV engines adopt the same conventional technology (conversion to OHV). Similarly, all affected 

OHV engines adopt the same conventional technology (piston and piston ring improvements, 

and improved combustion and intake system). Our report similarly assumes that there will not be 

a mix of conventional and new (FCM) technology applied within a market segment, but rather 

full adoption of FCM or full adoption of conventional technology. 

The fixed and variable costs of each scenario—the use of the EPA-assumed conventional 

technology, and the adoption of the FCM technology—are shown in Tables 4 and 5. 

Table 4. Unit Costs of Meeting Phase 2 Standards 

Using Conventional Technology, in 2002 Dollars

 (1) (2) (3) = (1) ÷ (2) (4) 

 Fixed Cost 

($1,000’s/family)

Average 

engines/family

Fixed Cost 

($/engine) 

Variable Cost 

($/engine) 

Class I: 

A. SV to improved OHV 18,276 634,105 28.82 14.91 

B. OHV to improved OHV 659 47,676 13.83 2.45 

Class II: 

C. SV to improved OHV 20,096 45,244 444.16 23.98 

D. OHV to improved OHV 659 90,625 7.28 2.45 

Source: EPA (1999). 

The variable costs shown in column 4 of Table 4 are taken from the EPA report and converted to 

2002 dollars. The fixed costs in column 3 of Table 4 are derived from information in the EPA 

report, as summarized in columns 1 and 2. Column 1 shows the lump-sum fixed cost to convert 

an entire engine “family” (line of similar engines) into compliance with Phase 2 standards. 

Column 2, the average number of engines per family, is computed by dividing the total number 

of engines by the total number of engine families, both of which are contained in the EPA report. 

 

28 The SV engine is converted to an OHV engine, which incorporates necessary improvements to meet Phase 2. It is 

identical to the “improved OHV” in market segments B and D. 
34

Fixed cost in column 3 is derived by dividing the fixed cost per family (column 1) by average 

engines per family (column 2). 

The variable and fixed costs of using FCM technology on the same quantity of lawnmower 

engines, based on data provided by Extrude Hone engineers, are shown in Table 5. Extrude Hone 

provided cost estimates for meeting the EPA regulations on both OHV and SV engines. 

Table 5. Unit Costs of Meeting Phase 2 Standards Using FCM Technology, in 2002 Dollars

 (1) (2) (3) = (1) ÷ (2) (4) 

 Fixed Cost 

($/cell) Engines/cell 

Fixed Cost 

($/engine) 

Variable Cost 

($/engine) 

Class I: 

A. SV using FCM 310,000 37,406 8.29 17.50 

B. OHV using FCM 309,000 49,875 6.20 14.00 

Class II: 

C. SV using FCM 310,000 37,406 8.29 17.50 

D. OHV using FCM 309,000 49,875 6.20 14.00 

Source: Extrude Hone. 

The basis for the Extrude Hone cost estimates is a single production cell, consisting of a single 

FCM machine and its associated equipment (such as electrical connections and mounts for 

lawnmower engines), operating on the cylinder head of a lawnmower engine. The base price of a 

single FCM machine is $300,000. Estimates of installation cost ranged from $8,000 to $10,000. 

For this report, the midpoint value of $9,000 was used. The total fixed cost per FCM machine, 

set up for OHV engines, is estimated to be $309,000. Fixed cost for an FCM machine set up for 

SV engines requires a more complex clamp estimated to cost an additional $1,000. The fixed 

cost for the FCM machine set up for SV engines is $310,000. 

The fixed cost per FCM machine for each type of engine is summarized in column (1) of Table 

5. Each FCM machine can process 37,406 SV engines or 49,875 OHV engines per year. These 

yearly production capacities are shown in column (2). Fixed cost in column (3) is derived by 

dividing the fixed cost per machine (column 1) by the number of engines that can be processed 

per machine (column 2). Extrude Hone also estimates that variable costs incorporated expected 

efficiency improvements in material handling, quality control, cleaners, supervisors, operators, 

and infrastructure. Variable costs are summarized in column (4) of Table 5. 

4.2 Total Cost Per Year of Using Conventional Technology Compared to FCM Technology

In this section we show the total costs to the industry of the Extrude Hone FCM process when 

compared with the conventional costs of meeting the EPA Phase 2 regulations, as estimated by 

the EPA. The total industry cost estimates are phased in according to the timing of the phased-in 

compliance with the regulations. The EPA Phase 2 regulation holds Class I and Class II engines 

to different standards, implemented in different years. Class I engines are analyzed over the years 

2007 to 2009, and Class II engines are analyzed over the years 2002 to 2007. 
35

Using the EPA market assumptions regarding the number of affected engines and the number of 

affected engine families, year-by-year cost estimates (shown in Tables 6 and 7) were 

developed.29 These total industry costs were derived by summing two components, the total 

industry variable cost and the total industry fixed cost. The unit variable cost is multiplied by the 

total production of compliant engines manufactured, by year. Fixed costs are incurred only in the 

year in which an engine family needs to be modified to meet Phase 2 requirements. The fixed 

cost per family (column 1 Table 4) is multiplied by the total number of engine families that are 

converted to meet the Phase 2 emissions requirements, by year. 

Using this method, we developed time paths of industry costs, shown in Tables 6 and 7. In 

market segments A and B (Class I), the time path is from 2007 to 2009. All Class I engines were 

assumed to be converted in 2007, incurring both fixed costs and variable costs. Two additional 

years (2008 to 2009) of variable costs were included to show the ongoing costs of production. In 

market segments C and D (Class II), the time path is from 2002 to 2007. All Class II engines 

were converted over the period between 2002 to 2005, according to the phase-in requirements of 

the regulation. Two additional years (2006 to 2007) of variable costs were included to show the 

ongoing costs of production. These schedules of yearly industry costs were used as inputs in a 

macroeconomic model. 

 

29 The EPA used estimates provided by Jack Faucett Associates in Small Nonroad Engine and Equipment Industry 

Study (December 1992). 
36

Table 6. Total Industry Cost of Meeting Phase 2 Standards Using Conventional Technology 

Compared to FCM Technology in Class I, in Thousands of 2002 Dollars

 Market Segment A: Class I SV Market Segment B: Class I OHV 

 Conventional 

technology FCM technology

Conventional 

technology FCM technology

2007 $305,022 $179,871 $6,210 $7,703 

2008 103,988 122,065 935 5,340 

2009 103,988 122,065 935 5,340 

Source: EPA (1999) and Extrude Hone Corporation. 

Table 7. Total Industry Cost of Meeting Phase 2 Standards Using Conventional Technology 

Compared to FCM Technology in Class II, in Thousands of 2002 Dollars

 Market Segment C: Class II SV Market Segment D: Class II OHV 

 Conventional 

technology 

FCM technology Conventional 

technology 

FCM technology

2002 0 0 882 1,830 

2003 63,542 3,500 222 1,269 

2004 130,338 9,376 1,104 3,099 

2005 306,290 23,460 2,208 6,198 

2006 24,949 18,211 889 5,075 

2007 24,949 18,211 889 5,075 

Source: EPA (1999) and Extrude Hone Corporation. 

The schedules of yearly industry costs for the conventional and FCM technology applied to the 

four market segments are shown graphically in Figures 12 to 15. 

In market segment A (Class I SV), the FCM technology is significantly less expensive than the 

conventional technology in 2007. As shown in Tables 4 and 5, fixed costs are concentrated in 

that year, and the FCM technology has lower fixed costs. However, the FCM technology has 

slightly higher variable cost on an ongoing basis. It is possible that the lower cost in 2007 might 

make FCM attractive to lawnmower manufacturers. 

In market segment B (Class I OHV), the FCM technology is always more expensive than the 

conventional technology. 

In market segment C (Class II SV) the FCM technology is always the lower cost option 

compared to the conventional technology. This is because, as we have already shown in Tables 4 

and 5, both the fixed costs and the ongoing variable costs of the FCM technology are lower 

compared to the conventional technology. 

Lastly, in market segment D (Class II OHV) the FCM technology is always more expensive than 

the conventional technology. 
37

The biggest cost savings from the FCM technology comes when it is applied to SV engines (in 

market segments A and C) that would otherwise have to be converted to OHV engines (other 

things being equal). Market segment C will definitely benefit from meeting Phase 2 regulations 

with the FCM technology. Additionally, market segment A might benefit from meeting Phase 2 

regulations with the FCM technology, given its lower fixed costs in 2007. We conducted a 

macroeconomic impact analysis for these two market segments: A (Class I SV) and C (Class II 

SV) to quantify the economic impact to the nation of using the FCM technology on lawnmower 

engines rather than the conventional technology assumed by the EPA to meet Phase 2 

regulations. 
38

Figure 12. Market A: Class I SV Costs Figure 13. Market B: Class I OHV Costs

Figure 14. Market C: Class II SV Costs

Figure 15. Market D: Class II OHV Costs

$0

$2

$4

$6

$8

2007 2008 2009

Year

$ millions (2002)

Conventional technology FCM technology

$0

$50

$100

$150

$200

$250

$300

$350

2002 2003 2004 2005 2006 2007

Year

$ millions (2002)

Conventional technology FCM technology

$0

$2

$4

$6

$8

2002 2003 2004 2005 2006 2007

Year

$ millions (2002)

Conventional technology FCM technology

$0

$50

$100

$150

$200

$250

$300

$350

2007 2008 2009

Year

$ millions (2002)

Conventional technology FCM technology
39

4.3 Introduction to the REMI Policy Insight Macroeconomic Impact Model

Several macroeconomic models are available for simulating the total national economic impact 

of changes to specific industries (in our case, the lawnmower engine industry).30 We used the 

REMI Policy Insight model of the U.S. economy because of its ability to handle a series of 

nonuniform economic shocks.31 

The REMI model was developed for analysts who need to estimate the impact of economic 

changes in the U.S. economy. In general, the model computes the total effect over time on all 

sectors of the U.S. economy resulting from a change to one or more sectors of the U.S. economy. 

The model is based on economic theory, input-output (I/O) accounting, and econometricallyestimated, time-dependent relationships between components of the economy. 

As summarized in Figures 12–15, fixed and variable costs of production will increase for all four 

market segments (A through D). Use of either the conventional technology or the FCM 

technology to meet the emissions requirements of the Phase 2 EPA regulations will increase the 

production costs of lawnmower engine manufacturers. In market segments A and C, use of the 

FCM technology will increase costs less than the conventional technologies (SV-to-OHV 

conversions and OHV improvements) and therefore can be considered economically viable for 

adoption by these segments, but not segments B and D.32 We therefore focused our REMI 

analysis on market segments A and C. We estimated the total industry production cost increase 

from each technology for market segments A and C, used REMI to estimate the direct and 

indirect impacts of each segment on the national economy, and compared the impacts. 

The simulation performed with REMI indicates that an increase in production costs in the 

lawnmower industry will cause an increase in the selling price of lawnmowers. This will cause a 

decrease in the number of units sold. As production costs rise, firms that seek to maintain current 

profit levels must increase their selling prices and/or reduce other production costs, including the 

costs of labor and capital (machinery). Decreased sales and cost cutting reduces employment in 

the industry and in industries that supply parts and services to it, thereby reducing aggregate 

income and the broad set of purchases this income supports (such as for purchasing cars, homes, 

services, and travel). 

Although REMI models the impacts for dozens of national economic impact variables, we 

selected three of the most comprehensive measures for this report: GDP, change in national 

employment, and change in personal income. GDP measures the value produced by labor and 

capital (such as machinery), and is computed as the sum of all sales of goods and services 

 

30 These include the REMI Policy Insight (Regional Economic Modeling, Inc., Amherst, MA), the DRI*WEFA 

Macroeconomic Model (Global Insight, Waltham, MA), and IMPLAN (Minnesota IMPLAN Group, Stillwater, 

MN). 

31 Using REMI, we were able to use discrete production cost increases in specific years within a single forecast. 32 If the EPA were to implement a “Phase 3” regulation on small nonroad, nonhandheld engines, this conclusion 

could change. 
40

produced in the country minus material costs.33 For example, the GDP of just the lawnmower 

engine industry would be the sum of all lawnmower engine sales minus the cost of the parts used 

to produce the engines. What remains is the cost of the labor and capital used to produce the 

engines; these “returns” to labor and capital are the measure of the contribution of the 

lawnmower engine industry to GDP. Change in national employment is the change in the total 

number of people employed in the country, and change in personal income is the change in 

income received by the employed. 

4.4 Macroeconomic Impact of the Costs of Conventional Technology Compared to FCM 

Technology, in Market Segments A and C

In this subsection we compare the macroeconomic impacts of using conventional technology 

versus the FCM technology, and present the impacts on GDP, national employment, and 

personal income.34 Detailed year-by-year graphs are in Appendix D. 

As a result of the EPA regulation, GDP, employment, and personal income will fall. In each 

market segment, we estimated the economic impact of using the FCM technology, and the 

impact of using conventional technology. For two market segments, A and C, the decrease in 

GDP is smaller with FCM technology than with conventional technology. For ease of 

interpretation, we illustrate the difference in the reductions in GDP or personal income from 

using the FCM technology instead of the conventional technology in markets A and C as a 

savings in GDP or personal income. 

Figure 16 shows that, in market segment A over the analysis period 2007 to 2009, the FCM 

technology saved $262 million in GDP. In market segment C, over the analysis period 2003 to 

2007, the FCM technology saved $982 million in GDP. 

Figure 17 shows that, in market segment A over the analysis period 2007 to 2009, the FCM 

technology saved $243 million in personal income. In market segment C, over the analysis 

period 2003 to 2007, the FCM technology saved $879 million in personal income. 

 

33 The Bureau of Economic Analysis (BEA) defines GDP as “the output of goods and services produced by labor 

and property located in the United States” (www.bea.gov). Sales, in contrast to GDP, is not a measure of everything 

actually created in this country. 

34 It is important to note that although we are considering only costs of the EPA Phase 2 regulation by two 

approaches, our analysis represents a full benefit-cost analysis. Both the conventional and the FCM technologies 

perform similarly regarding reductions in pollution and fuel consumption. In this case study, we are comparing the 

impact of the two technologies that meet the same objectives. Therefore, we do not offset the economic costs by the 

benefits (pollution reduction and fuel savings). The negative economic impacts on GDP, employment, and personal 

income of the conventional and the FCM technologies are estimated and compared in this study. We provide an 

estimate of the impact of each and net impact of one choice versus the other, represented as cost savings. 
41

Figure 16. Savings in GDP from Using the FCM Technology Instead of the EPA-assumed 

Conventional Technology to Meet Phase 2 Regulations, in Market Segments A and C

Figure 17. Savings in Personal Income from Using the FCM Technology Instead of the EPAassumed Conventional Technology to Meet Phase 2 Regulations, in Market Segments A and C 

 262

 982

$0

$200

$400

$600

$800

$1,000

A (Class I SV) C (Class II SV)

Market Segment

2002 $s (millions)

 243

 879

$0

$200

$400

$600

$800

$1,000

A (Class I SV) C (Class II SV)

Market Segment

2002 $s (millions)

43

Section 5. Summary and Conclusions

An economic case study of the potential adoption of the Flow-Control Machining (FCM) 

technology in lawnmower engines was used to develop and demonstrate the use of technology 

adoption indicators (TAIs). Extrude Hone, the developer of the FCM technology, initially 

identified lawnmower engines and aircraft engines as two potential uses of the FCM technology. 

The TAIs were developed to facilitate a systematic comparison of the likelihood for technology 

adoption in each industry. 

5.1 Development and Interpretation of the TAIs from the SCP Model 

The SCP framework guided the development of the TAIs. The framework asserts (and supports 

through extensive research) that the structure of a market (e.g., the number of buyers and sellers) 

influences the conduct of firms in that market, which ultimately determines the market price, 

quantity, and other performance attributes. In addition (and as it turns out, crucial to our 

assessment of the lawnmower industry), government regulations and more generally public 

policy can affect the structure or conduct of the market. Public policy and regulations belong in 

the standard set of TAIs, but require customized development from specific information about 

the industry of interest. We recommend that any study of a particular industry’s likelihood to 

adopt should include an assessment of current government regulations affecting that specific 

industry. 

The broadly defined—using 4-digit SIC and 6-digit NAICS industry classifications—TAIs of 

industry concentration (CR4, CR8, and HHI), as well as patents and RJVs, were used to compare 

the likelihood of technology adoption in two industries: the small engine and airplane engine 

industries. (The four- and eight-firm concentration ratios, along with the Herfindahl-Hirschman 

Index, were introduced in 2.1.) 

For lawnmower manufacturers, almost all the measures (including the narrowly defined public 

policy information specific to the lawnmower industry) support the conclusion that the 

lawnmower industry is likely to be more receptive than the aircraft engine industry to new 

technology. (The broadly defined CR4 and CR8 are unable to support the selection of either 

industry.) 

We extended the case study analysis based on the broadly defined TAIs to narrowly defined 

TAIs using data specific to the lawnmower industry. The CR4, CR8, and HHI were calculated 

based on private data available specifically for the lawnmower engine industry. The patent 

measure was calculated manually using searches of the USPTO database. Specific instances of 

regulations and the history of technology adoption in the lawnmower industry were also 

analyzed. 

The narrowly defined lawnmower industry data show that, according to the CR4 and CR8, the 

lawnmower industry is too highly concentrated to adopt technology. The HHI, a more 

comprehensive measure of concentration, supports technology adoption by the lawnmower 

industry. The narrowly defined TAI data on patents per the HHI measure, public policy, and 
44

historical technology adoption show that the lawnmower industry should be receptive to new 

engine technology. The EPA regulations are likely to provide a market pull. 

5.2 Economic Analysis of the FCM Technology 

We assessed the economic benefits from using the FCM technology by estimating the national 

economic impacts of using the conventional emissions-reducing technologies assumed by the 

EPA, or alternatively the FCM technology, and then comparing the two. Four lawnmower engine 

market segments, determined by engine displacement volume (Class I and Class II) and engine 

technology (SV and OHV) were identified. 

In each market segment, we estimated the cost increase from using conventional technology to 

meet emissions requirements. Over the period of analysis, the cost of the FCM technology was 

somewhat lower in market segment A, Class I SV engines, and significantly lower in market 

segment C, Class II SV engines. We extended our analysis to national economic impacts for 

those two segments where FCM appeared cost competitive with the conventional technology. 

We modeled the national economic effects of use in market segments A and C using the REMI 

macroeconomic model of the U.S. economy. For both the conventional technology and the FCM 

technology, the additional production costs cause lawnmower engine sales prices to rise, GDP 

and sales to decrease, employment in affected industries to decrease, and total personal income 

to decrease, but these effects are greater for the conventional technology. 

In market segment A (Class I SV engines), the FCM technology has significantly lower initial 

fixed cost but slightly higher variable cost than the conventional alternative. The reductions in 

GDP, employment, and income initially are quite a bit smaller for the FCM technology 

compared to the conventional technology. Using the FCM technology rather than the 

conventional technology saves $261 million in GDP over the three years 2007 to 2009, and saves 

$244 million in personal income. 

In market segment C (Class II SV engines), the FCM technology exhibits lower fixed and 

variable costs of production. The FCM technology is cost advantageous for market segment C 

and results in significant savings in GDP, employment, and personal income. Overall, the FCM 

technology saves 93% of GDP, employment, and personal income that would have been lost by 

using the conventional technology. Using the FCM technology rather than the conventional 

technology saves $982 million in GDP and $878 million in personal income over the five years 

2003 to 2007. The advantage of the FCM technology in market segment C is obvious. 

5.3 Application of TAIs 

The TAIs are a useful framework for assessing whether a particular industry will adopt new 

technologies.35 There are widespread differences across U.S. industries in size and distribution 

 

35 We envision the TAI framework to be useful guidance in a methodological investigation of industry 

characteristics affecting technology adoption. In this case study the public policy TAI, in the form of an EPA 

regulation, turned out to be a decisive factor indicating the adoption of the FCM technology. Other case studies may 

not have such a decisive factor. 
45

of firms, in patents, in research joint ventures, and in the use of new technologies. Increased 

understanding of the relationships between these variables will help ATP encourage research 

proposals from those sectors of the economy that are likely—or have the potential—to adopt new 

technology, and assess the potential economic impact of the proposed projects. There are four 

uses of the TAIs. First, they can be used to identify promising case studies for project evaluation. 

Second, the TAIs offer a consistent and effective methodology for conducting prospective case 

studies of economic benefits of ATP projects. Third, the TAIs can assist in evaluating business 

plans of ATP proposers. Fourth, the TAIs can be used to advise ATP awardees on which 

industries are more likely potential adopters of their technologies. 

The TAI methodology provided valuable insight into industry characteristics affecting 

technology adoption, and, after the analysis of all TAI measures for the two industries, leads us 

to conclude that the lawnmower industry exhibits the stronger case for likely adoption of the 

FCM technology. Analysis of the potential impact of the FCM technology using the REMI 

Policy Insight macroeconomic model suggests substantial benefits to the U.S. economy. 

47

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51

Appendix A 

EPA Phase 1 and Phase 2 Regulations on Nonroad Nonhandheld Small 

Engines

Under the Clean Air Act (1990), the Environmental Protection Agency was directed to study and 

regulate the emissions of nonroad engines (engines used in vehicles not driven on roadways). 

The EPA found that nonroad engines contributed significantly to pollution, emitting nitrogen 

oxides (NOx), hydrocarbons (HC), nonmethane hydrocarbons (NMHC), carbon monoxide (CO), 

and particulate matter (PM). Subsequently, under Phase 1 in 1997, lower emissions were 

required for new nonroad engines less than 19 kW (25 HP). In March 1999, the EPA finalized 

Phase 2 emissions standards for new nonroad non-handheld spark-ignition engines at or below 

19 kW that phase in between 2001 and 2007. 36 The Phase 2 emissions standards also addressed 

handheld engines (such as those used in leaf blowers and lawn-and-garden-type trimmers and 

edgers). The EPA plans to propose regulations for nonroad engines in recreational uses as well as 

nonroad engines larger than 19 kW (25 HP) in 2001. 

The Phase 2 emissions limits are defined by engine displacement class. Displacement categories, 

measured in cubic centimeters (cc) for engine classes I and II, are shown in Table 8.37 Most 

walk-behind lawnmowers use engines that fall into engine Class I, and the remaining larger 

mowers fall in engine Class II. 

Table 8. Definitions of Engine Class I and II

Engine Class Displacement is 

greater than or equal to . . . 

(in cubic centimeters, cc) 

. . . and displacement is 

less than 

(in cubic centimeters, cc) 

I 100 225 

II 225 no limit 

Source: Code of Federal Regulations (GPO 2000).

In 1997, Phase 1 emissions standards addressed the total hydrocarbons and oxides of nitrogen 

(HC + NOx) exhaust emissions in engine classes I and II. Phase 1 standards were applied to a 

newly manufactured engine. New Class I engines were allowed to emit up to 16.1 g/kW·h of 

HC + NOx, measured at a steady state. Class II engines were allowed to emit up to 13.4 g/kW·h 

or HC + NOx, measured at a steady state. Classes I and II were not to exceed carbon monoxide 

(CO) emissions of 519 g/kW·h. 

 

36 The March 1999 Phase 2 emissions standards are detailed in the Code of Federal Regulations Title 40: Protection 

of Environment Volume 14, which includes Section I: Environmental Protection Agency, Part 90, “Control of 

emissions from nonroad spark-ignition engines,” dated July 1, 2000 (GPO 2000). Part 90 has 13 subparts, A through 

M, that address different aspects of the regulation. The sections numbered 90.1 through 90.7 fall under subpart A, 

those numbered 90.103 through 90.126 fall under subpart B, numbers 90.201 through 90.220 under subpart C, and 

so on, culminating with subpart M, containing sections 90.1201 through 90.1249. Part 90, in its entirety, requires 

129 pages in the Federal Register (pages 166 to 294 in Title 40 Volume 14). 

37 Throughout this report, the abbreviation “cc” is used to denote cubic centimeters (cm3

). The use of “cc” is 

standard industry practice when measuring the displacement of engines, and is therefore followed in this report. 
52

Phase 2 regulations added new emissions test limits using a new test method. Tests are to be 

done after the engine’s service accumulation period (based on the engine’s useful life) so that 

emissions are stabilized. Engine Class I was subject to Phase 2 regulations in 2003 and 2007, as 

shown in Table 9. Limits on emissions of nonmethane hydrocarbons plus oxides of nitrogen 

(NMHC + NOx) are applied only to engines fueled by natural gas, and take the place of the 

limits on HC + NOx emissions. Regulations on Class II engines were phased in from 2001 to 

2005, as shown in Table 10. 

Table 9. Phase 2 (1999) Emissions Regulations for Engine Class I 

Engine Class HC+NOx NMHC+NOx CO Effective Date 

I 16.1 g/kW·h 14.8 g/kW·h 610 g/kW·h 2003 or 2007 A

A Engine families initially produced on or after August 1, 2003, must meet the Phase 2 

regulations when introduced. Pre-existing engine families must meet Phase 2 regulations by 

August 1, 2007. 

Source: Code of Federal Regulations (GPO 2000). 

Table 10. Phase 2 (1999) Emissions Regulations for Engine Class II

Engine 

Class 

Emission 

Requirement 2001 2002 2003 2004 2005 and later 

HC + NOx 18.0 16.6 15.0 13.6 12.1 

II NMHC + NOx 16.7 15.3 14.0 12.7 11.3 

CO 610 610 610 610 610 

Source: Code of Federal Regulations (GPO 2000).

In their Phase 2 economic analysis, the EPA proceeded under the assumption that SV engines 

would be converted to OHV engines. At the time of the report, manufacturers did not indicate to 

the EPA that there existed any conventional technology that could be applied to SV engines to 

reduce their emissions to Phase 2 levels. Some existing OHV engine families also required 

improvements to meet Phase 2. 
53

Appendix B 

Patents Issued by SIC Code 

Figure 16. Thousands of Patents Issued (Whole Count) for 1996 to 2000, by SIC Code 

Source: USTPO (2001).

108.3

5.6

0.6

3.6

1.9

8.7

1.9

16.4

27.8

37.7

9.1

141.9

145.2

15.2

7.0

8.2

24.3

18.3

59.6

21.6

195.0

18.3

71.2

45.5

28.8

77.5

13.3

17.5

11.1

12.1

179.3

34.9

1.9

56.9

3.1

3.3

5.4

17.5

6.4

0 50 100 150 200 250

Prof. & Scien. Instr. (38, ex. 3825)

Misc. Trans. Equip. (379, ex. 3795)

Guided Missiles & Space Veh. (376)

Motorcycles, Bicycles & Parts (375)

Railroad Equip. (374)

Other Trans. Equip. (373-375, 379, ex. 3795)

Ship Build & Repair (373)

Aircraft & Parts (372)

Motor Veh. & Equip. (371)

Trans. Equip. (37, 348)

Misc. Elec. Mach. & Equip. (369)

Elec. Components & Acc. (366-367)

Comm. Equip. & Elec. Comp. (365-367)

Radio & TV Equip. (365)

Elec. Lighting & Wiring Equip. (364)

Household Appliances (363)

Other Elec. Mach. & Equip. (363, 364, 369)

Elec. Industrial Apparatus (362)

Elec. Equip. (361-364, 369, 3825)

Elec. Transmission Equip. (361, 3825)

Elec. Mach. & Equip. (36, 3825)

Misc. Mach. (359)

Refrigeration & Service Industry Mach. (358)

Office Computing Mach. (357)

Gen. Industrial Mach. & Equip. (356)

Special Industry Mach. (355)

Other Mach. except Elect. (355, 356, 358, 359)

Metal Working Mach. & Equip. (354)

Const., Mining & Mat. Handling Mach. (353)

Farm & Garden Mach. & Equip. (352)

Engines & Turbines (351)

Mach., Except Elect. (35)

Motor Veh. & oth. Trans. (348, 371, 373-376, 379)

Ordnance Except Missiles (348, 3795)

Fabricated Metal Products (34 ex. 3462, 3463, 348)

Prim/Sec Non-Ferr. (333-336, 339 ex. 3399, 3463)

Prim. Ferrous Prod. (331, 332, 3399, 3462)

Primary Metals (33, 3462, 3463)

Stone, Clay, Glass and Concrete Products (32)

Industry

Patents (1,000s)
54

Figure 17. Thousands of Patents Issued (Fractional Count) for 1996 to 2000, by SIC Code 

Source: USTPO (2001).

93.2

2.0

0.4

1.1

0.8

5.5

1.5

5.9

14.3

27.9

6.9

124.3

132.3

8.1

6.1

3.7

16.7

13.2

45.1

15.2

177.4

8.2

62.4

24.3

21.7

56.6

10.7

10.7

5.4

4.7

150.5

22.0

1.8

36.8

2.5

2.1

4.6

10.1

2.4

0 20 40 60 80 100 120 140 160 180 200

Prof. & Scien. Instr. (38, ex. 3825)

Misc. Trans. Equip. (379, ex. 3795)

Guided Missiles & Space Veh. (376)

Motorcycles, Bicycles & Parts (375)

Railroad Equip. (374)

Other Trans. Equip. (373-375, 379, ex. 3795)

Ship Build & Repair (373)

Aircraft & Parts (372)

Motor Veh. & Equip. (371)

Trans. Equip. (37, 348)

Misc. Elec. Mach. & Equip. (369)

Elec. Components & Acc. (366-367)

Comm. Equip. & Elec. Comp. (365-367)

Radio & TV Equip. (365)

Elec. Lighting & Wiring Equip. (364)

Household Appliances (363)

Other Elec. Mach. & Equip. (363, 364, 369)

Elec. Industrial Apparatus (362)

Elec. Equip. (361-364, 369, 3825)

Elec. Transmission Equip. (361, 3825)

Elec. Mach. & Equip. (36, 3825)

Misc. Mach. (359)

Refrigeration & Service Industry Mach. (358)

Office Computing Mach. (357)

Gen. Industrial Mach. & Equip. (356)

Special Industry Mach. (355)

Other Mach. except Elect. (355, 356, 358, 359)

Metal Working Mach. & Equip. (354)

Const., Mining & Mat. Handling Mach. (353)

Farm & Garden Mach. & Equip. (352)

Engines & Turbines (351)

Mach., Except Elect. (35)

Motor Veh. & oth. Trans. (348, 371, 373-376, 379)

Ordnance Except Missiles (348, 3795)

Fabricated Metal Products (34 ex. 3462, 3463, 348)

Prim/Sec Non-Ferr. (333-336, 339 ex. 3399, 3463)

Prim. Ferrous Prod. (331, 332, 3399, 3462)

Primary Metals (33, 3462, 3463)

Stone, Clay, Glass and Concrete Products (32)

Industry

Patents (1,000s)
55

Appendix C 

Research Joint Ventures by SIC Code

Figure 18. Research Joint Venture Filings, by SIC Code

Source: CORE Database, 2000.

2

19

131

190

50

17

24

19

0

3

113

85

3

0

0

3

0

0

1

3

2

15

116

153

42

15

21

18

0

3

80

75

2

0

0

3

0

0

1

3

0 50 100 150 200

Misc. Manuf. Industries (39)

Instruments and Related Prod. (38)

Transportation Equip. (37)

Electrical and Electronic Equip. (36)

Machinery, Except Electrical (35)

Fabricated Metal Prod. (34)

Primary Metal Industries (33)

Stone, Clay, and Glass Prod. (32)

Leather and Leather Prod. (31)

Rubber and Misc. Plastics Prod. (30)

Petroleum and Coal Prod. (29)

Chemicals and Allied Prod. (28)

Printing and Publishing (27)

Paper and Allied Prod. (26)

Furniture and Fixtures (25)

Lumber and Wood Prod. (24)

Apparel and Other Textile Prod. (23)

Textile Mill Prod. (22)

Tobacco Manufactures (21)

Food and Kindred Prod. (20)

Industry

Number of RJV Filings

Whole Fractional

57

Appendix D 

REMI Forecast Detail

In Figure 19, the first bar in year 2007 shows that the conventional technology would reduce 

GDP by $1,120 million. The second bar shows that the FCM technology would reduce GDP by 

$660 million. Therefore, in market segment A, the FCM technology would save $460 million (= 

1,120 – 660), or would reduce the cost of meeting the regulation by 40% in terms of GDP 

compared with the conventional technology in 2007, the first year of analysis. Figure 20 shows 

the estimated reductions in employment. Using the FCM technology would save 5,826 jobs in 

2007 (jobs saved cannot be combined between years). Figure 21 shows the estimated reductions 

in personal income. Using the FCM technology would save $356 million in personal income in 

2007. Over the analysis period (2007 to 2009), total savings would be $261 million in GDP and 

$244 million in personal income. 

Figure 19. Market A (Class I SV): Estimated Reductions in GDP

Figure 20. Market A (Class I SV): Estimated Reductions in Employment 

253 109

660

169

1,120

392

$0

$250

$500

$750

$1,000

$1,250

2007 2008 2009

Year

2002 $s (millions)

Conventional Technology FCM Technology

14,170

1,094

2,875 1,906

4,688

8,344

0

5,000

10,000

15,000

2007 2008 2009

Year

Employment

Conventional Technology FCM Technology
58

Figure 21. Market A (Class I SV): Estimated Reductions in Personal Income 

The estimated reductions in GDP, employment, and personal income are then shown for market 

segment C in Figures 22, 23, and 24. In 2005, the year with the biggest impact from using FCM 

technology, $415 million in GDP would be saved, 5,594 jobs would be saved, and $350 million 

in personal income would be saved. Over the analysis period (2003 to 2007), total savings would 

be $982 million in GDP and $878 million in personal income. 

862

257 166

506

340

195

$0

$250

$500

$750

$1,000

2007 2008 2009

Year

2002 $s (millions)

Conventional Technology FCM Technology
59

Figure 22. Market C (Class II SV): Estimated Reductions in GDP

Figure 23. Market C (Class II SV): Estimated Reductions in Employment

Figure 24. Market C (Class II SV): Estimated Reductions in Personal Income

190

376

446

11 27 31 22 18 1 0 $0

$100

$200

$300

$400

$500

2003 2004 2005 2006 2007

Year

2002 $s (millions)

Conventional Technology FCM Technology

2,734

6,047 5,188

16 16 141 16 0

391 453

0

2,000

4,000

6,000

8,000

2003 2004 2005 2006 2007

Year

Employment

Conventional Technology FCM Technology

144

293

377

67 62 27 8 20 6 4

$0

$100

$200

$300

$400

2003 2004 2005 2006 2007

Year

2002 $s (millions)

Conventional Technology FCM Technology

ABOUT THE ADVANCED TECHNOLOGY PROGRAM 

The Advanced Technology Program (ATP) is a partnership between government and private industry to conduct 

high-risk research to develop enabling technologies that promise significant commercial payoffs and widespread 

benefits for the economy. The ATP provides a mechanism for industry to extend its technological reach and push the 

envelope beyond what it otherwise would attempt. 

Promising future technologies are the domain of ATP: 

• Enabling technologies that are essential to the development of future new and substantially improved 

• projects, processes, and services across diverse application areas 

• Technologies for which there are challenging technical issues standing in the way of success 

• Technologies where the development often involves complex “systems” problems requiring a collaborative 

• effort by multiple organizations 

• Technologies that will go undeveloped and/or proceed too slowly to be competitive in global markets 

without ATP 

The ATP funds technical research, but it does not fund product development. That is the domain of the company 

partners. The ATP is industry driven, and that keeps it grounded in real-world needs. For-profit companies conceive, 

propose, co-fund, and execute all of the projects cost-shared by ATP. Smaller companies working on single 

company projects pay a minimum of all the indirect costs associated with the project. Large, Fortune 500 companies 

participating as a single firm pay at least 60% of total project costs. Joint ventures pay at least half of total project 

costs. Single company projects can last up to three years, and joint venture projects can last as long as five years. 

Companies of all sizes participate in ATP-funded projects. To date, more than half of the ATP awards have gone to 

individual small businesses or to joint ventures led by a small business. 

Each project has specific goals, funding allocations, and completion dates established at the outset. Projects are 

monitored and can be terminated for cause before completion. All projects are selected in rigorous competitions that 

use peer review to identify those that score highest against technical and economic criteria. Contact ATP for more 

information: 

• On the Internet: www.atp.nist.gov 

• By e-mail: atp@nist.gov 

• By phone: 1-800-ATP-FUND (1-800-287-3863) 

• By writing: Advanced Technology Program, National Institute of Standards and Technology, 

100 Bureau Drive, Stop 4701, Gaithersburg, MD 20899-4701 

ABOUT THE AUTHORS 

Hayden Brown is an economist in the Office of Applied Economics at NIST. He has conducted economic analyses 

of the assistance provided by Manufacturing Extension Partnership (MEP) Centers, and is currently studying the 

selection of firefighting protective gear as well as exterior housing elements, based on multiple attributes. Dr. Brown 

received his Ph.D. in economics from the University of Kentucky. 

Mark A. Ehlen served as an industrial economist in the Office of Applied Economics at NIST from June 1995 to 

February 2002. During that time, he conducted studies on the economics of automotive, manufacturing, and 

construction technologies. Dr. Ehlen received his Ph.D. in economics from Cornell University. 

The Office of Applied Economics, part of the Building and Fire Research Laboratory at NIST, provides economic 

research and consulting to industry and government agencies in support of productivity enhancement, economic 

growth, and international competitiveness.