Program Design and Firm Success in the 

Advanced Technology Program: 

Project Structure and Innovation Outcomes

By

Michael R. Darby

University of California, Los Angeles 

Los Angeles, CA 90095

National Bureau of Economic Research

Cambridge, MA 02138

Lynne G. Zucker

University of California, Los Angeles

Los Angeles, CA 90095

National Bureau of Economic Research

Cambridge, MA 02138

Andrew J. Wang

Advanced Technology Program

National Institute of Standards and Technology

Gaithersburg, MD 20899

December 2002

U.S. DEPARTMENT OF COMMERCE

Donald L. Evans, Secretary

TECHNOLOGY ADMINISTRATION

Phillip J. Bond, Under Secretary of Commerce for Technology

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

Arden L. Bement, Jr., Director

UNITED STATES OF AMERICA

DEPARTMENT OF COMMERCE

NISTIR 6943

ABSTRACT

iii

Abstract

This study finds that the Advanced Technology Program (ATP) has a positive

effect on innovation in firms. Participation in ATP projects increases firms’

patenting, relative to patenting by the same firms prior to the ATP award. For

firms analyzed in this study, covering ATP award years 1990-1995, patenting

increased on average by between 5 and 30 patents each year during the period of

ATP project participation. Furthermore, both joint venture project participation

and university participation in a project appear to have a positive impact on firm

patenting. Joint ventures or alliances between firms are expected to have a

positive effect on innovation outcomes through the sharing of research capabilities

and expertise, and through increased networking and learning. University

collaborations are expected to have a positive impact on innovation due to

sharing of knowledge and expertise. 
Acknowledgments

We thank the University of California Industry-University Cooperative Research

Program, especially its director, Dr. Susanne L. Huttner, for providing support to

obtain licenses to limited-use data, and to develop a “fuzzy matching” algorithm.

The support provided for the data at the Institute on Primary Resources (IPR) at

UCLA, and allowed us to match ATP participant companies to the data archive.

We thank Adam Jaffe for making the Case Western Reserve/NBER patent data

through 1996 available to us. We are indebted to a remarkably talented UCLA

research team, especially David Johnson, Wenjin Kang, Qiao Liu, David

Waguespack, and Xiaogang Wu, and also Stephanie Hwang, Andrew Jing, Henry

Tang, and Mo Xiao. We also acknowledge helpful comments and assistance from

NIST colleagues John Hewes, Dennis Leber, John Nail, Stephanie Shipp, and

Connie Chang in reviewing and producing the final report. 

Program Design and Firm Success in the ATP

iv
Executive Summary

In evaluating the Advanced Technology Program, an important indicator of

program success is increased innovation by participant firms. In this study, we

measure innovation outcomes by successfully granted patents. First, we measure

the overall change in successful patent applications by participant firms during the

period of ATP support. Second, we investigate what might explain differing

impacts of the ATP on firm success; in particular, we consider the effect of

program design. The ATP makes awards for two types of programs: joint venture

(JV) projects involving explicit collaboration between two or more firms (and

possibly additional organizations such as universities and federal laboratories);

and single-firm (SF) projects proposed by individual companies. This study

examines the effects of both program structure and university participation on

firms. 

Besides providing funding awards to participants, the ATP promotes new

organizational structures that facilitate innovation. First, the ATP supports firms

willing to pursue novel approaches at the technological frontier, and encourages

industry to initiate higher-risk projects with greater potential for broader

economic impact. Second, the ATP encourages cooperation and collaboration in

R&D activities, among JV partners, and through subcontracting relationships

with universities, firms, and other organizations. When selecting, monitoring, and

reviewing projects, the ATP emphasizes linkages that are key to innovation and

technology transfer among firms and organizations. Because ATP project

participation enhances firms’ social network for R&D, we also expect that the

impact on innovation outcomes will extend beyond the project level to the firm

level. Therefore, we assess the impact of the ATP in before-and-after comparisons

of innovation outcomes at the firm level.

The sociological view of the ATP’s role in supporting innovation emphasizes the

changes in social relationships and behavior that result from firms working

together in R&D. Close contact among researchers in collaborative R&D is most

likely to transmit new knowledge, often tacit in nature, that is close to the R&D

frontier. Relaxation of boundaries around firms increases the flow of information

and hence learning across organizations. Institutional structures such as

agreements and monitoring can be developed to support trust between

organizations, which leads to greater confidence in successful coordination.

EXECUTIVE SUMMARY

v
This study conducts a statistical analysis of patenting by firms before and after

ATP project participation in order to assess innovation outcomes. The general

finding from the analysis is that the ATP has a positive effect on innovation in

firms. Participation in ATP projects increases firms’ patenting, relative to

patenting by the same firms prior to the ATP award. For firms analyzed in this

study, covering ATP award years 1990–1995, patenting increased on average by

between 5 and 30 patents each year during the period of ATP project

participation. Furthermore, the findings of this study support the idea that both

joint ventures and university collaboration have a positive impact on innovation,

as measured by increases in firm patenting.

Program Design and Firm Success in the ATP

vi
vii

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iii

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iv

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .v

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

2. ATP Program Design as Institution-Building . . . . . . . . . . . . . . . . . . . . . . .2

3. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

3.1. Sampling Criteria and Panel Design . . . . . . . . . . . . . . . . . . . . . . . . .8

3.2. Variable Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

4. Empirical Results: The ATP’s Effects on Firm Success . . . . . . . . . . . . . . .12

4.1. Effects of ATP Participation on Patenting . . . . . . . . . . . . . . . . . . . .12

4.2. Separate Effects of Participation and Funding Amount . . . . . . . . . .12

5. Conclusions and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

About the Advanced Technology Program . . . . . . . . . . . . . . . . .inside back cover

About the Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .inside back cover

List of Tables

Table 1. Firms in ATP Projects (1990–1995), Panel Dataset . . . . . . . . . . . . . .9

Table 2. Firms in ATP Projects (1990–1995), by Size . . . . . . . . . . . . . . . . . . .9

Table 3. Descriptive Statistics for Regression Sample of ATP Firms . . . . . . . .11

Table 4. Patenting by All Firms: Type of ATP Project Participation—

OLS Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Table 5. Patenting by All Firms: Intensity of ATP Project Participation—

OLS Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Table 6. Patenting by All Firms: Intensity of ATP Project Participation—

Fixed Effects Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

Contents
viii

Program Design and Firm Success in the ATP

Table 7. Patenting by Public Firms: Intensity of ATP Project Participation—

OLS Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

Table 8. Patenting by Public Firms: Intensity of ATP Project Participation—

Fixed Effects Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

List of Figures

Figure 1. Number of Projects and Firm Participants, and Cumulative Number 

of Unique Firm Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Figure 2. ATP Projects and Participants, by Single Firm and Joint Venture Type,

1990–1998 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Figure 3. Distribution of Firms in ATP Projects (1990–1995), by Type of

University Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Figure 4. Estimated Increase in Patents per Year, by Type of Participation . . . .16

Figure 5. Estimated Increase in Patents per Year, by Type of University Role . .16
INTRODUCTION

1

1. Introduction

The Advanced Technology Program (ATP) at the National Institute for Standards

and Technology (NIST) funds the development of enabling technologies that firms

are not likely to pursue in a timely way without the ATP. The role of ATP is to

bridge the gap from demonstrating a promising but risky idea to garnering the

organizational and financial resources to develop and commercialize a product or

process. In doing so, ATP increases the prospect of commercial introduction of

advanced technology. The ATP made its first awards in 1990, based on peerreviewed proposals submitted by either individual firms or joint ventures of two

or more collaborating firms. Over its twelve-year history, ATP has made over 600

awards involving well over 1,000 participants and subcontractors.

A key element of program success for the ATP is that it contributes to innovation

success of participant firms: If participant firms do not innovate and benefit from

new technology, others are less likely to benefit from new technology. Hence, as a

first step in evaluating program success, we search for evidence of ATP’s overall

impact on firm success. Our second step in evaluation is to investigate what might

explain differing impact on firm success; in particular, we consider the effect of

program design. The ATP makes awards for two types of programs: projects that

explicitly involve collaboration between two or more firms (and possibly

additional organizations such as universities and federal laboratories); and

projects proposed by individual firms that include no formal collaborative

framework. The former we term as joint venture or JV projects, and the latter we

term as single firm or SF projects. Our study examines effects on firms’ outcomes

related to these differing project structures, as well as effects related to

participation by universities (as a full member in a JV, or as a subcontractor in

either an SF or JV project).

We evaluate the ATP’s effects in terms of overall change in successful patent

applications before and during the period of ATP support. Patents are a useful

measure of innovation for all ATP participants, including small, privately held

firms, larger public firms, universities, and other research organizations. From

1988 to 1996, ATP participant firms and organizations accounted for over 40

percent of all patents granted to U.S. entities by the U.S. Patent and Trademark

Office (USPTO). Public and private firms comprise 88 percent of all ATP

participants, and account for over 80 percent of patents awarded to all ATP

participants. Patenting of innovations in advanced technology appears to be

important to most firms and organizations participating in the ATP.
In our view, the ATP not only provides funding awards, but also fosters

“institution-building” by promoting new organizational structures among

applicants and participants which help to facilitate innovation—which is the

capture of inventions in technologically advanced commercial products.

Institution-building takes place through the ATP in a number of ways. First, the

ATP supports firms willing to experiment and develop approaches that are novel

and at the technological frontier. Industry is thereby stimulated to initiate

projects that are higher in technical risk and have greater potential for broader

economic impact. Second, ATP encourages cooperation and collaboration in

R&D activities, among JV partners, and through subcontracting relationships

with universities, firms, and other organizations. When selecting, monitoring, and

reviewing projects, the ATP emphasizes linkages that are key to innovation and

technology transfer among firms and organizations.

In social science terminology, ATP project participation changes participants’

“social embeddedness” in networks of relations with other firms and

organizations. While this effect may be especially prominent for joint venture

participants, firms in single firm projects also note the importance of R&D

subcontractors and relationships for achieving project objectives. Because ATP

project participation enhances firms’ social network for R&D, we also expect that

the impact on innovation outcomes will extend beyond the project level to the

firm level. Therefore, we assess the impact of the ATP in before-and-after

comparisons of innovation outcomes at the firm level.

2. ATP Program Design as Institution-Building

The “social embeddedness” perspective on economic behavior of individuals and

firms emphasizes the social context and interactions of economic actors. In

traditional economic theory, economic behavior is analyzed in terms of rational

choice and utility-maximizing individuals or profit-maximizing firms, and

relatively little emphasis is placed on specific historical and social contexts.

Sociologists, on the other hand, have emphasized the importance of understanding

how specific social relationships shape economic behavior and economic

outcomes. Economic behavior is embedded in a social context, and the

characteristics of particular social relations affect economic behavior and

determine economic outcomes (Granovetter 1985). When disputes arise in

business, they are “frequently settled without reference to the contract or

potential or actual legal sanctions” (Macaulay 1963: 61). Instead, personal

relationships based on cooperation and trust are important to solving problems

Program Design and Firm Success in the ATP

2
and reaching agreements. Similarly, subcontracting or sales and purchasing

decisions are often made on the basis of long-term relationships and ongoing

social interactions or networks.

When the ATP makes an award and funds a project, the participating firms and

other organizations establish R&D and business ties, thus extending and

enhancing their social network for innovation. By fostering organizational

interactions, the ATP helps build and further develop the institutional basis for

innovation. From their networks, firms gain access to knowledge and

complementary expertise of R&D partners, as well as business and marketing

resources of partner firms. The social-embeddedness perspective on R&D and

innovation emphasizes that ATP project participation is an active institutionbuilding process. Firms partner with other firms and organizations, with support

from the ATP, in order to establish R&D arrangements that are favorable to highrisk research, and conducive to socially beneficial modes of behavior such as

research cooperation and information sharing. 

We highlight below a few aspects of social embeddedness—social relations that

structure economic behavior and outcomes—that are particularly important

aspects of ATP’s institution-building:

• Close collaboration among researchers in R&D work. Such relationships are

most likely to transmit new knowledge, often tacit in nature, which is close to

the knowledge frontier (Zucker et al. 1998a).

• Relaxation of boundaries around the firm. Cross-firm interaction and

knowledge transfer is accepted because information gains are expected to be

sufficiently valuable to offset any losses of proprietary information (Zucker et

al. 1996). Boundary permeability allows more flow of information and hence

more learning across organizations than would otherwise be the case. Boundary

design is often part of the strategic arsenal of a firm (Helper et al. 1998).

• Development of institution-based trust. Trust between organizations can be

developed on the basis of institutional structures rather than interpersonal or

specific characteristics of the other party (Zucker 1986). Two examples of

institution-based trust fostered by the ATP include: (1) Third-party (ATP)

monitoring of participants’ behavior in joint ventures to ensure cooperation (see

Zucker et al. 1996); and (2) Administrative structures and agreements (such as

intellectual property agreements and JV administrative structures) to increase

confidence in successful coordination (see Das and Teng 1998).

We believe that the implicit design of the ATP fosters firm behaviors that are

socially beneficial and in the collective interest, in particular encouraging firms to

collaborate and share knowledge. Sociology theory argues that actors contribute

ATP PROGRAM DESIGN AS INSTITUTION-BUILDING

3
to a collective good when two conditions are met: first, their action is likely to

have efficacy—that is, be effective; and second, there are norms of fairness that

encourage them to match the contributions of others (Gould 1993). The ATP

provides an institutional structure that makes efficacy and fairness more likely.

First, ATP projects define and structure the collective benefits expected from

research collaboration. Second, ATP project participation builds experience with

R&D collaboration, and hence norms for collaborative work. Firms participating

in ATP benefit not only from the R&D project per se, but also from the R&D

experience. In other words, firms benefit from “technology learning,” which

relates to the actual R&D work, and also from “organizational learning,” which

relates to the organizational processes for initiating and managing R&D. 

ATP institution-building is also evident in the ATP guidelines for structuring

projects and partners to maximize research synergy. For example, ATP encourages

including a variety of different types of JV partners in order to further prospects

for R&D success and technology commercialization and diffusion: “Joint ventures

should aim to include companies of diverse size, including smaller companies, and

possibly other organizations, such as universities and national laboratories” (ATP

1999: 34). Of particular interest is that many ATP projects involve universities.

Universities are often at the center of new discoveries, especially discoveries that

represent radical change from prior knowledge (see Jaffe 1989; Liebeskind et al.

1996; Zucker and Darby 1996; Zucker et al. 1998). As such, university

relationships may be important to many ATP projects.

The ATP has an express mission of encouraging collaborations among firms, and

between firms, universities, and other federal and non-profit research

organizations that make up the innovation network in the United States. The ATP

supports JVs with project awards that include potentially higher award levels over

more years of funding than SF projects. The ATP requires that JVs establish an

agreement and governance structures for management of the JV. The ATP also

encourages knowledge sharing among JV members. In effect, JVs tend to relax the

boundaries of participants’ organizations. Joint governance, collaborative R&D

work, and joint access to intellectual property created within the JV all have the

effect of opening up boundaries. Knowledge “spillovers” or transfers of

knowledge to other JV members occur within the enlarged “information

envelope” that encompasses the JV (Zucker et al. 1996). In particular, internal

task routines that are especially difficult to observe from outside of organizations

may be transferred among members within the expanded boundaries (see Nelson

and Winter 1982: 123–124).

Expanding the effective boundaries of organizations to encompass new research

collaborations results in greater information sharing by two means. Firstly, in JVs,

Program Design and Firm Success in the ATP

4
knowledge created by one participant organization becomes more observable to

other participants—internal task routines that are often unobservable across

organizational boundaries become transparent through joint work among

scientists, engineers, and other technically trained workers. And secondly, JV

collaboration defines a new “commons,” an area of mutual benefit around the

shared project, which draws in additional shared resources and establishes

additional collaboration and information sharing.

Our thesis is that JV participants operate in—are “embedded in”—a different

social context as a consequence of participating in a JV through an ATP award.

The JV participant organizations have changed social relationships (more contacts

and different contacts). These relationships provide an expanded information

network, and increased knowledge transfer through learning processes (Doz 1996;

Hamel 1991).

The expanded JV learning context includes firms, universities, federal labs, and

organizations outside of the JV—other organizations that the JV partners

collaborate with or are linked to in some way. These connections multiply access

to other kinds of knowledge, which provides additional expanded information

advantage, an example of the “strength of weak ties” in social networks

(Granovetter 1973). JVs organized in order to apply for ATP funding often bring

together firms that have not worked together before. Even more, each JV partner

brings contacts that are new to the other JV members.

Comments by ATP participants in JVs support the idea that collaboration

expands knowledge transfer among different organizations, and expands contacts

across a wider network of potential partners. As one JV participant states: 

Excellent collaborative environment and complementary technical capabilities

have improved the quality of technical output and effectiveness of the team.

There has been tremendous synergy between the companies that are

collaborating on this project. Each company brings a particular expertise that

the others don’t have and which would be difficult to develop. Each party is an

enabler for the others. (Powell and Lellock 2000: 23) 

As another JV participant indicates: 

In general, the collaboration has allowed us to contact new potential

collaborators and markets. Some of these markets are for new equipment using

our technology in ways we had not considered. Due to the success of the JV,

the various members are investigating projects outside the ATP. (Powell and

Lellock 2000: 25)

ATP PROGRAM DESIGN AS INSTITUTION-BUILDING

5
3. Methods

Institution-building by the ATP and the resulting organizational and

informational advantages held by ATP participants are factors that enter into the

innovation process in ATP projects. How do we best measure the impact of ATP

participation? To assess changes in organizational learning and knowledge

through ATP projects, we will study and compare change in innovation outcomes

before and after ATP participation. A major purpose of the ATP is to increase

commercial introduction of advanced technology by accelerating the development

of high-risk, enabling technologies. Patents, in representing an active business

decision to protect commercially valuable inventions, are arguably the single best

proxy measure of innovation. Patents are in fact commonly used to protect

intellectual property created with ATP support: 76 percent of organizations report

that patenting is a primary or secondary strategy for protecting intellectual

property, with only 12 percent reporting that patenting is unlikely (Powell and

Lellock 2000: 43).

In the analysis to follow, we assess whether ATP projects have a more general

effect on formation of new intellectual property within the firm as a whole. An

ATP project may represent only one R&D effort among many at a firm; but to

the extent that ATP involvement changes firm behavior, institutional settings, or

social embeddedness, the impact of the ATP project may extend beyond the

project to affect the firm more generally. Our key indicator of impact on firm

innovation is whether the overall rate of patenting by a firm increases after

participation in ATP projects begins. We focus on project structure (joint venture

vs. single firm), and university participation (university partner in a JV project, and

university subcontractor in a JV or SF project). We use a patent count measure

based on archival data assembled by the National Bureau of Economic Research

(NBER) and Case Western University, and significantly augmented by the Center

for International Science, Technology, and Cultural Policy (CISTCP) at UCLA.

Our first step is to set the unit of analysis. Archival data on patents are generally

available only for the firm or organization as a whole, and not for specific

locations of multi-location firms. Our analysis of whether participation in the

ATP has a positive effect on firms is therefore centered on the firm/organization as

the basic unit of analysis. Figure 1 shows the number of ATP projects and firm

participants from ATP award years 1990–1998. Figure 2 shows the distribution

by SF or JV type for projects and all participants, from ATP award years

1990–1998. The number of single firm projects is about twice the number of joint

venture projects. But because joint ventures involve multiple participants, the

number of JV participants is more than twice the number of SF participants.

Program Design and Firm Success in the ATP

6
METHODS

7

Figure 1. Number of Projects and Firm Participants, and Cumulative Number of

Unique Firm Participants

0

50

100

150

200

250

300

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

0

100

200

300

400

500

600

Single firm projects

JV projects

JV firm participants

Cumulative unique firms

(right axis)

Number of projects

and firm participants

Cumulative number of

unique firm participants

Figure 2. ATP Projects and Participants, by Single Firm and Joint Venture Type,

1990–1998

274

134

274

663

431

225

0

100

200

300

400

500

600

700

Single firm

projects

Joint venture

projects

Single firm

projects

Joint venture

projects

Number of participants

Total

Unique organizations

Number of projects
Some firms/organizations have participated in more than one ATP project, and

some have been in both JV and SF projects. So Figures 1 and 2 also present the

total number of “unique” firms or organizations, which counts each

firm/organization only once, even if it has been a participant in more than one

ATP project.

3.1. Sampling Criteria and Panel Design

Patenting by ATP awardee firms is tracked before, during, and after they become

ATP participants, allowing us to assess patenting performance for periods with

and without ATP support. In this way, an ATP participant can serve as its own

comparison group. For our analysis we include all firms conducting R&D in

projects that started by the end of 1995. (We exclude participants involved only

in administrative functions, and participants involved only in projects cancelled

before completion.) Firms enter our analysis panel in the year the firm was

founded, or in the first year of our panel, 1988, if the firm was founded before

1988. We chose 1988 as the first year for the panel to allow for pre-ATP

observation years, even for firms entering in the first ATP cohort (1991). The

panel ends in 1996 because number of patents dated by year of application is our

key variable of interest. By 1997 the count of patents by year of application

becomes truncated, because many patent applications from 1997 have yet to emerge

from the patent process, given that our data on patents granted ends June 30, 1999.

Table 1 presents the panel structure for two samples of firms. In order to match

to patent data, multiple establishments of the same firm are counted as one unit,

even though different locations of a firm may be participating at different times in

the ATP. The first sample is all firms that have participated in the ATP, and the

second sample is publicly traded firms that have participated in the ATP. New

entrants to the panel, in years other than the first year of the panel, are due to

founding of a new firm. Table 2 shows the distribution of firms by size category.

We establish a hierarchy to define firms as joint venture or single firm, with or

without university partner:

• If a firm has been a full partner in a JV project from ATP award years

1990–1995, then it is considered to be a “JV firm.”

❍ JV firms that have had both a university partner and a university

subcontractor in this period are defined as “JV firm—university partner and

subcontractor.”

❍ JV firms that have had only a university partner or only a university

subcontractor are defined as “JV firm—university partner” or “JV firm—

university subcontractor.”

Program Design and Firm Success in the ATP

8
❍ The remaining JV firms are defined as “JV firm—no university.”

• Single-participant firms are classified as either “Single firm—university

subcontractor” or “Single firm—no university.”

Figure 3 shows that nearly three quarters of all firms in ATP projects have had

university partners or subcontractors.

3.2. Variable Construction

Our analysis of ATP impact is based on measurement of changes in patenting

success by firms during and after participation in the ATP. The overall rate of

patenting depends on the “propensity to patent,” which is affected by the value of

METHODS

9

Table 1. Firms in ATP Projects (1990–1995), Panel Dataset

A. All Firms in ATP Projects

Year 1988 1989 1990 1991 1992 1993 1994 1995 1996

Total firms 232 245 260 274 285 349 349 350 350

Active in ATP 0 0 0 20 71 90 117 341 319

Not active in ATP 232 245 260 254 214 259 232 9 31

B. Public Firms in ATP Projects*

Year 1988 1989 1990 1991 1992 1993 1994 1995 1996

Total firms 93 96 99 108 116 122 131 151 151

Active in ATP 0 0 0 6 36 45 56 149 137

Not active in ATP 93 96 99 102 80 77 75 2 14

*Public firms are defined as firms appearing in the Standard & Poor’s Compustat database of

publicly traded firms.

Table 2. Firms in ATP Projects (1990–1995), by Size

All firms Public firms

N=350 N=158

Freq. % Freq. %

Small 195 56 57 36

Medium 88 25 47 30

Large 67 19 54 34

Small = fewer than 500 employees

Medium = 500 employees or greater, but not Fortune 500 firm

Large = Fortune 500 firm
getting a patent and the ease of obtaining a patent (Griliches 1990). In recent

years, the U.S. Congress and the courts have strengthened patent rights, and the

U.S. Patent and Trademark Office has hired more patent examiners. As a result,

both the rate of patent application and the speed with which patents are granted

have increased. A simple before-and-after comparison of patenting is therefore

subject to the criticism that it reflects trend increases in patenting rather than

identifying real program impact. Accordingly, we develop a “deflated” patentcount measure, which adjusts for year-to-year changes in the average rate of

patenting, measured by average number of “patents per assignee” for all U.S.

assignees of U.S. patents.

All dollar amounts (that is, ATP award amounts and company cost-share

amounts, as well as R&D expenditures for public firms) are deflated to 1996

dollars using the GDP deflator. We construct an R&D stock variable to measure

the cumulated “R&D capital” of the firm. Annual R&D expenditures are

available for public firms from the Standard and Poor’s Compustat database of

publicly traded firms. Annual R&D expenditures are cumulated and discounted

to produce the R&D stock variable.

Table 3 provides descriptive statistics for the variables used in the empirical

analysis for three groups of ATP participants: (1) All firm participants; (2) public

Program Design and Firm Success in the ATP

10

Figure 3. Distribution of Firms in ATP Projects (1990–1995), by Type of

University Participation

All Firm Participants

Single firm - university 

subcontractor

20%

Single firm - 

no university

11%

JV firm - no university

16%

JV firm - university 

subcontractor

24%

JV firm - university 

partner

8%

JV firm - university 

partner and 

subcontractor

21%
firm participants, defined as firms appearing in the Compustat database; and (3)

all organization participants, including universities and other nonprofits in JV

projects. The firm size categories are based on ATP definitions of firm size (as

shown in Table 2). The technology area categories are based on ATP definitions

of the technology area of the ATP project. 

METHODS

11

Table 3. Descriptive Statistics for Regression Sample of ATP Firms

All firms Public firms

N=2694 N=1067

Variable Mean S. D. Mean S. D.

DEPENDENT VARIABLE

Patents, deflated a 39.35 141.83 87.37 206.93

ATP PARTICIPATION INDICATORS

ATP participant b 0.29 0.42 0.33 0.44

ATP JV participant b 0.20 0.37 0.26 0.42

JV with university partner b 0.08 0.26 0.12 0.31

JV with university subcontractor b 0.11 0.29 0.14 0.32

SF with university subcontractor b 0.07 0.24 0.08 0.25

Cumulative ATP award stock ($000s) c 272.39 748.74 389.78 1,020.57

Cumulative ATP JV award stock ($000s) c 132.59 543.65 229.55 773.68

FIRM CHARACTERISTICS

Small firm d 0.50 0.50 0.25 0.44

Medium firm d 0.28 0.45 0.32 0.47

Large firm d 0.22 0.41 0.43 0.49

Biotechnology e 0.13 0.32 0.11 0.30

Chemicals e 0.07 0.24 0.11 0.28

Electronics e 0.14 0.32 0.14 0.30

Energy e 0.04 0.18 0.05 0.19

Information technology e 0.23 0.41 0.21 0.39

Manufacturing e 0.22 0.40 0.20 0.37

Materials e 0.17 0.35 0.18 0.34

Cumulative R&D stock ($millions) a — — 1,759.78 4,605.06

Notes:

a. Continuous variable for firm-year.

b. Numerical fraction variable for firm-year: (Number of months during year where participation

indicator is True)/12.

c. Continuous variable for firm-year: Sum of monthly pro-rated award amount for firm in year.

d. Indicator variable for firm.

e. Numerical fraction variable for firm-year: Variable defined by technology area of ATP project,

and is numerical fraction when firm is in more than one project and technology area of projects

differ.
4. Empirical Results: The ATP’s Effects on Firm

Success

4.1. Effects of ATP Participation on Patenting

Our basic hypotheses concern the effects of ATP participation on patenting. First,

we hypothesize that participation in ATP projects has a positive effect on

patenting at the firm level; that is, that the benefit of ATP project participation

extends beyond the project to the firm level. Second, we hypothesize that

participation in JV projects provides greater benefit to firms than participation in

SF projects, so we expect the ATP participation effect on firm patenting to be

greater for firms in JVs. The argument is that JV membership expands and

deepens connections among organizations, which is “social capital” for firm

innovation. Third, we hypothesize that the effect of ATP participation on

innovation, as measured by firm patenting, is greater if the firm has a university

partner or subcontractor. This hypothesis derives from studies that have shown

the importance of academia to science-driven industries (Jensen and Thursby

2000; Thursby and Thursby 2000; Zucker and Darby 1996, 1998; Zuckeret al.

1998a, 1998b).

We now turn to our main analysis of the panel of all firms that began

participation in the ATP by the end of 1995. In Table 4, we control for firm size

and project technology area, and then include dummy variables that describe

firms’ type of ATP participation in each year. In regression 3.1, we see that ATP

participation is associated with an increase of 29 in number of patents awarded to

the firm—nearly a 75 percent increase in patenting relative to the mean number

of patents per year for firms in the sample. In regression 3.2, we see that ATP JV

project participation has a positive effect relative to SF project participation. In

regression 3.3, we see that joint ventures with a university partner receive an

additional positive effect on patenting, and in regression 3.4, we see that

university subcontractors have a positive effect on firm patenting. We conclude

that JV participation and university participation are important to higher rates of

patenting by firms in ATP projects.

4.2. Separate Effects of Participation and Funding Amount

We can extend our analysis by taking into account the total amount of ATP

award funds received, and also the amount received through JV project awards.

In this case, the degree or extent of ATP participation (or the intensity of the ATP

“treatment effect”) is indicated by the amount of ATP award funding received by

the firm. Following typical practice in constructing measures of capital, we

Program Design and Firm Success in the ATP

12
cumulate these funds over time, incorporating a depreciation rate of 20 percent

per year, to create an ATP award stock variable. For firms that have participated

only in JV projects, the total award stock and JV award stock variables will be

equal, while for firms that have participated in both SF and JV projects, the total

award stock variable will sometimes be greater than the JV award stock variable.

The measured effect on patenting of an additional dollar of SF award funding is

equal to the coefficient on the total award stock variable, while the measured

EMPIRICAL RESULTS: THE ATP’S EFFECTS ON FIRM SUCCESS

13

Table 4. Patenting by All Firms: Type of ATP Project Participation—

OLS Regression

Dependent variable Patents, by date of application (deflated; one year lag)

Specification 3.1 3.2 3.3 3.4

Estimation OLS OLS OLS OLS

Constant –50.718*** 50.561*** –48.298*** –50.300***

(10.936) (10.896) (10.897) (10.753)

Small –13.225* –10.952 –10.303 –9.924

(5.770) (5.770) (5.762) (5.681)

Large 156.503*** 154.309*** 153.634*** 149.691***

(7.021) (7.011) (7.001) (6.916)

Biotechnology 55.545*** 57.723*** 53.647*** 59.946***

(12.382) (12.345) (12.384) (12.233)

Electronics 99.215*** 98.965*** 99.200*** 102.851***

(12.176) (12.130) (12.108) (12.023)

Energy 50.194** 54.170*** 53.145*** 52.123***

(16.643) (16.604) (16.576) (16.350)

Information technology 58.400*** 58.643*** 54.854*** 59.365***

(11.095) (11.054) (11.093) (10.954)

Manufacturing 41.138*** 37.514*** 34.262** 35.801***

(11.180) (11.166) (11.189) (11.031)

Materials 50.952*** 49.001*** 47.263*** 48.703***

(11.831) (11.795) (11.785) (11.628)

ATP participant 28.867*** –4.844 –4.980 –70.538***

(5.701) (9.279) (9.262) (12.422)

ATP JV participant 48.334*** 31.871** 68.127***

(10.520) (11.625) (15.131)

JV with university partner 38.438*** 26.327*

(11.645) (11.579)

JV with university subcontractor 47.057***

(11.545)

SF with university subcontractor 101.406***

(13.210)

Adjusted R-squared 0.239*** 0.245*** 0.247*** 0.269***

N 2694 2694 2694 2694

Significance levels: *p ≤ .05, **p ≤ .01, ***p ≤ .001
effect on patenting of an additional dollar of JV award funding is equal to the

sum of the coefficients on the total award stock and JV award stock variables. 

Table 5 reports results for regressions that include firm size and technology area

controls, ATP participation and JV participation variables, ATP award stock and

JV award stock variables, and several university involvement variables.

Interpreting the regression results is somewhat complicated. First, estimates for

the effect of ATP participation on patenting must be presented by specific

category of participant (such as JV with university partner) at the sample mean

for the category. Second, because the ATP award stock variable is a stock

variable, the effect of participation persists beyond the period of active

participation. In Figures 4 and 5, the left bar in each pair presents a conservative

interpretation of regression 4.4 by showing the estimated increase in patenting

within the sample period for the indicated groups. (The estimates of patenting

increases per year of participation are computed by multiplying the relevant

coefficients for ATP participation and award stocks by the sample means for each

of the specified groups, summing the results, and dividing by the mean number of

years of participation.) The estimate is conservative in that only about one-third

of the full effect from the award-stock variables occurs within the sample period.

Even under this conservative approach, we estimate that the average ATP

participant firm increases its patenting by 34 patents per year of ATP

participation during the sample period. Thus, even without allowing for the

future effects of knowledge created under the ATP program, we find a very

substantial effect on patenting attributable to ATP participation. There is also

evidence that the effect on patenting is greater for those firms that partner with

universities during their ATP participation.

Table 6 and the right bar in each pair in Figures 4 and 5 present results from

similar regressions with fixed effects for each firm to control for all unobserved

firm-specific heterogeneity. We find an average increase in patenting by 6 patents

per year of ATP participation during the sample period. 

Tables 7 and 8 present results from similar regressions for the subsample of firms

that are publicly traded. For these firms we have data to compute a cumulated

R&D stock variable in the same way as the ATP award stock variable. For these

publicly traded firms, the regular and fixed-effect regressions produce estimates of

increase in patents by 19 patents and 5 patents per year of ATP participation

during the sample period.

Program Design and Firm Success in the ATP

14
EMPIRICAL RESULTS: THE ATP’S EFFECTS ON FIRM SUCCESS

15

Table 5. Patenting by All Firms: Intensity of ATP Project Participation—

OLS Regression

Dependent variable Patents, by date of application (deflated; one year lag)

Specification 4.1 4.2 4.3 4.4

Estimation OLS OLS OLS OLS

Constant –46.904*** –41.265*** –40.674*** –40.404***

(10.960) (10.511) (10.399) (10.399)

Small –11.907* –14.556** –12.329* –11.577*

(5.790) (5.536) (5.498) (5.483)

Large 159.125*** 143.168*** 139.682*** 138.768***

(7.034) (6.792) (6.740) (6.720)

Biotechnology 59.906*** 46.164*** 48.746*** 48.063***

(12.408) (11.895) (11.776) (11.835)

Electronics 104.810*** 79.819*** 77.738*** 82.578***

(12.181) (11.750) (11.629) (11.689)

Energy 53.224*** 38.722* 42.009** 42.248**

(16.709) (15.985) (15.836) (15.789)

Information technology 61.732*** 52.236*** 51.705*** 50.649***

(11.127) (10.652) (10.540) (10.587)

Manufacturing 44.145*** 40.375*** 35.867*** 33.421**

(11.215) (10.726) (10.639) (10.644)

Materials 52.779*** 45.644*** 42.447*** 42.398***

(11.880) (11.356) (11.244) (11.228)

ATP participant –20.843*** –95.280*** –104.890***

(6.365) (11.485) (12.840)

Cumulative ATP award stock ($000s) 0.056*** 0.090*** 0.083***

(0.004) (0.007) (0.008)

ATP JV participant 97.561*** 76.474***

(12.632) (15.751)

Cumulative ATP JV award stock ($000s) –0.051*** –0.045***

(0.010) (0.010)

JV with university partner 31.973**

(11.208)

JV with university subcontractor 30.554**

(11.254)

SF with university subcontractor 26.104

(14.008)

Adjusted R-squared 0.232*** 0.300*** 0.315*** 0.320***

N 2694 2694 2694 2694

Significance levels: *p ≤ .05, **p ≤ .01, ***p ≤ .001
Program Design and Firm Success in the ATP

16

Figure 4. Estimated Increase in Patents per Year, by Type of Participation

Figure 5. Estimated Increase in Patents per Year, by Type of University Role

–10

5

20

35

50

65

80

95

110

125

140

Firms participating

only in SF projects

Firms participating

only in JV projecs

Firms participating

in both SF and JV

projects

All firms

OLS regression estimates

Fixed effects estimates

9.8

23.9

130.9

33.7

13.0

–3.6

29.8

5.5

–10

5

20

35

50

65

80

95

110

125

140

Single firm -

no university

Single firm -

university

subcontractor

JV firm -

no university

JV firm -

university

subcontractor

JV firm -

university

partner

JV firm -

university

partner and

subcontractor

All firms

OLS regression estimates

Fixed effects estimates

–8.5

19.1

11.7

37.6

27.5

77.5

33.7

11.8 13.6

5.4

–0.2

4.6

1.9

5.5
EMPIRICAL RESULTS: THE ATP’S EFFECTS ON FIRM SUCCESS

17

Table 6. Patenting by All Firms: Intensity of ATP Project Participation—

Fixed Effects Regression

Dependent variable Patents, by date of application (deflated; one year lag)

Specification 5.1 5.2 5.3

Estimation Fixed effects Fixed effects Fixed effects

Constant 38.381*** 37.869*** 37.815***

(1.087) (1.079) (1.081)

ATP participant –7.933** –26.099*** –26.423***

(2.617) (4.924) (5.663)

Cumulative ATP award stock ($000s) 0.012*** 0.033*** 0.033***

(0.002) (0.003) (0.004)

ATP JV participant 21.791*** 26.030***

(5.693) (7.306)

Cumulative ATP JV award stock ($000s) –0.032*** –0.032***

(0.005) (0.005)

JV with university partner 0.552

(5.366)

JV with university subcontractor –7.500

(5.365)

SF with university subcontractor 0.680

(6.540)

Adjusted R-squared 0.896*** 0.898*** 0.898***

N 2694 2694 2694

Significance levels: *p ≤ .05, **p ≤ .01, ***p ≤ .001
Program Design and Firm Success in the ATP

18

Table 7. Patenting by Public Firms: Intensity of ATP Project Participation—

OLS Regression

Dependent variable Patents, by date of application (deflated; one year lag)

Specification 6.1 6.2 6.3 6.4

Estimation OLS OLS OLS OLS

Constant –53.504** –47.167* –46.005* –46.186*

(20.015) (19.643) (19.140) (19.207)

Small –14.990 –18.182 –18.858 –17.303

(13.098) (12.858) (12.562) (12.634)

Large 112.381*** 108.875*** 105.712*** 105.019***

(12.656) (12.371) (12.064) (12.062)

Biotechnology 75.316** 66.516** 75.078*** 74.947***

(23.841) (23.371) (22.961) (23.085)

Electronics 168.140*** 147.106*** 142.296*** 147.340***

(23.512) (23.240) (22.655) (22.927)

Energy 35.455 23.647 19.729 22.630

(29.801) (29.182) (28.653) (28.717)

Information technology 71.224*** 68.329*** 65.698*** 66.442***

(20.245) (19.851) (19.346) (19.625)

Manufacturing –4.701 –3.904 –5.763 –5.985

(20.553) (20.089) (19.582) (19.669)

Materials 54.413* 48.480* 42.785* 42.536*

(21.919) (21.429) (20.892) (20.905)

Cumulative R&D stock ($millions) 0.023*** 0.021*** 0.021*** 0.021***

(0.001) (0.001) (0.001) (0.001)

ATP participant –28.738* –133.849*** –152.559***

(12.065) (22.832) (25.402)

Cumulative ATP award stock ($000s) 0.039*** 0.110*** 0.095***

(0.005) (0.011) (0.013)

ATP JV participant 127.730*** 132.109***

(24.264) (29.901)

Cumulative ATP JV award stock ($000s) –0.106*** –0.093***

(0.014) (0.016)

JV with university partner 11.558

(19.331)

JV with university subcontractor 14.396

(19.841)

SF with university subcontractor 50.318

(28.264)

Adjusted R-squared 0.456*** 0.481*** 0.507*** 0.508***

N 1067 1067 1067 1067

Significance levels: *p ≤ .05, **p ≤ .01, ***p ≤ .001
CONCLUSIONS AND IMPLICATIONS

19

Table 8. Patenting by Public Firms: Intensity of ATP Project Participation—

Fixed Effects Regression

Dependent variable Patents, by date of application (deflated; one year lag)

Specification 7.1 7.2 7.3

Estimation Fixed effects Fixed effects Fixed effects

Constant 92.380*** 85.931*** 86.577***

(4.411) (4.510) (4.588)

Cumulative R&D stock ($millions) –0.004 0.000 –0.001

(0.002) (0.002) (0.002)

ATP participant –10.445 –35.389** –21.368

(5.871) (11.792) (13.797)

Cumulative ATP award stock ($000s) 0.013*** 0.044*** 0.049***

(0.003) (0.006) (0.007)

ATP JV participant 27.911* 13.507

(13.215) (17.088)

Cumulative ATP JV award stock ($000s) –0.048*** –0.053***

(0.009) (0.009)

JV with university partner 20.598

(11.551)

JV with university subcontractor –12.424

(11.280)

SF with university subcontractor –29.199

(15.723)

Adjusted R-squared 0.905*** 0.908*** 0.908***

N 1067 1067 1067

Significance levels: *p ≤ .05, **p ≤ .01, ***p ≤ .001

5. Conclusions and Implications

We find that patenting generally increases after ATP participation under a number

of different program and participant variations. For firms in the sample, patenting

increased on average by between 5 and 30 patents each year during the period of

participation. These estimates are conservative since future effects from the ATP

project participation are not included, even though they are implied in our

regression models. The findings of this study also support the idea that joint

ventures and university collaboration have a positive impact on firm patenting

and innovation.

Positive effects on innovation for firms participating in ATP are significant and

robust in the analyses we report in this paper. Our proxy measure of innovation—

firm patents—suggests that the effect of the ATP project spreads beyond the
project and has impact on the entire firm. We may interpret this result as evidence

that ATP project participation supports firm-wide behavioral or organizational

changes that foster an increased rate innovation. Alternatively, “internal

spillovers” of knowledge or other benefits from one project to other projects may

help explain the broad firm-wide effects of ATP participation.

This study considers the effect of program design—particularly project structure

and university participation—on the innovation success of firm participants. The

findings indicate that joint venture collaboration and university participation have

a positive effect on innovation outcomes as measured by patents. These results are

interpreted from a sociological perspective that emphasizes institution-building

and social relationships as fundamental to the innovation process. From this

perspective, ATP as a public-private partnership program fulfills a role in

fostering institutions and social processes that facilitate innovation.

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Program Design and Firm Success in the ATP

22
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 whose development often involves complex "systems" problems requiring a collaborative effort by multiple

organizations;

• Technologies which 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 firms 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 company pay at least 60 percent of total project costs. Joint ventures pay at least

half of total project costs. Single-company projects can last up to three years; joint ventures can last as long as five years.

Companies of all sizes participate in ATP-funded projects. To date, more than half of 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, which use peer review to identify

those that score highest against technical and economic criteria.

Contact ATP for more information:

• On the Internet: http://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, Mail Stop

4701, Gaithersburg, MD 20899-4701

About the Authors 

Michael R. Darby

Michael R. Darby is the Warren C. Cordner Professor of Money and Financial Markets in the Anderson Graduate School of

Management and in the Departments of Economics and Policy Studies at UCLA, and Director of the John M. Olin Center for

Policy in the Anderson School. Darby has served in senior U.S. government positions, including Assistant Secretary of the Treasury

for Economic Policy (1986–89), Under Secretary of Commerce for Economic Affairs (1989–92), and Administrator of the

Economics and Statistics Administration (1990–92). He has served on editorial boards for American Economic Review,

Contemporary Economic Policy, Contemporary Policy Issues, Journal of International Money and Finance, and International

Reports. 

Lynne G. Zucker

Lynne G. Zucker is Professor of Sociology and Policy Studies, Director of the Center for International Science, Technology, and

Cultural Policy in the School of Public Policy and Social Research, and Director of the Organizational Research Program at the

Institute for Social Science Research at UCLA. Zucker has served on editorial boards for Administrative Science Quarterly,

American Journal of Sociology, American Sociological Review, Pacific Sociological Review, and Symbolic Interaction. She has also

served on the National Science Foundation Young Presidential Scholar Award Panel and the National Science Foundation Sociology

Panel, as Consulting Sociologist with the American Institute of Physics, and Economist with the Statistics of Income Division of the

U.S. Internal Revenue Service.

Andrew J. Wang

Andrew J. Wang is an economist in the Economic Assessment Office, Advanced Technology Program (ATP), National Institute of

Standards and Technology (NIST). He received his Ph.D. in Economics from Harvard University in 1997. His work has focused on

the development, design, and analysis of special surveys and data analysis projects used to evaluate ATP.