Womens Work: Gender Equality vs. Hierarchy in the Life Sciences 9781588269768

Citation preview

S/D-FM 6/16/04 12:48 PM Page i

WOMEN’S WORK

S/D-FM 6/16/04 12:48 PM Page ii

S/D-FM 6/16/04 12:48 PM Page iii

WOMEN’S WORK Gender Equality vs. Hierarchy in the Life Sciences

Laurel Smith-Doerr

b o u l d e r l o n d o n

S/D-FM 6/16/04 12:48 PM Page iv

Published in the United States of America in 2004 by Lynne Rienner Publishers, Inc. 1800 30th Street, Boulder, Colorado 80301 www.rienner.com and in the United Kingdom by Lynne Rienner Publishers, Inc. 3 Henrietta Street, Covent Garden, London WC2E 8LU © 2004 by Lynne Rienner Publishers, Inc. All rights reserved Library of Congress Cataloging-in-Publication Data Smith-Doerr, Laurel, 1969– Women’s work : gender equality versus hierarchy in the life sciences / Laurel Smith-Doerr. p. cm. Includes bibliographical references and index. ISBN 1-58826-264-2 (hardcover : alk. paper) 1. Women life scientists. 2. Women in science. 3. Sex discrimination in employment. I. Title. QH305.5.S53 2004 331.4'8157—dc22 2004001841 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. Printed and bound in the United States of America

∞

The paper used in this publication meets the requirements of the American National Standard for Permanence of Paper for Printed Library Materials Z39.48-1992. 5

4

3

2

1

S/D-FM 6/16/04 12:48 PM Page v

To Bill

S/D-FM 6/16/04 12:48 PM Page vi

S/D-FM 6/16/04 12:48 PM Page vii

Contents

List of Figures and Tables Acknowledgments

ix xi

Introduction

1

1 Explaining Sexual Apartheid in Science

9

2 A Brief Life Story of the Life Sciences

33

3 Life in the Commercial Laboratory: Institutionalizing the Network Form

51

4 Coming In on Queue? Women’s and Men’s Entry into Biotech

79

5 Networks vs. Hierarchies in Promoting Women Scientists

99

6 Flexibility, Flexibility, Flexibility: Narratives of Gender Equality in Biotech

117

7 Conclusion: The Knowledge Economy, Innovation, and Equality

135

Appendix: Combining Qualitative and Quantitative Methods to Study Scientists

155

References Index About the Book

179 199 205

vii

S/D-FM 6/16/04 12:48 PM Page viii

S/D-FM 6/16/04 12:48 PM Page ix

t

Figures and Tables

Figures

t

1.1 Annual salaries of scientists and engineers, by gender and years since PhD, 1999 1.2 Annual salaries of scientists and engineers, by field, gender, and years since PhD, 1999 2.1 “Petrolagar,” pharmaceutical product, 1926 2.2 “Enbrel,” biotechnology product, 1998 4.1 Percent change in life scientists’ odds of entering biotech 4.2 Percent change in life scientists’ odds of entering biotech, by industry period

13 14 38 44 89 91

Tables

3.1 Tensions in legitimacy narratives: connecting the individual level to the institutional level 5.1 Characteristics of life scientists in the statistical sample 5.2 Characteristics of life scientists in the statistical sample, by gender 5.3 Likelihood of scientists moving into supervisory positions, across all organizational settings 5.4 Likelihood of male and female scientists moving into supervisory positions, in biotechnology firms compared to hierarchical settings A1 Top ten U.S. metropolitan areas in number of dedicated biotechnology firms, 1997 A2 Number of semistructured interview respondents, by setting, 1992–2000

ix

74 103 104 105

107 163 164

S/D-FM 6/16/04 12:48 PM Page x

x A3 A4 A5 A6 A7

Figures and Tables

Information recorded on each PhD career Hierarchical order of job titles for PhDs Organizational affiliation categories Types of variables measured for statistical analysis Pearson’s correlation coefficients for relationships between main independent variables A8 Effects of gender on entry into the biotechnology industry, by period: results of logistic regression analyses A9 Effects of gender on mobility into leadership positions, by form of economic organization: results of logistic regression analyses A10 Complementary strengths and weaknesses of quantitative and qualitative methods

165 167 168 169 169 171 173 176

S/D-FM 6/16/04 12:48 PM Page xi

Acknowledgments

When one is grateful for something too good for common thanks, writing is less unsatisfactory than speech—one does not, at least, hear how inadequate the words are. —George Eliot, Middlemarch (1872)

If “it takes a village to raise a child” is a truism about child development, then “it takes a village to write a book” is just as true about the development of a manuscript. My village has been unfailingly generous. My first thanks go to Woody Powell, who in many ways mentored me through graduate school and continues to be a good colleague who challenges me to develop my research acumen. He sets the bar high, as when he told me during my first year in graduate school that I would be a famous sociologist someday (still working on that one, Woody!). But along with high standards, he supplies encouragement and resources to achieve goals. I was blessed not only with the best of intellectual mentors in Woody; I also benefited from his excellent luck in getting Marianne to marry him. Marianne Powell is an accomplished life scientist who generously answered my naive questions and kindly offered suggestions that were extremely helpful as I made my way into this research project. Woody and Ken Koput started me on this journey into examining the organization of the life sciences with their belief in my potential as a young graduate student, sharing their exciting new research project with me. I had the privilege of working with these two talented organizational scholars on a quantitative analysis of interfirm network connections in the biotechnology industry. Woody and Ken were generous in providing me with experience in analyzing data, publishing articles, and writing grants. They were openhanded with their National Science Foundation grant (97-10729) to support my travels for gathering qualitative data. In the course of my research, in addition to Woody’s gift with organi-

xi

S/D-FM 6/16/04 12:48 PM Page xii

xii

Acknowledgments

zational theory and Ken’s skill in quantitative analysis, I had the benefit of Paula England’s expert understanding of gender stratification research, Lis Clemens’s encyclopedic knowledge of sociology and detailed comments on my chapter drafts, and Cal Morrill’s advice on the qualitative study of organizations. Indeed, this project profited in many ways from its origins in my work at the University of Arizona sociology PhD program. There I received many helpful comments and enjoyed a warm collegial climate created by a first-rate faculty and my fellow graduate student colleagues. I give special thanks to Kelly Moore and Michael Mulcahy, who put me onto leads in their personal networks that helped me gain access to life-science organizations. Additionally, I received research grants from the Department of Sociology and the Social and Behavioral Sciences Research Institute at Arizona. My current department at Boston University has also been helpful in the completion of this book. Colleagues John Stone, Dan Monti, Nazli Kibria, Susan Eckstein, and Pat Rieker have given input on everything from suggested titles to detailed comments on chapter drafts. I thank all of my colleagues and students at BU who have indulged me in discussions of this project and have asked good questions to help clarify my writing. I thank Jeff Furman for bringing Sinclair Lewis’s book Arrowsmith to my attention, as well as for helpful conversations about the pharmaceutical industry. My deep gratitude goes to all of the life scientists who gave generously of their time in responding to my questions or allowing me to be present in their workplace. Without exception, these are very busy people, and almost without exception they agreed to talk to me about changes in their profession. The one or two who were unavailable to meet personally gave phone or e-mail interviews. The scientists I spoke with were thoughtful (in both senses of the word), articulate, and honest. Everyone at BioNow, the startup firm I observed (note: throughout this book, companies and persons are identified by pseudonyms), allowed me full access to the organization and to observing their work activities. At the National Institutes of Health, I was welcomed and aided in my quest for quantitative data by everyone from Wally Schaffer—the head officer of research training programs—to the temporary office worker (whose name I didn’t catch) who mailed me the large heavy box with my copies of files in it. The program and information officers at the National Institute of General Medical Sciences greased the wheels of the government bureaucracy and made it easy for me to get the information I sought. I also must acknowledge my favorite science-fiction and mystery writers: Lois McMaster Bujold, Spider Robinson, Connie Willis, Sara Paretsky, and Dorothy Sayers. Their influence on this project was to provide not only

S/D-FM 6/16/04 12:48 PM Page xiii

Acknowledgments

xiii

t

much needed diversion from time to time, but also greater attunement with the gestalt of scientists’ driving curiosity, as well as the mysteries of data analysis. Alert readers may recognize that I received more direct inspiration from these authors in generating pseudonyms for the scientists I studied. A series of helpful, friendly editors at Lynne Rienner Publishers aided this book project, beginning with Bridget Julian and then Alan McClare. Lynne Rienner saw the project through to completion with patience and enthusiasm. Her press embodies a flexible, creative, womanowned business. Reviewers Jennifer Croissant and Mary Frank Fox read the entire manuscript and provided many insightful comments for making improvements. I am grateful to the University of California Press and the Pacific Sociological Association for permission to reprint material previously published in my article “Flexibility and Fairness: Effects of the Network Form of Organization on Gender Equity in Life Science Careers” (Sociological Perspectives 47, no. 1 [March 2004]). The community of sociologists, too numerous to name, who listened to me describe this project in presentations always offered helpful questions and suggestions that improved the book in ways large and small. My intellectual “village” did its part to point out errors in my logic and analysis; I bear sole responsibility for any mistakes that remain. Finally, I thank my family, God, and especially my husband, Bill, for their moral support during the unfolding of this book project. Bill ensures that our home is filled not only with gender equality, but also with love. I dedicate this book to him with deepest affection.

A Note on Inconvenient Data (or Acknowledging One’s Biases)

While investigating interorganizational networks in the biotechnology industry with Woody Powell and Ken Koput, at the same time that I was studying the sociology of science with Lis Clemens, I developed a deep curiosity about what this new industry would mean for science careers. When gender issues cropped up, my initial expectation about biotech was pessimistic. I expected that the networks would tend to exclude women from decisionmaking power in biotech firms. Here, I thought, would be the old story of old boys. As a liberal-leaning sociologist, it was somewhat inconvenient for my political views that the less formal, rule-bound workplace in biotech turned out to be a better option for women scientists than the more bureaucratic settings in academe and big pharmaceutical outfits. An upside of this inconvenient finding was that I perhaps worked harder to

S/D-FM 6/16/04 12:48 PM Page xiv

xiv

Acknowledgments

be really sure it was accurate—by gathering qualitative observations and quantitative data and by asking pointed interview questions. I came to realize that my findings made sense from the perspective that the informal underbelly of organizations is where the action often lies. The purposes fulfilled by rules for fair practices, when accommodated by flexible structures, serve women better than does a set of rules that function only as formal window dressing. I hope that this book makes it clear that I am not joining those who call for an end to affirmative action; rather, I think the data show that equal opportunity laws and rules may be a necessary condition for equity—but they are not sufficient. What we really need are women and other underrepresented groups to be included as experts. This can happen in flatter organizations in which more people can call the shots in creative ways. This system does require trust on the part of those who have been traditionally excluded (i.e., women and minorities) and those in power (management, boards of directors, financiers). Women need to risk making careers across organizations, without a secure ladder in one bureaucracy governed by rules. Managers need to give more than lip service to diversity and entrust with real authority people who are probably not like alumni of their college fraternities. But willingness to risk relying on birds of a different feather can have great rewards in creating innovative enterprises and exciting careers. Through sharing more fairly, we can increase the size of the pie. Another background perspective I bring to this project is that of outsider. This viewpoint has its strengths, as anthropologists have long shown. Outside observers often better see the taken-for-granted aspects that an insider misses. But because my last coursework in the biological sciences was anatomy/physiology in high school, I was at a disadvantage in understanding the scientific jargon and content of the work I observed in labs and meeting rooms. Although this ignorance did seem to put at ease some gatekeepers who were concerned about proprietary knowledge leaving the lab, other scientists may have spent more time discussing their results with me if I had had a more extensive background in biology. As it was, my questions and observations were mainly limited to the social and organizational aspects of scientific work. My findings on the relative equality in biotech fed into another bias that I had to watch out for—distrust of bureaucracies. During the 1990s I watched as both my father and father-in-law, after giving many productive years of long hours and loyalty to two different large bureaucratic organizations, were prematurely forced from their jobs. It’s not as if hierarchical corporations are great places for white men to work anymore, either. The biotech industry, rather than an individual firm, seemed to provide a contrast as a place where hardworking men and women could create value. I have tried to provide balance in the book, but I am perhaps still too opti-

S/D-FM 6/16/04 12:48 PM Page xv

Acknowledgments

xv

mistic about the network form. We should be aware that such a flexible mode of organizing can quickly change for the worse as well as for the better; dynamism is a key characteristic of network ties. Although this picture of biotech in the 1980s and 1990s is a particular context, I hope I succeeded in drawing out general lessons about innovation, the organization of work, and gender equality.

S/D-FM 6/16/04 12:48 PM Page xvi

S/D-Intro 6/16/04 12:49 PM Page 1

Introduction

While there are . . . women[’s] . . . just claims which are not to be listened to, large objects which may not be approached. . . . She may pursue only fancifully, and under pain of ridicule: science, only as a pastime. —Harriet Martineau, Society in America (1837)

In Chicago in 1971, three years after the infamous antiwar demonstrations during the 1968 Democratic National Convention, women scientists engaged in their own quiet protest. Thirty-five women, in town for the annual meetings of the Federation for the American Societies of Experimental Biology (FASEB), crowded into a hotel room, shared their common frustrations in facing sexism and injustice in their careers, and decided in the spirit of the women’s rights movement to do something about it. That night in April, the professional association American Women in Science (AWIS) was born (Braselmann 1999). Neena Schwartz, a first copresident of AWIS, reflected: That small group of us who squeezed into a hotel room at the FASEB meeting and formed AWIS will always regard that evening as the high point for us, of taking over some control of our professional lives and declaring that women really belong in the scientific enterprise. (Mandula 1996, cited in Braselmann 1999)

AWIS continues to advocate for women in science. In 2002 the organization rather politely announced the availability of their Internet site (www.chillyclimate.org) to assess whether one’s academic department provides a warmer or chillier climate for women. The somewhat restrained approach to protesting inequality is not unusual for women scientists.1 Mostly, women face the barriers to their path in science alone, whether they internalize the blame or realize that they face disadvantages toward the upper tiers of science. The organizational level is

1

S/D-Intro 6/16/04 12:49 PM Page 2

2

Women’s Work

difficult to see from the perspective of the individual on the career path and even harder to change. When change does occur at the organizational level, the effects on careers are not always easy to predict. In the life sciences, the biotechnology industry burst onto the scene in the early 1980s. Not only did the young industry take advantage of new developments in scientific knowledge; it organized itself in ways that scientists had never seen in industrial enterprise. Biotech firms helped to create the new knowledge economy with their strong ties to universities, flatter project-based employment structures, and cutting-edge approaches to science. How would such a radical organizational change affect the long-standing inequalities women faced since the formative days of science careers in the United States when Harriet Martineau observed the ridicule of women in science? This book is about the work of men and women in cutting-edge, science-based firms, as well as the surprising egalitarianism found in forprofit enterprise. The central finding is that the organizational context structures career outcomes; new start-up firms in the field of biotechnology show marked differences for women’s career opportunities compared to university settings and established pharmaceutical companies. This finding is surprising given the argument made by scholars and feminist organizations that in order to have equal opportunities for promotion, women and minorities need the formal policies, rules, and long job ladders that large bureaucracies provide. Irene Padavic and Barbara Reskin (2002) summarize the scholarly literature on these issues: “women’s greater concentration in small, entrepreneurial firms and nonprofit organizations reduces their odds of promotion relative to men” (p. 109). Furthermore, “because the bottom line is usually the top priority, fines and other financial sanctions raise equal opportunity on employers’ agendas” (Padavic and Reskin 2002: 119). These ideas are not limited to scholarly studies. Consider the organizational policy advice given by Catalyst, a prominent consultant to business for women’s advancement: “Fundamental change in any work environment is never achieved through a piece-meal approach. All organizations that have made progress would agree that solid execution of an integrated change strategy is a requirement for success” (www.catalystwomen.org/companies.htm, December 2003). Catalyst then gives examples of “best practices” from large multinational bureaucracies—JP Morgan Chase and DeLoitte & Touche—that have implemented formal policies to advance women. It is in light of such arguments about large hierarchical organizations with formalized policies supporting gender equality that my findings are surprising. My finding that flatter, more interorganizationally connected biotechnology firms are better workplaces for female scientists is based on quantitative analyses of more than 2,000 life-science PhD careers, as well as qualitative interviews of forty-seven scientists in twelve biotech industry

S/D-Intro 6/16/04 12:49 PM Page 3

Introduction

3

and academic settings. The book is an effort to contribute to theories of organizations and gender stratification, on the one hand, and to describe some key aspects of the experience of working in the new world of commercial science on the other. Analyses of gender relations in science are largely based on studies of the academy, yet the greatest growth in employment and some of the most remarkable developments in medicine have come from the private sector. The number of U.S. life scientists working outside the academy grew from 83,000 in 1980 to 181,000 in 2000 (National Science Foundation 2002: fig. 3-1). When they are included in studies, scientific positions in “industry” are typically regarded as interchangeable despite considerable variations in practice. The organizational context of science/technology work has rarely been investigated systematically by close observation of one industry. Although there are many concerns about the consequences of the privatization of science, it is notable that both new career opportunities for women and new forms of human resource management have been developed much more rapidly in the private sector than in the university or government. Understanding the organizational contexts that release the potential for women scientists to contribute to technological innovation may suggest possible reforms in the more traditional settings of the academy, government, and large corporations. The knowledge foundations of this book originate in three lines of scholarship. The first comes from contemporary organizational theory and economic sociology. Based on core sociological concerns going back to Max Weber and Emile Durkheim (is it what you know or whom you know?), this scholarship has shown how economic decisions are embedded in social and organizational contexts (Granovetter 1985; DiMaggio and Powell 1983). Moreover, one line of work has emphasized the differences between hierarchical organizations, steeped in formality and bureaucracy, and more fluid organizations with so many ties to external parties that they resemble a spider’s web, or a network, much more than a pyramid (Powell 1990). Among organizational actors, those employing a network form of organization are “any collection of actors that pursue repeated, enduring exchange relations with one another” (Podolny and Page 1998: 59). Although there are a range of studies of these organizations with pronounced network features—manufacturing in northern Italy, garment districts in New York, and Japanese business groups, as well as the biotechnology industry—no one has considered how gender stratification is affected by these flatter and more lateral structures. The second line of relevant scholarship is the study of science and technology careers. Robert Merton’s theories about the institutions of science and patterns of stratification among scientists have been developed through empirical studies that have shown durable gender stratification is

S/D-Intro 6/16/04 12:49 PM Page 4

4

Women’s Work

widespread within the academy (Zuckerman, Cole, and Bruer 1991; Long and Fox 1995). The kind of sexual apartheid experienced by the scientists who formed AWIS placed women as second-class citizens in academic science. Salome Waelsch, an eminent life scientist who founded Albert Einstein College’s Department of Genetics in 1963, described her careerlong struggles with gender inequality in a 1991 interview. Waelsch, for example, described the inequity in how faculty advisers meted out PhD projects and publishing credit (Zuckerman, Cole, and Bruer 1991: 72–73): You were told what to work on for your PhD thesis. [My faculty adviser] gave me a problem that was very boring; in retrospect it was an insult to have been given such a project for my dissertation. Whereas a young man who was my colleague was given a very exciting problem, namely to find out whether a particular germlayer in the vertebrate embryo was responsible for the formation of the pattern of the extremities. The work was to be done with two species of salamanders, with different patterns of limb development. I was asked to sit down and describe the pattern of development of each species, whereas the young man was asked to transplant tissues between them, a really exciting experimental problem. I was asked to provide the groundwork, which was the most boring thing that you could imagine. . . . Another women whom I knew was also given a boring project. She did what I did, which was to play around at night with experiments outside her thesis project. She came up with a very exciting finding which really laid the groundwork for a lot of later research on mechanism of embryonic development, embryonic induction, and so on. At first, she was considered crazy when she came up with these data. Then somebody repeated her experiment and confirmed her results. The paper that eventually came out reporting the work did not carry her name. It was totally omitted.

While the gender biases female academics now face may be less blatant than having credit for their scientific discoveries stolen, the barriers are nonetheless real (Etzkowicz, Kemelgor, and Uzzi 2000). The study of scientists working in industry, however, is not as well developed as the study of scientists and engineers in the academy. Understanding trends in the lifescience workforce is key to encouraging the diversity of people needed to develop innovative medical technologies that will improve human health around the globe. The third line of scholarship comes from more universal theories of gender inequality in the labor market (Reskin and Roos 1990; England 1992; Kanter 1993; Jacobs 1989). Gender inequality is evident when men and women have different amounts of valued resources, like powerful positions in science and technology organizations. For example, a study published in Nature (Wennerås and Wold 1997) on the peer-review process found that female applicants for a prestigious postdoctoral fellowship in

S/D-Intro 6/16/04 12:49 PM Page 5

Introduction

5

t

Sweden must publish the equivalent of ten more journal articles to receive the same subjective ranking in “scientific competence” as a male applicant. From this perspective on gender, social structures provide unequal access to valued outcomes, and individual choices are shaped by the differential treatment of men and women. Studies of gender stratification have sometimes been criticized for focusing on the stability of gender inequality to the exclusion of investigating social changes toward gender equity (Chafetz 1990). Here, gender equality is used interchangeably with gender equity and refers to relative equality of opportunity. The data presented in this book provide additional insights into how organizational structures and discrimination matter a great deal for gender equality in professional careers. This book combines quantitative and qualitative methods to study gender relations in the life sciences. By life sciences I mean areas of research in human biological sciences—molecular biology, biochemistry, biomedicine, cell biology, and genetics, excluding agricultural sciences and the clinical practice of medicine. The data afford comparisons between male and female scientists in the academy, government, large pharmaceutical corporations, and newer, science-based biotechnology firms. The quantitative data come from a one-of-a-kind dataset that I constructed from information submitted by universities to the National Institute of General Medical Sciences (NIGMS). These data are unique in allowing for comparisons of career outcomes (at the job level) for men and women by the type of organization in which they work. There are two sources of qualitative data used in this book. One is the ethnographic observation of a biotechnology firm BioNow (a pseudonym) and a university laboratory. The other source is the semistructured interviews with scientists in a variety of lifescience organizations. The qualitative data permit a deeper look into the hows and whys of gender equality across life-science organizations.2

Outline of the Book

Chapter 1 describes the situation for men and women in academic science and the theories that have been developed to explain women’s long-standing disadvantages in the labor market. I argue for looking at the issues within an organizational context, particularly within the context of different organizational forms. Chapter 2 portrays the expansion of the modern life sciences and the emergence of the biotechnology industry as a fundamentally new way of organizing science and drug development. To understand why women were attracted to these novel employment settings, we need to know what work-

S/D-Intro 6/16/04 12:49 PM Page 6

6

Women’s Work

ing in biotechnology firms means to scientists. How do scientists perceive relatively new biotech careers—and describe these firms as legitimate places to work? Chapter 3 is set mainly in one biotechnology firm, BioNow, to observe how scientists understand the legitimacy of working in biotech. Life scientists legitimate, or institutionalize, biotech work using comfortable academic meanings (like publishing). At the same time, however, scientists talk about biotech as legitimate for reasons that conflict with academic frames—such as the collective rewards for teamwork. Although there is tension in the narratives scientists use to make sense of new biotech careers, the result is a legitimate career path. Who was the first to tread this path? We know from studies of labor markets that work is usually gendersegregated. Thus, who enters the biotech arena first—men or women? Chapter 4 addresses the question of whether, because the biotechnology industry is viewed as a legitimate career location for top scientists, men found themselves at the head of the queue in entering the industry. One might expect to find either that women entered into the biotechnology industry because it was not yet widely viewed as legitimate, or that men entered first because it was. On the contrary, the findings show that the new industry was not gender-segregated. As we shall see, female and male PhDs are equally likely to go into biotech. In Chapter 5, the issue posed is whether biotech firms are like universities, where male faculty members have a much better chance of promotion into a position of authority. The form of organization matters a great deal for women’s career chances. To summarize the key finding of the book: female scientists working in dedicated biotech firms have a much higher probability of being in a position to lead research teams than do their female colleagues in more hierarchical life science organizations. In network-intensive biotechnology firms, female PhDs are nearly eight times more likely to lead scientific projects than in more hierarchically organized academic and pharmaceutical corporate settings. Chapter 6 then explores the reasons why biotech evinces more gender equality. The flexibility of the network form (demonstrated through author interviews with scientists employed in a variety of settings) explains how women can maneuver around discrimination and obtain credit for scientific contributions. Flexibility allows female PhDs to get around discriminatory hurdles that constitute more rigid obstructions in hierarchies. Chapter 7 concludes the book with a discussion of how gender equality is not only good for the sake of fairness but proves to be a good business strategy, especially when innovation is crucial to economic enterprise. I also discuss implications of the findings for affirmative action policies and thinking about the future of knowledge industries and the university.

S/D-Intro 6/16/04 12:49 PM Page 7

t

Introduction

7

Notes

1. Scientists do not always go along quietly. Kelly Moore (1996) has investigated the dual development of scientist and activist identities among some, but most scientist-activists in her analyses seem to be men (e.g., Moore and Hala 2002). 2. The qualitative work answers questions about the mechanisms by which the network form facilitates gender equality that could not be discerned from the quantitative analysis. Through observing interactions and narratives, I gained insight into the processes by which women gain advantage in some contexts. See the Appendix for further discussion of the complementarities of quantitative and qualitative methods.

S/D-Intro 6/16/04 12:49 PM Page 8

t

S/D-1 6/16/04 12:50 PM Page 9

1

Explaining Sexual Apartheid in Science

There are persisting differences between men and women scientists. . . . These differences are almost always in the direction of comparative disadvantage for women. —Harriet Zuckerman (1991)

While many women of her cohort followed Rosie the Riveter’s lead and worked as factory assemblers, Judith1 had earned a physics degree from a women’s college and went to work as an industrial scientist during World War II. After the men returned to American shores and Judith married and bore a child, she left her high-paying R&D job and became a high-school teacher. Jeanne, a member of the baby-boom generation, feminist, and biologist, started out as a high-school teacher, then earned her PhD and worked in a series of postdoctoral positions during the 1980s. After marrying another life scientist and having a baby, Jeanne successfully rallied a group of female scientists to persuade her university employer to provide nursing rooms for breastfeeding mothers. But discouraged by her lack of upward mobility, Jeanne took her ambition to law school in the 1990s and became a highly paid attorney who advises clients on intellectual property. Lotty, who belongs to Generation X and eschews political labels, is a biologist who earned her PhD and began her first postdoctoral fellowship at the end of the 1990s. She is thinking about leaving the life sciences for a job where she can venture into more creative places than she finds in her current postdoctoral cul-de-sac. The individual stories of Judith, Jeanne, and Lotty illustrate some of the changes and continuities in the post–World War II generations of women in science careers. One of the largest changes is that women are pursuing higher education more often, including in the sciences. As a woman in a scientific/technical field, Judith was an anomaly in the 1940s and 1950s, Jeanne was still clear-

9

S/D-1 6/16/04 12:50 PM Page 10

10

Women’s Work

ly in the minority in the 1970s and 1980s, but Lotty had more company in the 1990s and 2000s. In 1975, women earned 45 percent of all bachelor’s degrees and 22 percent of natural-science and engineering degrees. By 1998, women were earning the majority (56 percent) of undergraduate degrees and 37 percent of natural-science and engineering degrees (NSF 2002, calculated from appendix, table 2-16). At the doctoral level as well, a woman is now much more likely to be a degree recipient. Women constituted 14 percent of doctoral recipients in 1970 and earned only 7 percent of new natural-science and engineering PhD degrees. By 1999, women were 42 percent of all the new PhD degree holders and earned 27 percent of the natural-science and engineering PhD degrees (NSF 2002, calculated from appendix table 2-24). The change in women’s pursuit of higher education parallels the shift in the female labor participation rate. In 1950, only 34 percent of U.S. women worked for pay (Fullerton 1999). By 1980, more than half found employment, and in 2000, 61 percent of women worked (U.S. Census 2000). Yet women have not often worked as scientists and engineers. Just 12 percent of college graduates working in nonacademic science and engineering occupations were female in 1980. By 2000, the female share of science and engineering jobs had increased to 25 percent. Yet the entire college graduate labor force in 2000 was 49 percent female. In comparison, African American and Hispanic college graduates, who are also scarce in science, have less percentage difference between science and engineering labor force participation and participation in the overall labor force. In 2000, African American graduates made up 6.9 percent of the science and engineering labor force and 7.4 percent of all college-educated employees. Hispanics were 3.2 percent of scientists and engineers and 4.3 percent of college-educated workers (NSF 2002, fig. 3-13). There have been large shifts in women’s pursuit of education and entrance to the labor force, but women are still underrepresented in science education and occupations. Women scientists have made some inroads working in universities and colleges. Although women are only 23 percent of academics, an individual female PhD is actually more likely to work in academia than is a male PhD (Long 2001). Perhaps women as a group need time to catch up to men in the academy. At the current rate of change by which women occupy academic positions (increasing about 1.5 percentage points annually; see Long 2001: 125), in 2013 women will make up half of all full-time academics. Although the number of women with science PhDs and academic jobs is growing, a gendered authority gap remains. The academic workplace is a stratified system. Some full-time academics have the security (or prospect) of tenure—if they survive the trial by fire, they are assured employment until retirement. Other academics, the “unfaculty” (Kerr 1963), work outside the tenure track in labs and classrooms, often without access to the

S/D-1 6/16/04 12:50 PM Page 11

Explaining Sexual Apartheid in Science

11

t

same departmental resources and employment benefits and always without the long-term contract and pay structure of the tenure system. Female scientists staff one-third of the unfaculty positions but only one-fifth of tenure-track jobs (Long 2001: 155). Among full-time academics in 1979, 85 percent of men and 71 percent of women had tenure-track jobs. Little change has occurred in the gender gap for faculty-track positions—in 1995, 82 percent of men and 69 percent of women academics were on the tenure track (Long 2001: 146–149). At that rate of decrease in the tenure track gender gap (1 percent over sixteen years), it is difficult to say how long it will take before women gain parity. If the aggregate change rate stays the same, it will not be for another 200 years. Within the tenure track, positions are stratified into three basic levels. Tenured associate professors have more power than untenured assistant professors but less prestige than full professors. At four-year colleges and universities, 29 percent of the women academics are tenured compared to 58 percent of the men (Trower 2001). Female faculty members occur in smaller proportions along the tenure track, and change in this relative scarcity has occurred slowly. In 1973, 94 percent of senior science and engineering faculty were male, and in 1999, 81 percent were male (calculated from NSF 2002, appendix, table 5-30). On average, female academics have not held their degrees for as many years as males, so the tendency is to excuse the gender gap in authority on campus with time lag. Studies that do control for the number of years since scientists received their doctorates, however, show that women and men who received their degrees at the same time do not have the same likelihood of moving up the academic job ladder (Ahern and Scott 1981; Fox 1996). A comprehensive report on academic scientists (Long 2001) provides detail on how men and women differ in the fields they pursue (most women are social or life scientists rather than physical scientists or engineers) and where they are employed (women are over-represented at two-year colleges, and underrepresented at Research I universities). This research finds that even after controlling for gender differences in the number of years scientists have held PhDs, field, and type of university, men still have about a 10 percentage-point advantage over women in becoming full professors (Long 2001: 176). Women academics are not yet as likely as men to be on the tenure track or promoted to senior positions.

Understanding Gender Differences

On a recent afternoon as I lunched with a colleague in the pub at our university, our conversation came to an abrupt halt as the classic rock station playing in the background blared into a noisy commercial for a testosterone

S/D-1 6/16/04 12:50 PM Page 12

12

Women’s Work

supplement. The bass-voiced announcer began, “Men, do you want to feel younger and more powerful? Take the manly alternative.” At the end of the spiel extolling the wonders of testosterone, listeners were admonished to “Feel like a man again!” My colleague’s jaw dropped, and he shook his head in consternation at what the world is coming to. I laughed, waiting for the punch line to the joke, then realized that the supplement was real and could be purchased online. Sheltered academics, we. Even knowing that we live in a society where male and female are constructed as polar opposites, we were surprised by the virulence of the emphasis on not being female. But it does in fact pay off in the labor market to be male. One metric by which scholars measure the relative advantage of men in the labor market is pay. In the United States in 2000, female full-time workers earned 72 cents for every dollar that male workers made. Although the gender gap in pay was on the decline during the twentieth century, there are some indications that a trend toward income equality may have stalled. A General Accounting Office study (2001) comparing 1995 and 2000 incomes found that in two-thirds of the industries studied the gap in pay between male and female managers was increasing rather than decreasing. Among scientists and engineers, women earn 77 cents for every dollar that men make. Figure 1.1 displays this overall pay gap in the first column pair. The rest of Figure 1.1 shows that at every career age men earn more than women. Women’s pay ranges from 80 percent of men’s in the first five years since the PhD to 89 percent at the end of their careers. These numbers account only for scientists and engineers who remain in the labor market. Perhaps the gender gap is smaller for the older cohort because women who received their PhDs before 1964 were more likely to leave the labor market, except for those who were more highly paid. The pay gap between men and women at the same career stage is smaller when field is taken into account. Figure 1.2 shows the median annual salaries for men and women who are computer and math scientists, life scientists, physical scientists, social scientists, and engineers, respectively, by the number of years since doctoral degree. Computer and math sciences have the most contradictory patterns in gendered pay differences. At the beginning of their careers, female computer scientists earn 76 cents for every dollar their male colleagues make, the largest gender gap across fields and career stages (save for physical scientists at twenty-five years from degree who have the same gap). But at ten years post–PhD, female computer scientists are the only group that earns more than their male cohort, making $1.03 for every dollar the men earn. This reversal from pay disadvantage to advantage could indicate that female computer scientists face high barriers early in their careers, but those who survive in the field for a decade are more likely to be in high demand. Among life scientists,

Annual salaries of scientists and engineers, by gender and years since PhD, 1999 (U.S.$)

Source: Based on data from NSF 1999, table G-11.

S/D-1 6/16/04 12:50 PM Page 13

Figure 1.1

S/D-1 6/16/04 12:50 PM Page 14

14

Women’s Work Figure 1.2 Annual salaries of scientists and engineers, by field, gender, and years since PhD, 1999 (U.S.$)

Male life scientists

Source: Based on data from NSF 1999, table F-10 (the NSF does not report data for some catagories because of small numbers).

women and men are paid equally early in the career (when most of them are postdocs), but by the end of their careers women are earning only 78 percent of what men earn. A disadvantage for everyone that arises from the persistent gender gap in pay and prestige in academic science is a steady loss of talent across generations of U.S. scientists. Judith’s and Jeanne’s skills and creativity were lost to science, and Lotty may soon be leaving as well. In an era when competitive advantage must be considered in global terms, we can illafford to lose some of the most highly educated, smartest people from the core knowledge-producing arena in our society. The so-called brain drain in Eastern European countries that have lost many PhDs to the United States has benefited this nation at the expense of other nations (Bosch 2003). Our own gendered brain drain means not only a loss of talent but also of diversity in scientific leadership.

S/D-1 6/16/04 12:50 PM Page 15

Explaining Sexual Apartheid in Science

15

As we have seen, statistical reports of the situation women face in academic careers and the labor market generally show a picture of durable inequality. Although women are in the labor force in record numbers, they are not making as much progress closing the pay gap and moving into positions of authority. Why do women experience consistent disadvantages? Economists point to individual choices made, sociologists to discrimination in a culture that devalues feminine traits. Analysis of how differences at the organizational level might affect women’s careers has been surprisingly slight. This limitation may result in part from a lack of good data at the organizational level, as well as greater statistical development of econometrics designed to analyze individual behavior rather than groups. The categorization of explanations for gender stratification has been influenced by economics as well, such as when scholars distinguish between the supply-side and demand-side approaches. Supply-side approaches focus on the choices made by individual women and men whether or not to enter labor markets. Demand-side approaches consider the behavior of employers, as well as characteristics of employing industries, in relation to unequal outcomes in the labor market.

Supply-Side Approaches Human capital theory, as developed by U.S. economists in the 1970s and 1980s (Becker 1981; Fuchs 1988), posits that gender differences in the labor market result from women not investing in their long-term human capital (education and training). This nearsightedness was a rational decision because women would stay in the labor market only a short time before leaving to raise children. A consequence of the changing gender composition of the highly educated population is that theories of gender inequality based on women’s lack of training have lost explanatory punch. Recent data showing that women attend college at higher rates than men and increasingly earn graduate degrees have falsified this human-capital hypothesis. Another way of looking at individual choices, in which culture plays a larger role, is to consider how people have been socialized into their appropriate gender roles since childhood. The socialization that children receive about the role of “scientist” appears to be unambiguous. Margaret Mead, the famous anthropologist, designed a creative test in the 1950s to see how high school students had been taught to perceive scientists. In her “Draw a Scientist Test,” students drew stereotypical scientists with lab coats, instruments, and eyeglasses and who were invariably white males with facial hair (Mead and Metraux 1957). When the same test is administered now, kids draw the same pictures of scientists (Barman 1999). Even when they personally know female or minority scientists, students draw pictures of hir-

S/D-1 6/16/04 12:50 PM Page 16

16

Women’s Work

sute, nearsighted men of European origins. The cultural message to girls and boys is clear: scientists are men. Presumably, this socialization leads males and females to make different occupational decisions. Generally, young women who aspire to work in female-typed jobs do end up working in those occupations (Okamoto and England 1999). A young woman who has been socialized to expect to work as a secretary or a home health aide is likely to be working in a female-dominated occupation later in her career. Robert Fiorentine (1987) refined socialization theory by arguing that the female gender role has more “normative alternatives” than the male. In explaining why, among premed students with the same academic records, females were less likely to end up as MDs than were males, Fiorentine pointed to differential socialization about paths to success. Women were socialized to either achieve occupational success or make a home for a high-achieving spouse. Men had no normative alternatives; occupational achievement was the only way to a successful life. Fiorentine contrasts the reactions of two undergraduates who consider what they will do if medical school does not pan out. The female student, reflecting on a recent visit to the home of her married sister, concluded: Males have to work and females don’t. . . . Females don’t really have to work unless they are single or want to. Well, maybe they might have to if there is not enough money, but it all depends. . . . I guess who she is married to . . . and what she wants to do with her life. (Fiorentine 1987: 1135)

The male student was clearer about his occupational options outside of medical school: I’m considering two options. One, I go back to school to become an architect. My uncle is an architect and doing very well. He would give me a job any time. Two, I stay with [a company . . . in which he has a low-level management position]. I know I will be able to make a lot of money if I stay. (Fiorentine 1987: 1136)

To me, Fiorentine’s exemplars seem to be revealing less about their normative understanding of the occupational requirements of their gender and more about their embeddedness in personal networks. The male student has network connections that will lead to good jobs, which the female student does not seem to have. In any event, the research showing that socialization dissuades girls from pursuing science and technology seems more helpful in explaining why females do not enter scientific careers in the first place. Socialization is a less compelling explanation for the outcomes of women scientists, who have already chosen to go into science despite traditional gender-role socialization. Yet some have argued that women scientists choose family life over a

S/D-1 6/16/04 12:50 PM Page 17

Explaining Sexual Apartheid in Science

17

more distinguished career, and that is why women publish relatively less than men. The problem with this idea, however, is that it does not hold up to the evidence. Married women seem to publish more than do single women, and having children does not decrease publication rates (Cole and Zuckerman 1991). Married women scientists publish more because they coauthor more, sometimes with their spouses, and at times with senior men who feel more comfortable working with female junior colleagues who are married (Schiebinger 1999). Arguing that socialization leads women to spend more time with family and less time publishing cannot explain why gender differences in academic promotion persist after controlling for productivity (Sonnert and Holton 1995). The number of citations per publication cannot explain the unequal outcomes by gender either, as women’s publications receive more citations on average than men’s (Zuckerman 1988). It becomes obvious that we must look beyond individual choice behavior when trying to understand gender stratification in science.2 A theory that attempts to explain women’s lack of standing in science as an aggregation of individual choices is the pipeline model. The pipeline is a metaphor to describe how the “flow” of females into science is increasingly weaker at every level (for reviews, see Schiebinger 1999; Kulis, Sicotte, and Collins 2002). Starting with a weaker stream of girls taking high school honors math and science, the water pressure decreases through college, graduate school, and early career points until the number of female full professors is less than a trickle. Not only is the flow of women smaller at the start of the pipe; the passage is also leaky, with women leaving at many points along the line. Some researchers (Kulis, Sicotte, and Collins 2002), however, compare different academic fields and show that the pipeline model does not explain why some disciplines have fewer women at the top when the flow of PhDs is the same. Others (Etzkowicz et al. 2000) note that “priming the pump,” or resocializing girls to recruit them into science and then waiting for things to even out in the next generation, is a naive viewpoint that neglects the demand side of organizational resistance to equality. Things do not seem to just even out on their own. During the 1970s, women made up nearly half of Britain’s life-science graduates, but in 2002 women still were less than 10 percent of the faculty in the life sciences (Greenfield 2002). Sociologist Jerry Jacobs (1989) has proposed a somewhat similar metaphorical theory to the pipeline model: the revolving door. To explain why women in the aggregate have not made progressively greater inroads into male-typed occupations, Jacobs suggests that a gendered revolving door is in operation. For every eleven women that enter a male-dominated occupation, ten leave. Rather than explain this lack of progress toward equity as mostly due to female gender-role socialization, however, Jacobs posits that individual women’s decisions are shaped through a combination

S/D-1 6/16/04 12:50 PM Page 18

18

Women’s Work

of normative expectations and discrimination. Consider the careers of Judith, Jeanne, and Lotty mentioned earlier. Each was successful early in her pursuit of science, but barriers to further success caused them to leave through the revolving door. It is not a pipeline supply problem: women are going into science and other male enterprises but then are leaving because of demand-side factors in the workplace.

Demand-Side Approaches Most sociological accounts of the demand side of gender inequality focus on the problem of discriminatory employers.3 When biased individuals occupy powerful positions in employing organizations, inequalities result. The hiring personnel do not have to be blatant discriminators; they may simply have a small preference for white men, which is perhaps to be expected in a culture where God is portrayed as a white man with a beard. Queuing theory develops predictions about what happens to the structure of occupations when such preferences are widely shared by employers (Thurow 1969). The queue is the central metaphor of this theory. Employers have a preferred lineup of categories of potential employees. Job-seekers also have a preferred ordered ranking of occupations. If most employers place men first and women second in their preferred gender queue, then men will have an easier time getting work in the desired job queue (Reskin and Roos 1990). What this means for occupational composition is that highly valued jobs are occupied by men, and devalued jobs are open to women. Occupations thus switch from being predominately male to female when they lose value in the job queue. For example, when clerical work was the first rung on a job ladder in business, men were office secretaries. With automation of office work (i.e., the typewriter), the job of secretary lost its business value so men left and women entered the job, queuing theory posits. Like other books (see, e.g., Reskin and Roos’s Job Queues, Gender Queues, and Jerry Jacobs’s Revolving Doors), this book focuses on the extent of gender inequality and offers a general structural theory to explain differences in outcomes for men and women. Chapter 4 directly assesses gender queues in the biotech industry. Indeed, this book is complementary to the works cited above in ascertaining when the exception to the rule occurs—when a new occupational arena is not sex-segregated. This book, however, takes a more in-depth look at one field—life sciences—rather than comparing occupations across the U.S. labor market and pays more attention to the organizations employing men and women. By focusing on life sciences, I can analyze the variation in organizational form within the field. Previous analyses of gender inequality among scientists have focused on academia, perhaps with good reason. There is ample discrimination to

S/D-1 6/16/04 12:50 PM Page 19

Explaining Sexual Apartheid in Science

19

document. Even scholars who do not set out to discover gender bias among scientists and engineers can find themselves revealing sexism in their study of academic life. For instance, in an investigation of how academic departments actually approach hiring and promotion underneath the meritocratic surface (Caplow and McGee 1958), researchers uncovered a distinct disadvantage for women and minorities in the 1950s. A male academic interviewed described how “egalitarian” members of his department were in contrast to others: We had one young woman come down here from one of the Big Ten. She had the MA and was working on her doctoral dissertation and we would have very much liked to have gotten her, but when she saw the Dean, he turned her down. He didn’t like the way she was turned out, thought she was too stylishly dressed. We had thought she looked very lovely. (Caplow and McGee 1958: 125)

The obvious double standard of judging female academics on their looks now seems almost a bit quaint and certainly an outdated mode of sexism. Nevertheless, even this old bias resurfaces in current reports on gender inequality in the academy. An eminent academic who was recently invited to serve on an internationally renowned science committee found she was the only female scientist in the group. During the introductions, the chairman belittled her contribution to the committee by remarking: “[scientist’s name] here will add some glamour to the discussion” (Greenfield 2002: 65). To explain how slights that seem small as single occurrences can add up to rather gaping gender inequalities across a career, Jonathan Cole offers a theory of cumulative disadvantage (see Cole and Singer 1991). Scientists throughout their education and work life receive a series of “kicks” from their social environment. Some of these kicks are positive (e.g., acceptance to a top program), whereas others are negative (e.g., discriminatory incident, rejection of a grant proposal). Over their careers, women scientists receive more negative kicks and less positive ones than do men scientists, adding up to a cumulative disadvantage. The way that individuals react to these kicks is also a factor in the cumulative disadvantage model. A career episode described by one of my respondents, Judith, may serve to illustrate. Judith, trained as a physicist, found work as an industrial scientist. After a few years on the job, she returned to school to take an engineering class that she felt would help her advance. At the university, they advised Judith to start in the introductory-level class and take the entire series of courses toward the degree in electrical engineering. She refused to let her industrial experience count for nothing and took the class she needed. In the class, the male students, according to Judith, “helped me get through everything.” Not allowing her to get her hands dirty to learn, and to falter, was no favor.

S/D-1 6/16/04 12:50 PM Page 20

20

Women’s Work

“I was completely lost,” she said. Judith’s reaction to the patronizing help was self-deprecation: “They were generous in passing me in that class.” She did not take any other engineering courses. The main lesson she learned was I do not belong here. This kind of internalization of negative kicks is a common reaction among women, according to social psychologists. The theory of cumulative disadvantage is a compelling explanation at the micro-individual level but misses how discrimination is embedded in the organizational level (e.g., how variation in organizational structures might permit different amounts of leeway for discriminators or egalitarians to act). Labor-market inequality occurs within the context of organizations; this includes scientific work. Sociologists of science have begun to examine the organization of academic departments to better understand the context of gender inequality (Etzkowicz, Kemelgor, and Uzzi 2000; Fox 1991; Fox 2000; Keith, Layne, Babchuk, and Johnson 2002). Henry Etzkowicz and colleagues (2000) describe science department cultures as either instrumental or relational. Instrumental departments have few women and tend to be populated by antisocial workaholics. Relational departments maintain a less hierarchical mentality, tend toward more cooperative research, and are woman-friendly. Likewise, Mary Frank Fox (2000) describes “good environments” of science departments that have increased the number of female colleagues as more focused on communication and having a supportive faculty, in addition to having resources and excellent personnel. These exploratory qualitative analyses of department cultures do not attempt to separate the academic environment from the positive or negative outcomes for women; instead they describe how academic context and women’s advances are correlated. My analysis differs in that I discovered a natural experiment of sorts, in being able to contrast a less hierarchical form (biotech) to more hierarchical organizational contexts (universities and large drug companies). In effect, I was able to measure organizational variation and then assess the likelihood that women scientists will thrive in these two kinds of environments (controlling for the proportion of women in the different settings). Yet in making this quantitative analysis, I combine academic contexts rather than making distinctions between them as others have. 4 In all research, there are trade-offs. Because the study of stratification in science focuses almost solely on academic scientists (for reviews, see Long and Fox 1995; Zuckerman 1988), I decided it was more important to look at variation across sectors. The lack of variation in types of science organizations usually studied means the interesting question—how the form of organization affects gender inequality among scientists—has been left unasked and unanswered. Organizational sociology and gender stratification research more gen-

S/D-1 6/16/04 12:50 PM Page 21

Explaining Sexual Apartheid in Science

21

erally have been like distant relatives who speak on occasion, when really they should be like close siblings in constant communication. Patricia Yancey Martin and David Collinson (2002) outline some of the reasons why organization studies and gender studies have not converged. Both are fragmented disciplines (i.e., include branches with different views on what constitutes evidence, particularly between U.S. and European social science). Additionally, the subfields themselves are sex-segregated, with gender scholars mostly women and organization scholars mostly men. Because academic citation patterns tend to be gendered (men in particular are more likely to cite and presumably to read the work of other men; Baldi 1998), these two areas of study—gender and organization—remain divided. Greater attention to the relative positions of men and women in organizations can reveal a fuller picture of gender equality. For example, in a study of the computer industry (Wright and Jacobs 1994), researchers find that the proportion of female workers is increasing but that women are more likely than men to leave the labor force. Perhaps more women are entering computing but not staying long, and therefore most are not moving into supervisory positions (but the women who do can earn as much as men). More detailed information on workers’ organizational positions, rather than general job categories, could help sort out the difference between ghettoization (women being employed mostly in a lower paying subfield) and inequality in level of authority (women being, on average, less likely to be promoted). In past research when sociologists have brought together the study of organizations and gender, the perspective has either involved networks at the individual level or, at the organizational level, more traditional demographic characteristics (e.g., size and sex composition of organizations). Individual networks do provide the conduit through which the majority of white-collar workers find their jobs (Granovetter 1995). As a result, researchers have studied the effects of gender differences in personal networks on careers (for reviews, see Raider and Krackhardt 2002; Ridgeway and Smith-Lovin 1999). Women’s networks tend to be centered in smaller voluntary organizations (McPherson and Smith-Lovin 1982). Women’s ties also tend to be more geographically local than men’s (Callender 1987; Hanson and Pratt 1991). These differences increase the segregation of work by gender, with women working in small, localized industries and men employed in larger, geographically widespread industries with more room for climbing hierarchical job ladders. In employment settings, women are more tied to one mentor or organizational sponsor (Burt 1997) and less likely to be the bridge contact between unconnected groups than are men (Burt 1992). These more dependent employment networks mean that women are less often in a position to receive promotions and bonuses. Yet

S/D-1 6/16/04 12:50 PM Page 22

22

Women’s Work

in the rare cases where women and men hold the same occupational and hierarchical positions, they have similar networks (Brass 1985; Moore 1990). A growing body of research has examined the effects of organizational level variables—particularly the sex composition of firms—on career outcomes for men and women.5 Sex composition refers to the percentage of women and men in a given setting. To understand wage inequalities for women, for example, scholars cannot look only at aggregate national-level data. In addition to individual human-capital investment and cultural devaluation of jobs usually done by women, organizational processes— which must be studied with organizational level data—matter. One study (Tomaskovic-Devey and Skaggs 2002) shows this by demonstrating how organizational processes like on-the-job training differ by sex composition of jobs. Job training is much less available in predominately female occupations, which contributes to the gender wage gap. In a European study, organizational factors contributed more to stable employment outcomes for young workers than did job-search strategies and even gender (RuizQuintanilla and Claes 1996). For example, efforts by employing organizations to integrate workers into their first jobs had a great positive effect. A helpful summary of the literature on the effects of sex composition in organizations notes that when women are in the minority, they experience greater visibility and stress and are assigned more stereotypical roles to perform (Reskin, McBrier, and Kmec 1999). Women employed in organizations where they are in the minority feel more isolated than men do when they are in the minority. The effects of gender composition on job satisfaction, and thus on turnover of male and female employees, are less clear. Men’s job satisfaction seems to be not much affected by sex composition, except when women are working at their hierarchical level. Then, men are less satisfied with their jobs, which may affect their turnover rates. Organizational sex composition also affects women’s chances at promotion into positions of authority. One study (Cohen, Broschak, and Haveman 1998) concludes that women financial managers face a difficult situation. The researchers find that savings and loans with women in prominent management positions have an increased likelihood of hiring and promoting women managers in lower levels. But how do women get to the top in the first place if they need women above them to increase their chances of promotion? Ryan Smith and James Elliott (2002) likewise find that women and minorities are more likely to attain authority where they exist in large concentrations. In addition to firm composition, Smith and Elliott learn that geographical and industry composition affect women’s chances of promotion—and that they do best in places subject to economic downturns. One of the places where women and minorities are highly concentrat-

S/D-1 6/16/04 12:50 PM Page 23

Explaining Sexual Apartheid in Science

23

ed, and find better chances of promotion, is the public sector. Organizational ecologists (Barnett, Baron, and Stuart 2000) show that within the public sector the sex composition of work units seems to matter less for promotion. California state employees in predominately male posts had fewer promotions with larger raises, whereas those in predominately female posts had more promotions with smaller raises, but as government employees they ended up with similar career outcomes. The sex composition of academic departments is a subject of research relevant to the careers of many scientists. One study (Wharton and Bird 1996) finds that as the percentage of women in a university department rises, they feel a corresponding sense of cohesion. Men in all-male departments feel the least cohesive, and men in predominately female departments feel the most cohesive. Feeling a sense of social cohesion, however, seems to be no guarantee of job satisfaction. Another study (Wharton, Rotolo, and Bird 2000) found that as women’s representation in university departments increases, both women and men express less satisfaction with their job. Furthermore, in the case of universities job satisfaction may not be related to turnover. The percent of women in academic departments is unrelated to men’s turnover, and sex composition reduces women’s turnover only when females made up more than a third of the department (Tolbert, Simons, Andrews, and Rhee 1995). As may be apparent, studies of effects of the organizational level on gender stratification have mainly focused on adding explanatory variables such as organizational sex composition. In general, studies that consider the effects of organizational features on gender inequality in employment take hierarchical organizations as their subject. Whether internal aspects like corporate strategy (Morgan and Knights 1991) and amount of slack (Tolbert and Oberfield 1991), or external characteristics like job market stability and legislative environment (Kulis and Miller-Loessi 1992), the assumption is that organizational features are hierarchical. Organizational form as a comparative context for inequality has not been studied, however (Smith-Doerr 2004). To understand the different labor-market opportunities for men and women, we must know something about the form of organizations in which they work. Organizations have undergone rapid changes since the Cold War era. Paul DiMaggio summarizes scholars’ understanding of key changes in economic organization: Throughout the world, the strong boundaries that once separated firms have become less distinct, while traditional arms-length market transactions have become more intimate. New forms of coordination—“relational contracting”—have emerged that entail much less commitment and control than bureaucracy, but more binding ties than simple market exchange. (2001: 4–5)

S/D-1 6/16/04 12:50 PM Page 24

24

Women’s Work

t

At the end of the twentieth century, some organizations held on to more traditional bureaucratic forms, whereas others had flatter hierarchies and more permeable boundaries. If innovative, flat network structures are key to the future of world economic organization (Powell 2001; Castells 2000), then what does this trend mean for the representation of women in visible positions of power?

A New Approach: Organizational Form, Innovation, and Inequality

My approach to the study of gender inequality goes beyond observing either the supply of individual workers into an occupation, or the demand of employing organizations for workers who will fit into their expected roles. Instead, I consider how the connectedness among organizations shapes the context of work, as well as the opportunities that workers have for reshaping their roles in network organizations. In organization studies and economic sociology there has been a lively line of research contrasting organizational forms in economies around the world. Evidence of the use of the network form of organization from Japanese automakers like Toyota to Italian textile manufacturers like Benetton demonstrates that the implications of this book go beyond biotechnology and science careers in the United States. The network form of economic organization has been contrasted to other forms—notably to markets and hierarchies (Powell 1990; Podolny and Page 1998). Network forms depend more on trust and collaboration between independent organizations (Powell and Smith-Doerr 1994). The basis of trust can be founded on common ownership and cross-holdings in Japanese keiretsu (Gerlach 1992), on location and kinship ties in the Third Italy (Piore and Sabel 1984), and on membership in a common technology community in Silicon Valley (Saxenian 1994). In networks, the formal independence of firms assures that these connections are more flexible than hierarchies, but the relationships formed create more durability than market exchanges (Powell 1990). Studies of economic organization have begun to look at the importance of forms other than hierarchies for productivity (Sabel 1989; Spenner et al. 1998); economic stability if not short-term profitability (Gerlach 1992); centrality and product development (Powell, Koput, and Smith-Doerr 1996), and intellectual capital (Smith-Doerr et al. 1999). The implications of other forms of governance for individuals’ careers have gone largely unexplored, however. What in fact has been thoroughly studied in the analysis of organizational structures is how decentralized configurations lead to innovation more frequently than bureaucracies do. The roots of economic sociologists’

S/D-1 6/16/04 12:50 PM Page 25

Explaining Sexual Apartheid in Science

25

and others’ analyses of the network form on this point tap an older interdisciplinary literature on organizational structure called contingency theory. Contingency theorists each seemed to have their own typologies to distinguish more and less centralized organizational structures. Joan Woodward (1965) contrasted large-batch and small-batch production. James Thompson (1967) described long-linked versus intensive technologies of production. Tom Burns and George Stalker (1961) viewed organizations as falling along a spectrum from mechanistic to organic types. In stable industries, organizations are mechanistic with centralized decisionmaking and reliance on rules. For example, the rayon mill that Burns and Stalker studied epitomized mechanistic organization with its factory “bible” that listed the procedures to guide every aspect of factory life. In companies located in dynamic industries, like the high-tech electronics firm they studied, changing markets and technologies mean the organization must be more organically assembled, that is, less rule-bound, with decentralized decisionmaking. The electronics firm hired engineers for their smarts, then told them to find a place in the organization, hoping for creative outcomes. Arthur Stinchcombe (1959) contrasted craft and bureaucratic organizations, using building construction as an example. Craft tasks require more skill and decisionmaking on the job and thus must be organized in decentralized ways. Although construction workers are dispatched by a central office to jobs, while on site the workers make their own decisions, such as how best to construct a doorway, using their skills and experience. Bureaucratic tasks are more routine, requiring the same actions for similar cases all day long. Workers complete specialized tasks by following rules, and decisionmaking about the direction of the organization is centralized in upper layers of management. The lower-level manager’s job is to monitor whether workers stay within the routine. Robin Leidner’s (1993) compelling analysis of the McDonald’s Corporation shows how this machine model of organization has been increasingly applied to service work. The job of shift managers is to make sure that fast-food workers keep up the pace of routine. Decisions about every detail, including how best to place pickles on a hamburger, are made at Hamburger University by upper-level management. For the most part, the contingency theory approach of analyzing a focal organization fell out of fashion by the 1980s, when organization theory shifted its attention from single firms to the level of industries and fields. Yet the question of how organizational structures are related to innovative outcomes and creative work did not go away. The approach to studying the problem and the language changed, however. Now the tools and imagery of social networks provide an interdisciplinary basis for understanding how organizational structures hinder or help innovation. Connections among organizations are examined by economic sociologists, geographers, anthro-

S/D-1 6/16/04 12:50 PM Page 26

26

Women’s Work

pologists, and management scholars to see how the combination of information, people, and ideas through networks sometimes produces novel outcomes. Annalee Saxenian (1994), an economic geographer, investigated the regional economies of Silicon Valley and the Route 128 corridor around Boston to understand which high-tech region was more successful during the 1980s. Saxenian criticized the long-held assumption that regional advantage can be explained by firms benefiting separately from their location near a highly skilled labor pool; for one, Silicon Valley’s advantage over Boston in the 1980s cannot be explained by a superior labor pool. Instead, Saxenian takes the perspective that scholars need to examine the form of economic organization—network or hierarchy—of firms in a given region. In the computer industry, hierarchical companies like Apollo and Digital Equipment Corporation (DEC) inhabited Boston, whereas Silicon Valley firms like Sun Microsystems and Hewlett Packard (HP) employed a network form of organization. Note that HP and Sun are still around today; Apollo and DEC are not. Survival is a key measure of success in the mercurial information technology (IT) arena. Sun’s organization in those days illustrated a concept developed by Saxenian’s colleagues (Sabel and Piore 1984): flexible specialization. Flexible specialization is a form of production that occurs through networks of close-knit firms in a region specializing in one kind of good. In Modena, Italy, small artisan shops each complete a phase of knitwear production to collaboratively produce high-quality, up-to-the-minute fashions (Lazerson 1995). Each link in the network has both the skill and the trust in their regional partners to share know-how in order to move quickly and flexibly to work with innovative materials and fashion trends. Saxenian found that flexible specialization gave Silicon Valley its advantage over secretive, hierarchical high-tech Boston firms. Sun, for example, designed hardware and software but let other companies manufacture their designs. Sun used open code (“free” software), providing a transparency in design that promoted collective learning within the Silicon Valley region. Their idea was to garner competitive advantage by introducing new products rapidly and staying one step ahead of the competition. Network ties with engineers and computer scientists in other firms give savvy organizations the ability to see what’s coming next in a fast-moving field. In contrast, Route 128 firms like Apollo and DEC worked with the goal of doing everything in-house. These more hierarchically organized firms, with clear organizational boundaries, suffered from inertia and did not see that early success and heavy investment in minicomputers and mainframes would cause them to fall behind in the personal computer revolution. In a knowledge-expanding industry, no one firm contains the expertise to continually ride the wave of innovation.

S/D-1 6/16/04 12:50 PM Page 27

Explaining Sexual Apartheid in Science

27

Older industries that emit less of a high-tech profile also show evidence of the connection between interorganizational linkages and innovation. In a study of health care organizations, new surgical technologies and ideas about alternative birthing centers were found more often in hospitals that remained independent but established collaborative links with others (Goes and Park 1997). A study of investment banks by Robert Eccles and Dwight Crane (1988) found that those with self-organizing teams were able to quickly form project networks around deals and did not leave them in place to create inflexible bureaucracy. Innovative banks could quickly restructure companies, as in First Boston’s deal with Union Carbide Corporation, through this decentralized, network structure. Laterally organized firms embedded in webs of relationships often foster the development of new ideas and entrepreneurship into new economic domains. Sometimes called learning organizations, those companies able to negotiate alliances with other information-rich organizations remain on the cutting edge.6 This is not to say that a network form is necessary to innovation. In the study of the relationship between organizational structures and innovative outcomes, contingency theory was abandoned in part because of its circular logic. Current network analyses must take care not to reinvent the tautology of structural functionalism. If we find ourselves on some level responding to the questions How do you know a firm is innovative? with the answer Because it uses a network form of organizing and How do you know an organization employs the network form? with Because it is innovative, then we are at a logical and empirical dead end. One way out of this morass is to cultivate an awareness of how cultural and national institutions play a role in the development of interorganizational networks. New institutionalism is a perspective that does focus on the cultural forces shaping organizations, usually into similar forms.7 Research by new institutionalists reveals similarities in economic organization across the globe as well as variation in the network form. The expansion of scientific research in nations around the globe seems to result more from a common culture of rationality rather than because it gives efficient returns on technological investments (Schofer, Ramirez, and Meyer 2000). Education is a key mechanism providing normative isomorphism in organizational structures and strategies (DiMaggio and Powell 1983). The education of the world’s business and political leaders in U.S. universities with similar curricula facilitates the diffusion of similar economic forms and policies globally. In business schools, future world leaders learn that scientific research leads to economic expansion and that high-tech entrepreneurs should use networks to set up business (e.g., Tushman and O’Reilly 1997). By contrast, local variations in networks in the economy show that the standard models one might pick up in business school apply differently in different national contexts. A study of the Far East employing

S/D-1 6/16/04 12:50 PM Page 28

28

Women’s Work

a new institutionalism framework demonstrates that the network form of organization has a different shape across even nearby nations (Hamilton and Biggart 1988). In Japan the keiretsu (business groups) forge horizontal networks between large conglomerates (e.g., Sumitomo and Toyota) as well as vertical networks between large firms and small subcontractors (e.g., Toyota and its seat manufacturer). South Korean business groups, in contrast, experience central coordination of their linkages through a strong national government. Taiwan’s network organizations are small, familyowned firms, that is, entrepreneurial efforts founded on kinship connections. Despite the varieties of connectivity, the trust between organizations that allows economic relationships to develop is a common theme in all variations of the network form. And whether managers cultivate interorganizational relationships and flatter organizational structures for relative effectiveness or cultural acceptability, network organizations often are a locus of innovation. The network form, then, has been found to be innovative, speedy, and facilitative of high-production-value goods. The question, however, is whether it provides a context of equal opportunity for men and women. This is a new question. On the one hand, organization studies that focus beyond hierarchy to variation in forms of economic governance overlook gender. On the other hand, feminist critiques of organization studies and economic sociology (Acker 1990; Milkman and Townsley 1994; Zelizer 1999), which alert us to the lack of attention to gender issues in organizations, have not raised the question of how gendered new and different forms of economic organization might be. The gender and work literature has approximated the issue of networks versus hierarchies in the discussion of the effects of formalization and rules on gender equality. Formal policies are said to prevent “social closure,” where those in power create barriers to the entrance of dissimilar others (Tilly 1998). Based on a survey of U.S. organizations, one team (Tomaskovic-Devey, Kalleberg, and Marsden 1996) argues that gendered social closure is lessened when employers have formal educational requirements and hiring practices rather than informal job training and recruitment. Another study (Reskin and McBrier 2000) likewise finds that recruitment of managers through networks usually disadvantages women. Such findings would seem to argue for the benefits of bureaucracy. Yet other research would seem to regard more network-like formations as beneficial to women. One study (Gorman 1999) of law firms described some firms as attuned to changes in the economy and legal environment, with more complex work patterns and a variety of weaker ties to clients rather than dependence on a few strong ties. These savvy law firms were more likely to form permanent positions for attorneys and to avoid the traditional up-andout system that has been shown to disadvantage women lawyers more than men (Epstein 1981; Kay and Hagan 1998).

S/D-1 6/16/04 12:50 PM Page 29

Explaining Sexual Apartheid in Science

29

t

The few scholars who have looked directly at high-tech, network-like organizations for gender equality have found opposite effects. Judith McIlwee and J. Gregg Robinson (1992) found that female engineers were disadvantaged in startups, which they argue are more network-based. Their study follows the line of argument that the formal rules in hierarchies provide an advantage for women. Females are often disadvantaged by informal organization norms, especially in technical jobs (Tierney 1995). Yet Mary Brinton (1993) found that women professionals in Japan did better in subcontracting firms having more equitable network relations rather than those more dependent on parent corporations. Moreover, bureaucratic rules often do not create equity for women. In a study of General Motors autoworkers (Milkman 1997), the researcher discovered that women were much more likely than men to “buy out” of the factory in the 1980s, even within the high-seniority ranks. By accepting a buyout, workers received about six months’ salary to leave rather than try to stay on in a reduced workforce. By the rules, workers with more seniority would receive preference in keeping jobs. Yet high-seniority women left, and they were less likely to find new work after leaving than the men (Milkman 1997). Women expressed little satisfaction in their jobs, perhaps because men earned three times as much as they did on average. The General Motors plant studied was bureaucratic, unionized, and rules-oriented. But women did not have equal pay, job satisfaction, or returns to seniority there. At heart, the emphasis on rules and formalization (e.g., Reskin 2003) seems to be about the transparency of work processes (i.e., in the selection of tasks, performance on the job, and distribution of rewards). When sexism is revealed to the light, it is more difficult to sustain. For life scientists, however, we will see that university rules appear to foster development of informal organization that favors men, whereas biotech firms’ “official informality” provides a clearer assignment of roles. Additionally, interorganizational performance of scientific R&D reveals who brings in good ideas and collaborators. Women have been constrained in their roles as scientific professionals in traditional life-science hierarchies. Biotech provides an alternative employment option with more opportunities for women’s advancement because there are fewer chances for sexism to thrive in the transparency and flexibility of the network form.

Women’s Work Life Chances in the Life Sciences

One of the most robust findings in the sociology of occupations is the persistence of gender inequality in careers. What do we currently know about the gendering of science and technology careers? Studies of academic careers and inequalities that women face in the labor market generally

S/D-1 6/16/04 12:50 PM Page 30

30

Women’s Work

show a picture of durable inequality. Although women are in the labor force in record numbers, including in the sciences, they are not making inroads into positions of authority. Why do women experience consistent disadvantage? Economists point to individual choices made, sociologists to discrimination in a culture that devalues feminine traits. Consideration of how differences at the organizational level might affect women’s careers has been surprisingly slight. Some studies have added organizational size or sex composition as an explanatory variable, for instance. The emergence of the biotechnology industry in the United States in the early 1980s offers a unique opportunity for analyzing data that speak to the relationship of organizational form to gender equality in work roles. When the role of formal organization is discussed in explanations of gender inequality, an assumption is usually made that hierarchical, bureaucratic organizations characterize work environments. Recent economic sociology and organization scholarship, however, has noted the importance of other forms of economic organization—notably the network form. Employment networks, however, are often portrayed as the domain of old boys. Thus, one might expect network organizations (like biotech firms) especially to rely on recruitment through old-boys’ networks and to promote stratification even more than hierarchies. Does gender inequality look the same across different forms of economic organization? This book examines the effects of network and hierarchical organization on the differential mobility of men and women into supervisory-level positions. To be competitive in the global knowledge economy, the United States must tap all potential talent in science and engineering. Instead of relying only on male scientists to lead us into the future because they have in the past, we have much to gain in scientific and technological innovation by including women in leadership roles. Compared to their roles within other natural sciences and engineering, women have been moving into the life sciences in large numbers. Currently, about 40 percent of life-science PhDs are female, up from less than 20 percent in the 1970s. Women still make up fewer than 20 percent of physicists and engineers. Yet large numbers do not necessarily mean more powerful positions: women academics in the life sciences continue to be underrepresented in full-time, tenured positions. And even when women make it to elite universities, they face discrimination, as demonstrated in the widely publicized news of Massachusetts Institute of Technology (MIT) confronting the deeply entrenched gender bias of academic science. The internal study (MIT 1999) found that gender inequalities accumulated during science and engineering careers. Senior women were especially less likely to receive equitable salaries, laboratory space, research grants and awards, other resources, and favorable responses to outside offers. That is, women faculty received less of these benefits in comparison to male faculty with equal professional accomplishments.

S/D-1 6/16/04 12:50 PM Page 31

Explaining Sexual Apartheid in Science

31

t

Additionally, women at MIT became increasingly marginalized by their departments over time, excluded from playing significant roles. As the title of this book suggests, life scientists work and distribute resources and control by gender in new ways in biotechnology firms. Like Judith McIlwee and Gregg Robinson’s Women in Engineering (1992), this book compares the outcomes for women in the technological workforce in different kinds of organizations. McIlwee and Robinson assess the fate of women in small and large engineering firms and find that larger corporations, like the aerospace giants, are better for women engineers. Why the difference between engineering and life sciences? In the small high-tech firms that they study, mechanical tinkering is perhaps a more gendered skill set than life sciences bench work seems to be (see also Oldenziel 1999 on the masculinization of engineering). In this study, biotech firms differ from other life-science settings not just in size and high-tech mentality but also in reliance on interorganizational ties. Biotechnology firms engage in significant relationships with diverse partners from multinational chemical companies to university labs to venture capital firms. Also consider that there are more female life scientists than engineers. The context for women may differ because of their token status in engineering versus their growing proportion in life sciences. Chapter 1 has introduced the central research problem of how organizational context shapes opportunities for women in science and technology occupations. The key question is how the network form of organization used by biotech firms compares to more hierarchical employment settings, like academe, for gender equality in the attainment of supervisory positions. Before examining these gendered organizational processes, however, it is important to first consider the context in which these professionals work. To this end, Chapter 2 offers a brief introduction to the development of the modern life sciences, an increasingly important arena in society during the twenty-first century.

Notes

1. Note that the names of individuals and organizations given throughout the book are pseudonyms to protect confidentiality. 2. Indeed, one team (Grant, Kennelly, and Ward 2000) points out that the gendered “productivity puzzle” that others (see, e.g., Cole and Zuckerman 1984) have studied fails to look critically at the academic context. The tenure system for no rational reason takes for granted a traditional male career pattern—an early peak to productivity during childbearing years—and disallows other patterns of productivity (i.e., for parents with primary child-care responsibilities). 3. Economists also have a demand side explanation called statistical discrimination. In this theory, employers attempt to make rational hiring decisions with limited information about job applicants. Because on average, the employers calculate,

S/D-1 6/16/04 12:50 PM Page 32

32

Women’s Work

women will miss more days of work it is statistically a better risk to hire a man. Thus, hiring discrimination against women is nothing personal, just the best business decision one can make in absence of reliable knowledge about applicants. Furthermore, if an employer is inaccurately making assumptions about demographic categories, the market will correct this error. The employer who is unfairly biased against women will go out of business because his competitors will hire women at lower wages while he pays more for men. There are many problems with this perspective (see England 1992 for a review of all the neoclassical economic permutations of this perspective and their weaknesses). Irene Browne and Ivy Kennelly (1999) show what the supposedly accurate statistical discrimination summary of applicants by gender and race looks like in real life outside of an economic model. Their study in Atlanta showed that male bosses’ stereotypes about single mothers led employers to discriminate against African American female applicants, not some statistical calculation. In fact, African American women in Browne and Kennelly’s survey data were actually no more likely to miss work or be late than any other demographic group. But this was a widely held stereotype that black women faced everywhere in employing organizations, and it was not about to be corrected by market forces. 4. I do consider the prestige ranking of university departments but do not distinguish between instrumental and collaborative academic environments, for example. 5. Note that there is also the larger macro, national context of gender stratification to consider, which has been a subject of much research. For example, Cotter and colleagues (1997) argue that U.S. women in female-dominated occupations benefit as much from gender integration as do women in predominately male occupations. Additionally, researchers (Cohen and Huffman 2003) posit that this effect occurs also at the specific job level: women in wholly segregated jobs benefit from working in labor markets with less occupational sex segregation. Integration seems to have a macro-level benefit to all women in a labor market. In cross-national comparisons of gender inequality, Maria Charles (1992) finds that corporatist political systems are related to occupational sex segregation, regardless of national egalitarian ideals. A study of nine industrialized countries (Rosenfeld, Van Buren, and Kalleberg 1998) found women moving more equitably into supervisory positions in nations with greater income equality, more generous maternity leave policies, and with less favorable economies. 6. For details, see reviews of the literature on networks and learning in economic sociology (Smith-Doerr and Powell 2004), as well as management of innovation at the organizational (Tushman and Smith 2002) and industry (Miner and Anderson 1999) levels. 7. Fortunately, alongside structural economic sociology, new institutionalism has also been developing in contemporary organization theory. This parallel growth provides a balance in the field between approaches that might be too structural and systemic and those too cultural and idiosyncratic. The complementary strengths of these trends in organizational sociology make for exciting times in scholarship. Some of the most innovative organization scholars have been able to combine the study of network structures and cultural institutions in some way.

t

S/D-2 6/16/04 12:51 PM Page 33

2

A Brief Life Story of the Life Sciences

It seems almost a miracle to me that fifty years ago we could have been so ignorant of the nature of genetic material and now can imagine that we will have the complete genetic blueprint of man. —James D. Watson (1992) You look at science as . . . something apart from real life, and which must be cautiously guarded and kept separate from everyday existence. But science and everyday life cannot and should not be separated. —Rosalind Franklin (1940)

The development of the life sciences has been heralded as one of the most important human achievements of the last half of the twentieth century. James Watson’s quote above reminds us of the swiftness of change in the life sciences during the span of one career. In June 2000, Craig Venter, who at the time was CEO of Celera Genomics, made a joint announcement with U.S. Human Genome Project director Frances Collins that all 30,000 or so genes of our human DNA (rather than the estimated 100,000) had been mapped, three years ahead of schedule. The scientific spotlight has shifted away from physics, which dominated the natural sciences in the early twentieth century and was developed inside government and academic laboratories. In contrast, the Human Genome Project announcement shows that the life sciences are being developed outside the government and academy, as well as within. This chapter discusses the emergence of new venues for scientists to pursue their careers: startup science-based firms. The employers in the life-science labor market, the organization of biotechnology firms at the industry level, and the interchange between university and industry domains are depicted in this chapter. The increasing overlap between university and industry domains in the life sciences is a story of personal and organizational relationships. Social relationships and science are intertwined; scientists have known

33

S/D-2 6/16/04 12:51 PM Page 34

34

Women’s Work

this, as Rosalind Franklin’s quote above demonstrates. The social aspects of science may be harder to see from the outside, however. Fortunately or unfortunately, depending on your perspective, insiders have permitted sociologists and anthropologists to observe the everyday world of science, and some scientists themselves have even described it directly to the general public, like Watson in his tell-all autobiographies. These forays into the laboratories, offices, and taverns where scientists work reveal the relationships and rivalries that infuse the swiftness of change in the life sciences. Watson recalls the teamwork that went into the momentous 1953 announcement he made with Francis Crick: Our writing of the tiny manuscript for Nature that would announce the double helix seemed even then an historic occasion. My sister Elizabeth, who had followed me to Europe two years before, did the typing, with Odile Crick using her artistic talents to draw the intertwined, base-paired, polynucleotide chains. Together with two experimental manuscripts from the warring King’s groups of [Maurice] Wilkins and [Rosalind] Franklin, it was dispatched to Nature’s editor. (Watson 2001: 11)

t

Every page of Watson’s lively memoirs shows how the discovery of DNA was embedded in social relationships. Just the few sentences above show how gender is a key aspect of the story of the life sciences. From sister and wife typing and drawing the diagrams for the famous manuscript, to the controversy surrounding the use of Franklin’s data for the double-helix idea, gender roles are inseparable from scientific work and the politics of discovery. Rather than scrutinize one infamous event, my focus is on the broader anatomy of a field—the organizational contexts in which men and women’s careers in the life sciences are worked out. This chapter discusses the emergence of new venues for scientists to pursue their careers—startup science-based firms—in contrast to the older alternatives of academia and large drug companies in the life-science labor market.

Let’s Get Physical:The Origins of Molecular Biology

During the first half of the twentieth century, physics was the undisputed ruler of the sciences. The rise of molecular biology toward the end of the twentieth century was not a separate development from the reign of physics; rather, like the “Matthew effect” discussed by Robert Merton in the careers of individual scientists, the role of physics as the elite “mentor” of molecular biology contributed to the rapid legitimation of the new field. Disciplines do not develop in social vacuums but come from existing lines of work and social relationships. Nicholas Mullins (1972) tracked the social networks of early molecular biologists in the bacteriophage group

S/D-2 6/16/04 12:51 PM Page 35

A Brief Life Story of the Life Sciences

35

led by Max Delbrück at the California Institute of Technology. Delbrück, student of the famous physicist Niels Bohr, along with several fellow physicists, decided to enter biology in the early 1930s because it “had the greatest number of unsolved problems which appeared open to fruitful investigation by physical methods” (Mullins 1972: 56). In constructing a new field, these scientists’ ties to physics infused the discipline with legitimacy. Physicists’ networks also infused nascent molecular biology with cash. Robert Millikan, the Nobel Prize–winning physicist and institution builder, made the initial decision to bring the biological sciences to Caltech. Millikan himself had been recruited to Caltech by the institute’s founder and astrophysicist, George Hale. Caltech had become the premier science institute on the West Coast by the 1920s, and Millikan wanted to add biomedical research to broaden the institute’s expertise beyond the physical sciences. Financial supporters from Carnegie and the Rockefeller Institute apparently supported Millikan’s expansionist plans. Millikan explained in a 1923 letter to Hale how the financiers have both been here within the month and coincide in the general view that if anything is done in Southern California in the field of biochemistry, biophysics, and medical education, it must be done in immediate contact with the present work of the Institute.

Millikan’s letter proceeds to relate how one financier described a Johns Hopkins bacteriologist whom he knew is suffering already from lack of contact with physics and chemistry. He says the Rockefeller Board will not be interested in any medical plan in Southern California which is farther away than across the street at most from the Institute. (cited in Kay 1993: 71)

The modern life sciences, although developed separately from the physical sciences, relied upon prominent physicists for both social and financial capital. This relationship continues: the U.S. Department of Energy, traditionally the governmental domain of physicists, was instrumental in the founding of the Human Genome Project during the 1980s. Another way that physics more tangibly and tragically contributed to the development of life sciences during the twentieth century was by providing the know-how for the atomic bombs that laid waste to Hiroshima and Nagasaki during World War II. After the war, the United States and Japan set up a joint venture, the Atomic Bomb Casualty Commission. The commission called upon biologists to study the genetic effects of radiation exposure from the atomic bombs. Although results of medical tests were inconclusive—it was unclear whether radiation had genetic effects on the

S/D-2 6/16/04 12:51 PM Page 36

36

Women’s Work

offspring of exposed parents—the social and political effects of the commission were real (Beatty 1991). Genetic and biomedical research received positive press and increased funding, and physics was viewed with heightened suspicion. As the twentieth century drew to a close, then, the natural sciences had a changing of the vanguard—a shift in which disciplines garner the most resources and attention and are considered to have had the most profound advances. The life sciences have taken the spotlight from physics, as the Cold War was pronounced dead and the war for life at the molecular level has taken center stage. Of the total federal moneys dedicated to research between 1971 and 1992, the life sciences’ share rose by 10 percent (to 40 percent of the total), whereas the proportion granted to physical sciences remained the same (National Science Foundation 1992). The budget for the National Institutes of Health (NIH)—a major funding source for lifescience research in academe and a prestigious fund for biotech firms— increased by 30 percent in constant dollars during the 1980s, whereas other federal funding remained stable or declined. A dramatic demonstration of this funding trend is the denial by Congress of funds for the construction of the Superconducting Supercollider even while full support was granted for the Human Genome Project to the tune of $200 million per year (NSF 1995). The trend toward funding the life sciences is paralleled by the increasing proportion of natural scientists going into the biological sciences compared to the physical. Twice as many new biological PhDs (39,000) completed their educations in 1980s—the first full decade of biotech—as did in the 1960s. In contrast, the number of new physical science PhDs matriculating in the 1980s (29,000) remained about the same as in the 1960s (NSF 1993). As Chapter 1 showed, the entrance of women into biology and not into physics accounts for some of this difference in the growth rates of doctorates. Physicists are aware that biology has now captured the scientific prestige, government funding, and industry resources that physics once enjoyed early in the twentieth century. Robert Laughlin, the 1998 Nobel Prize–winner in physics, now works on theoretical biology. Laughlin’s reason for switching fields almost eerily echoes Delbrück’s vision of biology seventy years ago: “Biology has provided physics with its new frontier” (Cook 2002: D3). Another prominent physicist who has moved into studying biological problems, John Hopfield, views disciplinary boundaries in the academy as restricting the flow of physics knowledge and human capital into biology: “The whole problem is that we are living in the 21st century with these 19th century guilds” (Cook 2002: D3). While universities have moved toward the creation of interdisciplinary biological and chemical science departments, physics remains largely separate. The shoe is on the other foot now, as physicists make connections with prominent molecular biologists for legitimation and funding of their discipline.

S/D-2 6/16/04 12:51 PM Page 37

t

A Brief Life Story of the Life Sciences

37

The Development of Basic Differences Between Academic and Industrial Biology

When physics dominated the sciences, roughly from World War I through the Vietnam War, young scientists were told that there were two basic career options: basic (i.e., academic) science or applied (industrial) science. The most famous statement of the distinction between the worlds of academic basic science and industrial applied science comes from Vannevar Bush (1945) in his report to President Franklin Roosevelt from his office as director of scientific research and development. Bush eloquently argued for the importance of basic science: Basic research leads to new knowledge. It provides scientific capital. It creates the fund from which the practical applications of knowledge must be drawn. New products and new processes do not appear full-grown. They are founded on new principles and new conceptions, which in turn are painstakingly developed by research in the purest realms of science.

Although basic science provides the font from which the material blessings of science flow, Bush warned that impure applied motivations must not be allowed to muddy the source waters: Industry is generally inhibited by preconceived goals, by its own clearly defined standards, and by the constant pressure of commercial necessity. Satisfactory progress in basic science seldom occurs under conditions prevailing in the normal industrial laboratory.

In his report, Bush expressed a widely held attitude toward science. This attitude was reflected in the organization of scientific institutions and careers. For biological and chemical scientists, a career developing pharmaceutical products in industry was a separate, sharply diverging path from the high road of pursuing basic science in the university. Even though this sharp separation of basic and applied life science in the early twentieth century was a stylized version of the truth to which exceptions abound, it’s important to recognize the extent to which this narrative shaped the organization of scientific work. When pharmaceutical companies established themselves in business, the strategy was to create economies of scale and scope in making an array of moderately priced products that would appeal to mass markets. Scientists in pharmaceutical corporations were charged with finding effective substances, not with understanding how they worked. The history of Wyeth pertinently demonstrates how the pharmaceutical industry emerged and has recently changed since biotechnology firms have come on the scene. In 1926, American Home Products (now Wyeth) was incorporated as a holding company. The company’s financiers required this consumer products conglomerate to

S/D-2 6/16/04 12:52 PM Page 38

38

Women’s Work

have a pharmaceutical foundation, because they saw that as a safe market. Thus, the corporation began by acquiring Deshell Labs in California, the maker of Petrolagar (a top selling laxative; see Figure 2.1). Reflecting national trends, the financial headquarters of the conglomerate was located in Manhattan, the manufacturing in Detroit. The next year the company purchased the makers of Old English Floor Wax and was on its way to holding a consumer product line through the 1980s that ranged from packaged foods to cleaning products to health care and infant formulas. By 2002, the pharmaceutical conglomerate had a different name and business strategy. Part of the impetus for a name-change may have been the late 1990s controversy surrounding the combination of two American Home Products–licensed diet drugs, the so-called fen-phen mixture. The phen drug, Redux, was licensed from Interneuron, a biotech startup. The combination carried a risk of severe pulmonary problems that led to perhaps as many as 123 deaths (Mundy 2001). American Home Products removed both drugs from the market and (according to fen-phen-settlements.com) has been negotiating a settlement to cover claims to in excess of $3 billion. Another reason for the name-change is apparently to give the

Figure 2.1 “Petrolagar,” pharmaceutical product, 1926 This is an advertisement for the laxative, Petrolagar, the first product sold by the pharmaceutical corporation American Home Products (now Wyeth) in 1926. Reprinted with permission of Wyeth.

S/D-2 6/16/04 12:52 PM Page 39

A Brief Life Story of the Life Sciences

39

t

corporation more of a hip biotech spin. Announcing “the future of medicine has a new name,” American Home Products changed its name to Wyeth in 2002. In reality, the name has been used for American Home Products’ drug division since 1931, when the company purchased the respectable pharmaceutical firm John Wyeth & Brother from Harvard University. Universities were not supposed to be in the business of making drugs or patent applications back then, in line with Vannevar Bush’s assertions. Today’s pharmaceutical corporations (not to mention universities) have been transformed by competition and collaboration with the upstart biotechnology industry. Wyeth is still interested in large-scale markets but has divested itself of Chef Boyardee, Wizard cleaning products, and candy making, set on becoming a research powerhouse instead of an American Home Products company. Wyeth’s website in 2003 featured its ties to biotech: “Strategic alliances with leading genomics and biotechnology companies around the world give Wyeth researchers access to extensive genetic databases, including the entire human genome sequence” (wyeth.com). Why did the pharmaceutical industry’s focus change so dramatically from diverse product lines and applied industrial science to human medicine and connections to basic science organizations? In the growth of legitimacy for industrial firms to pursue basic science and for universities to pursue patent applications, the biotechnology industry figures prominently. Small biotech firms outnumber large pharmaceutical corporations in the business of making drugs. One analysis of 482 firms developing human therapeutics and diagnostics provides information about the core population of the biotechnology industry (Powell, Koput, Bowie, and Smith-Doerr 2002). In contrast, a sample of just ten companies provides a good picture of the core population of large pharmaceuticals (Cockburn and Henderson 2001). Universities with active life-science research programs are numerous. The Carnegie classification of doctoral research universities includes 261 schools in the United States (McCormick 2000), and the National Research Council (1994) has ranked nearly two hundred PhD programs in molecular biology. Their intellectual-social capital, not their numbers, is what lends biotech the credibility to influence the established institutions of the pharmaceutical industry and academe.

Magic Bullets and Ivory Towers: Drug Development and Academic-Industry Relations

Two years prior to winning a 1908 Nobel Prize for his work in immunology, Paul Ehrlich predicted that the young pharmaceutical industry would be in the business of finding “magic bullets.”1 Magic bullets would kill disease-causing microorganisms without harming humans who have the disease. Ehrlich’s term has been handed down through generations of life-

S/D-2 6/16/04 12:52 PM Page 40

40

Women’s Work

science PhDs. During the early days of the biotechnology industry in the late 1970s and early 1980s, monoclonal antibodies were widely touted as the incarnation of the long predicted magic bullets to cure cancer. A 1980 review published in Immunology (“Whither Monoclonal Antibodies?”) refrained from using “magic bullets” language but did laud the “exquisite specificity” of monoclonal antibodies, which allowed scientists “to isolate or identify antigenic molecules that have hitherto been elusive” (cited in Cambrosio and Keating 1995: 120). Success with mouse and animal models, however, proved difficult to translate into success in humans. The first therapeutic product of monoclonal antibodies biotechnology did not hit the market until nearly twenty years after the hype. In 1997, Rituxan, a cancer treatment developed collaboratively by biotech firms Genentech and IDEC Pharmaceuticals, finally received FDA approval. Although the promise of new science coming from biotechnology sometimes leads to disappointing delays (and corresponding dips in stock market prices), small knowledge-producing biotech firms have become the model for larger pharmaceutical conglomerates to emulate and have been publicly pursued by forty-one U.S. governors attempting to inject new life into their states. The as yet limited success of biotech has attracted attention not just because of the new science but also because of the industry’s novel mode of organizing firms and careers. Biotechnology firms are connected through webs of interorganizational networks. These industry networks connecting laterally organized firms constitute a new model of organizing science that looks very different from earlier models of the large pharmaceutical conglomerate and the academic ivory tower.

Changes in Knowledge, Regulation, and Organization The life sciences have garnered increasing attention largely because of the rapid scientific and organizational changes they have undergone. The scientific and technical changes in the 1970s and 1980s include a shift in drug discovery emphasis from organic chemistry to molecular biology, the advent of recombinant DNA techniques, and the development of oncogenetics to study cancerous genes. Other changes in the life sciences during this period might be considered more a mix of scientific and organizational innovation. Cloning of human interferon genes led to collaborations for big cancer application projects between biotech firms and large pharmaceuticals such as Genentech and Hoffman-LaRoche and also Biogen and Schering-Plough. The Human Genome Project also represented both scientific and organizational changes in the life sciences. In the mid-1980s academic scientists outlined the project, and in the late 1980s federal funding was organized by the Department of Energy and the National Institutes of Health for the project. A national center for human genome research was

S/D-2 6/16/04 12:52 PM Page 41

A Brief Life Story of the Life Sciences

41

headed by James Watson (quoted in one of the epigraphs at the beginning of this chapter). Funding of the human genome project was at first controversial from a medical-ethical standpoint; bioscientists also criticized it from a concern with their research autonomy and a desire to avoid large project coordination of resources, à la physics, into “Big Science.” Changes in the economics of funding research in the life sciences during the early years of the biotech industry probably affected perceptions even more profoundly than budgets. Moreover, these perceptions certainly affected the environment of academic science. There was a fear of federal funding lapsing, although the NIH budget was rising. As the human genome project and money dedicated to AIDS research increased as a proportion of the NIH budget, however, there was a brief decline in the amount available for untargeted research for principal investigators (PIs). The cost of setting up a new assistant professor’s molecular biology laboratory had risen to at least $500,000, so university and federal funds could not provide the resources to expand the number of new academic positions significantly. Also, the pressure of having more PhDs without a parallel rise in the number of available academic jobs changed the dynamics of hiring so that not as many new PhDs obtained positions as academic PIs, compared to the proportion that did in the 1960s (National Research Council 1998). In addition to changes in the knowledge and organization of the life sciences, the conditions under which the biotechnology industry emerged and flourished included a favorable regulatory environment. For example, the 1980 Patent and Trademark Law Amendments Act, commonly known as the Bayh-Dole Act, allowed patents on ideas formulated with federal funding support to be held by the inventor. Thus, university scientists could legally profit from holding patents and forming startups. This and other U.S. laws (allowing more freedom of patenting, more research manipulating DNA, and more liberal FDA approval for new drugs), along with readily available financing from venture capital, provided a relatively friendly regulatory environment for the fledgling biotechnology industry. The emergence of the biotechnology industry is a result of the applications of life-science discoveries, a melding of the basic and applied sides of science to a greater extent with previous science organizations, and an organizational change in the formation of an industry of firms reliant on extensive collaboration for most activities. The biotechnology industry is populated by small firms, which for the most part have not been acquired by larger corporations, such as the giants of the pharmaceutical industry. Biotech firms are more likely to merge with each other than be acquired by pharmaceutical corporations. In 2001–2002, for instance, more than a halfdozen biotech firms announced mergers (Jaffe 2002). Dedicated biotechnology firms—the focus here and the core of the industry—may be defined as research-intensive organizations primarily concentrating on genetic

S/D-2 6/16/04 12:52 PM Page 42

42

Women’s Work

engineering and molecular biology for human therapeutic and diagnostic applications. Given the expanding, knowledge-intensive industry that biotechnology is, learning occurs in interorganizational networks rather than within single firms. Evidence of this learning through networks includes the fact that firms with the most collaborative R&D experience and centrality in industry networks develop more ties, develop more diverse portfolios of collaboration, and are more likely to go public (Powell, Koput, and Smith-Doerr 1996). Colleagues and I tested how interfirm relations in biotechnology are best explained. Our data provided the most support for a networks of learning argument—more central firms are larger and have more diverse ties— and the least support for the traditional economic model in which larger firms should be conducting more activity internally rather than relying on networks. Thus, at the industry level, rewards appear to come to firms that have extensive, diverse interorganizational connections—having many partners for different functions (e.g., R&D, clinical trials, distribution). The biotechnology industry is rife with interorganizational networks, and the firms more centrally located in industry circles are the most innovative and profitable (Powell, Koput, and Smith-Doerr 1996; Stuart, Hoang, and Hybels 1999; Baum, Calabrese, and Silverman 2000). Take Geron, for example. Researchers at this biotechnology company, alongside scientists from McMaster University and Cold Spring Harbor Laboratory, discovered telomerase, a cancer marker that is present in 80 percent of cancer samples. The company also collaborates with Roche, a large pharmaceutical corporation, to evaluate the clinical potential of telomerase (i.e., how sensitive it is compared to other commercially available tests for cancer). Innovation arises through such organizational relationships. Simultaneously managing ties with universities and research institutes for basic science, with pharmaceutical corporations for clinical testing, and with diagnostic companies for marketing the test kits contributes to a biotech firm’s ability to remain on the cutting edge in a fast-paced industry. Successful biotech firms maintain diverse ties with universities and pharmaceutical corporations, as well as with venture capital, hospitals, and government agencies. As befits an industry where organizations rely on partnerships to succeed, many new startups trace their origins to other biotech firms. Genentech in South San Francisco and Millennium Pharmaceuticals in Cambridge, Massachusetts, have been virtual incubators for the geographic agglomeration of firms in the two metropolitan areas of largest concentration of biotech in the world. Companies started by entrepreneurs from Genentech are now spinning off their own firms. In fact, Mark Levin founded Millennium Pharmaceuticals after working at Genentech, providing another example of a successful firm that traces its roots back to the first biotech firm. Companies are also started often by prominent academic

S/D-2 6/16/04 12:52 PM Page 43

A Brief Life Story of the Life Sciences

43

scientists with backing from venture capital (Zucker, Darby, and Brewer 1998), as in the founding of Genentech. Generally, ties to prominent organizational partners provide an important signaling function in the biotech industry and allow startups greater access to capital (Stuart, Hoang, and Hybels 1999).

Products of Networks The output of biotech firms is thus the product of their interorganizational networks. Biotech drugs and vaccines on the market or in clinical trials target more than 200 diseases, including a variety of cancers, Alzheimer’s, diabetes, AIDS, and arthritis. Startup firms with cooperative agreements have been more likely to produce innovative output than others (Shan, Walker, and Kogut 1994). Canadian biotech startups with alliances to diverse network partners perform better early on, with larger revenue streams and more patents (Baum, Calabrese, and Silverman 2000). The large pharmaceutical corporations have relied upon blockbuster drugs that bring in huge profits, like Lilly’s Prozac. Lilly lost an appeal to the U.S. Supreme Court in January 2002 to have its patent protection on Prozac extended. As the patent protection on big drugs runs out, the large pharmaceuticals have sought alliances with small biotech firms for new product ideas. Lilly’s website features a quote from CEO Sidney Taurel: “Successful alliances are more critical than ever to our strategy. We are working hard to be recognized as the pharmaceutical industry’s premier partner by consistently creating value for our partners and for Lilly” (http://alliances.lilly.com). Alliances between biotech and pharmaceutical companies and universities produce drugs that are developed interorganizationally rather than behind the closed doors of one secretive laboratory. The proprietary information is shared between organizational partners who trust each other to work toward a collective goal and is reinforced by the pressure to maintain a good reputation in order to form future alliances in the small world of the life sciences. Enbrel, a best-selling rheumatoid arthritis drug, is the product of biotechnology firms and their interorganizational networks. The Seattlebased biotech firm Immunex discovered that genetically engineering a cell receptor that binds the tissue-damaging factor at the site of chronic inflammation resulted in a therapy to reduce pain and swelling and that also slows the progression of joint destruction (Robbins-Roth 2000: 130). In developing its immunology research, Immunex scientists benefited from close ties to university researchers, some of whom they brought into the company full-time. Thomas O. Daniel, for example, directed the Vanderbilt University Center for Vascular Biology before becoming senior vice president of discovery research at Immunex. To perform the clinical trials and

S/D-2 6/16/04 12:52 PM Page 44

44

Women’s Work

marketing phases, Immunex forged an alliance with Wyeth-Ayerst, then the pharmaceuticals division of American Home Products. In 2002, Immunex was acquired not by Wyeth but by Amgen, another biotechnology firm. Amgen and Wyeth began collaborative marketing of the arthritis drug. Wyeth seemed to have learned caution about full disclosure in marketing drugs from fen-phen lawsuits: in every statement about Enbrel, Wyeth described how deaths had resulted from infections that some patients developed while taking the drug. Amgen took over marketing in 2003; Amgen’s approach to selling Enbrel to investors is illustrated in Figure 2.2. Figures 2.1 and 2.2 offer a striking contrast between traditional drugs and biotech drugs, especially interesting because both were once marketed by the same corporation. Pharmaceutical corporations traditionally developed and marketed medicines that worked, paying little attention to the scientific mechanisms that made them work. Biotech firms are science-intensive, searching for “beautiful molecules” (Werth 1994) to engineer as therapies by seeking in-depth knowledge of what makes them work. The distinction made by organizational theorist James March (1991) between exploratory and exploitative learning captures the different competencies of biotech and pharmaceutical companies. Organizations with a singular exploratory learning focus—like biotech firms—are good at striking out into uncharted knowledge domains, whereas organizations good at exploitative learning—like pharmaceutical corporations—do well at extending and filling the gaps in the maps created by more exploratory organizations. Biotech firms seek to form collaborative ties to pharmaceutical corporations for the drug-development competencies of the large-scale

Figure 2.2 not available

S/D-2 6/16/04 12:52 PM Page 45

A Brief Life Story of the Life Sciences

45

firms in financing and coordinating clinical trials and marketing campaigns. Triangle Pharmaceuticals, a small biotech firm, had an HIV and hepatitis B treatment in phase 3 clinical trials in 2002. AIDS activists have effectively persuaded the FDA to allow shorter and smaller trials for HIV drugs (Epstein 1996); even so, Triangle lacked the resources to deal with the scale of the clinical trials for its drug. The biotech company formed an alliance with the large pharmaceutical corporation Abbott Laboratories. Chris Rallis, president of Triangle, explained in July 2002 why his company collaborated with Abbott: “I’ve been astounded at the sheer cost of third-stage trials. But even with hundreds of patients instead of thousands of patients, it’s a colossal effort. We never could have done it by ourselves” (Jaffe 2002). Pharmaceutical corporations seek to collaborate with biotechnology firms because of the innovative capacity of the small, laterally organized, science-intensive companies. Michael Lytton, a venture capitalist at Oxford Bioscience Partners, put it bluntly: “The pharmas [pharmaceutical corporations] are getting desperate. They can merge together [only] so many times and they’re under tremendous pressure to come up with new products” (Jaffe 2002). Recently, however, biotech firms have made fewer alliances with large pharmaceutical companies. Recombinant Capital, a venture capital firm specializing in biotech, reported that the 524 alliances announced between biotech and pharmaceutical companies in 2000 fell to 442 in 2001. At the same time, publicly reported ties between biotech firms increased from 728 to 745 (Jaffe 2002). From a resource-dependence perspective, this shift in numbers of strategic alliances might mean that biotech is increasing its independence from the pharmaceutical industry and thus gaining relative power in the drug marketplace. Biotech firms do seem to be attempting to maintain independence from the large pharmaceuticals. Just days after the quote from Triangle’s Rallis was published, Triangle broke its ties to Abbott. The company’s president explained why in a press release: We have moved aggressively to reacquire full rights to our products not only because of our enthusiasm for the compounds but also because we believe this is a win-win outcome for Triangle and Abbott. For Triangle, we believe that the reacquisition of the rights to [four drugs, including the HIV/hepatitis B drug] all currently in clinical development, allows us to maximize the potential return on investment for the portfolio. (HIV and Hepatitis News 2003)

The bid for remaining independent was only partially successful. Triangle was not swallowed up by a pharmaceutical giant, but in 2003 Triangle Pharmaceuticals was merged into another biotech firm, Gilead Sciences. Still, fewer biotech-pharma ties might just reflect leaner economic

S/D-2 6/16/04 12:52 PM Page 46

46

Women’s Work

times. Perhaps pharmaceutical corporations are tightening their belts and investing less in risky potential future biopharmaceuticals and focusing more exclusively on current product lines. A vice president of pharmaceutical development at Abbott, John Leonard, indicated that the company agreed to break the alliance so it could focus on exploiting existing lines of research: “The agreement with Triangle to end our alliance will enable Abbott to focus exclusively on our core areas of expertise and scientific success in HIV and hepatitis C research” (HIV and Hepatitis News 2003). Although the relationship between biotech and pharma may run hot and cold, the close ties between biotech and universities remain constant. Pharmaceutical corporations and universities collaborate often with biotech firms and certainly have been affected by these ties. But the core mission of a large corporate conglomerate or an institution of higher learning, as opposed to a biotech firm, is not in jeopardy if it is not savvy at forming and maintaining diverse interorganizational relationships. For universities, former Harvard president Derek Bok (2003) argues that indeed the opposite is true: universities with commercial savvy are in trouble. Bok views universities that pursue commercialization of their research as being in danger of losing sight of core academic values, as secrecy and corporate vested interests replace them. Sociological studies of the overlap between the academic and commercial domains provide a complex picture of the effects of collaborating with biotech on the university (e.g., Croissant and Restivo 2001). Jason Owen-Smith (2003) finds that universities with more highly cited publications on average also have more patents, but only after the mid-1990s. Previously, universities that patented a lot did not have as many highly visible publications. Much of the patenting in universities— those in the elite group and those climbing the reputation ranks—has been in the life sciences. The divide between commercial and academic science on campus became more blurred at precisely the same time that the biotechnology industry grew rapidly. Between 1992 and 2001, biotech industry revenues more than tripled from $8 billion to $27.6 billion (bio.org). In Chapter 3, I consider the effects on the careers of scientists of the development of the biotechnology industry during this period of increased blurring of academic and commercial activities. Other thinking on the close ties between biotech and universities has been less critical and more caught up with enthusiasm for growth in the knowledge economy. Politicians in particular view with unbridled optimism new ideas for potential products, startup firms, and jobs that can spill over from academic organizations. The metropolitan centers for biotech in the United States—San Francisco, Boston, and San Diego—have become role models for cities across the nation. Attempts to make policy to grow the knowledge economy even in apparently fertile soil, however, have had mixed results. Having a top life-sciences research university, even with

S/D-2 6/16/04 12:52 PM Page 47

A Brief Life Story of the Life Sciences

47

governmental and lobbyist support, is not enough to create a top biotech region. Michigan has few biotech firms, and they are latecomers to the industry. This paucity of commercial life science exists despite several state initiatives to attract biotech, including MichBio, a nonprofit organization formed to support and lobby for the industry. The University of Michigan’s biological sciences departments consistently rank in the top-twenty, but the university ranks in the bottom third for royalties and inventions per research dollar (Blumenstyk 2002). The close proximity of multiple research universities that engage in patenting as well as publishing, venture capital firms that can provide hands-on management for new startups, and a large diverse cultural center to attract a highly educated workforce seem to be other ingredients for a successful knowledge economy that are missing in places like Michigan (Florida 2002). These elements of social/economic infrastructure are difficult to legislate.

Biotech Futures The attraction to biotech may be declining toward the mid-2000s as the industry falls on harder times. Stocks in the biotech sector fell 41 percent during 2002 (Hamilton 2002), following the bursting of the 2000 high-tech stock bubble. Biotech firms have not had the same failure rate as dot-coms but still have been adversely affected as stockholders became wary of the market in general and technology investments in particular. Unlike information technology’s very short product cycles, where software can be obsolete in a year or two, biotech is on a much longer product cycle in which drug development takes at least a decade. Frank Moss, chairman of biotech firm Infinity Pharmaceuticals and formerly CEO of a high-tech software company, compares the timelines of the two industries: “In biotech, you have a 10-year life cycle for developing a product. The product development cycle in high-tech is more like 10 months” (Kirsner 2003: C1). The long product cycles mean that biotech executives and investors must take the long view. During rough periods in the market, if biotech firms are forced to take a short-term outlook it may adversely affect the health of the industry. Cubist Pharmaceuticals reduced its staff 15 percent in the first quarter of 2003, mostly cutting scientists working on long-term research. Cubist’s chairman, Scott Rocklage, explained that this reduction in the publicly traded firm is because long-term research is “harder for investors to appreciate right now” (Kirsner 2003: C2). Publicly held firms may have a more difficult time during downturns than privately held companies with access to venture capital. Venture capitalists closely tied to biotech firms take a longer view than typical investors. Terry McGuire of Polaris Ventures claims that people in the venture capital community “aren’t anticipating the severe contraction you’ve seen on the IT side. In

S/D-2 6/16/04 12:52 PM Page 48

48

Women’s Work

t

the early 90s, the last lean time for biotech, it was amazing how resilient the companies were” (Kirsner 2003: C2). Venture capital has, however, faced severe losses as the market has fallen. Further, as venture capital firms, particularly those managing retirement funds, are asked to divulge return rates publicly and frequently, they may face strong pressure to abandon the long-term view necessary to sustain biotech. Because network ties with venture capital firms and other organizations sustain the core activities of biotech firms, this means that the vulnerabilities of biotech firms’ partners may have stronger repercussions than they would in a less interdependent industry. As biotech firms experience difficulty in finding funding, employment demand for life scientists decreases. Biospace.com marketing director Ian King said job listings with his firm in biotech decreased from 1,500 in 2002 to 1,200 in 2003. He believes this decline occurred because “a vast amount of venture capital, which could be spurring development, is now on the sidelines” (Bushnell 2003: G7). When biotech firms hit harder times, will fewer women and minorities be hired under the old unwritten rule of last hired, first fired? This remains to be seen, although it did not seem to occur in the early 1990s, when the biotech industry faced its last stall in funding (see Chapter 4 for an analysis of sex segregation in biotech). In any case, life scientists do not face the dire circumstances that unemployed information technology workers face in the mid-2000s, perhaps in part because life-science specialists form a smaller labor pool. Hundreds of IT workers compete for few job openings. Even biotech firms are deluged with applications from desperate IT professionals. Genzyme’s director of workforce development, Jo Norton, indicated that the biotech firm would be hiring about 180 specialists in 2003, and while it would be carefully searching for life scientists, “we now have hundreds of resumes from [IT workers] and don’t need anymore” (Bushnell 2003: G7).

Working in Biotech

Total employment numbers in the U.S. biotech industry are hard to come by. The U.S. Bureau of Labor Statistics does not track the biotechnology industry separately from the pharmaceutical industry (demonstrating the slow rate of change in keeping government statistics in a classically bureaucratic organization). For the broader category, economist Doug Braddock of the Bureau of Labor Statistics reported that “pharmaceutical employment increased significantly from 237,000 in 1990 to 315,000 in 2000 and is projected to reach 390,000 by 2010” (Ross 2002: G6). The biotechnology industry’s lobby organization, BIO, claims that the industry employed 179,000 people in 2003, “more than all the people employed by

S/D-2 6/16/04 12:52 PM Page 49

A Brief Life Story of the Life Sciences

49

t

the toy and sporting goods industries.”2 In addition, although biotech firms do have a high ratio of PhDs to other workers, not all employees in the industry are scientists. On this admittedly crude basis, a rough estimate is that somewhat fewer than 100,000 scientists work in the U.S. biotech industry. In the fast-paced biotech industry, where science and available funding are continuously changing, one of the most important resources firms have is human capital—the highly educated people who put the knowledge in “knowledge economy.” As we have seen in this chapter, biotech presented a new mode of organizing commercial life science that differed dramatically from traditional pharmaceutical corporations. How was this new industry received by the scientific community? With the complex changes in life science and the organization of scientific work, knowing how scientists make sense of a new job path is important to understanding whether the job is considered women’s work or men’s work. In Chapter 3, I go into the laboratory to examine how life scientists talk about the legitimacy of working in biotechnology firms. In addition to the structural differences between biotech firms and pharmaceutical corporations outlined in the present chapter (i.e., in reliance on interorganizational collaboration), inside views are key to knowing how the legitimacy of different industrial careers in science is related to women’s employment. This view from inside the laboratory shows how scientists themselves interpret the boundaries between biotech, pharmaceuticals, and universities. To explain this kind of “boundary work,” Thomas Gieryn (1999) draws on cartographic metaphors in describing the cultural representations of contrast accomplished by scientists. Gieryn focuses on the construction of boundaries between science and not-science: “As knowledge makers seek to present their claims or practices as legitimate (credible, trustworthy, reliable) by locating them within ‘science,’ they discursively construct for it an ever changing arrangement of boundaries and territories and landmarks” (1999: xi). In Chapter 3 I focus on life scientists’ interpretations of boundaries within their field rather than the field’s outside borders. Still, these narratives are about constructing legitimacy (credibility) for biotech. This legitimation process is an important context for the analysis of women’s employment in biotech that will follow. Can women easily enter highly valued fields?

Notes

1. In this section, I draw on a variety of excellent sources of information on scientific, technological, social, and economic changes in the life sciences and the biotech industry (including Werth 1994; Robbins-Roth 2000; Kevles and Hood 1992; Hall 1987; Kenney 1986; Angier 1988; Teitelman 1989); the Biotechnology

S/D-2 6/16/04 12:52 PM Page 50

50

Women’s Work

Industry Organization (www.bio.org); and Boston Globe features on the biotechnology industry in the Boston area. 2. Subtracting BIO’s number of biotech employees from the Bureau of Labor Statistics’s broader 2000 category would mean that only 136,000 worked in pharmaceutical companies—fewer than in biotech firms—which does not seem reasonable given that one of the largest pharmaceutical companies in the world, GlaxoSmithKline, employs more than 100,000. Because there is no industry clearinghouse that keeps track of total employment, estimates vary.

t

S/D-3 6/16/04 12:53 PM Page 51

3

Life in the Commercial Laboratory: Institutionalizing the Network Form GOTTLEIB (the pure, true scientist): “I do not approve of patenting serological processes. They should be open to all laboratories. And I am strongly against premature production or even announcement. I think I am right, but I must check my technique, perhaps improve it—be sure. Then I should think there should be no objection to market production, but in very small quantities and in fair competition with others, not under patents, as if this was a dinglebat toy for the Christmas tradings!” HUNZIKER (the evil head of a pharmaceutical corporation): “My dear fellow, I quite sympathize. Personally, I should like nothing so much as to spend my whole life in just producing one priceless scientific discovery, without consideration of mere profit. But we have our duty to the stockholders of the Dawson Hunziker Company to make money for them. Do you realize that they have—and many of them are poor widows and orphans—invested their Little All in our stock, and that we must keep faith? I am helpless; I am but their Humble Servant. . . . I don’t like to make any demands, but on this point it’s my duty to insist, and I shall expect you at the earliest possible moment to start manufacturing.” —Sinclair Lewis, Arrowsmith (1924)

In his satiric masterpiece Arrowsmith, Sinclair Lewis directs his barbs at the less-than-ideal character of academic, scientific, and medical life. No one comes in for harsher caricature than the pharmaceutical industry, as the epigraph above demonstrates. Poor Gottlieb—the brilliant idealist. When forced into working at a large drug company, he finds the place every bit as bad as its reputation for putting short-term profit before good science and reputable business ethics. Even though the fictional Gottleib and Hunziker are extreme sketches, the long-standing conflict between scientific and industrial values loudly resounds in the way scientists have thought about careers in the pharmaceutical industry. It still echoes faintly in the laboratories of biotech firms. One of the very first items that Rob told me about himself when I met him at the biotech startup firm was that he had attracted job opportunities at

51

S/D-3 6/16/04 12:53 PM Page 52

52

Women’s Work

t

universities but decided instead to work at BioNow. An MIT PhD, Rob wanted me to know that his going into biotech was out of choice, not desperation. Rob was not the only one who seemed to need to justify working in a commercial laboratory. My ethnographic observations at BioNow, and in a university laboratory for comparison, revealed the considerable attention that scientists paid to making sense of their career paths into the fledgling industry. The particular historical moment in which I observed the institutionalization of biotech firms organized in the network mode was the early to mid-1990s. By 2003, when George W. Bush was the first U.S. president to attend the annual meetings of BIO, the industry’s association and lobbying group, it had perhaps become more difficult to imagine biotech and the network form needing legitimacy. And yet there is still debate within the academy over whether commercialization is harmful or beneficial to science. Prior to addressing the question of how the biotech industry’s emergence has affected gender stratification in the life sciences, it is important to understand what it means to work in biotech. According to queuing theory, jobs that are desirable will be filled by men, and only when jobs become undesirable do women move into them. In this chapter, I examine how highly educated scientific professionals choose and frame work in novel settings—small science-based firms—that resemble neither older industrial jobs nor traditional academic careers. The newness of the biotechnology industry presents a strategic site for the study of the processes by which work is legitimated. In the new economy of high-technology, fast-paced change, and small entrepreneurial organizations, how do professionals make sense of an emerging career path?

Changes in Scientific Careers

The scientific labor market has changed in a number of key respects. First, industry is more attractive but is not traditional industry of the past. Interviews with academics forty years ago (Caplow and McGee 1961) show the attitude toward scientists who must “settle” for leaving the university. One interviewee described a “failed” academic colleague: “He was blocked for promotion because he didn’t publish [so] he went to a corporation” (Caplow and McGee 1961: 52). A job in industry for life scientists prior to the early 1980s meant employment in large pharmaceutical corporations, that is, until the emergence of the biotechnology industry presented another option. In his detailed description of the early years of Vertex, a biotech firm founded in 1989, Barry Werth (1994: 27) provides an example of how sci-

S/D-3 6/16/04 12:53 PM Page 53

Life in the Commercial Laboratory

53

entists believe the new biotechnology industry has the old pharmaceutical industry beat in doing cutting-edge science. Jeremy Knowles, a Harvard enzymologist and Vertex board member, quizzed Joshua Boger, the founder, about how the startup would compete with large pharmaceutical corporations that dominated the drug market: “I mean, yes, there are some splendid ideas here, and some superb people, and we will do it. But what’s to stop boring old Glaxo [maker of Zantac] from saying, ‘Oh, oh, we see. Maybe we can do what you’re telling us ourselves.’” Boger replied, “But they can’t do it, Jeremy.” Knowles countered, “But Merck can.” “No,” Boger paused resolutely. “Merck can’t either.” As Werth observed, “It had already become an article of faith at Vertex—as at most startup companies—that large corporations were dinosaurs: too unadaptable and slow moving to compete at the forefront of research.” This new perspective on commercial firms also presented a career option that was qualitatively different from what working in “industry” meant in the past. Typical job listings in the back pages of the journals Science and Nature and on their websites reveal the differences between work for life scientists in the academy, biotech industry, and pharmaceutical corporations. Three ads, published in 1997 (just after my observation period at BioNow), are indicative. A university with a molecular biology department among the top-fifteen ranked programs requests: We invite applications for a tenure-track position at the Assistant Professor level from candidates investigating cellular and molecular mechanisms of development. The Group . . . is a consortium of faculty . . . with common research and teaching interests. [T]he successful applicant will be encouraged to participate in interdepartmental graduate programs.

A large international pharmaceutical corporation outlines its expectations for potential employees: To maximise the use of [our] database, we are now embarking on a planned, phased expansion and therefore we have several new exciting opportunities in key areas. . . . With a PhD or equivalent . . . you will be committed to practical bench science. All scientists are expected to work and communicate effectively in a multi-disciplinary research environment.

Note the emphasis on a planned course of practical research by the pharmaceutical corporation. In contrast, a recent ad aimed at young PhDs for a biotechnology project group leader reads: This post offers the opportunity to work with leading-edge technologies in a stimulating environment where high scientific standards, innovation and

S/D-3 6/16/04 12:53 PM Page 54

54

Women’s Work success are the norm. The company, which has collaborations with other biotechnology and pharmaceutical companies worldwide, is well financed and expanding rapidly. In addition to working closely with scientists within the company, he/she will be responsible for setting up and monitoring projects involving in vivo models with external organizations. Candidates should possess: . . . a PhD and, perhaps, some postdoctoral experience in an academic or research institution. Personal qualities should include scientific credibility . . . and team-player skills.

In all three advertisements, interdisciplinary research is important, signaling the transformation of life science as a knowledge area. But in the biotech firm, there is a unique emphasis on interorganizational collaboration. General scientific credibility appears important in academic and biotech ads, whereas specific skills are requested by the pharmaceutical corporation. The ads for universities and biotech firms are successful at attracting the same kinds of candidates. I interviewed five young PhDs with prestigious postdoctoral experience in different settings. Four of them indicated that they had only applied for positions in academia or biotechnology firms. The one exception, who had an interview at a large pharmaceutical corporation, actually did not go through with the interview. He claimed at the last minute that he was sick and never rescheduled the job talk. In contrast, biotechnology represents what some consider a legitimate place to do good basic science outside of academe. A second change in the life-sciences labor market is that university positions have not increased in number, whereas PhDs have (National Research Council 1998). The intensified requirements of publishing and postdoctoral experience have rendered university careers more demanding, if not less appealing (Freeman et al. 2001). Half of all biological PhDs who had postdoctoral appointments during the 1960s spent an average of less than two years in the apprentice position, whereas the median time spent as postdocs in the 1980s doubled to nearly four years and seems to be increasing (Regets 1998). This postdoc-mill context undoubtedly has helped to make nonacademic employment more attractive to new PhDs, but the push or pull factors for biotech careers are not as predictable as might be expected. In the old days, scientists at less elite universities made the move more often to industry; in contrast, elites have moved more frequently into biotech (Robbins-Roth 2000). Evidence of a pull factor makes more sense in realizing that the entrance of elites to the biotech industry represents a kind of natural excludability. Life scientists at elite universities were closest to the new discoveries and best able to capitalize on them. Still, the interesting sociological question is: How do these highly trained scientists find their way to and legitimate working in novel settings?

S/D-3 6/16/04 12:53 PM Page 55

t

Life in the Commercial Laboratory

55

Institutional Change and Legitimacy

Beginning in the late 1990s, the number of organizational studies focusing on institutional change swelled to form a virtual new wave of neoinstitutionalism (e.g., Clemens 1997; Haveman and Rao 1997; Hoffman 1997; Rao, Morrill, and Zald 2000; Scott, Ruef, Mendel, and Caronna 2000; Lounsbury and Ventresca 2002). The incoming tide of social-movement research raised this school over the earlier criticisms that its explanations of similarities in organizational fields were too static. In the theoretical model, based on empirical studies of changing institutions among organizations, deinstitutionalization of old forms is key to the process of legitimating the new replacement (Clemens 2002; Scott 2001). Perhaps because of the influence of social movements scholarship on the model, institutional change is usually viewed as revolutionary—the new displaces the old— however gradually the shift may occur. For example, one study (Armstrong 2002) of prohomosexual organizations shows how the more bureaucratic gay-rights associations of the 1960s gave way to the loosely organized identity politics groups of the 1970s. I argue that when the new structure to be institutionalized is a network form of organization, rather than doing away with old modes, seemingly contradictory institutions coexist. The old commingles with the new as linkages across organizational fields bring together disparate institutional logics. By making this argument, I seek to combine insights in economic sociology on recent structural changes in the knowledge economy with neoinstitutionalist analysis of cultural changes in organizations. The research context is the biotechnology industry—an important part of the new knowledge economy. As Daniel Kleinman and Steven Vallas (2001) explain, a seemingly paradoxical combination of institutions is exemplified in the way knowledge workers negotiate the blurred boundaries between academia and industry. This chapter explores the micro-institutional context of career paths to understand the processes by which life scientists give legitimacy to working in the biotechnology industry—a new option that nevertheless fails to delegitimate the old academic path. The data are based on ethnographic observations of a young biotechnology firm I call BioNow. These are supplemented with observations at a university laboratory engaged in similar research problems and interviews of life scientists in a variety of organizational settings. In considering the legitimacy of scientific work, some features may be special to the field. Andrew Abbott (1988) argues that scientists do not need to resort to efficiency claims for the legitimacy of their profession; science is assumed to be the height of rationality and thus legitimacy in the modern era. Also, scientific occupations are highly ranked as prestigious in general

S/D-3 6/16/04 12:53 PM Page 56

56

Women’s Work

population surveys. Is legitimacy, then, a real problem? Legitimacy is relative: scientists tend to compare themselves not to the general population but to colleagues in their scientific community (Cole and Cole 1973). When biotech, a new variant of the traditionally less reputable industry path, emerged, the work was not granted immediate legitimacy in the scientific community. Legitimacy, in this and other institutional analyses of organizations, denotes structures and practices that are widely diffused and taken for granted as an appropriate way of doing things (Powell and DiMaggio 1991).1 A common focus in new institutionalism concerns how organizational agents—managers and professionals—use the structure of their organization to signal legitimacy (Meyer and Rowan 1977). For instance, a businessperson might have learned about matrix-type organizations in business school and start a company using that form in name, if not in practice, in order to sustain the myth of rational management. A strong criticism of earlier models was that neoinstitutionalists paid little attention to institutional formation and change. For example, Ronald Jepperson (1991: 159) warned: “Institutional effects should not be narrowly associated with explanations of stability or thought to be irrelevant to change.” As with criticisms that neoinstitutionalism does not allow enough room for individual agency or structural power, the accusations that change is neglected, “given recent developments . . . seem a bit outdated” (Scott 2001: 194). In fact, institutional change has provided a central theme around which a series of studies has grown. Inspired by sociological studies of social movements, this new wave of neoinstitutionalism has examined radical change in a variety of organizational fields.2 One study (Davis, Diekmann, and Tinsley 1994) describes the change from the conglomerate form of the for-profit corporation in favor of the “firm as portfolio” model. Patricia Thornton (1995) notes the shift in book publishing from an editorial to marketing logic. Andrew Hoffman (1997) demonstrates how green environmentalism in chemical corporations went from being heresy to dogma in a relatively short time period. Another study (Scott, Ruef, Mendel, and Caronna 2000) explains how health-care organizations switched from the logic of professional dominance by physicians to a cost containment, managed-care mentality. Michael Lounsbury (2002) charts the transformation of institutional logics employed by professional finance associations, from regulatory to market logics. Jennifer Howard-Grenville (2002) views institutional change in the semiconductor industry as more gradual than abrupt. Nonetheless, she shows the sharp departure from the old logic of responsibility for the effects of one’s products to the new logic of voluntary partnership with the Environmental Protection Agency when members of the World Semicon-

S/D-3 6/16/04 12:53 PM Page 57

Life in the Commercial Laboratory

57

ductor Council set ambitious goals for themselves to reduce perfluorocarbons (PFC) emissions. Based on the growing body of empirical studies, new institutional theorists have outlined some of the general processes of institutional change. Elisabeth Clemens (1997, 2002; Clemens and Cook 1999) explains the role of politics in institutional transformation. Social movements, in particular, play the role of bringing grievances to light, which aids in the deinstitutionalization needed to clear the way for institutional innovation (Clemens 2002). Following Anthony Giddens (1984), Richard Scott (2001) refers to this process of removing the old institution to make way for the new as “destructuration.” As has been empirically demonstrated, social movements can have effects on the deinstitutionalization of old frames. In the context of solid waste management, environmental activists’ criticisms served to revolutionize industry institutions in the case of recycling solid waste (Lounsbury, Ventresca, and Hirsh 2003). Generally, it is theorized that for legitimation of a changing institutional landscape to occur, the old must first be cleared away. The question I pose for this model of institutional change is whether structural context produces variation in this cultural process. As such, this chapter combines an institutionalist perspective on the content of legitimacy logics with economic sociology concerns about patterns of relationships among social actors. Particularly, I draw from one line of work in economic sociology that has emphasized the differences between hierarchical organizations, steeped in formality and bureaucracy, and more fluid organizations with so many ties to external parties that they resemble a spider’s web, or a network, much more than a pyramid (Powell 1990). Among organizational actors, those employing a network form of organization are “any collection of actors that pursue repeated, enduring exchange relations with one another” (Podolny and Page 1998: 59). There are a range of studies of these organizations with pronounced network features—in manufacturing in northern Italy (Lazerson 1995), garment districts in New York (Uzzi 1997), and Japanese business groups (Gerlach 1992), as well as in the biotechnology industry (Powell, Koput, and Smith-Doerr 1996). A network-form of organization differs from others in having more permeable boundaries, relying on close connections for all organizational functions including core activities. In biotech, firms engage in key R&D projects jointly with other firms, universities, and nonprofit research institutes. One might say that in network organizations a core competency is to establish and maintain interorganizational relationships. Maintaining a position at the nexus of network ties permits social actors (whether individuals or firms) access to diverse information, which in turn facilitates innovation. For example, the creative rise to power of the Medici during the

S/D-3 6/16/04 12:53 PM Page 58

58

Women’s Work

Florentine Renaissance was due to the family’s unique position in spanning disconnected social networks (Padgett and Ansell 1993). In the context of connective, spanning ties, as in the biotechnology industry, the institutional change process looks different. Rather than deinstitutionalizing the older mode to make way for the replacement institution, in the network form institutions—like the social actors who construct them—may be linked. Even seemingly contradictory, taken-for-granted practices may coexist in the network form. In the life sciences, past academic frames are conjoined with emerging institutions about work in the biotech industry. Tension exists between the old and new constructions, but mostly the conflicting elements are ignored. I observed little evidence of cognitive dissonance even among informants using seemingly conflicting frames. In some social situations, however, problems appear as the tacit social agreement shows signs of strain. When this happens, the tensions are glossed over. Three common frames used to discuss the legitimacy of biotech careers (resources, social networks, asset of newness) each carry within them implicit tension. The resources frame denies that incentives of a biotech career are primarily mercenary. Being able to do good science is a draw that goes beyond monetary compensation. But it is precisely the amount of money in a well-funded biotech firm that allows for good science to be done. To obtain funding, scientists need to be able to translate their work to finance-speak for the upper management who will sell it to venture capitalists and other investors. The old academic ideal that money is not the most important thing is in evidence in framing biotech, but it is simultaneously in tension with the need to justify budgets to venture capitalists and stockholders (although academics work incessantly on grants, something biotech supporters are quick to point out). Financial resources do, of course, matter to getting science done, but how they are spoken about in biotech firms is related to academic frames and is also unique. Perhaps not surprisingly, when following the money we can see scientific ideals in tension with financial ones. The social network frame—the idea that knowing someone connected to biotech is key to seeing the industry as a legitimate career location— builds off of but stands in some tension to the prior academic ideal of mentorship. PhD students, traditionally, are meant to model themselves after their faculty advisers. This obviously implies a career as an academic in a research university. In social network parlance, the prospective PhD should get a job based on strong ties. Sometimes, academic scientists are also affiliated with biotech firms (sitting on the scientific advisory board, for instance), and this means their students can still follow in their footsteps somewhat by entering the biotech industry. Still, graduate students may have mentors of the old guard who eschew any industry career for students;

S/D-3 6/16/04 12:53 PM Page 59

Life in the Commercial Laboratory

59

t

some of these PhDs enter biotech as well. The tension is that, for some, one’s own adviser may dismiss all industry careers—including biotech—as second-rate, whereas weak ties to other scientists who are perhaps more prestigious provide the deciding information on the legitimacy of biotech. Again, this seeming discrepancy is not explicitly acknowledged. The third narrative frame I call the asset of newness (following Arthur Stinchcombe’s famous “liability of newness” concept). In contrast to the legitimacy problems of new hierarchical forms outlined by Stinchcombe (1965), in network organizations like biotech firms newness and innovative practices are viewed as assets. Tension also took a backstage role in comparison to the quality of work in universities and industry. Biotech workers discuss how the industry is like academe, then in the same conversation raise how biotech’s newness differs from academe—yet do not describe these pieces as conflicting. In this frame, however, some of the elements did more obviously come into conflict—as in a meeting (described below) in which the debate over publishing versus maintaining secrecy arose. Despite these tensions between institutional logics, old academic as well as new biotech industry institutions coexist in the process of legitimating biotech work.

Biotechnology Industry Setting

Biotechnology is one of the two main pillars of the knowledge economy (the other being information technology). Knowledge economy is a term that acknowledges the university origins and continuing close connections between new economy industries and academe. The life sciences are rife with academic-industry relationships at both the organizational and individual levels. In such a cutting-edge setting, why would scientists need to legitimate their work? Even among elite life scientists, not all are convinced of the benefits of increased interaction between commercial and academic science. One study (Owen-Smith and Powell 2001) describes how famous academics’ personal connections to industry influence how congenially they view the blurred boundaries between university and biotech science. Although some biotech founders view the industry positively, part of the enduring academic legacy in the biological sciences is the idea that any PhD worth her salt obtains a university position; other options are clearly second-best. When biotech firm scientists discuss their choice of work, they must contend with this traditional idea that academic jobs are the default legitimate option. Rather than deinstitutionalizing academic models, however, scientists combine them with new (and sometimes contradictory) sources of legitimacy in biotech firms. Where deinstitutionalization does come into

S/D-3 6/16/04 12:53 PM Page 60

60

Women’s Work

play somewhat in the legitimation process is when scientists separate biotech firms from large pharmaceutical corporations. The main focus of their narratives, however, is the comparison of biotech to academe. The legitimacy narratives surrounding biotech work emerged from my ethnographic observations, primarily in the biotech firm BioNow. Field studies can reveal new questions or dimensions that the researcher may not have set out to investigate. Yet field research is sparse in sociological studies of work (Barley and Kunda 2001), industrial science (Kleinman and Vallas 2001), and institutions (Lounsbury and Glynn 2001). Institutional change is more often studied with archival rather than interpersonal data. This study contributes a needed look at the process of legitimating work in a new kind of employment setting. The legitimacy narrative patterns emerged as I analyzed the data from ethnographic observations. I conducted the fieldwork for this chapter over the period 1992–1996. BioNow was one of hundreds of young biotechnology firms in the United States founded during the 1990s; its research focuses on human diseases. (See the appendix for details on the ethnographic research methods.) In many ways, BioNow was typical. From the beginnings of the industry, university labs have provided the spawning grounds for new biotech companies (Kenney 1986). BioNow was no exception: scientists from laboratories at the state university research center founded the firm. Like the majority of similar biotechnology companies under five years old, BioNow was partially funded by venture-capital companies and held patents as its main assets (Smith-Doerr et al. 1999). The ratio of scientists to other employees is typically quite high in a biotech firm, and this was the case with BioNow. BioNow’s physical plant consisted of two buildings across the street from each other. There were three departments at BioNow, divided by scientific disciplines: biology, chemistry, and biochemistry. Each department had its own laboratory space. The older building housed the biology and most of the chemistry department. The newer building had the smallest number of scientists, accommodating the tiny biochemistry department and a few chemists. In this flat organization, there were only three official levels of researchers: department heads, other PhDs, and laboratory technicians. Department heads had private offices, other PhDs usually shared an office, and technicians in a department shared common office space. All of the administrators and secretaries had offices in the new building. The vice president of research organized the scientists into project teams based on certain disease targets (e.g., prostate cancer). The project teams crossed department lines so that scientists worked more closely with others trained in different disciplines than with scientists in their own department. Biotechnology is an industry in which the most successful firms forge ties with multiple organizations (Powell, Koput, and Smith-Doerr 1996).

S/D-3 6/16/04 12:53 PM Page 61

Life in the Commercial Laboratory

61

R&D ties, in particular, are often based on informal linkages between scientists in different organizations. As biotech scientists have interacted with others connected to their firms’ networks from a variety of organizations— universities, pharmaceutical corporations, nonprofit research institutes, government agencies—a new career path across sectors and organizations has developed. Of the scientists I interviewed, Miles and Luther were particularly attentive to the larger structural changes in careers for life scientists. Miles notes the career movement both between and within different forms of organization: Q: What do life scientists’ careers look like now? Miles: Scientists aren’t staying in the same place, they are moving around. Q: Do you think they move between organizations of the same type—like from university to university, or between different settings? Miles: They are both moving within and between different settings. And even within the same organization, they are not doing the same things. At biotech firms some scientists are in sales and marketing, some are at the bench.

Luther described his willingness to leave the pharmaceutical corporation and take a biotechnology position partly in terms of the greater interorganizational movement between academia and biotech. When asked what led him to taking the BioNow position, he observed: Luther: It had been established in biotechnology that people could go back and forth between the university and the industry—as long as they published enough while they were in a company. Q: Do you want to go back to academia someday? Luther: I think I will.

t

This career movement back and forth between biotech and university is related to how scientists speak about work in biotech, giving it meaning especially in relation to academic frames.

Common Narratives Used to Legitimate Biotechnology Industry Work

In the analysis of my field notes, three basic frames consistently organized scientists’ comments about biotechnology careers. The legitimacy of biotech as a distinct career option for life scientists appears to be constructed principally on frames that I refer to as the resources, social networks, and asset of newness narratives.

S/D-3 6/16/04 12:53 PM Page 62

62

Women’s Work

Resources The abundance of resources in firms would seem to be an obvious factor in explaining how biotechnology became an acceptable career route. Both the greater competition for obtaining academic support and the increasing amount of resources for biotechnology are themes that scientists discuss as part of the attraction of biotech. Scientists are generally concerned with the changes in funding for academic science in the post–Cold War era. One researcher (Hackett 1990: 272) quotes a scientist who expresses the sentiment of many others in saying that no longer does easy money in academia exist, as perhaps it once did. Although the NIH budget was well-funded throughout the 1990s, life scientists I talked to feel a sense of restriction surrounding academic positions. Sal, a PhD scientist at BioNow, focused on the competition for tenure-track positions when I asked him what the job market was like: “Well, of my cohort from graduate school no one I knew got an academic position. It used to be twenty or thirty people would compete for one spot, but now it is extremely competitive and hundreds of people apply for one position.” There does seem to be a push factor at work, with PhDs overflowing from the ivory tower. But how do scientists describe a pull factor, the legitimate desire to enter the biotech industry? A simple resource motivation story based on financial rewards or research budgets would actually favor large, established pharmaceutical corporations with billion-dollar drug markets over small, high-risk, startup biotech firms. Yet biotechnology firms differ from pharmaceutical corporations in providing young scientists the opportunity to lead their own projects and publish the results. Biotech scientists carefully point out that there is more to the resource story than dollars. Specifically, scientists assert that biotechnology allows greater access to resources needed for research and publication at the same time that academia is less able to provide these resources generously. The older academic idea is that financial compensation is not the most important incentive for true scientists. Todd, a full professor and the head of a laboratory in a research university, describes the ideal typical motivation for academic scientists: “Basically, scientists want to be famous; they want to be known for discovering something fundamental. If you’re out to make money, you’re in the wrong business!” In an informal interview with Rob, he explained the motivation of scientists (old frame) as well as the attractive resources for research in biotech firms (new frame). Rob, known as a super PhD because of his postdoctoral training at a leading research university, was employed as a scientist at BioNow. As a postdoc, he also had experience collaborating with an elite biotech firm (one of the most well-connected organizational players in this highly networked field). When I asked Rob whether he thought people’s consideration of careers in

S/D-3 6/16/04 12:53 PM Page 63

Life in the Commercial Laboratory

63

biotech has anything to do with the possibility of making more money, he responded: I don’t think so. It’s more that biotechnology has a greater visibility and people believe it will be intellectually stimulating. Scientists (at least good ones)—whether in industry or the university—are more interested in being able to do their own work than in fat paychecks. Or else they would be venture capitalists [laughs].

Rob’s remarks demonstrate the influence of the academic perspective on framing biotech careers. But the resource frame legitimating biotech also includes new material. Biotech work, according to the frame, is valuable beyond monetary compensation (like academic science work) yet offers other resources for doing research that university positions do not provide. Rob argues that the time constraints on academic scientists decrease their ability to do research: A PI [principal investigator] carries the weight of the world on their shoulders. They are responsible for making smart decisions about the direction of research—will this line of work pay off, getting students jobs, et cetera. It’s a tremendous load of responsibility. In industry you do not have to scrounge up money. It is a team effort. Scientists can rely on administration to get funding, administrators can rely on scientists to produce. The lines are more distinct, more specialized in industry. I had academic opportunities, but I wanted to focus on research rather than writing grants and recruiting students.

Ironically, he argues that more science can be done outside of academia, where pure research has traditionally been one of the few purported goals, along with training students. The blending of specialties in a team effort allows for better science, so the narrative goes. Richard, a BioNow technician, had another perspective on academic resource scarcity in working at the bench: At the university everything is funded by grants so you have to worry where money is coming from, since everyone is paid off the grant. Funding is such a problem that even getting equipment is a problem—if you want an enzyme you can’t just order one when you need it, you have to go borrow one. It slows down the process. The person I worked for [in an academic setting] did good research, but it wasn’t in a hot topic area so it was harder to get money. Science has fads, so you have to tie your research to what’s fashionable, like AIDS research is now.

Richard’s point is that academic science topics are constrained by available resources, perhaps not unlike the constraints on industry scientists who need to be concerned with the bottom line.

S/D-3 6/16/04 12:53 PM Page 64

64

Women’s Work

This bottom-line concern in for-profit enterprises is a tension that arises in the resource frame. In order to be doing good science in biotech firms, scientists must be able to think about its implications for the firm to communicate about their work with financiers. So, the resource story is not about money—or is it? Sal, like many of his coworkers, describes science in biotech firms like BioNow as profit driven: “Science is focused here— you listen to the speech Damon [the CEO] has prepared for the venture capitalists and realize that there is supposed to be a product somewhere down the road.” Indeed, Damon, the thirty-something CEO (whose experience was as a venture capitalist rather than as a scientist before heading up BioNow) focuses on the importance of cash infusion to a healthy firm— and what makes the money flow: Investors buy stories. And they look at the credibility of the people telling them. Like any sale—there is always a story. You have to make them believe that a company will be more than it is today and that it won’t crash on the way there. They look for new technology and solid management. Some don’t give a rat’s ass about management, they fund a technology and will put in their own management. Others don’t give a rat’s ass about technology, they fund good management and will license a technology. But at some point you need both.

Note that Damon, from his financial background, frames market outcomes as the basis of the successful biotech firm. Sal’s comment above shows that BioNow scientists incorporate the CEO’s viewpoint into their sensemaking but still tend to fail to realize that lovely science is only part of the race and that new medical therapies, rather than beautiful molecules, are the finish line. As part of the pattern I observed, however, this tension about the funding process goes unacknowledged for the most part. For example, later in the same conversation with Rob quoted above, in which he said “scientists can rely on administration to get funding,” he discussed the role of finance in a fledgling startup: “As a scientist in a firm like BioNow, you have to be able to talk to financiers and to show them that your work will have a payoff, at some point down the line.” All of the people with whom I spoke were articulate and self-reflective individuals. By pointing out that they did not see contradictory elements of their collective stories, I do not wish to imply that they are ignorant but rather that social constructions are often difficult for anyone to see. They could talk about biotech scientists (like academics) as being above money-grubbing, and at the same time discuss how the whole industry is driven by capital. Two ideas emerge to form a resource frame that is logically disjointed but narratively seamless. Perhaps because of the materialistic discourse in popular culture, scientists found it easy to tell resource stories about the emergence of biotech

S/D-3 6/16/04 12:53 PM Page 65

Life in the Commercial Laboratory

65

careers. One way to classify the different resource tales used to frame biotechnology legitimacy is to say that scientists seem concerned with the availability of resources used for a variety of ends. Rather than centering all of their attention on monetary resources for personal individual consumption, biotech scientists, like academics, talk of resources for their science. The discussion of resources is a complex portrait in which scientists are concerned about what they see as too intense competition for academic resources, as well as gratified by the research resources—including the value placed on publication—available in biotechnology compared to other industry settings. This is not to say that self-interest is not an important part of the story but that the resource frame is multiplex rather than a simple economic compensation narrative.

Networks Another factor in framing legitimate careers is the role of social networks. Network connections predict the attainment of good jobs (Granovetter 1995; Granovetter notes that because most of the best professional jobs are not advertised, information gathered through personal network ties proves invaluable in procuring positions). Information gained through friends and acquaintances is trusted more than job ads by candidates or résumés by employers. To refer to a cultural legitimacy narrative in a networks frame may seem to be confounding culture and structure. But in the way scientists talk about their networks, in using them to make sense of the legitimacy of their work, network ties are symbolic rather than structural. So networks are not only conveyors of information but also serve as frames for discussing job choices. In science, the most significant relationship for a young scientist contemplating a career path is that with her mentor/lab director. Nearly every scientist I spoke to about choosing a career path mentioned the person in whose lab they were trained as influential in the decision. This taken-for-granted academic model provides the basis for descriptions of entering the biotech industry only on the advice of respected, well-established scientists. Luther’s account of the major reason why he took the BioNow position fits Granovetter’s weak-tie hypothesis well: “I went into the company because of [a] scientist I knew. I didn’t work with him but I trusted him. He’s a good scientist.” Rob found his way into the biotechnology industry, and to BioNow in particular, also through network connections that helped to frame biotechnology firms as locations of legitimate science: I did my postdoc at MIT. I worked with someone who had a connection at EliteBio [all company and personal names here and below are pseudo-

S/D-3 6/16/04 12:53 PM Page 66

66

Women’s Work nyms] so I worked there too. BioNow was promising. I had my “intellectual eye” on it. I talked to people in biotechnology—people I knew at EliteBio—and talked to people here about the game plan to see if it fit in the biotechnology community. People told me it had a sound research basis.

Note that scientific networks serve to legitimate the biotech industry at the same time that they provide information on job availability. It is perhaps not surprising that in an industry rife with interorganizational collaboration (Powell, Koput, and Smith-Doerr 1996; Baum, Calabrese, and Silverman 2000), chronicles of biotech firm foundings are headlined by personal networks. Frank, one of the founders of BioNow, identifies himself primarily as an academic. He describes the importance of network connections to starting up the firm: [The basis of starting up the firm] was a concept first, an idea I brought up myself. I got Ethan McMaster to help me set up a lab to test it. We proved that the process works and had to disclose it to the university because of rules about patents. The university’s consultant on patents said they weren’t interested. So we did some more tests and came back, but the university still wasn’t interested so the patent was allowed to go back to me, the inventor. I, and fellow researchers, put up a small amount of money. Ethan McMaster’s ties with venture capitalists and financiers were used. The financier put us through vigorous tests with well-known scientists to see if it would be profitable.

The description Miles gives of the beginnings of the biotechnology industry, and his personal relationships with scientists like the founder of one of the first biotech firms, clarifies some of the grounds for his early entry into the fledgling industry: I was there when NewTech was first conceived. Alan Stewart—the guy who started NewTech—his first venture . . . only lasted for two years. I was in the living room when he was looking for ideas for a new venture. NewTech did well. It had more money than EliteBio, but didn’t focus—it had too many irons in the fire. Life Co. began in 1979. There were thirty employees when I joined.

Clearly networks play a critical role in spreading news about opportunities, and access to these opportunities furthers the propensity to frame biotechnology jobs as legitimate. The industry originally gained legitimacy by association with credible scientists who played a key role in the origins of the field (Zucker, Darby, and Brewer 1998). Now, a frame that the “stars” of life science legitimated the industry is used to explain why other scientists take biotech jobs. The reverse also seems to be true: scientists are given credibility through their association with prominent biotech firms.

S/D-3 6/16/04 12:53 PM Page 67

Life in the Commercial Laboratory

67

For example, Frank, a BioNow founder, was tenured after his experience with the biotech industry; some of his colleagues argue that his biotech patent was the main reason why he was promoted to associate professor. The result of the academic model (follow in your mentor’s footsteps) preceding the biotech heuristic (biotech is legit, just ask the eminent Dr. X) seems to present a discrepancy in narrative content. PhD students in Todd’s program encountered conflicting stories about desirable career paths. Matt is a full professor who was collaborating with Todd on one particular research project that I observed as it unfolded. Matt’s response to my question about what advice he gives to graduate students about biotech reflected the traditional academic frame: I would not advise good students to go into industry. Industry only does a little science at the beginning [of a project]. I think I look down on industry because it does not do basic science. It’s too product oriented, and does not explore interesting things that aren’t marketable. [In firms] they only publish to support their product.

Todd’s view was tempered by his positive relationships to scientists working in biotech: I now know some good people working in industry, in biotech firms. Since my graduate school career there has been more interdisciplinary collaboration, inside and outside the academy. The progress in knowledge in DNA and molecular biology has been applied to many systems. It breaks down barriers between chemists and cellular biologists, and university and industry, that used to exist. It’s because of the advent of new technology.

When I asked graduate students and postdocs on the project about these two professors’ different perspectives on biotech, they were not seen as conflicting, but the students clearly favored the new network frame. One postdoc confided to me: Todd knows more of what he’s talking about than Matt. He knows more people all over the world in universities, and in biotech, too. Todd would probably be okay with his student going into industry, if it’s a good company, but Matt is much more old-fashioned. As long as you’re doing good science and publishing, Todd will be happy.

Note that Todd’s more extensive professional networks were acknowledged as key to seeing biotech as a legitimate career path. Building off of the traditional idea that one’s mentor is one’s career model, the students know that networks are important to choosing the right career path. But their framing also includes the element of legitimating a route that does not

S/D-3 6/16/04 12:53 PM Page 68

68

Women’s Work

lead (at least not directly) into academe. The conflicting elements of the network frame, however, are glossed over by saying “as long as it’s good science.”

The Asset of Newness A lack of legitimacy is part of the liability of newness and contributes to the main problem of young firms: organizational failure. According to Stinchcombe (1965: 148): “The process of inventing new roles, the determination of their mutual relations and of structuring the field of rewards and sanctions so as to get maximum performance, have high costs in time, worry, conflict, and temporary inefficiency.” Yet instead of describing the hassles of new roles in biotech, scientists viewed the newness as an asset. The innovation, creativity, and connections with other cutting-edge labs that result from inventing new roles were viewed as sources of legitimacy, if more complex than other narrative frames. Among the legitimacy narratives, scientists discussed resources and networks in rather concrete terms. Life scientists also had more symbolic means of expressing the legitimacy of biotech. Here discussions of quality, endorsement, and interaction come up that seem somewhat more difficult to explain than funding or connections, but they are nevertheless given importance. In conversations, comparison and contrast patterns emerged in how scientists constructed a rhetoric of biotechnology as a good venue for life science research. On the one hand, biotech research was compared to the standard frame: the well-established legitimacy of academic science. On the other hand, biotechnology was framed as a new, exciting place to do cutting-edge work. The difference between the one construct (academia is always the number-one choice) and the other (biotech work has new advantages over academe) is clear. Tension becomes apparent when scientists in biotech firms are deciding on the criteria for and outcomes of their work: Should they publish right away or wait until after further developments are patented? Miles seemed a bit defensive about the old frame that academe is the place to do real science: Scientists in academia are not the only people that care about their research, you can care about your research in industry, too. But there is this attitude [in academia], that if you’re not doing curiosity driven research that you’re being co-opted, and the work is low quality. That is totally false! The work being done in industry is at least equivalent to the quality of research in academia.

Some of Miles’s fellow scientists must agree that biotech research is at the same level of quality as that in academe. As Frank explains, BioNow

S/D-3 6/16/04 12:53 PM Page 69

Life in the Commercial Laboratory

69

has successfully competed with universities for funds from peer-reviewed federal grant programs: Q: How has the recent NCI grant BioNow received [3.1 mil] influenced the firm? Frank: It is really just a small amount—a drop in the bucket. [pauses] Okay, maybe several drops in the bucket. But more importantly, the grant from NCI (National Cancer Institute) showed that they accept the method, it was an endorsement. Q: So it’s mainly important for scientific prestige? Frank: Yes. Another type of agreement we have more for endorsement is with NIH and [another biotechnology firm].

Note that Frank is arguing strongly that the grant money is more important as a symbol of scientific legitimacy, or an endorsement in his words, than as a financial resource. Frank’s response also reveals that collaboration with other biotechnology firms, not just with universities, can be an important indicator of scientific credibility. In contrast to these connections and comparisons to academic legitimacy, biotech scientists also frame their industry as new and exciting, separate from any other way of organizing science. A particular theme in expressing the excitement of biotechnology that came up repeatedly was collaboration. The scientists constructed a frame that says biotech leads to greater collaboration in research, more so than universities. Sara, the first female PhD to be hired at BioNow, describes the excitement of collaboration in a new firm in a new field, contrasted to academia: Here everything is new, everyone is excited and we’re working together on a common goal. You don’t see the type of political fights that go on in the university. I think they are unnecessary. There is more competition in the university. I was working in a lab at [a university] and one day two PIs were having a fight in the hallway—shouting at each other about supplies. They weren’t really fighting about pipettes and light bulbs, but were just rivals in every way.

Luther also compares academic organization of science unfavorably to biotech, where the collaboration is ironically more collegial: “In academia you’re isolated from the people working down the hall. Here we’re directed to the same goal. There’s a greater will to get things done. Things happen faster.” Rob turns to a metaphor to explain the difference between academic and biotech research. He comments on the ease of collaborating at BioNow: It’s not that way in academia. You have to worry about who gets their names on papers. As science has progressed the need for collaborators has risen but it’s harder in academia. Let me make an analogy of bead

S/D-3 6/16/04 12:53 PM Page 70

70

Women’s Work stringers and polishers. In academia you have to worry about how much time you polish as compared to how much time stringers spend, for who gets their name on the paper; and worry about who gets to write what in their grant since they can’t submit the same grants. But at the same time you have to have collaborators send a letter saying that they can do what you need them to do in your grant proposal. In industry they can hire expert bead polishers and stringers to work at the same firm. Here they can hire collections of pre-assembled teams. I know cellular biology at the molecular level but nothing about chemistry. But I can just go down the hall and speak to Alexander [the department head of chemistry]. In academia there are problems with who gets credit, personal acclaim, and grants that prevent spontaneous collaboration like we have here.

Rob is arguing that working in biotech has teamwork advantages, which results in doing better science, something he never experienced in the university. This same narrative that I heard comes across in biochemist Cynthia Robbins-Roth’s how-to for scientists (Alternative Careers in Science: Leaving the Ivory Tower, 1998). Robbins-Roth’s own chapter title, “A Scientist Gone Bad,” is a tongue-in-cheek reference to the expectation that real scientists are assumed to be academics. When she took a job at Genentech—a pioneering biotech firm—in 1980, it was a decision that she described in ways parallel to many of the scientists I spoke with: academe and pharmaceutical corporations were just not as exciting (Robbins-Roth 1998: 3): Academia was not for me—I couldn’t stand the thought of teaching one more medical student lab course. I started interviewing at pharmaceutical companies, but I was discouraged by their apparent propensity for hiring only middle-aged white guys as scientists. . . . [Genentech] changed the ground rules for doing science in a corporate setting . . . and we didn’t have to write grants or teach medical students! I was working with some of the best scientists in a broad range of disciplines—protein chemistry, immunology, tumor biology, molecular biology, X-ray crystallography, amino acid sequencing, cell assay development, and so on. I was in heaven.

In my conversations with life scientists I never heard biotech firms described as heavenly, but certainly Robbins-Roth’s description of the benefits of easy interdisciplinary collaboration to doing cutting-edge science has a familiar ring. The comparative framing of biotechnology involves connecting to the legitimacy of the academy, yet it also includes outlining differences from other settings including the university. This conflict of ideas was not explicitly talked about (at least not in my presence), but I observed some of the consequences of the tension between competing frames. In one research

S/D-3 6/16/04 12:53 PM Page 71

Life in the Commercial Laboratory

71

meeting, the underlying question (Are we like academia or not?) seemed clear. In this meeting, Luther wanted to publish, a common enough practice in biotechnology. Gabriel, an administrative PhD fresh from a large pharmaceutical corporation, defended why publishing rules in biotech are not the same as in academe. I recorded their exchange at the meeting: Luther: The other issue is when to go public with this. Gabriel: Not for a long time. Luther: Well, we should publish something. Gabriel: We don’t have anything to gain at BioNow right now by publishing. I don’t think we should. Luther: Don’t you think it would impress investors? Gabriel: But I don’t think we’re at that point. We already tell investors we’re successful, but don’t say with what. They have accepted this; they’re satisfied. It will not give us any more success to publish. If you reveal too much, you give it to the competition. Like you look in BioWorld [a trade publication] and NIH advertises their findings—why would anyone license them? Anyone can figure it out. You don’t want to give away your lead structure—you give away everything. That’s the basis of the whole industry. [He pauses while Luther folds his arms across his chest, takes in his breath sharply and raises his eyebrows in a skeptical facial expression. Then Gabriel seems to address Luther’s nonverbal negativity, looking at him as he continues to talk.] It’ll probably be publishable sooner than the stuff with [another new biotechnology firm with which BioNow is collaborating]. They’re either dead or not, so they’re not anxious to publish squat. It’ll mean a lot more to [BioNow’s collaborator on the project being discussed in this meeting] and us in a year or so.

An interesting aside is that Gabriel is not talking about limiting secrecy of their research to BioNow’s organizational boundaries but that the information is limited to networks of collaboration dependent on each different project. The issue around publishing was seemingly resolved until later in the meeting: Gabriel: The exciting thing is [describes a result]. We’re not sure, but it’s a hypothesis. We weren’t even sure it could get close. Luther: If we show [a result], we should publish. Gabriel: Write it up, and we’ll see. Luther: [chuckling, says in stage whisper] Then I’ll see how I could sneak it out of here [to publish]! [There is general group laughter; Gabriel smiles and shakes his head indulgently.]

The tension between organized perspectives or frames was clear in the laughter in the room. But the humorous moment also allowed Luther and Gabriel a way to bring the whole group together again to that sense of col-

S/D-3 6/16/04 12:53 PM Page 72

72

Women’s Work

t

laboration the scientists talk about at BioNow. Even when conflict between conceptions of work is obvious, as between Luther and Gabriel in the meeting, the differences are glossed over—with humor in this case. In my observation, the scientists at BioNow seemed intent on maintaining the collegial spirit, assuming that bitter disagreement would mar the ethos of working toward a common goal. Early on, I thought perhaps this framing was more evident because BioNow was a startup firm, but a similar orientation appears in older, larger biotech firms. In light of the collective rewards in biotech—higher stock value, new equipment, day-care centers—it is perhaps less surprising that the team ethos shows up more than in academe where the main incentive—tenure and promotion—is individualized.

Assessing the Institutionalization of Legitimacy

This chapter has shown how contradictory institutions, although they may not be seen as such by scientists, become part of the interwoven texture of the network form of organization in the biotechnology industry. In three emergent themes from scientists’ narratives, industry and academic logics seem paradoxical yet coexist in legitimation of biotech work. First, scientists discuss resources from the academic perspective of being above mercenary motives in favor of scientific curiosity, but they also describe the monetary resources in biotech as enabling good science. In other words, money is simultaneously a bad motivation and a good motivation. Second, narratives surrounding social networks in science incorporate the idea that one’s faculty mentor is always right, but they also allow that elite scientists who know both worlds of university and biotech are the most legitimate sources of career advice. Academic advisers, then, are to be trusted and ignored. Third, the asset-of-newness theme acknowledges that academia has been seen as the number-one source of new ideas in biology, but the time-honored institutions of the university may be second-best, as when team efforts and lateral networks in the biotech industry produce more innovations. The university is both the gold standard and the old (outdated) standard. How are these seemingly contradictory themes reconciled? Participants simply fail to perceive them as conflicting, most of the time. This study contributes to the organizational literature by outlining variation in the institutional change process when the structural context is the network form. In the growing literature on institutional change in organizations, deinstitutionalization precedes the legitimation of a new form (Clemens 2002; Scott 2001). But in a network organization, multiple coexisting institutional logics go beyond being just a stage that will soon resolve itself in a paradigm change or a kind of Marxian shift from thesis to antithesis. Instead, multiple paradoxical perspectives work together as a

S/D-3 6/16/04 12:53 PM Page 73

Life in the Commercial Laboratory

73

legitimate institutional logic in the network form, because individuals maintain a variety of diverse ties connecting them to seemingly contradictory information. In biotech firms, successful scientists cultivate relationships across organizational boundaries with academics and PhDs in other firms. But I could be wrong. Perhaps the biotechnology industry, founded in the late 1970s and early 1980s, is still too young and the institutional change process is incomplete. Perhaps the academic viewpoint is on its way to being delegitimated and the newer biotech legitimacy framework will be the new paradigm. It does not yet look that way after the first twenty-five years, however.

Connecting the Institutional and Individual Levels This chapter has illustrated how scientists legitimate working in biotech, with all of the attendant tensions. This individual, microlevel process of legitimation, I have argued, has repercussions for the institutional level of the legitimation of the network form of organization used by biotech firms. How, exactly, are these two levels linked? In one sense, we might make the fundamental observation that individuals’ occupational identity is naturally linked to the employing institutions in which they expect to work (Becker and Carper 1956). Howard Becker and James Carper found during their interviews with students in the 1950s that physiologists identified with the narrow, circumscribed roles available to them in academia or pharmaceutical corporations, whereas engineers had broader aspirations tied to a wide-open field that could lead them to general management positions in a variety of organizations. Since the 1980s, shifts in the institutional landscape of the life sciences and defense-related industries that employ many engineers may have reversed these individual occupational identities. As the life sciences expand, more biological scientists view themselves as scientist-entrepreneurs with many options, whereas engineers observe downsizing of stable technical jobs in aerospace and other large industrial corporations. More specifically to the network form, the mechanism by which these flatter organizations thrive on contradictory institutional logics is their multiple, disparate sources of information. A firm that relies on ties to diverse other organizations receives information flows that are often conflicting. For example, David Stark (2001) charts how architects of emergent network organizations in Eastern Europe after the collapse of the Soviet Union bring together the divergent logics (and ties) of capitalism and state-owned enterprises. When contradiction is incorporated into the everyday life of the firm, the process of glossing over tensions in organizational identity mirrors individuals’ glossing over tensions in occupational identity, such as that I observed at BioNow. Table 3.1 illustrates the specific connections

S/D-3 6/16/04 12:53 PM Page 74

74 Table 3.1

Women’s Work Tensions in legitimacy narratives: connecting the individual level to the institutional level

Biotech narrative

Individual-level logics, scientists in biotech

Institutional-level logics, network form

Resources

Biotech scientists must be above monetary motivation for good science. Biotech scientists’ work must translate to finance for good science to result.

Biotech firms’ priority is scientific reputation through prestigious publications. Biotech firms’ priority is stock price and product sales (Kleinman and Vallas 2001).

Networks

Academic mentor and other reputable scientists may give conflicting career advice.

Diverse biotech firm collaborators (e.g., in universities and pharmaceutical corporations) may give conflicting information (Powell et al. 1996).

Asset of newness

Biotech is like academe (publishing); biotech is unlike academe (teamwork).

Emphasis on both newer collective and old-fashioned individual rewards (Zucker et al. 2002).

between the individual-level logics of scientists working in biotech and the institutional-level logics of the network form of biotech firms—and the tensions inherent in these legitimacy narratives. In the narrative around resources, biotech firms’ key ties to universities and venture capitalists mean that there are sometimes contradictory emphases on publication and on meeting financial goals (Kleinman and Vallas 2001). The networks narrative means relying on collaborative partners for information. This arrangement may lead to conflicting advice from the variety of ties held by successful biotech firms (Powell, Koput, and Smith-Doerr 1996). Finally, the mobility of scientists between the university and biotech firms perhaps means that the narrative about assets of biotech newness (such as newer collective rewards like research team recognition and bonuses) conflicts with the older logic of individual rewards to star scientists (Zucker, Darby, and Torero 2002).

Implications of Institutionalizing the Network Form This chapter observes scientists institutionalizing the network form. These processes probably operate similarly for other laterally organized firms in the biotech industry, and in other industries governed by interorganizational ties. But further study, of course, is needed to determine whether these are general themes that apply in other settings. Although this is a qualitative study focused on one organization, other authors have similarly described

S/D-3 6/16/04 12:53 PM Page 75

Life in the Commercial Laboratory

75

scientists’ narratives legitimating biotech work (Rabinow 1996; Rayman 2001; Werth 1994; Dubinskas 1988). A biotech scientist quoted by Paula Rayman (2001:128) elaborates on the difference between scientific work in biotech and pharmaceuticals: “The pharmaceutical companies aren’t out to cure the roots. They are just out to make you feel better. Biotech is out to cure diseases. We’re finding out how disease progresses and finding ways to prevent it from happening.” Even more specific to this chapter’s findings, Paul Rabinow’s (1996) interviews with Cetus employees demonstrate that scientists widely use the network frame to legitimate working in a biotech firm. Rabinow’s scientists also refer to their reliance on ties with credible scientists to describe how they made the decision to take employment in one of the first biotech firms: “The people I respected said I should take the job at Cetus” (43); “I had known one of the founders” (56); “I met some of the people . . . and I was impressed with the scientific credentials” (73). A critic might assume that the legitimation effort found here is simple rationalization because there are not enough academic jobs to go around, whereas plenty of industry jobs exist. Certainly, there are now fewer academic posts relative to the number of applicants across the sciences (Fox and Stephan 2001). Where once nearly half of all biological PhDs had a tenure-track position ten years after graduation, in 1995 only 34 percent of midcareer PhDs were tenured (or tenure-track) in academe (Regets 1997). If scientists rationalize jobs as desirable based on availability, however, work in global pharmaceutical corporations would be the focus of the legitimation process rather than careers in biotech firms. Roche employs more than 66,000 people, and Millennium, a prominent firm in the biotechnology industry, employs slightly less than 2,000. In exploring perceptions about changing professions in a turbulent environment, this study touches on issues in the sociology of work. Work in the new economy is a burgeoning topic in social science (for reviews, see Smith 1997; Applebaum and Batt 1994; Leicht 1998). Yet one lacuna in scholarship on the new economy has been the understanding of how professional work—including the identity of professionals in changing employment conditions—becomes legitimated (Leicht and Fennell 1997). People need to give meaning to their activities and roles; this idea is axiomatic in sociology. Professionals probably spend more time than others spinning narratives about their work, as it is central to their identity. I have outlined a specific process of attributing meaning to work in network organizations: older frames are used and new ones are constructed which may be conflicting but are not seen as such because of the multiplicity of ties. Scientists simply borrowed ideas from different connections and constructed narratives in a bricolage fashion (Levi-Strauss 1966; Clemens 1996). In describing the scientists as bricoleurs, however, I do not intend to give an overly rationalistic impression of their construction activity. Of course, in some

S/D-3 6/16/04 12:53 PM Page 76

76

Women’s Work

ways scientists are attempting to shape their careers and the perceptions of their field, but rather than approaching a maximizing of legitimacy they are pulling together different frames in a rather spontaneous way. This bricolage is not always completely successful, as shown in the excerpt from the rather tense research meeting. My analysis contributes to a growing trend in institutional scholarship that seeks to unpack the multiple roles of agency in legitimation processes and the emergence of new institutions. During periods of institutional change, individuals may become more aware of taken-for-granted structures and use talk to work on frames perhaps more than at other times. It may also be that during such a time of constructing collective meanings individuals (because they are working to convince themselves and others) believe frames more strongly than during relatively quiescent periods. Although I found that scientists’ narratives of their careers held conflicting elements and that their social constructs were combined in ways beyond what would be most clearly rational, they did collectively serve to legitimate biotech industry employment. Other recent work has focused on intended collective action aimed at constructing new fields and institutions, for example, in the arts (DiMaggio 1991), politics (Clemens 1997), economics (Fligstein 1996), law, and nonprofit sectors (Morrill and McKee 1993; Rao, Morrill, and Zald 2000). My analysis focuses instead on unintended collective action in which individual actors operating in their particular milieu seek to legitimize their careers. To the extent that such framing occurs in the aggregate (as suggested by common patterns described in this chapter), new cultural resources for legitimating emergent institutions become available. What might seem like sensemaking from an individual perspective carries the possibility of becoming a broader legitimation process. Although this chapter focuses on legitimacy narratives about biotech industry work, the findings may have implications for the conflicting goals of commercial and academic science in the university. Within academic careers as well, there are now two conflicting legitimacy narratives. The traditional model of legitimacy is the post–World War II Vannevar Bush ideal of university basic science separate from its application in industrial technology. The new academic model of legitimacy has developed out of the increased blurring of boundaries between basic science versus applied science and university versus industry (Croissant and Restivo 2001). Now, academic scientists can legitimately be entrepreneurs. Top scientists in biotech firms have moved into academe as deans of schools of medicine and public health, department chairs, and cancer-center directors. Junior faculty expect technology transfer, and industry-funded projects will help them over the bar for tenure (Hackett 2001). A concern is what the implications of such a shift might mean for the independence of academic research

S/D-3 6/16/04 12:53 PM Page 77

Life in the Commercial Laboratory

77

t

from market influence. One study (Slaughter and Leslie 1997) describes shifts in U.S. science policy toward a competitiveness ideal. Indeed, the academic scientists who obtain the most resources do science that is cutting-edge and has commercial potential (Owen-Smith and Powell 2001). If we cannot draw lines between science for profit and science for its own sake, social inequality may be exacerbated. It would be high irony if the biotech industry culture increasingly takes the high moral road—recruiting PhDs who want to do good science and cure AIDS—while university culture escalates its rhetoric of profit and returns on investment (Kleinman and Vallas 2001; Bok 2003). Allowing short-term market interests to shape the distribution of resources to life-science research agendas inside the university as well as industry likely will result in even fewer resources to solve public health problems of the world’s poorest populations. Even if the outcome of close university-industry relations is not quite so grim, the trend is one that bears watching by sociologists.

Legitimacy and Gender Equality

What does it mean to work in the biotechnology industry? This chapter has explained why scientists were attracted to these novel and unorthodox settings, as well as how they formulate stories about the legitimacy of working in biotech firms. Prior to the emergence of biotech, jobs in industrial settings were seen as less legitimate than academic careers. I examined how scientific professionals make sense of work in novel settings—small science-based firms—that resemble neither older industrial jobs nor traditional academic careers. In the formation of a professional arena, individuals make sense of new work by drawing on existing legitimate frames from academe as well as by crafting new narratives. Although these old and new frames may conflict, the tensions in social constructions of careers go unnoticed or are glossed over. Three broad frames emerged from scientists’ legitimacy narratives about work in the biotech industry: resources, networks, and assets of newness. Tensions within frames arise from transformations in institutions (such as the increasing overlap between academic and for-profit science). For example, scientists made comparisons with academic work in which they would claim biotech firm science is just like in the university (i.e., publication-oriented), then in the next breath discuss how working in biotech is so different from academe (i.e., much more collaborative). This chapter sets the stage for the key concern of the book: how women scientists fare in this new, legitimate job market in industry. I next turn to the question of whether the organizational transformation produced by the emergence of the biotech industry has affected gender inequality among scientists. Are women making gains in the biotechnology

S/D-3 6/16/04 12:53 PM Page 78

78

Women’s Work

t

industry? This query can be asked in two ways: (1) Does the occupation show evidence of sex segregation in who is hired first? and (2) Are there differences in promotion to positions of authority by gender? These questions will be addressed in Chapters 4 and 5.

Notes

1. Neoinstitutionalism builds on symbolic interactionist insights in sociology (i.e., Berger and Luckmann 1966; Goffman 1974), as do management scholars (following Weick 1995) who discuss the role of sensemaking in organizations. Additionally, the use of symbolic interaction to study science is not new (for a review see Clarke and Gerson 1990). The focus of science studies, however, is the social construction of knowledge rather than the organization of science (Kleinman 1998). 2. Another branch of research on institutional dynamics places more emphasis on the emergence of novel organizational structures and professions than on change in existing fields. Paul DiMaggio’s study (1991) of the professionalization of art museum administration, and Heather Haveman and Hayagreeva Rao’s study (1997) of the evolution of the thrift industry, are well-known examples. Biotech is a new network organizational form for life scientists to work in, but it arises in an existing professional field and contends with older, dimmer views of science in industry. Thus, for this chapter the literature on institutional change is more relevant than that on origins.

t

S/D-4 6/16/04 12:54 PM Page 79

4

Coming In on Queue? Women’s and Men’s Entry into Biotech

I wouldn’t characterize anyone as a dime a dozen. —Cathy Houck, program manager at Cal-gene, describing scientists entering biotech in an interview with The Scientist (1991)

Judith’s experiences searching for a job as an industrial scientist reflect the separate queues in which women and men have waited to be hired. Judith was an independent young woman coming out of a women’s college at the advent of World War II. She wore the socially required hats, gloves, and heels but chose the smallest beanie-type hats she could find and bobbypinned them to her hair to keep them out of the way. Despite her parents’ and teachers’ best efforts to socialize Judith into becoming a teacher, she wanted to work in industry to “really do something in the world, something useful” that teaching did not supply, at least to her mind: “My mother wanted me to become a teacher. And my professors told me that I would never get a job in industry. But I was stubborn and I wanted to do something that was hands on. I just refused to teach.” Judith soon found that employers reserved the scientific and technical job queue for male candidates: I sent out fifteen letters to companies seeking work in a technical position. Half of them did not respond; the others wrote back and said “We don’t hire women as engineers or scientists.” They really said that back then: “We don’t hire women.” I would never have gotten a job if it hadn’t been 1942, and you know, the war had just started. And companies finally figured out that there weren’t any men around! So I was able to get a job in industry.

When employers ran out of men in the science and engineering queue because the men were overseas fighting a war, they finally came down to the end of the queue and hired women like Judith. Today, employers would not blatantly admit that they refused to hire a scientist because she was a

79

S/D-4 6/16/04 12:54 PM Page 80

80

Women’s Work

t

woman, in part because they might face a lawsuit. Yet sociologists continue to find evidence of segregated job queues in many industries. In valued occupations, the queuing theory posits, women are placed at the end of the line. In less valued occupations, men do not queue up, so women have a greater chance of being hired. Through these queues, jobs end up being either “women’s work” or “a job for a man.” This chapter answers the question of whether the biotechnology industry, as a legitimate new place for scientists to work, was originally a male or a female domain.

Segregation—No Girls Allowed

Americans live in a segregated society; one-third of all blacks live in one of sixteen urban centers, in neighborhoods where virtually no whites live (Massey and Denton 1993). Occupationally, however, gender segregates people into work roles even more than race does. Women and men of similar ethnic backgrounds, of course, live in the same neighborhoods and households. They often work in the same organizations. But men and women work in different jobs. Even if they work in the same occupation, they work in different sectors: men drive city buses, women drive school buses. Just as cities may be racially integrated at the metropolitan level but segregated at the neighborhood level, women and men may be integrated at the broader occupational level but segregated at specific job levels. Scholarship on gender segregation in the labor market utilizes language similar to that of spatial segregation by race. Sex segregation usually refers to the broader occupational differences (e.g., the proportion of female bus drivers or physicians); ghettoization refers to more local job differences within an occupation (e.g., the proportion of male pediatricians or neurosurgeons). In order to achieve occupational-level integration, 52 percent of the women in the U.S. labor force would have to change jobs to predominately male occupations (Padavic and Reskin 2002). Thus if one’s acquaintances are like the average U.S. workforce, more than half of all the working women one knows would have to change jobs (e.g., from working as nurses, food servers, secretaries, elementary school teachers, and cashiers to working as car salespersons, truck drivers, computer scientists, carpenters, and executives). Or men would have to switch jobs from male occupations to predominately female occupations. Even then, men and women still might not work side by side if, for example, the female food servers mostly worked in diners and the male food servers worked in upscale cafés. Segregation by gender would perhaps be less troubling if men and women actually were separate but equal in the labor market. The gender of a job, however, is closely linked to its pay—and female jobs are paid less.

S/D-4 6/16/04 12:54 PM Page 81

Coming In on Queue?

81

The gendered composition of jobs in the United States accounts for at least 5 percent of the sex gap in pay, or $.40 to $1 an hour in 1992 dollars (England 1992: 181). All other things being equal—including job experience, education level, skill requirements, and so forth—a person employed in a female occupation (whether a man or a woman) will earn about a dollar an hour less than someone in a male occupation. Professional occupations are also gender-segregated, including scientific and technical professions, as the figures in Chapter 1 show. A female PhD is much more likely to work in the social or life sciences than the physical or computer sciences. And gender composition of a scientific field is related to pay: physical scientists earn average annual salaries of $75,000, whereas life scientists earn $62,000 (NSF 1999, table F-2).1 This kind of gender segregation is not unique to the United States; it begins in educational organizations around the globe in which students enter different majors by gender. A study by Maria Charles and Karen Bradley (2002) examined postsecondary education across industrialized nations and found significant gender segregation by academic specialties in all of the countries. Their results somewhat contradict the argument by neoinstitutionalists that more universalistic gender norms are part of the increasingly global influence of Western individualism (Meyer, Boli, Thomas, and Ramirez 1997; Ramirez and Wotipka 2001). While women and men of the industrialized world now achieve educational degrees in similar proportions (i.e., community college or graduate degrees), integration of specialties has not occurred (Charles and Bradley 2002). Men are still more likely to be physics and engineering majors, women to be humanities and social science majors. Sex segregation is a robust pattern in industrialized labor markets. Many different explanations have been offered by social scientists to explain this gendered pattern. Economists tend to favor the explanation that workers’ individual tastes guide rational investments in different skills and education levels (Becker 1993; Fuchs 1988). Sociologists usually focus instead on how individual decisions are made in the context of social relationships and cultural expectations. The difference is certainly not as simple as the old joke: “economics is all about how people make choices; sociology is all about how they don’t have any choices to make” (Duesenberry 1960, cited in Granovetter 1985: 485). But there is a grain truth in this. Sociological studies of occupational segregation have often analyzed the constraints on individuals in the labor market, in contrast to economic hypotheses. For example, a national study of U.S. workers (Kaufman 2002) found that rather than individual preference or skill, a better explanation for occupational segregation is the sociological theory of queuing, in which male and female workers end up in stereotyped jobs through a discriminatory hiring process.

S/D-4 6/16/04 12:54 PM Page 82

t

82

Women’s Work

Queuing—Ladies Last, Please

Commonly, a field opens to women after it has become devalued and men have left for other jobs (Reskin and Roos 1990). From the queuing perspective, employers have implicit ideas about how to fill jobs, namely, that males are more valued applicants and should be hired first. When jobs become less desirable in income, status, or mobility prospects, they become feminized. Because men no longer want the less desirable jobs, employers are led to hiring female workers further down the queue. Historical examples of U.S. jobs that became devalued then feminized include clerical work, book editing, and pharmacy work (Reskin and Roos 1990). Studies of gendered job queues have directly addressed the question of who will enter a new field first, thus providing an important backdrop for the question of whether the early biotech industry was mostly populated by male or female scientists. Research on new fields shows that as jobs increase their desirability they change from women’s work into men’s work. Women were selected for the very first computer programming jobs in the 1940s because the work superficially resembled clerical tasks (Donato 1990). The arrangement of punch cards, for example, might at first seem like filing papers. Once men recognized that programming demanded logical, mathematical, and electronic skills, however, they filled those jobs. Later, when programming was parsed into skilled and deskilled work in the 1960s, women entered the lower-paying, less prestigious computer jobs like keypunching. The early years of computer programming are a story of a new field comparable in some ways to biotechnology. A rapidly growing field with no specific tradition of gender-role inequality characterizes both. But computer work developed primarily in hierarchical organizations—the defense-related departments of government and large corporate settings like IBM. The bureaucratic organizational context meant that job placement decisions came from above, that is, from white men within the power elite. Thus rapid shifts in the gender composition of programming resulted in women being pushed out (Donato 1990). As with computer programming, novel writing began as a field open to females (Tuchman 1989). In Victorian England, female authors like George Eliot, Jane Austin, and the Brontë sisters became novelists when the occupation was déclassé. As novels increased in readership and novelists were afforded more visibility and reward, men redefined the field. Gaye Tuchman (1989) describes the masculinization of novel writing as an empty field phenomenon. When a woman’s job comes to be seen as valuable, males view the field as empty because competition from females is considered negligible. Just as partygoers might return from a social gathering at which they did not find any friends and claim that no one was at the party, male writers saw no one authoring novels. After the turn of the centu-

S/D-4 6/16/04 12:54 PM Page 83

Coming In on Queue?

83

t

ry, high-brow literature was defined by manly realist novels written by U.S. male authors like Ernest Hemingway and Jack London. These macho authors and their imitators filled the field as it became more prestigious, and editors could place women writers farther down the list.

Insiders, Outsiders, and Innovation

Who moves first into a new scientific employment arena—male or female PhDs? This question connects to the broader one of whether professionals from the core or on the periphery of their occupation are more likely to pioneer employment into new territories. In the sciences, females inhabit the “outer circle” (Zuckerman et al. 1991), or periphery. Core scientists are those with educational privilege—PhDs from institutions with cutting-edge resources and research connections. When science studies take up the question of core-periphery diffusion, the focus is on scientific knowledge rather than the movement of people into new social space. Individual scientists, however, are the carriers of knowledge, and thus some of the abstract conceptualization of core-periphery diffusion may provide insight into careers. In this section, I first review scholarship considering the core-periphery diffusion of science. Then the above-reviewed gender studies of male/female entrance into empty fields are taken into account. These literatures are examined in light of the organizational characteristics of the focal research setting—the biotechnology industry; a hypothesis is formed about who is likely to enter the new arena first. Given the stratified character of science in which a small minority of PhDs garner the majority of widely read publications, research grants, and prestigious awards (Zuckerman 1977), there are clear insiders and outsiders in science. Do new ideas about career paths and scientific knowledge come from insiders or outsiders? To consider the diffusion of innovations, one direction is for innovations to move from the periphery of a field to the central core; a second direction is the reverse, in which innovations flow from the core to the periphery. The distinction between core and periphery comes from an image of power in a social structure in which the central, powerful actors have connections to the most resources. If we imagine a more traditional vertical hierarchy of power, an alternative description of the dichotomy is that innovations can diffuse from the bottom up or from the top down. On the one hand, some theorists of scientific and technological innovation characterize the diffusion of new ideas as flowing from the periphery to the core of a field. In a text written between the world wars and only discovered by science studies in the 1980s, Ludwik Fleck (1935) describes how the formation of new knowledge fields emerges from the intersecting

S/D-4 6/16/04 12:54 PM Page 84

84

Women’s Work

peripheries of what he terms thought collectives and what others (e.g., Kuhn 1970) might call groups with a shared paradigm. Thus for Fleck, new fields form where edges overlap, through peripheral ideas and people, rather than in the core of thought collectives. More well known is Kuhn’s (1970) concept of scientific revolutions, which parallels Fleck’s earlier discussion of innovative ideas. According to Kuhn, once scientists are vested in a core paradigm they engage in normal science—incremental accumulation of knowledge. Revolutionary ideas come only from the young turks outside of the dominant paradigm, from the periphery. On the other hand, some theorists of scientific innovation take the opposite perspective, maintaining that ideas diffuse from the core to the periphery (albeit with a somewhat different view of what constitutes the core and periphery). It has been argued (Goonatilake 1993) that cuttingedge science invariably originates in first world nations and makes its way to the third world only years later. A more avowedly Marxist perspective on diffusion would likely view innovations as emerging from the rational interests of the ruling class, claiming that ideas benefit the core at the expense of the peripheral classes (e.g., Gramsci 1971; Gorz 1980).2 The process of the accumulation of resources in science has more often been portrayed as a contingent feedback loop rather than conscious exploitation by powerful actors. The “Matthew effect” (Merton 1968) recognizes that scientists with a name and prestigious position have more attention paid to their ideas—and thus more rewards. Other scholars (e.g., Latour 1988) portray a similar, if more active, picture through actor networks: those in the core are more able to enroll others to the credit of their ideas. Leaving aside the issue of intentions, there seems to be an assemblage of scholars that believes scientific ideas move from the core to the periphery. This project, rather than focusing on the diffusion of abstract scientific knowledge, explores the diffusion of the acceptance of new career structures. Another way of looking at how the core influences the periphery comes from research on the formation of scientific disciplines. The diffusion of ideas and people from prestigious disciplines into new areas gives them legitimacy. Some (Mullins 1972) describe the flow of more prestigious physicists into molecular biology as the primary factor influencing the discipline’s formation. Likewise, others (Ben-David and Collins 1966) attribute the reception of psychology as a science to the movement of physiologists into psychology. Academic disciplines considered to be the core of science influence the emergence of new fields on the periphery. Similarly to the scientific domain, in classic diffusion of knowledge theory the diffusion from central to peripheral actors is a common proposition. Innovations are said to need advocates. If the advocates are prestigious professionals, it bodes well for the general adoption of the innovation (Barnett 1953), because early adopters of successful innovations are often

S/D-4 6/16/04 12:54 PM Page 85

Coming In on Queue?

85

seen as role models for others (Rogers 1995). At the same time, however, instances of innovation that flow from peripheral subcultures and then to elites (e.g., working-class African Americans’ influence on U.S. popular culture, including the invention of jazz and rap) have been noted (Rogers 1995). Thus, arguments for core to periphery or periphery to core can be made from both the scientific innovation literature and the general diffusion of innovation literature. An empirical question for this project is whether PhDs from the core or periphery of the life sciences are more likely to be the innovative early incumbents of new career positions. The core bastions of knowledge in the sciences—elite universities—produce the scientific professionals to whom credibility and legitimacy are most often attributed. Scientists with greater educational privilege have access to cutting-edge resources and research during their training. Educational prestige is thus one of the most salient characteristics of professionals, especially for scientists early in their careers. To find a process of diffusing career trends that does not reflect differences in educational background seems unlikely. One might expect that PhDs educated in more peripheral departments would be likely to enter a new industry setting first because traditionally, elite-educated PhDs have been directed exclusively toward academe. Industry jobs have been thought of as more the domain of those who are “not scientists’ scientists” (Kornhauser 1962) or are even “mediocre” scientists (Whyte 1956). Thus it may be the case that PhDs with nonelite educational backgrounds will be more likely than elite-educated PhDs to enter biotechnology industry positions in the early period. This statement follows from the arguments that innovation first occurs at the edges of a field, then diffuses to the center (e.g., to scientists from elite universities in this case). The biotechnology industry, however, has been closely connected to universities from its inception. Biotech firms are largely founded by academic scientists who are among the most prominent in their specialty areas in the life sciences (Zucker, Darby, and Brewer 1998). Considering that most workers, including white-collar professionals, end up in their jobs through the facilitation of network ties, I expect that the networks of founders will connect them to elite-educated PhDs first. In other words, the new career arena of biotech is likely first to be filled with elite-educated scientists because of the legitimacy of star founders, as well as their connections to the core of the life sciences. Thus to gauge whether biotechnology is first a man’s field or woman’s field, it will be important to control for the effects of educational prestige—to compare the careers of men and women from the same kinds of schools. The question is whether women, who tend to be more peripheral in science, were the innovative pioneers in biotech, or whether men in the core

S/D-4 6/16/04 12:54 PM Page 86

86

Women’s Work

of the field were the early adopters of the new career path. From research of job queuing and empty fields, one would expect that women would move into an open employment arena before it is legitimated and desired by male candidates. In other words, females, as more peripheral scientists, are more likely to enter a new field than are male scientists. Historical research on Victorian novelists and computer programmers shows that as jobs in new fields increase their desirability, they change from women’s work into men’s work. Perhaps, then, in the earliest years, before biotech was fully legitimated and men moved in, the industry would have had disproportionate numbers of female scientists. Yet as Chapter 3 demonstrated, biotech has been seen as more legitimate than other industry jobs in traditional pharmaceutical companies. Following the theory (Reskin and Roos 1990) that desirable occupations have a gendered queue where men are hired first, one might expect that the early years of biotech would have seen more men moving into biotechnology firms. The beginnings of the biotech industry were unlike when novel writing and computer programming were new; biotech did not experience a crisis of legitimacy in the same way. All new fields experience what organizational sociologist Arthur Stinchcombe called the “liability of newness” in his famous essay (1965)—the problem of being seen as a legitimate organizational type. Biotech, however, had more legitimacy from the start because the new firms were founded by prominent academic scientists. This propitious start for the industry perhaps made it more likely that men would enter from the beginning. In other words, the trailblazers of the innovative career path into biotech would likely be the life sciences’ core insiders rather than more peripheral outsiders. Science is not much like popular culture, where new language, music, and fashion fads may originate with teenagers of limited means on the fringes of society. New scientific experiments—whether at the bench or on one’s CV—require large investments of financial and social capital. In a knowledge-expanding field like the life sciences, usually no single organization possesses all of the specialized know-how in-house to make a major breakthrough. Biotech firms, with their facility at bringing together diverse interorganizational partners, build the social capital that provides the basis of innovation. The most central firms, the core of the industry, are the most visible in publishing, patenting, and selling drugs (Powell, Koput, and Smith-Doerr 1996; Smith-Doerr et al. 1999). As innovation in knowledge diffuses from the resource-rich core of life science organizations to the periphery, so too were the early entrants into biotech careers life-science insiders. Lynne Zucker and colleagues (2002) found that academic scientists whose papers are more highly cited (in other words, the most famous, core life scientists), the “stars,” were more likely to move into biotech firms. In Zucker et al.’s statistical analyses, scientists’

S/D-4 6/16/04 12:54 PM Page 87

Coming In on Queue?

87

wages had little effect on the decision to enter biotech. The way scientists at BioNow talked about deciding to go into the industry—the credibility of the science and scientists in biotech, rather than the money—seems to be a general sentiment. Reputation is key to the biotech industry’s ability to attract top scientists. For individuals, reputation in professional networks is key to getting a job. Mark Granovetter (1974), in a classic sociological study, investigated how white-collar men residing in Newton, Massachusetts, gathered information that had led them to good jobs. The help-wanted ads did not help much. Instead, 80 percent got a job through social network connections. Furthermore, helpful job information was likely to arrive through weak ties—acquaintances and friends of friends—rather than through strong ties. The strength of weak ties, as Granovetter (1973) put it, is that they provide you with new information on job leads, while your closest friends mostly can tell you what you already know. Granovetter’s findings have been replicated around the globe—a majority of professionals find jobs through personal networks. Although information about scientific position openings is more widely posted than in other white-collar occupations, scientists still often get jobs through networks. A white male academic in his fifties, head of a department at a top-ten school, described how hiring really works: We hire assistant professors every year. We do advertise widely and look at hundreds of applicant files, but this year we ended up hiring the person whom we had encouraged to apply for the position. Some of us already knew him from conferences and whatnot and asked him to consider applying. If you know people who would be a good fit, they will often end up being the best candidates.

After admitting this common practice in academic hiring, he shrugged, raised his eyebrows, and tilted his head as if to say with his body language, “It’s not exactly the best practice, but what can you do?” He concluded by remarking, “That’s just the way it works.” In this instance, a young male scientist had the right network connections to lead to a top academic job. Generally in leaving PhD training, men are more likely than women to have had the opportunity to form network connections to scientists who give them a leg up in entering the job market (Etzkowicz et al. 2000). What about biotech? Would male or female PhDs have been more likely to seek out biotechnology firm positions early in the industry’s growth? From the advent of the industry, male academic star scientists have founded biotech firms, sat on their advisory boards, and have published some of the most highly cited papers in molecular biology through their affiliations with biotech firms (Zucker, Darby, and Brewer 1998). Nearly all—96 percent—of these boundary-spanning, academic-biotech stars are men (Zucker

S/D-4 6/16/04 12:54 PM Page 88

88

Women’s Work

t

et al. 2002). If young male PhDs are more likely to have networks with connections to the core actors in the field, they may disproportionately have better access to information about the desirability of biotech jobs compared to traditional industry jobs. From a queuing perspective, males should be the first entrants into a new job arena if they consider it to be a desirable one. Gendered hiring queues would then lead men into the industry earlier than women. Put plainly, male PhDs would be more likely to enter biotechnology industry positions before female PhDs.

Who’s In First?

The sociology of science literature shows good evidence of a competitive queue for tenure-track academic positions. Males, and PhDs from more prestigious programs, head the line into academe as they disproportionately garner positions compared to other PhDs (Long and Fox 1995). But what is the effect of the emergence of the biotechnology industry on life-science careers? Has there been a queue of life scientists into the biotechnology industry as there has been into tenure-track life-science jobs? The question addressed here is whether who goes into biotech—mostly male or female PhDs—differs by industry period. In this section, the effect of gender on timing of entry into the biotechnology industry, holding prestige of doctoral degree constant. In biotechnology industry history, the early period comes prior to 1989. One indicator that the biotech industry had reached viability, and some level of maturation, after 1988 is that at that time an independent press (Oryx) began recording and selling information on biotech firm activity in a volume with monthly supplements titled BioScan. An industry publication such as this (marketed toward venture capitalists and other interested investors) signals a level of legitimacy and certain stability that a compendium of information on the industry is viable. Thus the time prior to such a trade publication is considered to be the early years of the industry (circa 1975–1988). The percent of PhDs in my sample from the early period is 38.4, and those whose career is measured in the later period (1989 and thereafter) make up 61.6 percent. To see whether being female or having a degree from a more peripherally ranked university would affect a scientist’s chances of entering the new, exciting, and scientifically credible field of biotechnology, I analyzed careers of 2,211 life scientists from information collected at the U.S. National Institutes of Health (see the Appendix for further details). What affects entry into jobs in the biotech industry? The first, basic look at this question resulted in the findings presented in Figure 4.1. This figure shows that entry into the biotechnology industry is affected by educational back-

Percent change in life scientists’ odds of entering biotech

Source: All else being equal, based on logistic regression results; see Appendix, Table A8.

S/D-4 6/16/04 12:54 PM Page 89

Figure 4.1

S/D-4 6/16/04 12:54 PM Page 90

90

Women’s Work

ground, all else being equal—including gender and time period in which the data are recorded. (Figure 4.1 is based on the results of a logistic regression model of the effects of PhD-granting department, gender, and period in biotechnology industry history on the probability of a PhD’s entrance into a biotech firm; see Table A8 in the Appendix for the full results.) Holding the effects of period constant, those with a middle-ranked education, in comparison to the elite-educated, see their odds of entering biotech decrease by 32 percent. PhDs from the least prestigious departments, compared to those from top-ranked schools, see their odds of entering the biotechnology industry decrease by 58 percent. Interestingly, gender is not a significant predictor of entering the biotech industry arena, and neither is the industry period. The key question in this chapter, however, is somewhat more complex than these basic chances for entering biotech shown in Figure 4.1. To connect to queuing theory, we need to know if the effect of gender differs by time period—that is, whether men would be more likely to enter the new, desirable employment arena in the earlier period. Figure 4.2 shows the unexpected answer. (See Table A8 in the Appendix for the complete results of the logistic regression model, including interaction terms for gender by period and education by period.) Based on the queuing literature and information on the early legitimacy of biotech, I expected that gender would significantly predict entry into biotechnology differently by time period so that men would step into the new industry first. The results shown in Figure 4.2 instead indicate that male and female scientists are equally likely to enter biotech across the entire industry time frame (up to 1995). There seems to be no difference in the odds for a male scientist to enter biotech earlier in the industry’s history rather than later.3 While female scientists are often treated as peripheral, the early entry of females into biotech alongside males marks the industry as an interesting case. Instead of a subsequent feminization (or masculinization) of the arena, the analyses show that male and female scientists are at all times equally likely to work in biotech firms. No gender queuing appears to be happening (see Figure 4.2). The gender integration in biotech firms does not seem to be accidental. Martin, an attractive white male PhD in his forties with thinning hair, took time out from recruiting scientists for his growing biotech firm to answer my questions. Martin explained the importance of gender and racial integration to building a thriving biotech firm with innovative capacity: Putting together a company is a bit like choosing sides for a football team. Some want people similar to themselves—I think you might see some of that particularly in big pharma—but the good leaders want to get the job done and will hire a range of candidates to do good work, and to do it in innovative ways. They want a team of people who are good in different ways. Our teams all include men and women, and a variety of nationalities.

Percent change in life scientists’ odds of entering biotech, by industry period

Source: All else being equal, based on logistic regression results; see Appendix, Table A8, Model 2.

S/D-4 6/16/04 12:54 PM Page 91

Figure 4.2

S/D-4 6/16/04 12:54 PM Page 92

92

Women’s Work

Perhaps because he somewhat inconsistently chose the gendered metaphor of football to describe a more gender-integrated approach to hiring teams of scientists, I wondered if Martin’s earnest expression of belief in diversity as a cornerstone of biotech firm success was for real. When I had passed by the desks in the front reception and office area, the firm for which Martin works looked like any other gender segregated workplace: the receptionist and secretaries were all female, most of them white. Yet when we passed through the labs, I could see that his claims for diversity appeared to be supported by the scientists at the bench—women and men who were Asian, East Indian, white American, Eastern European (chatting in Czech), and Latino. African American scientists were noticeably missing, not only from the labs in Martin’s company but also from the dozens of other laboratories I visited at universities, government institutes, nonprofit research institutes, and other biotech firms. An alternative story to the gendered female-to-male or male-to-female diffusion of job entry, one that seems to fit the data, is that there is no difference in who enters a new arena by gender. Although a negative hypothesis cannot be definitively tested, it appears that there is provisional evidence that gender does not predict when PhDs are likely to enter the biotechnology industry.4 The lack of support for the expectation that men enter first (or for its converse—that women enter first) suggests that standard queuing theory does not easily explain entry into the biotechnology industry. In biotech, then, men and women entered the industry proportionately in the early days. Moreover, scientists’ educational prestige does not explain this nonfinding; it is not the case that women from less prestigious universities and men from more prestigious universities go into biotech (or vice versa). Figure 4.2 also demonstrates the effects of educational background on entrance into the biotechnology industry. In comparison to life scientists educated in a top-ten department, PhDs with degrees from departments ranked lower than the top fifty see their odds of going into the biotech industry decrease by 62 percent. There is no significant difference in the odds of PhDs from the middle-ranked and top departments for ending up biotech.5 PhDs with elite educations are more likely to enter biotechnology than other scientists—but it does not vary by industry period. Like expecting more centrally connected men to arrive before more peripheral women, elite-educated PhDs should be more likely to enter the biotechnology industry during the early period than other PhDs, and this effect should be more profound in the early period than later in the industry lifespan. The prestige effect occurs—that is, PhDs with more elite educations are more likely to enter biotechnology compared to other scientists—but it does not vary by industry period. The lack of significant difference between periods indicates that where a scientist received his or her PhD continues to matter

S/D-4 6/16/04 12:54 PM Page 93

Coming In on Queue?

93

across the industry timeline. Elite-educated PhDs are more likely to enter biotech at all stages of the industry’s lifespan. To put it another way: PhDs with more peripheral educations were less likely to enter biotech in the later years as well as the early years of the industry. Rather than core to periphery (or vice versa) adjustment of biotech legitimacy over time, the constant influx of elite-educated PhDs signals the stable “core-ness” of biotech jobs, in much the same way that the group of top universities retains their graduates as professors.6 The constant, rather than time-varying, effect of educational background on entry into the biotechnology industry may have different interpretations. One explanation is that the data simply do not have the specificity to measure the effects of time. Because the data are recorded when the application was submitted (see Appendix for data details), the length of tenure in a position is unknown. Perhaps the precise date of entry into the industry, rather than the period in which the data are recorded, interacted with education might better predict the different effects of educational background by time. Another explanation, based on substance rather than measurement, is that even with more specific time data the lack of period effect would hold because the importance of educational background is so significant. If the biotechnology industry has always been seen as a desirable arena for scientific talent, then it is not surprising that the most elite-educated PhDs are more likely to obtain biotech positions. And if the supply of elite PhDs remains sufficient to the demands of the biotech industry, the importance of educational background and corresponding lack of variation by period are likely to continue. A recent National Research Council survey (NRC 1998) found that the number of life-science PhDs grew 42 percent during the 1990s. Commentary on the study summarizes the extent of readily available candidates: “the supply of newly minted Ph.D.s in the life sciences vastly outstrips the availability of desirable jobs” (Holden 1998: 1584). Given such employment trends, the likelihood of continued attraction of elite-educated PhDs into the biotech industry seems good. A key lesson from this chapter is that despite the continuing importance of elite educational credentials, biotech seems equally open to male and female scientists. The findings hold across time: elite-educated PhDs are more likely to obtain jobs in biotech firms, but there is no gender difference in who enters biotech. In other words, although the difference between core and peripheral PhDs did not predict the timing of entry into a new arena, the educational core seems to have first choice. There may be a variety of reasons why gender does not appear to affect the place of PhD-level scientists in the queue for entering the biotechnology industry. Women’s equal access to higher education and a knowledgeexpanding field, however, are key features of the biotech story. Female stu-

S/D-4 6/16/04 12:54 PM Page 94

94

Women’s Work

t

dents have made great strides into top PhD programs for life scientists. Biotech firms may appeal to female candidates who also have access to star scientists through ties formed during their education at elite universities and who thus make the top of employers’ lists. Biotech is not the only hightech industry where gender queues are less evident. One study (Wright and Jacobs 1994) of job queuing in modern computing—a high-tech industry located in many of the same geographic areas as the biotechnology industry—argues that the queuing dynamic of lowered rewards leading to feminization (as described by Reskin and Roos 1990) does not seem to explain what has occurred in computer occupations. Computing actually increased in desirability as more women entered the field. The findings in biotech and IT led me to formulate a proposed modification to queuing theory: the significance of gender queues will decrease in knowledge-expanding fields (e.g., high-tech industries) where women have access to the same educational advantages as men. The above proposition, though based on good evidence in late twentieth-century U.S. biotech and IT, should be further tested in employment arenas in other historical periods and national contexts. The historical and national context may indeed play a role. Biotechnology has been a predominantly U.S.-based industry. Because the second wave of the U.S. women’s movement occurred before the industry’s emergence, there may be less inertia toward male-dominated bias in comparison to the gender bias entrenched in academe and the older pharmaceutical industry (in which firms were founded prior to the 1960s). Once women enter the industry very early, the model for what a biotech scientist looks like is not necessarily male and may contribute to the flexibility of biotech firms’ search for candidates. Even though there are fewer biotech jobs, there is perhaps more diversity among the scientists for whom firms search. This search process has perhaps led to the result of no significant gender queues in entry to the field. Careful study of comparable occupations across other times and places will help sort out which aspects are unique to U.S. biotech and which are part of more general employment stratification processes.

Girl Ghettos

Although gender segregation does not appear in generally viewing who goes into biotech, perhaps women enter the field only to find themselves segregated into “girl ghettos”—devalued specialties within the industry. Just as female managers often are shunted aside into the devalued, feminized realm of public relations or human resources, maybe female life scientists have a ghetto of their own. There may be a potential educational

S/D-4 6/16/04 12:54 PM Page 95

Coming In on Queue?

95

basis for ghettoization—more specialized gender segregation within lifescience occupations. Women and men do cluster somewhat by specialty in the life sciences. Between 1986 and 1996, of the life scientists who received a PhD in bioengineering and bioinformatics, only 21 percent were female, whereas 49 percent of those who received their degree in embryology and developmental biology were female (National Research Council 1998: Table E.4). Yet molecular biologists, one of the more popular specialties, are the same proportion female as all life-science doctorates: 40 percent. Thus academic specialization among life scientists is somewhat, but not overwhelmingly, gendered. More ghettoization appears among life scientists who are less likely to go into biotech firms—those outside of the biomedical research fields. More than 80 percent of agricultural life-science PhDs are male, and more than 60 percent of public health PhDs are female. In the biotech industry, the team basis of production means that PhDs work alongside those from different specialties more often than they would in university departments. One day on a visit to the startup company BioNow, I was looking for Rob in the biology lab. I found him over in the chemistry lab. He greeted me warmly and began speaking as if we were continuing our conversation from where we had left off the last time I was at the firm: “As you can see, we’re not so compartmentalized in the company. Especially in a startup like this, it’s great that if I need something for a project that’s more advanced chemistry than I know, I can just come over here and ask an expert like Evangeline here.” He looked over at Evangeline, at work with some reagents at the bench. She looked at him out of the corner of her eye, smiled, and bowed her head over her work, apparently both a bit flattered and embarrassed at Rob’s compliment. The other chemists at BioNow were male, but Rob was more comfortable asking Evangeline’s advice on chemistry issues because she was on his project team. Even if male and female life scientists come from different specialties, they work together. To the extent that women are more likely to be the biologists and men the chemists, gender will provide a signal about who is likely to play a certain role on a project team. On the teams I observed at BioNow, this was a pattern: the chemists were usually male, biochemists were invariably male, and the molecular biologists were mostly female. From what we know of many other occupations—bus driver, editor, pharmacy worker, even physician and college professor—when a job specialty within it has been labeled female it is less desirable and pays less. If particular biotech team roles in the future become institutionalized as women’s work, we might expect them to then become less valued and see the kind of queuing that has occurred in other lines of work. Right now, there is some mild segregation by specialty in the life sciences, with gender-integrated teams in commercial firms.

S/D-4 6/16/04 12:54 PM Page 96

t

96

Women’s Work

If Not Horizontal Segregation,Then Vertical Stratification?

t

Just because men and women work together on scientific project teams, however, does not mean that there are no formal leadership roles and promotions to be had. In this chapter we’ve seen that while females are often treated as peripheral to science, the early entry of women into biotech alongside men marks the industry as an interesting case. Typically, men and women work in different jobs. Even if they work in the same occupation, they work in different sectors: men drive city buses, women drive school buses. The biotechnology industry is seen as more legitimate than other industry jobs. Following Reskin and Roos’s theory that desirable occupations have a gendered queue where men are hired first, one might expect that the early years of biotech would have seen more men moving into the firms. Historical research on Victorian novelists and computer programmers shows that as jobs in new fields increase their desirability, they change from women’s work into a job for a man. Thus another expectation, counter to the feminization idea, is that in the earliest years, before biotech was fully legitimated and men moved in, the industry would have had disproportionate numbers of female scientists. The results show that neither expectation holds true: men and women entered the biotech industry proportionately in the early days. Moreover, scientists’ educational prestige does not explain this nonfinding; it is not the case that women from less prestigious universities and men from more prestigious universities go into biotech (or vice versa). Gender does not appear to affect the place of PhDlevel scientists in the queue for entering the biotechnology industry. Rather than a subsequent feminization or masculinization of the industry, the analyses in this chapter have shown that male and female scientists have been equally likely to work in biotech firms. But if horizontal segregation between men and women in life-science organizations is less apparent, what about vertical stratification? Does lack of segregation and ghettoization simply mean that stratification takes the form of women scientists working in low-profile jobs under the authority of male bosses? Chapter 5 looks at the question of who has authority in life-science organizations.

Notes

1. Within academic scientific disciplines, there appears to be less of a sex gap in pay when controlling for number of publications, although the gap does not disappear (Levin and Stephan 1998). 2. Of course, the focus on life science PhDs means that those both in the core and periphery are part of the same social class, broadly speaking. 3. It is also possible that statistically speaking the problem of lower power (due to smaller numbers of PhDs in biotech than other settings) or other factors not

S/D-4 6/16/04 12:54 PM Page 97

Coming In on Queue?

97

included in the model (e.g., individual productivity) might account for the lack of significant results. 4. One caveat with a hypothesis stated negatively rather than positively is that the results do not constitute a strong test of it. The results may simply mean that there may not be sufficient data to reject the proposition. Rather than testing whether the data sustain it, supportive results demonstrate that as far as the data show, the proposition cannot be rejected. 5. This lack of significance may be due to the loss of degrees of freedom to nonsignificant coefficients in comparison to the model used for Figure 4.1. See Appendix Table A8. 6. Although the results in this chapter indicate that biotech industry innovators come from the core, this does not refute the importance of innovation originating from the periphery in other fields. For instance, institutional innovation in radio (Leblebici, Salancik, Copay, and King 1991) and alternative legal practice (Morrill and Owen-Smith 2002) came from the periphery. The biotech industry is a case in which features of the life-science core have resonated with the industry’s legitimation. See Chapter 3 for details on the legitimation of biotech careers.

S/D-4 6/16/04 12:54 PM Page 98

t

S/D-5 6/16/04 12:55 PM Page 99

5

Networks vs. Hierarchies in Promoting Women Scientists

Academia limits you to this tunnel vision. . . . There are good opportunities in industry—especially in biotechnology. At pharmaceutical corporations, however, industry is closer to academic roles—more rigidly defined. —Miles, academic scientist with industry experience Academic life is a mad hazard.

—Max Weber (1918)

During the heady days of 1960s–1970s U.S. student activism, when hopes for change were built up through campus groups, more than a few young college women decided to storm the male bastion of science. Women like Dorothy, a life scientist and a baby boomer, pursued a doctorate as a way of enacting her feminist beliefs as well as her personal ambitions. From her campus experience in the late 1960s and early 1970s, Dorothy formed the expectation that the academy rather than the buttoned-down corporate world would be open to women’s advancement—and to change. Reality, however, shattered her idealism. “I feel like I was misled by the women’s movement for years. There was no support for us in the academic workplace,” she told me. Although Dorothy is now a successful and highly placed scientist-administrator in the biotechnology industry, she had received little encouragement in the academy. When she was a postdoc, Dorothy had quickly tired of working for men in academia who received all of the credit for the work of the entire lab, little appreciated her contributions, and gave her long odds of ever gaining a tenure-track job. Postdoctoral appointments, by their nature, perhaps foster the development of cynicism. Lotty, a thirty-something biologist entering her fourth year of a postdoc at a top university, had become so jaded from the lack of creativity in her position that she decided to look for job opportunities outside of science altogether.

99

S/D-5 6/16/04 12:55 PM Page 100

100

Women’s Work I don’t think things would be much better for [female scientists] outside of the academy, either. There would probably be even less creativity if you’re working for someone else, and worrying about the profit margin, too. Unless you’re running your own lab, you are limited in trying new things.

Lotty’s perspective was that of a seasoned postdoc who expects that women would not have any greater likelihood of leading scientific research in the for-profit sector, based on the academic reality she knows. Whether one is an idealist, as Dorothy used to be, or like Lotty a cynic about the opportunities for female scientists in academic labs, either perspective would lead one to believe that the possibilities for women’s leadership would be even more remote in companies. Men and women have worked side by side in the biotechnology industry since the early days. Chapter 4 demonstrated that industry participation has been unbiased, yet to understand the whole story of gender relations in the life sciences we need to know who is leading the direction of innovation. Are women mere laborers, or do they get promoted into positions that allow them to lead scientific projects? To obtain the full benefit of gender diversity for the production of new ideas to cure disease, women must also lead. If female scientists are simply doing what Simon says, then Simone’s innovative ideas are less likely to be pursued. The question is, What kinds of organizations would promote women? Does the university engender equality, or should we look to new forms of organization for more diversity in leading scientific positions? Bureaucracy is said to aid women, in that rules correct gender discrimination in hiring and promotion decisions. Barbara Reskin and others (Reskin and McBrier 2000; Padavic and Reskin 2002) have argued that bureaucratic rules enforcing equity in hiring and promotion are a solution to job segregation and the glass ceiling. As a bureaucratic organization, the university, with its clear hierarchy and rules supporting equal opportunity, should promote women equally, following the logic of this theory. Indeed, science and engineering departments with written guidelines for doctoral study are more likely to have increased the proportion of degrees granted to women (Fox 2000). Yet we know from classic ethnographies of organizations (Dalton 1959; Gouldner 1954) that instituting more bureaucratic rules and levels of hierarchy also tends to increase the power and salience of informal, hidden modes of operation. Under certain conditions, then, hierarchy and formalized rules might not help women’s careers; instead more flexible organizations with interfirm connections may permit the transparency that dispels bias in decisionmaking. Reskin (2000) recognizes that if we study the organizational variation in sex inequality we can gain a better understanding of the processes underlying widespread stratification in the workforce. My data, based on careers like Dorothy’s and Lotty’s, sug-

S/D-5 6/16/04 12:55 PM Page 101

Networks vs. Hierarchies in Promoting Women

101

gest that we need to move beyond reliance on the rigidity of bureaucratic rules for enforcing diverse organizational leadership to recognizing how hierarchical structures can stultify diversity. Large hierarchical organizations often prove disadvantageous to women’s careers. In Rosabeth Kanter’s classic study (1977) of a large corporation, women faced barriers to moving into management positions. Those who were able to move up the management track successfully had to have powerful male sponsors. Other studies of managers in large corporations continue to find the same result. Women need to establish a strong informal tie to a male mentor—basically borrowing his social capital—to move up the career ladder, while their male colleagues can form their own informal ties to advance their careers (Morrill 1995; Burt 1998). Large bureaucracies are detrimental to the careers of women in other industrialized nations as well, showing the generality of this structural disadvantage. In a study of Japan (Brinton 1993), men and women enter large firms in equal proportions, but 22 percent of men enter the permanent employment career track, whereas only 7 percent of women do. Women in Japan are much more likely to shift from larger to smaller firms over their employment lives, again suggesting that hierarchies are unfriendly to women. What we don’t know from the existing literature is whether other modes of organizing—such as the network form—will foster women’s careers or make things worse. As she grew disenchanted with academia, Dorothy landed one of the first postdoctoral appointments ever offered by a biotechnology firm, an opportunity she discovered through a personal tie to one of the academic scientists who founded the company. Her experience demonstrates a more general finding. One problem with relying on formal measures to correct employment bias is that when individuals search for a job, formal employment mechanisms rarely succeed and almost always need to be supplemented by references from informal networks (Granovetter 1974). Personal networks lead to jobs in all kinds of employing organizations. The difference between network ties in hierarchies and interpersonal ties in flatter firms characterized by webs of interorganizational relationships is that the former are less visible. The old-boy networks in hierarchies are more insidious, as they hide beneath a formal facade of rules and a chain of command that supposedly maintains fairness. In some cases, hierarchy fosters not only inequality but also exploitation. In 2002, the popular media revealed the informal relations between priests hidden by the hierarchy of the Roman Catholic Church, as hundreds of parishioners who had been molested as children came forward. Over decades, the church hierarchy quietly transferred known pedophiles from one parish to another. Such use of informal relationships between clergy would have been much more difficult to accomplish in a transparent network organization. As in biotech firms,

S/D-5 6/16/04 12:55 PM Page 102

102

Women’s Work

when organizational members have working relationships with colleagues outside the firm that are just as strong as those inside, the company’s activities become more visible. It is more difficult to hide inequality in the network form of organization. We might expect, then, more equality in small, interconnected, flatter firms than in large, vertical pyramids with well-defined bureaucratic boundaries. Biotech firms, characterized by their permeable boundaries and webs of relationships to other firms, universities, hospitals, venture capitalists, and large drug companies, provide a clear model of the network form of organization. Universities and pharmaceutical corporations, in contrast, are hierarchies. Where do scientists who are ambitious like Dorothy and creative like Lotty go to lead innovative projects? From the organizational perspective, a clue comes from knowing when gender diversity in leadership becomes a priority to pursue rather than something to passively accept as the rules. In biotech, scientists interviewing applicants have a different mind-set than in more hierarchical science settings. In my conversations in the field, when I asked supervising PhDs about what they look for in hiring young lab workers, those in biotech readily supplied looking for candidates from diverse backgrounds as one criterion. In universities, I had to probe further to ask whether employers would be interested in hiring applicants from underrepresented groups in science (women, minorities)—which, of course, was answered positively. The open-ended question responses speak to the differences between the settings. In the biotech firms, diversity is pursued up front; in universities, it is seen as good, if it happens. The two organizational configurations, and the way innovative science is performed in hierarchies and networks, can help explain the different emphasis on diversity. Science in hierarchies is like playing coordinated solitaire: a principal investigator holds all the cards (though he or she delegates the card placement to subordinate researchers). There is one clear agenda and way to win—and only one winner. Solitaire players may trade cards collaboratively now and again, but each is really working on playing out her or his own card pile (getting published) before the competition does. Scientific work in biotech firms is more like a novel form of communal poker, in which many different gaming tables represent multiple projects, and players at each table pool their cards to create a winning hand. Each table needs a different combination of cards depending on whether they’re going for a straight or four-of-a-kind, so a diverse group of players is best. And in community poker, one can call on trusted friends from outside of the firm, who know the game well, to play a hand or two. This type of team-based innovation that characterizes biotech firms makes gender diversity of project directors desirable—in theory. Let us turn to the sample of more than 2,000 scientists to see if this expectation plays out in fact.

S/D-5 6/16/04 12:55 PM Page 103

103

t

Networks vs. Hierarchies in Promoting Women

The Statistical Sample

The scientists whose careers provide the basis for statistical analysis here are much like the general population of U.S. life scientists. Table 5.1 presents descriptions of the sample. (Detailed discussion of the methods and measures can be found in the Appendix.) The ratio of females to males is proportionate to other national samples of PhDs in the biological sciences (Fox 1996; Davis et al. 1996; NRC 1994). In 1999, the percent of female life scientists overall was 28.6 (NSF 2002, appendix table 3-38); in my sample the percent female is 28.3. The prestige of the universities where the scientists obtained their PhD varies, although a majority of the PhDs come from programs ranked in the top fifty. In the general population of PhD recipients in biochemistry and molecular biology in 1993, 75 percent came from top-fifty schools (calculated by author from NRC 1994, appendix table N). In my data, recipients from these programs similarly constitute 67 percent of the sample. These PhDs are relatively young; on average they were about five years out of graduate school when the information was recorded. Many of them hold nonsupervisory positions (i.e., postdoctoral fellows). Table 5.2 demonstrates that the male and female scientists in this sample look quite similar in their educational background and career placement. The notable exception is that a greater percentage of male PhDs hold supervisory positions—about 32 percent compared to about 23 percent of female PhDs.

Table 5.1

Characteristics of life scientists in the statistical sample

Characteristic

% in category (or mean)

Range of attributes

Gender

Male Female

71.7 28.3

Prestige ranking of PhD program

Lower ranked (50–200) Middle ranked (11–50) Top-ten programs

33.3 43.9 22.9

Years since PhD

0–39 years

Biotech employment

Not employed in biotech Employed in biotech firm

93.7 6.3

Supervisory position

Nonleadership position Has a supervisory role

71.3 28.7

Note: Total PhDs = 2,214.

5.30 (st. dev. = 4.50)

S/D-5 6/16/04 12:55 PM Page 104

104

Women’s Work

Table 5.2

Characteristics of life scientists in the statistical sample, by gender

Characteristic Prestige ranking of PhD program

Category Lower ranked (50–200) Middle ranked (11–50) Top-ten programs

Years since PhD

Male % (or mean)

Female % (or mean)

31.9 37.6 30.5

25.7 42.3 31.9

4.54 (st. dev. = 4.02)

3.97 (st. dev. = 3.59)

Biotech employment

Not employed in biotech Employed in biotech

91.9 8.1

91.7 8.3

Supervisory position

Nonleadership position Has a supervisory role

68.4 31.6

77.2 22.8

t

A deterrent to studying the organizational structure of gender relations is that data are often not readily available at the organizational level. My study of life scientists has the advantage of looking at relatively stable job roles, as well as organizations that can be categorized into: (1) academic settings or pharmaceutical corporations traditionally organized with hierarchical career ladders; or (2) biotechnology firms with project-based teams, flatter organizational structures, and multiplex relations with external collaborators. About 8 percent are employed in biotech science-based firms focused on research and development of human therapeutics. In all, this group of more than 2,000 PhDs looks much like the general population of U.S. life scientists, except in being somewhat younger. Although my dataset does not include information on compensation, scientists employed in industry generally are paid more than those in academe. In a survey of members of the American Association for the Advancement of Science, scientists employed in academia received a median annual salary of $80,000 compared to $96,000 for others (Holden 2001). That means that academics earned 83 cents for every dollar made by nonacademic scientists, on average. Within a given sector, however, income is often not the best measure of success for professionals or managers, because pay is too closely tied to job ranking. A better measure of career success is time to promotion (Burt 2000). In my sample of younger scientists, achieving a position of leading research is a sign of career success.

Gender Stratification Among Life Scientists

Who is likely to have early upward mobility in organizations? As discussed in Chapter 1, the natural sciences present no exception to the rule

S/D-5 6/16/04 12:55 PM Page 105

Networks vs. Hierarchies in Promoting Women

105

of gender inequality. Female PhDs spend more time delayed in low-paying, postdoctoral, non–tenure track positions (NRC 1994). Although the life sciences have a greater percent of female PhDs than physics or engineering, female life scientists earn the lowest median income among their scientific and engineering sisters (NSF 1998: table 3-13). This low pay is probably due to their more extensive periods as postdocs. Across scientific disciplines, male PhDs are more likely than female PhDs to achieve higher academic ranks (Cole and Zuckerman 1984), and male scientists are found in higher proportion in research universities compared to other educational institutions (Fox 1996). In this sample of scientists as well, males are more likely to become leaders (i.e., hold a tenure-track or senior scientist position). As shown in Table 5.3, being female decreases one’s odds of attaining a supervisory role by 32 percent (see Appendix Table A9 for the full logistic regression results). This female disadvantage exists net of one’s experience as a doctorate-holding scientist. These results do not mean that female PhDs have less prestigious degrees; I also held constant the rank of university programs in molecular biology. In other words, if a male and female scientist graduated in the same year from universities of the same prestige level, she would be 32 percent less likely than him to be leading her own lab. Antonia, a life-science PhD who did lead her own lab in a well-known biotechnology firm, commented on having a scientific career: “It’s never easy. Science is demanding and made difficult for anyone, but especially so for women.” She compared her struggle in building a science career to her husband’s, also a life scientist. He had also left the university path to pursue a career in biotech (not at the same firm), and he wanted to take an active part in their children’s lives. Antonia viewed university science, in particular, as antithetical to family life for women or men scientists who take their parental roles seriously. “But there always seem to be extra hurdles for women,” she added. “Having to prove oneself and one’s work may be just a little harder because one is a woman, and then there’s always the expectation, even if it’s not true, that you’re the one responsible for the

Table 5.3

Likelihood of scientists moving into supervisory positions, across all organizational settings

Characteristic

% change in odds of supervising

From middle ranked university, compared to top ten From lower ranked university, compared to top ten Female, compared to male Number of years since received PhD

26 decrease No difference 32 decrease 26 increase per year

Source: All else being equal, based on logistic regression results reported in Appendix, Table A9, model 1.

S/D-5 6/16/04 12:55 PM Page 106

106

Women’s Work

t

children and the household.” Perhaps the everyday interaction context for female scientists that Antonia points out—in which the expectation of slightly less valuable science and commitment to work is played out in the lab—contributes to this gender gap in promotion.

Networks vs. Hierarchies

Thus stratification in science careers indeed exists. Overall, women are less likely to hold leadership positions. The key question, however, is whether the form of organization affects the likelihood of gender equality in the promotion of scientists. Will gender still have the same effects in highly networked biotech firms as it does in more hierarchical science-based organizations? In biotechnology firms, the tasks are less minutely specialized than in pharmaceutical corporations, and scientists are less dependent on one principal investigator as a powerful sponsor than in academe. Teamwork is more than just rhetoric in network organizations; it is the basic organizational structure. One might expect that gender becomes more salient to work roles in a network organization because of fewer rules for fair apportionment (i.e., the old-boy networks take over). I have a different expectation: work organized into project teams may mean less attention is drawn to gender differences than to individual contributions to the group. Incentives at the team level change the predisposition to stereotypical roles. In one laboratory at BioNow, when a project team had successfully reached a breakthrough, the company held a party in the team’s honor. Congratulations for the research and the team’s name were printed on special labels affixed to the champagne bottles distributed at the festivities. The team was honored as a whole. In contrast, the academic labs I observed always seemed to have the background agenda of credit for the individual. Even the way the telephone is typically answered differs. At the university, the caller is greeted with the principal investigator’s last name—“Paretsky lab”). At biotech firm labs, the caller is often greeted with the name of a functional unit (“Biochemistry”). Because academic rewards accrue to individuals (tenure is a notable example), scientists are given credit (i.e., name order on publications) primarily based on their hierarchical role. When roles are assigned hierarchically because of the connection to rewards, and relative levels of subordination and power are clearly marked, this structure makes it easier for females to fall into assisting rather than leading roles. When less attention is paid to differentiation of work roles, males and females move more equally into supervisory roles. At BioNow, the team leader of those honored at the champagne party was a female who had not been allowed to lead research projects in academic positions she had held.

S/D-5 6/16/04 12:55 PM Page 107

Networks vs. Hierarchies in Promoting Women

107

Among life-science organizations, networked biotech firms should display more gender equity than more hierarchical organizations. So is a female scientist more likely to obtain leadership of a laboratory group in a biotech firm? In a word, yes. Table 5.4 summarizes the results for the statistical sample, comparing the effects for male and female scientists of employment in network and hierarchical organizations. In more bureaucratic settings like universities and traditional pharmaceutical corporations, women scientists are 60 percent less likely than men to run a laboratory. Male scientists do not seem to be much affected by organizational setting; their odds of leading do not change whether in biotech firms, universities, or other science-based organizations. Female scientists in biotech firms, however, are nearly eight times more likely than their sisters in hierarchical organizations to direct scientific projects or manage the firm in some capacity. These findings of significant advantages for female life scientists in biotech hold constant the number of years since attaining one’s PhD and the prestige ranking of one’s PhD program. Put differently, if two women received their doctorates in the same year from university programs of the same prestige level, the woman working in a biotech firm would be 7.9 times more likely than the woman working at a university to be supervising her own lab. To put the magnitude of this biotech benefit for female scientists in perspective, consider a sporting analogy. If a life scientist had to negotiate the length of a balance beam to be promoted in a biotech firm, her colleague in a bureaucracy would have to tiptoe along an additional balance beam plus a beam the length of a basketball court for a similar promotion. The reason for women doing better in network organizations is not simply due to the existence of either more females or more leadership positions in biotech firms compared to other organizations. Network organizations have similar gender and leader distributions as other organizations. In this sample, the percent female for PhDs in the biotechnology industry is 28.7, nearly the same female percentage as in all other organizations (28.3).

Table 5.4

Likelihood of male and female scientists moving into supervisory positions, in biotechnology firms compared to hierarchical settings

Gender

Change in odds of supervising in biotech

Change in odds of supervising in hierarchy

Male Female

No difference 7.9 times more likely

No difference 60% decrease in odds

Source: All else being equal, based on logistic regression results reported in Appendix, Table A9, model 2.

S/D-5 6/16/04 12:55 PM Page 108

108

Women’s Work

The percentage of PhDs who are leaders in the biotech industry is 18.4, compared to 33.8 in universities and 29.4 in supervisory roles at other employing organizations. If anything, biotech firms have proportionally fewer people in leadership roles because they tend to have flatter job ladders than hierarchical forms. The real difference occurs in the representation of women in leading roles. The percentage of supervisors who are female in biotech firms (39.4) is nearly twice the proportion of scientific leaders who are women in universities (20.2).1 Other published accounts of life scientists’ preference for the biotechnology industry support the results from my original dataset. Paula Rayman and colleagues at the Radcliffe Institute likewise found in their study of Boston-area firms that women who were passionate about science but faced obstacles in academe often turned to biotech. One of their interviewees, Georgia, explained, “Biotech offered me a place to make a good living, stay connected to the science I loved, and gave me a chance to move up and still have time for other things in my life” (Rayman 2001: 117, emphasis added). Una Ryan, the CEO of Avant Immunotherapeutics, was quoted in the Boston Globe (Earls 2002) as saying that “the commercial sector offers women much more—greater opportunities for advancement and higher salaries.” Cynthia Robbins-Roth (2000: 12) describes her own move from academia into the biotech firm Genentech: “Genentech and its biotech brethren created a new haven in which to do innovative science without the trappings of the big pharma environment, and away from the crazed politics of government-sponsored academic research.” Robbins-Roth in this passage does refer to biotech firms using the masculine term brethren, which may be an unconscious acknowledgment that most of the founders of biotech firms have been men. Yet her comment about her career decision also reveals that the bureaucratic “trappings” and “crazed politics” perhaps create even greater hurdles for women scientists in pharma and academe. My own conversations with life scientists also support the validity of the strong statistical finding for biotech’s female-friendly environment. Jeanne, a life-science PhD who had previously worked in the university, explained her exit: I left because I could see myself working as a postdoc forever and never getting the chance to move up into being a “real” scientist. I felt like I had to leave academia to be treated as a real adult. It was amazing how much respect I got in this [corporate] environment. I was the expert!

Not only women but also members of other minority groups in science seem to feel excluded from the standard university professorial identity of straight, white, able-bodied, and male. Jeremy, a young gay man, talked about his desire to leave academic science in terms of the unfriendly envi-

S/D-5 6/16/04 12:55 PM Page 109

Networks vs. Hierarchies in Promoting Women

109

t

ronment as well. He seemed to feel forced to decide between his sexual and professional identity: “You have to be a certain way to really fit in, to succeed as a scientist, as a faculty member. I just don’t have that.” Jeremy’s dilemma sounds much like Leslie Barber’s (1995: 231) claim that “to enter the prevailing culture of science, women must assimilate, leaving important parts of their identities behind.” Barber reported that of her grad-school cohort in molecular biology, the five men (of varying competence) all prospered in tenure-track jobs while the five women left science or languished as postdocs. My data confirm that this gendered outcome constitutes a general trend in the university and bureaucratic drug companies. Biotech provides the interesting exception. In organizations that are flatter and less bureaucratic, women can prosper. Women seem to be doing well in biotech outside of the United States, too. As biotech industries begin to grow in Europe and even in some second world nations, women are making their way into positions of authority. In Israel, “high-tech” usually means military-related, male-dominated companies. But Israeli biotechnology startups have visible leadership by female CEOs and COOs (Hershman 2002). Some women are even breaking through in business environments that have traditionally been exclusively male enclaves. In South Korea, Regen, a biotech firm, was founded by Bae Eun Hee. Dr. Hee explained her entrepreneurial motivation: “I want to excel as a businesswoman, not just as a scientist” (Du Mars 2001). Relative gender equality in biotech may be more than a U.S. phenomenon.

Innovation and Equality

Still, the main business of biotech firms is to discover new therapies for treating human disease and to make money doing it. Can biotech be less gender-biased and at the same time be successful in scientific and business innovation? Pharmaceutical corporations have difficulty keeping up with biotech firms’ cutting-edge innovation. Talent flocks to biotech or the university, as Chapter 4 showed, and ideas in the form of patents and publications flow from their laboratories. Yet university and biotech labs differ in the structure and gendering of scientific work. In these two settings, how is innovation related to equality? On one day in the rather cramped university lab, Jonathan and Colin, both postdocs, were joking around at one bench. I was talking with another postdoc, Victor, while he worked at a second bench. Hannah, a PhD student working at a third bench, was covertly watching Jonathan’s and Colin’s interaction. Jonathan and Colin cracked loud jokes about the sexual anatomy of the animals that were the experimental subjects and related this to their own sexual prowess. For the study of disease of the prostate gland, the

S/D-5 6/16/04 12:55 PM Page 110

110

Women’s Work

male subject animals had enlarged testicular areas. “You have to have balls to work on this project!” Jonathan quipped. Colin laughed and commented that he was the man for the job. During this routine, the two men occasionally glanced over to see if Hannah would react. I was out of their line of sight, so they did not appear to be putting on the male territorial behavior to impress me, the outsider female. Victor was busy preparing an experiment and showing me what he was doing and appeared not to notice Jonathan and Colin’s conversation. Hannah pretended to ignore them, but her fairskinned neck and cheeks began to flush before she exited in a rush through the lab door while Jonathan and Colin laughed raucously. After Hannah left the lab, Jonathan and Colin returned to their conversation about their scientific results. They debated using different tests to assess the material under study. Jonathan claimed, “I’ll bet if we got [a result] with [a test], Todd would want to publish it. Everyone else has used [another test] on it. Let’s ask him at the meeting.” Apparently, Jonathan and Colin felt more comfortable conducting their work after they had driven Hannah, their observer, from the lab. Later that same day, Hannah continued to avoid the male postdocs as much as possible in the confined space of the laboratory. When she carried a tray of test tubes into the centrifuge room, which was across the hall from the lab and about the size of a large walk-in closet, she found Victor in there talking with me because I was following him around the lab that day. As soon as she opened the door, her face flushed and she said rapidly, “Oh, you’re in here!” then spun on her heel and quickly shut the door. Victor began to say something to her, but she had already left. He turned to look at me and said, pointing at a centrifuge, “There’s another machine here she could use.” Then he joked with a half-smile as he gestured to the small space, “Plenty of room in here for everyone.” Although Victor had not been actively involved in the aggressive masculine display earlier, Hannah appeared nervous around him as well. She could have been reacting to my presence as the observer in the lab, embarrassed at knowing I might have witnessed the unprofessional behavior in the lab that occurred while Todd, the PI, was away. Hannah had fixed her eyes on Victor, however, when she opened the door and her face had colored and she stopped in her tracks, then directed her “you” at him, and only included me in her gaze as she turned to leave. By this behavior, I surmised that she was avoiding the male postdocs in particular. In avoiding not just Jonathan and Colin but also Victor, Hannah was cut off from scientific conversations like the one that Jonathan and Colin had about formulating a publishable finding. Todd regularly included her in conversations about lab projects at the more formal weekly meetings, but in the meetings that I observed over several months she did not take the initiative as Jonathan and Colin did in telling him “here’s a publishable piece of research that we discovered.” This lack of

S/D-5 6/16/04 12:55 PM Page 111

Networks vs. Hierarchies in Promoting Women

111

initiative may have been due to her status as a graduate student, but it seemed to be exacerbated by the way the two male postdocs informally excluded her in gendered ways. I would not have included this episode with Hannah and the “Wonder Twins” (as Victor sarcastically called them) if it had not represented part of a broader ongoing pattern of disrespect and ridicule that I heard about in interviews and witnessed in the lab. In some way, at each lab meeting that I attended over the course of months, Jonathan and Colin subtly discredited Hannah. When she got up the nerve to speak, they interrupted her (sometimes even when the PI asked her a question), rolled their eyes at what she said, smirked, and passed notes while she spoke. More than once when she ventured a suggestion for an experiment, one of them would ask under the guise of innocent curiosity, “Hasn’t that already been done by [so and so]?” Typically, the PI would respond and explain to them exactly how that other experiment was different from the one she proposed. The two postdocs would seem to listen to him carefully but usually did not take notes at this point. He often expressed impatience with Jonathan and Colin, frowning at them if he caught them rolling their eyes and so forth, but they were careful in meetings not to cross the line too far. With Hannah, the PI attempted to draw her out and support her ideas. It was on a day that the PI was out of the lab when they no longer kept their harassment of Hannah covert. Although this project was not about sexual harassment and I did not ask questions about or seek to observe blatant sexism, in interviews with scientists everyone seemed to tell a similar story from at least one lab they had spent time in during their career. An ice-cold climate for women scientists is a hazard in a hierarchical setting where informal practices can be hidden. Even when the male authorities are sympathetic, women can be subtly excluded. In contrast, at BioNow the bench scientists made sure to include everyone in informal laboratory life. At lunchtime, all of them would eat together. Someone would ask “Where’s Zoey?” if she did not join them. “Do you have a lunch? Would you like some of mine?” a technician asked me the first time I sat down with them at the table, before I was even introduced. Lunchtime was when discussion across departments would occur among the technicians. Even though experiments often tied technicians closely to their benches, the doctoral scientists stopped by each other’s labs or offices across department lines throughout the day to discuss a project, to ask a question, or to gossip. At lunchtime, the PhDs ate at their desks while reading journals, in the conference rooms during research meetings, or went out with colleagues from the firm or other life-science organizations. One day at lunch with the technicians, the topic turned to national differences, a natural one as scientists from China, the Czech Republic, Russia, Australia, and Great Britain as well as the United States worked at

S/D-5 6/16/04 12:55 PM Page 112

112

Women’s Work

BioNow. They discussed the differences between equipment and facilities, although Richard, an American, reminded the group that a lot of variation existed within the United States between university labs with more and less money, and the difference was especially great between academic and better-equipped commercial labs. As the lunch group discussed how they liked the more extensive interaction between scientists from different nations and disciplines at BioNow, Carl chimed in with a reference to the biohazard project that he claimed could not have been done without cooperation between biologists and chemists. Carl asserted that on the project team, “We really came up with an innovation.” Richard joked that next time the venture capitalists came around, “we should ask them to fund us,” and pointing in a circular motion around the lunch table to the technicians, “We have all the great ideas.” Murray, a foreign national, added, “Yeah, they should give us our own company!” Everyone laughed at this exchange. Carl looked down with an embarrassed smile, and another technician changed the subject. When I later engaged in one-on-one conversation with Carl, I asked about the research teams on which he participated. He drew a deep breath before he announced that he, a technician, was the leader of a project that “would never happen at a big company or university lab.” “Of course, it’s only the project to deal with the company’s biohazardous waste,” he demurred, “but still.” He did not spontaneously expand on the innovative nature of the project as he had during the lunchtime conversation. Richard’s subtle ridicule seemed to have kept Carl in his place as a technician. Richard had no problem with the PhD-level scientists of any gender or nationality expounding on the innovations that their team brought to the firm, but when Carl, a fellow technician, seemed to reach beyond his status, Richard used humor to keep him in line. Humor played a key role in delineating social boundaries but was used differently in the incidents in the commercial and university labs. Jonathan and Colin used gendered humor to exclude Hannah. Richard and Murray used humor for status-leveling—to continue to include Carl as “one of us.” In both cases, the potential for scientific innovation was limited by the drawing of these social boundaries even while solidarity was strengthened. These examples of the informal organization of laboratory life show that informal ties are not always inclusive forces for gender equality or innovative thinking; the type of formal organization in which interaction occurs affects the outcome—and who occupies positions of leadership matters. At the biotech firm, PhD scientists were more likely to respect the contributions of technicians than were other technicians. Max, a newly hired bench scientist on Sara’s research team, was having trouble with an experiment one day. He asked Sara about the procedure. She gave him some ideas about how to work it out but did not directly show him how to do it. She

S/D-5 6/16/04 12:55 PM Page 113

Networks vs. Hierarchies in Promoting Women

113

also told him to consult Claire, a more experienced technician with a master’s degree who worked in another department. Several hours later, Sara checked in with Max and asked him how the experiment was now going and what he had learned from Claire. He replied enthusiastically, explaining a new technique he had learned. When we talked later, Sara confided to me that she could have shown Max exactly how she would have done the experiment, but she liked for people to learn for themselves, and that way new refinements and techniques would be introduced into the lab. When I asked Max about working at BioNow as he wrote additions to his experimental findings in his lab notebook, he exclaimed how much better it was than at the university, where the more interesting bench science work with greater autonomy was done by postdocs or graduate students. He added, “And if I ask questions, the PhDs here treat me more like a fellow scientist than some annoying student who you have to take time to teach, taking you from your work.” Consuelo was working back-to-back with Max at a bench parallel to where we stood chatting. She turned around to join our conversation and nodded her agreement to his assessment: “They really take you seriously here.” Max and Consuelo seemed to have figured out Sara’s strategy for respecting the ability of scientists at all levels to learn for themselves, and they liked to work on her research team. A bench scientist who would have preferred being told and shown exactly what to do might not have appreciated working in a group that required such active engagement. Innovation and equal treatment are thus not mutually exclusive in the network form of organization; indeed, new ideas flourish where scientists collaborate across gender, educational degree, national, and disciplinary lines. In hierarchies, however, invention coexists with inequality. To return to the card-game metaphor I employed above, Jonathan and Colin viewed their labmate Hannah as a competitor for the PI’s attention and approval and wanted to play their scientific cards close to the chest. In the BioNow lab group, Sara encouraged Max to borrow cards across departments, and she herself collaborated with PhDs outside the company on the project, though everyone worked to keep their playing strategy secret from competing organizations’ networks. Bureaucracy, as Max Weber famously characterized, ideally consists of hierarchical positions, specialized jobs, impersonal treatment, and adherence to specific written rules. In reality, organizational sociologists have long revealed the unofficial power structures, elite cliques, and differential application of rules and documentation that flourish in direct correspondence with the extent of bureaucratization. Neither the formalistic rules nor unofficial discrimination that make up the ideal and real character of bureaucracy fosters the development of cuttingedge ideas for new products and markets. Network organizations are learning organizations that promote innovation by bringing together diverse information flows; the people that inhabit these firms create ties and organ-

S/D-5 6/16/04 12:55 PM Page 114

114

Women’s Work

ize in ways fundamentally different from bureaucratic structures. In network organizations, the formal design includes (1) job ladders with flatter profiles (e.g., at BioNow, the main distinction is between technician and PhD scientists); (2) specialization with a different emphasis—although workers still specialize in particular knowledge domains, they forge connections across specialties; and (3) avoidance of too many specific rules, because collaborating with different organizational partners on various projects virtually guarantees that each project must be conducted differently. New ideas and combinations of diverse human resources coalesce in organizations reliant on interorganizational networks. The weaknesses of the network form, however, are the strengths of hierarchical bureaucracy. Network organizations may be innovative and equality-reinforcing but are not suited to produce large batches at low costs like bureaucracies. Universities are good at providing standard education to large numbers of students. Biotech firms are not as good at training neophytes and instead rely on the university to provide scientists with basic skills and knowledge. Each new person in a biotech firm usually must discover his or her own way around the job, figure out whom to ask the right questions of, and generally have good communication and people skills. In Rob’s first week on the job at BioNow, I asked what he was doing. “Luther just told me to go read some journals. This is kind of a new area for me. So I’m reading,” Rob said as he gestured with the open journal issue he held. He set the journal down on his desk and looked up at me where I stood, not far from his chair: “To tell you the truth, I’m not really sure what to do yet. I’m still figuring this [job] out.” By the next week he had figured it out by attending meetings and observing and asking Sara questions as she worked. When I asked Sara what the job had been like at first for her, she said that she started by thinking about the projects going on in the lab at the time. She proposed an extension of the firm’s proprietary procedure to a new disease area. Both the CEO and her department head responded enthusiastically and told her to form a new project team. They made sure she had the necessary resources, and she hired some new technicians from university labs, including Max and Consuelo, to work on the project. The university educational system had prepared them, and many others like them, to work as bench scientists. Biotech firms do not hire people out of high school to do technical procedures in the lab; this kind of mass education is not in the repertoire of network organizations. The statistical data show that Sara’s success story in the biotech firm is not an isolated incident. Women have greater opportunity for promotion in network organizations, and this gender diversity in positions of authority seems to directly benefit the bottom line in the innovative ideas and products coming from biotech labs.

S/D-5 6/16/04 12:55 PM Page 115

t

Networks vs. Hierarchies in Promoting Women

115

Equality in Biotech

Whereas Chapter 4 showed that there do not appear to be gender differences in the entrance to biotech, in this chapter the question is whether gender stratification of PhDs might occur within life-science organizations. Given the consistent finding of gender inequality within the academy, it might be expected that women would be less likely to supervise scientific research across other sectors as well. Such stratification in science careers was indeed found: women are less likely to hold leadership positions (i.e., tenure track or senior scientist). And the result apparently is not due to gender differences in doctoral training, as prestige of education is held constant. Yet the form of organization in which scientists work matters greatly for stratification. In networks, the formal independence of firms assures that interorganizational connections are more flexible than those between hierarchical departments but that the relationships formed create more durability than market exchanges. This interorganizational connectivity means that a network organization is more transparent, rather than a closed social setting where discrimination is easier to accomplish. The data support the book’s central argument that stratification by gender is less pronounced in network organizations. Females are more likely to be in higherlevel positions in a network form of organization, such as a biotech firm. Biotech firms do not get a larger share of female scientists; thus greater numbers do not explain the finding. Dedicated biotech firms do have more women from top-ranked PhD programs but have more men from elite schools as well; graduates of elite universities are just as likely to be employed by biotech firms as by top universities. Men do not receive either benefit or detriment by going into biotech; their chances of authority positions were the same across settings. The significant, robust finding across statistical models is that young women PhDs were much more likely—in fact, about eight times as likely—to move into positions to lead research in biotech firms than in any other life-science organization. The main business of biotech firms, however, is producing innovative intellectual property. Can biotech be less gender-biased and at the same time be successful in scientific and business innovation? Comparisons of the biotech firm with the university lab, where new ideas also arise, show how equality among diverse scientists is more closely related to innovation in the less bureaucratic biotech firm. Male bench scientists in the university used sexualized humor to exclude women from the lab. In the biotech firm, technicians used sarcasm for status-leveling. The organizational form in which science occurs matters greatly for how leaders can create equality among scientists and innovation in their labs. In interviews with Todd, a

S/D-5 6/16/04 12:55 PM Page 116

116

Women’s Work

t

university PI, and Sara, a senior scientist at a biotech firm, both discussed at length the importance of equal opportunity and inclusion of women and underrepresented minorities in science. Both of them hired and encouraged female and minority scientists. Sara’s scientists, however, easily followed her lead in collaborating with diverse colleagues in other departments; they were used to working relationships outside their lab. Todd also suggested that his scientists consult with others outside of the lab; but students more often ignored this advice because they knew that their names would appear only on publications emerging from Todd’s lab. Although the hierarchical form of the university allowed for greater inequality in informal homogeneous cliques and narrow formal specialization, it also facilitates the mass education of scientists, upon which commercial labs rely for trained employees. Overall, innovation and relative equality coalesce in network organizations. These small innovative firms are providing the cutting edge not only in medicines but also in gender diversity. The data in this chapter illustrate that employment in a biotechnology firm benefits the careers of female PhDs, but they do not tell us why that is. For explanation, Chapter 6 turns to the narratives woven by scientists to describe why relative equality resides in network organizations.

Note

1. Unfortunately, at the individual level, quantitative data were not readily available on publication records. Possibly, counter to my argument, female scientists simply do not publish articles frequently enough, and the greater quality of their publications (Cole and Zuckerman 1984) matters less for promotion, and thus they fail in academe. Then perhaps many females enter biotech by default and are smarter on average than male PhDs in biotech, because smarter men can be productive and stay in university positions. So these smarter, less-published females are promoted in biotech because they are better scientists than the males in biotech. In contrast to this scenario, I assume that there are not gender differences in the abilities of scientists entering the biotech industry. The educational ranking variable provides data that perhaps may be related to this scientific productivity issue. There is no difference among scientists who enter biotech firms—male and female—by the ranking of their PhD program, which may indicate that those entering biotech are not necessarily failed academics. But it’s probably a stretch to assume similar average productivity for those from the same schools, particularly since women often do not receive the same mentoring that men do in the same departments (Etzkowitz et al. 2000). From the qualitative interviews, an admittedly nonrepresentative sample, there is some indication that women in biotech are just as highly published as those in academe and that highly sought candidates—male and female—choose biotech firms as a place to do science and publish without the distraction of constantly writing grants and teaching. However, systematic publication data are needed to rule out the counterargument.

t

S/D-6 6/16/04 12:56 PM Page 117

6

Flexibility, Flexibility, Flexibility: Narratives of Gender Equality in Biotech Being a woman and a scientist is like trying to climb a tree with your hands tied behind your back. —Dorothy, PhD life scientist

When I spoke with her, Sara was a young PhD who had prestigious postdoctoral experience, and the publications that resulted, on her résumé. She took a job in a fledgling biotech company partly because of the pull of doing novel science and having a chance to direct research in a cuttingedge firm, and partly because of the push of being discriminated against in the university. As we discussed the academy, she described one such instance in which she felt demeaned during a job interview: [In academia] it’s hard for women. If you’re a man it’s not the same way. I had an interview at [a research university] with the chair of [a] department, in his office. One of his colleagues walked by the open door and stopped in to say hello. When I was sitting right there, he told this other fellow, “I have a little girl here from England” [using a mocking tone]. He thought he was being nice, but he wouldn’t have said if I were a man, “I have a little boy here from England” [using the same sing-song tone of voice].

After recounting this event, Sara next immediately remarked on the difference in her experience at the startup biotech firm: I haven’t experienced discrimination here. Just before I got here, the vice president of research, the highest position for a scientist, was offered to [woman’s name]. That they would actively try to recruit a woman to a senior position says a lot.

According to Sara, the constant annoyance of being treated differently because she was a female scientist led her to look for other options outside

117

S/D-6 6/16/04 12:56 PM Page 118

118

Women’s Work

the academy, where she was not disappointed in the science or the workplace. Sara’s story is not unique but portrays a common theme that I encountered in talking to women who worked in the life sciences during the 1990s. I interviewed and observed the lab work and meetings of Sara and her colleagues at the small startup biotech firm BioNow. I also interviewed lifescience PhDs working at six different organizations in the San Francisco area, including universities and biotech firms of various sizes. Additionally, I observed the work lives of scientists in a university lab, and scientists working in three government and nonprofit research institutes gave me interviews. Cumulatively, I conducted forty-seven interviews, including unstructured interviews in labs, lunchrooms, and hallways, as well as semistructured interview meetings in scientists’ offices. As I analyzed the interview transcriptions and field notes, patterns in the scientists’ narratives about workplaces and gender emerged. (See Appendix for further details on data collection and analysis.) One puzzle that I wanted to understand arose from the results that were discussed in Chapter 5. My statistical data show that women scientists on average do better in biotechnology firms than in hierarchical—including academic—settings. Why? Qualitative analyses of interview data provide evidence for more or less plausible explanations of why biotech firms are relatively better places for women. If, as I have argued throughout the book, the network form of organization that biotech firms employ is key to gender equality, what exactly is it about network organizations that promotes equal opportunity? The answer that kept reappearing in my qualitative data is flexibility. Firms that maintain connections to diverse organizational partners develop adeptness in being flexible. Modes of communicating and rules spoken and unspoken differ from company to company; thus a network organization that collaborates with various companies is constantly learning new ways of doing complex day-to-day tasks (i.e., beyond just purchasing commodities) like R&D. Take, for example, one meeting I observed on an R&D project that included BioNow researchers Frank (a university researcher) and Paul (a scientist from another biotechnology firm). In a small conference room at BioNow we sat around an oval table, and periodically someone would use the whiteboard on the wall. The BioNow researchers were discussing a laboratory process that they had developed; they were willing to share it with the outside collaborators but were also aware of the value of this intellectual property. Paul initiated the conversation by suggesting a way around a problematic result: Paul: “Is it possible to [perform a process]?” Luther: “No.”

S/D-6 6/16/04 12:56 PM Page 119

Flexibility, Flexibility, Flexibility

119

[A little later in the conversation Frank stands in front of the white board to draw a diagram.] Frank: “One thing I’d like to add: if you put [substance X] in here [pointing to his drawing with the pen], you can get a direct finding that way.” Luther: “You have to wash off [X]; during the wash it associates with [substance Y].” Frank: “You need to be real fast.” Luther: “It washes very quickly. I don’t know why you can’t do it. It doesn’t make sense.” Paul: “How often do you wash?” [Frank sits back down in his chair, as Paul did not direct the question to him.] Luther: “We wash quickly, in a minute.” [Gabriel, the executive, speaks up now and directs his eye contact and earnest comments to Paul.] Gabriel: “These methods are highly proprietary. It took a long time to find something that would work. But it’s obviously not a patentable solution.”

Gabriel’s perspective, honed by experience in a large drug company, somewhat clashes with the spirit of scientific problem-solving that developed in the project meeting between Luther the BioNow scientist, Frank the university scientist, and Paul the scientist from an outside biotech firm. Yet even he simply warned Paul against conveying the “proprietary but not patentable” process to other competing networks of organizations researching the bioscience application on which they were collaborating for this project. In this meeting, Paul learned not only about a new technical method but also about the norms for sharing information at BioNow. Flexibility also comes into play by working on expanding technologies; one can never be certain which paths will lead to successful outcomes (e.g., a profitable drug). What I mean by flexibility is that the organizational capacity for change is highly developed, particularly at the project level. New projects can be pursued, and new collaborators inside and outside the organization can be drawn upon. Flexibility on projects often translates into flexibility in more formal organizational roles. In talking to scientists about the working environment of biotech firms, I discovered a shared perception of more open opportunities for groups of people traditionally disadvantaged in science, especially women. Miles, a middle-aged white male academic who had experience working in a biotech firm, commented on how he thought the flexibility would especially help women and people of color to move up job ladders: “I think there are more opportunities in biotechnology. There, titles are fluid, and if the person is gung-ho and has the skills, they can move more quickly into positions of responsibility.” Miles went on to contrast this fluidity in biotech firms to the rigidity he saw in the university and large drug companies:

S/D-6 6/16/04 12:56 PM Page 120

120

Women’s Work Academia limits you to this tunnel vision. You go the way your mentor did, and that’s the only measure of success. There are good opportunities in industry—especially in biotechnology. At pharmaceutical corporations, industry is closer to academic roles—more rigidly defined. There’s a stigma against you for moving up to more senior positions.

During our conversation Miles implied that female and minority scientists especially were likely to encounter a lack of opportunity when there are rigid definitions around jobs. Flexibility of work roles in a network form contributes to gender equality by allowing the old molds to be broken. Miles had secondhand knowledge of the discrimination his female colleagues had faced, but the women life scientists with whom I spoke all had direct experience of blatant and subtle discrimination in their profession. As Dorothy, a white female in her late forties, succinctly put it, “Being a woman and a scientist is like trying to climb a tree with your hands tied behind your back.” In university research laboratories particularly, female scientists were treated differently and not given opportunities because they were women. Sara, in the same job interview with the professor who called her a “little girl,” faced more of what she called “typical macho bullshit.” The principal investigator asked her why she had relocated. When she told me about this episode, her comment on how she had responded was, “I felt quite uncomfortable, because I knew my answer wouldn’t suit him, but I told him the truth. I really shouldn’t have done.” As she said this, she broke eye contact, dropped her head to look down, and made a throwaway gesture with her hand, as if to erase the memory of a past mistake. The “mistake” that Sara had made was to confess that she had moved to the area to be with her then-boyfriend (now her husband). In response, the interviewer asked whether she was married yet. She raised her voice for emphasis as she commented on his question: “That’s illegal, you know! But of course I couldn’t say anything against it.” She told the PI that she was not married. It was Sara’s opinion that she was not hired because the interviewer saw her as “an unreliable female.” He seemed suspicious of her foreign, unmarried status and disdainful of her decision to pursue a love relationship. Sara thought that the faculty member did not see her as a serious scientist because of this. She told me that she knew a foreign-born male postdoc in the department for which she was interviewing, and he had not been asked the same questions when he was hired. Sara’s negative experience in being asked inappropriate questions on the academic job market that are not asked of male colleagues is unfortunately not an isolated incident. A National Science Foundation study (Silverman 1997) reported similar patterns of hiring discrimination. One postdoctoral fellow in her mid-thirties, apparently relatively sheltered in graduate school, described her and female colleagues’ difficulties in getting a job without accounting for family issues first:

S/D-6 6/16/04 12:56 PM Page 121

Flexibility, Flexibility, Flexibility

121

When I got out of grad school, I didn’t think these [gender and family] issues would be a problem. But after I got my doctorate and tried to get a faculty position, I saw it was a striking trend. I doubt if someone will ask a guy if personal life will affect the job, but from what I’ve heard from other women who are job-hunting, it comes up a lot.

The barriers in academe led this PhD to look for a job in biotech, like many other talented women scientists. The constant background noise of being treated differently because of one’s gender seems to be part of life as a female scientist. Sara also chose to enter the biotechnology job market. She described her interview at BioNow as being very different from that at the university. “They didn’t ask me any questions about my motivations for being here, only about my scientific experience. They were very impressed with my postdoc at [internationally renowned research institute].” After she was hired, Sara discovered the freedom of doing science in a startup firm. I asked how she had come to lead the project whose meetings I was observing. While working on [a related project], I had an idea about [a diagnostic process related to a disease], and they pretty much said, “Get together a research team and do it.” There are so many opportunities here and so much less crap to deal with [than in the university].

Less gender bias seemed to go hand in hand with flexibility in scientists’ accounts of life in commercial biotech labs. Yet could life as a female scientist at a biotech firm be that rosy? I asked Sara if there were drawbacks to being female and working at BioNow. Her astute observations reflected a subtler kind of gender inequality at the biotech firm. There’s nothing obviously different about being a female scientist here. But compared to [her department head] and some of the other men who have stay-at-home wives to buy the groceries, do the wash, and raise the children, we’ll never have it as easy.

After a pause, Sara elaborated, I mean, [the department head] has time to go cycling, maintain a university affiliation, and so forth, on top of his regular work. Sometimes I wonder if they could ever understand how it is for women PhDs. Or even for the men who don’t have stay-at-home wives, either.

She reflected on these differences in family situations, “but I don’t suppose there’s anything the company could do about it.” Perhaps being a woman and a scientist at a biotech firm is like climbing a tree with only one

S/D-6 6/16/04 12:56 PM Page 122

122

Women’s Work

hand tied behind your back. It is better than more hierarchical academic and commercial settings, but still there is room for improvement. In U.S. culture—where Men Are from Mars, Women Are from Venus is not just a best-selling book but also a board-game and radio talk show—one proffered explanation for why women do better in networked biotech firms is simply that male and female scientists are different. Perhaps because girls are socialized to play together at relationships and boys are socialized to win competitive games, women do better working on collaborative projects in biotech firms and less well in hierarchical organizations. Just such a speculation was offered by one scientist whom I interviewed—Henry. Henry was an academic administrator when I interviewed him, but previously he had been an administrator in a biotech firm. I asked him, based on his experience in the two settings, if female scientists would generally find it easier to work in academe or in the biotech industry. Henry paused and regarded the question as if he had not considered it much before. (For my other questions, he had responded more rapidly, as our interview time was short.) After thinking a bit, Henry did not pass on the interview question but responded: “Collaboration is fundamental in biotech. Women are team players and generally get along better, so they have an easier time moving into biotech firms, I think.” Although having a certain logic, this explanation seemed less plausible when I realized that none of the women scientists I spoke with offered the same explanation. I put the question directly to Amanda, a carefully reflective interviewee whom I spoke with after talking to Henry. Amanda also had administrative experience in academe and in the biotech industry. “Do you think women do better in biotech firms because they are more collaborative, team players?” I asked. Her response was, after a pause to consider, “No, I don’t think that’s it. I’ve known plenty of male scientists who got along with others at least as well.” Amanda went on to more nuanced explanations about the conditions of work in the university and biotech labs she had known, as well as the pharmaceutical corporation in which her husband worked, to explain why female PhDs have advanced in careers in biotechnology. Later in the chapter we will see that Amanda’s account, like others, incorporates the key idea of flexibility. Flexible organizational boundaries, flexible project teams, and flexible roles seem to mitigate the gender discrimination that accompanies more rigid hierarchy. Dorothy’s career provides an example of this flexibility. Dorothy had been among the first postdocs in biotechnology in the early 1980s. She noted that the flexibility of the biotech firm that employed her was responsible for her ability to shift her career focus substantially in taking on an extra-scientific leadership role:

S/D-6 6/16/04 12:56 PM Page 123

Flexibility, Flexibility, Flexibility

123

At Life Co. they were quickly developing research and the board wanted a patent expert in-house. I was interested even though I was a scientist and had absolutely no experience with law. They knew I was bright and adaptable—from all of the different scientific projects I had worked on—and so they paid me to acquire the knowledge to become a patent agent.

Both Miles and Dorothy express that the biotech industry offers less standardized roles than can be found in other life-science organizations. The flatter structure of biotech firms seems to permit more varied opportunity for all scientists to take positions of responsibility. In the previous chapter, Lotty and Dorothy offered contrasts in their expectations of whether industry would provide more equitable opportunities than the university. Dorothy recalled her idealistic days as an undergraduate when she thought the university, as the center of social movement activity, would be the first to change. Lotty, a postdoctoral fellow, was more cynical about the university based on her experience of the perennial postdoc period, but she did not expect industry to be any better. Dorothy, disappointed in her actual experience at the university in graduate school, moved into one of the first biotech firms in the 1980s. Lotty was currently searching for a position in a biotech firm. As a member of Generation X, Lotty had less experience of the blatant sexism that Dorothy’s cohort faced at the university. No one said to Lotty’s face that “girls can’t do math and science,” as they did to Dorothy. Yet the outcome of discouragement in the university was the same. Although both had prestigious PhDs, publications, and ambition that would seem to mark them for professorial careers, both scientists reported receiving overwhelming support from male faculty mentors to leave the academic career path. Sometimes encouragement in one direction results in discouragement to follow another. The advice that Peter received differed from the messages given to Dorothy and Lotty by their male advisers. Peter was a postdoc in a research center at a public university when I met him. He worked on a project whose lab meetings I observed. The subject of one meeting was how Peter would best present his research at an upcoming colloquium at the research center. The following excerpt from my field notes occurred when I was hanging out in the lab one day and shows how Peter felt about his upcoming job interview at a traditional drug company: Peter came in looking for something on his bench. I said hi, and asked how his talk went. He said, “It went well. I’m giving it next week in a job interview.” He didn’t say where the interview was. I asked if he was nervous. He shrugged and said with a nonchalant tone and facial expression, “I guess maybe a little bit—it’s at a large pharmaceutical company in [a big city],” as if saying its being at a large pharmaceutical company was less cause for nervousness than its location. I asked at which company and he

S/D-6 6/16/04 12:56 PM Page 124

124

Women’s Work told me. I responded positively with recognition and said that is a really well known international corporation. He grunted an agreement without making eye contact again, and quickly exited the lab with his notebook under his arm, seeming to me to lack pride in the job interview.

Later, I found out that Peter did not actually go to the interview. He said that he had to cancel because he was suddenly ill, then never rescheduled the job talk. I asked him how Todd, the lab director, felt about his interview at the big corporation. He replied that Todd was “not really against industry like some professors [referring to another professor who had made disparaging remarks about industry in lab meetings], but I know he thinks I can do better.” For Todd, that meant an academic position. When I asked Todd about Peter and the job market, Todd mentioned the interview Peter landed at the large drug company but concluded that “he’ll have other opportunities.” Thus Peter received encouragement to stay in academe, in the form of discouragement for leaving. Neither Dorothy nor Lotty found discouragement to leave the academic path from their male mentors. In entrepreneurial biotech firms, however, encouragement is available to promising female scientists. Martin was in charge of hiring PhD-level scientists to work in the laboratories of a growing biotech firm that had just relocated to a larger facility when I interviewed him. During our interview, he took a phone call from a potential job candidate. I was close enough to hear the female voice on the other end of the line. Martin began discussing the merits of the scientific projects being researched at the company. I busied myself with my notebook, prepared to eavesdrop on the rest of the conversation, but Martin politely excused me from his office. After I returned to the office, later in the interview I asked what kinds of things he looked for in job applicants. He had previously been describing a cutting-edge R&D project to me—one that brought together two different scientific disciplines—on which the company collaborated with another firm. Martin tied together his response on the job applicant pool to the innovation in biotech: “In biotech, the speed of change is very fast, project management flexibility is incredibly important, so what I look for when hiring is good people.” I queried what he meant by good people, and he expanded on the importance of scientific training in graduate school and lab experience. I asked if there were any other aspects of applicants’ background that affected hiring decisions at the company. Martin replied: Scientific merit is the main criterion. And—as far as hiring women and minorities—diversity is good. Good comes from mixed backgrounds. As I said, being able to be flexible on projects matters for the bottom line of what we do. Being able to work with people of different backgrounds helps with that flexibility.

S/D-6 6/16/04 12:56 PM Page 125

Flexibility, Flexibility, Flexibility

125

Interestingly, rather than arguing for merit as opposed to diversity, Martin was arguing for merit through diversity. He was also careful to note that rather than having formalized quotas, he had the flexibility to hire a diverse group of scientists who would contribute to the health of the firm. He said, “We don’t have formal diversity targets here; no one’s going to tap me on the shoulder and say, ‘We need so many of such and such.’” I must admit that it was somewhat difficult to tell what was the recruitment pitch for the company and what was Martin’s personal opinion, but he did go on to further illustrate his position on the importance of a diverse leadership in the scientific workplace. He told me a story of how a female friend of his won a prestigious scientific award in graduate school but faced too many obstacles in the academy to continue to flourish there. Martin pointed to a framed photograph of two teenaged girls and said, “I hope that my daughters will not have to face stuff like that.” He elaborated, “But what is the university’s loss is my gain. I find that I have an edge in recruiting some of my best scientists who, for whatever reason, are not staying in the university—sometimes those from foreign countries, or women.” The idea that rapidly changing science is best researched by a diverse workforce is more than just rhetoric in most biotech firms. Biotech firms have been at the leading edge in creating company policies to reduce conflicts between work and family commitments. These policies disproportionately help women, who still work a double shift—completing most of the unpaid tasks upon coming home from full-time employment at the office (Hochschild 1989). Many firms have instituted family-friendly practices (Eaton 1999; Eaton and Bailyn 1999; Rayman 2001) and on-site day-care centers. Take, for example, the policies of Genentech, which was among the first biotech firms founded and has been ranked in the top 100 companies for working mothers since 1991 by Working Mother magazine. The company sponsors Second Generation, one of the largest on-site child-care facilities in the United States, contributes up to $5,000 and six weeks’ paid leave to employees who adopt a child, and maintains rooms for nursing mothers across its campus. Of course, just because company policies exist does not mean that informal norms support taking advantage of their benefits. The on-site day care, however, shows evidence of heavy usage. At an interview in his newly constructed, spacious office in the San Francisco area, George reflected on raising a family while working either in academia or biotech. His career had taken him into both settings for substantial periods, and he spoke about the life sciences from the vantage point of being at the top of his field and at the top of his game in his fifties. George remarked: “In biotechnology, there is more flexibility in terms of hours; it’s more difficult to accommodate a family in academia, especially with the tenure clock.” He expanded:

S/D-6 6/16/04 12:56 PM Page 126

126

Women’s Work When you’re working toward tenure, you’re spending fourteen or sixteen hours in the lab, there isn’t time for anything else. I think it’s particularly hard for women because tenure comes, in the sciences, at a time for people when they’re usually in their late thirties.

George did not feel that the university policy of taking a year off of the tenure clock for having a child would help most scientists. “You lose your momentum. Things are moving so fast, the science is progressing so quickly. To take a year off is an eternity, when you’re trying to carve out your niche and make a name for yourself.” The collaborative nature of science in the biotech industry, he thought, would make family life more manageable, although not easy. There are constantly projects going on in a firm. You would have work to go back to, though probably on a different project. If you’re particularly invested in a project, you might consult with others working on it or even go in for a few hours when you have an infant.

Although George had observed female colleagues managing family responsibilities, he did not personally experience the difficulty of juggling work and family life. Antonia, in contrast, had a small child and found that both the on-site day-care facilities at her biotech workplace and the project-based nature of the work permitted her to be a good scientist and mother at the same time. Her office revealed this role duality: she had childish drawings posted on the walls next to shelves bulging with the latest scientific journals. “It’s never easy for women,” Antonia declared. “Your career and your family are at critical junctures at the same time. I was writing up a project that was published in the New England Journal of Medicine—and led to a patent for the company—when I was nine months pregnant with my daughter.” She continued, “I finished the paper, sent it off, and went into labor the next day. Literally. I sent in the paper on a Friday and had my daughter in my arms on Monday.” Antonia did not think that she would have been able to have children in an academic setting. “While I was on maternity leave here, I could keep in touch with my colleagues [both at her company and other organizations] who kept it moving forward.” She contrasted this ability to trust collaborators with her perspective on academia: “When I was a postdoc at [a prestigious academic institute], people collaborated somewhat, on the fringes of their work, but still had their main turf which they guarded carefully.” The flexibility of project organization, and the trust in colleagues that develops through collaboration on important projects, contributes to female scientists’ ability to succeed in biotech firms despite family constraints. Perhaps, however, just as children are more flexible than adults, indus-

S/D-6 6/16/04 12:56 PM Page 127

Flexibility, Flexibility, Flexibility

127

tries lose their flexibility with maturation. Maybe the biotechnology industry is too young, founded in the late 1970s, to have seen the extent of its eventual bureaucratization. Further, because the firms were founded after the 1960s women’s movement, perhaps generalized cultural ideologies and laws explain women’s inroads into the industry rather than the form of organization. Time, through industry age or period, would explain the relative equality. This counterargument is difficult to refute with data gathered from a single scientific field (broadly speaking, the life sciences constitute one case). Consider another technical industry that developed in the United States at roughly the same time: cellular telecommunications. Wireless communication in 2002 had six large service providers that owned 75 percent of the market share (Telecommunications Industry Association 2002; the industry has been subject to megamergers since then). In contrast, biotechnology industry analysts speak in terms of the top-100 biotech companies (Robbins-Roth 2000). As of 2000, sixty-five new drugs from biotech labs were on the market, representing scientific innovation that could not be accomplished in a handful of firms. The interorganizational networks that sustained this R&D draw together hundreds of biotech firms and their venture capital, pharmaceutical, and university partners (Powell et al. 2002). In the innovative industry of telecommunications, are women doing as well as their sisters in biotech? Put differently, as telecommunications companies increase the scale on which they operate, and hire more people to manage this scale of operations, what is the result for women in the industry? A survey conducted by the Women in Cable and Telecommunications Foundation (WICT 1998) provides some clues. This industry study found that promotion widened the gap in compensation between men and women in telecommunications. The overall salary gap is 11.5 percent, but for those who were promoted by their current employer, the pay gap is 14.5 percent. Unfortunately, my data do not supply comparable information about salaries, but in comparing the qualitative responses that men and women have to opportunities in telecommunications to what I heard in biotech, the differences are striking. For example, a female manager for a telecom equipment supplier described the glass ceiling in the large bureaucratic firms that make up the industry (WICT 1998): I’m paid 25 to 30 percent less than men with the same title and less years with the company or industry experience. Women are not promoted to upper management. I have been bypassed for promotions that less experienced males have received. This is a feeling shared by other female workers in this industry. Many feel discriminated against.

The variety of job levels in the hierarchical telecom organizations allows women to be promoted (the industry survey found that the majority

S/D-6 6/16/04 12:56 PM Page 128

128

Women’s Work

of women as well as men had been promoted by their current employer); but it also permits women’s lack of mobility into positions of real authority to be hidden behind the window dressing of promotion rates. Provisionally, the founding of the telecommunications industry during the same time period as biotech does not mean that women experience the same level of equality. Telecom firms have become giants, with multiple layers of hierarchy, in contrast to the still relatively flat employment patterns that characterize scientific positions in biotech firms. Additionally, telecommunications companies tend to buy out rival companies and upstream supplier or downstream distributors to absorb other companies rather than to collaborate with them. We would not expect to see the kind of interorganizational project flexibility in telecom that biotech firms rely upon to generate new ideas and products. And if women technical workers in the telecom industry do not have access to the interorganizational flexibility and visibility of project management, this may explain their lack of power in telecommunications. The telecommunications comparison raises the question of whether the successful gender integration of biotech scientific positions may be due to the size of the organizations, if not the age of the industry. To give an idea of the scale of pharmaceutical corporations in contrast to biotech firms and universities, in 2003 Merck employed about 78,000, Millennium Pharmaceuticals (a biotech firm) had around 2,000 employees, and MIT retained a staff of about 9,200. Perhaps because biotechnology firms are uniformly small, women’s contributions are inherently more visible to everyone in the company and thus are equitably rewarded. While the number of scientists employed at biotech firms is certainly much smaller than in the large bureaucratic drug companies and universities, there is variation in the size of firms in the biotechnology industry.1 Genentech employs more than 5,000 people, but startups like Genmetrics, which began in 2001 with the five founders working (Graebner 2001), typically employ less than fifty. All successful biotech firms rely on interorganizational partnering to innovate, regardless of size. Among the scientists I studied, the network form of organization appears to facilitate women’s careers across firms of various sizes as well. Sara, for instance, worked at BioNow when it was a small startup and received promotion to leading a key project for the company as well as visibility for her scientific contributions. Antonia had a similar background as Sara: after a prestigious postdoctoral appointment at an academic institute in Europe she accepted a position at a U.S. biotech firm. But Antonia worked at a large, well-established biotech firm rather than a startup. She, too, received a promotion, responsibility for an important project, and accolades for her scientific contributions. Both women were recognized not only by their companies but also by their collaborators outside the firm as up-and-coming players in the field.

S/D-6 6/16/04 12:56 PM Page 129

Flexibility, Flexibility, Flexibility

129

Another positive feature of the network form is that the project-based nature of work allows women greater choice in selecting research collaborators. Amanda is a PhD who has worked in both academic and biotech leadership roles during her career. She used a story to contrast the university to the biotech industry in one’s ability to choose fellow scientists: A friend of mine left a tenured position at [an elite university] to go to [a biotech firm]. This person called the university department under [Professor X] an “autocracy.” They just wanted to do science, didn’t care about the prestige, and could do science there [at the biotech firm]— working with who they wanted to rather than dealing with [X].

This relative choice in colleagues, especially in collaborators on one’s projects, may particularly benefit female scientists. For example, if a female PhD in a biotech firm is working with a male chauvinist from another organization on one project, she can avoid working with him in the future. But in a hierarchy she would be more likely to be stuck working with the chauvinist or spending time and energy having him removed. Yet if biotech scientists find great colleagues through collaboration who treat them with respect, they can often form a long-term relationship that goes beyond a spot contract. Network organizations have projects that more routinely span organizational boundaries, heightening variability in working relationships and allowing for multiple modes of organizing work. The downside of flexibility, however, is that it may exacerbate inequalities at the boundary between PhD and other scientists. In my research with PhDs, from the scientist in his first week on the job fresh from school to the chief scientific officer, not one person had to ask permission to talk with me or let me tag along in the lab and to meetings. The technicians, however, were a different story. I felt somewhat chastised when I was in the lab one day for an extended conversation with three technicians and their department head poked his head in the door and sarcastically commented, “Are you still talking? How is the experiment going?” PhDs have collaborative relationships with outsiders for various projects, so an unknown person conversing with them at length is not unusual. But for technicians, the direct monitoring of their work at the bench assumes they are not free to create the flexible organizational boundaries of the firm. Still, in biotech there seems more opportunity to transgress the boundary between the role of technician and scientist as Miles’s comments earlier in this chapter suggest, and in the way Jake asked for the technicians’ advice (see Chapter 3). BioNow also recognized the commitment and skill of Brooke, a technician who was often consulted by PhD-level scientists for a crucial, frequently used procedure. The company paid for Brooke to return to graduate school for her PhD. Several scientists commented that this opportunity was something they had never seen at a university or a large drug company. It is

S/D-6 6/16/04 12:56 PM Page 130

130

Women’s Work

interesting that the way senior scientists dealt with Brooke’s hard-tocategorize position (technician or scientist?) was to make sure that she would be clearly within the boundaries of the scientist job. Dorothy’s quote about climbing trees with tied hands is illustrated by Amanda’s career. An experienced scientist and executive, Amanda worked for years in prestigious universities and some well-known biotechnology firms. Amanda claims: “In biotech the ‘rules’ are clearer. In the university there are more political decisions; you have to do things right as well as do good research.” Amanda’s tone of voice clearly placed quotes around “rules” in making this statement—indicating that the informal expectations in biotech were actually clearer to her than how to play the game of getting around the formal rules in the university. She described how the informal atmosphere of competition combined with male bonding in academic politics obscured her contributions to science. Amanda explained: At [an elite university] where I was a postdoc, everyone was in competition. Postdocs were pitted against each other, and you would hope the competition would make a mistake. As a female scientist, I found that I was professionally lonely in academe. I had a very negative experience at [another elite university]; there were many political decisions. I was the first female faculty in [the department], as an assistant professor. I did good work but was not mentored. So I didn’t do things right, like get invited to the right meetings. A male who came into the department at the same time was mentored by male faculty and so he did things right. He got senior people in the department to invite him to the meetings where the “right” people would see his work. So even though my research was just as good and important, he was promoted to tenure and I was not.

Amanda’s work went unrecognized because once one scratched beneath the surface of the formal rules for fair hiring and promotion in the university, the unwritten rules for playing the game were surreptitiously veiled from a lonely, green, junior faculty member. Nor was Amanda alone in her assessment of the cold climate for women in the university. Many of the male scientists I interviewed were sympathetic observers of the difficulties that female colleagues faced. Martin, for example, pithily summarized his view on the idea of the academic universalistic haven: “I think, just from what I’ve seen as an observer, that in academia women have very little breathing room. The idea that academia is always the right job is crap.” Amanda’s experience of not being able to gain tenure in academia, while proceeding to accomplish much and make a name for herself in biotechnology, illustrates how academia is not always the right job for talented women. After disclosing the negative period in her scientific career, Amanda contrasted it to her experience of working in the biotech industry:

S/D-6 6/16/04 12:56 PM Page 131

Flexibility, Flexibility, Flexibility

131

In biotech it is much less competitive; there is more cooperation. You are less likely to be competing with someone down the hall. Rather than vying for tenure, you can cooperate on getting the research out. And as someone who saw the management side in biotech, I could see that in recruiting [scientists] this made positions attractive—this collaborative teamwork, and having more opportunities to contribute, including for women.

Ironically, the formal university hierarchy fosters less clarity in its rules than is provided by the transparency of informal, unwritten rules of collaboration in biotech firms. I will admit that the skeptic in me wondered whether Amanda’s testimony reflected sour grapes about academia. Her story rang true, however, as I continued to read the gender and science literature and to collect interviews in which other scientists arrived at similar conclusions, including those who had met with more success in the university. Claud, for example, a full professor and also a research director at a biotechnology startup, successfully straddles both worlds. Overall, Claud was less critical of academia than others who had left it behind. But even from his position as a famous, successful academic, he saw a gender bias in the university in which fewer opportunities were open to women scientists. And like Amanda, he sees the biotechnology industry as more collaborative and merit-driven and consequently as providing greater gender equity. Claud explained the similarities and differences between biotechnology firms, traditional pharmaceutical companies, and the university: A similarity of biotech firms with the university is that the science has to be good. This means top academic training, and having a basic science background. For part of your expertise in biotech compared to more traditional industry, is to understand what you’re doing. But compared to the university, in academia you’re concerned with your own fame and what you can do for yourself. In the biotech industry your project is part of the team working together; it is more civilized.

Claud expanded on what he meant by biotech’s “civilization” and its relationship to gender equality: Academia is difficult; there are few opportunities, especially for women. I hate to say it, but some of my colleagues still have some old-fashioned ideas. There are more opportunities for women in biotech. Gender is not a factor in the industry; they look at your performance and if you do well, the company does well.

Again, the transparency of working on collaborative projects benefits those whose work is often overlooked in more hierarchical settings.

S/D-6 6/16/04 12:56 PM Page 132

132

Women’s Work

Interorganizational collaboration, the hallmark of the network form, may especially benefit women PhDs in biotech firms. In contrast to personal networking for individual rewards within bureaucratic organizations, which has been shown to increase gender stratification, connections made by those in a network form are perceived to add to the general good. Antonia, a senior scientist at a well-known biotechnology firm, notes that support for collaboration contrasts to her academic experience: From my experience at [an academic setting] I could tell you many a true story about political infighting—especially over funding. It was very stressful and not a pleasant experience. Now I think there is more camaraderie here because of the secure funding, among other things. I was excited to work at [the biotech firm]; it’s a great environment. No one is miserable. We are not compartmentalized—and get to work with many good scientists both here and outside the firm. And we choose who to work with based on nonfinancial considerations, like how good they are in their field.

t

Male and female PhDs alike are supported in building their interorganizational networks in biotech firms, because the health of the firm is reliant on scientific collaboration. Innovation for new products is sustained through the relationships that Antonia, and scientists like her, have with others. This networking support tends to help women in biotech, in comparison to female colleagues in more hierarchical settings. The network form explanation for equality appears quite plausible: women in particular seem to benefit from the more flexible scientific roles.

Three Things to Know About Gender Equality in Biotech

Why does biotech, relative to other settings, provide more equal footing for male and female scientists’ advancement into supervisory positions? Just as there are three things to know in real estate (location, location, location), it is important to know three things when understanding gender equality in the biotechnology industry: flexibility, flexibility, flexibility.2 Flexibility of organizational projects and personnel is key to both innovation and gender diversity in the biotechnology industry. This chapter has shown how flexibility helps to foster the mobility of women into powerful positions in the industry, in contrast to other explanations for why women generally do well in biotech. Chapter 5 demonstrated that biotech firms provide more equal footing for male and female scientists in advancement into supervisory positions, relative to other settings. This chapter has shown why. The narratives from scientists working in various settings—large and small biotech firms, universities, pharmaceutical corporations, nonprofit research insti-

S/D-6 6/16/04 12:56 PM Page 133

Flexibility, Flexibility, Flexibility

133

t

tutes, and government agencies—were surprisingly similar. Women had experiences of both blatant and subtle discrimination, particularly while working in university research labs. For example, Sara described a job interview in which the university lab’s principal investigator illegally asked why she was new in town and whether she was married. She felt that she was not hired because the PI expressed disdain—he saw her as an unreliable female—when she revealed that she had moved to the area to be with her boyfriend. This constant background noise of being treated differently because of one’s gender seems to be part of life as a female scientist. Biotech firms were certainly not spoken about as utopian havens, but relative to other types of employment settings they were described as places to have one’s scientific and technological expertise appreciated. Socialization explanations—that women have been socialized to be more cooperative and thus like network organizations better—did not appear in scientists’ narratives generally. Instead, the flexibility of biotech firms—supplying opportunities to do science and to get around many of the discriminatory gender hurdles women face—was a common theme from interviews with scientists. Chapter 7 concludes the book by drawing out the broader implications of this study for innovation in science and technology, changing economic organization, and labor force diversity.

Notes

1. Biotech firms vary in other ways as well. Fiona Murray (2002, 2003) makes an interesting case that even within the small world of tissue engineering there are gains to studying the variation in biotech firms—i.e., in their patenting and publishing networks—in order to better understand innovation processes. 2. There are many operative definitions of flexibility in the social science literature. Workplace flexibility often means the ability to change set working hours, or so-called flextime. Arthur Stinchcombe (2001) has argued that flexibility even occurs sometimes within organizational formality—in budgets, and markets, for example. Budgets are formalized means for allocating funds, but the amount is flexible from year to year. The main way that I regard flexibility is in the flexibility of network relationships—at both the organizational and individual levels. What I mean by flexibility here is the interorganizational flexibility of biotech firm networks, which translates into autonomy at the individual level for project leaders.

S/D-6 6/16/04 12:56 PM Page 134

t

S/D-7 6/16/04 12:57 PM Page 135

7

Conclusion: The Knowledge Economy, Innovation, and Equality Centralized bureaucracies always protect the status quo. They don’t innovate. Creativity comes from small groups. Small groups gave us the electric light, the automobile, the personal computer. Bureaucracies gave us the nuclear power plant, traffic jams, and network television. —Bruce Sterling, Islands in the Net (1988)

In Bruce Sterling’s cyberpunk fantasy novel Islands in the Net, a character who attempts to protect her large multinational corporation from smaller rogue economic competitors produces the above quote on bureaucracy and innovation. The character acknowledges that the power of her enemies lies in their decentralization and creativity, foreshadowing the storyline. This science fiction is just one example of how the idea that bureaucracies stifle creativity has become part of popular culture. Rather than the omnipotent governmental bureaucracy of Orwell’s 1984, Sterling (along with William Gibson and other writers in the 1980s cyberpunk movement) created visions of corporate globalization challenged by decentralized networks of skilled high-tech dissidents. Without going too far afield into the domain of literary criticism, it is interesting to note the connection between research interests in academic circles and stories told in popular culture—like the appearance of the network form of organization as an alternative to bureaucracy in science fiction. Scholars of organizations have long touted the link between decentralized organizational structures and creativity. From contingency theory of the 1950s and 1960s to current studies of networks of innovation in economic sociology, centralization is found to lead to routine rather than to novel outcomes. Bureaucratic forms may provide power, stability, and efficiency but not radically new ideas.

135

S/D-7 6/16/04 12:57 PM Page 136

t

136

Women’s Work

There Ought to Be a Law!

There is little controversy that more flexible, decentralized organizations better facilitate creative productivity. But do networks allow for equity, or instead do old-boy networks exclude women from power? Typically, organization through flexible networks is viewed as mutually exclusive with gender equality. In a large corporation studied by Rosabeth Kanter ([1977] 1993), white men’s informal relationships—sometimes referred to as oldboy networks—excluded women and minorities from positions of power. The acceptance of male executive networks as the legitimate informal organization appeared in many of Kanter’s examples. On one occasion, a female manager challenged the symbolic meaning of men’s golf as a legitimate business pursuit by publicly leaving the office early to attend ballet lessons instead (Kanter 1993: 215). The male managers presumably saw golf courses as places to build business contacts, and they trusted each other to make the decision to leave early, but their untrustworthy female colleague leaving early for ballet was shirking, not networking. The visibility of token female managers increased the pressure to perform better than anyone else (and for most of them without the support of the informal networks that male colleagues enjoyed). In the 1993 afterword to her classic ethnography, Kanter argues that the 1990s organizational landscape differs from the one she studied in the 1970s. In leaner, meaner corporations of the 1990s, all managers faced increasing performance pressures in flatter organizations. The answer was not competition between men and women over how to slice the economic pie but how to grow the pie. Her conclusions (Kanter 1993: 312) are difficult to dispute: “When men and women are in similar situations, operating under similar expectations, they tend to behave in similar ways.” My questions are these: How can we create similar situations for men and women? and Can we also increase the pie at the same time? In the United States and other developed nations, innovation in the knowledge economy is the most realistic way to increase the size of the economic pie, as we cannot compete with developing countries in cheap, mass production. Barbara Reskin’s 2002 presidential address to the American Sociological Association reveals one answer sociologists often give for creating equality in promotion and hiring: “Generally, the more bureaucratized personnel practices are, the less freedom managers have to act on their own stereotypes, biases, or impulses to favor ingroup members” (Reskin 2003: 13). Bureaucratic rules, Reskin and others posit, provide the most unbiased information on employees and transparent process for how jobs are allocated. A study of engineers (McIlwee and Robinson 1992) seems to support the hypothesis that bureaucratic rules help women move up. Engineers at a high-tech computer firm using informal work arrangements were contrast-

S/D-7 6/16/04 12:57 PM Page 137

Conclusion

137

ed with engineers at a bureaucratic missile manufacturer. The computer firm had a strong, masculine engineering culture that women liked technically but in which they found themselves excluded from the informal networks and thus the formal positions of power. Women did not like the blatant sexual harassment at the defense contracting firm or the boring, routine jobs that disallowed technical innovation. Women engineers were, however, more likely to be promoted in the bureaucracy than at the more laterally organized high-tech firm. U.S. social movements in pursuit of civil rights for blacks, women, homosexuals, and people with disabilities have found their clearest agenda in seeking to change discriminatory laws and to secure legal rights through legislation and the courtroom. There are benefits as well as cautions to consider in relying on one set of rules (i.e., laws) to protect equal employment opportunity (EEO) across all organizations and occupations and thereby ignoring the social context of the workplace. The benefit, if courts uphold the laws, is that employees have recourse beyond their bosses to compensation for blatant gender and race discrimination. But relying on laws to fix social problems is often too simplistic; the application of legal policies cannot be separated from the work context. Lauren Edelman’s research has shown how law and business institutions are interwoven in many ways (Edelman and Stryker 2004). Affirmative action laws are not just external mandates that come from outside an organization to create equality. For one thing, laws tend to constrain organizational procedures more than the outcomes, so appearing compliant (e.g., having an EEO office) while violating the spirit of civil rights laws is a common occurrence (Edelman 1992). Further, Edelman and colleagues (2001) find that managers interpret civil rights law based on emerging business norms of diversity, including personality types. Thus the way managers uphold equal opportunity laws may little resemble the laws as written. Others (Kelly and Dobbin 1998) make a similar argument but focus on how EEO/affirmative action specialists within corporations developed a vested interest in maintaining diversity rhetoric, even while the enforcement of the laws under Ronald Reagan’s administration declined. As organizational sociologists have long documented, formal policies take on a life of their own in the everyday operation of the workplace. A classic example is a study of a gypsum processing plant (Gouldner 1954) where the no-smoking rule was ignored by management and workers alike, except when the outside inspector came. The problem with expecting laws to protect women from discrimination across all organizational contexts is that more subtle forms of discrimination may be especially difficult to prosecute in professional circles. Certainly, laws are vital in halting quid pro quo sexual harassment (“sleep with me or else”). But what about when a PhD enters a workplace as the first woman scientist and a senior male col-

S/D-7 6/16/04 12:57 PM Page 138

138

Women’s Work

league greets her with, “So nice to have a young filly around”? She has to be socially savvy enough to know to whom she could jokingly reply “And so nice to have an old goat around” as a veiled warning against sexist comments. Formal workplace rules would not help her with this kind of discrimination, unless the outcome she was seeking was ostracism from the scientific community. An important question to ask about formal policies is whether one can actually use them. Universities have lots of EEO rules. When Sara faced a discriminatory job interview at a research university (see Chapter 6), she knew that questions about her personal love relationship were illegal and were not asked of a male applicant. She suspected that her answers to these illegal questions were the reason she was not offered the position. What could she have done? If she had filed a complaint with the university’s EEO office, she might not have landed an interview with the interviewer’s colleague, a founder of the biotech firm where she came to work. Others (Etzkowicz et al. 2000: 229) came to a similar conclusion about rules in their study of university scientists: [Affirmative action policy] allows the appearance of a neutral doubleentry bookkeeping system in recruitment efforts: an overt procedure with white males and women and minorities interviewed, covering a hidden decisionmaking process in which white males are typically offered positions.

Whether one can use the formal rules against discrimination depends on the local informal norms and who is in power. Ironically, if one can use the rules with the informal support of powerful, egalitarian sponsors in the organizational hierarchy, often one does not need to use them. After her negative experience in academic settings, Sara was keen to the visible antidiscrimination policies and norms that were made crystalclear to everyone entering the biotech firm. She expressed appreciation that top managers saw that it was in the interest of the small company to discuss intolerance, sexism, and racism instead of merely listing an EEO statement in job advertisements. Affirmative action policies, then, seem necessary but not sufficient to secure gender equity for professional women. What really made the difference in Sara’s career in biotech (as for other female life scientists) is the flexibility and transparency provided by the interorganizational webs connecting scientists across organizational boundaries. When women lead project teams, network formation and discontinuation allow them the flexibility to form longer-term ties with trusted colleagues and to abandon ties with others who engage in subtle sexism. The permeability of biotech firms’ boundaries resulting from these webs of relationships also means that women’s contributions to the vitality of companies is visible to the scientific community. If they were working in a large hierarchy under

S/D-7 6/16/04 12:57 PM Page 139

Conclusion

139

t

several layers of male managers, their contributions would be much less visible. The surprising finding in my research was that the flexible network form of organization—in biotech, more decentralized authority on interorganizational scientific projects—is more closely linked to gender equality than to discrimination. Biotech is a desirable destination for life scientists who want to remain on the cutting edge of science without having to spend time away from their research to write grant proposals or to grade undergraduate exams. In contrast to many other occupations that are clearly gendered, biotech jobs have seen men and women enter in similar proportions. Beyond the gender integration of biotech, women scientists move into positions of authority in firms much more often than their female colleagues in more hierarchical settings. The existence of multiple organizational forms with different levels of friendliness to women may have implications for scientific and technological innovation as well. Consider how organizational form and gender diversity might affect the emergence of new ideas. In bureaucracies, for example, one would expect innovation to occur in routine, incremental modes in response to the priorities of superiors. Especially if male authorities are directing women at the bench, fewer new ideas may come from bureaucracies. In network organizations, one would expect innovation to be more creative and radical, by bringing together interorganizational expertise, as well as the perspectives of a broader range of scientists—male and female—to come up with new ideas.

Making a Difference

Whether greater diversity does lead to more innovative productivity, or just to more conflict in the workplace, is an important question. A female engineer described working in the bureaucratic hierarchy of a missile manufacturer: “What I really disliked about the job was that you had to ‘group think.’ You got pressure and were squashed into not being innovative” (McIlwee and Robinson 1992: 133). This engineer refers to the popularized notion of groupthink, a term coined by psychologist Irving Janis (1971), wherein homogenous groups come to value social conformity over conflicting evidence in making decisions. Janis uses the example of John F. Kennedy’s close group of advisers during the Bay of Pigs fiasco—a homogenous group of white men who fostered an in-group identification so strong that it ended in political disaster. Does diversity in organizations better avoid groupthink and stimulate effectively innovative outcomes? Much research has been conducted on the topic of whether diversity in work groups enables creative, productive outcomes. The answer is problematic; it depends on the context in which diversity occurs (Amabile 1994;

S/D-7 6/16/04 12:57 PM Page 140

140

Women’s Work

Oldham and Cummings 1998; Higgins and Kram 2001). A review of forty years of research on the effects of diversity in organization studies notes: “Diversity is a mixed blessing and requires careful and sustained attention to be a positive force in enhancing performance” (Williams and O’Reilly 1998: 120). In laboratory studies, small mixed-sex groups result in more conflict (Alagna, Reddy, and Collins 1982), less optimism (Kent and McGrath 1969), and less efficient performance (Clement and Schiereck 1973) compared to same-sex groups. Yet conflict in the lab stemming from race and gender diversity resulted in more creativity in laterally organized groups (Chatman, Polzer, Barsade, and Neale 1998) and in further search for better-quality solutions (Hoffman, Harburg, and Maier 1962). Not enough common ground, however, may result in a level of conflict that is unproductive.1 Research conducted in organizations rather than the laboratory finds that gender diversity decreases job tenure (Pelled, Cummings, and Kizilos 2000), increases emotional conflict, and decreases perceived productivity (Pelled 1997). Men seem to experience larger negative subjective effects in gender-integrated workplaces (Tsui, Egan, and O’Reilly 1992), with lower job satisfaction and esteem in mixed-sex settings than women (Wharton and Baron 1987, 1991). This despite the fact that men’s pay, on average, surpasses women’s even in gender-balanced jobs (Budig 2002). When workers depend on each other to complete tasks and share the same values, however, gender diversity leads to greater morale and higher performance outcomes (Jehn, Northcraft, and Neale 1999). Because I study life-science PhDs, their educational background and similar professional socialization may mean that they share similar values and thus gain more benefit from gender diversity. Given a certain amount of trust, diversity produces innovation. Is it worth it to pay attention to recruiting a broader range of scientists? Some argue that because a glut of scientists already exists in the academic job market in particular, it’s a good thing that the tough road weeds out those who have less talent for science. But the question is whether the weeding-out process is one of survival of the fittest or simply a means for social replication—where those in power nurture the careers of others who look and think like them. If such a selection bias like that found in science does not leads to stagnation, then it leads, at a minimum, to less innovation. Further, if a stratification system becomes calcified and individuals lose their belief in the ideal of equal opportunity, the result will be either social conflict or profound apathy—neither of which is desirable. Especially as belief in the benefits of a meritocratic system is a key scientific value, ensuring gender equity is vital to science. Gender equity is also good for business and for society. When women have the opportunities to pursue scientific and technical careers, we all benefit. In one survey (Eaton 2003), the researcher found that scheduling flexi-

S/D-7 6/16/04 12:57 PM Page 141

Conclusion

141

t

bility (needed most by mothers) leads biotech scientists to report not only more job satisfaction but also better performance. These findings make perfect sense. I saw a similar pattern in a broader kind of flexibility: female scientists with more research autonomy and flexibility expressed greater job satisfaction and had accomplished, productive careers. Further, feminists who believe that women bring different experiences than men to the workplace argue that gender equality is important for producing a society with better, more representative, and safer technologies. Because women are often more peripheral to scientific and technical communities, they may bring more innovative ideas than scientists who are part of the homogeneous core (see Chapter 4 for a discussion of core-periphery scientists and innovation). As Jane Fountain (2000) cogently argues, when there are few women engineers designing information technologies for use by women and girls, the software and hardware product choices are often less than optimal (e.g., Barbie’s makeover computer game as the girl software option compared to myriad games for boys). Similarly, if we do not have the benefit of a diverse group of scientists working to discover and develop biomedical technologies for women’s health, less-than-optimal treatments may be adopted. Hormone replacement therapy (HRT) for menopausal women has been developed and marketed by the international pharmaceutical giant Novo Nordisk (under the ungainly brand-name Vagifem). The U.S. Women’s Health Initiative, in researching the effects of HRT, ended the study prematurely in 2002 because of results showing increased risks of breast cancer, heart disease, and strokes tied to HRT. The women’s health movement has made women better-informed consumers of therapies, but if women are only on the receiving end of treatment—as clinical doctors and patients—this has limits. According to the difference feminism perspective, unless women scientists are designing drugs and running scientific companies and academic departments, there will be little change in the diversity of therapies pursued.2

Organization Matters

Whether women do well in professional careers depends on the organizational context in which they work. When gender diversity exists in seniorlevel positions, the likelihood of women’s promotion in lower-level management increases (Cohen, Broschak, and Haveman 1998), and female attorneys cooperate rather than compete with each other (Ely 1994). Women who are in the minority of a work group feel as if they receive more support from members of the opposite sex than from other women (South, Bonjean, Markham, and Corder 1983). In advertising, women get social support from other women, but to attain a high-profile career women

S/D-7 6/16/04 12:57 PM Page 142

142

Women’s Work

rely on network ties with men outside their unit in the firm (Ibarra 1992, 1997). Greater attention is being paid to the demand side of how organizations provide a friendly or unfriendly context for women rather than just to the supply of women—but there is much work still to be done (Tomaskovic-Devey 1993). This book has shown how, in the life sciences, women’s career life chances differ dramatically by the organizational context of employment. Surprisingly, the vastly interconnected biotech industry networks do not seem to belong solely to good-old boys. Rather than finding evidence of gender queuing into the valued new arena of the biotechnology industry, I found that entrance into the new field appears to be proportional to the number of male and female life-science PhDs overall. But where access to supervisory positions is concerned, gender inequality in formal organizations is found to differ across settings with different organizational forms. Gender equality is not solely supported by bureaucratic rules (as in academe) or large internal labor markets (as in pharmaceutical corporations), as some scholars argue. Neither is women’s standing in organizations always threatened by informal networks, as one might believe in light of traditional beliefs about old-boy networks. In biotech firms, flexible network relationships reduce gender discrimination. The cooperation between scientists within and among organizations on projects helps to provide a variety of experience and people to work with. Some will be discriminatory, but because women have diverse ties they can maneuver toward less discriminatory individuals and groups. The contextual factors that influence gender inequality for highly educated life scientists also influence gendered outcomes in other social settings. Studying the science and technology labor market reveals more similarities than differences between the elite and nonelite members of the knowledge economy in gender stratification. Scientists are not objectively pursuing knowledge and technology without regard to gender. To be sure, scientists are focused on their scientific work, but their labor is organizationally patterned and gendered just like people in less powerful positions. Mass-production jobs in the knowledge economy in many ways look like factory jobs in the bad old days of the early industrial revolution. They are done by women under appalling working conditions for less than a living wage. Women in third world IT assembly jobs work in traditional, hierarchical, Fordist organizations. These women are stereotyped as having nimble fingers and a docile nature that will avert unionization. In reality, this docility is created by the structure of their work and control by managers of their lives as well as their work: In Korea, young women are employed only for a few years before marriage; in Thailand, they are employed on ten- to twenty-day contracts so they can be easily fired in case

S/D-7 6/16/04 12:57 PM Page 143

Conclusion

143

of union activity; in the Philippines and Taiwan married women are banned from working; in Malaysia, women workers are dismissed for having their eyesight ruined by working in microelectronics or for a record of union participation; in Mexico, pregnancy tests are administered in the factory; in India and the Philippines sterilized women are preferred hires (Webster 1996: 49–51). Even though women in biotech firms on average attain more authority than their sister bench scientists in universities and pharmaceutical giants, women still face a glass (or rather cash) ceiling in the industry. The founding entrepreneurs who find venture capital available for startups are almost exclusively male. Venture-capital networks still seem to be tied to the oldboy structure of universities—where top academics who become entrepreneurs are supposed to be male. Even when a female academic has persevered to a tenured position and invented a novel discovery that provides the scientific bases for a biotech startup, she does not always secure the funding and credit to be called the firm’s founder. For example, one biotech firm based on a woman PhD’s pioneering research with porcine stem cells does not reference her as an active founder of the company (Murray 2003). Opinions differ as to whether she was kept out by the venture capitalists and given false information that having a professor found a company was against the university’s rules, or whether she was not aggressive enough to pursue entrepreneurship and get full credit for her ideas. On either side of this argument, there seem to be different roles attributed to male and female scientists because of gender stereotypes. As one scientist observed in a rather sarcastic tone when commenting to me on the lack of female founders of biotech firms, “Women seem to lack that ‘vision thing.’” The scientist implied that arrogance rather than vision was the operative item in entrepreneurship. Indeed, some founders of biotech firms embrace the arrogance label. Joshua Boger, founder of Vertex, at first wanted to call the company Veritas, after Harvard University’s sacred motto. He had to be strong-armed by his Ivy League board of advisers into realizing that this was taking his presumption too far (Werth 1994). Yet Vicki Sato rose to the office of president at Vertex. The network form as compared to traditional hierarchy gives women scientists relative power, but there is still a dividing line in biotech, albeit higher up the chain of authority. Men have more access to capital to become founders/CEOs of startup biotech firms. In all, whether women in the knowledge economy assemble semiconductor chips in a third world factory or lead scientific discovery in a nimble biotech firm in South San Francisco, gender stereotypes do matter when women find themselves in structural positions with a lack of opportunities. To avoid stereotypical, unequal chances, attention needs to be paid to the organizational context of work and entrepreneurship.

S/D-7 6/16/04 12:57 PM Page 144

t

144

Women’s Work

IT vs. Biotech: What’s the Difference?

Taking context into question also means considering whether one’s results apply outside the population under study. Do the findings about women’s advances in entrepreneurial, interconnected firms in biotech generalize to other fields? A natural comparison to biotechnology companies would seem to be information technology firms, as the centers of these knowledgeeconomy industries are located in the same places: the Silicon Valley/San Francisco area, and the Cambridge/greater Boston area. One possible explanation of women’s relative success in biotechnology, based on popularized notions that more bohemian cities spur the growth of creative firms (Florida 2002), is that biotech’s location in more liberal-leaning cities promotes gender equality. Yet biotech firms are located in the same areas as IT firms, which have not shown as much evidence of gender equity for women engineers. Nor do universities located in San Francisco and Boston show greater tendencies to promote female scientists. To make a true comparison to the findings in this book, one would need to contrast bureaucratic firms like Saxenian (1994) described in Boston in the 1980s to the more networkconnected, laterally organized IT firms of Silicon Valley. True, at Hewlett Packard—one of Saxenian’s examples of an IT firm embedded in Silicon Valley high-tech networks—Carly Fiorina has been CEO since 1999, and the company listed five female senior or executive vice presidents in 2003. DEC, the comparable large Boston high-tech firm during the 1980s, was acquired by Compaq in 1998, which was bought by HP in 2002. So in a way one could say that DEC also has Fiorina as its CEO. Of course, one example does little more than suggest that systematic research needs to be completed to compare the fate of women engineers in more- and lessbureaucratic IT firms. As in other male-dominated professions, a glass ceiling limits women’s promotion in engineering (Toohey and Whittaker 1993). A 1992 study of a small startup computer firm and a large bureaucratic aerospace firm (McIlwee and Robinson 1992) seemingly contradicts my finding that the network form is more conducive to women’s promotions. In the decentralized high-tech startup, 26 percent of the male engineers had been promoted to senior engineer or middle management, compared to only 12 percent of the women. In the bureaucratic aerospace corporation, 10 percent of the men, but 25 percent of the women, were senior engineers (calculated from McIlwee and Robinson 1992, table 10). Beyond the fact that McIlwee and Robinson’s data come from 246 engineers employed in only two organizations, compared to my analysis of 2,062 life scientists employed in hundreds of different organizations, I think that the situations of female engineers and female life scientists are actually quite different. The situation for women in biotech is different from IT for two rea-

S/D-7 6/16/04 12:57 PM Page 145

Conclusion

145

sons: the critical mass of female life scientists, and the revolving door for women in IT. Critical mass (also known as the pipeline hypothesis) is the idea that if only there were enough women in the pipeline for science and engineering jobs, then inequality would disappear. The main problem, as viewed from this perspective, is that not enough girls are encouraged to pursue math and science classes; thus not enough women become science majors or take graduate-school degrees in science and engineering. The presence of fewer and fewer women along the pipeline makes it difficult to find female scientists to hire and even harder to promote them. A variety of criticisms have been leveled against this critical-mass hypothesis (see Schiebinger 1999 for a review). Henry Etzkowicz, Carol Kemelgor, and Brian Uzzi (2000) point out that critical mass is not able to explain the problems women face in academe; their data show that female academics face many of the same discriminatory hurdles regardless of the number of other women in their departments. I agree in that critical mass is not a sufficient explanation for gender inequality and that the demand side of organizational factors are more important for women’s outcomes than the supply side. Yet while having a number of women in a field may not be sufficient to produce equality, it may be a necessary starting point. The life sciences, for example, have a critical mass of PhDs, but this is not sufficient for equity: the organizational context matters. Women find hierarchical life-science firms unfriendly to their career advancement, but biotech firms with more decentralized project teams promote women at a fast clip. The lack of a critical mass of women in IT may be a drawback for female engineers. Many researchers have sought to explain why so few women pursue careers in information technology and engineering. Part of the reason may be that computing is a culture that is very unfriendly to girls. In their book Unlocking the Clubhouse: Women in Computing, Jane Margolis and Allan Fisher (2002: 88) quote a computer science major who describes the added pressure of being female: They [male students] have the pressure to do well, but they don’t have excess pressure from us [women] saying, “You know, you’re pathetic, you just got in because you’re a guy,” or something. We don’t give them that. . . . Their confidence hasn’t hit rock bottom because of that. They tell us that all the time, and it isn’t something we like to deal with. We shouldn’t have to deal with it.

Other women computer science majors in Margolis and Fisher’s study were put off by the overidentification of self-styled male computer geeks who stayed up all night typing code and socialized only through the medium of the computer. The geek culture of living and breathing computers obsessively does not seem to have the same counterpart in the life sciences. Biologists certainly work long hours in the lab and are committed to their

S/D-7 6/16/04 12:57 PM Page 146

146

Women’s Work

work, but I never met one who didn’t have a hobby or life of some kind outside of his or her experiments. Because female engineers are still almost always in the numerical position of tokens in educational and work settings, the interaction context they face is different than that of many female biologists. In one laboratory study (Sackett, DuBois, and Noe 1991, cited in Williams and O’Reilly 1998) of how people develop performance evaluations, when women were less than 20 percent of the group being evaluated all females received lower average evaluations. Yet when women constituted the majority of the group under evaluation, they received higher average ratings than did the men. In actual performance evaluations by managers, when women or minorities are few in number they give each other inflated evaluations, presumably to make up for biases of the predominant group. But this effect decreases as women and minorities’ presence increases to plurality in the work group (Burt 1997). Generally, in white-collar work groups that are male-dominated, more blatant sexism exists (Konrad, Winter, and Gutek 1992). High-tech computer firms, then, may be less friendly to women because computer science and engineering are so predominately male, even when women find themselves in more interorganizationally connected startups like the one studied by McIlwee and Robinson. Individual high-tech firms with a greater proportion of female engineers, however, might look more like biotech firms; this would provide an interesting test case. The second reason why IT is a different story for female professionals compared to biotech is that computing is more of a revolving-door occupation than the life sciences. One study of computing in the 1980s (Wright 1997, table 3-12) found that the expected tenure of women in computer work ranged from just four to nine years. Actually, computing tends to be a short-term occupation rather than a lifelong career for most of the men who go into it, as well as for the women. But the outflow of women from IT is higher than men when subtracting entrance rates from exit rates (Wright 1997). Men also have a greater likelihood of moving into management from technical computing jobs. Women may leave IT jobs more often for other occupations that allow more time for the second shift of household and caring labor that they face after returning home from paid work (Hochschild 1989). Although numbers comparable to Wright’s analysis of IT workers’ tenure in the field are not available for the biotechnology industry, the educational investment of life scientists, at least at the PhD level, probably makes it less likely for them to leave the field. Qualitatively, from my observations, interviews, and similar observations from the Radcliffe study of women in biotechnology, biotech scientists remain in the industry with durable careers, if not at the same firm. As Rayman, the principal investigator of the Radcliffe study, noted in a press release in 1999:

S/D-7 6/16/04 12:57 PM Page 147

Conclusion

147

In the workplace of the future, employees will have to re-invent the concept of a safety net. In biotech, we found workers were changing their attitudes and focusing less on job security than career durability. They hopscotch from one biotech company to another, carrying their skills like turtles on their backs. (www.radcliffe.edu/news)

t

This interorganizational mobility of highly skilled workers contributes to the flexibility they can create for themselves. As we saw in Chapter 6, Dorothy had the flexibility to design her own career path in the biotech firm where she worked. When her children were small, she left everyday life in the lab for training and a position as the patent specialist. Although she had left the science side (i.e., left a line job for a staff position), her work remained vital to the health of the company. In most industries, this change would be the kiss of death for career advancement. If an engineer leaves R&D for public relations, her career path is at a dead end. The interorganizational nature of biotech, however, provides more opportunities for reentry into key R&D positions as well as for promotions. Several years after Dorothy’s detour into patents expertise, a biotech startup company recruited her back to the science side as a senior executive. Statistical, longitudinal data, however, are needed to assess the average amount of time that male and female life scientists spend in different organizations during their careers. Such data on individual scientists’ careers might show how interorganizational networks overlap and shape career movement—and whether career moves are gender-proportional, or whether men have an advantage in going back and forth between university and biotech posts, for example.

Generalizability of the Biotech Case—In General

From the above comparison of the biotechnology industry with the IT field, we can glean at least three scope conditions for the findings presented in this book. Women (and perhaps other minority groups) will have improved career chances in a network form of organization when (1) the occupation consists of skilled or educated workers; (2) there is a critical mass of women, that is, women have access to training and working conditions facilitate stable employment rather than a revolving door; and (3) laterally organized employers provide a valued, legitimate option for work. A fourth scope condition might be where resources come from. When financial support comes from multiple sources and different organizational networks (e.g., venture capital, government funding, pharmaceutical partners, and angel investors), professionals are less dependent on one bureaucratic source of funding for their work. One main funding source (e.g., the federal government) often means that knowledge workers are either inside or out-

S/D-7 6/16/04 12:57 PM Page 148

148

Women’s Work

side the informal funding network. And women, on average, find themselves outside. Multiple funding options can help women become entrepreneurs in the knowledge economy. I have shown how all of these conditions apply in the life sciences. The critical mass of women is missing in engineering, however, as well as in other sciences like physics. The dearth of female engineers and physicists can more often be attributed to lack of opportunities in educational and employing organizations than to women’s lack of interest and aptitude. Anecdotes (like the one I heard about the eminent physicist at a technical university who, as everyone knows, refuses to train any female graduate students) are still not uncommon. The social sciences, in contrast, now matriculate a majority of female PhDs, but social scientists have few opportunities for employment in network organizations. Where the network form does exist as a viable governance structure, however, may be precisely the locations around the globe that offer opportunities for a more diverse population of workers to succeed in their careers and in creating innovative products. David Stark (2001) analyzes the coexisting logics in postsocialist firms in Hungary and the Czech Republic. Diversity in thinking stems from interorganizational networks (Stark 2001: 74): “The organization of diversity is an active and sustained engagement in which there is more than one way to organize, label, interpret, and evaluate the same or similar activity.” It is not difficult to imagine that the organizational diversity he observes would provide an environment amenable to gender and racial diversity among managers as well. Eleanor Westney (2001) demonstrates that Japanese network organizations look different from those in Eastern Europe or the United States. Yet as she summarizes: Networked internally in a production system based on teams and crossfunctional flows of people and information, networked as an organizational form in the extended enterprise of the vertical keiretsu, and networked externally in webs of collaborations and alliances, associations, and policy networks, the large Japanese firms exemplified the network enterprise. (Westney 2001: 133)

Japanese women have not had the access to educational and occupational training in the past to create gender equality in large corporate settings of the vertical keiretsu (Ogasawara 1998). However, we might predict that as more horizontal interfirm networks develop (Westney 2001), and if women were to have equal access to training, Japan could more easily develop gender equality at the top of corporations than could other nations that rely less on network enterprise. Innovation and diversity are benefits of interorganizational ties that seem to apply worldwide. But there are also dangers to consider as well, particularly in the context of universityindustry relationships.

S/D-7 6/16/04 12:57 PM Page 149

t

Conclusion

149

Dangerous Liaisons

One of the most profound changes in the organization of scientific institutions has been the changing interconnections between academic and industrial research. Although some researchers focus on how commerce between the ivory tower and the for-profit lab bench goes back to the turn of the twentieth century (Noble 1977), these two realms have seen unprecedented overlap since the 1980 U.S. Bayh-Dole Act (Etzkowicz, Webster, and Healey 1998; Kleinman 2003).3 The linkages between university and industry not only produce positive outcomes like innovation, economic growth, and speedy knowledge transmission but also raise many concerns. The collaboration between industry and university poses potential dangers; such close associations require oversight. Perhaps in the 2000s we still view university and industry ties as novel enough that the media and the general public are watching carefully. But some social scientists are worried that we are beginning to take them for granted (e.g., Croissant and Restivo 2001). Consider just one example demonstrating the potential tangle of academic and industrial interests and publicity. David Noble, a social historian of science and technology, succeeded in drawing some media attention to a controversial tenure case at the University of California– Berkeley (Abate 2003). Ignacio Chapela, a junior faculty member and outspoken critic of the large pharmaceutical and chemical company Novartis, came up for tenure under a cloud of conflicting claims about the university’s relationship with agricultural biotech. Chapela coauthored a controversial article published in Nature in 2001, claiming that bioengineered corn genes had spread to native maize in Mexico. In 2002, the editors apologized for publishing the original paper because the data were not of sufficient merit for Nature. Jasper Rine, a professor on the university’s tenure committee, is an outspoken critic of Chapela (and allegedly used the Nature paper as an example of a hoax in one of his courses). Rine is also the founder of biotech firm Acacia Biosciences, from which Novartis licensed a patent (one of Rine’s) for crop protection. He is credited as being the architect of the deal between the university and Novartis that Atlantic Monthly (Press and Washburn 2000) said exemplified how the Berkeley campus is a “kept university.” Noble raised the question as to whether it was a conflict of interest for Rine to remain on Chapela’s tenure committee. Whether or not Rine stepped down from the committee before Chapela was denied tenure in 2003 (Dalton 2003), it is clear that the close ties between university and industry should be monitored for conflicts of interest. The tenure committee composition and decision process have been kept behind closed doors (Walsh 2004). The UC-Berkeley administration argues that the unusual features in this tenure case mean that no policy need be established. Yet if association between firms and universities continues to

S/D-7 6/16/04 12:57 PM Page 150

150

Women’s Work

t

increase in the future, academic administrators will need to pay more attention to how these ties affect all decisions, not only those made in the technology-transfer office. A growing scholarly literature highlights concerns with the blurring boundaries between academic and commercial science more generally. Corporate patrons may unduly influence research of their university collaborators, resulting in an “academic capitalism” inseparable from corporate interests (Slaughter and Leslie 1997). This focus on the direct effects of university-industry ties is similar to arguments about the cooptation of politicians by corporate donors. Daniel Kleinman (2003) takes a different tack by charting how commercial interests have more of an indirect influence on academia. For example, early input by corporate firms in research agendas may shape the questions and answers pursued by biologists for years to come, even in the absence of direct collaboration with industry. Daily practices in the lab, including the substance of graduate education, may also be affected. Whether the blurring boundaries between academic and commercial science pose a threat to the integrity of the main research and training goals of the university remains to be seen, but the issue certainly should be watched carefully.

Gazing into the Future

Predicting the future organization of biotechnology in the United States is difficult. Biotechnology is a volatile stock market. Some firms can rely on solid venture-capital backers who invest in rounds of financing based on longer-term scientific milestones rather than the vagaries of public perceptions of hot stocks. The Amex biotechnology index, for example, showed a 191 percent increase in 1991 when the Food and Drug Administration approved several early biotech drugs, but it went down 20 percent in 1992. With the dot-com bust in the early 2000s, biotech stocks felt the backlash against tech holdings, and a 42 percent decrease in the biotech index occurred in 2002. Yet with positive clinical trials on cancer drugs in the first half of 2003, the index rose 35 percent (Aoki and Caffrey 2003) and continued to rise in early 2004. Year to year, biotech firms simply cannot rely on individual investors. Layoffs do come with downturns and even with success. Millennium Pharmaceuticals, one of the larger biotech firms, announced that it would lay off more than 700 people in 2002–2003 and bring its staff down to 1,700 so that it could show a profit for the first time in 2006. These cuts included research scientists. Venture capitalist Michael Lytton, however, views this as a boon for the startups that he funds: “Millennium is laying off scientists who focus on discovering new drugs. That’s exactly the skill set that is most needed in younger companies”

S/D-7 6/16/04 12:57 PM Page 151

Conclusion

151

t

(Aoki 2003). Millennium did lay off 600 workers in June 2003 (Hollmer 2003). The company’s financial picture subsequently brightened, but then 2003 was a good year in the U.S. stock market, generally. It is unclear whether or not such an extreme employment cut is a good strategy for the long run. An aspect of the unpredictable future of biotech to consider is whether it will negatively affect women’s gains in the industry. Susan Eaton (2003) has shown that biotech firms vary in how much they allow family-friendly policies like flextime actually to be used. Some firms have both formal and informal policies to allow key scientific personnel scheduling leeway. As one human resources manager quoted by Eaton (2003: 145) put it: “In small biotech firms, you know everyone well, you know their personal situations, and you can make accommodations. I bend over backwards because individual people are our most important asset. I try to create an environment supportive of scientists, who are expressive and creative, like artists.” But in one firm that Eaton studied, no one was allowed to work nonstandard hours even though there was a formal policy allowing it. If resources become even scarcer in biotech, women, as well as their autonomy in scheduling and in leading research, might be edged out.

Good Things Come in Small Packages

Although smaller, flatter organizations are the building blocks of a healthy knowledge economy, a larger, rather than smaller, scientific workforce is needed. Processes of exclusion and entrance into science are not examined here, but improvements in the workplace presumably would follow from having more people in science. One deterrent to women’s entrance to science and technology careers is the horribly long hours expected in scientific and computing work. An increase in the scientific workforce through greater investment in science education, and the creation of two jobs in the place of one current job, would make the career more accessible to a variety of people. Such a change would require shifts in the organization of science careers to allow for research teams to be assessed for effectiveness and rewarded accordingly, rather than individual merit alone. The greatest change, perhaps, would be required of the university and its principal investigator model. As a creative thought experiment, consider the issue of tenure in a different light. Imagine if team tenure were an option, in which three or four academics would be evaluated on their collective productivity, perhaps even for teams of scholars working in different universities. The classic problem of free-riding individuals receiving a collective good would disappear among high-commitment professionals in the face of pressure from teammates in small interdependent groups who might be compet-

S/D-7 6/16/04 12:57 PM Page 152

152

Women’s Work

ing against other research teams working in the same area. Less radical changes are perhaps easier to imagine yet could also foster more lateral organization within the university. For example, universities could give faculty credit for the ability to establish and maintain successful research collaborations, and for nurturing students, rather than viewing these activities neutrally or even negatively. Concern with the effects of organizational context reaches beyond the boundaries of science/technology organizations to the future of gender inequality in U.S. society. Recently, scholars have argued that future gender equality should be obtained through policies of more laws and enforcement of rules for hiring and promotion. Yet these policy measures have met public resistance. This book offers an alternative, structural policy by underscoring the role of organizational form in gender equality. Because many people will continue to live much if not most of their lives in organizations, we cannot separate gender inequality issues from organizational issues. Globalization is an organizational issue that has come under increasing scrutiny. Economic sociologists debate whether global rationalization is the main trend now and for the future of economic development (Meyer, Boli, Thomas, and Ramirez 1997), or whether globalization is an old trend and fails to mitigate the power of national elites (Fligstein 2001). Beyond the debate over the extent of globalization, however, there are features of global bureaucratization to beware—features that lead us away from gender equality and diversity. John Meyer and colleagues (1997) imply that a kind of global groupthink (Janis 1971) has emerged in the homogeneous focus on individual rights that has become taken for granted around the world. Although this individualized rationality may allow some women greater access to higher education (Ramirez and Wotipka 2001), the lack of different visions for citizenship, political economy, and careers is troubling. Further, a tendency to blame individual women for their lack of career advancement exacerbates rather than levels gender stratification. Global bureaucracies create vast divides between the haves and the have-nots. Poor women in developing countries often face the worst working conditions in order for profits to flow to hierarchical multinational corporations. Workers at Nike shoe factories in Vietnam, for instance, reportedly inhale air containing more than 100 times the legal limit for carcinogens while working sixty-five hours a week for the grand sum of $10 (Greenhouse 1997). Tendencies toward vertical integration and global bureaucratization are dangerous; equality will depend on the inclusion of other organizational forms in the economy. Acrophobia, where organizational structure is concerned, is not a bad thing. To be wary of the dangers of tall hierarchies and vertical integration—global inequality, gender stratification, and a dearth of creativity and innovation—is less paranoia than keen insight.

S/D-7 6/16/04 12:57 PM Page 153

t

Conclusion

153

Notes

1. Also, having a team-based employment structure rather than traditional hierarchy may lead to more conflict even among less diverse, homogeneous groups (Morrill 1995). One aspect of conflict in more flexible workplaces is whether teambased initiatives come from grassroots demand by employees or by executive fiat. One researcher (Vallas 2003) finds conflict between management and unionized labor in the paper-milling industry regardless of who initiated the team-oriented continuous quality-control production ideas. 2. The difference feminism viewpoint argues that men and women are fundamentally different. Women will thus create different outcomes if they are included as scientists and engineers (see Schiebinger 1999 for a review). 3. The importance of the Bayh-Dole Act itself in increasing the interplay between commercial and academic science has been under some debate. One study (Mowery and Ziedonis 2002), for example, disputes the role of Bayh-Dole with evidence that the quality and quantity of university patenting was on the rise prior to 1980.

S/D-7 6/16/04 12:57 PM Page 154

t

S/D-Appendix 6/16/04 12:58 PM Page 155

Appendix

Combining Qualitative and Quantitative Methods to Study Scientists

t

About the relationship between scientists and STS [science and technology studies] . . . I am a scientist. I notice that the way we talk (I, too) about scientists is usually as “them.” Why? We share the same fate, use their results, enter into dialogue with each other. —Susan Leigh Star, Ecologies of Knowledge (1995)

Gathering Qualitative and Quantitative Data

To the topic of gender inequality among life scientists, this book takes a combined qualitative-quantitative approach. The data provide for general comparisons between male and female scientists in biotech and other lifescience settings, as well as for information on the daily experiences of individuals working in biotechnology firms. In actual practice, the way that the methodology of this project unfolded demonstrates how qualitative and quantitative approaches are complementary. I began by conducting an ethnographic study of a biotechnology firm and a university laboratory. My qualitative approach was to explore the everyday organization of scientific work in two different settings. Thus, I did not begin by asking questions of gender inequality in science/technology, yet these emerged from my observations. Women scientists, when asked to compare working in biotech firms and universities, spontaneously related stories of discrimination they had faced because of their gender. Patterns in such discussions revealed that biotech was perceived as a more friendly place to be a female scientist. I wondered whether this was just the experience of the women I had talked to, or if there was a more general trend to be found. I also wondered whether gender equality was related to findings that colleagues and I were making in another project about interorganizational connectivity and innovation in the biotech industry as a whole. These questions about generalizability led me to search for a quantita-

155

S/D-Appendix 6/16/04 12:58 PM Page 156

156

Appendix

tive data source. While national surveys of scientists are available to the public (e.g., NSF’s SESTAT), none specified more detailed information on employment than that a scientist worked in industry. For one of my main independent variables of interest—organizational form—I had to be able to contrast careers of scientists in dedicated biotechnology firms (network organizations) to others in more hierarchical settings. Before thinking of attempting to conduct my own national survey, I looked for available archival data that would serve my purposes. One day in my ethnographic study of the university laboratory, a principal investigator was doing some good-natured complaining about having to reapply for his program’s training grant. When I inquired about what he meant (I had not been exposed to training grants in my graduate experience in the social sciences), he informed me that the National Institutes of Health funds the training of predoctoral students through Institutional National Research Service Awards (also known as training grants). After I expressed interest, the PI gave me a spare grant application form and had the secretary get me a copy of the ream of paper (three inches thick) that constituted their last proposal. I was excited to see that the training-grant materials required programs to report on the careers of graduate students and postdoctoral researchers who had gone through the funded labs for the past ten years. Also, this information—which includes reporting the specific organization at which individuals are employed—was required for all past students whether or not they had been funded by the grant. I thought that if I could get my hands on these data for all universities with training grants, I would have a good national sample of life-science careers. I arranged to meet with several administrators at the NIH to gain official access to their files and to get direction on which of the twenty-six institutes and their numerous training grant programs would be the most useful sample. With support from grants from my university, I traveled to Bethesda, Maryland, and set up base camp at the Holiday Inn that is nearly on the NIH campus. From the office of the director, a knowledgeable insider suggested going to the National Institute of General Medical Sciences (NIGMS), the institute that funds programs in the biological sciences most widely in terms of numbers of programs, diversity of disciplinary areas, universities at all levels of prestige, and regions of the country. While I was at the NIH, I also conducted a half-dozen qualitative interviews with PhDs. I asked about the differences of working in academe and government institutes, what scientists’ impression was of work in the biotech industry, and what they thought of the representation of women in science. Unexpectedly, in interviews I was referred to a few people at NIH who had worked in biotech firms. In their interviews I also asked them to compare biotech work to doing science in other settings, and what it was like for women and ethnic minorities to work in biotech.

S/D-Appendix 6/16/04 12:58 PM Page 157

Appendix

157

At the NIGMS, a grants information manager provided vital assistance, even though he was not convinced that I would find usable data. When I told him I wanted to copy the career information that universities reported on past students in their grant proposals, he was very skeptical. He had never systematically compared the information across universities; it was generally assumed that the reporting of this information varied widely by university—that many PIs did not put the information in tabular form by individual student but instead just reported the aggregate numbers of students in university and other jobs in paragraph format. I still wanted to see the training-grant applications for myself. Obligingly, the information manager told me about all the training grants administered by the NIGMS. He suggested that the broadest grants are those in cellular and molecular biology and also told me about the relatively new biotechnology training grant. Further, he gave me a list of all the grants and universities that were funded under them—the population of programs funded by NIGMS training grants, with their index numbers. He took me down to the basement, showed me how to use the electronically operated moving filing cabinets, and where the training grants were stored by number. He introduced me to the senior members of the clerical staff and asked them to give me free access to the files and any help I needed. The filing staff manager was on vacation, but the second in command, a stern-looking African American woman, did not seem pleased at having a young scholar giving more work to her filers. In her presence, the grants manager told me to come ask him if I had trouble with anything. I vowed to myself to do as much as I could on my own; the experience from summers I spent filing at my father’s office would prove useful. I selected systematic random samples of programs to look at from the alphabetical lists of grants in cellular and molecular biology, biotechnology, and genetics. On each grant application that I looked at, I was gratified to see, every PI had clearly put the information on past students in a table, labeled, and in the same place—just as instructed by the grant application form. Perhaps the NIH staff was basing their pessimism about the information consistency on years prior to the 1980s when the grant materials had not been so standardized, but for my purposes—to look at applications from the 1980s and 1990s—I had what I needed. The NIGMS grants information manager not only allowed me access to all of the files but also permitted me to use the vital resource of an on-site photocopier. He even generously provided cardboard boxes and shipping for the reams of paper I had copied. After creating a database, coding the photocopied application materials into variables, and cleaning the data, I analyzed quantitative data on more than 3,000 PhDs. Using logistic regression models, I discovered that my hypotheses from the ethnographic study were supported: biotech firms are

S/D-Appendix 6/16/04 12:58 PM Page 158

158

Appendix

much more likely to promote women than other industry or academic organizations that employ life scientists. And yet this statistical finding left me with further questions. In particular, why do women do better in biotech? In order to explore this question, I turned to qualitative interviews of scientists with experience in the biotechnology industry. The biotech industry has grown in close geographic proximity so that scientists can easily interact across organizations, which is to be expected in an industry of network organizations. This aspect was fortunate for me in planning where to conduct interviews. I arranged to visit the San Francisco area, the most biotech-intensive region in the world, in order to interview female and male PhD scientists in different organizational settings and career stages. I was interested in interviewing people at multiple organizations, in contrast to my earlier qualitative study, which focused on observations of scientists in one firm and one university. In this way, I hoped to transcend the viewpoints from just one organizational culture. I decided this benefit outweighed the limitation that I could do fewer interviews per day since I was traveling between sites. To get names of potential interviewees, I used contacts I already knew in the life sciences, friends, and colleagues in the social sciences. I contacted Bay Area life scientists by email to explain my interest in studying the organization of scientific work and in comparing university and industry labs. I inquired about their availability during the week I would be there. Only two scientists refused to interview, and one of them only because he was out of town that week. He was so interested that he requested a telephone interview, which I conducted with him after my trip. With those who agreed to an interview, I set up a time and also asked them for names of Bay Area scientists they knew at other organizations who might be interested in talking with me. In this way, I also built a snowball element into the sample. These dozen interviews provided me with further insights into why the quantitative data show that biotech firms exhibit less gender inequality than other employment contexts (i.e., academe and pharmaceuticals) in hiring and promoting PhDs. I enjoyed interviewing life scientists. I liked their direct style. Most would answer questions as carefully and completely as possible rather than trying to evade the question. Almost without exception, these biological scientists closely identified with their work and seemed eager to talk about their careers and changes in their field. This highly educated group, however, was extremely busy and smart and does not suffer fools gladly. Luckily as an outsider and graduate student (and younger in age), I had some leeway in being naive about the organization of scientific work. The PhDs gave me more patience than I sometimes saw, like when they caught students or technicians dawdling in the lab. In interviewing scientists, one difference that I discovered from textbook advice for interviews was in rephrasing questions. For important

S/D-Appendix 6/16/04 12:58 PM Page 159

Appendix

159

t

questions, often interviewers are advised to ask the same question multiple ways to bring out different nuances in responses. But the scientists saw through this tactic and would become impatient with it. Fortunately, I have a good short-term memory, which was helpful in remembering what had already been said in a given interview. I learned quickly not to repeat topics, which turned out to be very important at keeping interviewees’ interest in this population.

The Research Samples

Quantitative Sample Samples from the general population often do not include a sufficient number of individuals from the highest-ranked colleges or enough with advanced degrees to make a good study of the elite-educated (Morgan and Duncan 1979). Therefore, samples of PhDs are commonly used to study scientists and higher education. The quantitative sample of life-science PhDs for this project was selected through their graduate or postdoctoral university program’s participation in an institutional national research service award, granted by the National Institute of General Medical Sciences. Among the twenty-six institutes in the National Institutes of Health, NIGMS provides universities with the largest number of institutional research service awards, or training grants, as they are commonly known. From a list of university programs awarded training grants in the areas of cellular/molecular biology and biotechnology, a random sample was drawn. Occasionally, a selected file would be missing from the stacks because it had been checked out. The program directors I spoke with knew of no current systematic, large-scale project that might bias which files were pulled. Thus I assumed that file removal was due to chance and selected another program from the list when one was not in the stacks. The cellular/molecular biology area was selected in consultation with NIH program directors as one of the broadest, covering many specialties. Molecular biology training grants record students moving into more diverse careers than some of the more focused grants. Combining the more specific biotechnology area with the more general cellular/molecular biology training grants was an attempt to oversample PhDs who might go into biotech firms, since the numbers are fairly small and a better picture of biotech employment is important to the project. Oversampling when strata are of primary interest is a commonly accepted practice for quantitative research methods (Sudman 1976). Of course, this means that the data do not perfectly represent the general population of life scientists. The upper strata are likely somewhat overrepresented (since I argue biotech firms are desirable

S/D-Appendix 6/16/04 12:58 PM Page 160

160

Appendix

employing organizations; see the measures section). The focus on comparing the legitimacy of biotech and academe means that a semistratified sample is appropriate. The NIGMS staff generously allowed information from training-grant applications and renewals to be photocopied and transported for coding purposes. The information copied from training grants was composed of tables in which the university programs listed the faculty members’ students, current and past, and the PhD student’s current organizational position. All students in a university program are included in the grant application information, not just those funded by the training grants. This practice of reporting career positions began when the NIH instituted a payback policy for training grantees at the inception of the grant agenda during the 1960s. The idea is that students funded by the grants are obligated to return as many years back in scientific service to the country (broadly defined) as they had received in training. Although in practice service payback has proven onerous to calculate and enforce, the application renewal process continues to track trainees and other students. University programs are allowed to create their own formats for the table relating career information on students. This lack of standardization is likely the main reason why these data had not been placed in a computer database prior to this study.1 In the instructions to applicants for the training grants, the directions for submitting the information are as follows: In a table, for each faculty member participating in this application, list all past and current students for whom the faculty member has served, or is serving, as thesis advisor or sponsor (limit to past 10 years). For each student listed, indicate: (1) the training level, either predoctoral or postdoctoral; (2) the training period; (3) the institution and degree received prior to entry into training, including date; (4) title of the research project; and (5) for past students, their current positions and for current students, their source of support. (U.S. Department of Health and Human Services 1995: V-4)

The entire group of past doctoral and current and past postdoctoral students for each university program was coded into the database. Thus, rather than sampling within the programs, the population of students with PhDs is recorded. Six university programs and 3,395 PhD careers have been coded. The university programs vary in prestige of school and regional location. Furthermore, the addition of career information from postdocs educated at universities all over the globe adds diversity to the eliteness-of-education variable. Even though only six universities are in the database, the information from those schools generates data for PhDs from more than 100 different U.S. universities (out of a population of almost 200 ranked programs). Information in the NIGMS training-grant applications is submitted by

S/D-Appendix 6/16/04 12:58 PM Page 161

Appendix

161

all faculty, with students in the relevant program, which may span departmental lines. Since the object of submitting this information is to obtain grants, I suspect that information on students who drop out of the programs may be underreported; therefore only students who actually complete the PhD are included in the database. The incentive to retain an NIH training grant is high, both for financial and prestige benefits; thus information on former students’ careers is mostly complete. NIH program directors supervising all the grants further reduce missing data in the applications/reapplications by asking for further information during site visits and other communications with the university project directors. Thus relatively few entries are marked unknown. Access to the NIGMS training-grant data was made possible by a written request and presentation of a research proposal to the chief of the NIGMS Office of Program Analysis and Evaluation, who in turn received approval from a superior at NIH. The Freedom of Information Act provides legal justification for access to successful federal grant recipients, but rejected grant applications are not available. Data were collected at the NIGMS archives in Bethesda, Maryland, in 1996. Two large boxes containing photocopies of career information were sent back to Tucson, Arizona, to be coded. A limitation of using data from PhDs who spent time as graduate or postdoctoral students at universities that received training grants is that they may represent a different, perhaps more accomplished, population than PhDs who never worked in a lab at one of these universities. Keep in mind, however, that my data cover all students in these universities’ programs, not just the ones who received training grants. The sample is limited to PhDs primarily because of data availability, rather than the belief that PhDs alone comprise the population of scientists. Social studies of science have noted that the emphasis on PhDs neglects important actors in the creative scientific process, notably technicians (Shapin 1989; Barley and Bechky 1994). This study of professionals concerns the legitimation of a career trajectory. The less formal, contextual knowledge of technicians means that they are less likely than PhDs to garner credibility and legitimacy as scientists in knowledge production and career positioning.2 Thus the choice of emphasis on PhDs is linked to theoretical concerns about the socially accepted requirements for who determines career legitimacy in the sciences. Another data-driven delimitation of the sample is the inclusion only of young PhDs. Here doctoral scientists are considered young by having completed their degree recently; calendar age is not available. Specifically, PhDs in the sample are those who have been affiliated with sampled university programs in the ten years prior to the application, either as doctoral or postdoctoral students. (As evident from the excerpt from the training-

S/D-Appendix 6/16/04 12:58 PM Page 162

162

Appendix

grant instructions quoted above, the NIH only requires information from universities on students to the past ten years before application). From the theoretical interests of this study, the emphasis on career beginnings is beneficial in some ways. A more detailed analysis of postdoctoral and first positions taken out of graduate school and/or postdoctoral training can be made, since other career events have not yet eclipsed this earlier period. Careers of young PhDs perhaps show best the effects of the eliteness of doctoral education. Scientists are still likely to be connected to doctoral departments, and thus one’s education is more socially meaningful in both prestige and network contacts to potential job positions than later in a PhD career. Because the effect of eliteness of education is a primary variable, the focus on early career positions seems more appropriate than not. Furthermore, early career trajectories are often good indicators of future ones (Cole and Cole 1973; Merton 1973; Zuckerman 1977), so any differences found here are only likely to be exacerbated by career advancement. As Cole and Zuckerman (1984: 222) explain: “We know from studies of scientific careers that scientists’ standing in the stratification system is fairly well set by the end of their first decade of work . . . [and] provide[s] a good basis for predicting their futures.” In other words, sociology of science recognizes that the eliteness of one’s education is important to early career positions and that early career positions are important to later career achievements. Thus, although the data are not available, one might assume that my findings based on origins of graduate training and early job placement are likely to affect later career prospects of this sample of PhDs.

Qualitative Sample Fieldwork and interviews with scientists were conducted by the author between 1992 and 1998 in various settings, including biotechnology firms, university life-science departments, a nonprofit research institute, and government agencies that grant money for scientific research. Interview data specific to key questions were collected from PhDs in biotechnology firms and research universities affiliated with biotech firms. Two U.S. cities among the most active in the biotechnology industry were among the three focal sites of inquiry. Table A1 shows the top ten biotech cities by the number of dedicated biotechnology firms (DBFs) with phone numbers in relevant area codes. Data for the table were compiled from BioScan 3 and include outlying suburban areas as well as city-centers in metropolitan locations. For international contrast, the most active foreign countries are also presented, showing the strong U.S. basis of the industry. Note that some U.S. cities have twice as many dedicated biotechnology firms as entire industrialized nations. This table shows that the greater part of biotech is located in the United States, on its coasts in particular.

S/D-Appendix 6/16/04 12:58 PM Page 163

163

Appendix

Table A1 Top ten U.S. metropolitan areas in number of dedicated biotechnology firms, 1997 Greater metropolitan area or nation

Area codes

Number of biotech firms

1. San Francisco Bay

415, 510, 408, 916

56

2. Boston

617, 508

44

3. San Diego

619

41

4. Greater Philadelphia

610, 215, 609, 908

23

5. Washington, D.C.

301, 410, 703

20

5. New York City

914, 201, 516, 212

20

Canada

all

20

7. Houston

713, 281

11

8. Seattle

206

10

United Kingdom

all

10

9. Los Angeles

714, 805, 213, 310

9

10. Chapel Hill, N.C.

919

5

France

all

5

Note: Top national totals are included for comparison.

The qualitative interview sample is generally described in Table A2. In order to preserve the identity of informants, specific organizational settings, as well as individuals, are not identified by name. While qualitative research informants are normally made anonymous in studies of street corners and other public or semipublic places, the confidentiality for private settings is perhaps even more important to gaining the cooperation of interlocutors. Particularly in the case of cutting-edge science and technology, where information is potentially highly valuable, the issues of gaining access and maintaining secrecy of proprietary information present critical tasks for the ethnographer. In Table A2 are listed only those semistructured interviews where respondents sat down with me in a private meeting space, I asked questions from a list of topics, and I recorded the interview. This table does not list the countless hallway and lab conversations, meeting observations, shared meals, phone calls, e-mail messages, and so forth that constitute the bulk of field methods. Another important factor to consider in reading Table A2 is

S/D-Appendix 6/16/04 12:58 PM Page 164

164

Appendix

Table A2

Number of semistructured interview respondents, by setting, 1992–2000

Setting Biotechnology firm Government or nonprofit research institute University

Number of organizations

Number of in-depth interview respondents

5 3 4

34 5 8

Notes: Total semistructured interview respondents = 47 (20 female, 27 male). Informant and organization names in the text have been changed to protect confidentiality.

t

that I recorded where scientists were primarily located at the time of the interview, but many of the respondents had careers that spanned settings (i.e., currently in a university but having worked in the biotech industry in the past). Beyond the general issues of ethical research behavior and maintaining bridges to settings (rather than burning them for the next researcher), a particular concern for social scientists studying natural scientists qualitatively is the problem of “studying up.” Natural science has had more prestige in U.S. academia than social science during the modern and even postmodern periods (Ben-David 1971; The Economist 1997). Because of this difference in how “scientific” a discipline purports to be, the social scientist studying the practices of natural scientists has different status hurdles than the social scientists studying working-class people on the street corner or a tribe of indigenous people in a remote village. One way to gain access and adjust to a setting is to take on a preestablished role. Among scientists, as a relatively young female, I found it easy to fall into the role of student. As asking for information is key to being a student, this had some benefits. But also to be respected as an expert in my own right on the organization of lifescience careers, part of the interview process was devoted to giving as well as taking in information. I summarized some of the research I have conducted on the biotech industry to encourage PhDs to feel like they were gaining something in return for their valuable time. Thus in managing interview role performance, I attempted to appear at least as a wellinformed and curious student, rather than a completely naive one.

Data Analyses

Quantitative Measures and Methods The educational prestige classification is derived from a ranking published by the National Research Council (1994) on PhD programs. Specifically,

S/D-Appendix 6/16/04 12:58 PM Page 165

Appendix

165

rankings of biochemistry and molecular biology departments are utilized as the disciplinary division most closely aligned with the NIGMS grant categories analyzed here: cellular and molecular biology, and biotechnology. Universities are ranked from 1 (most effective) to 200. Ties in ranking do occur. The 1993 scores are attributed to university programs at all time points in the analysis because the 1984 National Research Council ranking did not include molecular biology programs, another indication of the relative newness of this scientific area. Because department rankings are generally stable over time, with 80 percent and above reliability, using the available year’s ranking seems a judicious measure of eliteness. Eliteness of education is collapsed into three categories for loglinear analyses. A score of 3 is given to the most elite category, schools ranked 1–10, so that a higher score is interpreted as more elite. Other elite-educated ranks are categorized as follows: rank 11–50 score of 2, rank 51 and more score of 1. These categories match scientists’ discussion of “top” schools, “B-level” schools, and “others.” The reversal of categories (i.e., 1–10 given highest score) is performed so that greater eliteness will produce a positive rather than negative effect on other variables. Data recorded on individual PhDs is summarized in Table A3. Geographical location data comes from postal ZIP codes. Gender is coded from first names. For postdoctoral students listed in the data source, the ranking and ZIP code of their PhD-granting university is recorded. While the relationship of adviser gender to student gender would be interesting to enter into regression equations, at least as a control, the amount of missing data on advisers is problematic at this point in time. Nearly all programs list faculty advisers by first initials only, even when students’ first names are listed, which does not allow faculty gender to be coded. Perhaps this

Table A3

Information recorded on each PhD career

University prestige ranking University ZIP code Date of grant application Individual identification Gender Year entered program Degree at entry Source of funding for training Faculty adviser identification Adviser’s gender Year completed PhD Current position/title Current organizational affiliation If university affiliated, ranking is recorded If biotechnology affiliated, firm ID is recorded

S/D-Appendix 6/16/04 12:58 PM Page 166

166

Appendix

practice reflects conventions for listing references in the natural sciences, or perhaps NIH directors are assumed to know the faculty listed. A more cynical interpretation might be that perhaps it is an indication that the vast majority of faculty advisers are male. The exclusion of race as a variable is due to a data limitation on two levels. One, the lack of people of color in the natural sciences in the United States is widely noted. Race is often not studied statistically because of this lack of variation among scientists. Additionally, the small-numbers problem is compounded when one wants to look at race and gender simultaneously. If one wants to study women of color in science, and further restrict that small group to the life sciences, the numbers unfortunately become minuscule (Clewell and Ginorio 1996). The modest number of foreign women of color compared to men and the declining number of African American men in higher education, let alone science (Malcom 1993), also make for very small and even nonexistent data cells. In a study such as this, which emphasizes types of organizational affiliations, there is an even more significant problem with small numbers (e.g., attempting to compare African American men in biotech and the academy). A second data deficiency is specific to my study: the race of students is not indicated on most grant applications. At the one university that did note racial minority status, a school especially successful at recruiting minority students, the numbers of nonwhite students were small (only about 6 percent). If this university is indeed an outstanding success at creating racial diversity among U.S. students, then the comparison of racial groups among PhD life scientists would be difficult, even if information were available. Unfortunately, data on race are not widely available here. Foreign students are much more likely to enter U.S. universities than U.S.-born minority students. At a later date, some analyses of the foreigneducated compared to U.S.-educated students are planned. Some data issues need to be solved first. Data on the country in which students received their PhD is available. If a foreign-born postdoctoral student received a PhD in the United States, however, he or she would not be labeled as a non-U.S. citizen.4 Likewise, an American who received a doctoral degree abroad would be indistinguishable from a foreign-born postdoc. For now, nationality (as well as race) is not measured. The emphasis in this project on entry into biotechnology, a predominately U.S. industry, makes the lack of good nationality data perhaps somewhat less problematic than a study primarily interested in large pharmaceutical corporations, for example. In coding the data on foreign-educated students, I did notice a trend toward scientists returning to the nation in which their PhD was received, after completing a stint as a postdoc in the United States (through which the student enters the database). Hierarchical level of job position is shown by ordered values in Table A4. A higher placement in ordering is associated with greater power in

S/D-Appendix 6/16/04 12:58 PM Page 167

167

Appendix

organizational hierarchies. Level of position integrates academic, industry, and government settings into one ordered scale. This scale was developed in consultation with bioscientists in the public and private sectors; triangulation contributes to its validity. In order to create a dichotomous variable out of positions, levels 1–5 are given a score of 0, and 6–10 are scored as 1. The cut-point of assistant professor delineates the relatively more stable and long-term positions from those less so. The leadership-level variable is dichotomized at the natural earlycareer bottleneck for natural scientists—the postdoctoral period. When a scientist has completed one or several postdoc/research associate positions and has finally obtained management of his or her “own lab,” it is a significant career step. In academe, this step is usually represented by the assistant professor position and, in industry, by the senior scientist or research group leader title. This measure of position also fits nicely with the data available, since the young scientists who have achieved leadership positions are usually not at the top (level 10) positions. Thus, the distribution of people in jobs along the ordered path in Table A4 from student in another discipline to president/CEO is such that most individuals in this sample are clustered right above and below the leadership line (i.e., postdocs and staff scientists or assistant professors and research team leaders). The dichotomous leadership measurement provides a significant boundary in the real world and in the available data.

Table A4

Hierarchical order of job titles for PhDs

Position Student in another discipline Research assistant, technician, intern Postdoctoral fellow Visiting scientist, advanced postdoctoral researcher Nontenure track staff, research scientist Assistant professor Research team director, independent scientist Associate professor Research department/section head Full professor Midlevel research administration Upper management, dean Board of directors, science adviser CEO, president Nonordered positions Nonscience position Nonemployed, not seeking employment Unemployed Unknown, deceased

Order 1 2 3 4 5 6 7 8 9 10

S/D-Appendix 6/16/04 12:58 PM Page 168

168

Appendix

The types of organizational affiliations are listed in Table A5. Dedicated biotechnology firms, at the practical level, are defined by the population of firms coded in collaboration with Powell and Koput (Powell et al. 1996; Koput et al. 1997; Smith-Doerr et al. 1999). Organizations are treated as interchangeable within types. Data are not readily available on the organizational level to compare characteristics such as size and age. For example, I assume that pharmaceutical corporations are largely similar to one another and that biotech firms are more similar to each other than to organizations of other types. Universities are treated as differing by prestige, however. Universities are the most common organization type to show up in the data, not surprising among PhD careers. Prestige is perhaps the distinguishing feature among universities at the organizational level, as is evident from all the different rankings made by the popular press as well as academic researchers. Even though I treat employing organizations, for the most part, as members of a type, this level of detail on type of organization joined is still better data than the majority of career research in which only individuallevel data are used. As discussed earlier, organizational-level data of any type are often not available in studies of individual mobility: a broad occupational code is often the only recorded employment context. Even when nearly flawless organizational-level data are used (as by Haveman and Cohen 1994), the authors are studying careers in organizations in only one population. My examination of jobs in organizations in different populations within one field means that the data on the organizational level are less detailed, perhaps necessarily, to fit within the confines of one project. An advantage of focusing on careers in one field and profession rather than an entire national economy is that the major technical and scientific changes apply across organization types and the education level is constant, allowing for the assessment of other effects on career outcomes such as ranking of education and gender.

Table A5

Organizational affiliation categories

University Other higher educational organization Government agency Nonprofit research institute Hospital Dedicated biotechnology firm focused on human therapeutics Other biotechnological firm (i.e., instruments, agricultural, unspecified biotech) Pharmaceutical or chemical corporation Other technical/scientific organizational affiliation Venture capital or law firm supporting biotech No organizational affiliation (i.e., private practice) Unknown, deceased

S/D-Appendix 6/16/04 12:58 PM Page 169

169

Appendix

Although standard panel data cannot be extracted from the quantitative data source, some over-time elements of careers are recorded. Panel, or time-series, methods cannot be utilized because the university programs do not have a universal deadline for applications and renewals; thus materials follow different chronologies for different programs. Also, career data for students are not recorded by dates; instead the current position at the time of application is simply noted. Thus position data are available more as cross-sections than over time, as some interim positions may be missing. But the ordering of positions is noted, so that multiple career positions reported are sequentially ordered in the database. The quantitative data consist of a large number of PhD careers, the majority measured at one time point. Some PhDs have career data recorded at two to four time points, but they make up only about 10 percent of the sample. Thus, cross-sectional forms of analysis constitute the statistical methods reported. Because of the different levels of variables measured for quantitative analysis, different statistical tests are employed to examine hypotheses. See Table A6 for a listing of the types of variables. Years since degree is a continuous, or ratio-level, variable because it is based on a true zero point. Organizational positions were first placed in hierarchical order, but level of position has been dichotomized for the analyses as discussed above. Table A7 shows the correlations5 between key explanatory variables, which seem to meet the assumption of statistical independence. Linear regression models also assume that pairs of error terms are independent.

Table A6

Types of variables measured for statistical analysis

Type of variable Ratio, continuous Ordered, categorical (collapsed categories) Ordered, categorical (dichotomized) Dichotomous, categorical

Table A7

Variables

Range

Years since PhD PhD education ranking

0–29 1–200 1–3 1–14 0,1 0,1

Hierarchical position Gender

Pearson’s correlation coefficients for relationships between main independent variables Biotech affiliation

Education rank Gender Years since PhD

.0968 .0028 .0271

Education rank

Gender

.0443 –.2746

–.0660

S/D-Appendix 6/16/04 12:58 PM Page 170

170

Appendix

But in the models for this project, I am estimating discrete dependent variables. As such, error terms are not independent of exogenous variables and are thus correlated. To estimate that the probability of y = 1, the effects of x must fall between 0 and 1. This means that the relationship between x and y is likely to be nonlinear at the boundaries, that is, more distant from the linear estimates at low and high values of x. So error is not independent of x values. For example, if x is prestige of education, then the estimates will be more off for the most and least prestigious educated. The common solution for this problem, adopted here, is to employ loglinear models to estimate relationships between variables (see Hanushek and Jackson 1977). Another assumption of linear models, and of logit models, is that x values are fixed. For example, a researcher may sample on gender as an independent variable. In this research project, the sample was gathered by university programs, which means x (i.e., gender) is not fixed. For example, there may be varying numbers of men and women in different university programs. Thus the appropriate statistical tool would be general loglinear models (GLMs). In GLMs, unlike logit models, there is no requirement to specify variables as independent or dependent in order to look at the relationships between them (Goodman 1978). In practice, however, which variables are exogenous and which are predicted can be assumed based on the researcher’s theory. Thus with GLMs, it is not necessary to assume that x values are fixed because x values are formally unspecified. However, GLMs are not commonly used and are more difficult to interpret than logit models. So an examination of the distributions by gender among different university programs was made. The university programs ranged between graduating 27–35 percent female students. Since there was not a large difference between programs, the fixed-x assumption does not appear to be violated to a great extent; thus logit models are presented. The more statistically appropriate GLMs were also run, and the results are essentially the same. Specifically, logistic regression (and general loglinear) models were run with the use of SPSS-windows software. The statistical models and logistic regression results for Chapter 4 concern the question of whether men or women entered the biotech industry in its earliest period. Before presenting the model testing the hypothesis of female entry early into the open industry, I report a basic model (1) without interacting the independent variables with the industry period. This model allows for the assessment of the main effects of the variables, and the results are found in Table A8. The log odds resulting from the model are transformed into percent change in odds in the third column of the table. Table A8 demonstrates the results of the logistic regression model of the effects of gender and period in biotechnology industry history, controlling for PhD-granting department, on the probability of a PhD’s entrance into a biotech firm. The general form of the logistic regression equation for model

Effects of gender on entry into the biotechnology industry, by period: results of logistic regression analyses Model 1

Variable Constant Education rank 11–50a Education rank below 50a Gender (F) Period (later)

Logistic coefficient (S.E.) –2.2312 (.2103) –.3819 (.1741) –.8652 (.2136) .0182 (.1726) .2497 (.1673)

Significance level

.0282

Model 2 % change in odds

.9161

32 decrease 58 decrease n.s.

.1356

n.s.

.0001

Education rank 11–50a x period Education rank < 50a x period Gender (F) x period –2 Log-likelihood Chi-Square Degrees of freedom

1232.134 20.606 4

.0004

Notes: N=2,211. n.s. = not significant. a. The relevant comparison is to education in PhD program ranked in the top-ten.

Logistic coefficient (S.E.) –2.4052 (.3257) –.4607 (.3053) –.9655 (.3737) .2880 (.3264) .5008 (.3839) .1088 (.3717) .1409 (.4554) –.3860 (.3854) 1231.379 21.360 7

Significance level

% change in odds

.1313

n.s.

.0098 .3777

62 decrease n.s.

.1920

n.s.

.7697

n.s.

.7570

n.s.

.3165

n.s.

.0033

S/D-Appendix 6/16/04 12:58 PM Page 171

Table A8

S/D-Appendix 6/16/04 12:58 PM Page 172

172

Appendix

1 in the table is: Prob (affiliation = biotech) = 1/(1+e-z), where z = constant + education + gender + period. The results of this model can be found in Table A8. The log odds are transformed into percent change in odds in the last column of the model. The model of interest, however, includes interaction terms for education by period and gender by period. The general form of the logistic regression model 2, used to test the hypothesis stated in Chapter 4, may be specified as follows: Prob (affiliation = biotechnology) = 1/(1+e-z) where z = education + gender + period + education x period + gender x period. The interaction terms were examined to answer the question of whether the effects of educational background and gender differ in the early period of the biotech industry compared to later. Table A8 presents the results of this model including the interaction terms, as well as the main effects seen in model 1. Percent change in odds due to each coefficient is also shown in the table. The results of model 2, in Table A8, show that the noninteractive independent variables have nearly the same effects as in model 1. The statistical models and logistic regression results for Chapter 5 have to do with the issue of whether gender inequality is evident in the sample, as well as whether women do better working in biotech. In Table A9, model 1 tests whether the origin of PhD education or gender affects a life scientist’s chances of attaining a position with supervisory responsibilities. The number of years since doctoral degree was obtained is controlled for in the model. The general form of model 1 can be described by the following equation for the logistic regression: Prob (position = leader) = 1/ (1+e-z), where z = constant + education + gender + years since PhD. The results of the logistic regression model are presented in Table A9. Again, the log odds results are mathematically transformed into percent change in odds (see the last column of model 1 in Table A9). Model 2 in Table A9 adds the key element of organizational form (biotech = network form) to the hypothesis about the effects of gender on having a leading position in life science. The logistic regression equation in general form is: Prob (position = leader) = 1/ (1+e-z), where z = constant + education + gender + biotech + years since PhD + education × biotech + gender × biotech. The results of the logistic regression procedure are presented in Table A9, as well as the calculated percent change in odds.

Qualitative Measures and Methods Interview schedules were developed primarily by the author but in consultation with experts in qualitative research: Morrill (e.g., 1995), Powell (e.g., 1985), and Clemens (e.g., 1997). Also, pilot interviews were conducted to evaluate the clarity, directness, and relevance of questions to PhDs in the life sciences. The questions for interviewing were used to generate,

Effects of gender on mobility into leadership positions, by form of economic organization: results of logistic regression analyses Model 1

Variable Constant Education rank 11–50a Education rank below 50a Gender (F) Years since PhD

Logistic coefficient (S.E.) –2.1387 (.1447) –.2988 (.1296) –.1064 (.1370) –.3837 (.1225) .2340 (.0159)

Significance level

.0212 .4375 .0017 .0001

Biotech affiliation Education rank 11–50a x biotech Education rank < 50a x biotech Gender (F) x biotech –2 Log-likelihood Chi-Square Degrees of freedom

2194.104 299.864 4

.0001

Model 2 % change in odds

26 decrease n.s. 32 decrease 26 increase

Logistic coefficient (S.E.) –2.7914 (.4879) .3991 (.5048) .6624 (.5740) –.9178 (.4506) .2504 (.0165) .6600 (.4999) –.7912 (.5221) –.9134 (.5902) 1.4087 (.4688) 2151.987 341.982 8

Significance level

% change in odds

.4292

n.s.

.2485

n.s.

.0417

.1867

60 decrease 28 increase n.s.

.1297

n.s.

.1217

n.s.

.0027

significant increaseb

.0001

.0001

Notes: N=2,062. n.s. = not significant. a. The relevant comparison is to education in PhD program ranked in the top-ten. b. Adding the biotech and gender interaction coefficients reveals the magnitude of the effect of being in a network form for females = 2.0687.

S/D-Appendix 6/16/04 12:58 PM Page 173

Table A9

S/D-Appendix 6/16/04 12:58 PM Page 174

174

Appendix

t

rather than to confine, discussion surrounding the major issues of the legitimation of biotech and interorganizational careers, the timing of the legitimation process, and the gendered environments of different science organizations. Interviews were approached with the goal of maximum information rather than strict reproducibility, thus the designation of the interviews as semistructured. When promising topics, if somewhat tangential to questions, arose in interviews, these avenues were pursued. Qualitative data were collected by two means simultaneously to make the record as complete as possible. Written notes and tape recordings of interviews and field observations were both made whenever feasible. Notes and tapes were transcribed into elaborated field notes as soon as possible after time spent in the field for maximal data retention. The interview transcriptions and field notes that I typed onto my computer served as the raw data for qualitative analysis. For the inductive analysis, I first categorized the data into broad, open topics, then narrowed these down into more specific patterns that emerged across the interviews and field notes. Thus the qualitative data generated inductive theory. At times qualitative data also served to verify some specific questions through the more structured interview queries. Thus data were coded both into emergent themes, as well as certain proposed areas of focus. Preliminary conclusions from interviews and observations were subject to member checks with PhDs—some involved and others not involved as interlocutors for the study. Overall, the phases of the qualitative research were construction of interview questions; pilot interviews; reconstruction of interview questions; interviewing and elaborating field notes; coding field notes; preliminary results; member check against conclusions and closure; and final results. This list shows the importance of interspersing immersion in the field with stepping back for analysis to the practice of qualitative research.

Combining Quantitative and Qualitative Methods

At first glance, it may seem contradictory to combine quantitative and qualitative methods because of their bases in different logics. Often, at least ideally, quantitative research designs are based on deductive reasoning, moving from a theory to examining variables that support one’s deductions. Qualitative research, in contrast, usually relies on induction, developing more general concepts from empirical observations. Induction and deduction may appear mutually exclusive, particularly if one attends to conflicts between social scientists who believe that theory must absolutely precede data collection or vice versa.

S/D-Appendix 6/16/04 12:58 PM Page 175

Appendix

175

When different research logics are combined in one study, deduction allows for confirmation of hypotheses connected to a priori theory, whereas induction allows for development of theory grounded in a real-world context with interlocutors. In order to combine theory-testing and theorybuilding, the inclusion of both deductive and inductive reasoning is here viewed as complementary instead of contradictory. Admittedly, this requires the reader to set aside the ideas that (1) any theory developed in the field is useless because of bias from the particular setting observed; and (2) no theory can ever be supported since all data are interpreted through the researcher’s own worldview. Although both of these critiques have some validity, the hope in combining methods is to mitigate rather than to exacerbate divisions between research strategies. By studying more than one particular setting, application to more general populations is possible; and by observing a small number of individuals and organizations in-depth, other voices besides the researcher’s are heard. As many social-science researchers will admit when not engaged in methodology wars, the best method of research depends on the questions asked. For analyzing relationships between variables to be generalized to a larger population, quantitative analysis is usually most appropriate. To study meanings of socially derived concepts and the unfolding of complex interactions, qualitative research is usually the appropriate tool. Because this study encompasses questions with different foci, methodological pluralism was adopted. I developed hypotheses about the relationships between quantifiable variables measured in a large sample of PhDs. The exploratory questions, in contrast, required a contextual knowledge of relevant organizations, as well as firsthand observations of scientists’ impressions of and behavior surrounding the process of legitimating new life-science career structures. Rather than conceive of these sets of questions as two different projects— one quantitative and one qualitative—I combined them to better understand the research setting and to allow for a richer discourse with theory through examining multiple sides of the subject field. Using a dual research design meant that I could not only evaluate the dynamics of an emergent career structure for educational and gender stratification, but also see how that stratification plays out in the work environment and how the legitimation process unfolds in a new area. These questions answered in a specific setting have relevance for socialscience theory: models of change in stratification, and mechanisms of legitimacy, are general concepts that can inform studies of other changing organizational landscapes. If only one set of questions or research methods were employed, the understanding of when, by whom, how, and why a new career pattern takes shape would not be as complete. The fol-

S/D-Appendix 6/16/04 12:58 PM Page 176

176

Appendix

lowing example of how quantitative and qualitative methods were combined in this project comes from Chapters 5 and 6. To address the question of whether gender affects attainment of leadership positions differentially within network forms of organization and hierarchical forms, quantitative data were employed. I then used interview and observational qualitative data to understand why biotech firms, representing the network form of organization, would permit greater gender equality in top positions. To illustrate how qualitative and quantitative research are generally two sides of the same coin, Table A10 lists examples of how the strengths of one complement the weaknesses of the other, and how this study exploits this methodological complementarity. The diagonals of the first two columns of Table A10 show how the strengths of qualitative research shore up the weaknesses of quantitative research, and vice versa. Quantitative generalizability and qualitative depth combine to mitigate weaknesses such as quantitative oversight of information important to interlocutors and qualitative limitation to small numbers. Although it is often not practical for researchers to attempt two methods of research in one data-collection effort (and certainly difficult to report in a single journal article), one possibility is to alternate qualitative and quantitative methods on subsequent projects addressing the same research area. Perhaps even a better solution is to practice research collaboration in which qualitative and quantitative researchers specialize in different aspects of a project. Of course, this research agenda would require laying down arms between qualitative and quantitative camps, but the payoff in benefits to understanding social institutions and developing theory that can result from merging methods of research makes the efforts worthwhile.

Table A10

Method

Complementary strengths and weaknesses of quantitative and qualitative methods

Strengths

Weaknesses

Examples from this study

Quantitative

Generalizable to population, allow group comparison/ variable analysis.

Context of response unclear (i.e., standard responses may be irrelevant).

Clarify how gender is related to trends in careers.

Qualitative

Understanding complex processes, details of interaction.

Not generalizable to broader population.

Understand how scientists construct legitimacy of biotech careers.

S/D-Appendix 6/16/04 12:58 PM Page 177

t

Appendix

177

Notes

1. NIGMS program directors were skeptical when I expressed my desire to photocopy career information from training-grant tables. They did not think it was practical to sort out the information from among the reams of material sent to their office (one university program typically has multiple folders that are more than six inches thick). I found that in the applications randomly selected all of the schools presented the career material in tables, even though directors who review these grants said that the practice of reporting career information in paragraph form was common. My years of data-coding experience were helpful for locating presentation patterns in the material. 2. However, I found that technicians, as semioutsiders to PhD career concerns, offered helpful observations and an interesting perspective on the field. 3. More extensive analyses of these data may be found (Powell et al. 1996; Koput et al. 1997); my definition of the biotechnology industry is in some ways a relatively narrow slice of biotechnology but a central one. Agricultural and environmental biotechs are not included; instead the focus is firms that work on therapeutic products for human diseases. 4. This is especially problematic considering that an estimated one-quarter (NRC 1998) to one-third (Burgess 1997) of U.S. doctoral degrees are awarded to non-U.S. citizens. 5. Strictly speaking, Pearson’s correlations assume continuous variables, and these violate that assumption, but the coefficients serve to estimate the lack of multicollinearity among independent variables.

S/D-Appendix 6/16/04 12:58 PM Page 178

S/D-Refs 6/16/04 12:59 PM Page 179

References

Abate, Tom. 2003. “Critic of Biotech Corn Fears UC Won’t Give Him Tenure: Junior Professor Fought School’s Ties with Industry.” San Francisco Chronicle, March 23. Abbott, Andrew. 1988. The System of Professions: An Essay on the Division of Expert Labor. Chicago: University of Chicago Press. Acker, Joan. 1990. “Hierarchies, Jobs, Bodies: A Theory of Gendered Organizations.” Gender & Society 4: 139–159. Ahern, Nancy, and Elizabeth Scott. 1981. Career Outcomes in a Matched Sample of Men and Women Ph.D.s. Washington, DC: National Academy Press. Alagna, Sheryle W., D. Reddy, and D. Collins. 1982. “Perceptions of Functioning in Mixed-Sex and Male Medical Training Groups.” Journal of Medical Education 57: 801–803. Amabile, Teresa M. 1994. “The Atmosphere of Pure Work: Creativity in Research and Development.” Pp. 316–328 in The Social Psychology of Science, edited by William R. Shadish and Steve Fuller. New York: Guilford. American Association of Pharmaceutical Scientists. 1996. “Employment Survey.” Pharmaceutical Technology (December): 28–56. Angier, N. 1988. Natural Obsessions. Boston: Houghton Mifflin. Aoki, Naomi. 2003. “Millennium to Trim 600 Jobs: Plans Research Cuts as It Turns Focus to Profit, Products.” Boston Globe, June 6: E1, 4. Aoki, Naomi, and Andrew Caffrey. 2003. “Promising Results: Advances Spur Hopes Biotech May Have Turned the Corner.” Boston Globe, June 3: D1–2. Applebaum, Eileen, and Rosemary Batt. 1994. The New American Workplace: Transforming Work Systems in the United States. Ithaca, NY: ILR. Armstrong, Elizabeth A. 2002. Forging Gay Identities: Organizing Sexuality in San Francisco, 1950–1994. Chicago: University of Chicago Press. Baldi, S. 1998. “Normative Versus Social Constructivist Processes in the Allocation of Citation: A Network-Analytic Approach.” American Sociological Review 63: 829–846. Barber, Leslie A. 1995. “U.S. Women in Science and Engineering, 1960–1990: Progress Toward Equity?” Journal of Higher Education 66: 213–234. Barley, Stephen R., and Beth A. Bechky. 1994. “In the Backrooms of Science: The Work of Technicians in Science Labs.” Work and Occupations 21: 85–126. Barley, Stephen R., and Gideon Kunda. 2001. “Bringing Work Back In.” Organization Science 12: 76–95.

179

S/D-Refs 6/16/04 12:59 PM Page 180

180

References

Barman, C. R. 1999. “Completing the Study: High School Students’ Views of Scientists and Science.” Science and Children 36: 16–21. Barnett, H. G. 1953. Innovation: The Basis of Cultural Change. New York: McGraw-Hill. Barnett, William P., James N. Baron, and Toby E. Stuart. 2000. “Avenues of Attainment: Occupational Demography and Organizational Careers in the California Civil Service.” American Journal of Sociology 106, no. 1: 88–144. Baron, James N., and William T. Bielby. 1980. “Bringing the Firm Back In: Stratification, Segmentation, and the Organization of Work.” American Sociological Review 45: 737–765. ———. 1984. “The Organization of Work in a Segmented Economy.” American Sociological Review 49: 454–473. Baron, James N., Brian S. Mittman, and Andrew E. Newman. 1991. “Targets of Opportunity: Organizational and Environmental Determinants of Gender Integration Within the California Civil Service, 1979–1985.” American Journal of Sociology 96: 1362–1402. Baum, Joel A. C., Tony Calabrese, and Brian S. Silverman. 2000. “Don’t Go It Alone: Alliance Network Composition and Startups’ Performance in Canadian Biotechnology.” Strategic Management Journal 21: 267–294. Beatty, John. 1991. “Genetics in the Atomic Age: The Atomic Bomb Casualty Commission, 1947–1956.” Pp. 284–324 in The Expansion of American Biology, edited by K. R. Benson, J. Maienschein, and R. Rainger. New Brunswick, NJ: Rutgers University Press. Becker, Gary S. 1981. Treatise on the Family. Cambridge, MA: Harvard University Press. ———. 1993. “Nobel Lecture: The Economic Way of Looking at Behavior.” Journal of Political Economy 101: 385–409. Becker, Howard S., and James Carper. 1956. “The Elements of Identification with an Occupation.” American Sociological Review 21: 341–348. Ben-David, Joseph. 1971. The Scientist’s Role in Society: A Comparative Study. Englewood Cliffs, NJ: Prentice-Hall. Ben-David, Joseph, and Randall Collins. 1966. “Social Factors in the Origins of a New Science: The Case of Psychology.” American Sociological Review 31, no. 4: 451–465. Berger, Peter L., and Thomas Luckmann. 1966. The Social Construction of Reality: A Treatise in the Sociology of Knowledge. New York: Doubleday. Blumenstyk, Goldie. 2002. “How Colleges Get More Bang (or Less) from Technology Transfer.” Chronicle of Higher Education, July 19. Bok, Derek Curtis. 2003. Universities in the Marketplace: The Commercialization of Higher Education. Princeton, NJ: Princeton University Press. Bosch, Xavier. 2003. “Brain Drain Robbing Europe of Its Brightest Young Scientists.” Lancet 361: 2210–2211. Boston Globe. 2000. “Sato Promoted to President of Vertex Pharmaceuticals.” December 6: D5. Braselmann, Sylvia. 1999. “Reluctant Rebels: Women Scientists Organizing.” San Francisco. www.ebawis.org/resources/reluct.pdf. Brass, D. J. 1985. “Men’s and Women’s Networks: A Study of Interaction Patterns and Influence in an Organization.” Academy of Management Journal 28, no. 2: 327–343. Brinton, Mary C. 1989. “Gender Stratification in Contemporary Urban Japan.” American Sociological Review 54: 549–564.

S/D-Refs 6/16/04 12:59 PM Page 181

References

181

———. 1993. Women and the Economic Miracle: Gender and Work in Postwar Japan. Berkeley: University of California Press. Browne, Irene, and Ivy Kennelly. 1999. “Stereotypes and Realities: Images of Black Women in the Labor Market.” Pp. 302–326 in Latinas and African American Women at Work: Race, Gender, and Economic Inequality, edited by I. Browne. New York: Russell Sage Foundation. Budig, Michelle J. 2002. “Male Advantage and the Gender Composition of Jobs: Who Rides the Glass Escalator?” Social Problems 49: 258–277. Burgess, David R. 1997. “Barriers to Graduate School for Minority-Group Students.” The Chronicle of Higher Education, October 10, pp. B7–B8. Burns, Tom, and George M. Stalker. 1961. The Management of Innovation. London: Tavistock. Burt, Ronald S. 1992. Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press. ———. 1997. “The Contingent Value of Social Capital.” Administrative Science Quarterly 42: 339–365. ———. 1998. “The Gender of Social Capital.” Rationality and Society 10: 5–46. ———. 2000. “The Network Structure of Social Capital.” Research in Organizational Behavior 22: 345–423. Bush, Vannevar. 1945. Science: The Endless Frontier. Washington, DC: U.S. Government Printing Office. Bushnell, Davis. 2003. “Where the Biotech Jobs Are: As Industry Hiring Slows, Companies with New Products Plan to Add Staff.” Boston Globe, March 9. Cahill, Spencer. 1983. “Reexamining the Acquisition of Sex Roles: A Symbolic Interactionist Approach.” Sex Roles 9: 1–15. Callender, Claire. 1987. “Women Seeking Work.” Pp. 22–45 in Unemployment: Personal and Social Consequences, edited by Stephen Fineman. London: Tavistock. Cambrosio, Alberto, and Peter Keating. 1995. Exquisite Specificity: The Monoclonal Antibody Revolution. New York: Oxford University Press. Campbell, John L., and Leon N. Lindberg. 1990. “Property Rights and the Organization of Economic Activity by the State.” American Sociological Review 55: 634–647. Caplow, Theodore, and Reece J. McGee. 1958. The Academic Marketplace. New York: Wiley and Sons. Castells, Manuel. 2000. The Information Age: Economy, Society, and Culture, Volume 1: The Rise of the Network Society. Rev. ed. Oxford: Blackwell. Chafetz, Janet Saltzman. 1990. Gender Equity: An Integrated Theory of Stability and Change. Newbury Park, CA: Sage. Charles, Maria. 1992. “Cross National Variation in Occupational Sex Segregation.” American Sociological Review 57, no. 4: 483–502. Charles, Maria, and Karen Bradley. 2002. “Equal but Separate? A Cross-National Study of Sex Segregation in Higher Education.” American Sociological Review 67: 573–599. Chatman, J., J. Polzer, S. Barsade, and M. Neale. 1998. “Being Different yet Feeling Similar: The Influence of Demographic Composition and Organizational Culture on Work Processes and Outcome.” Administrative Science Quarterly 43: 749–780. Clarke, Adele, and Elihu Gerson. 1990. “Symbolic Interactionism in Social Studies of Science.” Pp. 179–214 in Symbolic Interaction and Cultural Studies, edited by H. Becker and M. McCall. Chicago: University of Chicago Press.

S/D-Refs 6/16/04 12:59 PM Page 182

182

References

Clemens, Elisabeth S. 1996. “Organizational Form as Frame: Collective Identity and Political Strategy in the American Labor Movement, 1880–1920.” Pp. 205–226 in Comparative Perspectives on Social Movements: Political Opportunities, Mobilizing Structures, and Cultural Framings, edited by D. McAdam, J. D. McCarthy, and M. N. Zald. Cambridge, UK: Cambridge University Press. ———. 1997. The People’s Lobby: Organizational Innovation and the Rise of Interest Group Politics in the United States, 1890–1925. Chicago: University of Chicago Press. ———. 2002. “Invention, Innovation, Proliferation: Explaining Organizational Genesis and Change.” Research in the Sociology of Organizations 19: 397–411. Oxford: JAI. Clemens, Elisabeth S., and James M. Cook. 1999. “Politics and Institutionalism: Explaining Durability and Change.” Annual Review of Sociology 25: 441–466. Clement, D., and J. Schiereck. 1973. “Sex Composition and Group Performance in a Signal Detection Task.” Memory and Cognition 1: 251–255. Clewell, Beatriz Chu, and Angela B. Ginorio. 1996. “Examining Women’s Progress in the Sciences from the Perspective of Diversity.” Pp. 163–231 in The Equity Equation: Fostering the Advancement of Women in the Sciences, Mathematics, and Engineering, edited by C. S. Davis et al. San Francisco: Jossey-Bass. Cockburn, Iain M., and Rebecca M. Henderson. 2001. “Scale and Scope in Drug Development: Unpacking the Advantages of Size in Pharmaceutical Research.” Journal of Health Economics 20: 1033–1057. Cohen, Lisa E., Joseph P. Broschak, and Heather A. Haveman. 1998. “And Then There Were More? The Effect of Organizational Sex Composition on the Hiring and Promotion of Managers.” American Sociological Review 63: 711–727. Cohen, Philip N., and Matt L. Huffman. 2003. “Individuals, Jobs, and Labor Markets: The Devaluation of Women’s Work.” American Sociological Review 68: 443–463. Cole, Jonathan R., and Stephen Cole. 1973. Social Stratification in Science. Chicago: University of Chicago Press. Cole, Jonathan R., and Burton Singer. 1991. “A Theory of Limited Differences: Explaining the Productivity Puzzle in Science.” Pp. 277–310 in The Outer Circle: Women in the Scientific Community, edited by H. Zuckerman, J. R. Cole, and J. T. Bruer. New York: W. W. Norton. Cole, Jonathan R., and Harriet Zuckerman. 1984. “The Productivity Puzzle: Persistence and Change in Patterns of Publication of Men and Women Scientists.” Pp. 217–258 in Advances in Motivation and Achievement, edited by P. Maehr and M. W. Steinkamp. Greenwich, CT: JAI. ———. 1991. “Marriage, Motherhood, and Research Performance in Science.” Pp. 239–258 in The Outer Circle: Women in the Scientific Community, edited by H. Zuckerman, J. Cole, and J. Bruer. New York: W. W. Norton. Cook, Gareth. 2002. “Bio Envy: As Biology Picks Up Steam and Money, Physicists Join the Juggernaut.” Boston Globe, August 13. Cotter, David A., JoAnn DeFiore, Joan M. Hermsen, Brenda Marsteller Kowalewski, and Reeve Vanneman. 1997. “All Women Benefit: The MacroLevel Effect of Occupational Integration on Gender Earnings Equality.” American Sociological Review 62, no. 5: 714–734. Croissant, Jennifer, and Sal Restivo, eds. 2001. Degrees of Compromise: Industrial Interests and Academic Values. Albany: State University of New York Press.

S/D-Refs 6/16/04 12:59 PM Page 183

References

183

Dalton, Melville. 1959. Men Who Manage. New York: Wiley. Dalton, Rex. 2003. “Berkeley Accused of Biotech Bias as Ecologist Is Denied Tenure.” Nature, December 11. Davis, C. S., et al., eds. 1996. The Equity Equation: Fostering the Advancement of Women in the Sciences, Mathematics, and Engineering. San Francisco: JosseyBass. Davis, Gerald F., Kristina A. Diekmann, and Catherine H. Tinsley. 1994. “The Decline and Fall of the Conglomerate Firm in the 1980s: The Deinstitutionalization of an Organizational Form.” American Sociological Review 59: 547–570. DiMaggio, Paul J. 1991. “Constructing an Organizational Field as a Professional Project: U.S. Art Museums, 1920–1940.” Pp. 267–292 in The New Institutionalism in Organizational Analysis, edited by W. W. Powell and P. J. DiMaggio. Chicago: University of Chicago Press. ———. 2001. “Introduction: Making Sense of the Contemporary Firm and Prefiguring Its Future.” Pp. 3–30 in The Twenty-First-Century Firm: Changing Economic Organization in International Perspective, edited by P. DiMaggio. Princeton, NJ: Princeton University Press. DiMaggio, Paul, and Walter W. Powell. 1983. “The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields.” American Sociological Review 48: 147–160. Donato, Katherine M. 1990. “Programming for Change? The Growing Demand for Women Systems Analysts.” Pp. 167–182 in Job Queues, Gender Queues: Explaining Women’s Inroads into Male Occupations, edited by B. F. Reskin and P. A. Roos. Philadelphia: Temple University Press. Du Mars, Roger Dean. 2001. “They Do Dishes and Deals.” Asiaweek.com, July 13. Dubinskas, Frank A. 1988. “Janus Organizations: Scientists and Managers in Genetic Engineering Firms.” Pp. 170–232 in Making Time: Ethnographies of High-Technology Organizations, edited by F. A. Dubinskas. Philadelphia: Temple University Press. Earls, Alan R. 2002. “Women Find Room at the Top in Biotech.” Boston Globe, October 20, p. G1. Eaton, Susan C. 1999. “Surprising Opportunities: Gender and the Structure of Work in Biotech Firms.” Annals of the New York Academy of Sciences 869: 175–189. ———. 2003. “If You Can Use Them: Flexibility Policies, Organizational Commitment, and Perceived Performance.” Industrial Relations 42: 145–167. Eaton, Susan C., and Lotte Bailyn. 1999. “Emergent Career Paths in Changing Organizations: Work and Life Strategies of Professionals in Firms of the Future.” Annals of the American Academy of Science 562: 159–173. Eccles, Robert G., and Dwight B. Crane. 1988. Doing Deals. Cambridge, MA: Harvard Business School Press. The Economist. 1997. “The Knowledge Factory: A Survey of Universities.” October 4, pp. 1–22. Edelman, Lauren B. 1992. “Legal Ambiguity and Symbolic Structures: Organizational Mediation of Civil Rights Law.” American Journal of Sociology 97: 1531–1576. Edelman, Lauren B., Sally Riggs Fuller, and Iona Mara-Drita. 2001. “Diversity Rhetoric and the Managerialization of Law.” American Journal of Sociology 106: 1589–1641. Edelman, Lauren B., and Robin Stryker. Forthcoming 2004. “Law and Economy: Labor Markets and Employment,” in The Handbook of Economic Sociology.

S/D-Refs 6/16/04 12:59 PM Page 184

184

References

2nd ed., edited by N. J. Smelser and R. Swedberg. Princeton, NJ: Princeton University Press and Russell Sage Foundation. Eliot, George. 1985 [1871]. Middlemarch. London: Penguin Books. Ely, R. 1994. “The Effects of Organizational Demographics and Social Identity on Relationships Among Professional Women.” Administrative Science Quarterly 39: 203–238. England, Paula. 1992. Comparable Worth: Theories and Evidence. New York: Aldine de Gruyter. Epstein, Cynthia Fuchs. 1981. Women in Law. New York: Basic Books. Epstein, Steven. 1996. Impure Science: AIDS, Activism, and the Politics of Knowledge. Berkeley: University of California Press. Etzkowitz, Henry, Carol Kemelgor, and Brian Uzzi. 2000. Athena Unbound: The Advancement of Women in Science and Technology. Cambridge, UK: Cambridge University Press. Etzkowitz, Henry, and Andrew Webster. 1996. “Science as Intellectual Property.” Pp. 480–505 in Handbook of Science and Technology Studies, edited by S. Jasanoff, G. E. Markle, J. C. Petersen, and T. Pinch. Thousand Oaks, CA: Sage. Etzkowitz, Henry, Andrew Webster, and Peter Healey, eds. 1998. Capitalizing Knowledge: New Intersections of Industry and Academia. Albany: State University of New York Press. Federal Glass Ceiling Commission. 1995. A Solid Investment: Making Full Use of the Nation’s Human Capital. Washington, DC: U.S. Government Printing Office. Ferree, Myra Marx, and Patricia Yancey Martin, eds. 1995. Feminist Organizations: Harvest of the New Women’s Movement. Philadelphia: Temple University Press. Fiorentine, Robert. 1987. “Men, Women, and the Premed Persistence Gap: A Normative Alternatives Approach.” American Journal of Sociology 92: 1118–1139. Fleck, Ludwik. 1935. Genesis and Development of a Scientific Fact. Chicago: University of Chicago Press. Fligstein, Neil. 1996. “Markets as Politics: A Political-Cultural Approach to Market Institutions.” American Sociological Review 61: 656–673. ———. 2001. The Architecture of Markets: An Economic Sociology of Twenty-First Century Capitalist Societies. Princeton, NJ: Princeton University Press. Florida, Richard. 2002. The Rise of the Creative Class. New York: Basic Books. Fountain, Jane. E. 2000. “Constructing the Information Society: Women, Information Technology, and Design.” Technology in Society 22: 45–62. Fox, Mary Frank. 1991. “Gender, Environmental Milieu, and Productivity in Science.” Pp. 188–204 in The Outer Circle: Women in the Scientific Community, edited by H. Zuckerman, J. R. Cole, and J. T. Bruer. New York: W. W. Norton. ———. 1996. “Women and Scientific Careers.” Pp. 205–224 in Handbook of Science and Technology Studies, edited by S. Jasanoff, G. E. Markle, J. C. Petersen, and T. Pinch. Thousand Oaks, CA: Sage. ———. 2000. “Organizational Environments and Doctoral Degrees Awarded to Women in Science and Engineering Departments.” Women’s Studies Quarterly 28: 47–61. Fox, Mary Frank, and Paula E. Stephan. 2001. “Careers of Young Scientists: Preferences, Prospects, and Realities by Gender and Field.” Social Studies of Science 31: 109–122.

S/D-Refs 6/16/04 12:59 PM Page 185

References

185

Freeman, Richard, Eric Weinstein, Elizabeth Marincola, Janet Rosenbaum, and Frank Solomon. 2001. “Competition and Careers in Biosciences.” Science 5550: 2293–2294. Fuchs, Victor R. 1988. Women’s Quest for Economic Equality. Cambridge, MA: Harvard University Press. Fullerton, Howard N., Jr. 1999. “Labor Force Participation: 75 Years of Change, 1950–1998 and 1998–2025.” Monthly Labor Review, December. Gerlach, Michael L. 1992. Alliance Capitalism: The Social Organization of Japanese Business. Berkeley: University of California Press. Gibbons, Michael, Camille Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott, and Martin Trow. 1994. The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies. London: Sage. Giddens, Anthony. 1984. The Constitution of Society. Berkeley: University of California Press. Gieryn, Thomas F. 1999. Cultural Boundaries of Science: Credibility on the Line. Chicago: University of Chicago Press. Gilligan, Carol. 1982. In a Different Voice: Psychological Theory and Women’s Development. Cambridge, MA: Harvard University Press. Goes, James B., and Seung Ho Park. 1997. “Interorganizational Links and Innovation: The Case of Hospital Services.” Academy of Management Journal 40: 673–696. Goffman, Erving. 1974. Frame Analysis: An Essay on the Organization of Experience. Cambridge, MA: Harvard University Press. Goodman, Leo A. 1978. Analyzing Qualitative/Categorical Data: Log-Linear Models and Latent Structure Analysis. Cambridge, MA: Abt Books. Goonatilake, Susantha. 1993. “Modern Science and the Periphery: The Characteristics of Dependent Knowledge.” Pp. 259–267 in The “Racial” Economy of Science: Toward a Democratic Future, edited by S. Harding. Bloomington: Indiana University Press. Gorman, Elizabeth H. 1999. “Moving Away from ‘Up or Out’: Determinants of Permanent Employment in Law Firms.” Law & Society Review 33, no. 3: 637–666. Gorz, André. 1980. “The Scientist as Worker.” Pp. 267–279 in Science and Liberation, edited by R. Arditt, P. Brennan, and S. Cavrak. Boston: South End. Gouldner, Alvin W. 1954. Patterns of Industrial Bureaucracy. New York: The Free Press. Graebner, Lynn. 2001. “Human Genome Map Not the End of the Road: Genmetrics Taking On the Daunting Task of Exploring the Functions of Genes, Creating a Database for Drug Companies.” Silicon Valley Business Ink, February 1. Gramsci, Antonio. 1971. Selections from Prison Notebooks. New York: International Publishers. Granovetter, Mark. 1973. “The Strength of Weak Ties.” American Journal of Sociology 78: 1360–1380. ———. 1974. Getting a Job. Cambridge: Harvard University Press. ———. 1985. “Economic Action, Social Structure, and Embeddedness.” American Journal of Sociology 91: 481–510. ———. 1995. Getting a Job: A Study of Contacts and Careers. 2nd ed. Chicago: University of Chicago Press. Grant, L., I. Kennelly, and K. B. Ward. 2000. “Revisiting the Gender, Marriage, and Parenthood Puzzle in Scientific Careers.” Women’s Studies Quarterly 28: 62–85. Greenfield, Susan. 2002. SET Fair: A Report on Women in Science, Engineering,

S/D-Refs 6/16/04 12:59 PM Page 186

186

References

and Technology from the Baroness Greenfield to the Secretary of State for Trade and Industry. Oxford, UK. Available at www.set4women.gov.uk. Greenhouse, Steven. 1997. “Nike Shoe Plant in Vietnam Is Called Unsafe for Workers.” New York Times, November 8. Hackett, Edward J. 1990. “Science as a Vocation in the 1990s: The Changing Organizational Culture of Academic Science.” Journal of Higher Education 61: 241–279. ———. 2001. “Organizational Perspectives on University-Industry Research Relations.” Pp. 1–21 in Degrees of Compromise: Industrial Interests and Academic Values, edited by J. Croissant and S. Restivo. Albany: State University of New York Press. Hall, S. 1987. Invisible Frontiers: The Race to Synthesize a Human Gene. Redmond, WA: Tempus Books. Hamilton, David P. 2002. “Gene Machines: As Others Sink, 2 Biotech Players Have Hit It Big.” Wall Street Journal, November 18. Hamilton, Gary G., and Nicole Woolsey Biggart. 1988. “Market, Culture, and Authority: A Comparative Analysis of Management and Organization in the Far East.” American Journal of Sociology (Supplement) 94: s52–s94. Hannan, Michael T., and John Freeman. 1989. Organizational Ecology. Cambridge, MA: Harvard University Press. Hanson, Susan, and Geraldine Pratt. 1991. “Job Search and the Occupational Segregation of Women.” Annals of the Association of American Geographers 81, no. 2: 229–253. Hanushek, Eric A., and John E. Jackson. 1977. Statistical Methods for Social Scientists. San Diego, CA: Academic Press. Haveman, Heather A., and Lisa E. Cohen. 1994. “The Ecological Dynamics of Careers: The Impact of Organizational Founding, Dissolution, and Merger on Job Mobility.” American Journal of Sociology 100: 104–153. Haveman, Heather A., and Hayagreeva Rao. 1997. “Structuring a Theory of Moral Sentiments: Institutional and Organizational Coevolution in the Early Thrift Industry.” American Journal of Sociology 102: 1606–1651. Hershman, Tania. 2002. “The Biotech Mystique: Number of Women Execs Growing.” BioIsrael.com 3, no. 9, September. Higgins, Monica C., and Kathy E. Kram. 2001. “Reconceptualizing Mentoring at Work: A Developmental Network Perspective.” Academy of Management Review 26: 264–288. HIV and Hepatitis News. 2003. www.hivandhepatitis.com/hep_b/news/073102a. html. Hochschild, Arlie. 1989. The Second Shift: Working Parents and the Revolution at Home. New York: Viking. Hoffman, Andrew J. 1997. From Heresy to Dogma: An Institutional History of Corporate Environmentalism. San Francisco: New Lexington. Hoffman, L., E. Harburg, and N. Maier. 1962. “Differences and Disagreement as Factors in Creative Group Problem Solving.” Journal of Abnormal and Social Psychology 62: 206–214. Holden, Constance. 1998. “Report Paints Grim Outlook for Young Ph.Ds.” Science 281: 1584. ———. 2001. “General Contentment Masks Gender Gap in First AAAS Salary and Job Survey.” Science 294: 396–411. Hollmer, Mark. 2003. “Post-merger Millennium on Track, Analysts Say.” Boston Business Journal, October 10. Howard-Grenville, Jennifer A. 2002. “Institutional Evolution: The Case of the

S/D-Refs 6/16/04 12:59 PM Page 187

References

187

Semiconductor Industry Voluntary PFC Emissions Reduction Agreements.” Pp. 291–308 in Organizations, Policy, and the Natural Environment: Institutional and Strategic Perspectives, edited by A. J. Hoffman and M. J. Ventresca. Stanford, CA: Stanford University Press. Ibarra, Hermina. 1992. “Homophily and Differential Returns: Sex Differences in Network Structure and Access in an Advertising Firm.” Administrative Science Quarterly 37: 422–447. ———. 1997. “Paving an Alternative Route: Gender Differences in Managerial Networks.” Social Psychology Quarterly 60: 91–102. Jacobs, Jerry A. 1989. Revolving Doors: Sex Segregation and Women’s Careers. Stanford, CA: Stanford University Press. Jaffe, Sam. 2002. “Biotech-Big Pharma Betrothals Declining: Fewer Biotechnology Companies Look to Big Pharma for Big Cash Bailouts.” The Scientist 16, no. 14, July 8. Janis, Irving L. 1971. “Groupthink: The Desperate Drive for Consensus at Any Cost.” Psychology Today, November. Jehn, Karen A., Gregory B. Northcraft, and Margaret A. Neale. 1999. “Why Differences Make a Difference: A Field Study of Diversity, Conflict, and Performance in Workgroups.” Administrative Science Quarterly 44: 741–763. Jepperson, Ronald L. 1991. “Institutions, Institutional Effects, and Institutionalism.” Pp. 143–163 in The New Institutionalism in Organizational Analysis, edited by W. W. Powell and P. J. DiMaggio. Chicago: University of Chicago Press. Kanter, Rosabeth Moss. 1993 [1977]. Men and Women of the Corporation. 2nd ed. New York: Basic Books. Kaufman, Robert L. 2002. “Assessing Alternative Perspectives on Race and Sex Employment Segregation.” American Sociological Review 67: 547–572. Kay, Fiona M., and John Hagan. 1998. “Raising the Bar: The Gender Stratification of Law Firm Capitalization.” American Sociological Review 63, no. 5: 728–743. Kay, Lily E. 1993. The Molecular Vision of Life: Caltech, the Rockefeller Foundation, and the Rise of the New Biology. New York: Oxford University Press. Keith, Bruce, Jenny Sundra Layne, Nicholas Babchuk, and Kurt Johnson. 2002. “The Context of Scientific Achievement: Sex Status, Organizational Environments, and the Timing of Publication on Scholarship Outcomes.” Social Forces 80: 1253–1281. Keller, Evelyn Fox. 1983. A Feeling for the Organism: The Life and Work of Barbara McClintock. San Francisco: Freeman. Kelly, Erin, and Frank Dobbin. 1998. “How Affirmative Action Became Diversity Management: Employer Response to Antidiscrimination Law, 1961 to 1996.” American Behavioral Scientist 41: 960–984. Kenney, Martin. 1986. Biotechnology: The University-Industrial Complex. New Haven, CT: Yale University Press. Kent, R., and J. McGrath. 1969. “Task and Group Characteristics as Factors Affecting Group Performance.” Journal of Experimental Social Psychology 5: 429–440. Kerr, Clark. 1963. The Uses of the University. Cambridge, MA: Harvard University Press. Kevles, Daniel J., and Leroy Hood, eds. 1992. The Code of Codes: Scientific and Social Issues in the Human Genome Project. Cambridge, MA: Harvard University Press.

S/D-Refs 6/16/04 12:59 PM Page 188

188

References

Kirsner, Scott. 2003. “Who’s Hurting the Worst? Evaluating Biotech, High Tech.” Boston Globe, March 10. Kleinman, Daniel Lee. 1998. “Untangling Context: Understanding a University Laboratory in the Commercial World.” Science, Technology & Human Values 23: 285–314. ———. 2003. Impure Cultures: University Biology and the World of Commerce. Madison: University of Wisconsin Press. Kleinman, Daniel L., and Steven P. Vallas. 2001. “Science, Capitalism, and the Rise of the ‘Knowledge Worker’: The Changing Structure of Knowledge Production in the United States.” Theory and Society 30: 451–492. Konrad, A., S. Winter, and Barbara A. Gutek. 1992. “Diversity in Work Group Sex Composition: Implications for Majority and Minority Workers.” Research in the Sociology of Organizations 10: 115–140. Koput, Kenneth W., Laurel Smith-Doerr, and Walter W. Powell. 1997. “Strategies of Learning and Industry Structure: The Evolution of Networks in Biotechnology.” Pp. 229–254 in Advances in Strategic Management, Volume 14: Organizational Learning and Strategic Management, edited by J. P. Walsh and A. S. Huff. Greenwich, CT: JAI. Kornhauser, William. 1962. Scientists in Industry: Conflict and Accommodation. Berkeley: University of California Press. Kuhn, Thomas. 1970. The Structure of Scientific Revolutions. 2nd ed. Chicago: University of Chicago Press. Kulis, Stephen S., and Karen Miller-Loessi. 1992. “Organizations, Labor Markets, and Gender Integration in Academic Sociology.” Sociological Perspectives 35: 93–117. Kulis, Stephen, Diane Sicotte, and Shawn Collins. 2002. “More Than a Pipeline Problem: Labor Supply Constraints and Gender Stratification Across Academic Science Disciplines.” Research in Higher Education 43: 657–691. Latour, Bruno. 1988. The Pasteurization of France. Cambridge, MA: Harvard University Press. Latour, Bruno, and Steve Woolgar. 1979. Laboratory Life: The Social Construction of Scientific Facts. Beverly Hills, CA: Sage. Lazerson, Mark H. 1995. “A New Phoenix? Modern Putting-Out in the Modena Knitwear Industry.” Administrative Science Quarterly 40: 34–59. Leblebici, Huseyin, Gerald R. Salancik, Anne Copay, and Tom King. 1991. “Institutional Change and the Transformation of Interorganizational Fields: An Organizational History of the U.S. Radio Broadcasting Industry.” Administrative Science Quarterly 36: 333–364. Leicht, Kevin T. 1998. “Work (if you can get it) and Occupations (if there are any)? What Social Scientists Can Learn From Predictions of the End of Work and Radical Workplace Change.” Work and Occupations 25: 36–48. Leicht, Kevin T., and Mary L. Fennell. 1997. “The Changing Organizational Context of Professional Work.” Annual Review of Sociology 23: 215–231. Leidner, Robin. 1993. Fast Food, Fast Talk: Service Work and the Routinization of Everyday Life. Berkeley: University of California Press. Levin, Sharon G., and Paula E. Stephan. 1998. “Gender Differences in the Rewards to Publishing in Academe: Science in the 1970s.” Sex Roles 38: 1049– 1064. Levi-Strauss, Claude. 1966. The Savage Mind. Chicago: University of Chicago Press. Lewis, Sinclair. 1998 [1924]. Arrowsmith. New York: Signet Classic. Long, J. Scott, ed. 2001. From Scarcity to Visibility: Gender Differences in the

S/D-Refs 6/16/04 12:59 PM Page 189

References

189

Careers of Doctoral Scientists and Engineers. Washington, DC: National Academy Press. Long, J. Scott, and Mary Frank Fox. 1995. “Scientific Careers: Universalism and Particularism.” Annual Review of Sociology 21: 45–71. Lounsbury, Michael. 2002. “Institutional Transformation and Status Mobility: The Professionalization of the Field of Finance.” Academy of Management Journal 45: 255–266. Lounsbury, Michael, and Mary Ann Glynn. 2001. “Cultural Entrepreneurship: Stories, Legitimacy, and the Acquisition of Resources.” Strategic Management Journal 22: 545–564. Lounsbury, Michael, and Marc J. Ventresca, eds. 2002. Social Structure and Organizations Revisited: Research in the Sociology of Organizations 19. Oxford: JAI/Elsevier. Lounsbury, Michael, Marc Ventresca, and Paul M. Hirsch. 2003. “Social Movements, Field Frames, and Industry Emergence: A Cultural-Political Perspective on U.S. Recycling.” Socio-Economic Review 1: 71–104. Maddox, Brenda. 2002. Rosalind Franklin: The Dark Lady of DNA. New York: HarperCollins. Malcom, Shirley. 1993. “Increasing the Participation of Black Women in Science and Technology.” Pp. 249–253 in The “Racial” Economy of Science: Toward a Democratic Future, edited by S. Harding. Bloomington: Indiana University Press. Mandula, Barbara. 1996. “AWIS Presidents: 25 Years of Outstanding Leadership.” AWIS Magazine, May/June: 9–12. March, James G. 1991. “Exploration and Exploitation in Organizational Learning.” Organization Science 2: 71–87. Margolis, Jane, and Allan Fisher. 2002. Unlocking the Clubhouse: Women in Computing. Cambridge, MA: MIT Press. Martin, Patricia Yancey, and David Collinson. 2002. “‘Over the Pond and Across the Water’: Developing the Field of ‘Gendered Organizations.’” Gender, Work, and Organization 9, no. 3: 244–265. Martineau, Harriet. 1837. Society in America. New York: Saunders and Otley. Massachusetts Institute of Technology. 1999. A Study on the Status of Women Faculty in Science at MIT. Cambridge, MA: MIT. Massey, Douglas S., and Nancy A. Denton. 1993. American Apartheid: Segregation and the Making of the Underclass. Cambridge, MA: Harvard University Press. McCormick, Alexander C., ed. 2000. The Carnegie Classification of Institutions of Higher Education. Menlo Park, CA: Carnegie Foundation. McIlwee, Judith S., and J. Gregg Robinson. 1992. Women in Engineering: Gender, Power, and Workplace Culture. Albany: State University of New York Press. McPherson, J. Miller, and Lynn Smith-Lovin. 1982. “Women and Weak Ties: Differences by Sex in the Size of Voluntary Organizations.” American Journal of Sociology 87: 883–904. Mead, Margaret, and R. Metraux. 1957. “Image of the Scientist Among High School Students: A Pilot Study.” Science 126: 386–387. Merton, Robert K. 1968. “The Matthew Effect in Science.” Science 159: 56–63. ———. 1973. “The Normative Structure of Science.” Pp. 267–278 in The Sociology of Science: Theoretical and Empirical Investigations, edited by N. W. Storer. Chicago: University of Chicago Press. Meyer, John W., John Boli, George M. Thomas, and Francisco O. Ramirez. 1997. “World Society and the Nation-State.” American Journal of Sociology 103: 144–181.

S/D-Refs 6/16/04 12:59 PM Page 190

190

References

Meyer, John W., and Brian Rowan. 1977. “Institutionalized Organizations: Formal Structure as Myth and Ceremony. American Journal of Sociology 83: 340–363. Milkman, Ruth. 1997. Farewell to the Factory: Auto Workers in the Late Twentieth Century. Berkeley: University of California Press. Milkman, Ruth, and Eleanor Townsley. 1994. “Gender and the Economy.” Pp. 600–619 in The Handbook of Economic Sociology, edited by N. J. Smelser and R. Swedberg. Princeton, NJ: Princeton University Press. Miner, Anne S., and Philip Anderson. 1999. “Industry and Population Level Learning: Organizational, Interorganizational, and Collective Learning Processes.” Pp. 1–32 in Advances in Strategic Management: Population Level Learning and Industry Change, edited by Anne S. Miner and Philip Anderson. Stamford, CT: JAI. Moore, Gwen. 1990. “Structural Determinants of Men’s and Women’s Personal Networks.” American Sociological Review 55: 726–735. Moore, Kelly. 1996. “Organizing Integrity: American Science and the Creation of Public Interest Organizations, 1955–1975.” American Journal of Sociology 101: 1592–1627. Moore, Kelly, and Nicole Hala. 2002. “Organizing Identity: The Creation of Science for the People.” Pp. 309–335 in Social Structure and Organizations Revisited: Research in the Sociology of Organizations 19, edited by Michael Lounsbury and Marc J. Ventresca. Oxford: JAI/Elsevier. Morgan, Glenn, and David Knights. 1991. “Gendering Jobs: Corporate Strategy, Managerial Control, and the Dynamics of Job Segregation.” Work, Employment, and Society 5: 181–200. Morgan, James N., and Greg J. Duncan. 1979. “College Quality and Earnings.” Research in Human Capital and Development 1: 103–121. Morrill, Calvin. 1995. The Executive Way: Conflict Management in Corporations. Chicago: University of Chicago Press. Morrill, Calvin, and Cindy McKee. 1993. “Institutional Isomorphism and Informal Social Control: Evidence from a Community Mediation Center.” Social Problems 40: 44–63. Morrill, Calvin, and Jason Owen-Smith. 2002. “The Emergence of Environmental Conflict Resolution: Subversive Stories and the Construction of Collective Frames and Organizational Fields.” Pp. 90–118 in Organizations, Policy, and the Natural Environment, edited by A. J. Hoffman and M. J. Ventresca. Stanford, CA: Stanford University Press. Mowery, David C., and Arvids A. Ziedonis. 2002. “Academic Patent Quality and Quantity Before and After the Bayh-Dole Act in the United States.” Research Policy 31: 399–418. Mullins, Nicholas C. 1972. “The Development of a Scientific Specialty: The Phage Group and the Origins of Molecular Biology.” Minerva 19: 52–82. Mundy, Alicia. 2001. Dispensing with the Truth: The Victims, the Drug Companies, and the Dramatic Story Behind the Battle over Fen-Phen. New York: St. Martin’s. Murray, Fiona. 2002. “Innovation as Co-evolution of Scientific and Technological Networks: Exploring Tissue Engineering.” Research Policy 31, nos. 8–9: 1389–1403. ———. 2003. “The Sociology of Spillovers: Towards a Process Model of Innovation in Scientific Networks.” Working paper presented to the MIT Economic Sociology Seminar. Cambridge, MA: Massachusetts Institute of Technology, April 24.

S/D-Refs 6/16/04 12:59 PM Page 191

References

191

National Research Council. 1994. Meeting the Nation’s Needs for Biomedical and Behavioral Scientists. Washington, DC: National Academy Press. ———. 1995. Research-Doctorate Programs in the United States: Continuity and Change. Washington, DC: National Academy Press. ———. 1998. Trends in the Early Careers of Life Scientists. Washington, DC: National Academy Press. National Science Foundation. 1992. Science and Engineering Indicators 1992. Washington, D.C.: U.S. Government Printing Office. ———. 1993. Survey of Earned Doctorates. Washington, D.C.: U.S. Government Printing Office. ———. 1995. The Human Genome Project. Washington, D.C.: U.S. Government Printing Office. ———. 1998. Science and Engineering Indicators, 1998. Washington, DC: U.S. Government Printing Office. ———. 1999. SESTAT Detailed Statistical Tables: Characteristics of Scientists and Engineers in the United States. Arlington, VA. http://srsstats.sbe.nsf.gov. ———. 2002. Science and Engineering Indicators, 2002. Washington, DC: U.S. Government Printing Office. Noble, David. 1977. America by Design: Science, Technology, and the Rise of Corporate Capitalism. New York: Oxford University Press. Ogasawara, Yuko. 1998. Office Ladies and Salaried Men: Power, Gender, and Work in Japanese Companies. Berkeley: University of California Press. Okamoto, Dina, and Paula England. 1999. “Is There a Supply Side to Occupational Sex Segregation?” Sociological Perspectives 42: 557–582. Oldenziel, Ruth. 1999. Making Technology Masculine: Men, Women, and Modern Machines in America, 1870–1945. Amsterdam: Amsterdam University Press. Oldham, Greg, and Anne Cummings. 1998. “Creativity in the Organizational Context.” Productivity 39: 187–194. Owen-Smith, Jason. 2003. “From Separate Systems to a Hybrid Order: Accumulative Advantage Across Public and Private Science at Research One Universities.” Research Policy 32: 1081–1104. Owen-Smith, Jason, and Walter W. Powell. 2001. “Careers and Contradictions: Faculty Responses to the Transformation of Knowledge and Its Uses in the Life Sciences.” Pp. 109–140 in Research in the Sociology of Work, Volume 10: The Transformation of Work, edited by S. P. Vallas. Greenwich, CT: JAI. Padavic, Irene, and Barbara Reskin. 2002. Women and Men at Work. 2nd ed. Thousand Oaks, CA: Pine Forge. Padgett, John F., and Christopher K. Ansell. 1993. “Robust Action and the Rise of the Medici, 1400–1434.” American Journal of Sociology 98: 1259–1319. Pattatucci, Angela M. 1998. Women in Science: Meeting Career Challenges. Thousand Oaks, CA: Sage. Pelled, Lisa H. 1997. “Relational Demography and Perceptions of Group Conflict and Performance: A Field Investigation.” International Journal of Conflict Management 7: 230–246. Pelled, Lisa H., Thomas G. Cummings, and Mark. A. Kizilos. 2000. “The Influence of Organizational Demography on Customer-Oriented Prosocial Behavior: An Exploratory Investigation.” Journal of Business Research 47: 209–216. Perrow, Charles. 1993. “Small Firm Networks.” Pp. 377–402 in Explorations in Economic Sociology, edited by R. Swedberg. New York: Russell Sage Foundation.

S/D-Refs 6/16/04 12:59 PM Page 192

192

References

Piore, Michael J., and Charles F. Sabel. 1984. The Second Industrial Divide. New York: Basic Books. Podolny, Joel M., and Karen L. Page. 1998. “Network Forms of Organization.” Annual Review of Sociology 24: 57–76. Powell, Walter W. 1985. Getting into Print: The Decision-Making Process in Scholarly Publishing. Chicago: University of Chicago Press. ———. 1990. “Neither Market nor Hierarchy: Network Forms of Organization.” Pp. 295–336 in Research in Organizational Behavior, vol. 12, edited by L. L. Cummings and B. Shaw. Greenwich, CT: JAI. ———. 2001. “The Capitalist Firm in the Twenty-First Century: Emerging Patterns in Western Enterprise.” Pp. 33–68 in The Twenty-First-Century Firm: Changing Economic Organization in International Perspective, edited by P. DiMaggio. Princeton, NJ: Princeton University Press. Powell, Walter W., and Paul J. DiMaggio, eds. 1991. The New Institutionalism in Organizational Analysis. Chicago: University of Chicago Press. Powell, Walter W., Kenneth W. Koput, James I. Bowie, and Laurel Smith-Doerr. 2002. “The Spatial Clustering of Science and Capital: Accounting for Biotech Firm–Venture Capital Relationships.” Regional Studies 36: 291–305. Powell, Walter W., Kenneth W. Koput, and Laurel Smith-Doerr. 1996. “Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology.” Administrative Science Quarterly 41: 116– 145. Powell, Walter W., Kenneth W. Koput, Laurel Smith-Doerr, and Jason Owen-Smith. 1999. “Network Position and Firm Performance: Organizational Returns to Collaboration in the Biotechnology Industry.” Pp. 129–159 in Networks in and Around Organizations: Research in the Sociology of Organizations, vol. 16, edited by S. Andrews and D. Knoke. Greenwich, CT: JAI. Powell, Walter W., and Jason Owen-Smith. 1998. “Universities and the Market for Intellectual Property in the Life Sciences.” Journal of Policy Analysis and Management 17: 253–277. Powell, Walter W., and Laurel Smith-Doerr. 1994. “Networks in Economic Life.” Pp. 368–402 in The Handbook of Economic Sociology, edited by N. J. Smelser and R. Swedberg. Princeton, NJ: Princeton University Press. Press, Eyal, and Jennifer Washburn. 2000. “The Kept University.” Atlantic Monthly, March. Rabinow, Paul. 1996. Making PCR: A Story of Biotechnology. Chicago: University of Chicago Press. Raider, Holly, and David J. Krackhardt. 2002. “Intraorganizational Networks.” Pp. 58–74 in Companion to Organizations, edited by Joel A. C. Baum. Oxford: Blackwell. Ramirez, Francisco O., and Christine Min Wotipka. 2001. “Slowly but Surely? The Global Expansion of Women’s Participation in Science and Engineering Fields of Study, 1972–1992.” Sociology of Education 74: 231–251. Rao, Hayagreeva, Calvin Morrill, and Mayer N. Zald. 2000. “Power Plays: Social Movements, Collective Action, and New Organizational Forms.” Research in Organizational Behavior 22: 237–281. Rayman, Paula M. 2001. Beyond the Bottom Line: The Search for Dignity at Work. New York: Palgrave. Rayman, Paula M., and Jennifer S. Jackson. 1996. “Women Scientists in Industry.” Pp. 290–320 in The Equity Equation: Fostering the Advancement of Women in the Sciences, Mathematics, and Engineering, edited by C. S. Davis et al. San Francisco: Jossey-Bass.

S/D-Refs 6/16/04 12:59 PM Page 193

References

193

Regets, Mark C. 1997. “What’s Happening in the Labor Market for Recent Science and Engineering PhD Recipients?” National Science Foundation 97-321. ———. 1998. “Has the Use of Postdocs Changed?” National Science Foundation 99-310. Reskin, Barbara F. 1978. “Scientific Productivity, Sex, and Location in the Institution of Science.” American Journal of Sociology 83: 1235–1243. ———. 2000. “Getting It Right: Sex and Race Inequality in Work Organizations.” Annual Review of Sociology 26: 707–709. ———. 2003. “Including Mechanisms in Our Models of Ascriptive Inequality.” American Sociological Review 68: 1–21. Reskin, Barbara F., and Debra Branch McBrier. 2000. “Why Not Ascription? Organizations’ Employment of Male and Female Managers.” American Sociological Review 65: 210–233. Reskin, Barbara F., Debra B. McBrier, and Julie A. Kmec. 1999. “The Determinants and Consequences of Workplace Sex and Race Composition.” Annual Review of Sociology 25: 335–361. Reskin, Barbara F., and Patricia A. Roos. 1990. Job Queues, Gender Queues: Explaining Women’s Inroads into Male Occupations. Philadelphia: Temple University Press. Ridgeway, Cecilia L., and Lynn Smith-Lovin. 1999. “The Gender System and Interaction.” Annual Review of Sociology 25: 191–216. Robbins-Roth, Cynthia. 1998. “A Scientist Gone Bad: Or How I Went from the Bench to the Board Room.” Pp. 1–10 in Alternative Careers in Science: Leaving the Ivory Tower, edited by C. Robbins-Roth. San Diego: Academic Press. ———. 2000. From Alchemy to IPO: The Business of Biotechnology. Cambridge, MA: Perseus. Rogers, Everett M. 1995. Diffusion of Innovations. 4th ed. New York: The Free Press. Rosenfeld, Rachel A., Mark E. Van Buren, and Arne L. Kalleberg. 1998. “Gender Differences in Supervisory Authority: Variation Among Advanced Industrialized Democracies.” Social Science Research 27, no. 1: 23–49. Ross, Sherwood. 2002. “Talent Hunt Is Still On in Biotech.” Boston Globe, August 11. Rossiter, Margaret W. 1982. Women Scientists in America: Struggles and Strategies to 1940. Baltimore: Johns Hopkins University Press. Rothschild-Whitt, Joyce. 1979. “The Collectivist Organization: An Alternative to Rational-Bureaucratic Models.” American Sociological Review 44: 509–527. Ruiz-Quintanilla, S. Antonio, and Rita Claes. 1996. “Determinants of Underemployment of Young Adults: A Multi-country Study.” Industrial and Labor Relations Review 49: 424–438. Sabel, Charles F. 1989. “Flexible Specialisation and the Re-emergence of Regional Economies.” Pp. 17–70 in Reversing Industrial Decline? edited by P. Hirst and J. Zeitlin. London: Berg. Sabel, Charles F., and Michael J. Piore. 1984. The Second Industrial Divide: Possibilities for Prosperity. New York: Basic Books. Sackett, P. R., C. L. Z. DuBois, and A. W. Noe. 1991. “Tokenism in Performance Appraisal: The Effects of Work Group Representation on Male-Female and White-Black Differences in Supervisory Ratings.” Journal of Applied Psychology 76: 263–267. Saxenian, AnnaLee. 1994. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press.

S/D-Refs 6/16/04 12:59 PM Page 194

194

References

Schiebinger, Londa L. 1999. Has Feminism Changed Science? Cambridge, MA: Harvard University Press. Schofer, Evan, Francisco O. Ramirez, and John W. Meyer. 2000. “The Effects of Science on National Economic Development, 1970 to 1990.” American Sociological Review 65: 866–887. Scott, W. Richard. 2001. Institutions and Organizations. 2nd ed. Thousand Oaks, CA: Sage. Scott, W. Richard, Martin Ruef, Peter J. Mendel, and Carol A. Caronna. 2000. Institutional Change and Healthcare Organizations: From Professional Dominance to Managed Care. Chicago: University of Chicago Press. Shan, Weijan, Gordon Walker, and Bruce Kogut. 1994. “Interfirm Cooperation and Startup Innovation in the Biotechnology Industry.” Strategic Management Journal 15: 387–394. Shapin, Steven. 1989. “The Invisible Technician.” American Scientist 77: 554–563. Silverman, Edward R. 1991. “Study Reports Middle Managers’ Pay Rising at Biotech Firms.” The Scientist, April 29. ———. 1997. “NSF Employment Study Confirms Issues Facing Women, Minorities.” The Scientist, April 14. Slaughter, Sheila, and Larry L. Leslie. 1997. Academic Capitalism: Politics, Policies, and the Entrepreneurial University. Baltimore: Johns Hopkins University Press. Smith, Ryan A., and James Elliott. 2002. “Does Ethnic Niching Influence Access to Authority? An Examination of Race and Gender in Three Metro Areas.” Social Forces 81, no. 1: 255–279. Smith, Vicki. 1997. “New Forms of Work Organization.” Annual Review of Sociology 23: 315–339. Smith-Doerr, Laurel. 1999. Career Paths in the Life Sciences: Processes and Outcomes of Organizational Change. PhD diss., University of Arizona, Tucson. ———. 2001. “‘Where’d You Get Your PhD?’: Education Ranking and Inequality Among Life Scientists.” Paper presented at the annual meeting of the Society for Social Studies of Science, Cambridge, MA. ———. 2004. “Flexibility and Fairness: Effects of the Network Form of Organization on Gender Equity in Life Science Careers.” Sociological Perspectives 47, no. 1: 25–54. Smith-Doerr, Laurel, Jason Owen-Smith, Kenneth W. Koput, and Walter W. Powell. 1999. “Networks and Knowledge Production: Collaboration and Patenting in Biotechnology.” Pp. 390–408 in Corporate Social Capital and Liability, edited by R. T. A. J. Leenders and S. M. Gabbay. Boston: Kluwer Academic Publishers. Smith-Doerr, Laurel, and Walter W. Powell. Forthcoming 2004. “Networks in Economic Life.” In The Handbook of Economic Sociology, 2nd ed, edited by N. J. Smelser and R. Swedberg. Princeton, NJ: Princeton University Press and Russell Sage Foundation. Sonnert, Gerhard, with Gerald Holton. 1995. Who Succeeds in Science? The Gender Dimension. New Brunswick, NJ: Rutgers University Press. South, S., C. Bonjean, W. Markham, and J. Corder. 1983. “Female Labor Force Participation and the Organizational Experiences of Male Workers.” Sociological Quarterly 24: 367–380. Spenner, Kenneth I., Olga O. Suhomlinova, Sten A. Thore, Kenneth C. Land, and Derek C. Jones. 1998. “Strong Legacies and Weak Markets: Bulgarian StateOwned Enterprises During Early Transition.” American Sociological Review 63: 599–617.

S/D-Refs 6/16/04 12:59 PM Page 195

References

195

Star, Susan Leigh. 1995. “Introduction.” Pp. 1–38 in Ecologies of Knowledge: Work and Politics in Science and Technology, edited by S. L. Star. Albany: State University of New York Press. Stark, David. 2001. “Ambiguous Assets for Uncertain Environments: Heterarchy in Postsocialist Firms.” Pp. 69–104 in The Twenty-First-Century Firm: Changing Economic Organization in International Perspective, edited by P. DiMaggio. Princeton, NJ: Princeton University Press. Sterling, Bruce. 1988. Islands in the Net. New York: Ace Books. Stern, Scott. 1999. “Do Scientists Pay to Be Scientists?” Boston, MA: National Bureau of Economic Research, Working Paper No. w7410, October. Stinchcombe, Arthur L. 1959. “Bureaucratic and Craft Administration of Production: A Comparative Study.” Administrative Science Quarterly 4: 168–187. ———. 1965. “Social Structure and Organizations.” Pp. 142–193 in Handbook of Organizations, edited by J. G. March. New York: Rand McNally. ———. 2001. When Formality Works: Authority and Abstraction in Law and Organizations. Chicago: University of Chicago Press. Stolzenberg, Ross. 1978. “Bringing the Boss Back In: Employer Size, Employee Schooling, and Socioeconomic Achievement.” American Sociological Review 43: 813–828. Streeck, Wolfgang, and Philippe C. Schmitter. 1985. “Community, Market, State— and Associations?” European Sociological Review 1: 119–138. Stuart, Toby E., Ha Hoang, and Ralph C. Hybels. 1999. “Interorganizational Endorsements and the Performance of Entrepreneurial Ventures.” Administrative Science Quarterly 44: 315–349. Sudman, Seymour. 1976. Applied Sampling. New York: Academic Press. Teitelman, Robert. 1989. Gene Dreams: Wall Street, Academia, and the Rise of Biotechnology. New York: Basic Books. Telecommunications Industry Association. 2002. Annual Report 2002. Arlington, VA. http://www.tiaonline.org/media/annual_report/AnRpt02.pdf. Thompson, James D. 1967. Organizations in Action: Social Science Bases of Administrative Theory. New York: McGraw-Hill. Thorne, Barrie. 1993. Gender Play: Girls and Boys in School. New Brunswick, NJ: Rutgers University Press. Thornton, Patricia H. 1995. “Accounting for Acquisition Waves: Evidence from the U.S. College Publishing Industry.” Pp. 199–225 in The Institutional Construction of Organizations, edited by W. R. Scott and S. Christensen. Thousand Oaks, CA: Sage. Thurow, Lester. 1969. Poverty and Discrimination. Washington, DC: Brookings Institution. Tierney, M. 1995. “Negotiating a Software Career: Informal Work Practices and ‘The Lads’ in a Software Installation.” In The Gender-Technology Relation: Contemporary Theory and Research, edited by K. Grint and R. Gill. London: Taylor and Francis. Tilly, Charles. 1998. Durable Inequality. Berkeley: University of California Press. Tolbert, Pamela S., and Alice A. Oberfield. 1991. “Sources of Organizational Demography: Faculty Sex Ratios in Colleges and Universities.” Sociology of Education 64: 305–315. Tolbert, Pamela S., T. Simons, A. O. Andrews, and J. Rhee. 1995. “The Effects of Gender Composition in Academic Department on Faculty Turnover.” Industrial and Labor Relations Review 48: 562–579. Tomaskovic-Devey, Donald. 1993. Gender and Racial Inequality at Work: The Sources and Consequences of Job Segregation. Ithaca, NY: ILR.

S/D-Refs 6/16/04 12:59 PM Page 196

196

References

Tomaskovic-Devey, Donald, Arne Kalleberg, and Peter Marsden. 1996. “Organizational Patterns of Gender Segregation.” Pp. 276–301 in Organizations in America: Analyzing Their Structures and Human Resource Practices, edited by Arne Kalleberg, Peter Marsden, Joe Spaeth, and David Knoke. Beverly Hills, CA: Sage. Tomaskovic-Devey, Don, and Sheryl Skaggs. 2002. “Sex Segregation, Labor Process Organization, and Gender Earnings Inequality.” American Journal of Sociology 108, no. 1: 102–128. Toohey, R. P., and J. Whittaker. 1993. “Engineering Women: A View from the Workplace.” In Journal of Management in Engineering 9, no. 1, January. Trower, Cathy Ann. 2001. “Women Without Tenure, Part 1.” Science Nextwave, September 14, http://nextwave.sciencemag.org. Tsui, Anne S., Terry D. Egan, and Charles A. O’Reilly. 1992. “Being Different: Relational Demography and Organizational Attachment.” Administrative Science Quarterly 37: 549–579. Tuchman, Gaye. 1989. Edging Women Out: Victorian Novelists, Publishers, and Social Change. New Haven, CT: Yale University Press. Tushman, Michael L., and Charles A. O’Reilly. 1997. Winning Through Innovation: A Practical Guide to Leading Organizational Change and Renewal. Boston, MA: Harvard Business School Press. Tushman, Michael L., and Wendy Smith. 2002. “Organizational Technology.” Chap. 17 in Companion to Organizations, edited by J. A. Baum. Oxford: Blackwells. U.S. Census Bureau. 2000. “Women in the United States.” PPL-121. Washington, DC: U.S. Census Bureau. U.S. Department of Health and Human Services. 1995. Grant Application PHS 398: Includes Research Career Awards and Institutional National Research Service Awards. Washington, DC: U.S. Government Printing Office. U.S. General Accounting Office. 2001. “Women in Management: Analysis of Selected Data from the Current Population Survey.” Washington, DC: GAO, 02-126. Uzzi, Brian. 1997. “Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness.” Administrative Science Quarterly 42: 35–67. Vallas, Steven P. 2003. “Teamwork Fails: Obstacles to Workplace Change in Four Manufacturing Plants.” American Sociological Review 68: 223–250. Vallas, Steven P., and John P. Beck. 1996. “The Transformation of Work Revisited: The Limits of Flexibility in American Manufacturing.” Social Problems 43: 339–362. Walsh, Sharon. 2004. “Berkeley Denies Tenure to Ecologist Who Criticized Universities’ Ties to the Biotechnology Industry.” Chronicle of Higher Education, January 9. Watson, James D. 1992. “A Personal View of the Project.” Pp. 164–176 in The Code of Codes: Scientific and Social Issues in the Human Genome Project, edited by D. J. Kevles and L. Hood. Cambridge, MA: Harvard University Press. ———. 2001. Genes, Girls, and Gamow: After the Double Helix. New York: Vintage Books. Weber, Max. 1918. “Science as a Vocation.” Lecture at Munich University. Published in From Max Weber: Essays in Sociology, edited by H. H. Gerth and C. W. Mills. New York: Oxford University Press. ———. 1978 [1921]. Economy and Society. Edited by G. Roth and C. Wittich. Berkeley: University of California Press. Webster, Juliet. 1996. Shaping Women’s Work: Gender, Employment, and Information Technology. London: Longman.

S/D-Refs 6/16/04 12:59 PM Page 197

References

197

Weick, Karl E. 1995. Sensemaking in Organizations. Thousand Oaks, CA: Sage. Wennerås, C., and A. Wold. 1997. “Nepotism and Sexism in Peer-Review.” Nature 387: 341–343. Werth, Barry. 1994. The Billion-Dollar Molecule: One Company’s Quest for the Perfect Drug. New York: Simon and Schuster. Westney, D. Eleanor. 2001. “Japanese Enterprise Faces the Twenty-First Century.” Pp. 105–143 in The Twenty-First-Century Firm: Changing Economic Organization in International Perspective, edited by P. DiMaggio. Princeton, NJ: Princeton University Press. Wharton, Amy S., and James N. Baron. 1987. “So Happy Together? The Impact of Gender Segregation on Men at Work.” American Sociological Review 52: 574–587. ———. 1991. “Satisfaction? The Psychological Impact of Gender Segregation on Women at Work.” Sociological Quarterly 32: 365–387. Wharton, Amy S., and S. R. Bird. 1996. “Stand by Your Man: Homosociality, Work Groups, and Men’s Perceptions of Difference.” Pp. 97–114 in Masculinities in Organizations, edited by C. Cheng. Thousand Oaks, CA: Sage. Wharton, Amy S., Thomas Rotolo, and Sharon R. Bird. 2000. “Social Context at Work: A Multilevel Analysis of Job Satisfaction.” Sociological Forum 15, no. 1: 65–90. Whyte, William H. 1956. The Organization Man. New York: Simon and Schuster. Williams, Katherine Y., and Charles A. O’Reilly. 1998. “Demography and Diversity in Organizations: A Review of 40 Years of Research.” Pp. 77–140 in Research in Organizational Behavior 20, edited by Barry M. Staw and L. L. Cummings. Stamford, CT: JAI. Women in Cable & Telecommunications Foundation. 1998. “The Technical Pay Gap.” Cablevision, October 26. Woodward, Joan. 1965. Industrial Organization: Theory and Practice. Oxford: Oxford University Press. Wright, Rosemary. 1997. Women Computer Professionals: Controlled Progress in a Male Occupation. Lewiston, NY: Edwin Mellen. Wright, Rosemary, and Jerry A. Jacobs. 1994. “Male Flight from Computer Work: A New Look at Occupational Resegregation and Ghettoization.” American Sociological Review 59: 511–536. Zelizer, Viviana A. 1999. “A Gendered Division of Labor.” Pp. 2–4 in the newsletter for the Organizations, Occupations, and Work Section of the American Sociological Association, Fall issue. Zucker, Lynne G., Michael R. Darby, and Marilynn B. Brewer. 1998. “Intellectual Capital and the Birth of U.S. Biotechnology Enterprises.” American Economic Review 88: 290–306. Zucker, Lynne G., Michael R. Darby, and Maximo Torero. 2002. “Labor Mobility from Academe to Commerce.” Journal of Labor Economics 20: 629–660. Zuckerman, Harriet. 1977. Scientific Elite: Nobel Laureates in the United States. New York: The Free Press. ———. 1988. “The Sociology of Science.” Pp. 511–574 in Handbook of Sociology, edited by N. J. Smelser. Newbury Park, CA: Sage. ———. 1991. “The Careers of Men and Women Scientists: A Review of Current Research.” Pp. 27–56 in The Outer Circle: Women in the Scientific Community, edited by H. Zuckerman, J. R. Cole, and J. T. Bruer. New York: W. W. Norton. Zuckerman, Harriet, Jonathan R. Cole, and John T. Bruer, eds. 1991. The Outer Circle: Women in the Scientific Community. New York: W. W. Norton.

S/D-Refs 6/16/04 12:59 PM Page 198

S/D-Index 6/16/04 1:00 PM Page 199

Index

Abbott Laboratories, 45, 46 Academic careers: and biotech careers, 61; and critical mass theory, 145; legitimacy narratives in, 76–77 Academic citations: gendered patterns in, 21; and university patents, 46 Academic gender discrimination: and academic specialization, 81, 95; affirmative action/EEO rules and, 138; department culture and, 20; in hiring practices, 18–19, 120–121; and marginalizing behaviors/policies, 30–31; in promotion and awarding of tenure, 10–11, 18–19, 30, 62, 105, 115; and sex composition of departments, 23 Academic rank, of female vs. male PhDs, 105 Academic science: biotech comparisons/connections with, 39, 42, 68–70; commercialization debate in, 51–52, 77; discouragement to leave, 123–124; gendering of scientific work in, 109–111, 130–131; hierarchy and equity-enforcing rules in, 100; hiring of underrepresented groups in, 102; patents and, 46; research funding, 62, 63 Academic-industrial research collaboration: and academic capitalism, 150; conflicts of interest in, 149–150 Affirmative action law: and gender equity, 137–138; and organizational procedures/outcomes, 137

Alternative Careers in Science: Leaving the Ivory Tower, 70 American Women in Science (AWIS), 1 Amex biotechnology index, 150 Arrowsmith (Lewis), 51 Bayh-Dole Act, 41, 149 Biotech careers: academic frame for, 61; and demand for life scientists, 48–49; and educational background, 88–89, 92–93; family roles and, 120–121; females’ early entry into, 90; and gender equality in work roles, 29, 30; and gender queues, 18, 86, 88, 93–94; interorganizational mobility of, 147; as legitimate career choice, 58–61; and network connections, 87–88; predictors of entry into, 88–93; and socialization of girls, 122; timing of entry into, 85–86, 92–93; of top life scientists, 86–88; women’s durability in, 146; women’s future in, 151; of young male PhDs, 88 Biotechnology industry: academic comparisons/connections with, 68–70; asset of newness in, 59, 68–71; competencies, 44–45; diverse workforce in, 90–92, 102, 123–124; familyfriendly practices in, 125; flexible boundaries/roles in, 119–120, 122, 123, 138; financial resources and motives in, 58, 62–65, 72; future organization of, 150–151; gender

199

S/D-Index 6/16/04 1:00 PM Page 200

200

Index

equality in, 90–92, 109, 122, 138–139; human capital in, 49; information technology (IT) firms compared to, 144–147; institutional change process in, 57–59, 72–73; interorganizational networks in, 40, 42, 43–47, 60–61, 128; lack of female founders in, 143; pharmaceuticals’ collaboration with, 42, 43, 44–46; product development timeline, 47; products, 43; regulatory environment for, 41; relative equality in, 127; research and publication in, 62–63, 71; search for magic bullets in, 39–40; social networks and, 65–68, 72; sources of legitimacy in, 59–60, 74–76; startup firms, 29, 42–43, 121; team ethos in, 71–72, 106, 122; and university researchers, 42, 43, 46; venture-capital backers of, 47–48, 150; women’s promotion/leadership in, 102, 106–109, 122–123 Bureaucratic organizations: equityenforcing rules in, 100–101, 136–137; gender equity in, 28–29; “group think” in, 139; invention and inequality in, 113; multinational, gender policies of, 2; old-boy networks in, 101–102, 136 Bush, Vannevar, 37 Catalyst (consultant firm), 2 Chapela, Ignacio, 149 Compensation: academic vs. industrial, 104; gender gap in, 80–81; of female life scientists, 105; in relation to scientific field, 81; in science career, 62–63 Competitive advantage, gendered brain drain and, 14 Computer industry, organization forms in, 26 Contingency theory, 25, 27 Core-periphery diffusion theory: and innovation, 83–85; and women’s ideas, 141 Cumulative disadvantage theory, 19 Dedicated biotech firms: defined, 41–42; elite PhDs in, 115

Delbrück, Max, 35 Developing countries, women’s work in, 142–143, 152 Difference feminism perspective, and women’s innovative ideas, 141 Diffusion of knowledge theory, 84–85 Diversity: in biotech workforce, 90–92, 102, 123–124; and innovative productivity, 139–140; and job tenure, 140 Drug development: and academicindustry relations, 39–40; and biotech product cycle, 47 Eastern European network organizations, diversity in, 148 Educational prestige: and biotech entry, 88–90, 92–93; and careers of young PhDs, 162, 165; and life science career choices, 85; and women’s promotion, 105 Ehrlich, Paul, 39 Emotional conflict, and gender diversity, 140 Enbrel (drug), development of, 43–44 Engineers. See Female engineers Equal employment opportunity (EEO), and social context of workplace, 137–138 Family-related policies: biotech vs. university, 125–126; gendered discrimination and, 120–122 Federation for the American Societies of Experimental Biology (FASEB), 1 Female engineers: compared to female life scientists, 144–146; and critical mass hypothesis, 145; deterrents to, 151; performance evaluations of, 146; status in information technology (IT), 127–128, 144–146 Female entrepreneurship, and venturecapital networks, 143 Female life scientists: and academic gender bias, 10–11, 30–31; and biotech employment advantages, 108–109; and cumulative disadvantage theory, 19–20; and family issues, 120–121; and gendered brain drain, 14; and gendered pay differences, 12–14; ghettoization of,

S/D-Index 6/16/04 1:00 PM Page 201

Index 94–95; in IT vs. biotech, 144–146; median income of, 105; narratives of discrimination by, 117–118, 120–122, 126, 127, 128–131; and organization structure, 20; publishing of, 17 Female PhDs: in low-paying positions, 105; in social sciences, 148 Funding. See Research funding Gender bias: and affirmative action policies, 137–138; and exclusion from male networks, 136 Gender equality: formal vs. piece-meal approach to, 2; and future policy measures, 152; and global bureaucratization, 152; social and business benefits of, 140–141; and work role flexibility, 120 Gender stratification: and academic department organization, 20; in academic hiring and promotions, 18–19; in academic science, 3–4, 10–11; demand-side explanations of, 18–24, 142; and gender segregation, 80–81; and income inequality, 12–13, 105; in labor market, 80–81; and old-boy networks, 136; organizational context of, 2–4, 23–20, 103–116, 128; pipeline model of, 17; revolving door theory of, 17–18; scholarship on, 3–5; and sex segregation, 80; and socialization theory, 15–17; supplyside explanations of, 15–18; and women’s level of authority, 21, 30 Gender studies, and organizational studies, 21 Gender wage gap, 12–14, 105; and sex composition of firms, 22 Gendered work roles, and organization structure, 28–29, 30 Genentech, family-friendly practices in, 125 Ghettoization: defined, 80; of female life scientists, 94–95; and inequality in authority levels, 21 Global bureaucratization, tendencies toward, 152 Higher education, changes in women’s pursuit of, 9–10

201

Hiring discrimination: and family roles, 120–121; and queuing theory, 18–19 HIV drugs, clinical trials for, 45 Hopfield, John, 36 Hormone replacement therapy (HRT), 141 Human capital theory, and labor market discrimination, 15 Human Genome Project, 33, 40–41; criticisms of, 41; federal funding for, 35 Immunex (biotech firm), 43–44 Information technology (IT) firms: compared to biotech firms, 144–147; critical-mass hypothesis and, 145, 148; female engineers’ status in, 127–128, 144–146; geek culture in, 145; geographic location of, 144; organizational form and women’s promotion in, 144; revolving-door treatment of women in, 146; tenure in, 146 Innovation: in bureaucracy vs. network organization, 139; and core-periphery diffusion, 83–85, 141; decentralization and, 135; diversity and, 139–140; and drawing of social boundaries, 109–112; and equal treatment, 109–114; knowledge economy and, 136; and relative equality, 109–114, 116–117; and selection bias, 140 Institutional change: legitimacy and, 57, 58–59; neoinstitutionalist perspective on, 55–59; and network-form organizations, 57–59, 72–73 Japanese network organizations, diversity in, 148 Knowledge economy, 49; and biotechuniversity collaboration, 46–47; defined, 59; innovation and competition in, 136; and radical organizational changes, 2, 55 Labor force: female participation rate, 10; gender segregation in, 80–81 Learning organizations, 27 Legitimacy: academic model of, 76–77;

S/D-Index 6/16/04 1:00 PM Page 202

202

Index

of biotech work, 59–60, 74–76; and educational prestige, 85; and organization structure, 56; of scientific work, 55–56 Life sciences: defined, 5; doctorate growth rate in, 36; early social networks in, 34–35; economics of funding research in, 41; funding bias in, 36; gender stratification in, 104–106; and industrial applied science, 37; and physics, 35–36; scientific and organizational changes in, 40–41 Life-science careers: in biotech, 86–88; and educational prestige, 85; gendering of, 29, 30, 95; micro-institutional context of, 55. See also Biotech careers Life-science career study: combined qualitative-quantitative approach to, 155, 174–176; data source in, 155–156; eliteness of education in, 165; exclusion of race from, 166; key explanatory variables in, 169–170; leadership-level variable in, 166–167; interviews, 158–159, 172–174; qualitative sample in, 162–164; qualitative measure/methods in, 172–174; quantitative measures/methods in, 164–172, 175, 176; quantitative sample in, 159–162; statistical models and logistic regression results, 170–172; and types of organizational affiliation, 168 Life-science PhDs: and gender diversity benefits, 140; increase in, 93; interviews with, 156, 158–159; quantitative sample of, 159–162; and tenuretrack positions, 75 Life-science research programs, 39; and profit motive, 77 Massachusetts Institute of Technology (MIT), 30–31 Mead, Margaret, 15 Meritocracy, as key scientific value, 140 Millennium Pharmaceuticals, 150–151 Millikan, Robert, 35 Molecular biology: “Matthew effect” and, 84; origins of, 34–36; PhD programs in, 39

Monoclonal antibodies biotechnology, 40 National Institute of General Medical Sciences (NIGMS), 5, 156, 157 National Institutes of Health (NIH), 36; and Human Genome Project, 41; payback policy for training grantees, 160; research funding, 62; traininggrants data, 156, 157, 159, 160–161 Network organization, 24; capacity for change in, 119; diversity in, 119; conditions for women’s success in, 147–148; and contradictory institutional logics, 73–74; formal design of, 114; information sharing norms in, 118–119; innovation and equality reinforcement in, 114, 116; institutionalization of, 55, 74–77; interorganizational connectivity and transparency in, 57–58, 101–102, 115; legitimation efforts in, 74–76; and national contexts, 27–28; project flexibility in, 119; studies, 3, 26–28; weaknesses of, 114; women’s status in, 28–29, 30, 102, 115, 128; work role flexibility in, 119–120. See also Biotechnology industry New knowledge fields: academic influence on, 84; and core-periphery diffusion, 83–85; and empty field phenomenon, 82–83; entry into, 86–94; formation of, 83–84; queuing theory and, 82, 86. See also Biotech careers Noble, David, 149 Occupational segregation, 80–81 Organization size, and gender equality, 128 Organization studies: diversity effects in, 140; and gender studies, 21 Organizational sex composition: and gendered career outcomes, 22; and promotion of women, 22–23 Organizational structure: decentralized, creativity and, 135; and flexible specialization, 26; and gender equality/stratification, 3, 23–30, 103–116; and gendered social closure, 28; innovation and, 24, 26–28; and interorganizational linkages,

S/D-Index 6/16/04 1:00 PM Page 203

Index 27–28; and labor-market inequality, 20; and regional advantage, 26; theoretical approaches to, 25–29; and women’s promotion, 100–102, 106–109, 115–116, 144 Organizational theory, 3; of decentralization and innovation, 135; neoinstitutionalist, 27–28, 55–57 Patent and Trademark Law Amendments Act, 41 Patents, and university’s cited publications, 46 Pharmaceutical corporations: biological PhDs’ employment in, 75; and blockbuster drugs, 43; and biotech firms, 37–39, 40, 42, 43, 44–46; competencies of, 44–45; drug marketing, 43–44 PhDs: elite-educated, in biotech careers, 85; impacts of increase in, 54; young male, in biotech careers, 88. See also Life-science PhDs Physics: lack of critical mass of women in, 148; life sciences’ relationship with, 35–36 Pipeline model of gender stratification, 17, 145, 147 Private sector, U.S. life scientists working in, 3 Productivity, and gender diversity, 140 Professional occupations: gender segregation in, 81; legitimation of, 75–76 Public sector, women’s promotion in, 23 Publishing: in biotech firms, 62–63, 71; citations, and university patents, 46; citation patterns, 21; gender bias in crediting, 4 Qualitative research: problem of “studying up” in, 164; qualitative interview sample in, 162–164 Quantitative research, sampling/oversampling in, 159–162 Queuing theory, 1, 52, 80, 81; and biotech careers of life scientists, 88, 90; and core-periphery diffusion, 83–85; empty field phenomenon in, 82–83; and entry into new field, 82; and gendered hiring queues, 86–88; and occupational segregation, 81;

203 and tenure-track academic positions, 88

Research: academic-industrial collaboration in, 149–150; biotech, 62–63, 71 Research funding: advantages of multiple sources of, 147–148; academic science, 62, 63; biotech, 58, 62–65, 72; for Human Genome Project, 35; in life sciences, economics of, 41 Research teams, team tenure option for, 151–152 Revolving door theory, 17–18 Rine, Jasper, 149 Schwartz, Neena, 1 Science careers: in biotech industry, 48, 54; and eliteness of doctoral education, 162; gender stratification in, 115; and increase in PhDs, 54; labor market for, 52–54; legitimacy and, 55–56; and monetary compensation, 62–63; and recruitment ads, 53; university, 54 Science departments, instrumental vs. relational culture in, 20 Science education, women’s underrepresentation in, 10 Scientific career: academic, gendering of, 109–111, 130–131; compensation in, 62–63; and time to promotion, 104 Scientific field: gender composition in, 81; salary in relation to, 81; selection bias in, 140 Scientific labor force, increase in, 151 Scientific revolutions, concept of, 84 Senior scientist position, gender stratification in, 105, 115, 141–142. See also Women’s promotion and leadership Sex segregation, defined, 80 Social networks: gender differences in, 21–22; in hierarchies vs. network organizations, 101; legitimizing role of, 65–68; male executive, 136, 142 Socialization theory, 15–17 Social-movement research, 55, 56, 57

S/D-Index 6/16/04 1:00 PM Page 204

204 Technicians, in network organizations, 114 Telomerase, discovery of, 42 Tenure/tenure-track positions: decline in, 75; gender discrimination in, 10–11, 30, 62, 105, 115; and gender diversity, 140; in information technology, 146; of life scientists, 75; and queuing theory, 88 Triangle Pharmaceuticals, 45 University of Michigan, biological sciences departments of, 47 Unlocking the Clubhouse: Women in Computing, 145 Venture capital: and biotech firms, 47–48, 60, 150; and old-boy networks, 143 Waelsch, Salome, 4 Watson, James D., 33, 34, 41 Weber, Max, 113 Women in Engineering, 31 Women life scientists. See Female life scientists Women’s careers: in developing countries, 152; in network organization,

Index scope of conditions for, 147–148; and organizational context, 2–3; and revolving door theory, 17–18; in science and technology, deterrent to, 151 Women’s health, biomedical technologies for, 141 Women’s promotion and leadership: in biotech firms, 102, 106–109, 122–123; and bureaucratic rules, 136–137; bureaucratic structural barriers to, 100–101, 137; and differentiation of work roles, 106; and diversity in senior-level positions, 141–142; and formal organizational roles, 119; and male mentoring, 101; in networks vs. hierarchies, 106–109; and organizational sex composition, 22; and organizational structure, 100–102, 106–109, 115–116, 144 Women’s rights movement, 1 Work groups: diversity and productivity in, 139–140; male-dominated and sexist, 146 Work roles, and organizational form, 28–29, 30 Wyeth (company), 37–39, 44

S/D-ATB 6/16/04 1:01 PM Page 205

About the Book

Women scientists working in small, for-profit companies are eight times more likely than their university counterparts to head a research lab. Why? Laurel Smith-Doerr reveals that, contrary to widely held assumptions, strong career opportunities for women and minorities do not depend on the formal policies and long job ladders that large, hierarchical bureaucracies provide. In fact, highly internally linked biotechnology firms are far better workplaces for female scientists (when compared to university settings or established pharmaceutical companies), offering women richer opportunities for career advancement. Based on quantitative analyses of more than two thousand life scientists’ careers and qualitative studies of scientists in eight biotech and university settings, Smith-Doerr’s work shows clearly that the network form of organization, rather than fostering “old boy networks,” provides the organizational flexibility that not only stimulates innovation, but also aids women’s success. Laurel Smith-Doerr is assistant professor of sociology at Boston University.

205