Female STEM Doctorate Holders in the Academic Workforce

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Female STEM Doctorate Holders in the Academic Workforce:

An Event-History Analysis

A dissertation presented to

the faculty of

The Patton College of Education of Ohio University

In partial fulfillment

of the requirements for the degree

Doctor of Philosophy

Laura L. Risler

August 2019

This dissertation titled

Female STEM Doctorate Holders in the Academic Workforce:

An Event-History Analysis

LAURA L. RISLER

has been approved for

the Department of Counseling and Higher Education

and The Patton College of Education by

Lijing Yang

Assistant Professor of Counseling and Higher Education

Renée A. Middleton

Dean, The Patton College of Education 3

Abstract

RISLER, LAURA L., Ph.D., August 2019, Higher Education

Female STEM Doctorate Holders in the Academic Workforce: An Event-History

Analysis

Director of Dissertation: Lijing Yang

This study is an examination of the inter-sector career mobility of women who earn STEM doctorate degrees from U.S. higher-education institutions, using data from the national Survey of Doctorate Recipients. The findings indicate that cumulative-hazard estimates for changing employment sectors increased more rapidly for women than for men overall, but among those who started their employment in Education (for this study, restricted to higher education), rates by gender were equal. Complementary log-log regression analysis of all respondents indicated that women were significantly more likely than men to change employment sectors, as were respondents whose first employment was not in Education. Significant differences in likelihood were also found between disciplines. Marital status did not have a significant effect. Women with children living at home were significantly more likely to change sectors overall, except women starting in Education.

Dedication

To my husband, daughter, parents, and parents-in-law, who never doubted I could do it.

Acknowledgments

Many thanks are due to several individuals and entities who have helped me make it through the dissertation process! Foremost, of course, is my family, who have had to negotiate around my dissertation-related clutter and put up with my extended disappearances to the data room on countless evenings and weekends.

I also want to express my deepest thanks to my dissertation committee members: advisor Lijing Yang, DeLysa Burnier, Laura Harrison, and David Nguyen. They all provided invaluable input to my proposal and the final product.

I’d also like to thank the dissertation writing group I participated in through the

Graduate Writing & Research Center. With guidance from our facilitator, Dr. Robert

West, I and fellow doctoral students Tanya Barnett, Joe Carver, and Chris Fowler provided feedback on each other’s early drafts as well as mutual encouragement.

I would also like to thank the team of researchers whose tremendously influential work informed the development of my own research project: Drs. Mary Ann Mason,

Marc Goulden and Nicholas Wolfinger.

And finally, I want to thank not only the National Science Foundation for administering the SDR but also the thousands of doctorate recipients who have filled out these surveys. Their willingness to share information about their lives and careers makes large-scale research efforts like this one possible. As a newly minted Ph.D. myself, I’ll soon be called upon to fill out a Survey of Earned Doctorates, and I hope that by following the example of those SDR respondents I’ll be paying it forward to a new generation of researchers. 6

Table of Contents

Page

Abstract ...... 3 Dedication ...... 4 Acknowledgments ...... 5 List of Tables ...... 9 List of Figures ...... 10 Chapter 1: Introduction ...... 11 Statement of the Problem ...... 11 Purpose of the Study ...... 17 Research Questions ...... 18 Delimitations of the Study ...... 18 Definitions ...... 19 Organization of the Dissertation ...... 22 Chapter 2: Literature Review ...... 23 Introduction ...... 23 Theoretical Frameworks ...... 23 Behavioral economics ...... 23 Human capital theory ...... 26 Social-role theory ...... 29 Pipeline/path construct ...... 33 Research on the STEM Workforce ...... 38 Research using SDR data ...... 39 Research using other data sources ...... 41 Research on Women Doctorate Holders ...... 44 Research Related to Specific Factors ...... 48 Discipline ...... 48 Employment sector ...... 51 Changing sectors ...... 55 Race/ethnicity ...... 57 Family status ...... 59 Carnegie classifications ...... 62 7

Carnegie type of doctorate-granting institution ...... 62 Carnegie type of employing institution ...... 64 Significance of this Study ...... 66 Data sources on faculty ...... 68 Chapter 3: Methodology ...... 69 Research Questions ...... 69 About the Survey Instrument ...... 70 About the Sample ...... 73 Variables ...... 75 Dependent variable ...... 75 Independent variables ...... 76 Gender ...... 76 Age ...... 77 Race/ethnicity ...... 77 Citizenship status ...... 78 Marital status ...... 78 Children ...... 79 Discipline ...... 79 Doctorate-granting institution type ...... 80 Control of doctorate-granting institution ...... 81 Starting employment sector ...... 81 Employing institution type ...... 82 Postdoctorate appointment ...... 83 Tenure status ...... 83 Female*children ...... 84 Female*tenure ...... 84 Event-History Analysis ...... 85 Limitations of the Study ...... 89 Data on children ...... 89 Race/ethnicity ...... 89 Nonresponse bias ...... 90 Multicollinearity ...... 90 Summary and Rationale ...... 92 8

Chapter 4: Results ...... 93 Descriptive Analysis ...... 93 Time-constant variables ...... 93 Time-varying variables ...... 97 Sector changes, by independent variables ...... 104 Cumulative hazard rates ...... 111 Complementary Log-Log Regression Analyses ...... 118 Analyses of all respondents ...... 118 Analyses of respondents starting in the education sector ...... 122 Chapter 5: Conclusions ...... 128 Significance ...... 130 Summary of Key Findings ...... 131 Characteristics of respondents ...... 131 Timing of sector changes ...... 133 Likelihood of sector changes ...... 135 Implications ...... 137 Applications and Recommendations ...... 143 Areas for Future Research ...... 144 Conclusion ...... 146 References ...... 147

List of Tables

Page

Table 1 Respondents to Survey of Doctorate Recipients, 2001–2013 ...... 73 Table 2 Number of new respondents to all SDR surveys from their initial year to 2013 ...... 74 Table 3 Independent Variables ...... 76 Table 4 Recategorization of Carnegie Classifications for Degree-Granting Institutions ..... 81 Table 5 Recategorization of Carnegie Classifications for Employing Institutions ...... 83 Table 6 Time-constant variables, percent or mean ...... 94 Table 7 Citizenship or residency status, all respondents ...... 98 Table 8 Percent reporting whether children living at home ...... 100 Table 9 Percent holding a postdoctoral appointment or not ...... 101 Table 10 Carnegie type of employing institution, if a higher-education institution ...... 102 Table 11 Tenure status of respondents employed in the Education sector ...... 103 Table 12 Tenure status of respondents with first employment in education, by gender ...... 104 Table 13 Percent of respondents changing employment sectors, by gender ...... 104 Table 14 Percent changing sectors by race, gender and starting employment sector ...... 106 Table 15 Percent of citizenship categories changing sectors, by gender ...... 107 Table 16 Percent changing sectors by marital status, gender and first employment sector 108 Table 17 Percent changing sectors by children, gender and first employment sector ...... 109 Table 18 Percent changing sectors by employer Carnegie type in prior SDR and gender . 110 Table 19 Percent changing employment sector by tenure status in prior SDR and gender 111 Table 20 Sector change by origin/destination and gender ...... 117 Table 21 Cloglog of sector change, all respondents ...... 119 Table 22 Cloglog of sector change, all respondents by gender ...... 122 Table 23 Cloglog of sector change, those whose first employment was in education ...... 123 Table 24 Cloglog: sector change by gender, with starting employment in education ...... 125 Table 25 Cloglog: sector change for those starting in education (no tenure variables) ..... 126

List of Figures

Page

Figure 1. Research model of factors influencing career choices ...... 70 Figure 2. Construction of respondent waves included in the study ...... 74 Figure 3. Discipline by gender, all respondents ...... 96 Figure 4. Discipline by gender, respondents starting in academic sector ...... 97 Figure 5. Employment sectors, all respondents ...... 98 Figure 6. Percent married, by gender (all respondents) ...... 99 Figure 7. Percent married, by gender (respondents who started in academia) ...... 100 Figure 8. Percent of respondents changing employment sectors, by first sector ...... 105 Figure 9. Cumulative hazard estimates by gender, all respondents ...... 112 Figure 10. Cumulative hazard estimates by gender, starting employment in education ...... 113 Figure 11. Cumulative hazard rates for all respondents, by race ...... 114 Figure 12. Cumulative hazard rates by discipline, females only ...... 115 Figure 13. Cumulative hazard rates by discipline, males only ...... 116

Chapter 1: Introduction

Statement of the Problem

In the 1950s, America was shocked and alarmed by the Soviet Union’s launch of the satellite Sputnik. The prospect of a technologically advanced Cold War nemesis outcompeting the U.S. in military might galvanized the nation to invest heavily in science, technology, engineering and math (STEM) education for its citizenry. These fields were initially dominated by White men. Yet more than half a century later, even as the tremendous benefits of a talented STEM workforce have only become more apparent, the nation’s lead in global STEM talent is in jeopardy. The highly educated Baby Boom generation is exiting the workforce in increasing numbers, with fewer native-born workers to replace them (Laudicina, 2005; Nelson & Brammer, 2007). Simultaneously, the nation’s historic ability to attract and keep talented individuals from around the world is also eroding, due in part to post-9/11 immigration policies and attitudes as well as increasing competition from other countries (National Academy of Sciences, National

Academy of Engineering, & Institute of Medicine, 2007, 2010). Employers and policymakers are recognizing the need to cultivate more native-born STEM talent to offset these losses and maintain America’s competitive edge. The largest underrepresented sub-population in the talent pool, constituting the greatest growth potential for native-born STEM talent, is women (National Academy of Sciences,

National Academy of Engineering, and Institute of Medicine, 2007; National Academy of

Sciences et al., 2010). 12

The significance of this issue led the National Academy of Sciences’ Committee on Science, Engineering, and Public Policy to convene a Committee on Maximizing the

Potential of Women in Academic Science and Engineering in 2005. The committee aptly characterized the situation as a matter of urgent national concern:

To maintain its scientific and engineering leadership amid increasing

economic and educational globalization, the United States must

aggressively pursue the innovative capacity of all of its people—women

and men…. Neither our academic institutions nor our nation can afford

such underuse of precious human capital in science and engineering. The

time to take action is now. (National Academy of Sciences, National

Academy of Engineering, and Institute of Medicine, 2007, p. xii)

A highly educated STEM workforce provides a major competitive economic advantage to the nation. In the increasingly knowledge-driven global economy, STEM research and innovation drive economic growth and prosperity; innovations including the

Internet, genetic research, life-saving vaccines, and agricultural production have transformed the modern world. Significant benefits also accrue to individuals—not only to the holders of advanced STEM degrees whose employment options and earning potential are high, but also to the rest of society, who benefit from the direct and indirect employment opportunities, economic growth, and quality-of-life improvements that flow from STEM innovations (Auriol, 2010; National Science Board, 2014).

As the demand for highly educated workers has grown, the supply has gradually grown as well. The number of bachelor’s and master’s degrees conferred in science- and 13 engineering-related fields reached new highs in 2011 (National Science Board, 2014). US higher-education institutions also awarded more than 38,000 doctorates in science- and engineering-related fields in 2011—a 38 percent increase from 2000, exceeding the 33 percent growth in doctorates in all fields (National Science Board, 2014). In addition to the sum-total growth, recent years have also seen an increase in the proportion of STEM

PhDs earned by women and minorities. Women earned 47 percent of all STEM doctorates awarded in 2011, up from 43 percent in 2000; during the same time period underrepresented minorities’ share of STEM doctorates increased from 6 percent to

8 percent (National Science Board, 2014).

Diversification in STEM accrues benefits beyond increasing the size of the workforce. Empirical research has shown that heterogeneous groups can be more successful at solving problems together than homogeneous groups, even if the latter are composed of high-ability individuals (Hong & Page, 2001; Luan, Katsikopoulos, &

Reimer, 2012; Watson, Kumar, & Michaelsen, 1993). This advantage may be attributed to the fact that members of diverse groups tend to bring with them different ways of thinking and approaching problems (McBride, 2017). In addition, individuals with diverse backgrounds and lived experiences can be attuned to different needs and opportunities. As Berman and Godfrey (2019) have noted, for example, family issues such as child-care costs did not become a Congressional priority until more women entered Congress, largely because most male legislators had never personally dealt with these issues and therefore did not view them as priorities. Women legislators also recognized a bias in medical research, which once routinely studied only male subjects, 14 and their efforts to ensure women are represented in medical studies have been essential to many life-saving improvements in women’s health care (Berman & Godfrey, 2019).

The ability to harness the creative and problem-solving potential of a diverse workforce, therefore, can confer tremendous competitive economic and social advantages.

In recognition of this fact, growth and diversification of the STEM workforce are fostered by many social and individual forces, not least of which are public policies such as the America COMPETES Act; initiatives such as the National Science Foundation’s

ADVANCE program and Louis Stokes Alliances for Minority Participation; the National

Institutes of Health Bridges to the Doctorate program; and private philanthropic initiatives such as the Ford Foundation Fellowship Program and the Alfred P. Sloan

Foundation’s Minority PhD Program (National Academy of Sciences, 2011). The U.S. also, like most other developed nations, invests a considerable amount of money and effort in monitoring the quality and quantity of the nation’s STEM-educated individuals through the National Science Foundation; other major associations and institutes also conduct research on subsets of this population (e.g., the National Academies of Science,

Women in Science and Engineering).

Having earned their doctoral degrees, many STEM Ph.D. holders return to higher education as employees. The United States is world-renowned for its higher-education system, which both creates and disseminates advanced knowledge. Many of the most transformational scientific and technological breakthroughs in the world owe their existence in large part to research conducted in American higher-education institutions.

In fulfilling the roles of education and research, higher-education faculty are the most 15 important resource an institution has, determining its quality and reputation. An esteemed faculty can bring in large grants, conduct high-profile research, attract students, and enhance the institution’s prestige. For this reason, higher-education institutions are engaged in a constant competition for the best and brightest scholars (van Balen, van

Arensbergen, van der Weijden, & van den Besselaar, 2012; van den Brink, 2009). And, when they are successful in recruiting these individuals, institutions have an interest in keeping them rather than losing them to either other higher-education institutions or nonacademic positions.

While faculty salaries may not be as high as those in the business/industry sectors

(Hanks & Kniffin, 2014; Nerad & Cerny, 2002; Webber, 2013), other benefits of faculty positions may include the potential for tenure, infrastructure and startup funds to support self-guided research agendas, the opportunity to teach, and the academic calendar.

However, from the individual’s perspective, in choosing among a variety of career options, academia is only one sector of a larger job market. Incentives such as higher pay or the potential for greater work/life balance draw some individuals outside academia to opportunities in other sectors, namely business/industry and government. The degree to which characteristics of jobs in one sector or another factor into an individual’s career decisions varies according to each individual’s interests and life circumstances. Different sectors may also present opportunities for individuals in some disciplines more than others, due in part to specializations that limit those opportunities to a narrow population; these disciplinary differences are characteristics of a segmented labor market (Aanerud et al., 2007; Lin & Powers, 2004). 16

A considerable body of research exists concerning women’s experience navigating gendered workplaces and career trajectories, both in and beyond STEM fields.

Some longitudinal research has also been done on STEM faculty, but an opportunity exists for further exploration using more-current data and looking not just at those STEM doctorate holders in academia but at individuals working in multiple sectors, an approach that enables inter-sector comparisons.

This study focused upon the career mobility of women who earn STEM doctorate degrees from U.S. higher-education institutions, analyzing their movements between higher education and the business/industry and government sectors. Through an event-history analysis of data from the national Survey of Doctorate Recipients on individuals who responded to surveys between 2001 and 2013, the researcher investigated the factors associated with an individual’s likelihood of inter-sector mobility and how they vary by disciplinary as well as personal and education-related characteristics discipline. The researcher also looked specifically at individuals employed in academia but broadens the lens from previous work by including not only tenure-track faculty at research institutions but also those working in different types of jobs and in multiple types of institutions. In summary, this study encompasses the broad range of employment opportunities available to STEM doctorate holders and the choices these highly educated individuals make in their career trajectories.

The theoretical framework for this study incorporated three theories particularly well-suited to considering career choices and movements along the career pathway, from the perspective of the individual: behavioral economics, specifically the process by which 17 individuals make decisions based on limited information and under sub-optimal circumstances; human capital theory, the conceptualization of education as a form of capital in which individuals invest with the hope of future benefits; and social-role theory, which posits that gender differences in career trajectories, salaries, and perceptions of climate result from differing expectations about the appropriate social roles for men and women.

The study also conceptualized career mobility as a pathway or road, focusing on the agency of the individual in making career decisions. This construct contrasts with the more commonly used hypothetical pipeline, which identifies “leaks” that employers might take action to “repair.” The path/road construct incorporates a temporal dimension, considering not only whether a career trajectory changes, but also when it occurs. Event history analysis, the quantitative analytical approach used in this study, is ideally suited to exploring the timing of such career changes.

Purpose of the Study

The purpose of this study was to examine the inter-sector career mobility of women who earn STEM doctorate degrees from U.S. higher-education institutions and begin their careers in higher education. Using data from the national Survey of Doctorate

Recipients, the researcher investigated the extent to which key personal and education- related characteristics are correlated with an individual’s likelihood of mobility from one employment sector to another, and with the timing of their inter-sector movements. 18

Research Questions

1. What is the likelihood that female STEM Ph. D. holders who start their

careers in academia will move to another employment sector?

2. How are the likelihood and timing of female doctorate holders’ inter-sector

career moves affected by the following?

a. Demographic characteristics including age, race/ethnicity,

citizenship status, marital status, and family formation

b. Education-related characteristics including discipline, control and

Carnegie classification of the doctorate-granting institution, and

postdoctoral appointments

c. Employment-related characteristics including starting sector, initial

Carnegie type of academic employer, and tenure status

3. How do the likelihood and timing of female doctorate holders’ career moves

compare to those of their male counterparts?

Delimitations of the Study

This study only included individuals who earned a PhD in a STEM discipline from a U.S. higher-education institution between 1999 and 2008 and who responded to all SDRs from the time they entered the sample through 2013.

The employment sector of Academia was filtered to exclude respondents whose starting employment is in a K-12 institution.

The race/ethnicity categories American Indian/Alaska Native, non-Hispanic;

Hispanic, any race; Non-Hispanic Native Hawaiian/Other Pacific Islander; and Multiple 19

Race contain few individuals, even with oversampling, and were therefore combined into for this analysis. Race/ethnicity was categorized as White, Asian, Black or Other.

Citizenship status was condensed into two categories: Citizen/Permanent Resident and Temporary Resident.

Disciplines were condensed into five categories: Computer and Math Sciences,

Life and Related Sciences, Physical and Related Sciences, Social and Related Sciences, and Engineering.

The Carnegie type of the degree-granting institution was condensed into two categories for manageability: Doctoral/research institution and four-year non- doctoral/research institution.

The Carnegie type of employer if an educational institution included the above two categories as well as a third: Other, which includes two-year institutions.

Definitions

The following terms are defined in order to clarify their use throughout this dissertation:

Academia is used to refer to higher/postsecondary education.

Carnegie classification is a system for categorizing higher-education institutions by degree-granting and research activities (e.g., Research I or II, Comprehensive, Liberal

Arts, Two-Year Institution).

Censoring, in the context of event history analysis, refers to subjects in a dataset for whom data is missing at the beginning, at the end, or in the middle of the time window being studied. Event-history analysis are designed to accommodate censored 20 data. Indeed, as Allison (2014) notes, when the number of censored cases is large, excluding them from the analysis can introduce substantial bias.

DRF stands for Doctorate Records File, which contains data from an ongoing census of research doctorates earned in the U.S. since 1920.

Education sector refers only to the higher-education sector throughout this study, unless otherwise noted.

Employment sector refers to the general category of an occupation or employer within the broad context of the U.S. economy. For the purposes of this analysis, sectors are categorized as higher education (public or private), business/industry, and government (federal, state, or local).

Event history analysis, also known as survival analysis, is a set of statistical techniques that studying the timing of an event in relation to explanatory variables.

Faculty refers to individuals with teaching responsibilities in a higher-education institution. For the purposes of this dissertation, “faculty” is further delimited to individuals employed full-time, unless otherwise noted.

Institution control type refers to the means by which an institution of higher education is governed, either public or private.

Leaving academia means leaving a faculty job for a job with an employer that is not a higher-education institution; becoming self-employed or unemployed; or leaving the workforce altogether.

NCSES is the National Center for Science and Engineering Statistics. A unit within the National Science Foundation, NCSES collects, analyzes, and disseminates data 21 on science and engineering activity in the U.S., including the status of the science and engineering workforce as well as the nation’s global competitiveness in STEM activity and STEM education.

NSF is the National Science Foundation.

S&E refers to Science and Engineering. This term is sometimes used interchangeably with STEM, and sometimes more narrowly to exclude technology and mathematics. For the purposes of this dissertation, S&E is used interchangeably with

STEM, unless otherwise noted.

STEM degree fields, in the Survey of Doctorate Recipients, include biological/agricultural/environmental life sciences, computer and information sciences, mathematics and statistics, physical sciences, social and related sciences (e.g., psychology), and engineering (Finamore et al., 2013).

STEM-related fields, in the Survey of Doctorate Recipients, include such fields as health, science and mathematics teacher education, technology and technical fields, architecture/ environmental design, and actuarial science (Finamore et al., 2013).

STEM occupations, in the Survey of Doctorate Recipients, include computer and mathematical scientists; biological, agricultural, and other life scientists; physical and related scientists; social and related scientists; and engineers (Finamore et al., 2013).

STEM-related occupations, in the Survey of Doctorate Recipients, include health- related occupations, S&E managerial position, and S&E precollege teaching, as well as occupations such as S&E technicians and technologists, architects and actuaries

(Finamore et al., 2013). 22

SDR is the Survey of Doctorate Recipients.

SED is the Survey of Earned Doctorates.

SESTAT is the Scientists and Engineers Statistical Data System. Created by the

NSF, it contains data from three NSF-sponsored demographic surveys: the National

Survey of College Graduates (NSCG), the National Survey of Recent College Graduates

(NSRCG), and the SDR.

STEM refers collectively to Science, Technology, Engineering, and Mathematics.

For the purposes of this dissertation, STEM is used interchangeably with S&E, unless otherwise noted.

Organization of the Dissertation

This dissertation is organized in five chapters. Chapter One introduces the research topic and explains the purpose and importance of the study. This chapter identifies the specific research questions addressed, limitations and delimitations of the study, and definitions of key terms used throughout the dissertation. Chapter Two contains a review of related literature and identifies the niche that this study addresses within that body of literature. Chapter Three is a detailed description of the design and methodology of the study. The contents of this chapter include the population to be studied, the national dataset to be used, the statistical approaches to be applied, the variables to be included, and the analytical procedures to be employed. Chapter Four contains a detailed discussion of the results obtained from the data analysis. Chapter Five contains a summary of the results and their implications and presents conclusions and recommendations for future research. 23

Chapter 2: Literature Review

Introduction

This chapter begins with an explanation of the theoretical frameworks used to conceptualize career mobility. The next sections contain a discussion of the most important research related to the STEM workforce and the current literature on each of the factors examined in this study. The chapter concludes with a summary of the relevant literature and how the proposed study adds to the knowledge base.

Theoretical Frameworks

Multiple theories and constructs are used in this study to envision career mobility and identify factors likely to affect that mobility. Three theories are particularly well- suited to considering career choices and movements along the career pathway, from the perspective of the individual: behavioral economics, human capital theory, and social- role theory.

Behavioral economics. Behavioral economics is an approach to conceptualizing decision-making processes that incorporates empirical research from the fields of cognitive and social psychology to help explain how and why people make the choices they do (Mullainathan & Thaler, 2018).

Traditional economic frameworks typically posit a decision maker who is perfectly rational, perfectly selfish, and perfectly calculating; this hypothetical individual has been given the pseudo-taxonomic classification Homo economicus (Mullainathan &

Thaler, 2018). The distinct species name is particularly appropriate given that this imaginary individual bears only a passing resemblance to actual human beings. 24

Behavioral economics argues that the traits ascribed to Homo economicus, with their assumptions of perfect information and the ability to assess probabilities and payoffs for every conceivable alternative outcome, are so unrealistic as to render them incapable of providing meaningful explanations of decision-making in the messiness of the real world—with its unanticipated consequences, “apples to oranges” comparisons, and the uncertainties inherent in predicting future outcomes (Simon, 1955, pp. 103–104).

A pioneer of behavioral economics, Herbert Simon, countered traditional economic frameworks with what he termed a “behavioral model of rational choice”

(Simon, 1955). He argued that even the most intelligent individuals almost never approach a choice knowing every possible alternative, outcome, probability, benefit and cost. Based on observations from the behavioral sciences, Simon proposed a model of

“bounded rationality”—i.e., that individuals are limited in both their information and their ability to process it to solve the kinds of complex problems they encounter in real life (Simon, 1955). He posited that they will resort to “computational simplifications” as a way to cut a decision process down to manageable proportions (p. 104). For example, rather than quantifying all potential payoffs and seeking the maximum payoff, an individual may simply classify potential outcomes as “satisfactory or unsatisfactory” (p.

104). Another approach, “partial ordering of payoffs,” can be useful when the priorities of each group member (e.g., spouses or other family members) may be different (p. 108-

109). Yet another instance might involve comparing two or more options (e.g., job opportunities), on multiple characteristics deemed to be most important to the decision maker, such as salary, work climate, appeal of the work, or prestige (p.109). 25

A similar theory based in behavioral economics, Kahneman and Tversky’s

“prospect theory,” posits a two-stage choice process similar to what Simon describes: In an early “editing” stage, the individual engages in a preliminary analysis of alternatives

(or “prospects”) and often simplifies them, and in a second phase the individual evaluates the edited prospects and selects the prospect of highest value (Kahneman & Tversky,

1979, p. 274).

Most traditional economic models of choice assume that all alternatives are known and evaluated at the same time. In many real-world situations, however, an individual will encounter and must weigh choices sequentially. For example, an individual looking for a job may receive offers over time rather than all together. In such a situation the individual must decide not only what would constitute a satisfactory offer, or aspiration level, but also how likely it is that a better opportunity will appear within an acceptable time horizon. The individual might ratchet the aspiration level upward if satisfactory alternatives appear to be plentiful, or downward if it appears they are scarce

(Simon, 1955, pp. 110–111).

Simon further suggests that if individuals fail to find a satisfactory option from among the known or considered alternatives, they may try to expand the number of alternatives they are willing to consider (Simon, 1955, pp. 111–112). For instance, a person who originally aspired to a tenure-track position at an RI institution might, under certain constraints, find the pool of such jobs too limited, and thus need to expand the job search to include positions at other Carnegie-classification institutions, or a non-tenure- track position, or a job outside higher education altogether. 26

Previous experiences may lead individuals to discover new preferences that alter their decision calculus going forward. For example, by teaching a community-college course on an adjunct basis, an individual may discover a love of teaching and thus seek a change in career trajectory (Anderson, Mattley, Conley, & Koonce, 2014). Or a faculty member may, through engagement with industry partners, decide to leave academia and enter the private sector (M. W. Lin & Bozeman, 2006).

Human capital theory. The conceptualization of education as a form of capital in which individuals invest with the hope of increasing future earnings is widely accepted today. As a basis for public policy human capital theory can be problematic for, among other reasons, undermining the concept of education as a public good and eroding public funding support for higher education (Marginson, 2017; Mehrotra, 2005; Netcoh, 2016).

With regard to personal decision-making, however, it has applicability to individuals’ choice of discipline and career trajectory and therefore is a relevant component of the theoretical framework of this study.

When the idea of human capital theory was proposed in the mid-20th century, it generated controversy for seeming to equate humans with inanimate assets such as equipment or land (Williamson, 1964). Yet Schultz (1961) argued that it was legitimate to acknowledge that education could increase a person’s earning potential and also lead to new knowledge that could provide economic benefits to the broader society.

Beyond formal education, Feldman (1996) notes that individuals also amass considerable human capital in the form of work experience, acquired through a series of positions that leverage and build upon the experience gained in previous positions. Such 27 positions need not be in one organization or even one sector. While those pursuing a career in academia as a tenure-track faculty member will pursue a well-defined trajectory, others might amass considerable experience in industry and later leverage that practical knowledge in other settings, e.g., teaching (M. W. Lin & Bozeman, 2006; Weimer,

2001).

Higher salaries are the most obvious and readily measurable form of return on individuals’ considerable investment in advanced education (Taylor, 2007; Yang &

Webber, 2015). Indeed, research shows that the expectations of future salaries influence many individuals’ choice of courses of study and career aspirations (Taylor, 2007).

However, research has shown that not everyone reaps the same return on their investments. Salaries can vary considerably by discipline (Campbell, 2015; Webber,

2013a), which may be expected given that labor markets for each are segmented

(Aanerud et al., 2007; Y. Lin & Powers, 2004)—i.e., highly specialized and not conducive to much mobility between them.

Such differences are especially pronounced between the humanities and disciplines like law, business administration and medicine. An AAUP (2011) survey of faculty compensation rates indicated, for example, that in 2009–10, full professors of business administration earned an average of 51 percent more than their counterparts in

English Language and Literature; the disparity was even greater with full professors of law, who earned nearly 60 percent more than the English faculty, who in turn earned more than faculty in Fine Arts and Education (p. 14). 28

Salary differentials within academia can serve as an indicator of potential competition from employers in other sectors: Faculty in disciplines for which there are higher-paying alternatives outside academia (usually in the private sector) will require higher salaries to attract and retain them in academia (AAUP, 2011). Other trends in the private sector may also be affecting faculty compensation levels. For example, the outsourcing of jobs in some fields, such as software engineering, has put downward pressure on salaries in corresponding domestic jobs both in and outside academia

(AAUP, 2007), while other fields such as medicine and law are less vulnerable (though not invulnerable) to outsourcing. In addition, the increased use of low-cost postdoctoral fellows in disciplines like the physical sciences may dampen salary growth for faculty in those fields (AAUP, 2007, p. 32). These potential explanations are, it should be noted, speculative in nature, as little research actually exists on the reasons underlying the growing disciplinary variations in compensation (AAUP, 2007).

In academia, where an individual earns her degree can matter almost as much as the field of study itself. The ranking of a higher-education institution or specific academic program among its peers can greatly influence a degree’s perceived worth when the graduate enters the job market (Bedeian, Cavazos, Hunt, & Jauch, 2010; Headworth &

Freese, 2016; Weeden, Thébaud, & Gelbgiser, 2017). Whether this boost constitutes merely a self-perpetuating caste system or derives from actual quality differences (e.g., more-rigorous academic programs, better facilities and professional networks, ability to participate in high-quality research) is debated, but evidence exists for the existence of a prestige effect in at least some disciplines (Headworth & Freese, 2016). 29

Critics of human capital theory note that the theory falls short in explaining all differences in the returns that individuals receive on their investments in higher education

(Marginson, 2017; Mehrotra, 2005). Researchers have found factors correlated with salary that, on their face, do not relate with inherent ability or amount of education—most notably, gender and race (Campbell, 2015; Webber, 2013b; Webber & Canche, 2015). In general these researchers have found a gender pay gap that favors males (Schieder &

Gould, 2016; Webber & Canche, 2015). Interestingly, some anomalies do appear, such as

Webber & Canche’s (2015) finding that the return on investment for minority men exceeded that of their White male peers—if those minority males were married.

Human capital theory also falls short in its narrow focus on education as a means of increasing economic productivity that can be quantified as the additional lifetime earnings accruing to an individual (Marginson, 2017). In reality, it is likely that compensation is only one of several criteria that most individuals consider in evaluating their career options, though its significance likely increases with the amount of money involved. Its weight may also fluctuate at different points in an individual’s career. A new or recent PhD recipient may value salary more than a tenured professor who has built most or all of his/her career in the professoriate and may be disinclined to leave a relatively secure employment situation (AAUP, 2007). Other motivations to pursue higher education may include intellectual passion, familiarity (perhaps having parents who worked in academia) or a desire to use knowledge to help others.

Social-role theory. The general concept of role theory posits that individuals behave in ways that conform to certain categories or social positions defined by widely 30 shared social norms (Eagly, 1987). The motivation to conform to these roles may be external, in the form of the social group expressing approval for conformity and ostracism or other punishment for violating them; on the other hand, individuals who regard the roles as legitimate may also derive internal reward from conforming to them and thus behaving in a prosocial manner. Social roles may include doctor, waitstaff, caregiver, scientist, faculty member, etc.

A variant of social role theory is the concept of gender-based roles—shared expectations of women being the female gender role, and shared expectations of men being the male gender role. Eagly (1987) identifies two dimensions that encompass most beliefs underlying gender roles: the communal and the agentic. “Communal” refers to consideration of the welfare of other people and is more associated with the female role, while “agentic” refers to a tendency toward assertiveness and control and is more associated with the male role (Eagly, 1987, p. 16).

A long-standing traditional explanation for the differences in female and male social roles is that they stem from physiological differences between the sexes. Men, being stronger, specialized in tasks like hunting and fighting, while women, being responsible for childbearing, took care of the children and domestic work. Even though people would generally acknowledge that these categories overlap—not all women are gentle and submissive and not all men are aggressive and dominant—they appear to believe that such attributes have a basis in innate biological differences (Eagly, 1987).

Several researchers, however, have found that the differences between men and women are not nearly so clear-cut and in fact are influenced by context (Eagly & Wood, 1991; 31

Jaffee & Hyde, 2000). Eagly (1987) argues that in settings related to other social roles with norms that contradict gender stereotypes, individuals’ actions are moderated by those expectations as well. For instance, a male chaplain may exhibit more caregiving behavior; a female attorney may show aggressiveness in the courtroom. The context in which an individual is operating determines which social role will be more salient (Eagly,

1987, p. 27).

An individual’s social status can also moderate gender roles. While gender itself is often observed as a social-status cue because of its correlation with power and dominance (Eagly, 1987), other markers of social status can encourage behaviors counter to gender roles; for example, a woman of high social status may exhibit dominant behavior in relation to a low-status man, depending on the rigidity of gender hierarchies in their social context. Another influence on individuals’ conformity to gender roles is whether they are in a group setting; the presence of others may encourage conformity to gender roles out of a desire to exhibit prosocial behavior (Eagly, 1987, p. 27).

Gender roles can affect individuals’ careers in a variety of ways. Researchers studying employees in a variety of sectors and disciplines have found significant gender differences in career trajectories, salaries, and perceptions of climate (August &

Waltman, 2004; Campbell, 2015; Fox & Xiao, 2013; Graham & Smith, 2005; Holliday et al., 2014; Kahn, 2015; National Research Council (U.S.) et al., 2010; Schieder & Gould,

2016; Webber & Canche, 2015). In most cases, these researchers have found that women face more challenges balancing work and personal commitments than do their male colleagues, perhaps out of a sense of responsibility to fulfill gendered expectations that 32 women should bear the primary responsibility for children and domestic life. For instance, women tend to report more hours per week spent on household chores (Suitor,

Mecom, & Feld, 2001). Mason and Goulden (2004) have found that even among tenure- track faculty, women also tend to be the primary caregivers if they and a partner have children. In some cases women elect to delay, scale back, or even forgo family formation in pursuit of their career goals (Mason & Goulden, 2004). Or conversely, they may choose jobs that they perceive as more conducive to work/life balance (Goulden, Mason,

& Frasch, 2010).

Within academia, women faculty tend to spend more time on service activities, including student advising and committee work, than their male counterparts (Acker,

1992; Guarino & Borden, 2017; O’Meara, Kuvaeva, Nyunt, Waugaman, & Jackson,

2017; Steiner, Lanphear, Curtis, & Vu, 2002). This disparity can be costly to women’s careers; even though they may be called upon to fill these roles, their service activities take valuable time away from their research and teaching, which usually carry more weight in their evaluation for promotion and tenure. They are also more likely to participate in service internal to the institution rather than external service, such as to a professional organization, that could enhance their scholarly visibility and reputation

(Guarino & Borden, 2017). As a result, women can be hampered in their career advancement. Perversely, their experiences can then provide reinforcement for gender stereotypes, suggesting that women are less ambitious or less research-oriented than their male colleagues. 33

Pipeline/path construct. Perhaps the most commonly used theoretical perspective used when discussing career mobility as a whole is the “pipeline” image

(D’Amico, Vermigli, & Canetto, 2011; Goulden et al., 2010; June, A. W., 2008; Kulis &

Sicotte, 2002; Sheltzer & Smith, 2014; Stage & Maple, 1996; Tatum, 2008; Touchton,

Musil, & Campbell, 2008; van Anders, 2004; N. H. Wolfinger, Mason, & Goulden, 2008,

2009). This construct represents a particular labor market, sector, or employer as a pipe, with individuals (students and/or employees) constituting an aggregate substance flowing through it.

The pipe has an entry point at one end, often conceptualized as individuals’ entry into a preparatory academic program or the labor market; and an exit point at the other end representing workers leaving the market (upon, e.g., retirement, taking a job in another sector, or leaving the workforce). The departure of individuals at any point in between is often described as “leakage” (Goulden et al., 2010; Sheltzer & Smith, 2014; van Anders, 2004).

From the employer perspective, the objective is to maintain an optimal “flow” through the pipeline. If they cannot find enough workers, they will pursue strategies that prepare or attract more workers to enter the pipeline. Beyond this concern, the focus in the pipeline model is often on where the most common points of “leakage” occur, what factors are associated with them; and how they might be “plugged.”

As many researchers have noted, however, the “pipeline” construct has several shortcomings (Branch, 2016; Etzkowitz & Ranga, 2011; White, 2005). For one, it depicts all career mobility proceeding in the same direction. In reality, however, individuals may 34 move back and forth (e.g., between industry and academia) or in and out of the workforce

(Hewlett & Luce, 2005; McGrath, Driscoll, Gross, Bamber, & Axt, 2005; Soe & Yakura,

2008), or take leaves of absence via stop-the-clock or extended family/medical leave

(Conley, 2005, 2005; Robinson, 2009; Rosser, 2004).

Another shortcoming of the pipeline metaphor is its implicit assumption that all employees experience it in the same way. In reality, however, there are marked differences between men’s and women’s experiences. One of the most obvious and frequently studied issues is the structure of the academic tenure system. Designed by and for men, this archaic system, with its typically rigid “tenure clock” and emphasis on research productivity, has worked fairly well for individuals who could count on a support system at home and had few or no domestic responsibilities—namely, men.

Many women, however, experience the tenure process very differently. For one, they typically lack the domestic support system men often have—in fact, they still bear a disproportionate share of the household work. The tenure clock also often overlaps women’s window of fertility, forcing some to make a difficult choice of family or career—a choice men have never had to consider. The thoroughly gendered tenure system thus creates a different experience of the faculty pipeline for women and men.

The pipeline metaphor views career mobility from an employer-centric perspective. It portrays individuals as a passive input and concerns about “flow” and

“leakage” in terms of the impact on the ability of employers or industries to function. In fact, however, individuals exercise a degree of autonomy in their career movements.

They enter and leave job markets, sectors, and employers based on their own calculus of 35 needs and preferences (except when workers are fired or laid off, for example, but these movements are not the focus of this dissertation topic). Thus, “leakage” is not a passive or even necessarily a negative phenomenon; it is rather the result of multiple independent decisions of individual actors in response to a variety of factors.

That is not to say that individuals’ career movements are always made in response to positive “pull” factors (i.e., from something good to something even better).

Considerable research has indicated that, particularly for women and members of underrepresented minority groups, a variety of “push” factors in the work environment can lead individuals to leave, such as a lack of support for employees with caregiving responsibilities (Adamo, 2013; Carr et al., 1998; Fox, 2005; Mason, Wolfinger, &

Goulden, 2013; Morrison, Rudd, & Nerad, 2011; Watanabe & Falci, 2014; N. H.

Wolfinger et al., 2009) a disproportionate load of lower-status service duties (Guarino &

Borden, 2017), disparities in compensation (Carr, Gunn, Kaplan, Raj, & Freund, 2015) and work resources (Committees on Women Faculty in the School of Science, 1999), and perceptions of professional isolation or a generally chilly climate (August & Waltman,

2004; Fox & Xiao, 2013; Gardner, 2012, 2012; Lindholm, 2004; Meyerson, D. E., 2008;

Walters & McNeely, 2010; Wuhib & Dotger, 2014; Young, 2012). Ahmad (2017) critiques the academic-career pipeline model in particular for reinforcing an “ideal worker” norm by imposing a rigid, time-bound structure upon tenure-track careers in academia; women are more likely than men to “opt out” of academic careers because these ideal-worker norms are unsupportive of family formation and caregiving (p. 1). 36

That many individuals’ career movements cluster around similar points in the

“pipeline” suggests that structural factors in the work environment are influencing their decisions (Branch, 2016; Committees on Women Faculty in the School of Science, 1999;

Goulden et al., 2010; White, 2005). With regard to academia, research by Mason et al.

(2013) and Wolfinger et al. (2008) has shown that the overlap between many women’s pursuit of tenure and their years of peak fertility can force them to choose between having children or having a successful academic career when institutional family-leave policies fail to provide support for new parents. As White (2005) notes, through such efforts as the report on the status of women faculty in science at MIT (1999) the conversation about the status of women in academia is putting more emphasis on institutional responsibility for issues of climate and working conditions rather than merely accepting high attrition rates of women from the faculty ranks as an inevitable occurrence.

Given the shortcomings of the pipeline model, several scholars have suggested that the “pathway” or “road” is a more appropriate construct for conceptualizing career mobility. One of the more obvious advantages of the path or road construct is that it focuses on the agency of the individual in choosing the career route, navigating obstacles, and deciding whether to turn onto alternate routes (or exit the road altogether). In this way the path/road construct reframes employers’ role in individuals’ career mobility; they cannot actually “repair leaks” in the sense of preventing employees from acting upon their career choices, but instead must seek to increase the attractiveness of joining 37 or staying with them relative to other opportunities in an employee’s decision-making calculus.

Furthermore, the path/road construct encompasses the multiple career options available to an individual—an improvement on the binary nature of the pipeline construct

(one is either in the pipeline or not). In reality, career options can span multiple sectors including not only academia but also business/industry and government. Furthermore, individuals may move back and forth between sectors; for example, a faculty member may leave academia for the private sector, then years later return to teaching on a full- or part-time basis. The options available to individuals are influenced by the human capital they have already acquired and the choices they have already made, and they continue make choices going forward based on the limited information they possess about future options and possible outcomes, as well as on the potentially subconscious influences of social roles.

The path/road construct also accommodates the recognition that individuals experience the journey along it in very different ways. While a road is already structured, it may have different lanes; vehicles may even be assigned to lanes, or only certain types of vehicles may be permitted to access certain advantaged lanes. Individuals with well- equipped luxury cars can navigate the road in relative comfort and safety, while those with less-reliable vehicles are more prone to breakdowns and more vulnerable to sub- optimal road conditions. As Branch (2016) notes, “Likewise, the presence or absence of tools and conditions will likely result in different outcomes for women compared to men on a scientific path.” (pp. xiv–xv) 38

The path/road construct also incorporates a temporal dimension, considering not only whether a career trajectory changes, but also when it occurs. Event history analysis, the quantitative analytical approach used in this study, is ideally suited to exploring the timing of such career changes. More significantly, event history analysis also enables exploration of how the timing of a career change is affected by multiple variables, including both variables that are constant over time (e.g., gender, discipline) and those that vary over time, such as marital status or children (Allison, 2014; Blossfeld &

Rohwer, 2002).

Research on the STEM Workforce

A considerable amount of data exists on the nation’s STEM existing and potential workforce. The NSF has surveyed STEM doctorate holders on an approximately biennial basis since 1972 through the SDR; in addition to making the survey data available in public-use and restricted-use data files, the NSF presents and analyzes key statistics from these surveys in regular reports including Science and Engineering Indicators (National

Science Board, 2012, 2014, 2016) as well as Women, Minorities, and Persons with

Disabilities in Science and Engineering (most recently published in 2017).

One of the most high-profile studies of the STEM workforce in the last decade was the influential report Rising Above the Gathering Storm (2007). Prepared by the

National Academies at the request of a bipartisan group of U.S. senators and

Congressional representatives, the study surveyed the nation’s STEM capacity and recommended strategies to strengthen it in order to remain competitive in the global community of the 21st century. The list of recommendations and action steps in that 39 report encompassed not only topics such as research funding and policies governing patent rights, immigration and education from K-12 through higher education, but also the state of the most highly educated STEM workers and the need to retain and grow this vital segment of the American workforce. Seeing what they deemed insufficient action toward the priorities outlined in Rising Above the Gathering Storm, the National

Academies released a more-urgent update a few years later titled Rising Above the

Gathering Storm, Revisited (2010). Pointedly, the report was subtitled Rapidly

Approaching Category 5, a reference to the highest level in the system for rating the strength and destructiveness of hurricanes.

Research using SDR data. In addition to statistical reports it publishes itself, the

NSF makes its SDR data available for analysis by others. For straightforward summary statistics, users can access the NSF’s online SESTAT (Scientists and Engineers Statistical

Data System) interface, which can generate summary data tables without exposing individual records, or download public-use files, which do not contain any individually identifiable information on respondents. For advanced research requiring access to individually identifiable information, the NSF can provide restricted-use data files through a strict licensure program that requires applicants to follow tight security protocols to protect confidentiality and data integrity.

A list of all published research using either the public-use or restricted-use data would be too long to include here, and much of it explores research questions unrelated or tangential to this study. Following are brief descriptions of several recent publications 40 whose authors used SDR data to explore research questions with some relevance to this study.

Bender and Heywood (2009) used data from the 1997 and 1999 SDR waves to examine the consequences of mismatch between worker skills and job requirements for science Ph.D. holders in terms of earnings, job satisfaction, and likelihood of turnover.

Bound, Turner and Walsh (2009) explored data points from several waves of the

SDR as part of their analysis of trends in the number and country of origin of foreign- born students earning STEM doctorates from U.S. universities.

Campbell (2015) used data from four cycles of the SDR (2003, 2006, 2008, and

2010) to study the intersectional effects of gender, race and discipline on STEM doctorate recipients’ career paths and salaries.

Webber and Yang used SDR data to examine the career trajectories of PhD earners who took postdoctoral appointments, including comparing their subsequent careers to those of their counterparts who did not complete postdoc appointments and the impact of completing a postdoc on future salaries, likelihood of securing a tenure-track faculty position, and publication productivity (Webber & Yang, 2015; Yang & Webber,

2015).

One of the best-known studies to use SDR data was conducted by Mason and

Goulden in collaboration with other researchers including Wolfinger. Their extensive analysis of several questions related to women STEM faculty yielded a number of publications including “Marriage and Baby Blues: Redefining Gender Equity in the

Academy” (Mason & Goulden, 2004), “Keeping Women in the Science Pipeline” 41

(Goulden et al., 2010), “Problems in the Pipeline: Gender, Marriage and Fertility in the

Ivory Tower” (N. H. Wolfinger et al., 2008), “Stay in the Game: Gender, Family

Formation and Alternative Trajectories in the Academic Life Course” (N. H. Wolfinger et al., 2009) and Do Babies Matter? (Mason et al., 2013). Their research primarily drew on

SDR survey data from 1981 to 1999, with some lines of research supplemented by smaller datasets such as single-institution surveys. As may be inferred from the titles of the above journal articles and book, they found that, in general, family formation had a bigger impact on the careers of women in academia than on men. (This summary glosses over some of their more-nuanced findings, some of which are described below in discussions of individual factors.)

Research using other data sources. Numerous other quantitative and qualitative research exists on the STEM workforce, as well as on individuals in what is often called the STEM “pipeline” (a construct examined in Chapter 1 of this dissertation). K–12 students’ proficiency in science and mathematics is assessed through standardized tests such as the National Assessment of Educational Progress, or NAEP (National Center for

Education Statistics, n.d.), and the Iowa Assessments (Houghton Mifflin Harcourt, n.d.)

And studies comparing students in different countries are made possible by standardized international tests, including the Programme for International Student Assessment, or

PISA (Organisation for Economic Co-operation and Development, 2017), and the Trends in International Mathematics and Science Study, or TIMSS (Institute of Education

Sciences, n.d.b). Other research on the K–12 population includes surveys of attitudes toward science, mathematics, and how they differ by factors such as gender, 42 race/ethnicity, and/or socioeconomic status (e.g., Hyde, Mertz, & Schekman, 2009;

Subotnik & Steiner, 1993; Subotnik, R. F., Stone, K. M., & Steiner, C., 2001).

At the postsecondary level, national datasets include the Survey of Earned

Doctorates, an annual census of all recent recipients of doctorate degrees from U.S. higher-education (National Center for Science and Engineering Statistics, 2017).

Conducted every year since 1957, the SED is sponsored jointly by the NSF, the National

Institutes of Health, the U.S. Department of Education, the U.S. Department of

Agriculture, the National Endowment for the Humanities, and the National Aeronautics and Space Administration. It casts a broader net than the SDR, including all new doctorate earners except those earning professional (e.g., M.D., D.V.M., or D.Min.) degrees. SED data is useful for assessing trends in doctoral education and among doctorate earners, and in fact it constitutes the sampling frame for the SDR (National

Center for Science and Engineering Statistics, 2013). However, because it collects data on individuals once, immediately after they receive their degrees, it cannot be used to analyze those individuals’ subsequent career trajectories.

Another major national dataset, the National Study of Postsecondary Faculty, holds information on individuals employed as faculty members in U.S. higher-education institutions (Institute of Education Sciences, n.d.a). This dataset is unique in its focus on faculty—in institutions ranging from large public universities to small community colleges, in all disciplines. NSOPF contains data from several years’ worth of surveys, but because individuals’ responses cannot be linked across years, it yields only a 43

“snapshot” view of the nation’s faculty in each survey year. The study was discontinued in 2004.

Individual research initiatives have also collected national-scale data on their own, efforts that are indicative of just how much interest there is in studying the nation’s most highly educated individuals. One such dataset on doctorate earners is Nerad and

Cerny’s PhDs—Ten Years Later (1997). Their study surveyed nearly 6,000 Ph.D. recipients from 61 U.S. institutions who earned their degrees between 1983 and 1985 in six fields: biochemistry, computer science, electrical engineering, English, mathematics, and political science. Respondents were asked about their employment decisions in the years since earning their Ph.D.’s, as well as their opinions on the usefulness of their Ph.D. and the quality of their degree program. The study was funded by the Andrew Mellon

Foundation and the NSF. While it provided a rich dataset for analysis and yielded several publications (Aanerud et al., 2007; Nerad, Aanerud, & Cerny, 2004; Nerad & Cerny,

1999, 2002), the survey has not been repeated on the same disciplines, although the researchers used a similar methodology to study PhD holders in art history (Sadrozinski,

Nerad, & Cerny, 2003) and the social sciences (Nerad, Rudd, Morrison, & Picciano,

2007).

Large-scale studies of the employment experiences of individuals in STEM fields have also been conducted by Fox (Fox, 2005, 2010; Fox & Stephan, 2001). In 1993–1994 she surveyed more than 1,200 full-time tenured, or tenure-track faculty members at doctoral-granting institutions in select STEM disciplines to study the relationship between family status, publication productivity and gender in academic science (Fox, 44

2005). During that same time she also fielded a survey of 3,800 doctoral students in select STEM disciplines, to assess the extent to which their preferences and perceived employment prospects were consistent with the actual employment experiences of recent

Ph.D. recipients in those disciplines, as gleaned from SDR data (Fox & Stephan, 2001).

And in a survey in 2003–2004 of a similar population of STEM faculty members in nine

U.S. research universities, she found that key social-organizational attributes including research-related interactions with colleagues, departmental climates, and work/family conflicts contribute to different experiences of the academic-science work environment for women and men (Fox, 2010).

Research on Women Doctorate Holders

Higher education has seen a steady and remarkable increase in the number and proportion of women students. According to national data on the number of degrees conferred, women surpassed men in the earning of associate’s degrees in 1978 and in the earning of baccalaureate and master’s degrees in 1982, though they only reached this milestone for doctorate degrees in 2005 (U. S. Department of Education, 2012).

Having earned their academic credentials, women are more likely to work in academia than men. According to the 2012 SED, women who reported definite postgraduation employment plans were more likely than men to anticipate being employed by an educational institution (59 percent of women versus 46 percent of men), while men were much more likely to find employment in the industrial or business sectors (37 percent of men versus 18 percent of women (NCSES 2012, Table 55). The mix of primary activities reported by these new doctorate holders also varied by gender, 45 with men much more likely to be involved in research and development (50 percent of men versus only 30 percent of women), and women being much more likely to teach

(43 percent of women versus 30 percent of men) or work in administration (14 percent of women versus 10 percent of men) (NCSES 2012, Table 55).

The numbers of women in faculty positions have increased, but their progress into the higher ranks has lagged behind their male counterparts, particularly at research universities and the more elite liberal-arts colleges (Córdova, 2011; Glazer-Raymo, 1999, p. 50; Lapovsky & Larkin, D. S., 2009; Mason, 2011; Touchton et al., 2008). According to 2007 data collected by the American Council on Education, women constituted

47 percent of assistant professors, 40 percent of associate professors, and a mere 26 percent of full professors (Córdova, 2011, para. 5). Cordova (2011) notes that “This drop- off has significant consequences, as many senior administrators are chosen from the higher ranks of tenured faculty” (para. 5). Some scholars assert that the common metaphor of a pipeline to describe the career progression of women is inadequate, suggesting at the very least that it is “leaking” (Mason & Goulden, 2002) or obstructed by

“stubbornly durable blockages” (Keohane, 2003, p. 6) related to institutional factors.

Touchton et al. (2008) go even further, suggesting that a more appropriate model is that of a “reservoir that hold[s] new college faculty, particularly women, in place” (p. 22).

Adding to the backlog of undertapped talent, the trend in faculty hiring has increasingly turned toward part-time and non-tenure-track positions, the ranks of which are disproportionately women (Schuster & Finkelstein, 2006). 46

“Critical mass” is a term borrowed from nuclear physics that refers to the minimum quantity of a fissionable material necessary to start a self-sustaining chain reaction. In popular usage, it has been adapted to refer to an amount of something that is necessary to have a significant, lasting effect or achieve a particular result in a given situation. The concept of critical mass was first applied to organizations by Harvard scholar Rosabeth Moss Kanter in her seminal 1997 book Men and Women of the

Corporation. She argued that once the number of women reached a critical mass in an organization, they would no longer be seen merely as “tokens” or symbols but would be seen as individuals (Kanter, 1977). The concept of critical mass can help explain the greater representation of women in leadership at institutions in which women have been present for a longer time (as a percentage of the institution’s life span) and in greater numbers, such as two-year colleges.

The critical mass concept has its limitations as a STEM workforce metaphor, however. As White (2005) points out, the expected increase in women in leadership ranks due to more women entering academia has not materialized. Instead, as the previously cited data on women in higher education show, women’s representation still declines with increased faculty rank and leadership levels. If critical mass is simply about numbers of individuals, the metaphoric chain reaction should be self-sustaining; instead, it seems to be running out of momentum.

Subsequent research has indicated a corollary to the critical mass concept that sheds light on White’s observation. A Harvard colleague of Kanter, Robin J. Ely, found that numbers alone will not bring about change in the way women are perceived and 47 promoted if the women are only at entry- or mid-level positions; the critical mass must be reached at the senior levels of an organization (Nichols, 1994, p. 11). Furthermore, Ely found that when a critical mass is achieved at the top levels, not only do men’s perceptions of women in the organization change, but women’s own perception of themselves changes, resulting in greater engagement, job satisfaction, and self-confidence

(p. 11).

Within academia the work of Mason, Goulden, and Wolfinger has provided evidence of the impact of family formation on women’s career prospects in academia.

Mason, Wolfinger and Goulden (Mason et al., 2013) found that, while family formation presents career challenges for both men and women in academia’s tenure track, the impacts are much larger for women. They found women’s marital status somewhat negatively correlated with the likelihood of obtaining tenure and moving up the ranks of faculty or administration; and women with young children were far less likely to hold tenure-eligible professorships and more likely to leave academia altogether.

Wolfinger, Mason and Goulden (2008) found support for the existence of a

“gender penalty” in their employment prospects for tenure-track faculty positions. They also found, however, that when they incorporated interactions between gender, fertility, and marital status into their employment models, single women without children were actually 16 percent more likely to get jobs than single, childless men (N. H. Wolfinger et al., 2008, p. 395). They therefore concluded that the “gender penalty” had less to do with gender than with family formation. “In other words, women suffer at the beginning of their academic careers because they marry and have children, not because they are 48 women” (p. 395). When considering the findings of their research in the context of related literature on women, and women faculty in particular, Mason and Goulden (2004) ultimately observed that the goal of gender equity remains elusive in what continues to be a male-dominated profession.

Research Related to Specific Factors

Following is a discussion of the literature related to each of the factors examined in this study.

Discipline. Researchers have explored differences in the career experiences and outcomes of individuals in different discipline areas, to determine the extent to which these disciplines may represent segmented labor markets (Blossfeld & Mayer, 1988; Lin

& Powers, 2004). For example, in a study of faculty turnover behaviors, Xu (2008a) used a disciplinary classification method developed by Biglan (1973) and updated by Malaney

(1986) to categorize disciplines into eight groups based on three dimensions: “Hard” vs.

“Soft,” Pure vs. Applied, and Life vs. Nonlife. In another study, Xu (2008b) further narrowed her focus to STEM disciplines characterized as “Hard,” (mathematics, chemistry, and other science and engineering programs). Her work showed substantial disciplinary differences in the major factors contributing to faculty turnover.

Seifert and Umbach (2008) took a somewhat different approach to incorporating disciplinary differences into their study of job satisfaction among faculty member. Rather than using select disciplines or groups of similar disciplines as variables in their analysis, they derived variables based on several characteristics of disciplines that they hypothesized to have an effect on job satisfaction; their derived disciplinary variables 49 included the proportion of women faculty; the average number of articles, books, and presentations produced by faculty members; the proportion of faculty who were primary or co-investigators on a funded project; and the average salary. Using this approach, they found aggregate disciplinary effects on faculty job satisfaction to be unclear, but they found some significant correlations in specific measures. For instance, Seifert and

Umbach found that research productivity in a discipline correlated positively with faculty members’ satisfaction with authority over the work itself, but it was negatively related with perceptions of equitable treatment. They also found the proportion of women faculty in a discipline negatively related with perceptions of equitable treatment but not with other measures of satisfaction. And average salaries in a discipline were positively related with perceptions of equitable treatment among faculty but did not correlate significantly with any other outcomes.

The literature indicates a keen awareness that the representation of women across disciplines has been uneven (Ahmad, 2017). Data from the Survey of Earned Doctorates

(SED) indicate that in 2012, women earned more than half of all doctorates in the life sciences, social sciences, education, and humanities, but less than 30 percent of all doctorates in the physical sciences and even fewer (22 percent) in engineering (National

Center for Science and Engineering Statistics (NCSES), 2012). Yet even in the latter two disciplinary categories, the proportion of women earning doctorates has increased substantially since 1992, when women earned only 19.8 percent of physical science doctorates and 9.4 percent of engineering doctorates (NCSES 2012).

Many researchers have examined the experience of women in specific disciplines 50 within academia, e.g., chemistry (Grunert & Bodner, 2011), academic medicine (Carr et al., 2015), biomedical science (Gibbs, McGready, Bennett, & Griffin, 2014), radiation oncology (Holliday et al., 2014), computer science (Fox & Xiao, 2013; Tillberg &

Cohoon, 2005), engineering (Bailey, Baillie, Impagliazzo, Riley, & Catalano, 2006;

Ingram & Mikawoz, 2006; Tyson & Borman, 2010), mathematics (Stage & Maple,

1996), physics (Czujko & Anderson, 2015), psychology (D’Amico et al., 2011), and sociology (Leahey, Keith, & Crockett, 2010).

Mason, Wolfinger and Goulden (2013) also incorporated disciplinary differences into their analyses of the experiences at various stages in the faculty career. In earlier phases of their study they looked across disciplines from the humanities to the social and bench sciences at the likelihood of securing a tenure-track faculty position; they found that women who had children within five years of earning their doctorates were significantly less likely than men in similar circumstances to do so, and that this pattern held true across disciplines. In a later phase of their work, which focused on STEM faculty, they found more-hostile academic cultures for women in the physical than in other sciences, while women in life sciences were more likely to face challenges in obtaining and keeping federal funding for their research agendas.

Kahn and Ginther (2017) also analyzed women’s representation in STEM using categories of STEM disciplines—in their case, differentiated by how math-intensive they are. Based on this criterion, Kahn and Ginther grouped the more math-intensive fields of geosciences, engineering, economics, math/computer science, and physical science into a category they dubbed the “GEMP” fields; and the remaining fields of the life sciences, 51 psychology, and the social sciences (not including economics) they collectively called

“LPS” fields. Their categories reflected a similar understanding to that of Mason,

Wolfinger and Goulden (2013), Xu (2008b), and others: that individuals’ career experiences in STEM differ in meaningful ways across disciplines.

Employment sector. The traditional higher-education faculty career involves a lengthy quest to secure tenure—essentially a guarantee of lifetime employment except in rare instances of gross misconduct or extreme financial hardship for the employing institution (Schuster & Finkelstein, 2006). This process has historically been so formalized that it is referred to as the “tenure track” (Benderly et al., 2014).

For individuals, tenure is often a powerful incentive to pursue a career in academia rather than other employment sectors. In addition to the job security tenure offers, tenure-track positions in STEM disciplines typically come with startup funds to help the new faculty member outfit lab facilities, pursue external grant funding, and engage in research and publication. After a probationary period, usually 7 years, the faculty member’s record of research productivity, teaching, and service are reviewed and tenure either granted or denied (Benderly et al., 2014). If it is denied, the individual has up to a year to find employment elsewhere.

Increasingly, however, many higher-education institutions are creating faculty positions off the tenure track (Schuster, 1995; Schuster & Finkelstein, 2006). These positions may be full-time or part-time, may involve different mixes of research, teaching, and service, and typically pay less and carry less prestige than tenure-track positions. Contingent faculty now constitute 76 percent of the academic workforce; they 52 are often paid by the course (averaging about $2,700 per course) and have little or no job security, benefits, or voice in institutional governance (Pannapacker, 2013). Contingent faculty represent less of a long-term financial commitment for the employing institution, and in a time of declining public investment in higher education, the cost savings and flexibility they provide are compelling institutions to increase the proportion of non- tenure-track to tenure-track positions (Leslie, 2007; Schuster & Finkelstein, 2006).

However, this trend is likely discouraging talented scholars from seeking jobs in academia and eroding full-time faculty members’ sense of job security, and it is distributing the burden of shared governance among a dwindling number of tenured and tenure-track professors.

At the same time that the growth in tenure-track faculty positions has virtually stalled in almost all STEM disciplines, two other trends are intensifying competition for these limited positions. First, the number of STEM Ph.D.’s awarded has continued to increase rapidly (Benderly et al., 2014); and second, in 1994 the mandatory retirement age for tenured faculty was abolished, reducing turnover (Schuster & Finkelstein, 2006).

Increasingly, newly minted doctorate holders wanting a tenure-track position must bide their time in one or more postdoctoral appointments, building their research portfolios and waiting for an opportunity to open up (Benderly et al., 2014; Webber & Yang, 2015).

These positions are typically low-paying and offer little or no job security, as they are often funded through “soft money” (i.e., grants or contracts).

Some survey data as well as anecdotal evidence suggests that current and potential scholars are dissatisfied with real or perceived conditions in academia and may 53 thus be disinclined to enter or remain in faculty positions (C. Trower, 2011; Vick &

Furlong, 2010). Trower (2011) surveyed 1,775 tenured associate and full professors at seven public universities and found that many respondents were frustrated about

“leadership turnover and the corresponding shifts in mission, focus, and priorities, and also about salary” (p. 1). Other complaints from survey respondents included inadequate support for research, lack of collaboration, and unclear criteria for promotion (p. 1).

For many STEM Ph.D.’s, career alternatives exist in other sectors besides academia. In a study of 2010 SDR data, Turk-Bicakci, Berger and Haxton (2014) found that about half of Black, Hispanic, and White female STEM Ph.D. holders and Black and

Hispanic male STEM Ph.D. holders were employed in nonacademic careers; two thirds of Asian female STEM PhD holders and almost three fourths of Asian male STEM PhD holders were employed outside academia; and approximately three fifths of their White male counterparts were in nonacademic careers (p. 2). The most common nonacademic employment sectors included private, for-profit organizations and government.

The proportion of academic to nonacademic employment options available to

STEM doctorate holders varies considerably by discipline (Benderly et al., 2014, p. 15).

Nonacademic employment sectors that provide opportunities for cutting-edge research include for-profit companies such as those in the pharmaceutical or biotech industries, as well as government-funded research institutions such as the Los Alamos, Lawrence

Livermore, and Oak Ridge National Laboratories (Vick & Furlong, 2010).

Using data from Nerad and Cerny’s PhDs—Ten Years Later study (Nerad et al.,

2004; Nerad & Cerny, 1997), Aanerud et al. (2007) found that, when non-academic labor 54 markets offer attractive alternatives to the academic labor market, women Ph.D. holders’ odds of obtaining tenure increase relative to men’s. They concluded that non-academic career options, whether an individual pursues them or not, have a discernible impact on academic career paths. Similarly, Fox & Stephan (2001) studied STEM doctoral students and found a direct correlation between the availability of high-paying industry jobs in a discipline and women’s likelihood of obtaining tenure-track faculty jobs; men were more likely to be employed in industry if the pay in that sector for their discipline was better.

Given the trend toward increasingly competitive grant programs for academic researchers, current or potential faculty may find the research opportunities outside academia preferable—to the extent that these other opportunities are not also hit by declining financial support due to reduced public investment or difficult business climates. In addition, private-sector employment opportunities tend to be less attractive to individuals who are more open to the free sharing of research findings (Fritsch & Krabel,

2012).

The tenure-track position in academia still persists as a goal for many. In discussion forums such as the Chronicle of Higher Education, conversation abounds about the career prospects in academia, but alternative career paths outside academia are less-often discussed, whether due to an academic culture that promotes faculty careers over alternatives outside academia (Sauermann & Roach, 2012) or merely an unfamiliarity with sectors beyond academia. Yet the perceived attractiveness of an academic career tends to wane over the course of a student’s Ph.D. program, even though 55 advisors strongly promote academic research careers over non-academic careers—and even over teaching-focused academic careers (Sauermann & Roach, 2012, p. 4).

Changing sectors. Literature examining the inter-sector career movement of

STEM doctorate holders is limited (Bozeman & Ponomariov, 2009). An increasing body of research is exploring collaboration between higher education and industry, particularly in terms of its impact on productivity as measured by increased licensing and patent activity (Dietz & Bozeman, 2005) and in terms of the entrepreneurial motivations of faculty (Bozeman & Gaughan, 2011; Fox & Xiao, 2013; Fritsch & Krabel, 2012). In many cases, however, these collaborations do not necessarily result in sector switching; increasingly, such partnerships are conducted through university-affiliated research centers (Corley & Gaughan, 2005; Dietz & Bozeman, 2005).

Interestingly, Corley and Gaughan (2005) found parity between men and women faculty affiliated with such centers in terms of the amount of time they spent working on grant proposals, administering grants, and conducting research, suggesting that the research centers hold promise as a context for greater gender equity. Other research also suggests that the experience of working with industry collaborators may increase faculty members’ interest in further collaborations or in switching to private-sector employment

(Dietz & Bozeman, 2005; Fritsch & Krabel, 2012).

Hardly any literature exists on sector-switching from the private sector to academia. Carrigan et al. (2017) conducted a qualitative study of participants in an “on- ramping” program that prepared women STEM Ph.D. holders to move from business/industry or government work to seek tenure-track faculty positions. The women 56 shared the considerable challenges they faced in making the transition, including moving from a system in which publishing was nonessential or even discouraged to one in which it was vital. Weimer’s (2001) advice to individuals considering moving from industry to academia, based on his own experience, identifies another challenge: mid-career switchers are often not eligible for the grant programs that could best help them establish their research programs because many such programs are restricted to applicants who earned their doctorates in the last five years.

It should be noted that both Weimer and Carrigan et al. focused on tenure-eligible faculty positions in research institutions. Many of the barriers they identified might be considerably lower at less-research-intensive institutions or in non-tenure-track faculty positions. Individuals who primarily want to teach may find such institutions and positions to be a better fit for their interests. Through interviews with women STEM faculty members at two-year institutions across the U.S., Anderson et al. (2014) found many who had come from positions in the private sector and expressed a strong preference for teaching; few if any expressed interest in pursuing a tenure-track position in a more research-intensive institution.

The above sector-switching literature focuses on qualitatively exploring the experiences of sector-switchers—their motivations as well as the challenges they face and how they have (or have not) overcome them. Quantitative research on the prevalence of sector switching is also sparse. Bozeman and Ponomariov (2009) analyzed data on sector-switchers from private to public sector (not exclusively academia). They found no statistical difference in the likelihood of sector switching between demographic factors 57 such as race/ethnicity, gender, or marital and family status; age, however, was slightly positively correlated with sector switching. Individuals reaped some career benefit of private-sector experience in subsequent public-sector employment, but that benefit actually seemed to have a limit—i.e., at some point a longer stretch of private-sector employment became negatively correlated with promotions and number of employees supervised (Bozeman & Ponomariov, 2009, p. 89). This inflection point, the authors suggest, indicates that there may be a strategic, limited window of time in which individuals should consider sector switching in order to reap the maximal career benefits.

Race/ethnicity. Assessments of the nation’s capacity to maintain its economic edge in the world recognize the need to expand the pool of STEM talent (National

Academy of Sciences, 2011; National Academy of Sciences et al., 2007, 2010). While a great deal of focus has been given to increasing the proportion of women in STEM, considerable attention has also been directed at increasing minority participation. In

Expanding Underrepresented Minority Participation: America's Science and Technology

Talent at the Crossroads (2011), the National Academy of Sciences noted that in recent decades international students have accounted for nearly all the growth in STEM doctorates at U.S. universities. Yet for multiple reasons including increasing international competition for these students as well as shifts in national immigration policy, the NAS urged an increased effort to grow domestic talent with an emphasis on minority 58 populations, the fastest-growing subgroup in the general population (National Academy of Sciences, 2011).

The issue of minority participation in STEM is complicated by the fact that not all minority groups are, in fact, under-represented. Due to the large influx of international students, particularly from China and India, over the last few decades, Asians now constitute a larger percentage of the overall STEM workforce than they do of the general population (National Science Foundation, National Center for Science and Engineering

Statistics, 2017). However, as Nelson and Brammer (2007) have found, when international students are removed from the analysis and only Asian-Americans with undergraduate degrees from U.S. higher-education institutions are considered, the appearance of Asian over-representation among faculty compared to the general U.S. population greatly decreases (Nelson & Brammer, 2007, p. 13).

The NSF recognizes three racial/ethnic groups—Black, Hispanic, and American

Indian—to be underrepresented because they constitute smaller percentages of STEM degree recipients and the STEM workforce than they do of the overall U.S. population

(National Science Foundation, National Center for Science and Engineering Statistics,

2017). Numerous studies have indicated that individuals from under-represented minorities (URMs) face numerous barriers to pursuing STEM careers. Young people from URMs are less likely to be academically prepared (Yeh, 2016b, 2016a); are less likely to take an interest in STEM careers (Grandy, 1998; Ilumoka, 2012; L. L. Leslie,

McClure, & Oaxaca, 1998; National Academy of Sciences, 2011); and report lower job satisfaction in STEM-related employment (Burnett, Bilen-Green, McGeorge, & Anicha, 59

2012; Dawson, 2013) than their White and over-represented minority counterparts.

Furthermore, even those who do secure positions in the STEM workforce are more likely to leave (Rosser, 2004; Xu, 2008b). Researchers have also found that the intersection of race/ethnicity and gender can put women from URMs in a “double bind” as they are more likely to cope with not only gender but also racial discrimination (Malcom, Hall, &

Brown, 1976; Ong, Wright, Espinosa, & Orfield, 2011)

While race/ethnicity appears to affect faculty career trajectories in STEM, analysis of the nuances and distinctions between multiple races and ethnicities is hampered by the fact that, as subcategories in a quantitative analysis such as this study become more numerous and fine-grained, the issues of both robustness and confidentiality become a concern. In some STEM disciplines, for example, the number of

Native Americans with Ph.D.’s is already vanishingly small, and even national-level aggregation may yield few to no individual cases (Nelson & Brammer, 2007). The extent to which race and ethnicity can be delineated in a study depends on the research question(s), the type of data, and the analytical approach being used.

Family status. Considerable research exists concerning the impact of marital status and family responsibilities upon women’s and men’s career development and indicated notable, persistent disparities. Ginzberg’s (1966) finding that employed married women with graduate degrees were still doing all or most of the household chores is now more than 50 years old, yet more-recent studies have shown that the division of domestic labor has still not reached parity (Carr et al., 1998; Fox, Fonseca, & Bao, 2011; Morrison et al., 2011; Perna, 2001; Wolf-Wendel, Ward, & Twombly, 2006). Not surprisingly, 60

Suitor et al. (2001) found that the amount of household labor increased with the presence of children in the home—an increase that fell primarily on women. Interestingly, in an analysis of data from the 2004 NSOPF, Leslie (2007) found that while women reported working fewer hours at their faculty jobs the more children they had, the exact opposite was true for men.

While the absence of children in the home does not eliminate the disparity in the division of household labor for married couples, it appears that the women faculty in these households are making the adjustments necessary to maintain their research productivity. Suitor et al. (2001) had hypothesized that research productivity would correlate inversely to hours spent on household work; however, they found no correlation between time devoted to research and scholarly productivity for either women or men without children—though when children were present in the home, time spent on household labor lowered women’s, but not men’s, scholarly productivity (Suitor et al.,

2001, p. 64). And in their research on women in academia, Mason, Wolfinger and

Goulden (2013) found that women with no children had at least as good a chance of earning tenure as men.

The adjustments these women are making are costly, however. When they looked at total time spent on any type of work (combining household chores and scholarly work), Suitor et al. (2001) found that women faculty worked approximately 10 hours more per week than their male counterparts; among just the women in tenure-track positions, the time disparity jumped to 20 hours per week (Suitor et al., 2001, pp. 63–64).

Bittman and Wajcman (2000) also found the quality of women’s leisure time to be lower 61 than that of men, as they were more likely to juggle their “leisure” with child care or household chores. And in their University of California Faculty Work and Family Study,

Mason and Goulden (2004) found that female faculty were less likely to have children than their male colleagues, and among faculty who were parents and past the age of likely fertility, women were more likely than men to express regret that they had fewer children than they wanted.

In addition to looking at the impact of family formation on academic careers,

Mason, Wolfinger and Goulden (2013) examined the impact from the opposite direction: the impact of academic careers on family formation decisions. They found that women in tenure-track jobs were far less likely than their male counterparts to ever have children; more than twice as likely to be single 12 years after earning their Ph.D., and more likely to be divorced (p. 3).

Budig (2003) explored the causal relationships between women’s fertility and employment through an event-history analysis of data from the 1979-1994 National

Longitudinal Survey of Youth. Consistent with Mason, Wolfinger and Goulden (2013), she found that both part-time and full-time employment were correlated with fewer pregnancies. Interestingly, she also found that mothers’ participation in the workforce varied depending on the age of their children. Those with preschool-age children were more likely to exit full-time employment (but not part-time employment); however, mothers with older children were less likely to leave either part-time or full-time employment, and if non-employed they were more likely to enter full-time employment

(p. 376). It should be noted, however, that Budig’s sample included only women aged 18- 62

37, and these women were of a variety of educational, socioeconomic and career backgrounds; this may limit the applicability of her findings to highly educated women in academic STEM careers.

Carnegie classifications. The Carnegie Classification system was created by the

Carnegie Commission on Higher Education in 1970, as a framework for categorizing the wide diversity of American higher-education institutions (Geiger, 1984). The system encompasses every accredited, degree-granting American college and university that is included in the national Integrated Postsecondary Education Data System. The Carnegie

Commission published its first version of the classification system in 1973 and has updated it in 1976, 1987, 1994, 2000, 2005, 2010, and 2015 (Indiana University Center for Postsecondary Research, n. d.).

While the purpose of the Carnegie Classification System is not to make determinations of quality or prestige, the criteria used in determining Carnegie classifications, including the amount of research funding an institution brings in, numbers of research-focused faculty, and number and types of degrees conferred (Indiana

University Center for Postsecondary Research, n. d.), essentially create a hierarchy in which institutions that have the largest research budgets and train the most researchers occupy the top tiers. U.S. News has used the Carnegie classification system as the basis for their the Best Colleges ranking categories since 1983 (Morse, Mason, & Brooks,

2017).

Carnegie type of doctorate-granting institution. Several researchers have considered the correlation between characteristics of degree earners’ doctorate-granting 63 institution and their subsequent career prospects. According to human-capital theory, indicators of the quality of academic preparation can be considered as measures of the extent to which individuals have accumulated human capital (Bentley & Adamson,

2003). One common proxy for academic quality that is particularly relevant to STEM disciplines is the Carnegie classification of the doctorate-granting institution. For graduate students in the STEM disciplines, high research funding and activity as well as emphasis on doctoral education can be compelling indicators of institutional quality as they apply for admission to doctoral programs.

At the graduate level in particular, departmental and program prestige can be just as important as institutional rankings if not more so. Researchers including Mason,

Wolfinger and Goulden (2013) have used doctoral-program rankings by the National

Research Council as a measure of “prestige” in their analysis of factors influencing the likelihood of getting a tenure-track faculty position. Where available to cross-reference with other datasets, such rankings can provide an even more granular indication of the prestige of an individual’s doctorate education. However, current prestige rankings in a format that could be cross-referenced were not available for this study.

Beyond the content and rigor of the doctoral program, research has shown that socialization within the graduate program shapes students’ expectations of careers

(American Association of Physics Teachers, 2009; Cech, Rubineau, Silbey, & Seron,

2011; Hagemeier, Murawski, & Popovich, 2013). This socialization often has a strong bias toward encouraging research-faculty careers over other potential career paths

(Sauermann & Roach, 2012). Being socialized in this environment, students may 64 therefore not be aware of the wide diversity of higher-education career opportunities that are available beyond the research university, or at least may regard those job prospects as inferior at best.

Carnegie type of employing institution. The same characteristics that make institutions attractive to aspiring doctoral students can also confer prestige upon the faculty who work in them. As mentioned previously, the research productivity of its faculty and their ability to produce significant numbers of researchers in turn factor into an institution’s Carnegie classification. Because in earlier iterations of the Carnegie system an institution’s classification was based heavily on how much external research funding it received, many institutions saw an incentive to support grant-seeking in order to receive a higher classification in the next Carnegie classification revision (Ehrenberg,

2003). For faculty either starting or seeking to further their research agendas, such institutional support is invaluable, setting them on a path to greater visibility in their discipline and connections with influential colleagues in and beyond academia. Bentley and Adamson (2003) have suggested that junior faculty in particular who secure positions in more-prestigious universities can accrue more human capital than they might at lower- prestige institutions.

To STEM faculty who focus on the research-related aspects of their work, research-intensive institutions may be preferable. However, many faculty members prefer the teaching portion of their responsibilities to the research. Women in particular are more likely to gravitate toward teaching over research (Ahmad, 2017; Goulden et al.,

2010; Townsend, 1998) and may seek employment in what the Carnegie system classifies 65 as more-teaching-focused institutions. Additional evidence suggests that women who work at community colleges choose to do so because they believe they will be better able to achieving a better balance between work and their personal lives. (Anderson et al.,

2014; Wolf-Wendel et al., 2006). After living with the results of such choices, many women indicate that they have found the teaching load and pressures for virtually 24/7 availability to students at be least as challenging as they anticipated for a career in a research-intensive institution. However, they still expressed a relatively high level of satisfaction with the community-college environment (Shaw, Callahan, & Lechasseur,

2008; Townsend, 1998).

Not all observers agree that these women’s career decisions are motivated by positive preferences, however. The expectation of research productivity at universities designated in the Carnegie classification as “research-intensive,” for instance, may present a negative motivation that contributes as much to women’s reported preference for teaching as any inherent affinity for the latter activity. Women who have gone through doctoral programs often relate that they did not aspire to tenure-track jobs at research institutions because they saw women in such jobs sacrifice either their personal lives for their careers or vice versa, and they did not want to have to make such choices.

Such evidence therefore calls into question whether women truly are more innately drawn to teaching over research or merely exercising an avoidance response to a perceived incompatibility between research and family formation (Ahmad, 2017; Fox et al., 2011;

Fox & Stephan, 2001; N. H. Wolfinger et al., 2009). 66

Many new doctorate recipients, who may only have experience with universities, may not even be aware that the faculty experience they have observed in their undergraduate and graduate programs is not the rule across all types of institutions. If they aspire to such a career for themselves, they may be in for a rude shock if they take a position at, say, a liberal-arts or a community college. By the same token, those who decide they do not want such a career may avoid academia altogether, unknowingly overlooking some teaching-intensive faculty opportunities that might suit their career goals very well.

Once an individual has joined a particular type of institution, research has shown that the degree of congruence between individual and employing institution affects the individual’s likelihood of staying with or leaving that institution (Lindholm, 2003, 2004;

Xu, 2008b). As faculty responsibilities and experiences can vary widely between different types of higher-education institutions (Adams, 2002; August & Waltman, 2004;

Lindholm, 2004; Nerad et al., 2004; C. A. Trower & Chait, 2002), individuals who find their employment experience unsatisfactory may thus seek a change, either within or beyond academia.

Significance of this Study

This chapter contains an overview of the considerable body of research exploring the gendered nature of employment both in and beyond academia. Many of the researchers have examined the intersection of gender and family status, gender and race/ethnicity, and specific disciplines or categories of disciplines. The literature on 67 academia typically focuses on women’s progression to tenure, productivity, salary growth, likelihood of securing a leadership role, attrition, and job satisfaction.

Relatively little of the existing research considers the broader job market for

STEM Ph.D. holders beyond academia, particularly movement between academia and other sectors. Some data analyses look at the likelihood of certain subgroups being employed in particular sectors. Such analyses cannot, however, capture information about the timing of individuals’ movement between sectors when the data are point-in-time.

The research most comparable to that proposed in this study is the work by

Mason, Goulden and Wolfinger (Goulden et al., 2010; Mason et al., 2013; N. H.

Wolfinger et al., 2008, 2009; N. Wolfinger, Mason, Goulden, & Frasch, 2009) on women in academia. Much of their analysis used national-level data from multiple waves of the

SDR to explore various questions about the careers and family-formation patterns of women in academia. Their SDR dataset included survey waves from as early as 1981 to as late as 2003. (Certain portions of their research also drew upon data from the U.S.

Census as well as surveys of the 61 universities with membership in the Association of

American Universities.)

This study used event-history methods similar to those employed by Mason,

Goulden, and Wolfinger but analyzes a more-recent span of SDR data, from 1999 through 2013. It also looks at the likelihood of individuals moving from one sector to another, the timing of their departure, and how the likelihood and timing of departures may vary by starting sector as well as demographic, disciplinary, and other factors.

Insights based on this study can inform and enhance higher-education institutions’ efforts 68 to recruit and retain talented STEM faculty, and to prepare STEM PhD holders for postgraduate employment opportunities.

Data sources on faculty. The SDR is characterized by Mason, Wolfinger, and

Goulden as “arguably the best employment data set in the United States” (2013, p. 2). It is certainly one of the only remaining comprehensive data sets on faculty at U.S. higher- education institutions. The National Study of Postsecondary Faculty was discontinued after conducting its last nationwide survey of faculty in 2004. The Survey of Earned

Doctorates, as mentioned before, surveys only newly minted Ph.D.’s and thus does not capture the experiences of individuals beyond that point in time. The Higher Education

Research Institute (HERI) Faculty Survey, the other major national faculty survey, does not collect information on respondents’ career paths and thus cannot provide insights on how faculty navigate through their careers.

The SDR instrument is administered every two to three years to cohorts of Ph.D. holders in science and engineering, both in and beyond academe. Furthermore, the SDR uses a longitudinal sampling design with the ability to link individual responses across multiple surveys using unique user IDs. Most significantly for the purposes of studying the movement of individuals between sectors, the inclusiveness of the SDR survey population and the longitudinal potential present a remarkably underused trove of data that can shed light on how and why individuals make the career decisions they do.

Chapter 3: Methodology

The purpose of this study was to examine the inter-sector career mobility of women who earn STEM doctorate degrees from U.S. higher-education institutions. Using data from the national Survey of Doctorate Recipients, the researcher investigated the extent to which key personal and education-related characteristics are correlated with an individual’s likelihood of mobility out of the initial employment sector and the timing of their inter-sector movements, particularly exploring the differences between women and men. The researcher also analyzed how outcomes vary between individuals starting in the

Education (for the purposes of this study, higher education) and those starting in other sectors.

Research Questions

1. What is the likelihood that female STEM Ph. D. holders who start their

careers in academia will move to another employment sector?

2. How are the likelihood and timing of female doctorate holders’ inter-sector

career moves affected by the following?

a. Demographic characteristics including age, race/ethnicity,

citizenship status, marital status, and family formation

b. Education-related characteristics including discipline, control and

Carnegie classification of the doctorate-granting institution, and

postdoctoral appointments

c. Employment-related characteristics including starting sector, initial

Carnegie type of academic employer, and tenure status 70

3. How do the likelihood and timing of female doctorate holders’ career moves

compare to those of their male counterparts?

These research questions were explored using event-history analysis. This approach has not been widely used for research involving career movements, due partly to the challenge of collecting longitudinal data that can make use of event-history techniques. The SDR, however, lends itself to such analysis due to the ability to connect an individual’s responses over multiple survey years. For the purposes of this study, the

“event” is a change in employer sector, either to a different sector or to non-employment.

Figure 1. Research model of factors influencing career choices

About the Survey Instrument

The Survey of Doctorate Recipients is a panel study of individuals who have earned a research doctorate in a science, engineering, or health (SEH) field from a U.S. higher-education institution. The study has collected demographic, education, and career- 71 related data on a nationally representative cohort of new and previously surveyed individuals every two to three years since 1973. Data from the SDR give federal agencies insight into the nation’s scientific and engineering workforce as they develop policies, forecast labor supply and demand, and generally assess the nation’s competitiveness in attracting and retaining science and engineering talent (NORC at the University of

Chicago, 2014). The SDR is primarily sponsored by the National Science Foundation, with additional support from the National Institutes of Health.

The sampling frame for the SDR is derived from the Survey of Earned Doctorates

(SED), a yearly census of individuals who research doctorates in any discipline from a

US university. Eligible individuals include those who earned a research doctorate from a

US college or university in a SEH field, are under 76 years of age, and are not terminally ill or institutionalized (National Center for Science and Engineering Statistics, 2013). The sampling frame includes two groups of respondents: new doctorate-holders since the last

SDR cycle, and previously surveyed individuals. The new-cohort frame is a primary frame consisting of all eligible cases from each doctoral award year since the last SDR cycle, while the “returning” cohort frame is a secondary frame of respondents to the previous survey cycle. In 2013, the entire sampling frame contained 116,508 eligible cases, of which 44,602 were returning cohort cases and 71,906 were new cohort cases, collectively representing a total population estimated at 838,000 doctorate holders

(National Science Foundation, Division of Science Resources Statistics, 2014).

A stratified random sample is drawn from this frame, with the goal of achieving as much homogeneity as feasible while oversampling certain small subpopulations (such 72 as minority female engineers) to ensure they are represented in the sample (Brown &

Henderson, 1998). In the 2013 SDR cycle, for example, stratification variables included degree field, sex, and race/ethnicity (National Science Foundation, Division of Science

Resources Statistics, 2014). Ultimately, 47,078 cases were selected for the 2013 SDR survey sample (National Science Foundation, Division of Science Resources Statistics,

2014).

The data used in this dissertation were available through a restricted-use data license granted by the NSF permitting the use of data from survey years 1999 through

2013. (A request was made to add data from the 2015 SDR to the current license agreement, but due to the extended shutdown of the federal government from late

December 2018 through early 2019 this study ultimately proceeded without it.)

In each SDR cycle used for this dissertation, sampled individuals could complete the survey by one of three methods: paper-based questionnaire, online (Web-based) questionnaire, and computer-assisted telephone interview. Any cases not responding received follow-up prompts encouraging them to participate. The overall response rate for 2013 was 76.4 percent (weighted), a rate comparable to those that have been obtained in previous survey cycles (National Science Foundation, Division of Science Resources

Statistics, 2014). 73

Table 1

Respondents to Survey of Doctorate Recipients, 2001–2013

SDR Survey Year Respondents

2001 31,366 2003 29,915 2006 30,817 2008 29,974 2010 31,462 2013 30,696

Source: Survey of Doctorate Recipients, National Science Foundation, 2001–2013

Of particular utility to those interested in analyzing SDR data over time, each individual respondent is assigned a unique identifier that remains constant across all future survey years. This identifier enables a researcher to connect individuals’ responses across years for a longitudinal perspective over their careers.

About the Sample

The researcher looked at four waves of SDR respondents who completed every survey immediately following the receipt of their doctoral degree, from 2001, 2003, 2006 or 2008 through 2013. The following figure illustrates how these waves were constructed.

SDR Survey Year 2001 2003 2006 2008 2010 2013 2015 First wave Second wave Third wave Fourth wave Figure 2. Construction of respondent waves included in the study

A preliminary analysis of these survey waves strongly indicated that it would be

more accurate to include not only individuals who earned their Ph.D. since the year of the

prior SDR survey but also those who earned their degree during the prior SDR survey

year. For example, in the second wave illustrated above, only five respondents reported

earning their doctorate in 2003, but the 2006 wave included 584 respondents who

reported earning their doctorate in 2003. A similar pattern showed up in other years,

suggesting that the timing of many individuals’ degree awards kept them from being

included in that year’s sampling frame. The following table shows the number of

respondents in each wave for both of these selection methodologies.

Table 2

Number of new respondents to all SDR surveys from their initial year to 2013

Initial survey # earning highest degree in # earning highest degree in Total year years since prior survey year of prior survey 20011 266 553 819 2003 284 563 847 2006 1,040 584 1,624 2008 462 877 1,339 Totals 2,052 2,577 4,629 1 Prior SDR survey year: 1999 75

The dataset was further refined by winnowing out respondents for whom variables essential to this study were suspect or disqualifying, such as individuals reporting receiving their doctorate degrees from two-year institutions; those whose reported disciplinary category was “non-S&E;” and those whose academic-sector employment was solely in K–12 education. After eliminating those respondents, the dataset included 4,516 cases.

Variables

Following are descriptions of the SDR variables used in the study.

Dependent variable

The dependent variable in this study was the event of a change of employment sector, whether to another sector or to unemployment. The change variable is derived from the SESTAT variable EMSECDT, which includes the following categories:

• 2-year college or Other School System

• 4-year college or Medical Institution

• Government

• Business/Industry

• Logical Skip (cases that report not being employed)

Note that two-year institutions and K-12 schools are combined in this variable. To exclude individuals employed solely at the K-12 level, the variable EDTP was used to separate these institution types. Once separated, two-year institutions were combined with other higher-education types to create an Education category. 76

Independent variables

Based on the literature, the following independent (predictor) variables were included in the event-history analysis to assess their explanatory effect on sector changes.

Table 3

Independent Variables

Time- Time- Variable Categoriesa constant varying Background characteristics Gender 2 X Age n/a X Race/ethnicity 4 X Citizenship status 2 X Education Discipline of doctorate 6 X Carnegie classification of institution 2 X Control of doctorate-granting institution 2 X Postdoctorate appointment 2 X Employment Starting employment sector 3 X Carnegie classification of employerb 3 X Tenure statusb 3 X Family characteristics Marital status 2 X Children living in household 2 X Interaction terms Female*children 2 X Female*tenure status 2 X a Dummy variables were created for all variables except age. b If employer is a higher-education institution.

Gender. The gender variable is represented by the variable GENDER from the

2013 SDR. The variable includes two categories: male and female. Traditionally gender 77 has been treated as a time-constant variable, ignoring the possibility that a transgender respondent may transition between gender identities. In consideration of this possibility, the GENDER values from respondents in 1999 and 2013 were compared. Only one record showed a change (it cannot be known whether this was due to a transitioning of gender identification or an error), indicating that it is reasonable to treat gender as a time- constant variable.

Age. The SESTAT variable AGE from the year of each respondent’s first appearance in the dataset was used. Although age is a time-varying variable, because every respondent’s age increases by the same increment its utility as such is limited.

Thus, it was treated as a time-constant variable for the purposes of this analysis.

Race/ethnicity. Race/ethnicity is treated as a time-constant variable. Although the categories available to respondents have changed over time, particularly with regard to the treatment of ethnicity and the option to select multiple races (National Science

Foundation, National Center for Science and Engineering Statistics, 2006), the individual’s race/ethnicity does not change over time. This analysis used the SESTAT variable RACETHM from the 2013 SDR. This variable includes the following categories:

• Asian, non-Hispanic ONLY

• American Indian/Alaska Native, non-Hispanic ONLY

• Black, non-Hispanic ONLY

• Hispanic, any race

• White, non-Hispanic ONLY

• Non-Hispanic Native Hawaiian/Other Pacific Islander ONLY 78

• Multiple Race

For this analysis, the above categories were condensed into four: White, Asian,

Black, and Other Race. This simplification was necessary because even with oversampling, some of the originally subcategories are prohibitively small.

Citizenship status. The citizenship status of respondents can change over the study period and is therefore treated as a time-varying variable. This analysis used the

SESTAT variable CTZN, which includes not only citizenship but also visa status. This variable includes four categories:

• U.S. citizen, native

• U.S. citizen, naturalized

• Non-U.S. citizen, Permanent resident

• Non-U.S. citizen, Temporary resident

For the purposes of this study the first three of these categories were combined, creating a new citizenship variable with two categories: Citizen or permanent resident and Temporary resident.

Marital status. The marital status of individuals is included as a time-varying variable. The literature indicates that marital status is correlated with different career outcomes for men and women (Fox et al., 2011; Kulis & Sicotte, 2002; Mason &

Goulden, 2004; Morrison et al., 2011; N. H. Wolfinger et al., 2008). For the purposes of this analysis, the presence or absence of a spouse is of interest rather than whether the respondent is widowed, divorced, etc. The SESTAT variable MARIND contains this level of detail for this analysis; it is a binary categorical variable with values Yes and No. 79

Children. Many researchers have found that family formation, including childbearing and child-rearing, impacts faculty career trajectories (Adamo, 2013; Bleske-

Rechek, Fuerstenberg, Harris, & Ryan, 2011; Carr et al., 1998; Fox, 2005; Mason &

Goulden, 2002, 2004; N. H. Wolfinger et al., 2009). The SDR collects some information on the numbers and ages of children in each survey cycle; however, not every question is asked in every cycle. The variable common to all cycles in this analysis, CHLVIN, records the presence or absence of children living in the household as a binary (Yes/No) categorical variable. This measure regrettably does not provide detail on the number of children or their ages, information that would add a quantitative dimension to the understanding of a respondent’s child-rearing burden. It does provide some insight to the analysis, however, due to its time-varying nature.

Discipline. The discipline categories used in the SDR have changed slightly over time. Since the disciplinary area of an individual’s doctorate degree is time-constant, it is reasonable to select discipline data from a survey year that categorizes disciplines at a level of detail most useful in the context of the given research question. The variable

NDGMEMG from the 2013 SDR cycle was therefore used. This variable used the following six categories:

• Computer and Math Sciences

• Life and Related Sciences

• Physical and Related Sciences

• Social and Related Sciences

• Engineering 80

• Non-STEM degrees

These categories are similar to the six categories used by Turk-Bacakci et al

(2014): Engineering, Biological Sciences, Physical Sciences, Agricultural Sciences,

Computer Sciences, and Mathematics/Statistics.

Doctorate-granting institution type. The Carnegie classification of the institution from which respondents earned their doctorate degrees is included as a time-constant variable. The SESTAT variable HDCARN, based on the 1994 Carnegie classification taxonomy, was used. The categories were greatly condensed for ease of analysis as shown below.

Table 4

Recategorization of Carnegie Classifications for Degree-Granting Institutions

Original category New category

Research University I Research University II Doctorate Granting I Doctorate Granting II Research/Doctoral Medical schools and medical centers Other separate health professional schools Schools of engineering and technology Comprehensive I Comprehensive II Liberal Arts I Liberal Arts II Theological seminaries, bible colleges Non-Doctoral Schools of business management Schools of art, music, and design Teachers colleges Other specialized institutions Indian Tribal Institutions

Control of doctorate-granting institution. The type of control (public or private) of the institution from which respondents earned their doctorate degrees was incorporated as a potential explanatory variable in this analysis, using the SESAT variable HDPBPR.

Starting employment sector. A component of this study involves analyzing differences in career movements between those who start their employment in the

Education sector and those who do not. This variable was used as a filter for analyses including only individuals who started in Education; it was also included as an independent variable in the regression analyses of the entire dataset. The variable for starting employment sector was derived from the EMSECSM value for each individual in 82 the year of their first SDR response. Starting sector was treated as a time-constant variable with two categories: Started in Education and Started in Other.

Employing institution type. For the portion of the analysis including only respondents whose first employment sector is Academia, the possible influence of the type of employing higher-education institution was studied. For Carnegie classification the variable CARNEG was used rather than the more-recent CARN05C, because the latter variable does not exist in SDR cycles before 2006. Categories of first employing institution were collapsed for manageability as follows:

Table 5

Recategorization of Carnegie Classifications for Employing Institutions

Original category New category Research University I Research University II Doctorate Granting I Doctorate Granting II Research/Doctoral Medical schools and medical centers Other separate health professional schools Schools of engineering and technology

Comprehensive I Comprehensive II Four-Year Non-Doctoral Liberal Arts I Liberal Arts II Two-Year Institutions Theological seminaries, bible colleges Schools of business management Schools of art, music, and design Two-Year and Other Teachers colleges Other specialized institutions Indian Tribal Institutions

Postdoctorate appointment. A postdoctorate appointment is a rite of passage for many STEM Ph.D. holders, particularly those who aspire to a tenure-track faculty position. Since individuals may move in and out of postdocs over the study period, this is treated as a time-varying variable using the SESTAT variable PDIX, an indicator

(yes/no) of whether the respondent held a postdoc.

Tenure status. For those individuals employed in the Education sector, a tenure- status variable was derived from the SESTAT variable TENSTA, which includes the following categories: 84

• Not applicable: no tenure system at this institution

• Not applicable: no tenure system for my position

• Tenured

• On tenure-track but not tenured

• Not on tenure track

These five categories were condensed into three: Tenure-track (which combines the categories of Tenured and On Tenure Track); Not on Tenure Track; and Not

Applicable (which combines the two original Not Applicable categories).

Two interaction terms were added into the analysis as well.

Female*children. An interaction term derived by multiplying the categorical variables for gender and children was incorporated to test for a potential interaction effect between gender and the presence of children in the household. For those respondents who are female and have children living at home the value of the interaction term is 1; for men as well as for women without children at home the value is 0. This interaction term was used to explore the potential influence of social roles related to parenting as a factor in individuals’ career decisions. Models were compared with and without this interaction term in order to observe its explanatory effect.

Female*tenure. An interaction term derived by multiplying the categorical variables for children and tenure-track/non-tenure-track was included to test for a potential interaction effect between gender and tenure status. For those respondents who are female and employed in a tenure-track position the value of the interaction term is 1; for men as well as for women not on the tenure track the value is 0. Since considerable 85 research has indicated correlations between gender and tenure status, this interaction term was used to explore the possibility of a more than additive interaction effect between these two variables. Models were compared with and without this interaction term in order to observe its explanatory effect.

Event-History Analysis

Event-history analysis, a statistical approach designed to investigate the time to a specified change in categorical status, was used for this study. Event-history analysis is not one method but a collection of statistical approaches used when the dependent variable of interest is not merely whether an event has occurred for an individual case but also the length of time until that event occurs (Allison, 2010; Kleinbaum & Klein, 2012).

The choice of method depends upon the type of data and the nature of the research question.

Event-history analysis has its origins in the biomedical and engineering fields. A typical biomedical application of event-history analysis would be to study the survival times of cancer patients—indeed, the approach is commonly referred to as “survival analysis” for that reason (Allison, 2014). The engineering disciplines have also adapted the technique to conduct reliability or “failure-time” analyses on structures and materials such as manufactured electronic component (Allison, 2014).

Event-history analysis is also increasingly used in a wide variety of the social sciences. For example, Lehrman (1989) has used it to assess influences on the rate of failure of commercial life-insurance companies. Guo (1993) examined the rates and timing of marital dissolution among American couples between 1970 and 1987. More 86 recently, Hiroi and Omori (2013) used event-history analysis to explore the causal factors underlying different countries’ experiences of coups d’état. And DesJardins, McCall,

Ahlburg, and Moye (2002) have used the approach to study factors influencing time to bachelor’s degree attainment.

The capabilities of event-history modeling also lend themselves to analyzing events related to employment and career mobility. Lancaster (1979) has used event- history analysis to study factors influencing the duration of unemployment. Sanz-

Menendez, Cruz-Castro, and Alva (2013) have used it to explore the effects of academic performance, social embeddedness and mobility on the time to tenure among faculty in

Spanish universities. And Mason and Goulden (2004) and Wolfinger, Mason and

Goulden (2008) have used event-history modeling to study various influences on career trajectories and family formation for women in academia, using SDR survey data from

1981 to 1999.

Panel survey data such as the SDR are well-suited to the use of event-history analytical techniques. Each cycle of the SDR collects point-in-time data on respondents with science and engineering doctorate degrees, while the unit IDs assigned to each individual enable the researcher to connect multiple responses over time by the same respondent. Given that individuals are regularly entering and leaving the SDR sampling frame and the possibility of nonresponse in one or more cycles, not all respondents will have the same number of observations within the time period analyzed. In event-history analysis such gaps in observations are referred to as censoring (Blossfeld & Rohwer,

2002). 87

This analysis includes STEM doctorate recipients since 1999 who respond to all

SDR surveys from the receipt of their degree to 2013, the year of the most recent SDR cycle available at the time of this study. Event-history analytical approaches are used to analyze the career trajectories of these individuals, specifically the likelihood and timing that they will leave their initial employment sector for another or for no employment. In the language of event-history analysis, the “event” in question is the change from the initial employment sector (the origin state), whether in Academia, Business/Industry or

Government, to the destination state of either a position in another employment sector or non-employment.

While a change in employment sector could occur at any point along a continuum during the study period (or not at all, in which instance the case is characterized as right- censored), the SDR dataset only records data at discrete intervals (typically two years, though two intervals are three years each). A change in employment sector between two

SDR cycles could thus have occurred at any point between the two cycles, but the exact timing cannot be known in any more detail. This situation is known as interval censoring

(Allison, 2010; Kleinbaum & Klein, 2012). Two event-history approaches can be used for discrete-time data: a logit model or a complementary log-log model. Allison (2010) notes that logit models are best suited for event times that are actually discrete, while the complementary log-log model is recommended for events that could occur at any time but are only observed at discrete intervals. The SDR dataset exemplifies the latter case; for this reason, Wolfinger et al. (2008, 2009) used complementary log-log regression in 88 their analysis of data from earlier waves of the SDR. The same modeling approach was used for this study.

A simple complementary log-log model can be mathematically expressed as:

log− log1 − �(�) = � + �� + ��(�) + . . . + �� + ��(�) where P(t) is the probability of experiencing an event at time t given that the individual has not yet experienced it (Allison, 2014). The design of this model ensures that P(t) is bounded by 0 and 1, which is vital because, being a probability, P(t) cannot be less than zero or greater than one (Allison, 2014). The above sample equation includes two types of explanatory variables. The first, represented by x1, and xi, are variables that remain constant over time (such as race/ethnicity or discipline). The second, represented by x2(t), and xj(t), are time-varying variables that may have a different value at each time t (for example, presence or absence of children in the household).

In practice, a complementary log-log model can include numerous variables of both types. When the b coefficients of each variable are exponentiated, the result is interpreted as a “hazard ratio” for the associated variable (Allison, 2014). For example, in analysis of the likelihood of an event, a coefficient of 0.40 for a binary variable of Female would indicate that the odds (or “hazard”) of an individual experiencing the event are

1.49 times higher for a female than for a male because e0.40 = 1.49.

After fitting a model including all individuals in all sectors, the researcher also conducted event-history analyses of subsets of respondents:

• A model including women only

• A model including men only, and 89

• A model including only those respondents whose starting employment sector

is Education

Models of the full dataset and the subset whose starting employment sector was in

Education were run with and without the interaction terms to assess their effects, if any, on the likelihood of changing sectors.

Limitations of the Study

Following are several limitations that should be noted when interpreting the results of the data analysis presented in this dissertation. They include limitations imposed by the survey instrument, bias introduced by the survey respondents (and non- respondents), and potential interrelationships between variables.

Data on children. The SDR survey instrument varies across years in the way it asks respondents to report the presence and number of children in their household (e.g., in the age ranges). Therefore, consistent with Mason, Wolfinger and Goulden’s (2013) solution to this problem, a binary variable for the presence or absence of children living in the household, not the number of children, was used.

Race/ethnicity. Another limitation of this study is the categorization of race/ethnicity into White, Asian, Black and Other Race. This decision was made in part to simplify analysis, and partly because some subcategories would otherwise become too small (e.g., Native American women tenure-track faculty in engineering at a private

Comprehensive university). Because Hispanic STEM Ph.D. holders are incorporated into the Other category, which also includes non-Hispanic respondents of other races), the dimension of ethnicity is lost. The categories Asian and Black do permit exploration of 90 some potentially meaningful differences. Even these terms have their limitations, however. The category “Asian” encompasses such disparate groups as Chinese,

Cambodian, Indian, Bangladeshi, Israeli, Kurdish, etc., and the category Black elides

African Americans and African immigrants. In the latter case the combination of this race variable and the citizenship variable could be used to explore potential differences between these groups, but the citizenship variable used in this study would not be able to distinguish between native-born African Americans and Africans who are permanent residents.

Nonresponse bias. As with most survey data, nonresponse bias is a potential issue. Fortunately, the SDR has a fairly high response rate. In 2013, for example, the unweighted response rate was 76.3 percent (and the weighted response rate 76.4%), comparable to response rates in other years (National Science Foundation, Division of

Science Resources Statistics, 2014, p. 5). Most nonresponse cases are either non-U.S. citizens (whose unweighted response rate in 2013 was 69.6%) or individuals who could not be located due to missing source data; this latter group accounted for nearly half

(49.4%) of all nonresponse cases in 2013 (National Science Foundation, Division of

Science Resources Statistics, 2014, p. 5).

Multicollinearity. Multicollinearity is the existence of a strong linear correlation between two or more predictor variables in a model. In most data analyses, at least some degree of correlation between variables is common (Field, 2013). When correlations are low they have little meaningful impact on the model estimates, but at higher levels, multicollinearity can inflate the correlated covariates’ standard errors and complicate 91 attempts to assess their statistical significance (Allison, 2010; Farrar & Glauber, 1967;

Field, 2013; Van den Poel & Lariviere, 2004). Multicollinearity does not affect the overall model or even the coefficients of the collinear variables; it only affects interpretation of the correlated variables (Allison, 2012). If the correlated variables are included in the model as controls and are not themselves the variable(s) of interest, it may not even be necessary to address their multicollinearity (Allison, 2012).

Multicollinearity can present a unique potential challenge for event-history analysis, because time-varying covariates can, by definition, change values over the period of time studied. To determine the extent of multicollinearity in their Cox proportional-hazard analyses, Mitra and Golder (2002) and Van den Poel and Larivière

(2004) added variables sequentially into their Cox models and monitored the stability of the parameters with each addition. (It should be noted that the Cox proportional-hazard model is a different event-history approach than that used in this study.) Allison (2012) recommends that because multicollinearity concerns linear covariate relationships, it is unnecessary to evaluate it in the context of a survival analysis (Allison, 2010, p. 417).

Nevertheless, some standard practices designed to minimize the potential for correlations were used in this study. For each dummy variable used in the study, the reference category is selected based on evaluation of which categories have the highest number of cases. This selection can be a challenge with variables that change over time, so the frequencies of categories in each year were considered in making the selection.

Also, Stata itself incorporates a safeguard, automatically dropping any variable that 92 perfectly correlates with one or more other variables; this safeguard was activated to omit one variable in one of the models estimated for this study (see Table 24).

Summary and Rationale

The above literature review contains a summary of the theoretical models and conceptual framework on individuals’ career decision-making processes that have informed the development of this study. In addition, an extensive review of existing research surveyed what is known about career movements in general; issues specific to women’s experience navigating gendered workplaces and career trajectories; and career experiences (both generally and specific to women) in individual STEM disciplines. This review shows that while some longitudinal research has been done on careers, even with

SDR data, this area presents an opportunity for further exploration using more-current data.

In addition, this researcher broadened the lens by looking not just at those STEM doctorate holders in academia but at individuals in multiple sectors, a perspective that enables inter-sector comparisons. Finally, the portion of the study that does focus on academia includes not only tenure-track faculty at research institutions but also those working in different types of jobs and in multiple types of institutions. In summary, this study encompasses the broad range of employment opportunities available to STEM doctorate holders and the choices these highly educated individuals make in their career trajectories.

Chapter 4: Results

This chapter begins with a description of the variables—both time-constant and time-varying—used in this study. Summary statistics are provided for the entire dataset as well as the subgroup consisting of those who started their postdoctoral careers in the academic sector. Preliminary event-history analyses are also presented, including cumulative-hazard graphs, to provide insight into the timing of sector changes for the overall dataset and key subgroups, including a comparison of rates for males and females.

The second section presents findings from the model generated using the entire dataset. Results are then presented for separate models generated by gender. These comparisons provide insight into the extent to which the effects of independent variables differ between men and women.

A third section presents the findings of models generated for those starting their careers in the academic sector. These models incorporated key variables that are only applicable to this group, including tenure status and characteristics of the employing higher-education institution. A separate model then excluded tenure status, to enable examination of the effect on the gender variable.

Descriptive Analysis

Time-constant variables. Summary data for each time-constant variable in the analysis are provided below in Table 6:

Table 6

Time-constant variables, percent or mean

Time-constant variable Respondents All starting in respondents Education (n=4,516) (n=2,508) Background characteristics Gender Female 41.66% 44.72% Male 58.34% 55.28% Race White 66.51% 69.45% Asian 23.59% 19.99% Black 3.75% 3.99% Other 6.15% 6.56% Starting age Mean 34.86 35.10 Median 33 33 Education Discipline Computer and Math Sciences 8.34% 8.34% Life and Related Sciences 25.63% 29.88% Physical and Related Sciences 14.74% 13.18% Social and Related Sciences 25.72% 29.56% Engineering 18.69% 10.74% S&E-Related 6.88% 8.29% Carnegie classification of doctorate-granting inst. Doctoral/research institution 97.09% 97.54% 4-year non-doctoral/research institution 2.43% 2.01% Control of doctorate-granting institution Public 67.04% 69.15% Private 32.52% 30.43%

Note: All figures are weighted.

Females constitute 41.66 percent of the overall group and slightly more (44.72%) of the subset who started their postgraduate careers in the academic sector. The majority of respondents (66.51% in the overall group, 69.45% of those starting in academia) are

White. The second-largest racial group is Asian (23.59% of the overall group, 19.99% of those starting in academia). The percentages of Black (non-Hispanic) and other racial groups are considerably smaller: 3.75 percent and 6.15 percent for the overall group and

3.99 percent and 6.56 percent for the subset starting in academia, respectively. The mean starting age of all respondents was 34.86 (35.10 for those starting in academia), with a median age of 33.

The largest disciplinary categories of respondents were Social and Related

Sciences (25.72% of the overall group) and Life and Related Sciences (25.63% of the overall group). These groups constituted an even larger percentage of those starting in academia, at 29.56 percent and 29.88 percent respectively. The percentage of respondents in Computer and Math Sciences was constant (8.34%) between the overall group and those starting in academia. All other disciplines constituted smaller percentages of the group starting in academia than the overall group.

The vast majority of respondents, more than 97 percent, earned their doctorate degrees from doctoral or research institutions. More than two-thirds (67.04%) of respondents overall earned their degrees from public institutions, a proportion that was higher (69.15%) among those who started in academia.

As Figure 3 shows, the distribution of disciplines varies by gender. Females were more likely than males to have earned their doctorates in the Social and Related Sciences 96 or the Life and Related Sciences. Males were more likely to pursue Engineering, Physical and Related Sciences or Computer and Math Sciences.

Figure 3. Discipline by gender, all respondents

Among those who started working in the academic sector upon receiving their doctorates,

Figure 4 shows that the proportion of males and females in the Life and Related Sciences were nearly identical, and the disparity in representation of males and females in

Engineering was smaller than that among all respondents. 97

Figure 4. Discipline by gender, respondents starting in academic sector

Time-varying variables. Following are discussions of the time-varying independent variables included in this study, including citizenship/residency status, employment sector, marital status, children, postdoctoral appointments, and Carnegie type of employing institution and tenure status (if employed in higher education).

The vast majority of respondents included in this study were citizens or permanent residents when they entered. As Table 7 shows, the percentage increased over the study, indicating that individuals who entered as temporary residents moved to permanent resident status or became naturalized citizens over time.

Table 7

Citizenship or residency status, all respondents

Status 2001 2003 2006 2008 2010 2013

Citizen or permanent resident 82.63% 86.14% 86.30% 88.25% 93.62% 97.62%

Temporary resident 17.37% 13.86% 13.86% 11.75% 6.38% 2.38%

Most respondents started their postdoctoral employment in a higher-education institution, as shown in Figure 5; the second-most reported employment sector was

Business/Industry. However, the table also shows that the percentage of respondents employed in the academic sector has declined since the 2006 survey year.

Figure 5. Employment sectors, all respondents

Business/Industry employment fluctuated but generally rose over approximately the same time period. And the percentage of Logical Skips, indicating individuals who 99 were out of the workforce either voluntarily or involuntarily, increased slightly over time.

The percentage of respondents who reported being married increased gradually during the study period, as indicated in Figure 6. Notably, however, the percentage of women who were married lagged behind their male counterparts across the entire study period; the magnitude of the disparity appears remarkably consistent over time.

Figure 6. Percent married, by gender (all respondents)

The same trend appears in the subset of respondents who started their employment in the academic sector (Figure 7).

100

Figure 7. Percent married, by gender (respondents who started in academia)

The percentage of respondents who reported having children living in the home exhibited a similar trend to marital status; the percentage increased over the study period, though females lagged consistently behind their male counterparts as shown in Table 8.

Table 8

Percent reporting whether children living at home

Among respondents starting in 2001, women initially were more likely to hold a postdoctoral appointment than men. In every subsequent year, however, the reverse was true. The percentage of respondents holding a postdoctoral appointment, predictably, 101 decreased over the study period. It did not, however, reach zero. Those still holding a postdoc in 2013 must have held it for a minimum of five years, as the last wave of respondents entered the dataset in 2008. The percentage of Logical Skips, indicative of respondents who were out of the workforce, was consistently higher for women than for men across the study period (see Table 9).

Table 9

Percent holding a postdoctoral appointment or not

Among those respondents who reported working in the Education sector at any point during the study period, the majority worked in a doctoral or research institution, as is shown in Table 10. No consistent distribution appears among higher-education categories, although interestingly, in 2001 women were more strongly represented in doctoral/research institutions than men were. Over time, however, a higher percentage of both men and women appeared in the Four-Year Non-Doctoral category and the Other category (the latter of which includes two-year institutions).

102

Table 10

Carnegie type of employing institution, if a higher-education institution

Table 11 shows the tenure status for everyone working in Education at any time during the study period. Logical Skips indicate respondents who are employed outside the Education sector or out of the workforce altogether. As the increase in the percentage of Logical Skips over time shows, employment in higher education is decreasing. The percentage of women in tenure-track positions tracks closely with the percentage of men on the tenure track. However, women were more likely than men to be off the tenure track or in positions or institutions in which tenure was not available.

103

Table 11

Tenure status of respondents employed in the Education sector

The tenure-status picture looks slightly different when confining the analysis to respondents who started their careers in Education. In this subset, both women and men were more likely to report working in a position or for an institution with no tenure system (see Table 12). In the 2008 SDR cycle, however, the largest category became

Tenured or On Tenure Track. In every year, the proportion of women in this category lagged behind their male counterparts. The increasing percentage of Logical Skips represents individuals who either are no longer employed in Education or have left the workforce altogether.1

1 Because the subgroup of respondents in Table 12 includes individuals who started in Education, all individuals in the 2001 wave are, by definition, employed and therefore there are no Logical Skips in 2001. 104

Table 12

Tenure status of respondents with first employment in education, by gender

Sector changes, by independent variables. A change variable is calculated for each survey year starting in 2003 by comparing the respondent’s employment sector in that year to the employment sector in the previous SDR cycle; any difference is recorded as a “1.” The percentage of respondents changing employment sectors is broken out by gender in Table 13. Among respondents in the wave entering in 2001, women were slightly less likely than men to report a different employment sector in 2003. It should be noted that leaving the workforce entirely is also recorded as a change. From 2006 to

2013, however, women became more likely than men to have changed sectors. The percentage of women changing sectors increased year over year, while the percentage fluctuated for men.

Table 13

Percent of respondents changing employment sectors, by gender

2003 2006 2008 2010 2013 Female 7.54% 9.74% 12.53% 14.14% 15.03% Male 7.85% 6.88% 11.50% 10.23% 10.36%

105

In general, individuals whose first reported employment after earning their doctorate was in Education had a lower likelihood of changing sectors compared to individuals starting in any employment sector, as shown in Figure 8.

Figure 8. Percent of respondents changing employment sectors, by first sector

Table 14 shows the percent of respondents in each racial category who changed sectors in each survey year, by gender and initial employment sector. The rates of change are, for the most part, lower for most races among the group starting in Education than for those starting in either Business/Industry or Government.

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Table 14

Percent changing sectors by race, gender and starting employment sector

2003 2006 2008 2010 2013 F M F M F M F M F M First employment was in Education

White 5.39% 5.86% 7.98% 6.75% 11.13% 9.92% 11.77% 10.13% 12.82% 9.48%

Asian 4.71% 9.81% 7.03% 5.17% 13.58% 16.33% 13.03% 10.93% 16.84% 14.35%

Black 8.59% 5.65% 3.75% 3.96% 5.33% 13.75% 10.04% 13.76% 15.67% 8.70%

All other 5.89% 4.33% 4.90% 6.18% 9.67% 13.25% 9.81% 7.69% 9.10% 13.20%

First employment was in Business/Industry or Government

White 9.20% 8.74% 13.32% 8.30% 15.16% 13.87% 15.50% 11.51% 17.89% 11.74% Asian 12.39% 8.68% 11.45% 5.22% 10.34% 6.58% 21.44% 6.57% 17.69% 6.75%

Black 23.24% 9.70% 17.38% 8.41% 14.24% 10.15% 24.41% 14.82% 10.60% 5.91% All other 9.67% 20.71% 12.27% 10.92% 18.67% 12.09% 21.43% 15.54% 16.34% 11.25%

Some anomalies appear, such as an apparent spike in sector changes in 2003 among Black women starting in Business/Industry or Government. One cannot draw reliable inferences from these anomalies, however, due to the small numbers of cases in these subgroups; the aforementioned instance, for example, involved a subgroup of fewer than 12 cases.

An examination of sector changes by citizenship/residency status is summarized in Table 15. As the table indicates, there is no consistent pattern by gender.

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Table 15

Percent of citizenship categories changing sectors, by gender

2003 2006 2008 2010 2013 F M F M F M F M F M

Citizen or 7.75% 7.91% 10.25% 7.60% 13.27% 11.84% 14.10% 10.04% 14.97% 10.47% permanent resident Temporary 5.18% 7.56% 3.59% 3.62% 2.85% 9.58% 14.96% 12.50% 20.66% 7.00% resident

A comparison of sector changes by marital status, gender and first employment sector (see Table 16) indicates that, for most of the study period, the rates of sector changes among all respondents (married and unmarried) whose first employment was in

Education were lower than for those who started their employment in Business/Industry or Government, whether they were married or not. For women the only exception to this trend was in 2003, when unmarried women in Education had a slightly higher rate of sector changes than their unmarried Business/Industry and Government counterparts, although their rate was still lower than that for married women in Business/Industry or

Government. And from 2006 to 2013, married men in Education switched sectors more often than their married (but less often than their unmarried) counterparts in

Business/Industry and Government; the only exception was in 2013 when married men in

Education changed sectors more often than married or unmarried men in any sector.

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Table 16

Percent changing sectors by marital status, gender and first employment sector

2003 2006 2008 2010 2013 F M F M F M F M F M First employment was in Education Unmarried 6.53% 9.30% 6.50% 3.96% 9.49% 10.79% 13.96% 12.35% 14.35% 9.66%

Married 4.89% 5.21% 7.88% 7.11% 11.89% 12.02% 10.98% 9.76% 13.03% 11.02%

First employment was in Business/Industry or Government

Unmarried 7.75% 8.50% 9.67% 10.42% 12.00% 13.40% 18.11% 15.39% 21.27% 10.89%

Married 11.95% 9.65% 14.88% 6.68% 15.48% 10.70% 17.25% 9.18% 16.13% 9.76%

Table 17 shows the percent of respondents, by whether they reported children living in their home or not, who changed employment sectors in each survey year, by gender and initial employment sector. As with marital status, in general the respondents who started their employment in Education did not change sectors as much as their counterparts starting in Business/Industry or Government. More notably, the disparities between the sector-change rates for those with and without children in the home are smaller among those who started in Education than among their counterparts in

Business/Industry or Government. In particular, in 2003 and 2006 women who started their employment in Business/Industry or Government and had children at home were considerably more likely to change sectors than their childless counterparts, while in

2010 and 2013 women in Business/Industry or Government who did not have children at home were much more likely to change sectors than those with children.

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Table 17

Percent changing sectors by children, gender and first employment sector

2003 2006 2008 2010 2013 F M F M F M F M F M First employment was in Education

No children 4.55% 7.40% 6.22% 5.42% 9.59% 12.50% 13.20% 12.39% 14.22% 12.37% at home Children at 7.21% 5.16% 9.22% 7.32% 13.03% 11.00% 10.45% 8.88% 12.68% 9.84% home First employment was in Business/Industry or Government

No children 6.87% 11.36% 10.82% 7.67% 13.36% 10.94% 21.79% 12.63% 23.24% 12.98% at home Children at 15.82% 7.23% 16.77% 7.40% 15.68% 11.53% 13.51% 8.66% 12.65% 8.24% home

Table 18 analyzes the likelihood of respondents leaving the Education sector, by the Carnegie classification of their employer in the previous SDR survey year—for example, what percent of those who were employed in a doctoral/research institution in

2001 had changed employment sectors in 2003. Logical skips represent respondents who were part of the dataset in the prior year but were either employed in another sector or out of the workforce at that time. As Table 18 shows, respondents in the Logical skip group were more likely to have changed sectors than any other group in either gender and in every year except 2008, when their rate was exceeded by male respondents previously in doctoral/research institutions. In comparing rates just among respondents whose prior employment was in Education, those formerly in doctoral/research institutions were more likely to change sector than those in either non-doctoral/research or two-year or other 110 institutions, except when narrowly edged out in 2010 among male respondents formerly in two-year or other institutions. Respondents of both gender categories formerly in non- doctoral/research institutions showed consistently low sector-change rates across all survey years.

Table 18

Percent changing sectors by employer Carnegie type in prior SDR and gender

2003 2006 2008 2010 2013 F M F M F M F M F M Doctoral/ Research 13.07% 12.59% 13.99% 13.21% 17.92% 19.85% 11.49% 9.21% 11.27% 10.39% Non-Doctoral/ Research 5.54% 6.71% 1.81% 2.18% 7.64% 4.03% 5.89% 4.47% 6.66% 3.42% Two-Year or Other 0.00% 12.25% 13.66% 8.98% 6.56% 8.79% 9.98% 9.97% 9.57% 7.19%

Logical skip 20.46% 17.90% 25.32% 13.79% 21.71% 15.96% 18.82% 12.08% 20.14% 11.78%

Another way of looking at sector changes among this subgroup of respondents who were employed in Education in the previous SDR survey is by comparing sector changes with tenure status reported in the prior SDR. These sector changes are summarized below in Table 19. In every survey year and for both genders, individuals on the tenure track were substantially less likely to report changing employment sectors than respondents in the other categories. In 2003, individuals who had reported occupying non-tenure-track positions in 2001 were the most likely to change employment sectors; in 111 every subsequent year of the study period, however, respondents whose prior employment was non-tenure-eligible had the highest rates of sector changes.

Table 19

Percent changing employment sector by tenure status in prior SDR and gender

2003 2006 2008 2010 2013 F M F M F M F M F M Tenured or tenure track 2.14% 3.27% 2.04% 2.74% 5.51% 3.17% 2.78% 2.99% 5.51% 2.35%

Not on tenure track 20.81% 20.98% 15.81% 15.84% 14.54% 20.74% 14.66% 9.18% 12.16% 12.72% Tenure not available at inst. or job 12.92% 16.80% 24.51% 21.40% 23.11% 27.71% 17.95% 15.01% 18.08% 20.61%

Cumulative hazard rates. The above analyses compare sector-change rates at an aggregate level, and across multiple years. Techniques of event-history analysis, however, allow the assessment of a particular individual’s likelihood of changing sector at any time during the study period. The cumulative hazard rate in survival analysis is the probability of the event occurring at a particular point in time given that it has not occurred up to that point. Figure 9 shows the cumulative hazard rate, by gender, for all respondents. (“Hazard,” in this context, refers to a change in employment sector. For this analysis, an individual remains in the risk set unless and until changing sectors, at which point they are dropped from the analysis.)

As Figure 9 shows, in 2003, the first opportunity for a change to be noted, the cumulative hazard estimate for females and males appears essentially equal. In 112 subsequent years, however, the cumulative hazard rate for females is higher than for males, and the disparity grows through the rest of the study period.

Figure 9. Cumulative hazard estimates by gender, all respondents

When the dataset is restricted to those respondents who began their postgraduate employment in higher education, however, a different pattern appears. As Figure 10 shows, the cumulative hazard estimates for males and females in this subgroup increase virtually in lockstep throughout the study period.

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Figure 10. Cumulative hazard estimates by gender, starting employment in education

Cumulative hazard rates are disaggregated by race in Figure 10. Black doctorate holders initially had a higher cumulative hazard rate than the other three racial categories, but by 2010 they were overtaken by their Asian counterparts. The racial category with consistently the lowest cumulative hazard rate was Other, which includes Hispanic,

Native American, Pacific Islanders, and respondents reporting multiple races.

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Figure 11. Cumulative hazard rates for all respondents, by race

Cumulative hazard rates also varied by discipline. Figure 12 and Figure 13 show these rates for women and men, respectively. In both charts, the disciplines are relatively closely clustered at the beginning of the study period but gradually diverge until by the end they are markedly different. For women, the cumulative hazard rate in 2003 was noticeably higher for Engineering. For most of the study period, those in the S&E-

Related Fields had the lowest cumulative hazard rate until the final year, when women in the Computer and Mathematical Sciences became the lowest. By the end of the study period, women in the Biological and Life sciences had the highest cumulative hazard rate, with Engineering and Physical and Related Sciences not far behind.

Among men, though the overall pattern of divergence among the disciplines was similar, that of individual disciplines was markedly different. Throughout the study period men in Engineering consistently had the lowest cumulative hazard rate, while men 115 in the Physical and Related Sciences were at or tied for the highest. Men in the Computer and Mathematical Sciences initially had the second-highest cumulative hazard rate, but by the end of the study period they had the second-lowest rate.

Figure 12. Cumulative hazard rates by discipline, females only

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Figure 13. Cumulative hazard rates by discipline, males only

Cumulative hazard rates indicate the volume of movement between sectors, but it does not, on its own, provide any information about the sectors that individuals are leaving or those to which they move. Table 20 addresses this question, providing a breakdown by gender of the movement patterns in each survey year. Derived from frequency tables comparing each respondent’s reported employment sector from 2003 to

2013 with that reported in the previous year, the figures in each column represent the percentage of all changes for the given gender, in the given year, corresponding to each possible pair of origin and destination sectors.

As Table 20 shows, the most common movements overall were Education to

Business/Industry and Business/Industry to Education. This finding is hardly surprising, 117 given that these two sectors are considerably larger than Government. Across all survey years, the proportion of men moving from Education to Business/Industry was larger than for women; remarkably, this movement pattern alone represented 41 percent of all changes made by males in 2008.

The Logical Skip category represents individuals who are out of the workforce for any reason. Table 20 indicates that movement between Education and Logical Skip—in both directions—was more common for women than for men in every survey year observed. Movement between Government and Logical Skip, in either direction, was uncommon overall for both men and women, but a slightly larger proportion of women than men moved between these sectors as well. No consistent trends appear in movements between Business/Industry and Logical Skip.

Table 20

Sector change by origin/destination and gender Origin sector and 2003 2006 2008 2010 2013 destination sector F M F M F M F M F M Education to Gov’t. 11% 12% 8% 9% 11% 6% 5% 10% 7% 4% Education to Bus./Ind. 19% 26% 29% 39% 29% 41% 18% 21% 19% 29% Education to Logical skip 13% 4% 7% 1% 9% 4% 16% 7% 11% 6% Gov’t. to Education 4% 12% 3% 3% 5% 6% 4% 9% 4% 7% Gov’t. to Bus./Ind. 1% 4% 4% 6% 4% 7% 8% 5% 10% 8% Gov’t. to Logical skip 2% 0% 3% 1% 1% 0% 1% 1% 2% 1% Bus./Ind. to Education 29% 19% 15% 10% 13% 14% 13% 22% 14% 17% Bus./Ind. to Gov’t. 3% 3% 4% 6% 5% 11% 8% 7% 5% 4% Bus./Ind. to Logical skip 11% 11% 4% 6% 9% 2% 7% 8% 8% 7% Logical skip to Education 4% 3% 9% 8% 8% 4% 7% 5% 11% 5% Logical skip to Gov’t. 4% 1% 3% 4% 2% 1% 2% 0% 1% 1% Logical skip to Bus./Ind. 0% 3% 10% 7% 5% 4% 10% 5% 10% 10% Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 118

Complementary Log-Log Regression Analyses

The next step of the analysis was to fit event-history models estimating the effect of both time-constant and time-varying variables on the likelihood of changing employment sectors. The method used for this study is complementary log-log (cloglog) regression, an analytical approach appropriate for discrete data such as the SDR dataset used here.

Analyses of all respondents. The first models incorporated all respondents in the dataset regardless of starting sector. The output for Model A, which did not include interaction terms, and Model B, which included the interaction term Female*children, is shown in Table 21. (Note that, because both models included respondents starting in any sector, tenure-related variables were not included in either model.)

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Table 21

Cloglog of sector change, all respondents Model A Model B Independent variable exp(b) a SE b exp(b) SE Age (starting) 1.007* 0.004 1.008* 0.004 Female 1.252*** 0.064 1.126 0.078 Race Asian 1.112 0.073 1.115 0.073 Black 1.061 0.097 1.063 0.098 Other Race 1.071 0.075 1.073 0.076 Temporary resident 0.694** 0.081 0.691** 0.080 Married 1.026 0.064 1.028 0.063 Children living at home 0.905 0.049 0.817** 0.059 Discipline Computer and Math Sciences 0.796* 0.091 0.797* 0.092 Life and Related Sciences 1.443*** 0.096 1.446*** 0.096 Physical and Related Sciences 1.214* 0.099 1.219* 0.099 Engineering 0.878 0.077 0.881 0.077 S&E-Related 0.963 0.099 0.964 0.099 Degree-granting institution type Non-doctoral/research 0.880 0.143 0.877 0.142 Private control 1.083 0.055 1.082 0.055 Held a postdoc 0.707*** 0.067 0.705*** 0.067 First employment not in Education 1.209*** 0.064 1.209*** 0.064 Female x children - - 1.236* 0.118

Note: The term “cloglog” is the standard shortened term for complementary log-log regression used in this and following tables. a Interpreted as the hazard ratio. b Standard error. *p < .05. ** p < .01. ***p < .001

As Table 21 shows, age was associated with a slightly higher likelihood of changing sectors in Models A and B. In Model A, which did not include the interaction term Female*children, females were 1.252 times more likely to change sectors than men, an effect that was statistically significant at the p<.001 level. Respondents with temporary 120 resident status were less likely to change sectors, a finding significant at the p<.01 level.

Individuals with degrees in Computer and Math Sciences, Engineering, and S&E-related

Fields were less likely to change sectors, though the coefficient was only significant for

Computer and Math Sciences. Those in Life and Related Sciences and Physical and

Related Sciences were more likely to change sectors, both coefficients being statistically significant. Those holding postdoctoral positions were less likely to change sectors, and those starting their employment in either Business/Industry or Government were more likely to change sectors than those starting in Education.

As Model B shows, the inclusion of the Female*children interaction term changed the effects of both the gender and children variables. The interaction term itself indicated a significantly higher likelihood of changing sectors for women with children living at home (1.236 times higher than for men and women without children at home). This interaction term appeared to explain most of the gender difference in sector-changing, as the Female variable became insignificant. The Children variable, however, actually became significant in Model B and showed a decreased likelihood of sector changing; as an effect distinct from the interaction term, it suggests men with children living at home may be less likely to change sectors.

Model A was also conducted separately for women and men. Results of these subgroup analyses are shown in Table 22. For both men and women, age was associated with a slightly higher likelihood of changing employment sectors, though it was only statistically significant for men. Temporary residents were less likely to change sectors, though the effect was stronger, and only statistically significant, for women. 121

Women who were married were slightly more likely, while men were slightly less likely, to change sectors, though the effect was not statistically significant for either gender. Respondents with children living in the home were less likely to change sectors.

Men were less likely than women to do so, and the effect was only statistically significant for them (a finding that supports interpretation of the significant Children term in Model

B of Table 21).

A degree in Life and Related Sciences was associated with a greater likelihood of changing employment sectors for both men and women, an effect that was significant at the p<.001 level. Those with degrees in the Physical and Related Sciences were also more likely to change sectors, though the likelihood was smaller than for the Life and Related

Sciences and the coefficients were statistically significant only for men. Women with a degree in Engineering were more likely to change sectors, while men with Engineering degrees were less likely to change sector, though neither effect was statistically significant. Meanwhile, women in S&E-related fields were less likely, and men in those fields more likely, to change sectors; in both cases the coefficients were not statistically significant.

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Table 22

Cloglog of sector change, all respondents by gender Females Males Independent variable exp(b) SE exp(b) SE Age 1.006 0.005 1.013* 0.006 Race Asian 1.221* 0.113 1.051 0.095 Black 1.041 0.124 1.035 0.149 Other race 0.975 0.095 1.141 0.115 Temporary resident 0.461** 0.104 0.845 0.117 Married 1.049 0.087 0.998 0.092 Children 0.982 0.074 0.838* 0.066 Discipline Computer and Math Sciences 0.696 0.137 0.842 0.123 Life and Related Sciences 1.408*** 0.124 1.481*** 0.152 Physical and Related Sciences 1.150 0.139 1.271* 0.145 Engineering 1.212 0.154 0.804 0.096 S&E-Related 0.918 0.116 1.142 0.201 Degree-granting institution type Non-doctoral/research 0.604* 0.150 1.308 0.276 Private control 1.071 0.078 1.097 0.078 Held a postdoc 0.738* 0.099 0.654** 0.086 First employment not in Education 1.404*** 0.100 1.044 0.081

*p < .05. ** p < .01. ***p < .001

Analyses of respondents starting in the education sector. Cloglog models were estimated for the subset of respondents whose first employment was in Education. These models incorporated variables related to Education employment, including the Carnegie 123 classification of the employing institution and the tenure status of the employment.

Results of these models, both with and without interaction terms, are shown in Table 23.

Table 23

Cloglog of sector change, those whose first employment was in education

Model C Model D Independent variable exp(b) SE exp(b) SE Age (starting) 0.987 0.014 0.989 0.014 Female 1.051 0.196 1.079 0.316 Race Asian 1.742* 0.403 1.710* 0.400 Black 1.148 0.491 1.146 0.488 Other Race 0.878 0.249 0.877 0.249 Temporary resident 0.370 0.193 0.377 0.198 Married 1.568 0.384 1.574 0.389 Children living at home 0.876 0.170 0.744 0.209 Discipline Computer and Math Sciences 0.621 0.277 0.612 0.273 Life and Related Sciences 1.275 0.318 1.275 0.315 Physical and Related Sciences 1.818* 0.542 1.838* 0.552 Engineering 0.706 0.279 0.706 0.279 S&E-Related 0.792 0.328 0.787 0.326 Degree-granting institution type Non-doctoral/research 1.015 0.759 0.991 0.748 Private control 0.783 0.168 0.782 0.167 Tenure status Not on tenure track 2.582*** 0.700 1.952* 0.643 Tenure not available a 3.491*** 0.854 2.613** 0.792 Held a postdoc 0.332*** 0.103 0.341** 0.106 Carnegie type of employer Non-doctoral/research 0.795 0.209 0.809 0.212 Two-year or other 1.348 0.545 1.365 0.551 Female * children - - 1.365 0.508 Female * tenure track - - 0.513 0.219 a Tenure not available either for position or at institution. *p < .05. ** p < .01. ***p < .001.

As can be seen in Table 23, gender is not significant in Model C or D, but being in a non-tenure-track position or one for which tenure is not even an option is significant 124 in both models. Asians were significantly more likely to leave the Education sector than non-Asians in both models. Among all disciplines, only Physical and Related Sciences showed a significant likelihood of changing sectors. Holding a postdoc was also strongly associated with staying in the Education sector.

When both the Female*children and the Female*tenure interaction terms were added in Model D, neither one was statistically significant. Taken together with the lack of significance for the Children variable, Model D indicates that for women who start in the Education sector, the presence of children in the home may not be strongly associated with the likelihood of changing sectors.Table 24 shows results of the same general model as Model C in Table 23, run separately for men and women. Here again the Children variable was not statistically significant for either men or women, although the hazard ratio indicates a higher likelihood for women and a lower likelihood for men. Men in the

Physical and Related Sciences were statistically much more likely to leave the Education sector. Both forms of non-tenure-track employment were associated with a significantly and substantially greater likelihood of leaving the Education sector for women, but for men, only the Not on Tenure Track variable was statistically significant.

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Table 24

Cloglog: sector change by gender, with starting employment in education

Female Male Independent variable exp(b) SE exp(b) SE

Age 0.971 0.018 1.010 0.020 Race Asian 1.629 0.530 1.712 0.570 Black 0.509 0.309 1.754 0.923 Other race 0.504 0.247 1.269 0.461 Temporary resident 0.409 0.300 0.346 0.253 Married 1.209 0.375 1.985 0.764 Children 1.099 0.278 0.703 0.200 Discipline Computer and Math Sciences 0.319 0.337 0.899 0.464 Life and Related Sciences 1.128 0.367 1.555 0.610 Physical and Related Sci’s. 1.418 0.694 2.435* 1.000 Engineering 0.491 0.402 0.988 0.474 S&E-Related 0.680 0.371 1.314 0.806 Degree-granting institution type a Non-doctoral/research omitted 2.527 1.918 Private control 0.880 0.264 0.743 0.228 Tenure status Not on tenure track 3.171** 1.393 2.131* 0.770 b Tenure not available 5.606*** 2.111 2.088 0.783 Held a postdoc 0.303** 0.133 0.419* 0.184 Carnegie type of employer Non-doctoral/research 0.799 0.322 0.765 0.269 Two-year or other 1.527 0.845 1.264 0.766 a Variable predicts failure perfectly. It was dropped and 98 observations not used. b Tenure not available either for position or at institution. *p < .05. ** p < .01. ***p < .001

When tenure status was removed as an explanatory variable, gender became significant. Table 25 shows versions of this model with and without interaction terms.

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Table 25

Cloglog: sector change for those starting in education (no tenure variables)

Model E Model F Model G Independent variable exp(b) SE exp(b) SE exp(b) SE Age (starting) 1.007 0.006 1.008 0.006 0.997 0.014 Female 1.201* 0.085 1.054 0.105 1.538 0.440 Race Asian 1.179 0.104 1.181 0.105 1.668* 0.385 Black 1.117 0.155 1.126 0.156 1.168 0.495 Other Race 0.893 0.093 0.891 0.093 0.844 0.240 Temporary resident 0.833 0.127 0.830 0.127 0.399 0.206 Married 1.040 0.093 1.042 0.093 1.550 0.384 Children living at home 0.872 0.070 0.774* 0.081 0.720 0.200 Discipline Computer and Math Sciences 0.939 0.155 0.933 0.154 0.588 0.263 Life and Related Sciences 1.693*** 0.154 1.693*** 0.154 1.387 0.332 Physical and Related Sciences 1.917*** 0.218 1.917*** 0.218 1.994* 0.584 Engineering 1.158 0.148 1.162 0.149 0.687 0.271 S&E-Related 0.937 0.141 0.935 0.141 0.768 0.320 Degree-granting institution type Non-doctoral/research 1.631 0.440 1.590 0.431 1.099 0.830 Private control 1.001 0.072 1.005 0.073 0.773 0.165 Held a postdoc 0.310*** 0.039 0.308*** 0.039 0.471* 0.141 Carnegie type of employer Non-doctoral/research 0.069*** 0.015 0.069*** 0.015 0.684 0.174 Two-year or other 0.162*** 0.060 0.163*** 0.060 1.398 0.558 Female x children - - 1.286† 0.175 1.443 0.530 Female x tenure track - - - - 0.246*** 0.084

*p < .05. ** p < .01. ***p < .001. † Borderline insignificant, at p < .064.

In Model E, which includes no interaction terms as well as no tenure-related

terms, women appeared significantly more likely to leave Education than men. This

finding indicates that tenure status has a gendered component, an effect that has been

observed in other research on faculty including salary-equity studies. When the 127

Female*children interaction term is added in Model F, however, gender became insignificant and its hazard ratio smaller, suggesting that what appeared to be a gender effect in Model E was actually related more with family status—though it should be noted that the interaction term itself was only borderline statistically insignificant

(p<.064).

Finally, with the addition of the Female*tenure track interaction term in Model G women on the tenure track appeared substantially less likely to leave the Education sector than either men or women not on the tenure track. This finding was due largely to the absence of other tenure-related variables in the model, as can be seen by comparing

Model G with the results of Model D in Table 23, which showed the Female*tenure interaction term to be insignificant and all other tenure-related variables significant.

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Chapter 5: Conclusions

A considerable body of research exists concerning the gendered nature of employment and institutions, both in and beyond academia. Many of these studies have been focused on the intersection of gender and family status, gender and race/ethnicity, and specific disciplines or categories of disciplines. The literature on academia typically focuses on women’s progression to tenure, productivity, salary growth, likelihood of securing a leadership role, attrition, and job satisfaction. Relatively little of the existing research considers the broader job market for STEM Ph.D. holders beyond academia, particularly movement between academia and other sectors.

The purpose of this study was to examine the likelihood of individuals moving from one sector to another, the timing of their departure, and how the likelihood and timing of departures may vary by starting sector as well as demographic, disciplinary, and other factors. Insights based on this study could inform and enhance higher-education institutions’ efforts to recruit and retain talented STEM faculty, and to improve their efforts to prepare STEM PhD holders for the wider range of postgraduate employment opportunities.

The study explored the following research questions:

1. What is the likelihood that female STEM Ph. D. holders who start their

careers in academia will move to another employment sector, and how does

that likelihood compare to their male counterparts?

2. How are the likelihood and timing of female doctorate holders’ inter-sector

career moves affected by characteristics including age, race/ethnicity, 129

discipline, marital status, family formation, and Carnegie classification of both

the doctorate-granting institution and the initial academic employer?

The theoretical framework for this study incorporated three theories particularly well-suited to considering career choices and movements along the career pathway, from the perspective of the individual: behavioral economics, particularly the theory of rational choice given limited, or bounded, rationality; human capital theory, the conceptualization of education as a form of capital in which individuals invest with the hope of increasing future earnings; and social-role theory, which posits that gender differences in career trajectories, salaries, and perceptions of climate result from differing expectations about the appropriate social roles for men and women. The study also conceptualizes career mobility as a pathway or road, focusing on the agency of the individual in making career decisions. This construct contrasts with the more commonly used hypothetical pipeline and incorporates a temporal dimension, considering not only whether a career trajectory changes, but also when it occurs.

The data used for this study are part of the Survey of Doctorate Recipients, a panel study of individuals who have earned a research doctorate in a science, engineering, or health (SEH) field from a U.S. higher-education institution. The SDR has collected demographic, education, and career-related data on a nationally representative cohort of new and previously surveyed individuals every two to three years since 1973.

This researcher looked at four waves of SDR respondents who completed every survey following the receipt of their doctoral degree through 2013. The data used in this dissertation are available through a restricted-use data license granted by the NSF. 130

The method used for this study was event-history analysis, a statistical approach designed to investigate the time to a specified change in categorical status. The study analyzed the career trajectories of these individuals, specifically the likelihood and timing that they will leave their initial employment sector for either a position in another employment sector or non-employment. After fitting a model including all individuals in all sectors, the researcher also conducts event-history analyses of subsets of respondents: a model including women only, a model including men only, and a model including only individuals whose starting sector is academia. The study also incorporated interaction terms to explore the extent to which gender and parenthood as well as gender and tenure status interact to influence the likelihood of changing sectors.

Significance

While a considerable amount of research has been done on gender differences in employment both in and beyond academia, a great deal of it focused on individuals working in the STEM disciplines, the literature has focused on women’s careers within the context of one employment sector and, often, one discipline. Relatively little research considers the broader job market for STEM Ph.D. holders beyond the academic sector.

This researcher has contributed to the literature by examining individuals’ movements between academia and other sectors, taking a longitudinal approach to looking at those movements over time and comparing the experiences of men and women.

The methodology used in this study, event-history analysis, is a relatively new tool in the exploration of career trajectories. The researcher used event-history methods similar to those employed by Mason, Goulden, and Wolfinger but analyzed a more-recent 131 span of SDR data, from 2001 through 2013. Insights based on this study can inform and enhance higher-education institutions’ efforts to recruit and retain talented STEM faculty, as well as facilitating efforts to prepare STEM PhD holders for the wider range of postgraduate employment opportunities that will be available to them.

This chapter begins with a summary of key findings presented in Chapter 4. It concludes with a discussion of implications and recommendations for future research.

Summary of Key Findings

Characteristics of respondents. The descriptive analysis yielded information about the distributions of respondents by gender, race, age, discipline, characteristics of degree-granting institutions, family status, employment sectors, and characteristics of the employer and position for those employed in higher education, as well as the directions of inter-sector movements. In general, there were more men than women in the respondent group, and the most common race was White, followed by Asian. Women overall were most strongly represented in the Social and Related Sciences and the Life and Related Sciences, followed by while men were most strongly represented in

Engineering and the Life and Related Sciences. The vast majority of respondents were

U.S. citizens or permanent residents, and the percentage in this category rose from nearly

82 percent in 2001 to nearly 98 percent in 2013. Most earned their doctoral degrees from publicly controlled institutions with a doctoral or research Carnegie classification. Most 132 respondents also started their employment in the Education sector after earning their doctoral degrees. However, the proportion doing so declined between 2006 and 2013.

The percentage of respondents who reported being married increased over the study period, as did the percent with children living at home. However, in both cases the rates for women lagged behind those for men, and the disparity between the two did not substantially decrease over the study period. The percentage holding a postdoctoral appointment was initially higher among women in 2001, but in every subsequent year, even as the overall percentage declined, it became higher among men. In contrast, women were much more likely to be out of the workforce than men.

Among respondents working in the Education sector, the majority worked in a doctoral or research institution. In 2001 women were actually more likely to work in one of these institutions in men, a trend that coincides with their greater likelihood of holding a postdoc. However, over time both men and women became more likely to work in an institution in either the four-year non-doctoral or the Other category.

In 2001 and 2013, men were more likely to hold tenured or tenure-track positions than women. Yet in the survey years in between (2003 to 2010), women were actually more likely to report holding these positions than men. Throughout the study period, even in 2001 and 2013, however, women were also more likely to be off the tenure track or in positions or at institutions where tenure was not available. And across the study period men were also more likely to be outside Education altogether.

When restricted to respondents whose first employment was in Education

(excluding those who moved into Education in subsequent years), the analysis showed a 133 more marked decline in the percentage working in positions or at institutions where tenure was not available and a larger increase in those in tenured or tenure-track positions—both trends that indicate individuals were either moving into different positions or changing institutions but staying within academia. Respondents were overall less likely to leave the Education sector if they began their employment there.

Timing of sector changes. Overall women were more likely to change employment sectors (which could include leaving the workforce) than men in every survey year after 2003. Initially men were slightly more likely to change sectors, but their likelihood fluctuated over the study period while women’s consistently rose. Respondents in all racial categories had lower rates of sector changes if their initial employment was in

Education versus Business/Industry or Government. The same trend was evident when categorizing respondents by marital and parental statuses. In addition, the disparities in sector-change rates for those with and without children in the home were small among those who started in Education than among those in Business/Industry or Government.

Interestingly, the presence of children was associated with a greater likelihood of changing sectors in some, but not all, years.

A comparison of change rates with the Carnegie status of employers reported in the prior survey year suggests that respondents who previously worked in doctoral or research institutions were more likely to change sectors than those who previously worked in either non-doctoral/research or other institution types. This may be due, however, to the larger percentage of individuals employed in doctoral/research institutions overall. Perhaps not surprisingly, respondents who had reported being on the 134 tenure track in the prior SDR cycle were substantially less likely to report changing employment sectors in the subsequent SDR year.

Analysis of cumulative-hazard estimates of respondents overall from 2003 to

2013 showed that in the first year the rates were even for women and men, but in subsequent years the cumulative hazard rate increased more rapidly for women than for men. Interestingly, however, among those who started their employment in Education, the cumulative hazard rates increased in virtual lockstep for women and men.

For both men and women, cumulative-hazard rates by discipline initially clustered fairly closely but diverged markedly over time. For women, Engineering had the highest hazard rate in 2003, but by 2013 women in Biological, Agricultural and Environmental

Life Sciences had the highest rate followed by Engineering and the Physical/Related

Sciences; Social and Related Sciences fell in the middle, while Computer and Math

Sciences had the lowest cumulative-hazard rate of all. Among men, however,

Engineering consistently had the lowest rates while Physical/Related Sciences was consistently at or tied for the highest.

A cross-tabulation of respondents’ employment sectors in each year with the sector reported in the previous year yielded insight into the patterns of inter-sector movement. The greatest volume of movement was between Education and

Business/Industry—hardly surprising since these are by far the largest two categories.

Notably, however, across all survey years the proportion of men moving from Education to Business/Industry was larger than for women. Meanwhile the movement between 135

Education and Logical Skip (the latter category indicative of being out of the workforce) was more common for women than for men in both directions and in every survey year.

Likelihood of sector changes. Complementary log-log regression analysis of all respondents indicated that overall, women were significantly more likely than men to change employment sectors. Marital and parental status did not have a significant impact on the likelihood of changing sectors. Respondents with degrees in Life and Related

Sciences and Physical and Related Sciences were significantly more likely to change sectors, while those in Computer and Math Sciences were significantly less likely to change. Holding a postdoc was associated with a significantly lower likelihood of changing sectors. Respondents whose first employment was not in Education were significantly more likely to change sectors.

When models were estimated separately for men and women, some differences emerged. Asian women, but not Asian men, were significantly more likely to change sectors. Women with temporary resident status were significantly less likely to change sectors, but their male counterparts were not. Marital status was not significant for either gender. Having children living at home was not significant by itself, but when a

Female*children interaction term was added it was associated with a significantly higher likelihood of changing sectors while the Children variable became associated with a significantly lower likelihood of change; the fact that this effect appeared only when the

Female*children interaction term was added suggests that it is unique to men.

Being in the Life and Related Sciences was significantly associated with an increased likelihood of changing sectors for both men and women, but the Physical and 136

Related Sciences was only significant for men. Holding a degree from a non-doctoral or non-research institution was significantly associated with a lower likelihood of sector change for women, but it was (non-significantly) associated with a higher likelihood of sector change for men. And while both women and men whose first employment was not in Education were more likely to change sectors, the effect was strongly significant for women but not significant for men.

The cloglog regression model including all respondents whose first employment was in Education did not show a significant effect for gender. There was a significantly lower likelihood of sector change for Asian respondents. Once again, marital and parental status were not statistically significant. Those with degrees in the Physical and Related

Sciences were significantly more likely to change sectors, but no significant effect was observed for any other disciplinary category. Individuals who reported not being on the tenure track or for whom tenure was not available were significantly more likely to change sectors. When interaction terms were added, Female*children was associated with a higher likelihood of changing sector while Female*children was associated with a lower likelihood of changing sectors, but neither interaction term was statistically significant.

When the cloglog regression on respondents whose first employment was in

Education was run separately for women and men, differences again emerged. Women who were not on the tenure track or employed where tenure was not available were significantly more likely to change sectors, and the effect was very strong for the latter category. Men were also more likely to change sectors, though the hazard ratios were not 137 nearly as high and were only significant for those not on the tenure track (as opposed to those for whom tenure was not available).

Finally, when the cloglog regression on all respondents whose first employment was in Education was estimated without the tenure-track variables, other differences emerged. Gender became significant, with women more likely to change sectors; this difference indicates that gender is still entangled with tenure status, making the tenure- status variable mask some of the effect of gender on sector changes. The Life and Related

Sciences and the Physical and Related Sciences were both associated with strongly and significantly higher likelihoods of changing sectors. And the Carnegie type of the employing institution became significant, with both non-doctoral/research and two-year or other institution types significantly associated with a far lower likelihood of changing employment sectors. When added to the model, the Female*children interaction term was associated with a higher likelihood of changing sectors but was borderline insignificant.

The addition of the Female*tenure track interaction term was associated with a significantly lower likelihood of changing sector, but this variable was likely reflecting the effects of the omitted tenure-status variables, as it was insignificant in the model in which those tenure-status variables were included.

Implications

This study is focused on the career movements of women STEM doctorate holders moving between employment sectors over a period of 12 years, and how the likelihood and timing of those movements was related to various demographic, disciplinary and employment-related factors. One of the major findings of this study is 138 that women who start their employment in the Education sector (which, for the purposes of this study, includes only higher education) are less likely to leave the sector than women who start in Business/Industry or Government. Another is that, perhaps surprisingly, family-related factors appear in general not to play a significant role in sector changing behaviors for either women or men who start in the Education sector, except when tenure status is not considered. This finding stands in contrast to the analysis of all respondents, which indicated that when the interaction between gender and children is considered, women are more likely to change sectors while men are less likely to do so.

The more positive interpretation of these findings is that, in general, women may find Education to be a desirable sector in comparison to other employment options.

Whether the nature of the work, the academic culture, or the benefit packages, it could be that women are more likely to choose academia over other sectors because they find it a better fit with their personal and career goals. Indeed, researchers have found that many women report teaching and mentoring students to be personally fulfilling, even if they initially began teaching as part of an unexpected detour in their original career path (e.g.,

Anderson, Mattley, Conley, & Koonce, 2014). Teaching and mentoring are activities associated with nurturing, which suggests that social-role conformity might also contribute to women’s affinity for this type of work.

The possibility that women affirmatively sort themselves into academia does not negate the work of various researchers who have found climate issues to be a problem in many academic organizational and disciplinary cultures. It should be noted that the sector-change variable in this study does not capture the movement of women who 139 change employers but stay within academia; it could be that women faced with a negative work climate respond by moving to another educational institution rather than leaving the

Education sector altogether. Having acquired work experience in higher education, these women may choose to stay in the academic career path to maximize the personal investment they have already made, a decision that would be consistent with a human- capital perspective. This choice would apply not only to women with faculty positions but also those who take on postdoctoral appointments, which require a considerable investment of time and foregone earnings but in many disciplines are an important stepping stone to an academic career, particularly one on the tenure track (Yang &

Webber, 2015).

It is also plausible that for some women, the lower likelihood of moving from the

Education sector reflects a more risk-averse decision calculus. While researchers have identified “chilly” work climates as a reason for dissatisfaction and attrition among women in academia, those problems are not confined within the ivy-covered walls; business and government are gendered workplaces as well. Some women may view aspects of employment in other sectors as undesirable or at least uncertain enough that incurring the frictional costs of such a significant move might seem ill-advised. As the study period bridges the Great Recession, the economic realities of that time may further have influenced individuals to take fewer career risks. This should not be interpreted, however, as license to neglect efforts to improve chilly work climates in academia. While these findings show that women may be less likely to leave the Education sector, the study does not capture movement between higher-education institutions. It is quite 140 possible that women are leaving those with chilly work climates and moving to better opportunities within the Education sector.

The fact that marital status and children did not show a clear impact on the likelihood of changing sectors (which, again, includes leaving the workforce) for women who start their careers in Education suggests that family formation may be less likely to derail women’s academic careers than they once were. The data alone are insufficient, however, to determine to what extent women are finding better work/life balance in a chosen career path and to what extent they are staying in place out of some necessity.

Taking time out of the workforce to raise children is economically difficult for many if not most families, and for highly educated individuals the opportunity cost of taking several years out of the workforce is considerable in terms of both lost earnings and lost career momentum. Also, the data indicated that the female respondents are less likely to be married or have children than their male counterparts; these trends suggest that, as

Mason, Wolfinger and Goulden (2013) found, some women are still feeling it necessary to choose between family formation and career—a choice that their male counterparts do not seem to face. The implications of the findings for these variables, therefore, are unclear.

This study indicated that women are still less likely to hold tenure-track positions than men. The inclusion of tenure-track variables in the model appeared to mute the significance of gender on the likelihood of changing sectors, because when the model was modified to remove the tenure-track variables, the gender variable became a significant predictor. Interestingly, comparisons of tenure status and change variables 141 show that in most of the study period, tenure-track women were actually less likely than tenure-track men to change sectors. In the two (of six) survey years in which women’s sector-change rates exceeded men’s, they were still barely above 5.5 percent. These findings suggest that when women do obtain tenure-track positions, they are somewhat more likely to stay with them. Perhaps they perceive more value in the potential future security they provide. Or, perhaps women are self-selecting, before they reach the point of obtaining one, and making the personal sacrifices they perceive as necessary to pursuing a faculty career.

The variances in cumulative-hazard rates and regression coefficients found in this study between STEM disciplines supports other researchers’ observation of distinct labor markets by discipline. Fields including Life Sciences and the Physical Sciences appear to have more employment prospects outside the Education sector than Computer and Math

Sciences, for instance. This finding supports conducting discipline-specific research into

STEM career trajectories; broad-brush analyses that encompass multiple disciplines may elide meaningful distinctions, actionable between field-specific labor markets.

Though it was not common for either gender, women in this study appeared to be comparatively more likely than men to move in and out of the workforce. Among men who left Education, the most common inter-sector movement was to Business/Industry.

From the perspective of employers in the Education sector, in order to compete for talent that may be employed in other sectors one strategy may be to build or improve “on- ramps” that facilitate the transition into academia (Carrigan et al., 2017). 142

While the findings of this study suggest that the Education sector may be perceived as a relatively desirable oasis of employment for STEM doctorate holders, the evidence also shows that it is a shrinking one. And when the retention picture is disaggregated by tenure status, a significant disparity appears between those fortunate few on the tenure track and everyone else. Clearly, tenure-track positions are an attractive selling point to individuals considering pursuing a long-term career in academia. Yet at the same time, the broad trend in higher education has been to reduce the proportion of tenure-track faculty and relying more heavily on non-tenure-eligible positions.

Underlying this shift is, in most cases, a desire to cut costs and make faculty positions more flexible (i.e., easier to abolish) in response to market demands. Yet by scaling back on tenure-track faculty positions, Education risks conceding a major competitive advantage to employers in other sectors in the competition for the most highly educated

STEM talent. Poignantly, that women seem to favor careers in Education at a time when those career opportunities are shrinking suggests that women’s participation in the broader STEM workforce may face future declines, unless employers in other sectors make intentional, systemic efforts to improve their work climates.

Finally, given the decline in academic employment, it is increasingly likely that many highly educated STEM graduates holders will spend all or part of their careers outside the Education sector. STEM programs should invest efforts in preparing their students to understand and negotiate the employment options they may face and help them develop strategies for building rewarding careers in their chosen disciplines. Such efforts will help mitigate the realities of “bounded rationality” by ensuring that these 143 individuals are aware of the options available to them and can better weigh the pros and cons of movements along their career trajectories.

Applications and Recommendations

This study provides a somewhat positive picture of higher education’s ability to retain women STEM doctorate holders relative to other sectors. As much as the literature catalogs women’s dissatisfaction with work climates in academia, women’s greater likelihood of staying in the Education sector indicates that they may be weighing their options and finding the alternatives less desirable.

This finding should not, however, be interpreted as encouragement of the status quo for higher-education institutions. The fact that women are staying in the Education sector does not mean that they are staying with the same employer. Institutions must continue to monitor their work climates and seek continuous improvement if they are to attract and retain talented STEM doctorate holders. The institutions that are most successful will ultimately be the ones to reap the greatest benefit from these efforts.

The findings might also indicate an opportunity for higher education to capitalize on a competitive advantage as a desirable place to work by creating “on-ramps” for individuals to move from other sectors into higher-education employment. Not only would this increase the career options available to STEM doctorate holders, but it could also benefit society by fostering new connections and intellectual cross-pollination between the entire STEM workforce.

While higher education may be a relatively attractive sector in which to work, the fact is that employment in this sector is declining and this trend is likely to continue for 144 the foreseeable future. STEM doctoral programs have an obligation to prepare their students for this reality and should make career exploration a more integral part of their programs. Students should be encouraged to look at options outside academia, not just as fallback options for those not good enough to secure research-faculty jobs but as valid aspirations in themselves. As they are investing considerable resources in pursuing a

STEM Ph.D., they should have as much information as possible to help them make wise choices about their future in the STEM workforce.

Areas for Future Research

This study was designed to yield a broad picture of the career trajectories of female STEM doctorate holders and the impact of various factors on the timing and likelihood of changing employment sectors. The dependent variable for this study was a change from one sector to any other sector—or departure from the workforce. The findings of this study indicate that event-history analysis of multiple destination states may be a potentially fruitful area for further research. For instance, while the cumulative- hazard graphs for men and women who start their careers in Education are strikingly similar, the graph only indicates departure from the Education sector, not where those individuals are going. Event-history techniques that account for multiple destination states could shed light on the factors associated with women moving to a particular sector

(or leaving the workforce altogether). In turn, the independent variable for starting in a sector other than Education does not enable the exploration of potential differences in movements from each of those other starting sectors. 145

Future studies could also benefit by encompassing a longer time frame than that covered by this study. While it spanned 12 years, the SDR data constituted only six points in time, which limited the utility of analyses such as the cumulative-hazard graphs.

Either adding more SDR survey years or finding other data sources could enable researchers to study longer career trajectories. The longer time horizon would also enable exploration of related research questions such as the likelihood and timing of multiple inter-sector career movements.

As mentioned previously, this study was focused only on movements between sectors, not movements within sectors. Future research could take a similar event-history approach to analyze intra-sector movements as well—for instance, movements between institutions in different Carnegie classifications. Historically, women have constituted a larger proportion of the faculty in lower-tier and teaching-focused institutions such as two-year colleges than in the more research-intensive universities. Further study could shed light on the career paths women take to these institutions. Do they move to the teaching-focused employment from more research-oriented higher-education institutions, or are they coming out of Business/Industry or Government? An analytical approach that encompasses multiple events could also look at how or whether women take time out of the workforce, and what “on-ramps” they take to move back into the workforce.

The researcher found disciplinary differences in the likelihood and timing of inter-sector movements. These findings support the other researchers’ observations of segmented labor markets. Encompassing all STEM disciplines into one model may thus be masking more-pronounced differences between them. Further research on individuals 146 in specific disciplines may therefore yield additional insights into their career trajectories.

Such information could be used to help STEM students envision the options available to them and map out their own career trajectories in their chosen discipline.

Conclusion

The goal of this researcher has been to contribute to a better understanding of the career movements of some of the most highly educated members of the American workforce. The career decisions these individuals make are of vital interest to the broader society, as their content knowledge and creativity yield innovations that grow the economy, create both STEM and non-STEM jobs, and improve our quality of life.

Furthermore, a more-diverse STEM workforce is a stronger workforce, with gender a crucial dimension of diversity. Accordingly, efforts to attract and retain women in STEM can benefit from a greater understanding of women’s career movements in the context of all the employment options available to them.

147

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