Three Essays: Hybrid Model Based Analysis of the Science Workforce

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Julie Ann Maurer

Graduate Program in Education Physical Activity and Education Services

The Ohio State University

2018

Dissertation Committee:

Dr. Joshua Hawley, Advisor

Dr. David Stein

Dr. Anand Desai

Copyrighted by

Julie Ann Maurer

2018

Abstract

Workforce related issues in the Science, Technology, Engineering and Mathematics

(STEM) fields are of great importance and have been the subject of many research studies.

Academic research is essential to a country’s ability to remain competitive in the global economy. Furthermore, the future economic strength of the United States is reliant on having a productive and well-prepared scientific workforce. Although behavioral and social science research (BSSR) is known to play a vital role in addressing health, security, and other complex challenges facing the country, most existing STEM workforce research does not consider BSS disciplines. Therefore, this dissertation explores academic BSSR workforce supply and demand dynamics, including various factors that influence its stability and size, by developing a simulation model framework.

This research considers academic science workforce system behaviors attributable to individual level factors that influence career decisions and the eventual outcomes associated with them. Traditional labor market economics studies are based on linear estimations that do not capture the complexity associated with the overall system, including macroeconomic contexts at the regional level and individual level heterogeneity. However, other recent studies have successfully used system dynamics models to understand some of the less intuitive workforce system behaviors, such as feedback loops, though they use aggregate level factors. Thus, individual level heterogeneity has not been well represented in past academic research workforce supply and demand studies.

This dissertation contributes to existing knowledge by investigating the distribution and attributes of BSS researchers employed in the academic workforce in the context of their dynamic interactions with top research universities. The primary research question is how does the relationship between individual BSS researcher characteristics and R1 universities’ hiring decisions combine to influence overall workforce outcomes in the context of regional dynamics?

The supply dynamics of the workforce system are simulated using an agent based model (ABM) that allows consideration of researchers’ individual level attributes and behaviors. In the ABM, graduates who choose to enter the academic workforce have three career options, including applying for an academic job, pursuing non-academic jobs in government or industry, or remaining unemployed. The use of ABM allows job seekers to develop emergent behaviors while also considering the hiring preferences of employers.

Agents’ workforce outcomes are also simulated within the context of national and regional macroeconomics represented by a system dynamics (SD) model used to simulate the demand dynamics. The resulting model framework is used to consider the effect of policies designed to address the gender pay gap, and to consider how fluctuations in federal funding of academic science research impact system outcomes.

The demographic characteristics of the BSS students and researchers initially populating the model are primarily informed by analysis of National Science Foundation’s Survey of

Doctoral Recipients (NSF-SDR) data. NSF-SDR data from 1993 through 2013 are analyzed to determine the employment trends represented in the model. Publicly available data including

NSF Survey of Federal Funds for Research and Development and Higher Education Research and Development Survey, U.S. Census, and other data sources are also used.

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Dedication

In loving memory of

Eugene Joseph Butler

Acknowledgments

I am overwhelmed with gratitude for those who have provided me with wisdom, guidance and support throughout this chapter of my lifelong learning journey. Dr. David Stein planted a seed nearly 15 years ago when he encouraged me by asking, “…so, when will you be back for your PhD?” As a first generation graduate student, the possibility of pursuing a doctorate degree had not occurred to me. I am so fortunate that both he and Dr. Josh Hawley believed in me, and recognized my unrealized potential as a scholar and researcher. As my advisor, I credit Dr. Hawley with contributing to my success with boundless patience and his belief in my capacity for learning and growth that far exceeded my “comfort zone.” I am thankful for the foundational knowledge Dr. Anand Desai taught me in the areas of public policy modeling, economic theory and the overall scientific process. His kind encouragement was pivotal in my development as a scholar and scientist. As well, I am thankful for the constant support, guidance and friendship of Dr. Hyungjo Hur (“Little Brother”) throughout this trek.

I am forever grateful for my family’s support as I’ve taken the “road less traveled.” I could not have taken the first, or the last, steps of this adventure without my husband Tom’s boundless patience, love and encouragement. Whether the long hours with Ben learning JAVA coding, or Sarah’s constant reassurance (“This is what you wanted, right?”), my children inspired me to persevere through every obstacle. I thank my mother, Judy Lou, for being a first

v generation college graduate and, as such, instilling the value of education in me at an early age.

She always encourages me to pursue my dreams and has proven that anyone is free to do so, regardless of their circumstances.

I want to thank all of my many coaches (friends!) who collaborated to give me strength and optimism over the past seven years: Dr. Eunice Hornsby, Dr. Christina Butler, Dr. Carol

Ventresca, Bryce Bate, my coworkers and the Girls (!). You have all contributed to my enjoyment of this experience in countless ways. I’m looking forward to what lies ahead and hope everyone will join me for my “next chapter.”

Finally, I appreciate the support I received from the National Institute of General Medical

Sciences of the National Institutes of Health through the grant, “A Model-Based Examination of

Behavioral & Social Science Workforce: Improving Health Outcomes,” awarded to The Ohio

State University. Please note that the discussion and conclusions of this dissertation are my own and do not necessarily represent the views of the National Institutes of Health, or The Ohio State

University.

Vita

June 1981 ...... Chico Senior High School

1987...... B.S. Civil Engineering, University of California, Irvine

1992-1995 ...... Instructor, Civil Engineering/Environmental Technology, Columbus State Community College

1995-1998 ...... Chair, Environmental Department, Columbus State Community College

1998-2002 ...... Administrator, Transitional Workforce Department, Columbus State Community College

2004...... M.A. Workforce Development and Education, The Ohio State University

2006-2013 ...... Administrator, Pataskala Campus, Central Ohio Technical College

2013-2015 ...... Program Coordinator, Office of Distance Education & eLearning, The Ohio State University

2015 to present ...... Lead Research Manager, Ohio Education Research Center, The Ohio State University

vii

Publications

Niyousha Hosseinichimena, Rod MacDonald, Ayaz Hyder, Alireza Ebrahimvandi, Lauren

Porter, Becky Reno, Julie Maurer, Deborah Andersen, George Richardson, Josh Hawley,

David Andersen (2017) Group Model Building Techniques for Rapid Elicitation of

Parameter Values, Effect Sizes, and Data Sources, System Dynamics Review, 33,1 (71-

84), http://onlinelibrary.wiley.com/doi/10.1002/sdr.1575/full

Hyungjo Hur; Maryam Alsadat Andalib; Julie Maurer; Joshua Hawley; Navid Ghaffarzadegan

(2017) Recent Trends in the U.S. Behavioral and Social Sciences Research (BSSR)

Workforce, PLOS ONE 12(2): e0170887. doi:10.1371/journal.pone.0170887

Fields of Study

Major Field: Education Physical Activity and Education Services

Workforce Development and Education Joshua Hawley, Ed.D.

Adult Learning Theory David S. Stein, Ph.D.

Social Policy Anand Desai, Ph.D.

viii

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments...... v

Vita ...... vii

Publications ...... viii

Fields of Study ...... viii

List of Figures ...... xiv

List of Tables ...... xvii

Chapter 1: Introduction ...... 1

Problem Statement ...... 3

Academic Science Job Market ...... 7

Purpose of research ...... 10

Research Methods ...... 14

Hybrid Dynamic Modeling ...... 15

Research Questions...... 19

Organization of the Dissertation ...... 21

Chapter 2: Behavioral and Social Science Research Workforce: A Closer Look at its

Composition, Behaviors and Outcomes ...... 22

Introduction ...... 22

What we know about BSS researchers and their career decisions ...... 24

Data and Methods...... 28

Results ...... 30

Who is in the BSSR workforce? ...... 30

BSSR annual earned income trends...... 32

Regional BSSR workforce trends ...... 34

Differences between BSS disciplines ...... 37

Regional differences in BSSR compensation ...... 40

BSS researcher employment sector choices ...... 42

Discussion and conclusions ...... 46

Chapter 3: Simulating the Scientific Research Workforce Demand Impacts of ...... 48

Government Higher Education Research Funding ...... 48

Introduction ...... 48

Higher education scientific researcher workforce demand as a complex problem ...... 50

Previous research on modeling workforce demand ...... 52

Prior models ...... 52

Econometric versus SD workforce demand models ...... 54

A conceptual model of scientific researcher workforce demand ...... 55

The academic science research funding process and its workforce impacts ...... 57

A system dynamics model of academic science researcher workforce demand ...... 60

Building up a system dynamics model ...... 61

Feedback loops in the academic researcher workforce demand system ...... 62

Implementing the model ...... 64

Simulation results ...... 68

Discussion & Conclusion ...... 70

Chapter 4: Exploring the United States Academic Science Research Workforce Through

Dynamic Modeling ...... 72

Introduction ...... 72

Background ...... 74

Academic BSS research workforce supply and demand ...... 76

A closer look at behavioral and social science researchers ...... 77

Gender diversity in the BSSR workforce ...... 78

NIH workforce modeling ...... 81

An academic BSSR workforce supply and demand framework ...... 83

Methods ...... 85

Job matching with employer willingness-to-pay versus job seeker willingness-to-accept ... 87

Hybrid Dynamic Modeling ...... 87

Purpose ...... 88

State Variables and Scales ...... 88

Process Overview and Scheduling ...... 90

Design Concepts ...... 91

Initialization and Input ...... 96

Data ...... 97

Baseline model behavior ...... 101

Policy Experiments ...... 105

Alternative funding policy simulation ...... 108

Conclusion and Future Work ...... 113

Chapter 5: Discussion ...... 114

Summary of findings ...... 114

Conclusions ...... 118

xii

Conclusions for Research ...... 118

Conclusions for Practice ...... 120

Limitations and Future Research...... 121

References ...... 124

Appendix A ...... 135

Appendix B ...... 149

xiii

List of Figures

Figure 1. Higher education R&D expenditures, by source of funds and R&D field: FYs 1972–

2015. Source: NSF-HERD data...... 4

Figure 2. Supply and demand for academic science researcher workforce ...... 12

Figure 3. Conceptual framework adapted from NIH Biomedical Research Workforce Working

Group Report (NIH, 2012, p. 32) ...... 13

Figure 4. Employment by field, 1990-2010. Reprinted from “Storm clouds on the horizon for

PhDs,” by D. K. Ginther, 2015, Issues in Science & Technology, 31(4), 75...... 26

$10,000). Individual earnings data are derived from a match to W-2 earnings data. Reprinted from “Wrapping it up in a person: Examining employment and earnings outcomes for PhD recipients,” N. Zolas, N. Goldschlag, R. Jarmin, P. Stephan, J. Owen-Smith, R. F. Rosen, B.

McFadden Allen, B. A. Weinberg, J. I. Lane, 2015, Science, 360(6266), 1370...... 27

Figure 6. Labor force status for BSS doctorates, (% of total). Source: NSF-SDR, 2013...... 30

Figure 7. Research career paths analyzed for the BSS researcher workforce (1993-2013)...... 31

Figure 8. Median annual earned income by employment sector, BSSR workforce (1995-2013). 32

Figure 9. Median annual earned income by employment status and gender, BSSR workforce

(1995-2013)...... 33

xiv

Figure 10. Median annual earned income (full time) by employment sector and gender, BSSR workforce (1995-2013)...... 34

Figure 11. Higher education expenditures of federal funds on R&D, 2004-2013. Source: NSF

Higher Education Research and Development Survey...... 35

Figure 12. BSSR workforce change by Region (1993-2013)...... 37

Figure 13. Distribution of BSSR workforce by field of study (1993-2013)...... 38

Figures 14 a-d. Northeast academic researcher median annual earned income, by gender (1993-

2013)...... 39

Figure 15. Headcount expenditures by R1 universities, by region (2010-2015)...... 41

Figures 16 a-d. Regional academic researcher median annual earned income, by gender (1993-

2013)...... 42

Figure 17. Employers of U.S. doctorate holders by employment sector and field of study (2013).

...... 43

Figure 18.Higher education R&D expenditures, by source of funds and R&D field: FYs 1972–

2015. Source: NSF-HERD data...... 49

Figure 19. A conceptual academic science research workforce demand and supply model ...... 57

Figure 20. Academic research and development expenditures, by source of funding: FYs 1972-

2016. Source: National Science Foundation, National Center for Science and Engineering

Statistics, Higher Education Research and Development Survey (HERD). (NSB, 2018) ...... 59

Figure 21. Causal loop diagram of academic science researcher workforce demand model ...... 63

Figure 22. Academic science researcher workforce SD model in AnyLogic (AnyLogic, 2017) . 67

Figure 23. Academic science research workforce SD model base run results ...... 69

Figure 24. Supply and demand for academic science researcher workforce...... 77

Figure 25. Conceptual framework adapted from NIH Biomedical Research Workforce Working

Group report (NIH, 2012, p. 32)...... 82

Figure 26. Higher education R&D expenditures, by source of funds and R&D field: FYs 1972–

2015. Source: NSF-HERD data...... 84

Figure 27. R1 universities’ federally funded research related human resource expenditures, by region: FYs 2010-2015. Source: NSF-HERD data...... 85

Figure 28. Researcher’s professional and personal life state charts...... 89

Figure 29. Annual federally funded research job growth in the U.S...... 90

Figure 30. Visualization of U.S. academic BSSR workforce simulation model interface...... 96

Figure 31 a-e. Baseline model outputs of academic BSS researcher employment trends for R1 universities in the U.S. and its regions...... 101

Figure 32a-d. Baseline model outputs of federal funding, target FTEs, (headcount) FTE gap and

HR expenditure rates...... 103

Figure 33 a-e. Baseline model outputs of enrolled BSS PhD students and unemployed, employed and on leave BSS researchers...... 104

Figure 34 a-d. Gender pay gap reduction policy: Average annual salary by gender...... 107

Figure 35 a-d. Alternative federal research funding policy: Employed BSS researchers by region.

...... 109

Figure 36 a-d. Alternative federal research funding policy: Headcount gap (FTE), hiring rate

(FTE/month) and firing rate (FTE/month) by region...... 111

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List of Tables

Table 1. State clusters representing study regions in the United States* ...... 36

Table 2 Estimating associations between demographic, funding source and job satisfaction variables and mobility between academic and nonacademic sectors ...... 44

Table 3. Outcomes related to academic science researcher workforce demand ...... 64

Table 4. Main elements in the SD model ...... 65

Table 5. Agent types with their simulated characteristics and data sources...... 98

Table 6. Federal obligations for S&E R&D to top research universities, by region: FY 2008-2016

($100K) ...... 100

Table 7. Mincer Equation: Willingness-to-Accept (Full time) ...... 135

Table 8. Mincer Equation: Willingness-to-Pay (Full Time) ...... 138

Table 9. Key parameters and variables used in the agent based model ...... 149

Table 10. Key parameters and variables used in the system dynamics model ...... 153

xvii

Chapter 1: Introduction

Since health, well-being, and security are proper concerns of government, scientific progress is, and must be, of vital interest to government. - Vannevar Bush, 1960

The federal government plays a critical role our nation’s scientific workforce in ensuring a strong economy and addressing the challenges facing our country (National Science and

Technology Council [NSTC], 2000). The guiding principles underlying the government’s policies concerning its use of public resources for science have been in place for more than 50 years when Vannevar Bush’s report titled Science, the endless frontier : a report to the President on a program for postwar scientific research (Bush, 1960) was published. The United States has led the world in the production of innovative science and technology for decades providing extensive government support for the research and development (R&D) enterprise. The partnership between the federal government and universities has helped to ensure America’s position as the leading producer of fundamental scientific knowledge in the world.

The strength of a nation’s economy relies significantly on the availability of a well prepared and productive scientific workforce. Academic research plays a fundamental role in developing the human capital that underlies technological advancements that drive the United

States’ ability to compete in a global economy, especially considering that other countries are making great progress in human capital development at the PhD level (National Sciences Board

[NSB], 2015; National Research Council [NRC], 2005). Shifting workforce dynamics over the

1 years, such as rapid changes in technology, steadily increasing numbers of women, and an overall aging population present challenges in efforts to develop strategies for ensuring a sustainable and well prepared scientific labor force (Ceci, Ginther, Kahn, & Williams, 2014;

Goulden, Mason, & Frasch, 2011; Lloyd, 2005; Xie & Shauman, 2003). Policy makers’ ability to anticipate trends in the academic science workforce supply and demand is particularly important considering its contributions to innovative research and development, and educating future generations of scientists.

Existing studies examining academic science workforce supply and demand do not fully consider system behaviors attributable to individual level attributes that influence career decisions and the eventual outcomes associated with them. The complexity associated with the overall system and its macroeconomic national and regional contexts, as well as its individual level heterogeneity, is difficult to capture using traditional labor market economics approaches that rely upon linear estimations. While some of the more difficult to understand system behaviors, such as feedback loops, have been effectively captured with system dynamics models based on aggregate level factors, individual level heterogeneity has not been well represented in past workforce supply and demand studies.

This dissertation explores academic science research workforce supply and demand dynamics, including various factors that influence its stability and size, by U.S. region. There has been extensive research examining the availability of talent to meet labor market demand in science, technology, engineering and mathematics (STEM) fields. However, less empirical research has focused on the behavioral and social science (BSS) fields1 as a subset of STEM disciplines. The importance of the BSS research workforce, and its contributions to efforts to solve a broad range of health, security, and other complex challenges facing the country, is well-

1 BSS includes the major fields of psychology, economics, political science, sociology and other social sciences. 2 recognized by the federal government (Obama, 2015; Riley, 2018). The workforce outcomes of doctoral BSS researchers, from graduation through their career span, and the effects of intervention strategies designed to improve them, is the focus of this study.

The purpose of this dissertation is to better understand the distribution and attributes of

BSS researchers employed in the academic science workforce in the context of their dynamic interactions with the R1 universities that employ them. The supply dynamics of this workforce system are simulated using an agent based model (ABM) to examine individual level behaviors and outcomes within the context of national and regional macroeconomics represented by a system dynamics (SD) model simulating the demand dynamics. In particular, I am interested in exploring how this model framework can be used to gain insights into the effectiveness of policies designed to reduce the gender pay gap and minimize the supply and demand gap.

Problem Statement

States’ ability to compete in a global economy, especially in the current global economy in which other countries are making great progress in fostering PhD level human capital development (NSA, 2005; NSB, 2015). Shifting workforce dynamics over the years, such as rapid advancements in technology, steadily increasing numbers of women in the labor force and an overall aging population, present challenges for ongoing efforts to ensure a sustainable and well prepared scientific labor force ( Ceci, Ginther, Kahn, & Williams, 2014; Freeman, 2006;

Goulden et al., 2011; Larson & Gomez Diaz, 2012; Lloyd, 2005; Xie & Shauman, 2003).

Federal government funding for research and development (R&D) at higher education institutions is a primary source of funding for the human capital required to support the scientific research enterprise as shown in Figure 1. Funding levels have been flat or declining in recent years, with the exception of the five-year period from 1998-2003 when there was a doubling of

NIH funding and increases from the 2009 American Recovery and Reinvestment Act or stimulus package in the wake of the Great Recession. These fluctuations in federal obligations have significant implications for the production of research at universities throughout the country and the researchers they employ (Hur, Ghaffarzadegan, & Hawley, 2015; Larson, Ghaffarzadegan, &

Diaz, 2012; Teitelbaum, 2014).

70.0

60.0

50.0

40.0

30.0 dollars) 20.0

10.0

0.0

Source of Funds, (billions of constant 2009 2009 ofconstant (billionsFunds, of Source

1972 1977 1982 1987 1992 1997 2002 2007 2012 Fiscal Year All R&D Federally funded Nonfederally funded

Figure 1. Higher education R&D expenditures, by source of funds and R&D field: FYs 1972– 2015. Source: NSF-HERD data.

The challenges facing the academic science workforce system resulting from unstable government funding are further complicated by the increasing rate of graduating doctorate recipients. Evidence of this increase for BSS fields is reported in the Council of Graduate

School’s Graduate Enrollment and Degrees: 2006 to 2016 Survey report (Okahana & Zhou,

2017). The total increase in doctoral degrees awarded in the period from 2006 to 2016 was 4.7 percent, with the PhD degree production rate increasing in most fields from 2014-15 to 2015-16.

However, the number of degrees awarded in BSS fields was found to decreasing by -1.6 percent between 2014-15 and 2015-16. Moreover, there is empirical research indicating that the growing number of doctorate recipients, coupled with the end of a mandatory age for faculty retirement and slowing hiring rates for full time faculty, has led to an oversupply of PhD holders in the academic science workforce (Ghaffarzadegan, Hawley, Larson, & Xue, 2015; Larson,

Ghaffarzadegan, & Xue, 2014; Larson & Gomez Diaz, 2012).

There is a growing literature of studies examining the implications for inter-sector mobility resulting from the lack of academic research jobs and, in particular, tenure track faculty positions (Ginther, 2015). The traditional career path for PhD holders has been to accept either a faculty or researcher position within the academy. In recent years there has been a shift away from this assumed trajectory with increasing numbers of doctorate recipients choosing nonacademic research jobs, instead (Auriol, Misu, & Galindo-Rueda, 2016; Fernandez-Zubieta,

Geuna, & Lawson, 2015; Zhou & Volkwein, 2004). Moreover, there may be implications for attracting new students into doctoral programs if jobs are not available when they graduate.

When one considers the time lags associated with average time to earn a degree, (seven years or more, for many fields), there is concern that the pipeline of new researchers available to support the demand for academic science research may not be sufficient.

The issue of the lack of gender diversity within the tenured faculty and senior researcher ranks has been of concern for government agencies (NSC, 2012). There is evidence showing that a disproportionate number of researchers leading academic science research projects are male and white (Ginther et al., 2011). This is related to the fact that most tenured faculty positions are held by white men, though women are achieving parity in some fields that are not math intensive

(Ceci, Ginther, Kahn, & Williams, 2015; Ginther & Kahn, 2006; Goulden et al., 2011). For women choosing academic science careers, there is an additional challenge with the average age of PhD graduates (37 years) occurring during peak child bearing years (Mullen, 2012). As well, for those pursuing tenure track faculty positions, the time to achieve tenure during which workloads are heavy and hours are long conflicts with their biological clock (Wolfinger, Mason,

& Goulden, 2008).

Currently, little is known about how these factors influence the job search and match experiences and eventual career outcomes of academic science researchers. Gaining insight into how individual researcher characteristics and universities’ hiring preferences combine to influence wages would be useful in the development of effective strategies for attracting and retaining a diverse pool of talented academic science researchers. Regardless of their position, women continue to experience a pay gap compared to their male counterparts (Blau & Kahn,

2017). Progress has been made toward parity, but a significant deficit still exists even when controlling for hours worked, discipline, sector, career stage, and other factors. With females receiving about 60% of all BSS PhD’s, threaten their success and retention in the academic science workforce (Andalib, Ghaffarzadegan, & Larson, 2018).

Academic Science Job Market

Many Western countries, including the U.S., are challenged by the problem of matching the supply of unemployed people with available jobs. The classic labor market research approach to gaining insights into supply and demand problems considers three underlying explanation.

The first is employers offering low wages that may not compete with alternative sources of income, either from not working, or accepting work that is not aligned with one’s career goals.

The second points to misalignment of skills between what is required for the job and what the unemployed person possesses. And the third being the costs associated with taking a job, such as moving, transportation or additional training (transaction costs), are too high. These three issues informed the design of the simulation model of the academic BSSR workforce for this study.

Given that the supply of unemployed BSS researchers included in this analysis hold doctorate degrees, the issue of skills attainment is focused on the horizontal match between their degree field and the disciplines required for available jobs (Robst, 2007), rather than the vertical match of education attainment level with skills required for job vacancies (Hartog, 2000). The issue of transaction costs is also minimized for the purposes of the current study since there is sufficient evidence in the literature to support the assumption that mobility is an accepted and frequently assumed cost that academic researchers are willing to forego. Even in the case of low paying entry level appointments, such as post-doctoral positions, the academic research career path often requires relocation to institutions offering opportunities that align with candidates’ career goals (Buffington, Cerf, Jones, & Weinberg, 2016).

Of the three explanations, the issue of employers offering low wages is the most relevant to this study. While it is widely accepted that all of these factors affect an individual’s willingness to accept a job offer, it is less obvious how academic researchers and the universities

7 that employ them differ in their decisions regarding these costs. Search theory based arguments suggest that unemployed BSS researchers must make concessions to find academic jobs. Yet, there are few studies that provide information relating to how unemployed doctoral researchers weigh wages and other job attributes when accepting jobs. Macro level considerations, such as levels of federal government R&D funding to higher education institutions in various regions

(Goldstein & Renault, 2004), result in differences between researcher job openings and unemployment rates (Salter & Martin, 2001; Stephan, 2012). These differences also have implications for wages offered (Moretti & Wilson, 2014). For some fields, such as biomedical, the willingness of unemployed BSS researchers to accept low wage jobs in other regions is found to be quite high (Garrison, Justement, & Gerbi, 2016).

On the micro level, the effects of variations in pay between fields and genders are less understood. For example, there is evidence of math intensive fields paying higher wages than other less technical fields. PhD researchers in economics earn much higher incomes overall than those working in psychology, social science and other social science disciplines. There is also evidence of a gender pay gap in all BSS fields, with women earning significantly lower median annual incomes than men (see Chapter 2). The empirical evidence of whether or not a gender pay gap exists is varied. For the U.S., some studies report that there is no clear connection between gender and income (Kolesnikova & Liu, 2011) while others conclude that men earn consistently higher wages in the academy than women (Blau & Kahn, 2017; Ceci, Ginther, Kahn, &

Williams, 2014; Wolfinger, Mason, & Goulden, 2008)

The issue of the persistent imbalance of gender diversity and pay equity among senior researchers in the academic science labor market is a complex problem. Efforts to analyze and address such problems often encounter challenges posed by the interaction of more than one

8 condition. Uncertainty in federal funding of science and engineering R&D, an aging workforce with increasing retirement rates, life course impacts relating to work-family balance and regional economic factors entangle to influence researchers’ career outcomes (Ceci et al., 2014; Larson &

Gomez Diaz, 2012; Stephan, 2012; Yi & Larson, 2015; Zolas, Goldschlag, Jarmin, Stephan,

Smith, et al., 2015). These problems are not simply complicated with moving parts, but are found to be complex systems in a more technical sense.

A complex system refers to tangled, connected problems where the behavior of a system as a whole is not able to be reduced to separate systems to be understood completely. To seek understanding regarding how the system will respond to certain interventions in an effort to solve the problem, it cannot simply be taken apart and its components analyzed independently. In analyzing complex systems, seeking to understand policy effects on the overall system in a reductionist way by examining various problems within it separately does not reveal how the behavior of the overall system will be effected (Ghaffarzadegan, Lyneis, & Richardson, 2011;

Sterman, 2006). Rather, the behavior of the whole is not equal to the sum of the behavior of the parts. A change in one part of the system will often ripple around and cause changes in other parts of the system.

Links between the underlying causes of the problems facing the academic science labor market system are often unclear given its fragmented nature. The behavior of the workforce system as a whole can be radically different from the behavior of individual systems operating within it. In fact, the traditional statistical techniques often used to conduct labor market research do not capture feedback effects that result from delayed, distal, reciprocal, and multi-faceted connections between these subsystems. Moreover, the effects of two or more interventions developed in isolation, when combined, can have very different overall effects on the system as a

9 result of how they interact. These additional interactions lead to nonlinear effects that can cause radically and surprisingly different results than were anticipated. Consequently, policies intended to resolve persistent problems often prove unsuccessful due to these unintended outcomes.

Using alternative approaches to gain insights into complex systems is important in a very practical sense. More efficient strategies for identifying high leverage, cost effective policies are needed given that limited resources are available to design and implement solutions. There are many examples of how the academic science labor market behaves as a complex system with numerous empirical studies employing systems strategies in an effort to better understand the outcomes of its workers (Hur, Ghaffarzadegan, & Hawley, 2015; Larson, Ghaffarzadegan, &

Xue, 2014b; Larson & Gomez Diaz, 2012; Xue & Larson, 2015).

Purpose of research

Academic science research and development is vital to ensuring continued economic growth at both the national and regional levels. The discoveries from basic research conducted at research universities spur innovation and applications that have direct benefits to the public good, as well as to business, industry and government institutions. Moreover, the research enterprise requires human capital that relies upon the talent of doctorate level faculty and staff researchers, as well as the development and training of current and future cohorts of doctoral students and new graduates.

There is a large labor economics literature that seeks to estimate workforce supply and demand across various sectors, including the academe. The accuracy of these estimations is often inhibited by the inability of their models to consider the effects of individual heterogeneity and the way in which unobservable factors interact to effect behavioral patterns, including those that

10 emerge from these complex workforce systems over time. Moreover, these models have been developed by focusing on components of the system, such as just the supply side or the demand side, rather than considering them as a system. Econometric methods have been the most common approach to investigating workforce outcomes.

More recently, researchers have used dynamic modeling methods to understand the balance between workforce supply and demand as an outcome of complex interactions. Flynn et al (2014) created a model framework for the Australian optometry workforce that recognizes the complexity of the system and considered individual employer, practicing optometrists’ and students’ decisions and attributes within the broader context of macroeconomic dynamics within various regions of the country. Teitelbaum (2008) posited that the disequilibrium that exists in the academic science workforce system as a result of changing federal government funding policies results in unanticipated reinforcing feedbacks within the system that lead to instability within its workforce. This argument was further validated by empirical research examining the effects of government funding on the biomedical research workforce by Larson et al (2012),

Ghaffarzadegan et al (2015) and Hur et al (2015).

These studies affirm that a dynamic and adaptive framework is most appropriate for understanding the academic science workforce system. I created the concept map in Figure 2 to illustrate the supply and demand dynamics of the academic BSS research workforce. Various endogenous and exogenous factors influencing this workforce system and researchers’ career and life course decisions within identified boundaries are represented. The majority of studies emphasize STEM fields as including only math intensive disciplines, which often prove to be the most challenging for achieving desired equity outcomes. However, the BSS fields within the

broader STEM disciplines are of great importance in forming diverse research teams and have

not been studied as extensively.

Career issues

Hours worked per

scientist per year

Funding agency expectations Research Annual

Production salary per Universities’ need for Rate scientist Unexpected PhD scientists Change needs per University year Universities’ expenditures scientific workforce on research Federal government capacity HR expenditures on higher

education research Figure 2. Supply and demand for academic science researcher workforce

Government agencies that invest heavily in higher education research production have

supported workforce research that provides insights into effective strategies for attracting and

retaining talent, often with a particular focus on women and underrepresented minorities. For

example, in 2011 the Director of the National Institutes of Health (NIH) established a scientific

research workforce working group to develop a model for a “…sustainable and diverse U.S.

biomedical research workforce that can inform decisions about training the optimal number of

people for the appropriate types of positions that will advance science and promote health” (NIH

Director's Advisory Committee, 2012, p. 7). A conceptual framework (model) was developed as

a result along with recommended data collection actions to facilitate development of a full

12 dynamic model in the future. I adapted the working group’s model, as shown in Figure 3, for use in framing the research completed for this dissertation.

Figure 3. Conceptual framework adapted from NIH Biomedical Research Workforce Working Group Report (NIH, 2012, p. 32)

I conceptualize the supply dynamics represented in Figure 3, combined with the factors influencing the overall system represented with green highlights in Figure 2, by representing the factors influencing the workforce system and researcher’s career paths within identified boundaries. This framework guides research completed for each of the essays included in this dissertation. The interdependency of heterogeneous researcher and employer agents is key to exploring the supply side hiring process. The salaries earned by working researchers are based upon their individual attributes and inform the use of the model framework to explore the gender pay gap policy outcomes. The model framework also links micro behaviors of agents with

13 regional macroeconomic patterns, including temporal and geographic dynamics, used to simulate workforce demand in a scenario exploring an alternative federal government research funding policy.

In this dissertation, I frame the academic BSS researcher workforce as a system, implement a Mincer wage model, an ABM and a SD model, for capturing the interdependency and interaction within the workforce system. I also discuss the usefulness of the framework and resulting conceptual hybrid model for providing insights for policy makers and other stakeholders. I use the BSS research workforce, gender pay gap and federal academic science funding policies to demonstrate the proposed modeling framework’s applicability and value.

Research Methods

Several methodologies are used to analyze the academic BSS workforce system, including the interaction of individual’s progression from graduate programs, graduation, entry into the system, and progression along their career trajectory through retirement. This research approach allows the regional research job growth over time to be considered, as well as the hiring and job destruction decisions of R1 universities.

System dynamics (SD) is a dynamic modeling method used to understand complex systems (Sterman, 2000). Stocks, flows and feedback loops are used to operationalize the observed trends in system changes over time with consideration of accumulations, non-linear behavior and delays (Forrester, 1968; Sterman, 2000). This method has been successfully used to better understand the healthcare workforce supply and demand dynamics (Flynn et al., 2014;

Ghaffarzadegan, Hawley, & Desai, 2014a; Ghaffarzadegan et al., 2015; Health Resources &

Services Administration [HRSA], 2018; Tomblin Murphy et al., 2009).

While the SD model is simulating the researcher demand component of the academic

BSS workforce system at the region level, an ABM is used to focus on individual heterogeneity at the individual (agent) level. The ABM component captures individual preferences and career related decisions of PhD graduates and experienced researchers (Bruch & Mare, 2006), including their adaptive behaviors (Axelrod, 1997). The temporal and spatial changes in agents’ movement between various states in the model are accounted for, with their behavior being determined by specific rules.

The ABM and SD supply and demand dynamics are enhanced with the application of behavioral economics concepts to evaluate individual choices based on underlying employer and researcher characteristics. The job matching algorithm employed within the ABM’s hiring process considers the relative utility of each job seeker’s attributes (human capital) to potential employers and each job’s utility to researcher applicants using an OLS estimation to determine the marginal benefit for each factor. The utility of a job seeker to an employer becomes the predicted salary value for the applicant. This approach assumes that agents (job seekers and employers) exhibit bounded rationality in their knowledge of available jobs/applicants in the current market and they seek to maximize utility. The neoclassical assumption of perfect information is relaxed. A conceptual model framework adapted from a healthcare workforce study conducted by Flynn et al (2014) informs construction of the narrative process models that develop at the region level over time and space. The multiple levels of the model interact to represent the academic BSS research workforce as a complex system.

Hybrid Dynamic Modeling

SD modeling and ABM methodologies are combined in this hybrid model to include both the demand and supply components of the academic BSS researcher workforce system consistent

15 with the approach used in workforce modeling efforts (Flynn et al., 2014). AnyLogic modeling software (AnyLogic, 2017) is used to implement the model design with an integrated environment facilitating the use of multiple techniques within a single model. A preliminary analysis is conducted using National Science Foundation Survey of Doctoral Recipients Survey data to generate individual preferences that underlie agents’ employment related decisions.

Discrete time is used to calibrate the probabilistics used with the ABM, and to determine the

BSS researchers’ life and career related decisions.

An individual choice behavior framework is used to represent agent’s choices in the model. Choice is involved, to some degree, in all decisions and actions we take in life. In particular, the decisions and actions of PhD researchers in the academic science workforce and their employers are examined using this approach in this study. Thurstone (1927) is considered to be the first researcher to use an individual choice behavior as an indication of broader population behavior on certain processes. He described food preferences with a utility function.

Choice models are now used in various disciplines including, psychology, housing, marketing, transportation, and many others. Many new choice models have been developed over the years with early computational challenges being resolved as technological advancements have occurred.

The following steps are specified by Ben-Akiva and Lerman (1985) as forming the sequential decision-making process leading to an outcome considered to be a “choice”: 1) definition of the choice problem, 2) generation of alternatives, 3) evaluation of attributes of the alternatives, 4) choice, and 5) implementation. A choice is not considered to be dependent on the alternatives being considered, but on the characteristics or attributes of the options, instead.

Decision makers apply certain decision rules to calculate the best alternative.

The decision rule describes the decision maker’s internal process used to consider the available information and make a unique choice (Slovic, Fischhoff, & Lichtenstein, 1977). The type of decision rule applied in this study uses the concept of utility, which assumes that a vector based on alternative’s attributes defines an objective function representing its attractiveness, or utility. Decision makers are assumed to want to choose the alternative that will maximize their utility. In this study, researchers are decision makers selecting which jobs to apply for from a choice set of vacant positions, or opportunities they are aware of (Luce, 1959). All alternatives are assumed to be feasible and known during the decision process and are mutually exclusive.

Thus, all possible alternatives are included in the choice set and are finite. This framework also assumes that decision makers exhibit rational behavior, and will select alternative A if it is more feasible than alternative B every time a decision must be made. Moreover, if alternative A is more feasible than B, and B is more feasible than C, the decision maker will prefer alternative A to alternative C.

Anand (1993) describes decision theory as being about “choosing the act that is best with respect to the beliefs and desires that an agent holds”(Anand, 1993, p. 1). He asserts that utility theory informs efforts to achieve this. Decision theory seeks to maximize utility given the attributes of a decision maker (or agent). A Bayesian design using historical data is used here and is helpful in identifying the choice sets that are most accurate in estimating each agent’s utility function. By situating the choice model within an ABM that draws from the actual career decisions made by PhD researchers, this study provides a helpful solution to the problem. The utility of each choice available to agents, including employer’s hiring decisions and applicant’s job acceptance decisions, are determined using a human capital theory based Mincer wage equation.

Human capital theory is based on the fundamental principle that the learning capacities of people employed at an organization are of value, similar to other resources necessary for the production of goods and services (Lucas, 1988). On an individual level, the human capital theory approach assumes that individuals gain human capital stock as they acquire skills and abilities from education and training, and that over time they will realize a return on their investment in higher education as a result of increased earnings over their life cycle (Becker, 1993; Schultz,

1961, 1963). The assumption that years of work experience play an important role in the evolution of earnings over a person’s career is represented by an age-earnings profile that is concave with the rate of increase in income highest during the early career stage, then peaking in mid-career and slowing as one approaches the end of their career (Borjas, 2016). A great deal of empirical research exists in the field of labor economics that investigates how the human capital model influences the age-earnings profile.

Mincer developed the human capital earnings function (Mincer, 1974) which represents the age-earnings profile generated by the human capital model as:

Equation 1. Mincer human capital earnings equation log w = as + bt – ct2 + other variables

where w is the wage rate, s is years of schooling, t is years of work experience and t2 is a quadratic on years of work experience that expresses the concave shape of the age-earnings profile. This function assumes that individuals’ unobserved ability do not differ. Age is used to measure an individual’s work experience and years of education through high school are included. The model was later expanded to consider the work histories of women stopping out for child bearing and rearing (Mincer & Polacheck, 1974). Key parameters in the wage regression model are difficult to estimate given unobserved heterogeneity, which leads to

18 inaccurate results. The model framework developed in this study uses the ABM to represent the individual heterogeneity which addresses this issue.

In this context, education is seen as a way for individuals to increase their skills and knowledge to improve their opportunities for higher wages, increased job opportunities and economic security. It is assumed that PhD researchers will take advantage of the best opportunities available, seeking to maximize their earnings when deciding to accept job offers.

As well, intensive research universities are competitive and want to maximize research and development funding. Therefore, these institutions compete for the best and brightest researchers in an effort to improve their reputations, attract top scientists to available positions and receive a higher share of available funding.

Research Questions

This study seeks to add to existing knowledge of BSS academic research workforce trends related to demographics and inter-sector mobility. Furthermore, it identifies workforce related demographic trends and researcher employment outcomes. A conceptual hybrid simulation model framework is developed for to examine workforce supply and demand dynamics leading to insights into labor market efficiencies. The resulting model is used to explore the effects of policies designed to address the issues of gender pay gap and unstable federal funding of higher education research. The primary research question addressed by this dissertation is how does the relationship between individual BSS researcher characteristics and

R1 universities’ hiring decisions combine to influence overall workforce outcomes in the context of regional dynamics? The following questions are considered, specifically:

RQ1. How do BSS researcher’s characteristics and career path decisions vary by gender, discipline and region, and what are the implications for annual median salaries?

This analysis will be descriptive in nature and identifies demographic related patterns for use in representing academic research workforce trends in the simulation model. It also examines researchers’ sector employment decisions to better understand the factors influencing their retention in the academic BSS workforce.

RQ2. At what rate will a policy requiring employers to pay women the same annual salary as men working in the same jobs close the pay gap? Is there observed variation by region?

The new policy is simulated by eliminating the gender pay gap and observing the rate of change by region as compared to the model’s baseline results. The simulation will control for various factors known to influence the outcomes, including full time working status (≥ 35 hours per week), major field of study, education level and years of work experience.

RQ3. Given the instability of federal government academic science research funding policies, and the effect of these decisions on regional BSS research production, how effective is distributing increases over longer time periods in stabilizing the workforce supply and demand gap?

This policy alternative is operationalized by recalibrating the model using data constructed to keep overall federal government research funding increases the same, but distribute the obligations over a longer time period to moderate the oscillatory nature of the SD demand component of the system. The results of this modification will then be compared to the

20 baseline model run to evaluate the effects on stabilizing the gap between workforce supply and demand.

Organization of the Dissertation

This dissertation consists of four chapters including this introduction to the study and three essays. The first essay (Chapter 2) is a descriptive study providing details concerning researchers comprising the BSS workforce. It includes an examination of the underlying factors influencing researcher’s career path choices as they pertain to inter-sector mobility, (i.e., migrating from academe to jobs in business and industry, or from nonacademic positions to academic jobs). Chapter 3 details the development of a basic system dynamics model for use in representing the regional macroeconomics influencing BSS research workforce demand dynamics. A gap analysis model structure is used to evaluate the number of full time research positions created or destroyed on an annual basis (i.e., job growth rate). Chapter 4 incorporates the analyses completed in Chapter 2, and the SD model developed in Chapter 3, to implement a hybrid model framework utilizing an ABM to consider BSS workforce supply dynamics. The framework is then used to evaluate policy scenarios and consider their effects on key workforce indicators and researcher career outcomes. Finally, a discussion overviews the findings, including implications, limitations, and potential next steps.

Chapter 2: Behavioral and Social Science Research Workforce: A Closer Look at its Composition, Behaviors and Outcomes

Introduction

The strength of a nation’s economy relies significantly on the availability of a well prepared and productive scientific workforce. The United States has maintained its position as a world leader in innovation and technological advancements for decades as a result of its ability -to attract and retain top scientists (Richard B. Freeman, 2006). From a national human resource development perspective, there are structural connections tying gains in high-skilled human capital with the growth of our national economy (McLean, Osman-Gani, & Cho, 2004).

Gregory Tassey with the University of Washington’s Economic Policy Research Center captures this in his Make America Great Again article (2018) with this statement: “Investing in research, technology development, worker training, and modern technological infrastructure is the only prescription that will maintain the health of the U.S. economy” (Tassey, 2018).

Population growth rates in many countries, including recently in the U.S., are declining at a rate that threatens the ability to produce more scientists (Vespa, Armstrong & Medina, 2018).

Competition for limited human capital resources is growing and other countries, including China, the Republic of Korea and Germany, are investing heavily in research and development (184%,

135% and 19% respectively) to increase their domestic industries’ productivity and sustain growth (Tassey, 2018). These countries also recognize the need to invest in human capital development through education of the STEM workforce at a faster rate than the U.S. ( Becker,

2012). The increasing retirement rate of scientists in the baby boomer generation combined with the declining population growth rates in the U.S. are combining to threaten the U.S. share of scientists ( Blau & Weinberg, 2017; Freeman, 2006; Vespa, Armstrong & Medina, 2018).

Given the importance of academic research to basic science advancements , which lead to industry innovation and technological advancements , the characteristics of BSS researchers in the academic science workforce are the focus of this study (Salter & Martin, 2001; Tassey,

2018). Unlike many existing empirical studies of the STEM workforce that focus primarily on biomedical, engineering, computer science and math fields, this study evaluates trends in BSS fields, including economics, psychology, anthropology, political science and social science. In recent years, the federal government has recognized the importance of diverse research teams for improving productivity and successful policy design and implementation (Obama, 2015). Having the ability to better understand how communities and individuals intended to benefit from health and welfare policies, such as those relating to the current opiate abuse crisis, greatly improves the likelihood that government interventions will be successful (Riley, 2018).

This study builds on previous research that focused on BSS researcher workforce trends related to demographics and productivity (Hur, Andalib, Maurer, Ghaffarzadegan, & Hawley,

2015). Major trends in the BSSR population, annual earnings across BSS disciplines and differences in these trends are examined. This is a descriptive study and is intended to identify demographic related patterns, particularly those related to gender, rather than determining causality. In addition to demographic trends, employment outcomes of BSS researchers are analyzed with the goal of informing the development of a simulation model of the U.S. academic science workforce with regional differences considered. This model, the subject of Chapters 3 and 4 of this dissertation, will provide a framework in which dynamic hypotheses can be tested

23 against empirical observations to further inform a theory of underlying academic science labor market efficiencies. This work aims to aid policy makers and key stakeholders in efforts to design policies to achieve greater gender diversity and wage equity in the BSSR workforce.

This preliminary analysis examines variations in BSS researchers’ characteristics and career path decisions by discipline and by region. As well, gender differences in aggregate median annual earnings are determined to better understand the status of the gender pay gap within the BSS workforce (Blau & Kahn, 2017). The lower labor force participation of females in senior research positions, as compared to their male counterparts, is cause for concern given the importance of attracting and retaining talent in this sector ( Ceci et al., 2014; Ginther &

Kahn, 2006). Beyond the benefits to the national and regional economies that occur when the

U.S. strengthens its academic researcher workforce by engaging more women, universities benefit from having more female researchers to participate in collaborative research efforts

(Campbell, Mehtani, Dozier, & Rinehart, 2013; Xie & Shauman, 2003). Moreover, gender diverse research teams have been shown to improve the quality of work produced by elevating the collective intelligence of groups (Cheruvelil et al., 2014; Page, 2017). These factors combine to motivate research into what measures might be taken to improve the career outcomes of all academic BSS researchers over time and, by doing so, strengthen the national and regional economies in the U.S.

What we know about BSS researchers and their career decisions

The findings from empirical research on the supply of scientists needed to meet labor market demand are wide ranging, with the majority concluding that in the case of academic science, the availability of tenure track positions has lagged behind the supply of graduates

24 seeking them (Ginther, 2015; Larson et al., 2014; Mangematin, 2000; Roach & Sauermann,

2010). Given the cost of doctoral degree attainment, most graduates expect to earn a return on their investment within a reasonable time period after graduating. This perspective drives career path decision making and motivates many PhD scientists to seek employment outside of the academe at increasing rates, even if this path veers from their original academic career goals

(Fuhrmann, Halme, O’Sullivan, & Lindstaedt, 2011; Zolas et al., 2015).

PhD graduates choosing careers in the academic sector are increasingly employed in postdoctoral positions (i.e., postdocs) or as non-tenured faculty, rather than finding more desired tenured faculty opportunities (Ghaffarzadegan et al., 2014; Ginther, 2015; Phou, 2017; Pool et al., 2016). This rise in postdoc positions began in the late 1980’s and early 1990’s and corresponded with an increase in nonacademic employment of PhD scientists. The graphs in

Figure 1, based on 1981-2010 Survey of Doctoral Recipients data, show employment trends in the fields of engineering, life sciences, physical sciences, and social sciences (Ginther, 2015).

A closer look at the workforce outcomes of PhD recipients using data from the

UMETRICS project, which contains administrative records on federally and non-federally sponsored research projects from eight universities, linked with U.S. Census data reveals a great deal. Findings of the study titled “Wrapping it up in a person: Examining employment and earnings outcomes for PhD recipients” (Zolas et al., 2015) indicate that one year after graduating, those who pursued jobs in industry had significantly higher earnings than their academic and government employed counterparts. Figure 2 displays the relative annual earnings of those employed in each of these three sectors indicating that while the majority of graduates were working in academia and government, they were earning far less than those employed in industry. In fact, PhD scientists working in academia had average earnings below $50,000

25 annually which may be indicative of the growth in the number of postdoctoral graduates and the increasing competition for the limited number of academic jobs.

Figure 4. Employment by field, 1990-2010. Reprinted from “Storm clouds on the horizon for PhDs,” by D. K. Ginther, 2015, Issues in Science & Technology, 31(4), 75.

While these results were descriptive and reflect conditions at large Midwestern research universities, they do suggest that annual earned income disparities between the academic and nonacademic sectors may have longer term implications for the attraction and retention of

26 talented scientists to not only the academic research enterprise, but to the academic programs whose graduates supply this labor market (Berger, 1988; Montmarquette, Cannings, &

Mahseredjian, 2002).

Figure 5. Annual earnings (U.S. $1 x 1000) of UMETRICS doctoral recipients by sector. The figure plots the smooths share of UMETRICS doctoral recipients (the probability density estimated using a Gaussian kernel model) at each level of earnings (with bandwidths of $10,000). Individual earnings data are derived from a match to W-2 earnings data. Reprinted from “Wrapping it up in a person: Examining employment and earnings outcomes for PhD recipients,” N. Zolas, N. Goldschlag, R. Jarmin, P. Stephan, J. Owen-Smith, R. F. Rosen, B. McFadden Allen, B. A. Weinberg, J. I. Lane, 2015, Science, 360(6266), 1370.

A recent study of the trends in behavioral and social sciences research workforce (Hur, Andalib, et al., 2015) reported that new PhDs (defined as those graduating within four years of completing the SDR survey) in the fields of sociology and psychology were mostly women, while political science had achieved gender balance and economics remained male dominated. Overall, BSS fields had more recent female doctoral recipients than the other STEM fields of biomedical and engineering.

This research also provided estimates related to the academic job market conditions facing new graduates finding that, overall, the number of new hires in this sector equals about

1/6th of the number of assistant professors and the annual growth rate of faculty positions was

27 nearly constant (< 2 percent). Data indicate that 29% of BSS PhD graduates would have a chance of securing tenure-track faculty positions with the other 71% taking non-tenure track positions or non-academic jobs.

Data and Methods

The National Science Foundation’s (NSF) Survey of Doctoral Recipients (SDR) data from

1993 to 2013 were analyzed for this study. The SDR is conducted by the US National Science

Foundation (NSF) and is a longitudinal survey designed to provide career history and demographic information about individuals with doctoral degrees. NSF’s National Center for

Science and Engineering Statistics (NCSES) is the primary sponsor of the survey, which began in 1973. Individuals with doctoral degrees in the sciences, including social science, who are under the age of 76 are included in the sample.

The SDR includes such variables as occupation, employment status, geographic employment location, educational history, race/ethnicity, date of birth, sex, degree field, labor force status, annual earned income and many others. Large sample size is considered to be one of the SDR’s strengths. For example, the overall sample size for the 2013 survey was approximately 40,000, which included new doctoral graduates along and response rates are considered to be good (i.e., in 2013, 76 percent of those surveyed responded). The analyses used survey weights to adjust for attrition bias to ensure more representative data. The NSF has de- identified and anonymized these datasets before making them publicly available for research.

The Ohio State University’s Behavioral and Social Science Institutional Review Board considers this dataset exempt.

Five major variables were used to measure demographic characteristics from this study: age, gender, ethnicity, citizen status, marital status and parental status. Six outcome measures were used to consider workforce outcomes in this study: 1) basic annual salary (principal job before deductions), 2) funding sources (if government funding, NIH or not), 3) satisfaction on job advancement, 4) labor force participation status (hours worked per week, change in employer, reason for not working) and 5) sector of employment. Only researchers with full time employment status were included. All median earned income outcomes were adjusted for inflation when compared to 2013 data. The data were narrowed down to the fields of economics, sociology, psychology, political sciences, and other social sciences.2 The goal of the descriptive analyses is to uncover major trends as opposed to identifying causal factors.

2 The detailed major fields are as follows:

 Psychology: educational psychology, clinical psychology, counseling psychology,

experimental psychology, general psychology, and industrial and organizational

psychology, Social psychology, Other psychology, Other social sciences.

 Economics: agricultural economics, and economics.

 Political sciences: public policy studies, international relations, and political sciences and

government.

 Sociology: sociology.

 Other social sciences: area and ethnic studies, linguistics, anthropology and archeology,

criminology, geography, and history of science.

Results

A preliminary study was completed using NSF-SDR data from 1993 to 2013 to better understand the BSSR workforce. This work builds upon the previous research into U.S. BSSR workforce trends conducted in 2015 (Hur, Andalib, et al., 2015).

Who is in the BSSR workforce?

In 2013, there were 234,956 doctoral recipients included in the survey whose degrees are in BSS fields. About half (48%) were employed as researchers in the U.S., while 35 percent were employed in non-research positions and just over 1 percent were retired. Figure 3 shows the primary activities of U.S. BSS PhD scientists in 2013, including the proportion working in research related (defined as 10% or more of their time spent doing research) versus non-research related jobs.

1.8 1.3 0.3

11.4 Employed Research (U.S.) Employed Non-research (U.S.) Retired 49.5 Not in Labor Force

Unemployed 35.7 Employed (Non-US)

Figure 6. Labor force status for BSS doctorates, (% of total). Source: NSF-SDR, 2013.

During the past twenty years, the proportion of BSS PhD scientists engaged in research related work was highest in 1995 at about 52 percent and has steadily declined since. Almost two thirds of those employed in research positions are employed by academic institutions with industry being the next largest employer (26%) and government employing the fewest (9%).

Employment trends of the two sectors are analyzed with four year colleges/universities, medical schools and research institutions included in the academic category and industry and government agencies composing the nonacademic sector. Figure 4 clearly shows the dominance of the academic sector as the primary employer of BSS scientists, with the proportion selecting this career path declining only three percentage points since its peak at 70 percent in 1999.

100% 90%

80%

70% 60% 50% 40%

30% % % Researchersof 20% 10% 0% 1993 1995 1997 1999 2001 2003 2006 2008 2010 2013 Year Academic Industry Government

Figure 7. Research career paths analyzed for the BSS researcher workforce (1993-2013).

Demographic characteristics of the BSSR workforce, as reported in the results of the

2015 study conducted by Hur et al, include gender, age, and race. The results of this earlier study indicate that the proportion of female BSS researchers is increasing (42% in 2003 to 48% in

2013) while the percentage of racial minorities remains low (14% in 2003 to 18% in 2013).

Efforts to attract and retain female and underrepresented minorities in the BSSR workforce, and to improve their numbers in senior researcher positions, are of increasing importance considering these observed trends. The current study focuses on the gender parity issue, in particular.

BSSR annual earned income trends Despite the fact that the median annual earned income of BSSR employed in the academic sector average about $20,000 less annually than those in nonacademic jobs, the traditional career path is preferred. An examination of the trend over time of earnings by employment sector is shown in Figure 8. While there are greater fluctuations in these levels for the nonacademic sector, income levels are consistently more than those in the academic sector peaking in 2010 at $23,320. How these trends moderate when considered by gender is also of interest.

110000

100000

90000

80000

70000

60000 MedianSalary(adjusted), $

50000 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013

Year All BSS Academic Nonacademic

Figure 8. Median annual earned income by employment sector, BSSR workforce (1995-2013).

Figure 9 shows BSSR median annual earned income for men and women by employment sector. These data indicate a persistent gender pay gap during the period from 1995 to 2013 with no indication of the differential converging. These trends are consistent with the findings of other empirical studies using SDR data (NSB, 2014), as well as those using other sources of income data, such as Current Population Survey (CPS) wage data (Blau & Kahn, 2017). It is interesting to note that the increase in male salaries was far greater than those of women from 2008-2010, with a sharper decline from 2010 to 2013. This analysis confirms that there is a significant and persistent trend of lower pay for female BSS researchers when compared to male researchers employed in the U.S.

110000

105000 100000 95000 90000 85000 80000 75000 70000 Annual Salary(adjusted), $ 65000 60000 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year

All FT Male FT Female

Figure 9. Median annual earned income by employment status and gender, BSSR workforce (1995-2013).

A closer look at median BSSR annual earned income trends by gender and sector (Figure

10) reveals that women earn less than men in both academic and nonacademic jobs. However, women in nonacademic jobs earn significantly higher incomes than men in academic research.

There is a noticeable drop in female median salaries in 2006 for those working in government

33 and industry, while their male counterparts did not experience the same decrease. Also of interest is that those working in nonacademic jobs realized sharp annual earned income increases in

2010, with the increase for male researchers being significantly greater than women’s. This occurred in the year following the great recession of 2007-2009. The median salaries of male researchers working in academia increased at a rate higher than those of female researchers in

2010.

140000

120000

100000

80000

60000

40000

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Annual Annual earned income (adjusted), $ Year

Ac FT Male Ac FT Female NA FT Male NA FT Female

Figure 10. Median annual earned income (full time) by employment sector and gender, BSSR workforce (1995-2013).

Regional BSSR workforce trends

It is beneficial to consider the BSSR workforce dynamics on a regional basis, as workforce issues vary considerably from region to region throughout the U.S. There are regional contexts that influence academic BSSR annual earned income trends such as competition between top research universities for limited research and development funding and researcher talent acquisition and retention (Weinberg et al., 2014). Economies vary between regions with

34 economic development implications linked to each region’s success in academic science research and development (Lan, Katrenko, & Burnett, 2015; Tassey, 2018). Figure 11 shows the distribution of expenditures of federal research and development (R&D) funding by higher education institutions by region. The NSF definition of R&D is “creative work conducted systematically to increase the stock of knowledge (research) and to use that knowledge to devise new applications (development).”3 Universities in the South have had the highest R&D expenditure4 rates of federal from 2004-2013 with the difference over levels of funding in the next highest region, the Midwest, increasing in recent years to more than $4 billion annually.

18000000

16000000

14000000

12000000

10000000

(thousands $)of 8000000

6000000 R1 universities' R&D expenditures , 4000000 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Fiscal Year

Northeast Midwest South West

Figure 11. Higher education expenditures of federal funds on R&D, 2004-2013. Source: NSF Higher Education Research and Development Survey.

3 Source: NSF Higher Education Research and Development (HERD) Survey Technical Notes, retrieved online at https://ncsesdata.nsf.gov/herd/2015/herd_2015_tech_notes.pdf 4 NSF-HERD Survey definition of R&D expenditures: “Institutions were asked to report R&D expenditures from the institution’s current operating funds that were separately accounted for. For the purposes of the survey, R&D included expenditures for organized research as defined by 2 CFR 220 (OMB Circular A-21) and expenditures from funds designated for research.” Source: NSF HERD Survey Technical Notes, retrieved online at https://ncsesdata.nsf.gov/herd/2015/herd_2015_tech_notes.pdf 35

The impact of these expenditures is beneficial not only to the universities receiving the funding, but also to the surrounding communities and the regions in which they are located

(Tassey, 2018). In addition to human resource expenditures that have a ripple effect with employees’ supporting their local economies, but there are other benefits such as spillover effects of innovations in business and industry and the purchase of goods and services required to support the research enterprise (Salter & Martin, 2001). This is known as a multiplier effect, where each dollar of government funding increases the local economy by an additional percent increase (Goldstein & Renault, 2004). The regions defined for this study align with those used by the U.S. Census Bureau. Table 1 displays the states included in each of four regions.

Table 1. State clusters representing study regions in the United States* Region States

Northeast CT, ME, MA, NH, RI, VT, NJ, NY, PA Midwest IL, IN, MI, OH, WI, IA, KS, MN, MO, NE, ND, SD South DE, DC, FL, GA, MD, NC, SC, VA, WV, AL, KY, MS, TN, AR, LA, OK, TX West AZ, CO, ID, MT, NV, NM, UT, WY, AK, CA, HI, OR, WA, AS, MP, PR, VI, other U.S. Territory * Based on U.S. Census Bureau regions

Figure 12 indicates the change in the distribution of BSSR workforce by region, which provides a view of the trends from 1993 through 2013 within each of the four regions studied.

The majority of researchers (60%) have moved to a different region since earning their doctorate.

All regions have experienced flat or declining rates of change in their BSSR workforce supply with the exception of the South, where growth in the South Atlantic sub region has been a driving factor. This area of the country has seen its share of researchers increase steadily from 19 percent in 1999 to a high of 23 percent in 2010, declining slightly to 22 percent in 2013 for an overall increase of 8,733 scientists. The Middle Atlantic area of the Northeast region has shown the most significant decline in the proportion of employed BSS scientists with its share dropping

36 from a high of 18 percent in 1995 to 16 percent in 2013. This brings the Northeast to the same standing as the West as the second largest BSSR workforce regions in the U.S. This alignment initiated in 2008 just after the Great Recession began.

35%

30%

25%

20% % % Researchersof

15% 1993 1995 1997 1999 2001 2003 2006 2008 2010 2013 Year Northeast Midwest South West

Figure 12. BSSR workforce change by Region (1993-2013).

Differences between BSS disciplines

A closer look at workforce trends within the various BSS disciplines is necessary to fully understand the experiences of researchers in each area. Changes in the proportion of BSS researchers by field for the twenty year period from 1993 through 2013 is shown in Figure 13.

Psychology remains the dominant field with a share of more than 40 percent of all BSS scientists. Economics is the next largest group at 16 percent since 2006, though it has been declining at a rate of about one percentage point every two years. Overall, the distribution of

BSS scientists between the various disciplines has remained relatively steady.

100% 90%

80%

70% 60% 50% 40%

30% % % Researchersof 20% 10% 0% 1993 1995 1997 1999 2001 2003 2006 2008 2010 2013 Year Psychology Economics Political sciences Sociology Other social sciences

Figure 13. Distribution of BSSR workforce by field of study (1993-2013).

It is important to consider how remuneration varies between BSS fields in an effort to better understand the workforce outcomes of researchers employed in academe. Between field variation in earned income is considerable and directly affects the career choices of these scientists. Moreover, there are regional differences in the median annual earned incomes of academic BSSRs that are reflected in Figures 14, below.

Overall, scientists with doctoral degrees in economics realize the highest returns to their investment in education with increasing median annual earned incomes since the mid-1990’s in all regions. Psychology was consistently the second highest paid field in the Northeast until

2013, when political science jumped ahead. All other fields than economics in the Midwest earn about the same median annual salaries, with psychology and other social sciences tied at $74,000 in 2013. Other social sciences have seen the largest gains over time in the South, holding the second highest spot with political science in 2013 at $75,000 annually. Political scientists also

38 earn the second highest median annual earned income in the West at $90,000 after sharp increases in 2010 and 2013.

a. Northeast 120000

110000

100000

90000

80000

70000

Annual Salary (adjusted), $ (adjusted), Salary Annual 60000

50000 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year Economics Political and related sciences Psychology

b. Midwest 130000

120000

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100000

90000

80000

70000

Annual Salary Annual (adjusted), $ 60000

50000 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year

Economics Political and related sciences Psychology Sociology and anthropology Other social sciences

Figures 14 a-d. Northeast academic researcher median annual earned income, by gender (1993- 2013).

c. South 120000

110000

100000

90000

80000

70000 Annual Annual Salary (adjusted), $ 60000

50000 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year Economics Political and related sciences Psychology Sociology and anthropology Other social sciences

d. West

120000

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70000

60000 Average Salary (Adjusted), Average Salary (Adjusted), $ 50000 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year Economics Political and related sciences Psychology Sociology and anthropology Other social sciences

Regional differences in BSSR compensation

The focus of this study is academic BSS researcher supply and demand at top research universities. Federal government funding of basic research at higher education institutions is a major determinant of academic research job creation rates. NSF HERD data provides the proportion of higher education expenditures of federal research funds on headcount, which is defined as the difference between the target workforce necessary to produce the funded research and the number of researchers currently employed. Figure 15 shows this spending over time in

40 each of the four regions. Funding levels have been declining in recent years resulting in greater competition for limited resources. While headcount expenditures in the South have increased by nearly $21 million, other regions have experienced flat or declining headcount spending rates.

These trends are consistent with the regional share of BSSR workforce employed in each region shown in Figure 13.

250000

200000

150000

100000

R1 universities R&D headcount 50000

expenditures expenditures , (thousands dollars)of 2010 2011 2012 2013 2014 2015 Fiscal Year

Northeast Midwest South West

Figure 15. Headcount expenditures by R1 universities, by region (2010-2015).

Figures 16 shows the median annual earned income trends for academic BSSRs by gender and region over the period from 1993-2013. Universities in the Northeast region pay significantly more than the other three areas of the country with male BSSRs earning nearly

$20,000 more annually than females. As of 2013, females earned a median annual earned income of $80,000 which was $5,000 more than the next highest region (West). Gender wage gaps were evident in all regions with the Midwest having the smallest difference at $14,000. The Midwest is the only region in which the difference between male and female BSSR salaries decreased

41 from 2010 to 2013, but also pays the lowest median salaries to women ($71,000). Female BSSRs in the West saw a sharp decrease in their median salaries from 2010 to 2013, dropping from

$79,500 to $75,000, causing the region to lose ground in its progress toward parity.

a. Northeast b. Midwest

110000 90000

100000

80000 90000

80000 70000

70000 Annual Annual Salary, $ Annual Annual Salary, $ 60000 60000

50000 50000 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year Year

NE Male NE Female MW Male MW Female

c. South d. West

100000 100000

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Annual Annual Salary, $ Annual Annual Salary, $ 60000 60000

50000 50000 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year Year

S Male S Female W Male W Female

Figures 16 a-d. Regional academic researcher median annual earned income, by gender (1993- 2013).

BSS researcher employment sector choices

Finally, given that BSS researchers face an increasingly competitive academic science job market with relatively low annual salaries when compared to nonacademic research opportunities, the factors influencing these career path decisions were explored more systematically. The focus of this inquiry is still on employed PhD BSS scientists employed in

42 research positions. As indicated in Figure 17, below, the distribution of employers of U.S. STEM doctorate holders by sector type and field of study confirms that the majority of those with BSS degrees are employed in the academic sector, while non-BSS trained scientists are predominately working in the nonacademic sector. This suggests that understanding more about the factors influencing researchers’ decisions to leave the academy for jobs in business and industry may provide useful insights for those interested in retaining them. Conversely, knowing more about the attraction of BSS researchers to academe from the nonacademic sector is also of concern.

Having a better understanding of these employment trends also helps to inform the academic science research workforce model development effort.

Non-S and E Fields S and E-Related Fields Engineering Social and related sciences Physical and related sciences

Computer and mathematical sciences

0% 10% 20% 30% 40% 50% 60% 70% 4-yr coll/univ; med schl; univ. res. inst. 2-yr coll/pre-college institutions Bus/Ind, for-profit Bus/ind, self-employed, not-incorporated Bus/Ind, non-profit Federal government

Figure 17. Employers of U.S. doctorate holders by employment sector and field of study (2013).

The sector employment outcomes within the BSSR workforce were examined by gender, parental status, and field. This section provides descriptive statistics then overviews statistical analysis used to investigate relationships between job advancement satisfaction, job security satisfaction and NIH funding and the likelihood of migrating from academic jobs to nonacademic (business and industry) employment and, conversely, from the nonacademic sector

43 to academe. This analysis controls for various individual attribute including gender, marital status, parental status, working hours, tenure status, and field of study.

Table 2 Estimating associations between demographic, funding source and job satisfaction variables and mobility between academic and nonacademic sectors

Academia Non- Academia Bus/Ind to Academia VARIABLES to to Non- to Bus/Ind Academia Academia Academia Satisfaction on 0.46** 0.50** 0.97 0.93 Job Advancement (0.15) (0.17) (0.21) (0.23)

Satisfaction on 0.86 0.98 0.68** 0.62** Job Security (0.25) 0.30) (0.13) (0.14)

NIH Funding 0.44 0.34* 2.31** 2.20* (0.25) (0.22) (0.93) (1.02)

Male 0.41* 0.49 0.96 1.29 (0.22) (0.28) (0.38) (0.57)

Children 0.38* 0.29** 1.09 1.27 (0.20) (0.17) (0.43) (0.55)

On tenure-track 1.25 2.21 (Ref: Tenured) (1.00) (1.92)

Not on tenure 6.49*** 15.12*** track (4.49) (11.97)

N 601 601 494 424 2 Pseudo R 0.32 0.36 0.09 0.12 Observations ***p<0.01, **p<0.05, *p>0.1 Notes: Standard errors in parentheses. The regression models are logistic models to estimate the chance of changing sector of employment, academic versus nonacademic (business and industry), during the time period from 2010 to 2013. Data for the model are NSF-SDR 2013, only individuals employed in BSS fields. Other variables controlled for in the regressions are Marriage, Children, Tenure status, Source of Funding (NIH or not), and Job Satisfaction on Advancement and Security.

Tables 2 displays the results of the logit analysis of various factors influencing BSS researcher sector mobility. The results of this analysis indicate that female academic researchers are more likely than males to leave academe. While both men and women with children were

44 less likely to move from their current employment sector, women in BSS fields were more likely than men to move from academic jobs to nonacademic work. Of all researchers, women in the academic sector earn the lowest wages. This combined with their lower rate of achieving tenure may be factors that influencing their increased risk of leaving.

Overall, researchers who are satisfied with their career advancement were much less likely to move from academic jobs to nonacademic jobs. This is consistent with the well documented challenges in, first, becoming employed in a tenure track position, then actually successfully achieving tenure. However, satisfaction with career advancement was not as significant as satisfaction with job security in its influence on decisions to leave nonacademic jobs for academia. Again, being tenured has direct implications for job security and is often indicated as the reason why unsuccessful candidates chose to leave academe.

Funding from NIH was determined to be a significant factor in decisions to leave the nonacademic sector, with recipients being far more likely to move to academic jobs than their peers. This finding has direct implications for efforts to attract talented researchers to academic science. Since academic institutions conduct the majority of basic research in the U.S., and much of the research NIH supports is basic, rather than applied, this outcome seems reasonable.

Research has become increasingly collaborative in nature which suggests that those working together on projects would have closer ties. The influence of these networks and their impact on researcher productivity and mobility decisions is a growing area of interest and warrants further investigation. These results inform the weights (beta values) used to calculate the utility of each choice to agents in the ABM component of the BSSR workforce hybrid model developed in

Chapter 4.

Discussion and conclusions

Given the academic labor market related findings of Hur et al in their 2015 study, there is not an overproduction of BSS PhD graduates as seen in the biomedical and engineering fields.

Their additional finding that the average age of the BSS researcher workforce has been increasing suggests that retirement rates will likely increase as the baby boomer generation enters the late career stage (Vespa et al., 2018). Based on NSF-SDR data, the average age of BSS researchers in 2013 was 54 years as compared to 48 years in 1993. These factors combine with the annual median income differences between BSS fields to suggest that attracting students to

BSS doctoral programs and, upon graduation, into the academic science workforce may pose challenges in the future.

The finding that, in the academic sector, male researchers’ median income increased at a higher rate than their female counterparts in 2010 may be an indication of the influence of the influx of federal government higher education research funding from the American Resource

Recovery Act of 2009. Given that more males than females working in the academic sector serve as principal investigators for research projects supported by these grants, their salaries may have been more directly affected by this increased funding (Ceci et al., 2015).

The variation in compensation between BSS disciplines is a factor most students consider when choosing which field to pursue a PhD in. As well, many graduates making career choices after earning their doctorates weigh the relative benefits of applying for jobs within the same region as their university against opportunities offered in other regions. In a tight job market, regions face increased competition for talented researchers and are not as likely to attract and retain these graduates if their median annual income levels are lower than other areas of the country. At an organizational level, top research universities with specialized projects in certain

BSS disciplines may have to make adjustments to their recruitment and retention strategies to remain competitive with institutions in other regions offering higher paying jobs.

Chapter 3: Simulating the Scientific Research Workforce Demand Impacts of Government Higher Education Research Funding

Introduction

Since the great recession, levels of federal funding of scientific research and development have been declining or flat with adverse effects on doctoral researcher career outcomes, especially early career doctorates. In addition to factors contributing to this trend at the individual and organization level, national and regional economies and federal funding for scientific research and development (R&D) combine to influence academic research related job growth (Ginther, 2015). These trends have negative effects that become amplified when considered over the course of researchers’ careers (Ghaffarzadegan, Xue, & Larson, 2017; Hur,

Ghaffarzadegan, et al., 2015; Larson et al., 2012). Economic theory suggests that there are potential negative consequences for the attraction and retention of student enrolling in doctoral programs if there is a perception of high risk in realizing a return on an investment in advanced degrees (Stephan, 2012). Moreover, there are negative long term implications for our nation’s economy if innovation from scientific R&D does not remain competitive with other countries

(Freeman, 2006).

In the case of academic science research, federal funding is a primary driver of workforce demand with trends in the rate of change of this funding reflected in the rate of change of university expenditures of federal funds on personnel (NSF 18-303). Figure 1 shows the change over time of federal funding for higher education science and engineering (S&E) R&D from

1972 through 2015. HHS (NIH) funding levels have consistently been the highest of all federal agencies with a budget of $20,659,000,000 in 2016. Since the Great Recession (2007-2009), federal funding of higher education research has been declining or flat overall, though there was a 2.5% increase to $38.8 billion in FY 2016 (NSF 18-303). Such fluctuations in funding levels are common, with the system structure experiencing uncertainty due to the influence of changing political climates and macroeconomic factors (Teitelbaum, 2014). These annual rates of change in expenditures have direct implications for net job growth in the academic science workforce

(Larson et al., 2012; Weinberg et al., 2014). The interactions between various components of the academic science workforce system combine to create an overall complexity that merits the use of simulation modeling. This approach has been successfully used to better understand possible effects of policies intended to positively influence labor market outcomes (Andalib et al., 2018;

Flynn et al., 2014; Ghaffarzadegan et al., 2017).

70.0

60.0 50.0 40.0 30.0 20.0

constant 2009 dollars) 2009 constant 10.0

Source of Funds, (billions of (billionsFunds, of Source 0.0

1972 1977 1982 1987 1992 1997 2002 2007 2012

Fiscal Year All R&D Federally funded Nonfederally funded Figure 18.Higher education R&D expenditures, by source of funds and R&D field: FYs 1972– 2015. Source: NSF-HERD data.

In this study, I describe a system dynamics (SD) simulation model that represents the fluctuations in government science research funding to top U.S. research universities. The system behavior responds to funding increases which lead to demand for researchers exceeding supply, considered as a positive gap, with increased salaries, low unemployment, lower research production capacity and, possibly, poorer quality of research. Conversely, when funding is decreased and the supply of qualified researchers exceeds demand for research production, unemployment rates increase for recent graduates and early career scientists, downsizing of the current workforce may occur and salaries will likely be reduced. The model makes explicit the effects of these variations on researcher workforce demand while offering a structure for answering the questions related to academic research job growth rates. It will be used to represent the demand component of a comprehensive academic science workforce supply and demand model presented in Chapter 4.

Higher education scientific researcher workforce demand as a complex problem

The issue of the impact of unstable federal funding of science and engineering research, combined with an aging academic science workforce with increasing retirement rates, life course impacts relating to work-family balance and regional economic factors in the academic science labor market, is one such problem. These factors entangle to influence the workforce supply and demand dynamics with potentially adverse impacts on researchers’ career outcomes. These problems are not simply complicated with moving parts, but are found to be complex systems in a more technical sense.

A complex system refers to tangled, connected problems where the behavior of a system as a whole is not able to be reduced to separate systems to be understood completely. To seek

50 understanding regarding how the system will respond to certain interventions in an effort to solve the problem, it cannot simply be taken apart and its components analyzed independently. In analyzing complex systems, seeking to understand policy effects on the overall system in a reductionist way by examining various problems within it separately does not reveal how the behavior of the overall system will be effected. Rather, the behavior of the whole is not equal to sum of the behavior of the parts. A change in one part of the system will often ripple around and cause changes in other parts of the system.

Given the fragmented nature of academic science labor market system, links between the underlying causes of the problems it faces are often unclear. The behavior of the system as a whole can be radically different from the behavior of systems that comprise its parts. In fact, the traditional statistical techniques often used do not capture feedback effects that result from delayed, distal, reciprocal, and multi-faceted connections between them. Moreover, the effects of two or more interventions developed in isolation, when combined, can have very different overall effects on the system as a result of how they interact. These additional interactions lead to time lags and other system behaviors that can cause radically and surprisingly different results than were anticipated. Consequently, policies intended to resolve pervasive problems often prove unsuccessful due to these unintended outcomes. By using an alternative approaches to gain insights into this complex system, it is possible to find more efficient strategies for identifying high leverage, cost effective policies are needed given that limited resources are available to design and implement solutions.

The purpose of this SD model is to provide estimates for the demand for academic research scientists to inform the agent based model of the workforce system developed in Chapter 3.

Thus, the model is uses simplistic assumptions and does not fully account for all of the

51 macroeconomic factors existing in the real world (Ghaffarzadegan, Lyneis, & Richardson, 2011).

This approach is considered to be effective for the purpose of providing a high level representation of the net job growth (Flynn et al., 2014) resulting from expenditures of federally funded research grants at the top 100 research intensive (R1) universities in the U.S.

Previous research on modeling workforce demand

The academic science workforce has been studied extensively across various disciplines.

Recent examples of the use of SD in the field of human resources include models of the biomedical and healthcare workforce supply (Ghaffarzadegan, Hawley, and Desai, 2014; Larson and Xue, 2015). The most relevant literature is highlighted within this section to provide context for the SD model developed for this study.

Prior models

Existing SD models effective in examining scientific workforce dynamics include studies of the supply pipeline of doctorate recipients graduating and moving into postdoctoral positions in biomedical research (Andalib et al., 2018), models of retirement policy changes on creating new faculty positions at universities (Larson & Gomez Diaz, 2012) and other labor market dynamics affecting this workforce (Ghaffarzadegan et al., 2014a). These models provide useful insights into how components of the workforce system behave with regards to the lag time associated with various government grant funding systems (Larson et al., 2012), the effects of retirement rates on faculty hiring (Larson et al., 2014; Larson & Gomez Diaz, 2012), and the availability of academic research jobs for early career doctoral researchers (Ghaffarzadegan et al., 2015; Hur, Ghaffarzadegan, et al., 2015). However, they do not consider the overall supply and demand dynamics of the system with consideration of the influence of individual researcher

52 attributes (such as degree field and demographics) on workforce outcomes. For example, these characteristics are important when considering employer’s preferences and their influence on hiring decisions.

Another model available for examining the academic science workforce is the STEM

Research and Modeling Network (SRMN) supported Business-Higher Education Forum (BHEF)

U.S. STEM Education Model (SRMN, 2009). It was developed by Raytheon Company and is now a publicly available simulation tool for use in exploring factors affecting the retention of students in the STEM education pipeline, which is considered to be an indicator of projected labor market supply. This model is not helpful for achieving the aims of this study since its boundaries are limited to include students’ high school to college transitions only. Thus, early career workforce dynamics occurring after graduation are not captured.

Other workforce simulation modeling efforts have focused on various healthcare workforce groups. For example, the Health Workforce Simulation Model (HWSM) developed for the Health Resources & Services Administration (HRSA, 2018) was developed to estimate the supply and demand, and distribution of, health care workers for the purpose of informing public policies designed to prevent surpluses and shortages. This comprehensive model is adaptable to many different applications and has been used extensively in efforts to project future labor markets in a variety of professions, including nursing, pediatrics, neurology, geriatrics, and many others. This HWSM microsimulation model is used to produce national and state-level estimates and describe the effects of policy options within the projection period. Model data are updated each year and use HWSM simulated individual-level data based on predicted probabilities estimated from recent data. Demand is modeled by using estimated health care services consumption driven by the population size of potential users.

Another example of a health care workforce supply and demand model is a framework developed to represent the dynamics of Australia’s optometry workforce (Flynn et al., 2014).

The approach used in this study is unique from the HRSA model in that it employs an ABM to represent the individual level characteristics of agents, while representing the macro level demand dynamics with a SD model. While this hybrid model approach captures the heterogeneity of the job seekers, the SD representation of demand is similar to the other existing health care workforce simulation models. It bases workforce demand estimations on population growth rates, given that healthcare services projections are largely based on needs that change with population dynamics. A different approach is needed for estimating the demand dynamics for the academic science researcher workforce, since it is not as directly affected by the population in a particular geographic area.

Econometric versus SD workforce demand models

Traditionally, economists use statistical models that are based on historical data, and focus on the intersection of supply and demand curves at equilibrium, to consider labor market gaps, but they do not consider how this intersection is reached. In contrast, system dynamists believe that the availability of a product (in this case qualified PhD research scientists), rather than its rate of production, affects demand. This inductive approach draws from all types of data relevant to the problem which, in this study, includes federal expenditures on academic research and the resulting human resource expenditures by U.S. universities. System dynamics models are considered to be more useful for examining short- to mid-term trends than statistical models

(Lyneis, 2000). Moreover, SD models allow practical scenarios to be explored as inputs to policies and decisions. Experiments examining policy alternatives by varying relevant model

54 parameters allow decision makers to consider how this complex system responds to the interactions of agents over time (Sterman, 2006).

Economic studies of labor market supply and demand generally estimate job vacancies resulting from turnover using industry or occupational separation rates from various sources, including U.S. Department of Labor (DOL) Bureau of Labor Statistics (BLS) Job Openings and

Labor Turnover Survey (JOLTS) data. Growth driven job openings are calculated using linear or econometric models assuming that rates determined by historical trends will continue into the future. Thus, resulting projections are based on the known demand equilibrium position equaling the base year employment, coupled with recent historical trends. U.S. DOL Employment and

Training Administration (ETA) sponsored state-level estimates by year of annual average job openings are, essentially, linear. This methodological approach does not account for business cycles occurring during the 10 year period the projections span. Estimating average job openings annually with general economic cycles considered, allowing supply and demand calculations on an annual basis, is preferred. Moreover, the challenge inherent in traditional econometric labor market supply and demand models of accounting for current jobs (usually assumed as a constant) is addressed in the SD approach. The SD model extends linear estimations to consider nonlinear behaviors playing out over time and space. This capability allows consideration of feedback within the academic science workforce system, such as the instability resulting from fluctuations in federal funding of academic science research.

A conceptual model of scientific researcher workforce demand

There are evidence based studies of the academic science workforce suggesting that some early career scientists, though highly trained at the doctorate level, lack the characteristics that

55 highly ranked research universities require to make them a good match for well-paying academic research jobs, including tenure track faculty positions. However, a lot is understood about the key attributes of successful applicants and what trade-offs employers are willing to make between these attributes in the top academic research institutions. Quantifying the demand for new researchers would make it easier to identify gaps in the supply (or training) of PhD scientists, give graduates a better understanding of what is expected when they enter the workforce, and give employers insights into the expectations of graduates.

The conceptual outline of the demand and supply for the academic science workforce is shown in Figure 2. This diagram highlights the fact that gaining insights into workforce dynamics goes beyond a simple linear flow of PhD graduates to entry level scientists to retirement. Instead, accounting for how various external factors influence the workforce as a whole and the individuals within it, including temporal and geographic dynamics and feedbacks build into quantitative models. The causal relationships between the factors thought to effect the overall workforce system, and feedback loops within the demand related segment, are further explored in this study. Those factors within the scope of this effort are indicated with green shading.

Career issues

Hours worked per scientist per year

Funding agency expectations Research Annual Production salary per Rate scientist Universities’ need for PhD scientists Unexpected Change needs per University year expenditures on research Universities’ HR scientific workforce capacity

Federal government expenditures on higher education research

Figure 19. A conceptual academic science research workforce demand and supply model

This SD model was developed as part of a comprehensive academic science researcher

workforce model simulating employment and employees’ experiences with general work-life

balance, life course phases, employer’s location, and entry or re-entry into the workforce. As

well, the full model examines when researchers leave the workforce to retire, change their career

path, or for other reasons. The interface between new doctorate graduates entering the workforce

and finding jobs is the initial focus of the model. Unemployed job seekers enter the BSS research

workforce and are hired based on whether or not their attributes align with available jobs, as

employers consider their qualifications, major field of study, compensation, and other factors.

The academic science research funding process and its workforce impacts

The federal government became a major contributor to universities’ research enterprise in

the 1950’s following the launch of Sputnik. It increased six fold between 1955 and 1967.

Existing colleges and universities expanded and new universities were created during the boom

57 period. This expansion was curbed by the Vietnam War when funding for research declined and remained flat until the 1970’s. The fluctuations in federal government funding continued as the

U.S. economy experienced another recession in the early 1980’s and by 1989, only 60% of higher education research funding came from the federal government (Stephan, 2012).

In 1998 there was a significant increase as NIH funding was doubled over a five year period. Rather than returning to “normal” funding levels after the doubling years, the amount of funding allocated to NIH and several other federal agencies supporting university research decreased. Flat funding rates continued until the American Recovery and Reinvestment Act of

(ARRA) stimulus funding in 2009. This generated a $21 billion influx of funding for research and infrastructure support, with the majority flowing into universities. This represented the first time that federal funding of higher education research occurred as a countercyclical measure. As previously mentioned, government funding of research at universities was flat or declining from

2009 through FY 2016, when a 2.5% increase occurred.

Many studies have recognized the critical influence of government funding on the production of scientific research at our nations universities and the impact fluctuations in these resources on human capital (Garrison, Justement, & Gerbi, 2016; Hur, Ghaffarzadegan, et al.,

2015; Lane, Owen-Smith, Rosen, & Weinberg, 2015; Larson et al., 2012; Pool et al., 2016b;

Salter & Martin, 2001; Stephan, 2012; Weinberg et al., 2014). The relative significance of government funding for higher education research, as compared to all other sources, is well represented in Figure 3.

The federal funding level is over twice the funding received from state and local governments plus the private sector combined in the 1970’s. However, more recently, as federal funding has trended downward, it is still over half of the total expenditures on academic R&D.

Empirical research has confirmed that the academic science researcher workforce dynamics are highly influenced by the funding process (Teitelbaum, 2008, 2014) with implications for outcomes such as research productivity, job growth rates, researcher salaries and replacement rates in the wake of retirement and attrition.

80.0

70.0

60.0

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40.0 Percent 30.0

20.0

10.0

0.0

Year Federal… State and local government Business Academic institutions All other sources

Figure 20. Academic research and development expenditures, by source of funding: FYs 1972- 2016. Source: National Science Foundation, National Center for Science and Engineering Statistics, Higher Education Research and Development Survey (HERD). (NSB, 2018)

The government’s primary goal for funding research at universities is to produce scientific discoveries that improve the public good and help the country remain competitive in the global economy (Neal, Smith, & McCormick, 2008). Increasing research funding in agency budgets allows them to fund more projects which, in turn, increases the amount of research activity for the workforce to complete. The underlying relationships between these desired outcomes and the

59 factors influencing the overall system are further conceptualized as feedback loops as the model structure is further developed.

A system dynamics model of academic science researcher workforce demand

The image of the world around us, which we carry in our head, is just a model. Nobody in his head imagines all the world, government or country. He has only selected concepts, and relationships between them, and uses those to represent the real system. (Forrester, 1971)

System dynamics (SD) is a dynamic modeling method used to understand complex systems (Sterman, 2000). In this approach, systems are represented as stocks, flows and feedback loops to represent how they change over time, including delays, accumulations, and non-linear attractions (Forrester, 1968; Sterman, 2000). SD has been used effectively to examine both the healthcare workforce (Flynn et al., 2014; SRMN, 2009; Vanderby, Carter, Latham, & Feindel,

2014), and the scientific researcher workforce (Andalib et al., 2018; Ghaffarzadegan, Hawley, &

Desai, 2014b), yielding valuable insights into supply and demand dynamics in these sectors. This approach has also been used to analyze the less intuitive impacts of federal research funding policies on workforce outcomes for the biomedical research workforce (Larson et al., 2012).

Systems theory is a basic acknowledgement that systems are open and are, therefore, influenced by the environments in which they are located (Boulding, 1956). SD is a quantitative research method that builds on this theory to model complex, dynamic social systems (Forrester,

1987). A SD framework allows challenging problems and issues to be examined and understood using simulation modeling. Moreover, SD models have been used effectively to simulate the outcomes of various public policy options to solve complex problems (Desai, 2012;

Ghaffarzadegan et al., 2011).

SD models make explicit the underlying assumptions, calculations and boundaries used to project how systems may react to various policy solutions, rather than relying solely on insights that may be gleaned from historical data based on results using statistical models

(Ghaffarzadegan et al., 2014a). Given the limitations previously noted in econometric workforce supply and demand models and the complexities inherent in the overall system, SD modeling provides a useful method for estimating academic research job growth rates.

Building up a system dynamics model

System dynamics models are useful for gaining insights into systems that are not intuitive and often result in changing our thinking about how the feedbacks affect the causal relationships driving them. A stock and flow structure provides the basic components of a SD model and is useful in modeling the demand dynamics of the academic science research workforce. Stocks are accumulations within the system that may be used to represent populations, inventories and monetary or other balances. Stocks are affected by the flows connected to them; flows are the rate of movement of elements between stocks in the system. Flows are not observable when the system is at rest though stocks remain. The amount of flow into and out of each stock is represented mathematically over time.

Sterman (2000, p. 194) uses a metaphor to aid in understanding stock and flow systems in which the bathtubs represent stocks, pipes represent flows, and faucets or drains represent the amount of inflow or outflow that control the amount of water in each bathtub. For the purposes of this study, stocks consist of annual funding available to hire academic researchers and the number of researchers currently employed at top research universities. This description of the SD structure is useful for communicating insights from these differential equation-based models to audiences without mathematical backgrounds.

This modeling effort is not intended to provide definitive predictions of future academic science research workforce demand. Given the complexities that exist in this system it is not feasible to represent it in a mathematical model. The model is intended to represent the complex interdependencies in the real system, rather than predict its future state. However, it does allow modeling of the future workforce demand using projections of future demand for researchers.

Feedback loops in the academic researcher workforce demand system

Causal loop diagrams (CLDs) are helpful for capturing the feedback structure of systems and representing the dynamic hypotheses about the causes of dynamics (Sterman, 2000). They are, essentially, the stock and flow diagram in a simplified graphic. The CLD shown in Figure 21 represents the workforce demand dynamics and captures the underlying hypotheses about the causes of these dynamics. It represents key variables considered and the causal links between them. These links are assigned either positive (+) or negative (-) polarity to indicate how the dependent variable changes when the independent variable changes. The loop identifiers show overall positive (reinforcing) or negative (balancing) feedback in either clockwise or counterclockwise directions within the system. Positive feedback loops amplify directions, generate growth and reinforce change, whereas negative loops move the system toward the desired state or goal with balancing, stasis and equilibrium actions. In the case of the workforce supply and demand system, as in the case with most systems, equilibrium is not attainable.

The purpose of this model is to represent the gap between the demand for academic researchers and the available supply. The system is characterized by goal seeking behavior given the desire to have the demand met by the supply at any given time. The main loop represents the relationship between research funding at higher education universities, human resource expenditures on research production, the desired workforce needed to meet the demand for

62 research and the production of research. This is represented as a reinforcing loop given the overall growth of the scientific enterprise over time. Although there are temporal increases and decreases in research funding levels that cause short term oscillations in the system behavior, the longitudinal trend is one of growth and expansion.

Annual HE + Research Funding

+ Research R Production HR Expenditures + + Gender - Male B + + Researcher Annual + Salary Target Workforce + - Years of Work Working Experience Researchers - - B +

- + Firing Rate - Headcount Gap Retirement/ Attrition R + Avg Duration of Employment + Hiring Rate +

Figure 21. Causal loop diagram of academic science researcher workforce demand model

Additional causal loops considered to have critical effects on the system involve the annual salaries paid to researchers and two loops associated with the headcount gap created when there are either more or less researchers available in the workforce than desired. A reinforcing loop occurs when hiring is in effect while workforce reduction results in a balancing loop. A variable representing the retirement and attrition rate influences the number of vacancies existing when hiring occurs in the system. This causal loop diagram serves as a concept map for the

63 creation of the stocks and flows in the system dynamics model. The system is used to represent the workforce demand dynamics in the hybrid model and is not intended to incorporate all of the factors relevant to the real system. It is intended to reflect the influence of annual expenditures of research funding by universities on human resources at a regional level.

Table 3. Outcomes related to academic science researcher workforce demand

Outcome Measure Job vacancies Annual human resource expenditures and researcher annual salaries Researcher salaries Annual median salary ($) Total research capacity Total annual full time equivalent (FTE) researcher jobs

Table 3 lists the outcomes of interest and the measures used to quantify them in the model. The number of job vacancies is based on the proportion of current higher education research funding available expended on human resources divided by median annual PhD researcher salaries. Annual funding and human resource expenditures data are available from the

National Science Foundation Higher Education Research and Development (NSF-HERD)

Survey and the NSF Survey of Federal Funds for Research and Development (NSF-Federal S&E

Support). Data from NSF Survey of Doctoral Recipients (SDR) are used to determine annual median salary values. The model calculates the total annual full time equivalent (FTE) researcher jobs by dividing annual human resource expenditures by annual median salary, with one FTE assumed to represent at least 35 hours of work per week.

Implementing the model

The model uses the SD framework for inventory workforce interactions which represents business cycle characteristics causing oscillations in the system. The simple model of the labor supply chain developed by Sterman (2000) and since used by others represents hiring rates by

64 using vacancies created when layoffs occur. Given that the academic science workforce is unique from more traditional sectors, such as manufacturing and agriculture, its business cycles are uniquely represented and are notably steadier. Therefore, feedbacks representing these fluctuations, and delays caused by the hiring process and production of research, are substantially different and are simplified in this application.

Given that federal funding of academic R&D in science and engineering is a major factor driving BSSR workforce demand at top research universities, this modeling approach provides a framework in which target workforce and the (headcount) gap existing between it and available

BSS researcher workforce are represented simply. Table 4 lists the main elements of the SD simulation. This SD demand model estimates the number of job vacancies each year based on higher education expenditures on scientific R&D headcount. It calculates the job growth rate based on several factors, including increase or decrease in university expenditures of federal and nonfederal research grant funding, average annual salaries of doctoral BSS researchers and estimated annual quit rate which includes retirements. The annual (headcount) gap in target workforce levels is tracked over time.

Table 4. Main elements in the SD model

Component Description Formulation Funding Represents the multiplier value Funding_Mpr(t) = Funding_Mpr(t-dt) + multiplier used to adjust the annual (Flow_1)*dt Funding Mpr funding levels adjusted for the annual percent increase Flow 1 Annual multiplier value Flow_1 = Funding_Mpr*annual_increase adjustment Funds in Flow of annual government funds_in = funding into R1 universities’ baseFunding*Funding_Mpr*govntPolicy pool of annual funding available for S&E R&D Government Used to represent sudden large govntPolicy = 1 initially; event triggers Policy Change impacts on funding levels change in value based on fraction of

budget increases or decreases Annual Funding Current pool of total funds annual_Funding (t) = annual_Funding(t- ($, per 1000) available for university S&E dt) + (funds_in – expenditures)*dt R&D determined by the level of funds flowing in minus the expenditures flowing out Target workforce The desired number of targetWorkforce = (1- researchers needed to produce propNonHR)*annualFunding/annualSalary the required government funded research as determined by current funds available for HR expenditures and median annual researcher salaries Expenditures Total rate of expenditures of expenditures = annualSalary*Workforce government funding on research related human resources by universities Headcount gap Difference between the desired headcountGap = targetWorkforce - and current researcher Workforce workforce levels; assumes 1 headcount = 1 FTE (≥35 hrs/wk) Hire rate Rate at which hiring must occur hire_rate = replacements + headcountGap when research demand exceeds current available workforce Workforce Current researcher workforce Workforce(t) = Workforce(t-dt) + (|hire_rate – fire_rate| - quit_rate)*dt Fire rate Rate at which firing must occur fire_rate = |replacements - headcountGap| when research workforce level exceeds current demand Quit rate Rate at which researchers are quit_rate = Workforce/avgDurationEmpl leaving the workforce as a result of retirement and attrition Replacements Number of vacant research quit_rate positions resulting from retirements and attrition; based on

The gap analysis SD model structure is combined with the workforce inventory approach to allow consideration of Phillips’s multiplier accelerator theory (1954). This approach models the future state of the system based on current conditions then, considers the desired state of the system. Scenarios are designed and implemented in an effort to close the gap between the desired

66 states and those expected under current conditions. SD provides an effective way to achieve the objectives of the gap analysis quantitatively, while accounting for the complex relationships between different factors influencing the system behavior (Richardson and Pugh, 1981). The basic model was initially developed using Systems Thinking for Education and Research

(STELLA) software. Once completed, it was transitioned to AnyLogic (AnyLogic, 2017) for use within the full supply and demand model.

Figure 22. Academic science researcher workforce SD model in AnyLogic (AnyLogic, 2017)

The goal (targetWorkforce), a function of annual salary and annual expenditure levels, is compared to the current workforce to determine the discrepancy or adjustment needed to maintain equilibrium. As such, this SD model will eventually be used to inform a full supply and

67 demand model in Chapter 4 by representing the demand for additional researchers or the need for workforce reduction. The annual net job growth rate will determine the number of full time research job positions (FTEs) available each year, with each position being generated with random attributes, such as pay scale and qualifications.

Simulation results

Early in the process of developing a comprehensive framework for modeling academic science research workforce supply and demand, discussions with workforce development experts and NIH officials confirmed that the method of representing demand using simulated population dynamics is not appropriate. Unlike healthcare services, there are no accurate measures available to estimate the number of researchers relative to the population density in a particular region.

Moreover, there is not direct connection between the demand for academic science researcher and the size of a population within the particular geographic area where research is produced.

Instead, the influence of federal research grant funds flowing to universities was identified as the primary factor affecting the net job growth rate. Other factors commonly included in other existing demand models, such as retirement and attrition rates and researcher salaries, remain relevant.

This insight was useful in developing the model structure which uses the expenditures of federal funds by universities on human resources necessary to support scientific research production. This structure allows consideration of the effects of different variables in the annual net researcher job growth rate necessary to inform the hiring process represented in the full supply and demand model. As previously noted, government funding of scientific research has proven to be quite unstable over time which has well documented effects on the strength of the

68 academic science workforce (Hur, Ghaffarzadegan, et al., 2015; Larson et al., 2012; Teitelbaum,

2008, 2014).

While there are fluctuations in government research grant allocations from one budget cycle to the next, the overall trend has been one of growth. Arguably, the greatest impact on expansion and contraction of workforce demand in this system occurs when there are major funding increases or cuts, such as the doubling of NIH grants from 1998 through 2003 and stimulus funding from the ARRA. Estimating how these policy events influence workforce demand is the goal of this model.

Figure 23. Academic science research workforce SD model base run results

This simplified representation of the system is not intended to forecast or predict, but rather, to inform the hiring process in the agent based model (ABM) of the hiring process as to

69 whether or not there is demand for additional researchers. The output of the basic model is shown in Figure 23 with the major government funding cut of 20 percent resulting from a policy change occurring in model year 10. The impacts of the event are evident in the corresponding job growth rate (hire_fire rate), as well as in the corresponding change in annual funding and workforce levels. These results demonstrate that the model behavior effectively generates the estimated job growth rates associated with major policy events necessary to inform the ABM.

Discussion & Conclusion

The purpose of this academic science workforce demand model is to generate net job growth rates for use in the agent based model representing the hiring process developed in

Chapter 4 of this study. An extensive review of past supply and demand research yielded many existing dynamic models using SD methods to represent workforce demand. A majority of these studies focus on the healthcare sector and various professions within it and utilize a population driven model structure with estimates of demand based on how many patients are served by each worker. However, this approach was not appropriate for estimating the demand for academic science researchers.

A new demand model structure was developed that is based on what is considered to be a more accurate predictor of demand for doctoral researchers – higher education expenditures of government funds on producing research. Although the model boundaries are limited, and the factors influencing the system are simplified, the results it yields are considered to be sufficient for fulfilling the needs of this study. The model provides a basic foundation on which to build in conducting future research to answer many questions facing policy makers, university human resources managers and other stakeholders concerned with issues confronting the academic

70 science enterprise. With additional resources, the issues relating to lag time in the system, unstable government funding streams and the implications for scientific research productivity can be explored using extensions of this basic model. Moreover, the model can be calibrated using discipline specific data to consider the unique needs of various researcher groups.

In Chapter 4, the model is integrated with an ABM to create a hybrid model structure in order to fully consider the hiring process doctoral graduates and incumbent researchers experience in academe. The stock and flows representing the workforce component of the system are replaced with endogenous data generated by the ABM during simulation. The SD demand model is calibrated using region level data to represent the variations in government funding of research at top research universities throughout the U.S. This unique approach leads to insights into academic science research workforce outcomes that are useful for informing agency and institution level policy decisions.

Chapter 4: Exploring the United States Academic Science Research Workforce Through Dynamic Modeling

Introduction

This study explores the complex dynamics of scientific research workforce supply and demand through a combination of system dynamics (SD) and agent based modeling (ABM). It provides insights into the ways in which individual doctoral researcher characteristics interact with attributes of the demand for academic science research by region. The model examines how the career path decisions of these scientists combine with employer’s hiring decisions to affect their workforce outcomes. The conceptual model is applied to the behavioral and social sciences research (BSSR) segment of the science, technology, engineering and mathematics (STEM) workforce in the U.S.

The ABM is combined with economic theory at the individual level to examine supply and demand concepts. The model observes the number of PhD graduates who enter the BSSR workforce as well as their pursuit of research positions in academic institutions, comparing federally funded research within the behavioral and social science fields. Workforce demand is considered using an SD model representing federal government funding for scientific research and development at R15 doctoral universities. The use of ABM allows PhD job seekers to

5 Highest research activity designation in the Carnegie Classification of Institutions of Higher Education 72 develop emergent behaviors while the SD simulation controls the dynamics of the job market conditions. Employer hiring processes are also included in this component of the model.

Heterogeneity at the individual level is introduced into the model by assigning PhD job seekers’ characteristics, such as career goals and life events. Doctoral graduates who decide to enter the scientific workforce choose from three career options, including applying for an academic job, pursuing nonacademic jobs in government or industry, or remaining unemployed.

The ABM also considers each agent’s personal life including transitions into being married and having children. The demographic characteristics of the researchers initially populating the model come from the National Science Foundation’s Survey of Doctoral Recipients (NSF-SDR) data.

Factors influencing agent’s decisions include salary and alignment of the job with their field of study. These empirical factors are based on the results of my preliminary study conducted to better understand BSSR workforce employment trends using survey data from 1993 through 2013 (see Chapter 2). The model framework is developed using this information, as well as, publicly available data including U.S. Census data, NSF Higher Education Research and

Development (NSF-HERD) Survey data, Federal Funds for Research and Development (NSF-

FFRD) Survey data, and Survey of Federal Science and Engineering Support to Universities,

Colleges, and Nonprofit Institutions (NSF-Federal S&E Support Survey) and others. Various scenarios are simulated to represent how policy changes might impact the academic science workforce composition. The model results are visualized using an easy to understand graphical interface to aid in BSSR workforce related decision making.

Background

The behavioral and social science workforce has not been the subject of many studies compared to other STEM fields (Castaños-Lomnitz, 2006). There has been far more research focusing on physical and natural sciences such as engineering, chemistry, biology, and physics

(United States, 1988; Ghaffarzadegan et al., 2014; McGee, Saran, & Krulwich, 2012). There is an expressed interest in the health of the behavioral and social sciences research (BSSR) workforce as demonstrated by former President Obama’s Executive Order (Obama, 2015). The

Order recognized the need to “strengthen agency relationships with the research community to better use empirical findings from the behavioral sciences.” Federal agencies were directed to improve programs and policies by integrating behavioral insights that move beyond the assumption of rational decision-making to consider policy design and implementation in a more realistic context. Although the order has recently been rescinded by President Trump, a framework now exists for use in policy applications, ensuring a continued interest in using BSS research to improve government efficiency, program integrity and health outcomes.

The NIH has a history of recognizing the importance of BSSR to health related policies, and its contributions to their “…understanding of the basic underlying mechanisms and treatment of mental and physical health and illness” (NIH, 2015). The growing opiate addiction crisis offers a stark example of this with its reliance on the BSS workforce for helping to develop solutions that employ behavioral and social changes (Bershad, Miller, Norman, & de Wit, 2018).

A Harvard Business Review article provides evidence of the increasing demand for this workforce, as well, with a recent article titled “Why the U.S. government is embracing behavioral science” (Gino, 2015). The engagement of various BSS disciplines, including

74 behavioral decision research, psychology and behavioral economics, in efforts to address policy problems such as climate-change, medical decision making and tax payments are described.

This examination of the U.S. BSSR workforce provides insights into the distribution and characteristics of researchers employed in academic science research jobs. These attributes inform this analysis of how doctoral recipients in BSS related fields apply for and accept academic science research positions. Moreover, the workforce supply and demand dynamics provide a baseline from which to consider outcomes of alternative scenarios addressing pressing issues challenging the system at the individual and regional levels, including gender pay equity and research funding instability.

Governmental agencies providing research and development funding to higher education institutions are interested in better understanding how PhD scientists transition from graduate studies into the scientific research workforce. For example, the transitions from student to early career researcher positions have implications for the eventual career outcomes of researchers.

The observed differences between male and female researchers’ compensation when controlling for work hours, degree attainment level and field of study are helpful in gaining insights into a path to improve gender parity in the academic science workforce. The issues of a lack of gender diversity in senior academic science research positions and a gender wage gap are pervasive, and are reinforced by the fact that individual universities train and hire the future workforce using policies that have developed over many years.

Efforts have been underway for well over a decade to increase the number of women in the scientific research workforce by drafting policies believed to negatively influence gender equality. Significant progress has been made in attracting more females to graduate education programs resulting in increased graduation rates of women earning doctorates in BSS fields

(Ceci et al., 2015; Ginther & Kahn, 2006). Despite this progress, and an overall increase in the number of females employed as academic science researchers, the goals of gender pay equity and gender parity in advanced researcher positions have yet to be achieved.

Academic BSS research workforce supply and demand

There are evidence based studies of the academic science workforce suggesting that some early career scientists, though highly trained at the doctorate level, lack the characteristics that highly ranked research universities require to make them a good match for well-paying academic research jobs, including tenure track faculty positions. The key attributes of successful applicants are well understood, as well as, what trade-offs employers are willing to make between these attributes. Quantifying the demand for new researchers will make it easier to identify gaps in the supply (or training) of PhD scientists, give graduates a better understanding of what is expected when they enter the workforce and give employers insights into the expectations of graduates.

The conceptual outline of the demand and supply for the academic science workforce is shown in Figure 1. This diagram highlights the fact that gaining insights into workforce dynamics goes beyond a simple linear flow of PhD graduates to entry level scientists to retirement. Instead, accounting for how various external factors influence the workforce as a whole and the individuals within it, including temporal and geographic dynamics and feedbacks should be built into quantitative models.

This model investigates employment and academic researchers’ experiences with general work-life balance, life course phases, employer’s location, and entry or re-entry into the workforce. As well, the model examines when researchers leave the workforce to retire, change their career path, or for other reasons. The interface between new doctoral graduates entering the workforce and finding jobs is the initial focus of the model. Unemployed job seekers enter the

academic science research workforce and are hired based on whether or not their attributes align

with available jobs, as employers consider their qualifications, major field of study,

compensation, and other factors.

Career issues

Hours worked per scientist per year

Funding agency expectations Research Annual Production salary per Rate scientist Universities’ need for PhD scientists Unexpected Change needs per year University expenditures Universities’ on research scientific workforce HR capacity

Federal government expenditures on higher education research

Figure 24. Supply and demand for academic science researcher workforce.

A closer look at behavioral and social science researchers

The BSSR workforce in the U.S. is a segment of the STEM workforce that is growing in

importance.. Government agencies, as well as many sectors of business and industry, recognize

the critical need to better understand of how to influence key behavioral and social factors to

achieve desired outcomes. This capability is particularly relevant to efforts by government

agencies to design and implement policies and programs intended to address persistent wicked

problems, such as drug addiction, poverty and cancer.

Behavioral and social sciences encompasses scientific fields that focus on human behavior and decision making such as psychology, economics, political science, sociology, and anthropology. A recent study of trends observed in the BSSR workforce (Hur, Andalib, Maurer,

Hawley & Ghaffarzadegan, 2017) found that the lack of gender equity and racial diversity continues to pose challenges for scientists in these fields. The model developed in the current study is applied to this workforce to observe how these observed trends affect the career outcomes of doctoral researchers. A preliminary analysis of NSF-SDR data was necessary to obtain the calibration data used in this effort. The results of this analysis are included in Chapter

2 of this dissertation.

Gender diversity in the BSSR workforce

Since the middle of the twentieth century, the economic status of women has changed dramatically as a result of their increasing labor force participation. Women have surpassed men in their in their higher education attainment rates in recent decades, as well, with their rate of earning doctoral degrees achieving parity with that of men. Despite these significant shifts, women have yet to close the gender pay gap. This gap is measured by the ratio of female to male wages and is most often less than one. While the average pay for women has made progress toward equity with the income earned by men, it remained at 80 percent in 2017 (Semega,

Fontenot, & Kollar, 2017). The persistence of this issue has negative implications for not only the women affected, but for their employers and others who value gender diversity in their organizations.

The federal Equal Pay Act of 1963 required equal pay for equal work, regardless of gender. This law was enacted to require employers to pay employees equally for performing work requiring equal skills, responsibility and effort and is performed under similar work

78 conditions regardless of gender, except where the difference is justified based on permissible factors such as seniority. Title VII of the Civil Rights Act of 1964 reinforces this policy by prohibiting wage discrimination on the basis of race, color, national origin or sex. Since these laws took effect there has been progress made toward closing the gender pay gap, (defined as the difference between pay for men and women after controlling for such factors as education attainment, seniority, work hours and experience), but the gap persists. More recent policies intended to close the stubborn, remaining gap focus on requiring greater transparency in employers’ disclosure of wage data.

From a human capital perspective, which considers education and years on the job, women have increasingly gained equality with men. Unfortunately, these same gains have not occurred in the distribution of child rearing and household responsibilities. The underlying factors influencing the pay differential for men and women have been the subject of extensive research with various studies attributing it to the demands of motherhood and stopping out to have children, lack of ability to negotiate for pay increases, reduced work hours and selecting degree programs in nontechnical fields associated with lower overall salaries in the labor market.

A study conducted by Blau and Kahn (2013) finds that, when compared to 22 other OECD countries, the labor force participation rate of women dropped from sixth highest in 1990 to 17th in 2010. However, women in countries with expanded “family-friendly” policies were found to be less likely to have full time jobs and to work as managers or professionals, than women in the

U.S. (Blau & Kahn, 2013). Thus, when considering the labor market in general there may be unintended outcomes resulting from implementing policies such as increased parental leave time and flexible work arrangements.

Research conducted by Goldin concludes that closing the remaining gap will be achieved through changes in how employers structure jobs and pay to offset the additional costs associated with temporal flexibility (Goldin, 2015). For example, in cases where it is costly to employers when employees take family leave, job share or work flexible hours, they are less likely to compensate them at the same level as those without flexible arrangements. The feasibility of this approach hinges upon colleagues’ ability to substitute for each other and perform at the same level of quality and efficiency. However, this is not the case for many research related positions.

Often it is not possible to find another person with comparable knowledge, skills and abilities to step into a researcher’s unique role on a project and produce at the same level.

With regards to the disciplines that women have traditionally chosen, women employed in Science, Technology, Engineering and Math (STEM) jobs earn approximately 30 percent more than those in non-STEM positions (Beede et al., 2011). In fact, this STEM versus non-

STEM pay difference is not as big for men as it is for women. In addition to the importance of higher paying STEM opportunities for women, the U.S. will struggle to remain competitive in the global economy without attracting and retaining more women into the academic science workforce (Executive Office of the President, 2013).

The results of a preliminary analysis comparing the average annual salaries of full time academic BSS researchers completed in Chapter 2 provides historical data which confirms that the gender pay gap exists and varies by discipline and region. Significant differences between male and female salaries within the nonacademic sector (business and industry) were also observed, though women working in nonacademic jobs earned more than males and females employed in the academy. These findings, coupled with the results of an examination of the factors influencing BSS researchers’ decisions to migrate between academic and nonacademic

80 sector jobs, suggest the need to further investigate the eventual impact of lower pay for women on their success in academe.

The negative relationship found to exist between job satisfaction of female academic BSS researchers and their decisions to leave the academic sector for opportunities in business and industry is consistent with the findings of other empirical studies of women in academic science

(Ceci et al., 2014). These trends indicate that challenges exist in efforts to improve the attraction and retention of women in this workforce. Past empirical research provides evidence of the positive relationship between wages and job satisfaction (Clark, Georgellis, & Sanfey, 1997;

Hagedorn, 1996; Okpara, Squillace, & Erondu, 2005). Moreover, recent research that surveyed younger women in the millennial generation reports that pay is a primary reason why they leave organizations (Noël & Hunter Arscott, 2016). These findings suggest further negative consequences for attracting and retaining talent for employers who are not successful in efforts to implement effective strategies for eliminating the gender pay gap. This suggests the need for additional research to examine whether or not female BSS researchers earning salaries commensurate with their male counterparts will have greater job satisfaction and will be less likely to leave the academic science. The goal of policy makers it to reduce the overall migration rate of scientists leaving academe for nonacademic jobs would be reduced, especially those that increase the gender diversity of research teams.

NIH workforce modeling

NIH established a working group of the Advisory Committee to the Director in 2011 In an effort to address the challenges facing the scientific research workforce, tasking it with

“developing a model for a sustainable and diverse U.S. biomedical research workforce that can inform decisions about training the optimal number of people for the appropriate types of

81 positions that will advance science and promote health” (NIH, 2012, p. 7). A modeling subcommittee was created to build a comprehensive model of the scientific workforce. This group developed a conceptual framework (model) and recommendations for action needed to fill existing data collection for use in future development of a full dynamic model. This conceptual framework was adapted for use in this study as represented in Figure 2.

Figure 25. Conceptual framework adapted from NIH Biomedical Research Workforce Working Group report (NIH, 2012, p. 32).

The working group’s efforts led to the identification of many challenges relating to the success of early career researchers working in academic science, especially following dramatic funding fluctuations. These issues included a large outflow of women employed as postdoctoral researchers, longer training periods with increasing average age for NIH research program grants, lower average starting salaries for researchers (as compared to other fields) and older average median age (37 years) of starting in tenure track positions. All of these factors threaten 82 the attraction and retention of new graduates into the academic BSSR workforce.

Recommendations for addressing these problems included strategies such as providing benefits such as health insurance and maternity leave for early career researchers and encouraging universities to establish programs to provide skills training to PhD students to improve their transitions to nonacademic careers.

An academic BSSR workforce supply and demand framework

The hybrid, multi-level design used in this study is informed by a workforce supply and demand model framework developed for the Australian optometry workforce in which SD and

ABM structures were effectively combined to take advantage of both approaches (Flynn et al.,

2014; Osgood, 2007; Swinerd & McNaught, 2012). This model draws from both the adapted

NIH Biomedical Workforce Working Group and the optometry workforce model frameworks in an effort to provide a structure for investigating BSS researchers’ career choices and related outcomes. More specifically, I formulate transitions occurring in this workforce as a complex system, integrating both ABM and SD methodologies to capture individual level career decisions within an overall system.

In this study, a SD model generates annual average job opening estimates during each model year. This stylistic representation of the labor market demand informs the rate of job creation in the ABM component of the academic science research workforce system. NSF-

HERD Survey, NSF-FFRD Survey and NSF Federal S&E Support Survey data are used to identify historical trends in the system, recognizing the close relationship between federal government research grant funding and higher education expenditures of government funds on research, as shown in Figure 3.

70.0

60.0

50.0

40.0

30.0

2009 dollars) 2009 20.0

10.0

0.0

Source of Funds, (billions of constant (billions Funds, of Source

1972 1977 1982 1987 1992 1997 2002 2007 2012 Fiscal Year All R&D Federally funded Nonfederally funded

Figure 26. Higher education R&D expenditures, by source of funds and R&D field: FYs 1972– 2015. Source: NSF-HERD data.

As suggested by the trends over time for human resource related expenditures by R1 research universities shown in Figure 27, the rate of expenditures of federal funding on research project full-time equivalent positions (FTEs) provides an approximation of the net researcher job growth rate for use in the model. In fact, the level at which federal grant dollars are allocated to fund academic research at universities has direct implications for the sustainability of this workforce. Overall, the federal funding trends are upward with slightly decreasing rates since

2010, though there have been periods of major fluctuations resulting in instability in the academic BSSR workforce system.

250000

200000

150000

expenditures, ($K) expenditures, 100000

R1 universities R&D R&D headcountuniversities R1 50000 2010 2011 2012 2013 2014 2015 Fiscal Year

Northeast Midwest South West

Figure 27. R1 universities’ federally funded research related human resource expenditures, by region: FYs 2010-2015. Source: NSF-HERD data.

Given the complexity of the workforce dynamics and the challenges of understanding the heterogeneity of its scientists, this hybrid model combines observed NSF Survey data and other public data sources, to simulate both the choices of scientists and their employers. In particular, this study aims to contribute to the existing literature by providing additional insights into how the academic BSSR workforce structure, and researcher and employer job search and match behavior, interact.

Methods

Several methodologies are combined in this study to represent the interactions of region level workforce demand and individual level career path dynamics. System dynamics is a dynamic modeling method used to understand complex systems (Sterman, 2000). In this

85 approach, systems are represented as stocks, flows and feedback loops to represent how they change over time, including delays, accumulations, and non-linear attractions (Forrester, 1968;

Sterman, 2000). SD has been used effectively to gain insights into the healthcare workforce, as discussed in Chapter 3 (Ghaffarzadegan et al., 2014a; Tomblin Murphy et al., 2009).

SD models are used here to consider system structures at an aggregate level, while an

ABM is used to focus on the individual (agent) level behaviors and characteristics. ABM allows the model to capture the individual dynamics of the individual preferences, heterogeneity, and career related decisions (Bruch & Mare, 2006) of PhD graduates within the larger population, including their adaptive behaviors (Axelrod, 1997). Agents exist in various states in the model with rules governing their behavior and how they move between states, which can change temporally or spatially.

The SD and ABM workforce supply and demand dynamics are combined with concepts from behavioral economics, to analyze individual choices and preferences based on underlying researcher and employer attributes. Narrative process models that unfold on a regional level over time and space are constructed in a manner consistent with the adapted model framework (Flynn et al., 2014). The conceptual model explores the feasibility of linking workforce supply and demand behavior with individual career decisions. The various levels interact to represent the complex academic BSSR workforce system. Various scenarios are examined in experiments designed to evaluate their effects on the academic science workforce system performance in each region.

Job matching with employer willingness-to-pay versus job seeker willingness-to-accept

This study focuses on identifying the features (attributes) of new PhD graduates seeking academic employment, as well as researchers employed in the academic science workforce, that are valued by potential employers and how much they are valued. The job match model used is assumed to be additive such that the utility an employer derives from a particular applicant is a simple weighted sum of the attributes of the job candidate. An employer is assumed to gain utility from an applicant’s attributes; different amounts of each applicant’s characteristics provide different utilities to the employer. Conversely, job seekers determine the utility of job opportunities based on each vacancy’s attributes. In both cases, weights are applied to the attributes to inform willingness-to-accept and willingness-to-pay estimates which, in turn, inform agents’ decisions to offer and accept jobs. These weights are based on actual (revealed) choices reflected in the NSF-SDR data. The results of the preliminary analysis conducted to calculate these weights are provided in Appendix A.

Hybrid Dynamic Modeling

ABM and SD modeling methodologies are combined in this hybrid model of both the supply and demand components of the academic BSSR workforce. This approach has been used effectively in developing other workforce supply and demand models (Flynn et al., 2014).

AnyLogic (AnyLogic, 2017) modeling software is used to build this model and provides an integrated environment allowing for the combination of multiple techniques in a single model.

Discrete time is used to calibrate the probabilistics used with the ABM, and to generate the individual preferences informing the BSS researchers’ career related and life course decisions.

NSF-SDR data are used to generate the individual preferences informing the BSS researchers’

87 employment decisions. The hybrid model is described here using a standard protocol (Grimm et al., 2006).

Purpose

The aim of this hybrid model is to represent individual BSS researchers’ choices over the course of their careers in our analysis of federally funded academic research based estimates of demand for BSS PhD scientists at top research universities in four U.S. regions. This effort explores whether trends and changes in the academic science workforce can be understood by modeling individual level choice processes. In particular, it considers various factors that influence BSS scientists’ career related decisions, as well as the implications of their decisions for the entire BSS research profession.

State Variables and Scales

Three hierarchical levels are included in the model ( BSS Researcher, U.S. regions and overall U.S. BSSR workforce). BSS researchers (Agent) are assigned socio-demographic variables (Figure 28): identity number, age, region where they live and work, gender, field of study, employment status (unemployed or employed), and marital and parental status. Additional variables related to their career choice decision preferences are also assigned.

Figure 28. Researcher’s professional and personal life state charts.

Each U.S. Region (Agent) has a dynamic level of funding for higher education R&D from the federal government and other sources, a list of BSS researchers that are graduating from doctoral programs or working in the Region, and a listing of vacant or filled federally funded research jobs. The workforce dynamics embedded in each Region are captured using an SD model (Figure 29). The SD model in each Region provides approximate estimates of the BSSR workforce demand. The existing BSS researchers in the Region interact with Region to fill open job positions annually. Region job market dynamics are represented in the model in a stylized manner. Vacant jobs are simply represented according to the flow of research funding into the universities and are created with random attributes, including pay scale and discipline.

Figure 29. Annual federally funded research job growth in the U.S.

The population is comprised of four regions of the U.S. and is represented by characteristics which include the number of academic science researchers, the number of enrolled doctoral students and the number of jobs. The geographic location of the regions considered through the current model does not include the concept of distance. As well, a researcher’s location is not included other than generally connecting them to their associated jobs and populations within a specific region.

Process Overview and Scheduling

The model uses a continuous-time abstraction, (meaning there are an infinite number of points in time between any two points in time), based on calendar time. Events and model time operate at a discrete level for the ABM and the SD model and. as such, events are evaluated in discrete time, (i.e., values of variables are viewed as occurring at distinct, separate “point in time”). Certain types of events such as statistics calculations are evaluated at once at the end of

90 each year. The temporal resolution of the ABM component is about one week. The model’s start time is July 1, 2008 and the duration varies. The model’s scope includes graduates of BSS doctoral programs from the time they enter graduate school until they retire or decide to leave the academic science workforce (Figure 28). This scope is sustained regardless of the reason for leaving the workforce (i.e., retirement, career change to a different profession, or decision not to actively seek work). The scope also does not include BSS researchers who emigrate from another country. Agents enter the model in their first year of graduate school and are assigned a set of randomly generated preferences and attributes.

Design Concepts

The model uses the natural alignment of the individual level capabilities of ABM and economic theory to represent details at the individual level to address concepts of supply and demand dynamics. This approach allows agents to be more complex and adaptive, and to exhibit a contextually adaptive responses and emergent behaviors (Flynn et al., 2014). Moreover, its ability to capture interagent interactions lends itself to representing supply and demand dynamics in which agents compete for limited resources, including doctoral programs and academic research jobs at top research universities. ABM is also capable of capturing interactions between agents within a population, making it a useful approach for considering workforce dynamics with competition for limited jobs. Given the heterogeneity and diversity of the academic BSSR workforce, and the need to consider individual preferences of both job seekers and employers,

ABM is an appropriate method to gain the desired insights. Agents’ decisions evolve with the

ABM’s ability to inform their choices with their individual histories, which also allows learning effects and policy options to be represented.

State charts are used to graphically depict related states that agents may exist in, with transitions between them governed by triggered in response to events occurring at regularly scheduled intervals (Fowler, 2004). For example, these events may be in response to messages the agent receives or fixed hazard rates. Agents occupy only one state within a state chart at any time. This state is known as the active state. Agents may have multiple state charts, if necessary, and these are independent from states and transitions in other state charts. Dimensions of an agent’s heterogeneity may be characterized by a particular state chart, with added dimensions represented across state charts. Nested state charts may be located within states, such as an agent being within the workforce (in workforce) state, and then within a sub state reflecting employment status.

Figure 28 shows the researcher’s state chart. Transitions between state charts are triggered by messages received or events occurring at regularly scheduled intervals. Decisions are made at regularly scheduled intervals coinciding with life events such as choosing to take a job, leave a job, retire, have a child, etc. Factors influencing decisions include salary and alignment with agents’ program of study. Mincer wage bargaining is used to characterize the hiring decisions of employer agents in the ABM model. This allows each agent’s decision criteria and choices to be parameterized using data from the NSF-SDR (see Chapter 2 and appendices for further details).

Agent decision outcomes are probabilistically determined by their characteristics and available options, with decisions as to which branch is followed (with contingent transition) depending upon them. Decision points are considered as nodes with connections between the nodes (edges) leading back to the state from which transitions originate. These edges represent a decision outcome of either “no action” or a delay in making a decision. The probability of

92 choosing a decision outcome is primarily computed using the available NSF-SDR data, with supplemental data estimated from related empirical research, to determine the probability of choosing outcome A. Individual agent choices are based on the likelihood that they will select a particular alternative based upon probabilities observed in available data. These concepts are examined by observing the number of PhD graduates who enter the workforce as well as their pursuit of research positions in the academic sector. Some individuals self-select out of the model by choosing either nonacademic sector jobs or not to seek employment.

BSS doctorate recipients who choose to enter the academic BSSR workforce evaluate their career options. The three options available to them in the model representing their professional life include applying for a job with an academic institution, applying for a job in the nonacademic sector (government or industry), or remaining unemployed. Factors included to govern their decisions include the anticipated income from a particular job and if the research aligns with their field of study.

A score measured in willingness-to-accept (WTA) is calculated for each job applicant using their individual linear combination of the relevant K attributes for available doctoral researcher positions. WTA is calculated using a Mincer equation, which determines the natural log of the market wage for each researcher based on a quadratic formula considering years of experience, education attainment level and other control variables. The Mincer “human capital earnings equation” determines the natural log of earnings and is modeled as:

Equation 2. Mincer human capital earnings equation used to calculate WTP and WTA values.

2 ln 푦 = 훽0 + 훽1푆 + 훽2퐸 + 훽3퐸 + 푋′훽4.

Y is the natural log of wages, S is education level, E is work experience and X is a vector of control variables. An ordinary least squares (OLS) estimation is used to estimate the beta values for each variable, which then become the marginal benefit of each variable and is used to

93 compute WTA (predicted salary) values for applicants. As well, the marginal benefits for available jobs are calculated in the same manner to compute willingness-to-pay (WTP) values for each vacancy (see tables in Appendix A for OLS estimates used in Mincer earnings calculations).

The interface between the SD demand model that simulates job growth and the ABM that represents academic BSSR workforce supply is represented within the hiring and firing processes. Within the SD model, the difference between the headcount gap and the current number of job vacancies (posting) is evaluated. If this value is greater than zero, and there are employed (working) researchers available to promote to supervising (hiring) positions, then a random researcher agent with more than six years of experience receives a message triggering their transition to the hiring state. Once in this state, they post the available job triggering the search and match sub model. Unemployed researchers (applicants) are each assigned WTA values and are available to fill all academic researcher vacancies in their field.

The employers’ job search process is initiated when job vacancies exist and is continuously iterates through the available employed (working) BSS researcher agents to select hiring supervisors with jobs to post. Each job is assigned a WTP value which is then used in the hiring process. The WTA values of all BSS researchers in the unemployed state (applicants) that are at or below the job’s assigned WTP value are considered and the candidate with the lowest

WTA (most qualified) receives an offer. Job seekers accept offers that are within ten percent of their reservation wage, considered to be a function of their WTA value which reflects their skills and qualifications as doctoral recipients.

The hiring process continues until the difference between the total number of hiring and employed researchers is greater than the headcount gap, as determined by the SD demand model.

The headcount gap is defined as the “target workforce,” or number of researchers required to meet the capacity for research production required by the funding received, minus the current number of employed researchers. The headcount gap value is essentially the job growth rate and indicates whether or not the current available job vacancies have all been filled or if hiring needs to occur.

The firing process begins when the headcount gap becomes negative as a result of a reduction in available research funding. Employed researchers with less than six years of experience are at greatest risk of being fired. This reflects the likelihood that early career scientists are less likely to hold tenured faculty positions, or non-faculty positions having less job security. This process ends when the headcount gap, (or difference between the target workforce and current number of employed BSS researchers), in the SD model returns to zero which signals that supply and demand have reached equilibrium.

The process models resulting from this effort to join the ability of the ABM to represent the individual choices of employers and researchers using Mincer wage values to analyze the wage bargaining underlying each match with the SD’s capacity to capture the aggregate system structure by accounting for regional level dynamics over space and time. The model permits the design and testing of virtual experiments representing the outcomes of various scenarios at both the institution and region levels, linking them to the academic BSSR workforce system performance. Figure 30 shows the main level visualization of the model simulation.

Figure 30. Visualization of U.S. academic BSSR workforce simulation model interface.

Initialization and Input

Demographics (such as age and gender) of the scientific researchers’ initially populating the model at model start up, as well as the attributes of those flowing in as time progresses, are primarily informed by analysis of NSF-SDR data. The results of preliminary analysis conducted to better understand the BSSR workforce trends, and the career choices made by PhD scientists, are shared in Chapter 2. These findings inform the proportion of BSS researchers in each employment state at model start up. These empirical factors are also informed by the results of a

BSSR workforce study conducted in 2015 (Hur, Andalib, et al., 2015).

For the current version of the model, agents:

. Use the same fundamental decision rule . Do not learn at either a collective or individual level . Do not belong to any social networks or collectives . Have complete information regarding their existing options, including what the vacant jobs in their region are and their particular characteristics . Do not make predictions regarding further options that may become available . Do not interact directly with other agents. When a vacant position is filled, it is no longer available to other job seekers (agents) . Have fixed Mincer equation coefficients determined by attributes that do not change over time, though their coefficients may evolve based upon their changes in their status (such as getting married) Data

Several data sources were used to develop and calibrate the current model. The dataset contains data for various components and stages of model development including creating the simulated population of doctoral students and employed researchers, region level academic science research labor market net job growth rates and various data tables and estimated parameter values.

Simulated Agent Characteristics

Several data sources were combined to determine values for various agent characteristics used in the simulation. The three types of agents in the model were defined by several factors. Table 5 lists these agents and their characteristics with corresponding data sources. Tables located in

Appendix B provide full details of the variables and parameters used in the ABM.

Table 5. Agent types with their simulated characteristics and data sources.

Agent Type Characteristics Data Source BSS Researcher Demographic factors, including: NSF-SDR age, gender, race, Marital and parental status Region-level location Employment status Sector of employment (academic or nonacademic) Field of study Employment outcomes (annual salary) CGS, IES-NCES, Digest of PhD graduation rate Education Statistics, Doctoral Recipients from U.S. Universities Region Federal funding academic science NSF Federal S&E Support research (BSS fields at top research Survey, NSF -FFRD universities) Average annual growth rate of federal obligations for research, by field Number of BSS researchers per region Region level median BSS researcher NSF-SDR salaries, by field and gender Number of BSS students enrolled per region CGS. IES-NCES Number of R1 universities per region Carnegie Classification of Institutions of Higher Education Main Fertility rate by ages CDC - NCHS Marriage rate United States Census Bureau Willingness-to-pay NSF – SDR used to determine Mincer coefficients (Table 7) Willingness-to-accept NSF – SDR used to determine Mincer coefficients (Table 8) NSF, National Science Foundation SDR, Survey of Doctoral Recipients NCSES, National Center for Science and Engineering Statistics FFRD, Survey of Federal Funds for Research and Development Federal S&E Support Survey, Survey of Federal Science and Engineering Support to Universities, Colleges, and Nonprofit Institutions CGS, Council of Graduate Schools IES- NCES, Institute of Education Sciences, National Center for Education Statistics CDC – NCHS, Centers for Disease Control and Prevention – National Center for Health Statistics

BSS Researcher and Regional factors

NSF-SDR data from 1993 through 2013 were analyzed to determine the employment trends of scientists employed as researchers in the BSSR workforce. This dataset is a nationally representative sample of the population of individuals earning research doctoral degrees and living in the U.S. The data has been collected biennially since 1973 and is a longitudinal survey designed to provide career history and demographic information about individuals with doctoral degrees. It includes doctorate degree holders in the sciences, including social science, who are under the age of 76.

NSF-HERD Survey data are used to calibrate the baseline system dynamics in the model.

NSF-HERD is an annual survey that includes all institutions of higher education in the United

States, Guam, Puerto Rico, and the U.S. Virgin Islands that grant a bachelor’s or higher degree in any academic discipline and had at least $150,000 in separately budgeted research and development (R&D) expenditures during the institution’s current fiscal year. The NSF-HERD

Survey gathers current fund expenditures for separately budgeted R&D, with total R&D expenditures now including science and engineering (S&E) fields and non-S&E fields. Before

2010, NSF contacted institutions that met the degree-granting criterion but were not in the population of the previous census to determine whether they met the $150,000 expenditure criterion. Since FY 2011, the review of the population of 4-year degree-granting institutions has been completed before survey collection. 906 institutions were in the Survey population in FY

2015, an increase from 889 in FY 2014.

The SD model calibration also utilized data from NSF’s Federal S&E Support and FFRD

Surveys. The Federal S&E Support Survey is an annual congressionally mandated survey and is the only source of data for institution level federal S&E funding to colleges and universities. Key

99 variables include type of institution, federal agency providing support, state, academic institution characteristics and other related information. The NSF-FFRD Survey is an annual survey and includes all federal agencies that conduct R&D programs, with the exception of the Central

Intelligence Agency (CIA). The Survey is the primary source of information about federal funding for R&D in the U.S. Federal obligations related key variables include agency, field of science and engineering, geographic location, type of organization completing the work, type of

R&D (basic, applied, or development) and others.

Additionally, publicly available data sources including United States Census, Centers for

Disease Control and Prevention, National Center for Education Statistics and Integrated Public

Education Data System are used to inform this model. Table B1 contains a complete listing of the data sources used to parameterize and calibrate the ABM while Table B2 displays the parameters and exogenous variables used in the SD model.

Calibrating the SD model with NSF Federal S&E Support Survey data

The SD component of the hybrid model was calibrated with NSF Federal S&E Support

Survey data. Table 6 shows data region level data for the top research universities located within each of the four geographic regions of the U.S.

Table 6. Federal obligations for S&E R&D to top research universities, by region: FY 2008-2016 ($100K)

2008 2009a 2010a 2011 2012 2013 2014 2015 2016 Northeast 50577 62524 62189 55932 55490 51635 62388 62699 65436 Midwest 50805 62372 61918 56269 539901 49841 118968 112677 119949 South 68616 87218 87762 80210 82288 76016 51458 51998 52292

West 53976 66234 68466 61142 61174 59151 19094 19025 20031 a Includes American Recovery and Reinvestment Act of 2009 obligations. Source: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Federal Science and Engineering Support to Universities, Colleges, and Nonprofit Institutions.

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Simulation results

Baseline model behavior

Initial ABM and SD parameters reflecting current data generate baseline views of the

U.S. BSSR workforce pipeline, as well as views for each region. The model is quite successful in

replicating the historical data trends, as shown in the simulation results in Figures 31a-e. In each

figure, the simulation outputs for the number of BSS researchers employed by R1 universities at

both the national and regional levels follow the observed trends closely with the historical data

points being represented as continuous dashed lines. The alignment results achieved between

simulation output and historical data in the West region is least accurate, and will be further

evaluated in the future.

a. United States 6000

4000

2000

1:100 0 2008 2009 2010 2011 2012 2013

Year Employed Researchers, Employed

Calibrated Historical

Figure 31 a-e. Baseline model outputs of academic BSS researcher employment trends for R1 universities in the U.S. and its regions.

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b. Northeast c. Midwest 2000 2000

1500 1500

1000 1000 1:100 500 1:100 500

0 0

2008 2009 2010 2011 2012 2013 2008 2009 2010 2011 2012 2013 Employed Employed BSS Researchers, Year Employed BSS Researchers, Year

Calibrated Data Calibrated Data

d. South e. West 2000 2000

1500 1500

1000 1000

500 1:100 500 1:100

0 0 2008 2009 2010 2011 2012 2013 2008 2009 2010 2011 2012 2013

Year Researchers, Employed BSS Employed Employed BSS Researchers, Year Calibrated Data Calibrated Data

Figures 32a-d depict simulation results for the target number of full time BSS researcher

positions (FTEs) required to fulfill the production demand for funded research, the (headcount)

gap between the current FTEs and the target FTEs, and human resources (HR) expenditures on

BSSR salaries in each region. The figures show that the target FTE levels, FTE gap and HR

expenditure trends are consistent with the annual changes in federal funding obligations for BSS

research at R1 universities in each region. The ideal supply and demand balance exists when

headcount gap values are at or near zero. When comparing regional differences it is evident that

when the annual federal research funding level changes in a given region there is a

corresponding change in the headcount gap after a time lag of approximately one year. This

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represents the amount of time necessary to expand or scale back capacity for research production

after major funding fluctuations. This is consistent with the time lags known to exist when churn

occurs in workforce supply and demand. Although the additional costs of turnover resulting from

this churn are not considered here, it is assumed that reductions in headcount gap values result in

reduced replacement, training and lost productivity costs.

a. Northeast b. Midwest

150000 5000 150000 5000

4000 4000 100000 3000 100000 3000 2000 2000

50000 1000 50000 1000 FTEs, FTEs, 1:100 0 0 FTEs, 1:100 0 -1000 0 -1000 2009 2011 2013 2015 2009 2011 2013 2015

Year Year Funding/Expenditures, Funding/Expenditures, $K Funding/Expenditures, Funding/Expenditures, $K Federal Funding HE Exp Federal Funding HE Exp Target Workforce Headcount Gap Target Workforce Headcount Gap

c. South d. West

150000 5000

4000 4000 100000 3000 100000 3000 2000 2000 50000 1000

50000 1000 FTEs, FTEs, 1:100

0 FTEs, 1:100 0 -1000 0 2009 2011 2013 2015 0 -1000 2009 2011 2013 2015

Figure 32a-d. Baseline model outputs of federal funding, target FTEs, (headcount) FTE gap and HR expenditure rates.

The visualizations of the ABM output representing the behavioral patterns of BSS

researchers as they progress through their careers are shown at both the national and regional

levels in Figures 33a-e. While the overall U.S. trends in these employment outcomes is relatively

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stable, the observed between region differences indicate that researcher migration occurs from

regions with reduced demand to regions where more opportunities exist, as expected. The

baseline simulates the historical patterns of federal government funding and the associated rate

of academic research HR expenditures, as well as the resulting effects on workforce demand (net

job growth rate) that triggers the hiring or firing processes in the ABM. These results serve as

evidence of the face validity of the model, though more work can be done with additional data in

the future.

a. United States 7000 6000 5000 4000

3000

2000

1:100 1000 0 0 20 40 60 80 100

Months BSS Students/Researchers, BSS Students/Researchers, Academic BSSRs Enrolled Students Unemployed BSSRs

b. Northeast c. Midwest 3000 3000 2500 2000 2000

1500

1000 1000

500

1:100 1:100 0 0 0 50 100 0 50 100

Months Months BSS Students/Researchers, BSS Students/Researchers, Enrolled Students Unemployed BSSRs Enrolled Students Unemployed BSSRs Academic BSSRs Academic BSSRs

Figure 33 a-e. Baseline model outputs of enrolled BSS PhD students and unemployed, employed and on leave BSS researchers.

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d. South e. West

, , 3000 2000 2500 2000 1500 1000 1000 500 0 0 50 100 0

1:100 Months 0 50 100 1:100

Students/Researchers Months

BSS Enrolled Students Unemployed BSSRs

Enrolled Students Unemployed BSSRs BSS Students/Researchers, BSS Academic BSSRs Academic BSSRs

Policy Experiments

The model framework developed here may be used to simulate policy scenarios to examine the model’s overall response to changes made to reflect interventions. The first experiment explores a counterfactual scenario to answer “what if” questions about the possible outcomes associated with an alternative approach to addressing the gender wage gap issue. The second experiment simulates the difference in workforce outcomes when an alternative government research funding policy is implemented The model parameters are modified to reflect changes in variables concerning these policies that decision-makers within government can implement.

Gender pay gap policy

Policymakers have been concerned with the issue of gender wage gap for decades spurring an extensive body of labor economics based research (Kunze, 2008). Typically, these studies are based on the human capital model (Becker, 1975) and often use the Mincer6 wage regression model to measure the gender wage gap. Most are based on an assumption that key

6 Mincer developed the original empirical model (Mincer, 1974) which is based on a life-cycle earnings model. Age is used to measure an individual’s work experience and years of education through high school are included. The model was later expanded to consider the work histories of women stopping out for child bearing and rearing (Mincer & Polacheck, 1974). 105 variables are not correlated with unobserved heterogeneity components and that the factors included in the wage growth models are exogenous. Discrimination is often assumed to exist when interpreting the unexplained portion of wage gap outcomes. As well, key parameters in the wage regression model are difficult to estimate given unobserved heterogeneity, which leads to inaccurate results. The model framework developed in this study uses the ABM to represent the individual heterogeneity, including time-out-of-work and actual work experience, which addresses some of the limitations inherent in more traditional gender wage gap models.

This scenario explores the effects of policy interventions, such as the Lilly Ledbetter Fair

Pay Act (2009), that require employers to be accountable for their compensation practices with increased reporting requirements. The increased transparency is presumed to reduce the likelihood that discrimination will occur, and will lead to a reduction in the gender wage gap. To model the effects of this policy, a gender wage gap factor that exists in the baseline model is reduced to zero. The differences between the alternative policy simulation output is compared to the baseline output to observe how the behavior of average salary trends change over time.

Outcomes related to average annual salaries for BSS researchers, by gender, are examined in Figures 33 a-h. Despite the consistently lower average annual salary levels in the experiment, there are significant increases in female salaries compared to the base case as the new policy is implemented and equal pay practices are improved. In the counterfactual run, there is a commensurate reduction in the average annual salaries of male BSS researchers due to the unchanged demand for research production and the limited resources available to achieve it. Also of interest is the more balanced positive effect on salaries for both genders when a major increase in funding occurs in the Midwest region. In the calibrated run, this positive outcome was observed only for male salaries.

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a. Northeast 100

gender ($K) 70

60 AverageAnnual Salary,by 0 10 20 30 40 50 60 70 80 90 100 Months Calibrated Run Avg Salary Calibrated Run Male Salary Calibrated Run Female Salary Fair Wage Avg Salary

b. Midwest 100 Funding increase

70 gender ($K)

AverageAnnual Salary,by 0 10 20 30 40 50 60 70 80 90 100 Months Calibrated Run Avg Salary Calibrated Run Male Salary Calibrated Run Female Salary Fair Wage Avg Salary Fair Wage Male Salary Fair Wage Female Salary c. South 100

70 gender ($K)

Figure 34 a-d. Gender pay gap reduction policy: Average annual salary by gender.

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d. West 100 90

80 70 60

gender ($K) 0 10 20 30 40 50 60 70 80 90 100 Months

AverageAnnual Salary,by Calibrated Run Avg Salary Calibrated Run Male Salary Calibrated Run Female Salary Fair Wage Avg Salary Fair Wage Avg Male Salary Fair Wage Avg Female Salary

Alternative funding policy simulation

The model currently considers all federal agency funding obligations for higher education research collectively. Job creation rates are adjusted by region to simulate the academic BSS research workforce impacts resulting from fluctuations in annual funding levels. The baseline model results, which were calibrated with data that include the ARRA funding period with large funding increases and subsequent decreases, indicate that there are negative implications for workforce outcomes when large oscillations occur.

This application simulates the effects of a policy that holds funding steady over time and compares the resulting workforce outcomes to those of the baseline case where outcome trends are based on actual funding levels. This experiment allows a closer examination of the differences in employment, headcount gap levels, hiring rate and firing rate trends in a stabilized funding environment. The outcomes are plotted together by region along with the calibrated run.

Figure 34 a-d displays the number of employed researchers for each region, as well as the annual federal funding trends. For all regions, the new policy had the intended stabilizing effect on the number of employed researchers. The observed change suggests that flat or slightly increasing funding growth rates foster a smooth employment trend.

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a. West

80000 1400 1200 60000 1000 40000 800 600 20000 400 200 0 0 0 10 20 30 40 50 60 70 Annual Annual Federal($K)Funding, Months Calibrated Fed Funding Flat Fed Funding Calibrated Employed BSSRs Flat Fund Employed BSSRs

b. Midwest 150000 2500

2000

100000

1500

($K) 1000 50000 (FTE) 500

Annual FederalFunding, 0 0 0 10 20 30 40 50 60 70 Employed BSS Researchers, Months Calibrated Fed Funding Flat Fed Funding Calibrated Employed BSSRs Flat Fund Employed BSSRs

Figure 35 a-d. Alternative federal research funding policy: Employed BSS researchers by region.

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c. Northeast 80000 1500 1400 60000

1300

40000 1200

($K) 1100

20000 (FTE) 1000

Annual FederalFunding, 0 900 0 10 20 30 40 50 60 70 Months Calibrated Fed Funding Flat Fed Funding Employed BSS Researchers, Calibrated Employed BSSRs Flat Fund Employed BSSRs

d. South

120000 4000 100000 3500 3000 80000 2500

60000 2000 40000 1500

1000 (FTE) 20000 500 0 0 0 10 20 30 40 50 60 70

Months Employed BSS Researchers,

Annual FederalFunding, ($K) Calibrated Fed Funding Flat Fed Funding Calibrated Employed BSSRs Flat Fund Employed BSSRs

Additional evidence of this policy’s effectiveness at maintaining employment stability in the academic BSS research workforce is shown in Figures 35 a-d. While the calibrated trends clearly demonstrate the disruptive impact of major funding increases and decreases on the hiring and firing rates in each region, the alternative funding policy reduces the volatility in the system. The same is true of the headcount gap that measures the difference between the target number of full time equivalent research positions required to produce the amount of funded research and the current number of working researchers (FTEs).

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a. West

2000 19

1000 14

0 9

-1000 4 Headcount, (FTE) -2000 -1 0 10 20 30 Months 40 50 60 70 Flat Fund HR Calibrated HC Gap Flat Fund HC Gap Hire/Fire Rate,(FTE/month) Calibrated HR Calibrated FR Flat Fund FR

b. Midwest

6000 15

5000 4000 10 3000 5 2000

1000 0

0 Headcount Gap, (FTE) -1000 -5

0 10 20 30 40 50 60 70 Hire/Fire Rate,(FTE/month) Months Calibrated HC Gap Flat Fund HC Gap Calibrated HR Flat Fund HR Calibrated FR Flat Fund FR

c. Northeast

1000 1

800 0.5 600 400 0 200 -0.5

0 Headcount Gap, (FTE)

-200 -1 Hire/Fire Rate,(FTE/month) 0 10 20 30 Months40 50 60 70 Calibrated HR Calibrated HC Gap Flat Fund HC Gap Flat Fund HR Calibrated FR Flat Fund FR

Figure 36 a-d. Alternative federal research funding policy: Headcount gap (FTE), hiring rate (FTE/month) and firing rate (FTE/month) by region.

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d. South

2000 20 1500 1000 15 500 0 10 -500 -1000 5

-1500 Headcount Gap, (FTE) -2000 0 0 10 20 30 40 50 60 70

Months Hire/Fire Rate,(FTE/month) Calibrated HC Gap Flat Fund HC Gap Calibrated HR Flat Fund HR Calibrated FR Flat Fund FR

Past research has shown that there are unintended negative outcomes when major federal academic research funding fluctuations occur (Larson et al., 2012; Teitelbaum, 2008). It is also recognized that steadily decreasing expenditures on academic research, and flat or declining federal academic research expenditure rates that fail to account for the effects of inflation, are of concern since they may also have adverse effects on the strength of the academic science research workforce. Unmeasured costs that are incurred with large changes in funding levels include loss of productivity and talent when layoffs are necessary, as well as the expense associated with efforts to attract, hire and train talent when hiring is required to build capacity.

Moreover, as discussed previously, there are consequences within the graduate education pipeline when students base their decisions on which major fields of study to pursue, and whether or not there will be a return on their investment upon earning a doctorate degree. This model considers the connectivity between the macro level dynamics of the system, and the individual level dynamics influencing the supply of researchers. This achieves a comprehensive framework used to examine the implications of this policy approach for transforming the overall behavior of the workforce system in a positive direction by improving stability.

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Conclusion and Future Work The combination of dynamic modeling approaches, SD and ABM, combined with economic theory shows promise as a useful tool for expanding our current understanding of

BSSR workforce dynamics. The combination of these approaches used in this model provides a proof of concept and allows the examination of trends in the academic science research workforce at both individual and region levels to better inform future policy planning. The model could be further enhanced with the analysis of additional administrative data to more accurately capture agents’ decisions and the associated outcomes. The availability of additional university administrative data would also permit greater job related details, including availability and type

(such as, post doctorate, staff and tenured and non-tenured faculty positions).

Supply dynamics, including “cobweb” effects, that result when students entering the higher education pipeline select their field of study and base their career goals on the strength of the current job market may be considered with future extensions of the model (Andalib et al.,

2018; Blume-Kohout & Clack, 2013). These effects are realized years later when the enrolled cohorts eventually graduate, resulting in a lagged reduction in workforce supply that is often out of sync with current demand. Additional policy scenarios will be explored to determine their ability to smooth such supply and demand fluctuations.

The current version of the model over simplifies the supply and demand dynamics of job creation and will be improved with more explicit representation of employers. The SD model driving job creation dynamics will be expanded to consider other macroeconomic influences effecting higher education expenditures on research related human resources. As additional resources become available, more extensive model validation and sensitivity analysis will be completed in an effort to fully develop the current conceptual model framework.

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Chapter 5: Discussion

This chapter includes three sections. The first section includes a summary of findings from Chapters 2 through 4, with each essay investigating a different component of the academic science workforce system. In the second section, I discuss the dissertation’s conclusions and implications for research and practice. In the third and final section, I identify the limitations of my research and overview future research opportunities for scholars based on my findings.

Summary of findings

This dissertation seeks to contribute to knowledge gained from past academic science workforce supply and demand research by identifying demographic trends and employment outcomes for doctoral level behavioral and social science researchers. A simulation model framework is developed to better understand the hiring decisions of universities, with consideration of individual level BSS researcher heterogeneity, in the context of regional macroeconomic demand dynamics. The framework was employed to examine the effects of two policy scenarios on BSS researcher workforce outcomes. These scenarios are designed to address questions concerning resolution of the gender pay gap and alternative federal funding strategies for academic research and development at R1 universities in the U.S.

In Chapter 2, I presented a descriptive study to illuminate BSS researcher career path decisions and how they vary by gender, discipline and region. A preliminary analysis of BSS researcher demographic and remuneration patterns were then used to represent academic science

114 workforce trends in the agent based model of individual level behavior. This effort was my first step in developing the academic science workforce model framework. Significant differences were found to exist between median annual salaries of male and female scientists in BSS fields in all regions of the U.S., with the lowest gap of $14,000 existing in the Midwest. However, this region also paid the lowest median salaries to women at $71,000 annually. Female BSS researchers in the West experienced a significant drop in annual salaries between 2010 and 2013, going from $79,500 to $75,000. Overall, it was evident that females in the academic research sector earned lower wages compared to male BSS researchers.

This initial study also provided insights into the sector mobility of scientists leaving academia for jobs in business and industry, as well as those who migrate to academe from the nonacademic sector. As one would expect, this analysis showed that BSS researchers who are satisfied with their career advancement were less likely to leave the academic sector. However, job security was found to have a greater influence than career advancement in decisions to stay or leave the nonacademic sector. Also of interest, researchers receiving NIH funding were far more likely to migrate to academic jobs from the nonacademic sector than those without NIH resources. Chapter 2 provided useful input data for initialization of the ABM developed in

Chapter 4.

Next, Chapter 3 overviews the development of a SD representation of academic science workforce demand that is based on federal funding of higher education research and development. This new approach to representing academic science workforce demand was necessary since existing workforce simulation models typically use population growth dynamics to represent the temporal changes resulting in job growth. A gap analysis model structure is used to construct a simple SD model to generate job vacancies at the regional level. Specifically, the

115 number of job vacancies available for the hiring process in the ABM is determined by the difference between the number of researchers necessary to produce the funded research and the current number of researchers employed. Conversely, when regional funding levels decline the commensurate workforce reduction results in the destruction of jobs. The resulting model is successfully used to generate the macroeconomic context for the ABM hiring process in the hybrid simulation model framework developed in Chapter 4.

As such, Chapter 4 builds upon the findings of Chapters 2 and 3 with the development of a hybrid simulation model framework. My review of existing literature confirms that traditional approaches to labor market supply and demand analysis are not effective at capturing the effects of human behavior and individual decision making with assumptions of homogeneous, fixed and linear contexts. The behavior of researchers and employers are operationalized by using a Mincer wage model in the ABM, with utility values calculated to inform job match related decisions.

Heterogeneity at the agent level is represented, including the time-out-of-work and actual work experience factors, which effectively resolves limitations inherent in traditional gender wage gap models. The model also accounts for lags occurring in the system, such as the time required for students to complete their doctoral programs, the delays occurring when changes in funding require hiring or firing to occur and the delays in researcher career progression when they stop out of the workforce for long term family leave.

Face validity of the model was achieved by comparing statistics of regional employment figures with historical data. This allows policy experiments to be conducted with comparisons to baseline model output yielding insights into their effectiveness. Two policy experiments are performed to demonstrate its use. First, the temporal effects of federal policies designed to ensure equal pay for equal work on reducing the gender pay gap is simulated for each region.

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The results of this run reveal that there was an overall increase in annual salaries for female BSS researchers over time as the policy effects were fully realized. There was a commensurate reduction in remuneration for male researchers due to the limited resources available for the same level of research production capacity. In the Midwest regions, there was an observed balancing effect with female and male researcher salaries increasing when funding increased, which was not the case in the baseline run showing the status quo.

The hybrid model framework is also used to consider an alternative approach to federal government funding of academic science research. This scenario is simulated with an evaluation of how overall supply and demand dynamics compared to those of the baseline model outcomes.

The key findings of this experiment indicate that it is more beneficial to hold funding levels steady than to have oscillations occur in the system as a result of increasing and decreasing rates of funding from year to year. On all outcome measures considered, including employment levels, hiring/firing rates and the gap between the target number of researchers needed and the number of working researchers, the flat funding growth rate produces significant positive results as compared to past funding policies. Thus, flat or slightly increasing funding growth rates result in more balanced academic BSS workforce supply and demand dynamics. The potential savings from reducing churn, combined with the potential for increased productivity as research projects are sustained with uninterrupted researcher human capital, provide motivation for further consideration of this policy alternative. These findings are consistent with previous studies by

Hur, Ghaffarzadegan and Hawley (2015) and Larson, Ghaffarzadegan, and Diaz (2012) that demonstrated the potential for negative workforce outcomes when major changes in federal funding of academic science research occur.

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Conclusions

Conclusions for Research

There have been many empirical studies on the STEM workforce in recent years that have focused on the ongoing challenges facing our country in attracting students into higher education programs, up to and including doctorate degrees, and retaining them on productive academic science research career path (Auriol, Misu & Galindo-Rueda, 2016; Fernandez-

Zubieta, Geuna,& Lawson, 2015; Ginther, 2015; Zhou & Volkwein, 2004). Gender diversity in senior researcher positions in STEM disciplines continues to be a concern for many policy makers, with a growing body of literature finding imbalance within these ranks (Ceci, Ginther,

Kahn & Williams, 2014; Goulden et al., 2011; Xie & Shauman, 2003). This dissertation contributes to the body of knowledge by focusing on BSS fields within STEM, in particular, and investigating the way in which individual characteristics and preferences influence academic researchers’ career path decisions, and employers’ hiring decisions, within the context of the overall academic science research workforce system.

The successful attraction and retention of females to academic BSS research is important to ongoing efforts to increase gender parity in senior researcher positions at R1 universities. The existence of a gender wage gap poses challenges to these efforts, especially when one considers the positive relationship between job satisfaction on career advancement and compensation.

Chapter 2 of this study analyzes the underlying factors influencing female BSS researchers’ decisions to leave the academy for higher paying jobs in the nonacademic sector and confirms the significant role of satisfaction on career advancement.

This research contributes to existing academic science workforce supply and demand studies by demonstrating the feasibility of using a hybrid model combining ABM and SD models

118 for analyzing university hiring processes and BSS researcher career pathways. Moreover, in

Chapters 4 the model framework developed is shown to be useful for considering federal policies addressing complex problems to be evaluated using heterogeneous actors. Regional supply and demand interactions are modeled and visualized, as are the individual researcher career trajectories. Chapter 4 demonstrates that hybrid models can successfully address complexity in the overall system that traditional analytic approaches cannot capture. Thus, the micro and macro contexts of the academic science workforce system are accounted for in a way that differs from previous approaches, many of which rely on traditional econometric models or, as in the healthcare workforce simulation models, consider demand in the context of population growth

(Flynn et al, 2014).

I validated the simulation model by comparing the employment statistics with those of the empirical data. This comparison confirmed that the simulation is accurately specified, since the patterns reasonably replicate historical data. Given the successful replication, the hybrid model framework has face validity and may be useful as a framework to test other assumptions, theories or policy alternatives. I use two policy experiments as examples of how the model may be used to simulate policy interventions that the federal government could implement to reduce the gender pay gap, and stabilize the negative effects of large federal funding fluctuations on the gap between supply and demand, in the academic science workforce system. I share the results of both simulations to show that the framework may be employed to conduct more extensive experiments to deepen our understanding of how policies may affect the overall system as well as the outcomes of individuals, institutions and other entities within it.

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Conclusions for Practice

The hybrid simulation model I developed provides a framework for further exploring assumptions, theories and policies related to the academic science workforce. The current version of the model is calibrated using BSS research related data and provides a proof of concept that effectively captures heterogeneity within the system. The model may be further elaborated and validated to improve the accuracy of its simulated output. Additional experiments will yield information that decision makers may use to develop more effective policies to address persistent issues undermining the productivity and diversity of the workforce. Exploring policies in this manner is far less time consuming and costly than actually implementing them and committing the resources required to complete randomized studies to evaluate their effects.

For example, one policy scenario explored here indicates that Regional differences in federal funding for academic research have important implications for workforce demand. BSS researchers migrate to regions offering more opportunities at higher compensation levels. While this behavior may be somewhat expected, the model framework demonstrates the importance of policies that stabilize the demand for production of research for insuring a future supply of students in the education and training pipeline. Top research universities with strategic interests in developing or maintaining excellence in BSS research must consider policies that ensure their ability to compete with institutions in other regions offering higher paying jobs.

The current model framework underrepresents the complexity of the workforce system by using randomly generated agent attributes and does not account for many factors that influence career decisions of researchers and hiring decisions of employers. The model simulations will be greatly enhanced once system stakeholders are engaged in identifying additional factors and data for inclusion in the model. Moreover, the way in which the model

120 makes underlying assumptions and rules explicit facilitates the effective communication of the various perspectives held by stakeholders resulting in a more accurate understanding of the overall system.

The model framework may be recalibrated using data from other sectors allowing it to be adapted to simulate supply and demand for other workforce systems. Many high demand sectors are either currently experiencing labor shortages, or are projecting future issues resulting from increasing retirement rates, rapidly advancing technology and slower population growth. Having the ability to represent these systems in a simulated environment will potentially reduce costs associated with implementation of ineffective policies as insights into their impact on workforce supply and demand are realized.

Limitations and Future Research

The model framework developed in this study serves as a proof of concept by demonstrating the value of combining ABM and SD methodologies, along with economic behavior theory, to capture the individual level characteristics and behaviors of the academic science workforce while considering the overall national and regional demand dynamics driving research production. There are several limitations that may be addressed in the future. For example, more extensive model validation and sensitivity analysis should be completed in an effort to fully develop the current conceptual model framework as additional resources become available.

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2018; Blume-Kohout & Clack, 2013). These effects are realized years later when the enrolled cohorts eventually graduate, resulting in a lagged reduction in workforce supply that is often out of sync with current demand. Additional policy scenarios should be explored to determine their ability to smooth such supply and demand fluctuations.

The ABM hiring process could also be expanded to more accurately reflect change over time in the Mincer wage coefficients associated with researchers’ attributes. Additional measures of productivity could be incorporated into the model to fully capture all components of the conceptual model. Data sources that provide accurate measures of researcher productivity over time should be considered in this effort. The use of administrative data, such as research university data available through UMETRICS, would allow a more accurate representation of employer hiring preferences. The availability of additional university administrative data would also permit greater job related details, including availability and type (such as, post doctorate, staff and tenured and non-tenured faculty positions).

The current version of the model over simplifies the supply and demand dynamics of job creation and would be improved with more explicit representation of employers. The SD model driving job creation dynamics should be expanded to consider other macroeconomic influences effecting higher education expenditures on research related human resources. GIS features may also be included to enhance the regional geographic characteristics and visualizations.

The current boundaries of the model exclude the nonacademic sector of the scientific research system. Expanding the framework to include both sectors to allow further analysis of the researcher migration between the academic and nonacademic sectors is of interest. Given the finding that NIH funding was a positive factor in attracting researchers to academic science from the nonacademic sector, it would be interesting to investigate the influence of collaborative

122 research and the associated networks researchers form. Currently, the model does not consider agents as members of social networks or collectives. There is increasing interest in the impact of researcher’s networks and their impact on researcher productivity, and adding the dimension of sector mobility would be appropriate.

There are many useful applications for the model framework that are possible as additional data becomes available. I look forward to further validating the model to improve its capabilities and accuracy for considering how various policies might impact workforce supply and demand related outcomes. There is an ongoing need for research that will help to inform the workforce related decisions of stakeholders and policy makers at the organizational, regional and national level. Having the ability to test various solutions to complex problems before implementing new programs and policies will help reduce unnecessary expenses and delays in achieving desired results.

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Appendix A Job Match Regression Analysis Results

( Thurstone, 1927) (Anand, 1993) (Manski, 1977) The ordinary least squares (OLS) estimation results displayed in Figures 1A and 2A are used to identify the β values for each variable in the willingness-to-pay (WTP) and willingness- to-accept (WTA) marginal benefits calculations used in the hiring process in the agent based model (ABM). It is assumed that the unobserved heterogeneity component in the error term and the components in X (vector of control variables) are uncorrelated, the sample of wage observations is drawn randomly from the population, and there is no measurement error in the variables. The results of a pairwise correlation matrix is shown in Table 3A.

Table 7. Mincer Equation: Willingness-to-Accept (Full time)

DV: Log salary Academic Non-Academic

Model 1 Model 2

Female -0.182*** -0.104

(0.038) (0.071)

Marriage status 0.125* -0.116

(0.068) (0.109)

White 0.088** 0.039

(0.042) (0.078)

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US citizen 0.028 0.022

(0.064) (0.191)

Age 0.049*** 0.117***

(0.016) (0.030)

Age squared -0.001** -0.002***

(0.001) (0.001)

Child 0.009 0.136*

(0.041) (0.073)

Spouse work -0.067 -0.169*

(0.047) (0.089)

NIH funding 0.147*** -0.011

(0.040) (0.091)

Professional society membership 0.251*** 0.129

(0.078) (0.081)

Major:

Political and related sciences -0.152* -0.12

(0.090) (0.139)

Psychology -0.308*** -0.346***

(0.057) (0.101)

Sociology and anthropology -0.397*** -0.382***

(0.060) (0.115)

Other social sciences -0.321*** -0.313**

(0.071) (0.134)

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Constant 9.883*** 8.931***

(0.402) (0.732)

Pseudo R2 0.3281 0.1913

Observations 483 222

Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

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Table 8. Mincer Equation: Willingness-to-Pay (Full Time)

DV: Log salary Academic Non-Academic

Model 1 Model 2

US citizen 0.046 0.145

(0.057) (0.150)

Age 0.043*** 0.125***

(0.014) (0.026)

Age squared -0.001* -0.002***

(0.001) (0.001)

NIH funding 0.134*** 0.076***

(0.037) (0.082)

Professional society membership 0.217*** 0.057

(0.075) (0.074)

Major:

Political and related sciences -0.260*** -0.165

(0.082) (0.130)

Psychology -0.404*** -0.411***

(0.053) (0.102)

Sociology and anthropology -0.477*** -0.385***

(0.057) (0.114)

Other social sciences -0.438*** -0.295**

(0.065) (0.138)

Constant 10.064*** 8.55***

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(0.352) (0.634)

Pseudo R2 0.309 0.1189

Observations 591 311

Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.

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Appendix B Agent Based Model Data

Table 9. Key parameters and variables used in the agent based model

Parameter Initial Source Description Value BSSR employment status Proportion seeking 0.001 1993-2013 employment average SDR propBSSToWorkforce 0.34 initial Calibration from NSF Doctorate Recipients value – from U.S. Universities 2015. Data tables 42, 43 0.69% Definite commitments at doctorate award of entering U.S. doctorate recipients, by science and workforce engineering field of study: 1995–2015. after https://www.nsf.gov/statistics/2017/nsf17306/r graduation eport/what-are-the-postgraduation-trends/job- X 49.5% market-science-and-engineering.cfm entering At least 10% of time on basic or applied research research proportion based on Status of workforce Doctorates in BSSR chart, 2993-2013 from (2013 NSF-SDR; Research employer in the US SDR) avgExtendedLeaveTi 6 weeks OSU Paid Available only to female faculty and staff with me or 0.12 Leave paid leave. Not available to new hires in yr Policy postdoctoral research positions. Paternity leave and adoption leave – 2 weeks. Percent not in labor 1.8 2013 SDR force Rate of retired Timeout Annual Average rate of .077 based on 1999-2013 ; random SDR; yearly rate used for calibration. 65-80 Proportion entering .34 1993-2013 149

BSSR workforce average (research ≥ 10% of SDR time) Proportion academic 65.6 2013 SDR Calibration with 1993-2013 SDR data; researchers imputed for years not included in biannual survey data Percent in 34.4 2013 SDR Calibration with 1993-2013 SDR data; nonacademic sector imputed for years not included in biannual survey data Rate of leaving BSSR 0.21 Annual Estimated from SDR data: rate of change in workforce nonresearch positions held by BSSRs from 1993-2013 Rate of quitting 0.57 Annual Dropout rated from Ph.D. programs; estimated from CGS data. Demographics Proportion female 46 2013 SDR Proportion male 54 2013 SDR Percent white 79 2013 SDR Percent non-white 21 2013 SDR BSSR age by gender Custom 2013 SDR Proportion of researchers represented by distribut gender and age in 5 year increments ion; random Ph.D. student age - 25, 49 Council of min, max Graduate Schools Retired 68 2013 SDR Employed 50 2013 SDR BSS graduates Percent psychology 43 2010 SDR Percent economics 16 2010 SDR Percent political 12 2010 SDR science Percent sociology 9 2010 SDR Percent other social 19 2010 SDR sciences Rate of graduation 0.125 Annual, Annual rate of BSS students graduating from per year assume 8 U.S. doctorate programs; Calibration data year source:: Doctorate Recipients from U.S. average Universities 2015. Data tables 31, 32 completion https://www.nsf.gov/statistics/2017/nsf17306/r time eport/what-influences-the-path-to-the- initially doctorate/time-to-degree.cfm Average salary ($)

150 min,max Academic 95,116 2013 SDR Non-academic Industry 128,786 2013 SDR Government 114,407 2013 SDR Number of BSS W – 2013 SDR Initial values used when Regions set up researchers 131,000 initially. Calibration data source: 1993-2013 MW – SDR data. 93,000 NE – 117,000 S – 154,00 Number of BSS Ph.D. 32,276 IES NCES Council of Graduate Schools: Total 2016 students Digest of doctorate program enrollment 59,621; Table Education C.11: First-Time Graduate Enrollment by Statistics Broad Field and Gender, 2006 to 2016; Social (2015) and Behavioral Sciences – Men: 2015 to 2016 Average Annual % Change -0.0%; 2011 to 2016 Average Annual % Change -1.0%; 2006 to 2016 % Change, -1.0%; Women: 2015 to 2016 +3.8%; 2011 to 2016 +0.7%; 2006 to 2016 -0.4%. 2005 cohort would be graduating in 2013 per assumed 8 year time to degree. Psych, 54,102 + Soci Sci 107,278 = 161,380; assume 20% doctorate/80% masters programs; 161380x.20=32,276 Number of universities 110 Carnegie 110 Research Universities (R1: Research Classificati Universities (Doctoral - Highest on of research activity) in the Carnegie Institutions Classification of Institutions of Higher of Higher Education Education Rate of quitting .441 CGS Doctoral program average completion rate (2007) 55.9% in social sciences; A Data-Driven Approach to Improving Doctoral Completion, Grasso, (2007) for Council of Graduate Schools paper series; http://cgsnet.org/ckfinder/userfiles/files/Paper_ Series_UGA_FrontMatter.pdf Average duration of 6 weeks; OSU 6 weeks paid maternity leave for faculty and extended leave 0.012 yr Policy 627 staff; none for postdoctoral researchers (hr.osu.edu) Proportion of females 0.44 1993-2013 Calibration with SDR data from 1993-2013;

151 having babies SDR initial value average Rate of Retirement 0.18 NSCG Currently using a timeout transition triggered by aging to 65-80 yrs. Rate of maternity 0.04 1993-2013 Calibrate using actual change over time of leave SDR proportion of BSSR females with children average Proportion BSSR 0.34 1993-2013 seeking employment SDR average Proportion BSSR on 0.035 Estimated Based on rate of female BSSR having children extended leave from 1993-2013 SDR data. ageSpecificFertilityRat Table CDC- National Center for Health Statistics, ePerThouFemaleAnnu data NCHS al marriageRateUS 16.5 U.S. ACS_13_1YR_GCT1251.US01PR marriage Census rate 2013 US Census propHavingBabyForFe 0.44 NSF-SDR Average from 1993-2013 male

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Table 10. Key parameters and variables used in the system dynamics model

Name Description Value Source baseFunding Federal obligations for Time series NSB S&E Indicators BSS research at R1 of funding (R1 fractions based on universities FY 2016 data) propNonHR Proportion of HE 0.57 Estimate based on NSB expenditures not related to S&E Indicators FY 2016 human resources HE R&D expenditures data annualSalary_$K Median salary of all 80 Estimate from NSF SDR doctoral researchers 2013 data employed full time in R1 universities (behavioral and social science fields, only) annual_increase Percent annual increase of 0.05 Estimate from NSF, funding multiplier NCSES, SFFRD Table A4-25 - Federal obligations for research, by detailed S&E field: Selected years, FYs 1990-2015 average annual growth rate data averageDurEmpl Average duration of 10 Estimate based on NSF researcher full time SDR 2013 employment (years) at R1 universities govtPolicy Percentage annual 1/0.8 Author’s assumption increase/decrease in (changes when government S&E R&D GovtFundingCut event budget occurs) NSB, National Science Board NSF, National Science Foundation SDR, Survey of Doctoral Recipients NCSES SFFRD, National Center for Science and Engineering Statistics, Survey of Federal Funds for Research and Development

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