Three Essays on Policies to Help Government Improve Workforce Resilience
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By
Hyungjo Hur
Graduate Program in Public Policy and Management
The Ohio State University
2017
Dissertation Committee:
Joshua D. Hawley, Advisor
Anand Desai
Robert Greenbaum
Copyrighted by
Hyungjo Hur
2017
Abstract
This dissertation investigates the effectiveness of human resource management policy through the lens of workforce resilience. This dissertation consists of a set of three interconnected essays. Chapter 1 provides a conceptual framework for workforce resilience. Chapter 2 explores the effects of different active and passive labor market policies among Organization for Economic Cooperation and Development (OECD) countries using panel regression and difference-in-difference analysis. This study finds that countries that spent more on active labor market policies have lower unemployment rate and greater resilience in the Great Recession of 2008. Chapter 3 examines the productivity and behavior of postdocs in US biomedical field by considering workforce diversity using a difference-in-difference analysis. This study shows the heterogeneous behavior and productivity of postdoctoral researchers by citizenship status. Under the more supportive funding environment, US citizens stayed longer in postdocs, permanent residents were more productive. Under less supportive funding environment, permanent residents were less productive. Chapter 4 analyzes how human resource management strategies affect US government employees’ turnover using logistic regression. This study finds that turnover increases when individuals have a gap between job expectation and satisfaction. Chapter 5 discusses the overall theoretical and practical contribution, policy implication, and limitation of this dissertation. This dissertation contributes to the understanding of how
ii government responds and prepares to unexpected social and economic crises (increasing global competition, technology innovation and demographic).
iii Dedication
This dissertation is dedicated to my family.
In memory of my lovely mother Ok Deok Kim (1948-1990) and my friend Jee Hoon Park
(1975-2015).
iv Acknowledgments
This work would not have been possible without the support of several individuals. First and foremost, I would like to express my sincere, deepest gratitude to my advisor Dr. Joshua Hawley for his incredible support, expert guidance, patience, understanding and encouragement. I am grateful for his life changing guidance and suggestions. He challenged me to examine my research and work in new ways, and provided indispensable mentoring and advice.
In addition, I would like to express my appreciation to Dr. Anand Desai and Dr.
Robert Greenbaum for serving on my committee and taking time to talk with me throughout the entire process. Their confidence in me and thoughtful comments were invaluable. I am also grateful to Dr. Navid Ghaffarzadegan for his support and advice.
Thanks also go to my friends in the John Glenn College of Public Affairs. I sincerely thank Julie Maurer for being a great help and providing unconditional support. I am grateful to Dr. Jaewon Lee, Dr. Sangheon Kim, and Dr. Youngbum Lee. Without their advice and encouragement, I could not have started and finished my PhD studies.
Finally, I would like to thank my friends. I have benefited from many discussions with
Kwang Wung Jeong, Sangyong Han, Kwangbin Bae, Namho Kwon, Hosung Sohn, Sun-
Ki Choi, and Sang Hyuck Yoon.
v Vita
July 12, 1978 ...... Born, Busan, South Korea
2003...... B.A. Public Administration, Pukyung
National University, Busan, South Korea
2007...... M.P.A. Seoul National University, Seoul,
South Korea
2010...... M.P.A. Robert F. Wagner Graduate School
of Public Service, New York University
2011...... M.A. Economics, Maxwell School, Syracuse
University
2011 to present ...... Graduate Research and Teaching Associate,
John Glenn College of Public Affairs, The
Ohio State University
vi Publications
Hur, H., Andalib, M. A., Maurer, J., Hawley, J., & Ghaffarzadegan, N. (2017). Recent
Trends in the U.S. Behavioral and Social Research (BSSR) Workforce. PloS
ONE, 12(2), e0170887.
Hur, H., Ghaffarzadegan, N., & Hawley, J. (2015). Effects of Government Spending on
Research Workforce Development: Evidence from Biomedical Postdoctoral
Researchers. PloS ONE, 10(5), e0124928.
Frazier, L. A., & Hur, H. (2013). The Legacy of Chester Barnard in Contemporary
Scholarship. In B. R. Fry & J. C. N. Raadschelders (Eds.), Mastering Public
Administration: From Max Weber to Dwight Waldo (pp. 263-270). Thousand Oaks,
CA: CQ Press.
Lee, J., & Hur, H. (2012). Assessing Training System for Social Service Workers in
South Korea: Issues and Policy Agenda. Asian journal of human services, 3, 60-
76.
Fields of Study
Major Field: Public Policy and Management
vii
Table of Contents
Abstract ...... ii
Dedication ...... iv
Acknowledgments...... v
Vita ...... vi
Publications ...... vii
Fields of Study ...... vii
Table of Contents ...... viii
List of Tables ...... xiii
List of Figures ...... xvi
Chapter 1 : Introduction ...... 1
History of Workforce Development ...... 2
Developing Resilient Workforce ...... 3
Problem Statement, Purpose and Research Question ...... 6
Research Questions...... 9
Problems, Policies and Outcomes ...... 12
viii Significance of the Study ...... 13
Organization of the Dissertation ...... 14
Chapter 2 : Government Expenditure on Labor Market Policies in OECD Countries:
Responding to the Economic Crisis ...... 15
Abstract ...... 15
Introduction ...... 16
Increasing Risk and the Effects and Importance of Risk Management ...... 19
Necessity of Risk Management and Choice of Policy Tools: ALMP and PLMP ..... 22
Policy Tools ...... 22
Different Policy Tools for Unemployment Problem: ALMP vs. PLMP ...... 23
Resilience ...... 28
Data and Measurement ...... 33
Dependent Variables ...... 34
Independent Variables ...... 35
Controlling for Other Relevant Variables ...... 36
Models ...... 39
Results ...... 41
ix Discussion ...... 49
Additional Tables ...... 53
Chapter 3 : Effects of Government Spending on Research Workforce Development:
Focusing on Behaviors and Productivity of Biomedical Researchers ...... 58
Abstract ...... 58
Introduction ...... 59
NIH Funding for Maintaining Resilience in Biomedical Fields ...... 61
Workforce in Biomedical Fields ...... 66
Postdoctoral Researchers: Training Position as Future Research Engine ...... 66
Workforce Diversity in Biomedical Fields: Asymmetric Effects of Funding ...... 69
Data and Measurement ...... 72
Method ...... 73
Dependent Variable ...... 75
Other Variables ...... 76
Results ...... 77
Policy Effects on the Whole Population ...... 77
Diversity of Workforce: Effects on Different Citizen Groups ...... 80
x Discussion and Conclusion ...... 84
Additional Figures and Tables ...... 88
Chapter 4 : Turnover Behavior among U.S. Government Employees ...... 108
Abstract ...... 108
Introduction ...... 109
Managing Voluntary Turnover and Organizational Resilience ...... 112
Human Resource Management Practices ...... 117
Job Discrepancy ...... 118
Discrepancy between Perceived Job Expectation and Actual Job Satisfaction ...... 118
Job-Education Mismatch ...... 121
Work-related Training ...... 122
Demographic Factors ...... 125
Data and Measurement ...... 127
Method ...... 127
Dependent Variables ...... 129
Independent Variables ...... 131
Results ...... 133
xi Human Resource Management Practices ...... 134
Demographic Factors ...... 138
Discussion and Conclusion ...... 144
Additional Figures and Tables ...... 149
Chapter 5 : Discussion ...... 172
Summary ...... 172
Contributions and Implications ...... 174
Limitation and Future Research ...... 176
References ...... 179
xii
List of Tables
Table 1.1. The Three Main Type/Definitions of Resilience ...... 5
Table 2.1. Descriptive Statistics...... 38
Table 2.2. Unemployment Rate, Long-Term Unemployment Rate: Year 2001-2013 ..... 42
Table 2.3. Percentage Point Change in Unemployment Rate ...... 45
Table 2.4. Percentage Point Change in Long Term Unemployment Rate ...... 47
Table 2.5. ALMP Expenditure per Unemployed Worker as a Percentage of GDP per
Capita ...... 54
Table 2.6. Descriptive Statistics of Higher ALMP Expenditure Countries by Pre-Post
Crisis ...... 55
Table 2.7. Descriptive Statistics of Lower ALMP Expenditure Countries by Pre-Post
Crisis ...... 56
Table 2.8. Unemployment Rate, Long-Term Unemployment Rate ...... 57
Table 3.1. Major NIH Funding Measures: 1998-2015 ...... 66
Table 3.2. Difference-in-Difference estimates for the Before (1995), During (2001,2003),
After (2008) Doubling Funding Effect ...... 79
xiii Table 3.3. Difference-in-Difference Estimates for the Before (1995), During (2001,2003),
After (2008) Doubling Funding Effect by Citizenship Status ...... 82
Table 3.4. Descriptive Statistics...... 92
Table 3.5. Difference-in-Difference Estimates: Time in Latest Postdoc ...... 94
Table 3.6. Difference-in-Difference Estimates: Time Since Graduation ...... 97
Table 3.7. Difference-in-Difference Estimates: Published Articles ...... 99
Table 3.8. Difference-in-Difference Estimates: Conference Papers ...... 102
Table 3.9. Difference-in-Difference Estimates within Only Government-Funded Postdocs
...... 105
Table 3.10. Difference-in-Difference Estimates by Citizenship Status within Only
Government-Funded Postdocs ...... 106
Table 4.1. Logit and Multinomial Estimates of Different Type of Turnover of
Government Employees: 2003-2006 Cohorts ...... 140
Table 4.2. Logit and Multinomial Estimates of Different Type of Turnover of
Government Employees: 2010-2013 Cohorts ...... 142
Table 4.3. Descriptive Statistics...... 151
Table 4.4. Logit and Multinomial Estimates of Different Type of Turnover of
Government Employees: 2003-2006 Cohorts ...... 155
Table 4.5. Logit and Multinomial Estimates of Different Type of Turnover of
Government Employees: 2010-2013 Cohorts ...... 159
xiv Table 4.6. Logit and Multinomial Estimates of Different Type of Turnover of
Government Employees: 2003-2006 Cohorts ...... 164
Table 4.7. Logit and Multinomial Estimates of Different Type of Turnover of
Government Employees: 2010-2013 Cohorts ...... 168
xv List of Figures
Figure 1.1. Problems, Policies and Outcomes of Three Studies ...... 13
Figure 2.1. Change in Unemployment Rates by Country, 2007-2009...... 20
Figure 2.2. OECD Unemployment Rate Before and After 2008 Economic Crisis ...... 21
Figure 2.3. Spending on Active and Passive Labor Market Policies (2001-2013) ...... 26
Figure 2.4. Percentage Point Change in Unemployment Rate ...... 27
Figure 2.5. Response of High/Low Resilience Countries to a Crisis Event ...... 31
Figure 3.1. Ratio of NIH Budget to Other Federal Research Spending ...... 65
Figure 3.2. Trends in NIH Funding FY1995-FY2016 ...... 89
Figure 3.3. Trends in Research Project Grants FY1995-FY2015 ...... 90
Figure 3.4. Motivation on Intellectual Challenge: Postdocs ...... 91
Figure 4.1. Overview of the Mobility of Government Employees ...... 113
Figure 4.2. U.S Government Turnover Rate (Federal, State/Local Government) ...... 150
Figure 4.3. Total Turnover Rate by Government Level ...... 150
Figure 4.4. Voluntary Turnover Rate by Government Level...... 150
xvi
Chapter 1 : Introduction
Nation states must be prepared to address the following issues: 1) global fluidity,
2) regular ambiguity and disruption in global economy, and 3) diverse actors on the policy arena (Sandeen & Hutchinson, 2010). Global competition, technology innovation and demographic shifts make the human capital much more important. Furthermore, workforce development faces challenges in dealing with multiple players in different fields (McLean, 2004; Swanson & Holton III, 2009). With global competition, technology innovation, and demographic shifts, mobilizing optimal level of human capitals becomes more important priority for policy decision makers (Foster &
Rosenzweig, 1996; Jacobs & Hawley, 2009; Levy & Murnane, 2004). In the era of increased interdependencies among nations, economic instability is increased. That is, one country’s economic problems do not limit to one country but it affects to other countries. Due to increased economic instability, therefore, it is important to train and retain the workforce.
With increasing competition among countries, technological improvements and innovations became the key elements for increased productivity and mobilization of human resources in more effective ways (Foster & Rosenzweig, 1996; Jacobs & Hawley,
2009; Levy & Murnane, 2004). Without adopting the advancement of technology, it is
1 difficult to increase the productivity through the workforce development. Increasing individual worker’s productivity through technological innovations will play important roles in workforce development disciplines.
Demographic changes, particularly aging in most developed nations, also make workforce development even more important if countries are to stay competitive
(Carnevale & Fry, 2001). As less people staying workforce due to retirement especially in government, transferring of knowledge accumulated by previous generation becomes more important than ever. Retirement of baby boomers and the need to improve the skill set of millennials are very important of demographic challenges. To ensure smooth transition into high tech society while keeping and mobilizing existing knowledge, government needs to make sure retaining skilled workers in the workforce longer
(Ashton, Green, James, & Sung, 1999; Carnevale & Fry, 2001).
History of Workforce Development
Workforce development encompasses activities that will help workers improve their capabilities and competitiveness within labor market through combination of public and private investment (Grubb, 2001; Harrison & Weiss, 1998; Jacobs & Hawley, 2009).1
1 Grubb (2001) argues that workforce development improves society by providing opportunities to improve the individual standard of living, and support organizations. He describes how workforce development encompasses not only individual-level characteristic such as skill training and motivation but also the relationship of other workers in the organizations. Jacobs & Hawley (2009) explain the workforce development as “the co-ordination of public and private sector policies and programs that provides individuals with the opportunity for a sustainable livelihood and helps organizations achieve exemplary goals, consistent with the societal context” (p. 2543). According to Harrison & Weiss (1998), workforce development involves various processes, from recruiting and placing new workforce based on their preferences and skills, to retaining them within the organization through closely monitoring their needs in order to create competitive workplace environment in order to minimize potential talent loss. 2
In the U.S., workforce development focuses on improving individual skill level and increasing international competitiveness of business. Government interventions in the workforce development system have changed over time. In 1913, the US government created the Department of Labor with the goal of “foster, promote and develop the welfare of wage-earners, to improve their working conditions, and to advance their opportunities for profitable employment.” During the Great Depression, New Deal legislation passed to stabilize the economy and provide job opportunities to the unemployed (1933-1938). During the war period (1939-1945), government funded vocational training for war production to World War II. The focus of government shifted to equity and education under Johnson’s war on poverty programs (Economic
Opportunity Act) in 1964. Various agencies were created to train those facing structural unemployment, such as Manpower Development & Training Act (MDTA) in 1962 and by the Comprehensive Employment and Training Act (CETA) in 1973. In the 1980s, government embrace a neoliberal ideology and eliminated public sector employment under Job Training & Partnership Act (JTPA) in 1983. In 1990s, there was an overhaul of the government workforce development system which led to the Workforce Investment
Act (WIA) in 1998 (Barnow & King, 2005).
Developing Resilient Workforce
Resilience can be defined in three ways (see Table 1.1). The first definition is
‘bounce back’ to the pre-crisis status in the face of adversity (Holling, 1973; Pimm, 1984) also known as engineering resilience (Martin & Sunley, 2014). The first definition
3 focuses on degree to which individuals have recovered their skills or wages from the challenges such as unemployment (Martin & Sunley, 2014; O'Neill, 1986). The second definition is the 'ability absorb’ the changes called, ecological resilience (Holling, 1973;
Martin & Sunley, 2014). The extent of how much shock and adversity have been absorbed is an important ability in ecological resilience (Martin & Sunley, 2007). The third definition is ‘positive adaptability’ to the shocks (Luthar, Cicchetti, & Becker, 2000;
Martin & Sunley, 2014). Adaptive resilience focuses on a robust transition enhancing the internal capacity to help to adapt to changes continuously (Marcos & Macaulay, 2008;
Martin, 2012). The concept of resilience involves both overcoming obstacles and also adapting to the new environment. Resilience is not a fixed characteristic, but it implies change in internal and external factors (Rutter, 1993).
Human resource management policies are defined as practices and programs for recruiting, developing, training, and retaining workers, which is related to increase individual or organizational productivity (Boachie-Mensah, 2006; Noe, Hollenbech,
Gehart, & Wright, 2003; Wright & Noe, 1996). Human resource management policies are important in every step of human resource management pipeline. Attracting, selecting, developing and retaining the workforce are all important to maintain the resilient workforce (Whittington & Evans, 2005). In other words, human resource management policies can be used as strategies of resilient workforce development and can help overcome the challenges. For example, human resource management policies of government can affect the behavior and performance of employees, which will lead to making government resilient (Noe et al., 2003). 4
The strategic human resource management policies of the government could be ready to provide knowledge and skills necessary for workforce to be prepared for changes. Human resource management policies need to be proactive in creating resilience for the community.
Title Definition Interpretation Main Fields of Use Bounce back ‘Bounce back’ from System returns, ‘Engineering the shocks ‘rebounds’, to pre- resilience’, found in shock state or path: physical sciences, emphasizes speed some versions of and extent of ecology recovery. Ability absorb ‘Ability absorb’ Emphasizes stability ‘Ecological changes and shocks of system structure, resilience’, found in function, and identity ecology and social in the face of shocks. ecology The size of shock that can be tolerated before system moved to new state/form. Positive ‘Positive The capacity of a Found in adaptability adaptability’ in system to maintain psychological anticipation of, or in core performances sciences and response to, changes despite shocks by organizational theory and shocks adapting its structure, functions, and organization. The idea of ‘bounce forward’. Source: Adapted from Martin & Sunley (2014)
Table 1.1. The Three Main Type/Definitions of Resilience
5
Problem Statement, Purpose and Research Question
Due to economic and social uncertainty and complexity, the government is forced to strategically reduce risk. In responding to challenges in the external environment, the government plays a vital role in managing risk because private markets for risk do not always work optimally on their own (Moss, 2004; Baker & Moss, 2009). For example, government intervenes in the area of the labor market that private sector cannot due to lack of profitability, or simply because of the sheer level of investment required. The
2008 economic crisis forced the government to address problems, and many countries are still struggling with high unemployment rates. The government also needs to spend on basic and applied level research for a similar rationale private companies do not see the advantage of investment in research for all fields. For instance, the US government doubled NIH funding from $13.6 billion to $27.1 billion during 1998-2003 (Smith, 2006) to provide advanced research infrastructure in biomedical fields to increase productivity and workforce development (Lawler, 1997; Mervis, 1997). By doing so, government takes a role of buffering risk and preparing for it.
In responding to internal challenges, the government can focus on human resource management policies in order to prepare the nation for demographic shifts. By 2019, nearly around 31 percent in the US federal government will be eligible to retire
(Government Accountability Office, 2015). If we do not solve these problems, we will face the critical skill gaps issues (Government Accountability Office, 2014).
Demographic shifts sheds light on the importance of strategic workforce development and management. Decision makers are seeking solutions to improve the human resource 6 management systems in order to optimize the processes of recruitment/selection, and training of employees.
In assessing the effectiveness of human resource management policies on workforce development, it is important to 1) understand how human resource management policy and government spending is working (how much money has been spent), and how the government radically changed the policy and management; 2) explain the policy significance of using the role of government concepts and theories in order to understand why policy and management have been changed and what for; 3) assess the impact of government policy and management by measuring their effects on workforce development, thus offering alternative policy and management that will help to build better performing policy and management.
Policy makers should deal with these issues as important because policies provide structural development in order to make a nation resilient in response to workforce challenges in the long-term solution. First, human resource management policy for workforce development can be changed with the different types of challenges we are facing. Second, the policy affects to not only individual behavior and productivity but also organizational management and productivity. That is, human resource management policy encourages and supports individual and organizational workforce development.
Third, these individual and organizational productivity and development make it possible to achieve the social goal with workforce development. Critical to evaluating resilient human resource management policy for workforce development is to figure out
7 individual level performance—i.e. people entering and participating in the workforce, or general labor pool—and the organizational level outcomes—whether organization benefit from human resource management policy in the form of profitability and productivity
(Jacobs & Hawley, 2009).
The notion of workforce development is emerging as an important policy objective among governments around the world in response to increased competition and uncertainties under globalization, development of new technology, and demographic shifts (Jacobs & Hawley, 2009). Human resource management policy has been considered as important factors for workforce development. Today, the concept of resilience is considered as important by experiencing the rapid change and shocks.
However, research is missing that bridges among workforce development, resilience, and human resource management policy which will be covered on dissertation.
With the understanding of the challenge of workforce development and importance of the role of government, the premise of this research rests on the idea that human resources management policy operates differently in internal (demographic shifts such as aging) and/or external (economic uncertainty, technological development) transitions. To develop this research, I will study the links between the effectiveness of human resource management policy and workforce development. Even though workforce development is hard to measured by short-term results due to their long-term result- bearing characteristics, it is necessary to evaluate how the population behaves and changes their behavior in response to human resource management policy.
8
Research Questions
This study will analyze the effectiveness of human resource management policy and government expenditures. The scope of this dissertation is to develop a robust understanding of workforce development. As external (economic uncertainty, technological development) and internal (demographic shifts such as aging) changes happen, the government needs to understand these changes and develop human resource management policy in response to those changes.
The first paper defines a role for government in preparing for unexpected crises.
To study the role of government, the paper focuses on the impact of government expenditures in labor market on the unemployment rate. Unemployment rate represents the labor market condition. Resilience in the labor market is defined as the capability to cope successfully with the adversity of high unemployment rate. Among Organization for
Economic Cooperation and Development (OECD) countries, the level of government expenditures on labor market policies differ. The ratio of government expenditure on active labor market policies also differs by countries.2 This study aims to analyze the variation in government expenditures among OECD countries. The paper focuses on the
2008 recession. Different policy responses to the labor market have varying results on socioeconomic development (i.e. unemployment rate) after 2008 economic crisis. Does a
2 In contrast to passive labor market policies, like unemployment insurance and transfers for income supplements, active policies redistribute employment opportunities and tries to encourage self-sufficiency by staying in the labor market and provide necessary adjustments to make sure everyone in the labor pool gets mitigations from changing economic situations (for example, new training for new technologies, support entrepreneurship, and employment counseling to help people find right jobs for their credentials) (Brown & Koettl, 2012). 9 high level of government expenditure on active labor market policies positively effects socioeconomic development (short-term and long term-unemployment rate)? After a crisis, does the unemployment rate to bounce back to pre-crisis level? Different levels of government expenditures on labor market policies among OECD countries allow me to help answer this research question. The emphasis of the analysis is to assess spending of impact on overcoming an unexpected crisis.
The second paper looks at the effects of the US federal government’s policy response to increasing global competition in biomedical science. In this paper, resilience is defined as researchers in biomedical fields keep research activities without discontinuity and losing capacity, and make innovation by improving their knowledge with learning advanced technologies and method. For keeping resilience to technological advancement, the US government has been investing in research and development (R&D) as a way to increase productivity. This study aims to investigate the changes in productivity and behavior of postdoctoral researchers in biomedical science by comparing before, during, and after NIH doubled funding periods (from 1998 to 2003).
Does the change in NIH funding level affect to productivity and behavior of postdoctoral researchers? And the productivity and behavior of postdoctoral researchers are different by workforce diversity (different citizenship statuses). Different motivation and incentive to their postdoc position by citizenship statuses allow me to help answer this research question. In order to understand the federal government’s impact, I look at specifically biomedical researchers as an example. But this analysis is relevant to bigger questions
10 about the role of research funding on outcomes matter to the Congress, the President and the public.
The third paper identifies the factors that influence government employees’ career mobility (turnover). The management of turnover is associated with organizational resilience in both stable and turbulent eras—especially when organizations are anticipating a decreasing number of experienced employees due to retirement (Cho &
Lewis, 2012; Government Accountability Office, 2014; Lewis & Cho, 2011; Tobias,
2001). Organizational resilience is associated with well-attuned human resource management during times of adversity (Vogus & Sutcliffe, 2007) in order to address unanticipated events and manage continuous environmental changes that affect employees (Mallak, 1998). This study aims to analyze the government employees’ turnover by taking a closer look at ‘do human resource management strategies
(differences between perceived expectations and actual job satisfaction levels, job- education mismatches, and work-related training) negatively effects on turnover?’ This study provides a better understanding about job-related transitions of government employees in order to prepare for impending changes (McKinsey, 2012; Wynen, Beeck,
& Hondeghem, 2013)—especially with the expected surge in the number of senior employees who are becoming eligible for retirement.
11
Problems, Policies and Outcomes
This section provides an overview of problems, policies and outcomes of this dissertation. Overall, Figure 1.1 describes how government responds to economic, technology changes and demographic shifts to increase the resilient of the workforce.
In Figure 1.1, ‘Problems’ refers to external (economic uncertainty, technological development) and internal (demographic shifts such as aging) shocks and changes. These factors substantially weaken the functioning of labor markets, increase the importance of adopting the advancement of technology, and a number of employees eligible to retire.
Second, ‘Policies government uses’ represents the government respond toward the challenges. To solve each challenge, the government can make a decision to implement active/passive labor market policy, increase R&D expenditure, and strategies for retaining employees.
Third, ‘Outcomes’ shows how well government human resource management policies works for the challenges. This dissertation measures the outcome using resilience concept (the change of unemployment rate, productivity, and turnover rate). That is, how well each policy absorbs well, bounce back, and adaptive well to each challenge.
12
Figure 1.1. Problems, Policies and Outcomes of Three Studies
Significance of the Study
The expected contribution of this study, which is consisted of three interconnected essays, is to advance the role of government in workforce development through human resource management policy. Based on understanding of how government has been carrying out human resource management policies on workforce development, this study contributes to the existing body of role of government in workforce development literature by offering resilience concept. Resilient workforce development is emerging topic which can give more information to government to do strategically reduce the risk under increasing uncertainty and complexity. This study contributes to provide a theoretical and conceptual picture of the role of government in overcoming crises. This study improves the understanding of how government responds and prepares to significant unexpected current and potential upcoming social and economic crises
(increasing global competition, technology innovation and demographic). This study provides more information of government’s human resource management policy affecting
13 workforce development from macro (OECD countries) to micro level approach (US postdoctoral researchers and government employees) using resilience concept.
Organization of the Dissertation This dissertation is organized into five chapters. This chapter serves as an introduction and background to human resource management policy and conceptual framework of workforce resilience. Chapter 2 shows the effects of government expenditure in labor market policies on the unemployment rate. In Chapter 3 examines the change of productivity and behavior of postdoctoral researchers under the change of
NIH funding. Chapter 4 explores the factors affecting to government employees’ turnover. Chapter 5 discusses the overall theoretical and practical contributions, policy implications, and limitation of this dissertation.
14
Chapter 2 : Government Expenditure on Labor Market Policies in OECD
Countries: Responding to the Economic Crisis
Abstract In most Organization for Economic Cooperation and Development (OECD) countries, the unemployment rate increased considerably during the early stage of the Great Recession of 2008. However, unemployment trends varied by country: some countries were more resilient, resulting in a faster recovery and lower or steady unemployment rates during the recession. This study evaluates the impact of different government expenditures on labor market policies (especially active labor market policies) among OECD unemployment rates. Based on panel regression and a difference-in-difference analysis utilizing panel data from 2001 to 2013, this study discusses how government active labor market policies have worked to reduce the unemployment rate, and how this policy helps resilient countries to adapt to unexpected economic crises.
Keywords: labor market policy, policy tools, resilience, government expenditure, OECD
15
Introduction
The 2008 global financial crisis caused serious economic disruptions in most countries (Gokay, 2009; Schmitt, 2011). The sudden drop in financial markets affected citizens’ daily lives most directly through unemployment. The unemployment rate shows the effects of an economic crisis on a country by representing the level of unproductive labor in its economy. When the crisis began in 2008, the Organization for Economic
Cooperation and Development (OECD) predicted that the total unemployment in OECD countries would grow from 34 million in 2008 to 42.1 million in 2010 (OECD, 2008).
However, the actual number of unemployed people was greater than expected: it dramatically increased from 30.6 million in the last quarter of 2007 to 47 million in the second quarter of 2010, representing the most rapid rise in OECD unemployment since the early 1990s (Junankar, 2011; OECD, 2008).
Unemployment still presents a serious risk, as the unemployment rate has been growing at a rapid pace since the financial crisis of 2008 (World Economic Forum, 2014,
2015, 2016).3 But while unemployment rate increased considerably during the early stage of the crisis in most OECD countries, unemployment rates varied by country. The unemployment rates in the United States rose by around 5 percentage points from 2007-
2009, but a number of countries such as Sweden and the Netherlands experienced only small increases in unemployment during the same period (OECD, 2016b). Some
3 According to the global risk reports of the World Economic Forum, survey respondents were asked to assess the likelihood and impact of their individual risks on a scale of 1 to 7, with 1 representing a risk that is not likely to happen or have impact, and 7 representing a risk very likely to occur and with massive and devastating impacts. Unemployment and underemployment had likelihood and impact values of 5. The global risk reports also show how global risks are interconnected, and unemployment is closely linked to fiscal crises. 16 countries also bounced back more effectively in the face of economic adversity; their unemployment rates returned to pre-crisis level in a relatively short time, or remained steady during the recession.
Why is there an uneven unemployment rate among OECD countries affected by the economic crisis? There are three possible explanations for these differences: the different size of negative demand shock, macroeconomic policy responses, and the structure of the labor market (Schmitt, 2011). First, the negative demand shock is hard to measure accurately by countries, but Gross Domestic Product (GDP) can be used to observe shocks on the demand side. However, the OECD (2010) found that the unemployment rate among countries varied during the recession, even though each country had similar reductions in GDP. Second, each country had different macroeconomic policy responses, such as expansionary monetary and fiscal policy, which are also difficult to quantify. In terms of the size of its fiscal stimulus package (as a share of GDP), the United States implemented a bigger macroeconomic policy than the average OECD county (Schmitt, 2011). The unemployment rate in the United States also fluctuated more than other countries (except Spain and Ireland). Third, the level of emphasis in each country’s labor market programs can be measured by expenditures on two types of labor market policies: active labor market policy (ALMP), focusing on incentive to work, and passive labor market policy (PLMP), focusing on income compensation (Vanhoudt, 1997). Each country had different levels of government expenditure in each type of program (OECD, 2011).
17
It is important to examine how different labor market policies affect the resilience of labor market after a negative shock. Accordingly, this study focuses on how different government expenditures on labor market policies among OECD countries affected unemployment rates differently, and specifically how nations adapted to the Great
Recession of 2008, because the labor market impacts from the 2008 economic crisis and recovery periods were different across countries (OECD, 2011). Countries that spent less on labor market policies prior to 2008 had a larger increase in unemployment. In particular, countries that spent less on active labor market policies countries experienced a larger increase in unemployment than countries that spent more on active labor market policies (OECD, 2011). Based on a panel regression and difference-in-difference analysis, utilizing panel data of OECD countries from 2001 to 2013, I explore the effects of different policy responses to the labor market among OECD countries. I also examine whether countries with higher active labor market policies expenditures before the crisis showed more resilience with respect to their unemployment conditions, such as a smaller change in unemployment rates after the initial crisis, in comparison to countries with lower active labor market spending. By examining the impact of active labor market policies in reducing the unemployment rate in this way, this study suggests that active labor market investments made countries more resilient during the Great Recession of
2008, and helped their citizens adapt to the unexpected crisis. In the next section, I describe the change in the unemployment rate due to the economic crisis, and review active and passive labor market policies among OECD countries. Based on the existing
18 literature, I then analyze the effects of labor market policies on the unemployment rate and discuss my findings.
Increasing Risk and the Effects and Importance of Risk Management
As seen in the 2008 economic crisis, and as experienced in previous crises, several events resulted in unexpected negative results that were not predicted prior to the crisis. The unemployment rate increased considerably during the early stage of the crisis in most OECD countries. Figure 2.1 shows the percentage-point change in the unemployment rate across countries. It covers a period that starts in 2007 – the year just before the downturn hit most economies – and ends in 2009 – the year that the economy reached its worst point in most countries (Schmitt, 2011).4
4 The National Bureau of Economic Research marks the beginning of the recession in the United States at December 2007, with the trough in June 2009. 19
Germany -0.9 Netherlands 0.2 Belgium 0.4 Austria 0.4 Switzerland 0.5 Norway 0.7 Luxembourg 1.0 Slovak Republic 1.1 Japan 1.1 France 1.1 Australia 1.2 Finland 1.4 Czech Republic 1.4 Portugal 1.5 Italy 1.7 Sweden 2.2 Denmark 2.2 Canada 2.3 United Kingdom 2.4 New Zealand 2.4 Hungary 2.6 United States 4.7 Spain 9.7 -2.0 0.0 2.0 4.0 6.0 8.0 10.0 Percentage Points
Figure 2.1. Change in Unemployment Rates by Country, 2007-2009
Even today, however, many countries still face difficulties in reducing high unemployment rates. As seen in Figure 2.2, the unemployment rate among OECD nations was 6.8% in 2015, showing only a minimal reduction since 2010 (8.3%). Long-term unemployment remained at 35.1% in 2013, compared to 31.5% in 2010. The increasing frequency of social crises or events increase pressure on the government to solve the underlying problems (Hogwood & Gunn, 1984; Kingdon, 1984). Public policy makers
20 have sought to anticipate unexpected crises in order to reduce the risk to human life and society. Their goal is to maintain a sustainable development despite threats posed by intermittently occurring natural and non-natural (e.g. man-made) hazardous events. These efforts identify emergency management5 as an important function of government. As the government and public policy studies have been slow to respond and meet these challenges, individuals and the larger society have in many respects, become victims of the crisis. More people and societies are thereby exposed to this extremely risky situation
(Petak, 1985).
33.8% 31.5% 28.5%
8.3% 6.8% 5.6%
2007 2010 2015
Unemployment Rate Long-Term Unemployment Rate
Figure 2.2. OECD Unemployment Rate Before and After 2008 Economic Crisis
5 Emergency management can be defined as the process of developing and implementing policies related to the government response to, as well as preventive solutions for, either upcoming or existing crises. 21
Necessity of Risk Management and Choice of Policy Tools: ALMP and PLMP
Policy Tools
When facing different kinds and levels of risk, governments around the world try to reduce that risk and increase resilience by intervening through policies. Governments use policy tools for dealing with risk, which may influence individual behaviors through a combination of punishments, rewards, incentives, and alerts (Bemelmans-Videc, 1998;
Elmore, 1987; Howlett, 1991; Howlett & Ramesh, 1993; King, 2007; Rist, 1998;
Schneider & Ingram, 1997; Stewart, 1993; Stone, 2002). Notably, incentives and disincentives included in policy tools are designed to promote expected behaviors while discouraging disapproved ones (Salamon, 2002a, 2002b; Stone, 2007). By influencing individual behaviors, government policy tools can effectively change a society
(Bemelmans-Videc, 1998). These systemized approaches are intended to produce expected changes in individual behavior, eventually resulting in “the necessary and sufficient conditions for a valued outcome” (Bobrow, 2006; Stone, 2002). In order to produce those outcomes, policy tools contain assumptions about behavioral patterns of the target population, and about the necessary means to achieve the intended outcomes
(Schneider & Ingram, 1997). In other words, policy tools by their nature are very susceptible to natural human behavior (Somit & Peterson, 2003). Using policy therefore leads to a government-expected outcome to a goal-oriented approach.
22
Different Policy Tools for Unemployment Problem: ALMP vs. PLMP
There are several reasons the unemployment rate rises. Broadly speaking, two factors are responsible: 1) a structural problem of mismatches in the labor market, and 2) cyclical problems where lower demand for goods and services creates less demand for employees (Nie & Struby, 2011).6 To solve the unemployment problem, a government intervenes in the area of the labor market where the private sector would not.
Government policies toward the unemployed generally fall into two categories: active labor market policies and passive labor market policies. The government uses two types of policy tools to manage the labor market. PLMPs aim to indirectly help the unemployed find jobs by providing them with income security, such as unemployment insurance and benefits (OECD, 2013). These policies are focused on static instruments to help people change their status after a crisis situation: not to help them escape unemployed status, but to support income sources for the unemployed. That is, this government tool serves as an automatic stabilizer for the economy by cushioning the declining aggregate demand
(Blinder, 2004; Nie & Struby, 2011). On the other hand, ALMPs aim to encourage the unemployed to find jobs directly, by supporting job searches, creating employment services, helping to increase skills by training, and enhancing labor demand by providing wage subsidies and job vouchers. So, the goals of ALMPs are to expand the workforce actively and directly by increasing employability (Nie & Struby, 2011).
6 Nie & Struby (2011) pointed out two more reasons for unemployment in the US: 3) Reduced mobility due to the house lock problem where the unemployed can’t sell their houses due to the depressed housing market, and 4) extended UI benefits that discourage the unemployed from finding jobs. This model can also be applied to most OECD countries, because they experience similar problems. 23
Each OECD country has different levels of expenditures on labor market policies, and different shares of expenditures on ALMPs and PLMPs. Differences in these policies represent institutionalized patterns of different cultures and experiences (Alesina &
Glaeser, 2004; Calmfors & Driffill, 1998; Kahn, 2012; Rodrik, 1988; Summers, Gruber,
& Vergara, 1993). However, while national perspectives on the labor market and government labor market policies vary, there are still debates about the effectiveness of active vs. passive labor market policies (Kahn, 2012), and about balancing the ratio of expenditures between the two (Layard, Nickell, & Jackman, 2005; Forslund & Krueger,
1997). Some studies argue that active labor market policies have little to no effect on unemployment (Glyn, Baker, Howell, & Schmitt, 2003; Bertola, Blau, & Khan, 2007;
Blanchard & Welfers, 2000). Most active labor market policies that have been evaluated are not cost-effective (Betcherman & Olivas, 2004), and there is little compelling evidence that active labor market policies are effective in preserving jobs during a crisis.
Still, other scholars defend active labor market policies, on the grounds that passive labor market policies give high social benefits to the unemployed and can reduce their incentives for work and employment. Active labor market policies may limit these adverse effects by improving labor market matching through establishing incentives for the development of skills and enhancing job search support and monitoring, thereby raising employment (Bassanini & Duval, 2009; Belot & Van Ours, 2004; Murtin, de
Serres, & Hijzen, 2013; Murtin & Robin, 2014).7
7 With wage and hiring subsidies, for instance, employers have incentives for hiring new employers (Brown & Koetti, 2015). 24
The financial and economic crisis that hit OECD countries in 2008 has made it evident that OECD countries’ labor markets fare differently during unexpected economic crises and prolonged recessions. It was clear that some countries were better prepared to deal with the economic crisis and were able to manage a faster recovery (i.e. were more resilient), therefore reducing social conflicts. OECD studies argue that there was “the tendency for the hardest hit countries to have invested relatively little in labor market programs prior to the crisis” (OECD, 2011, p. 32). Many studies also point to cross- country differences in the resilience of employment to shocks – most prominently those between the United States and Continental European countries (Burgess, Knetter, &
Michelacci, 2000; Balakrishnan & Michelacci, 2001; Amisano & Serrati, 2003;
Dustmann, Glitz, & Vogel, 2010; Ormerod, 2010). In this context, previous research suggests that structural policy settings and labor market institutions can amplify or mitigate the employment effects of shocks, and make them more or less persistent
(Bassanini & Duval, 2006; OECD, 2010, 2011). As seen in Figure 2.3, different government expenditure levels (% of GDP) in active and passive labor market policies represent the different priorities of each government. Based on Figure 2.3 and Figure 2.1, countries that had higher unemployment rate changes spent less on active labor market policies than countries that had lower rate changes. Therefore, we can expect that different levels of government expenditure on different labor market policies might lead to the different unemployment rates of each country.
25
Relative Labor Market Policies Spending (2001-2013)
Active Labor Market Policies Passive Labor Market Policies
Denmark Sweden Netherlands Germany France Finland Spain Belgium Austria Norway Portugal Switzerland Luxembourg Hungary Italy New Zealand United Kingdom Australia Canada Slovak Republic Japan Czech Republic United States 0 0.5 1 1.5 2 2.5 3 3.5 4 Expenditures as a share of GDP (%)
Figure 2.3. Spending on Active and Passive Labor Market Policies (2001-2013)
Figure 2.4 shows the change in unemployment rates among two types of countries: those with higher spending on active labor market policies,8 and those with
8 I calculate the average of ‘ALMP expenditure per unemployed worker as a percentage of GDP per capita’ from 2001 to 2007 (before the 2008 crisis) of each country. Then I divide the countries into two groups, split at the median. Above-median countries are Austria, Belgium, Denmark, Finland, France, Germany, Luxemburg, Netherlands, Norway, Sweden, and Switzerland. Below-median countries are Canada, the Czech Republic, Hungary, Italy, Japan, New Zealand, Portugal, Spain, the United Kingdom, the United States, and the Slovak Republic. See Table 2.5 in the additional Tables section. 26 lower spending on active labor market policies. Prior to the 2008 economic crisis, the change in unemployment rate of both groups is inconsequential, even though there is some fluctuation. After the 2008 economic crisis, both groups experienced greater changes in the unemployment rate. However, countries with higher ALMP expenditures had fewer rate changes than their low-ALMP counterparts. The gap between the two groups increased after the 2008 economic shock.
5%
4%
3%
2%
1%
0%
-1% 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Lower Spending on ALMP Higher Spending on ALMP
Figure 2.4. Percentage Point Change in Unemployment Rate
27
Resilience
The literature focuses on the aftermath of economic shocks, discussing rescue, recovery, and emergency aspects (Bozeman & Pandey, 2004). Overall, after an economic crisis we tend to focus on minimizing the damage. Many polices thus emphasize responding effectively to the crisis, and trying to recover from it. Even with the development of sophisticated technology for economic forecasting, it is hard to predict exactly what can and will happen. However, it is necessary to take a broader perspective on how we can make society and systems more robust to an unexpected crisis. There are many studies regarding responses to an unexpected natural crisis, such as Hurricane
Katrina, that can be utilized in developing resilience to economic catastrophes.9
Resilience can be defined in three ways (Martin & Sunley, 2014). First, it is the ability to bounce back to the pre-crisis status in the face of adversity (Holling, 1973;
Pimm, 1984), also known as engineering resilience. This definition is focused on the recovery speed and amount: e.g., whether individuals have recovered their skills or wages from challenges such as unemployment. Some predictive ability is important to maintain stability for engineering resilience (Martin & Sunley, 2014; O'Neill, 1986). A second definition is the ability to absorb shocks (Holling, 1973; Martin & Sunley, 2014), called ecological resilience. The extent of how much a given shock and adversity has been absorbed is an important facet of ecological resilience (Martin & Sunley, 2007). The third definition is a positive adaptation in the context of adversity (Luthar et al., 2000). This
9 Resilience has growing importance for both organizations and individuals (Martin & Sunley, 2014). The concept originated in psychology, but is rapidly spreading to other fields, including workforce development. 28 adaptive resilience focuses on a robust transition, enhancing internal capacity to help adapt continuously to shocks (Marcos & Macaulay, 2008; Martin, 2012). The concept of resilience is not confined to overcoming challenges, but also includes expanding to adapt to the new environment.
From the standpoint of a nation, resilience is defined as its ability to maintain the status quo and not go down, or at least to minimize the damages caused by the existing economic downturns without losing capacity, and ideally to bounce forward by adapting to the adversity (Aiginger, 2009). Making a country more resilient requires a long-term strategy for the structural developments of the national economy (Bonanno, 2004).
Furthermore, more sophisticated policy options are necessary to maintain and to adapt to relative stability, to minimize the effects of economic troubles. That is, a nation’s resilience ability can be improved depending on internal and external factors, including risk, protective factors, and environmental changes from within and outside the national boundaries (Rutter, 1993). Resilience is a dynamic process: protective factors such as internal and external resources can influence and improve resilience (Glantz & Sloboda,
1999). By allocating resources efficiently, resilience can therefore be used as adequate risk management (Perrings, 2006). In this study, labor market resilience is defined as the capacity of a labor market to resist or quickly recover from negative exogenous shocks and disturbances, and to renew, adjust, or re-orient itself in order to benefit from positive shocks (Fenger, Koster, Struyven, & Veen, 2014).
The unemployment rate represents the labor market’s condition because it shows the market’s economic vitality and capacity. So, the change in the unemployment rate is a
29 good measurement of resilience in the labor market. Each country’s government implements labor market policies (active and passive) to enhance individual resilience from unemployment, and to prevent massive societal panic from widespread unemployment. Figure 2.5 shows how different countries’ resilience levels result in different unemployment rates after facing the same crisis event. There was no significant difference between the groups before the crisis. After the crisis, however, one group of countries displayed a low degree of resilience, and featured a higher change of unemployment rate and a slower return to the previous status. However, the other group of countries had a high degree of resilience, with a lower change of unemployment rate and a quick return to the previous status.
30
Figure 2.5. Response of High/Low Resilience Countries to a Crisis Event
From this point, an individual’s level of resilience in the labor market is defined as the capability of the individual to cope successfully with the adversity of unemployment by having assertive job search behaviors (Moorhouse & Caltabiano,
2007). Since resilience is a series of processes (Glantz & Sloboda, 1999), it is assumed that positive outcomes are the result of resilience that moderates the adverse effects of unemployment. This is related to the labor market intervention policies of the country, and the risk of each country would differ by national resilience level. Therefore, their different levels of unemployment are related to the inherent vulnerability and nurtured resilience of each government’s labor market policies, toward the risk of being affected by external shocks. The resilience in unemployment can be referred to as the government 31 policy-induced ability to recover from or adjust to the negative impacts of adverse exogenous shocks.
Previous debates and findings show that active labor market policies affect unemployment rates and resilience to adversity (Bassanini & Duval, 2009; Belot & Van
Ours, 2004; Murtin et al., 2013; Murtin & Robin, 2014). However, it is necessary to show empirical analysis of the impact of active labor market policies on unemployment rates and resilience to the 2008 economic crisis, using recently updated data. By focusing on the active labor market policy, this study asks two questions: 1) how is government expenditure on active labor market policies related to the unemployment rate, including long-term unemployment? 2) How is government expenditure on active labor market policies related to resilience (the change of unemployment rate, including long-term unemployment)?
Hypothesis 1.1: Higher spending on active labor market policies is positively
associated with lower unemployment.
o Hypothesis 1.1a: Higher spending on active labor market policies is
positively associated with a lower unemployment rate.
o Hypothesis 1.1b: Higher spending on active labor market policies is
positively associated with a lower long-term unemployment rate.
Hypothesis 1.2: Countries that spent more on active labor market policies before
a crisis are more resilient during that crisis.
32
o Hypothesis 1.2a: Countries that spent more on active labor market
policies before a crisis have less change in unemployment rates during the
crisis.
o Hypothesis 1.2b: Countries that spent more on active labor market
policies before a crisis have less change in long-term unemployment rates
during the crisis.
Data and Measurement This analysis is based on a dataset covering 23 countries, compiled from the
OECD and World Bank data from 2001 to 2013.10 The main data elements are unemployment rate, government labor market policies (active labor market policies vs. passive labor market policies), and other variables. To control for country-specific factors
(because each country has its own characteristics and political environment), this study used time and country fixed-effect components in panel regression analysis.
10 Due to limited data for 2001-2013, I only include 23 countries: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Hungary, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Slovak Republic, Spain, Sweden, Switzerland, United Kingdom, United States. 33
Dependent Variables
Unemployment Rate
I look at both the unemployment rate and the long-term unemployment rate11 as dependent variables, as the long-term rate measures structural unemployment. The long- term unemployment rate indicates an unemployment problem that can’t be solved by an increase in aggregated demand, such as the mismatch of labor suppliers and demanders
(Bivens & Shierholz, 2014).12
Resilience
I use changes in the unemployment rate as the measurement of resilience. The change of unemployment rate includes three elements of resilience (engineering resilience, ecological resilience, and adaptive resilience). This change illustrates the dynamics of how the unemployed lost or got jobs, and moved into and out of the labor market during the crisis. That is, the change of unemployment shows how well people bounce back from the crisis, and how well they absorb and adapt to the adversity. With this understanding, I examine how the protective factor (government labor market policies, especially active labor market policies) helps increase resilience (less change of unemployment rate, and less change of long-term unemployment rate) toward the adversity (2008 economic crisis).
11 I define long-term unemployment rate as unemployment lasting 12 months or more (OECD, 2016d). 12 When workers don’t have skills that employers want, employability decreases. 34
Independent Variables
To examine factors that affect the unemployment rate, I use active labor market policies and passive labor market policies as the key independent variables, in order to analyze their effect on reducing unemployment. Different expenditure levels are primarily a result of the different roles assigned to active labor market policies and passive labor market policies in the context of national economic and employment policies (Schmid, 1988). Countries have different strategies for labor market improvement, and this is shown by government expenditures on active and passive labor market policies (% of GDP). The relative comparison of labor market policy expenditures represents the labor priorities of each government. In the measure of spending on labor market policies, I do not directly use the ‘government expenditure on labor market policies as % of GDP.’ Instead, I use a standard measure of ‘expenditure per unemployed worker as a percentage of GDP per capita’13 to adjust for differences across countries in unemployment rates and the size of the economy (Bassanini & Duval, 2006; Layard et al., 2005; Nie & Struby, 2011; Schmitt, 2011).
As explained above, I divide countries into two groups by their levels of expenditure on active labor market policies. I use the same criteria that I used in Figure
2.5 to form these groups, and I calculate the average of ‘active labor market policies’
13 Expenditure (as a share of GDP) per percentage point of unemployment is a standard measure of the generosity of national ALMP programs. Using only expenditures (as a share of GDP) would exaggerate the generosity of ALMP programs in the case of countries with high levels of unemployment. In the case of Denmark, dividing the total expenditures (as a share of GDP) by the unemployment rate emphasizes that the Danish system is exceptionally generous – per unemployed worker” (Schmitt, 2011). It is calculated as ‘Government expenditure on labor market policies as % of GDP * (total population / number of unemployed).’ 35 expenditure per unemployed worker as a percentage of GDP per capita’ from 2001 to
2007 (before the 2008 crisis), for each country. Then I divide the countries into two groups, split at the median (see Table 2.5).
Controlling for Other Relevant Variables
In addition to government expenditure on labor market policies, many other factors affect the unemployment rate (Nie & Struby, 2011). Just as each country has different emphases on active labor market policies and passive labor market policies, each one likewise has different emphases on other policies and different social/economic environments. Therefore, it is important to carefully consider other factors affecting the unemployment rate. I include a set of several such control variables in my analysis. First, a tax wedge is defined as the difference between the cost of a worker to the employer and take-home pay, and is related to increased unemployment rates (Blanchard, Jaumotte, &
Loungani, 2014). In terms of employment protection affecting unemployment rates, I used trade union density (Nickell, 1997).14 To control the effects of the business cycle on unemployment, output gaps (differences between actual and potential GDP as % of potential GDP) are used as control variables (Nie & Struby, 2011). I also consider several other control variables: openness,15 manufacturing (% of GDP),16 inflation rate,17 long-
14 Trade union density is defined as “the ratio of wage and salary earners that are trade union members, divided by the total number of wage and salary earner” (OECD, 2016e). 15 This captures the impact of external factors that each country has, defined as ‘openness to trade’ (trade % of GDP): “Trade is the sum of exports and imports of goods and services, measured as a share of gross domestic product” (The World Bank, 2016). 16 Share of manufacturing in GDP. 17 Inflation (annual %) shows the rate of price change in an economy as a whole (The World Bank, 2016). 36 term interest rate,18 the labor force participation rate of women, elderly population,19 and government debt.20 Descriptive statistics for all variables are reported in Table 2.1.21
18 “Long-term interest rates refer to government bonds maturing in ten years. Long-term interest rates are one of the determinants of business investment. Low long-term interest rates encourage investment and high interest rates discourage it” (OECD, 2016d). 19 Total % of population over 65 years old (OECD, 2016a). 20 General government debt-to-GDP ratio (OECD, 2016c). 21 I also show descriptive statistics of Higher and Lower ALMP Expenditure Countries by Pre-Post Crisis in Tables 2.7 and 2.8, in the additional Tables section. 37
Variables N Mean SD Min Max
Unemployment rate 287 7.13 3.56 2.10 26.60 Long-term unemployment rate 286 31.36 15.68 4.33 73.12 Change of unemployment rate 287 0.16 1.01 -3.50 6.60 Change of long term unemployment rate 285 0.22 3.83 -19.04 12.83 ALMP Expenditure 287 22.22 17.56 2.57 123.15 (per unemployed worker as a percentage of GDP per capita) PLMP Expenditure 287 32.15 22.12 3.27 131.42 (per unemployed worker as a percentage of GDP per capita) Higher ALMP Expenditure Countries 287 0.49 0.50 0.00 1.00 (=1, Lower ALMP Expenditure Countries==0) Post crisis (After 2008 =1, before year =0) 287 0.48 0.50 0.00 1.00
38 Tax wedge 287 39.03 9.71 15.87 57.10
Output gap (% of potential GDP) 276 0.07 2.61 -8.01 7.79 Manufacturing (% of GDP) 277 16.62 4.96 5.44 27.64 Openness (Trade % of GDP) 287 90.91 57.79 20.26 352.90 Union Density 287 31.19 19.71 7.55 79.08 Inflation rate 287 2.26 2.18 -5.39 15.65 Long-term interest rates 281 4.26 1.54 0.65 10.55 Labor force participation rate of women 287 68.16 7.31 50.73 79.86 Elderly population (% of population over 65 old) 287 15.78 2.43 11.42 24.15 Government Debt (% of GDP ) 273 54.89 30.48 3.67 195.99 Table 2.1. Descriptive Statistics
Models First, I examine the impact of different government expenditures on OECD countries’ labor market policies on the unemployment rate. I hypothesize that unemployment rate differences across countries are attributable to differences in government expenditures on active labor market policies, after controlling for passive labor market policies and other economic and demographic factors. Since the different policy factors of different countries are likely to have different impacts on their unemployed populations, the unemployment rate is used as dependent variable. My hypothesis predicts a negative value for β1 (less government expenditure on active labor market policy increases the unemployment rate). I used the one-year lagged independent variables to solve endogeneity problems.
Yit = β0 + β1ALMPit-1 + β2Cit-1 + µit + εit (1)
where i denotes each of 23 countries (i = 1, …23) and t is the years of the period
2001-2013 (t = 2001, ... 2013); the dependent variable Yit is the unemployment rate
(including long-term unemployment); ALMPit is the key variable of interest (labor market policies expenditure per unemployed worker, as a percentage of GDP per capita);
Cit is the vector of control variables that influence unemployment rate (tax wedge, output gap, union density, debt to GDP ratio, manufacturing ratio, trade openness, inflation rate, long term interest rate, labor force participation rate of women, and percentage of population aged 65 or over); µit is the unobserved fixed effect for each country and each year; and εit is the error term. I performed Hausman tests to check whether the fixed- effects model was more appropriate than a random-effects model. Although significant
39
Hausman tests would suggest that a fixed-effects model is appropriate, I also show the random-effects regressions to compare regression coefficient estimates.
Second, I examine the effects of active labor market policies on the resilience of the labor market. Using a difference-in-difference analysis, I compare the change of unemployment rate in countries that spend more on active labor market policies to that in countries that spend less on active labor market policies. Specifically, I run the following model with five dependent variables: before crisis to year 1) 2009, 2) 2010, 3) 2011, 4)
2012, and 5) 2013.
Yit = β0 + β1 HigherALMPit + β2 PostCrisisit + β3 (HigherALMPit × PostCrisisit) +
β4Cit-1 + µit + εit (2)
In this equation, 푦푖푡 is the outcome of interest (percentage point change in the unemployment rate, including long-term unemployment) for country i given time t. The dummy variable HigherALMP captures possible differences between the treatment
(higher ALMP spending countries)22 and comparison groups (lower ALMP spending countries). The variable Postcrisis is equal to 1 for the second time period (from 2008 to
2013), and equal to zero for 2001 to 2007. The variable C represents other control variables (tax wedge, output gap, union density, debt to GDP ratio, manufacturing ratio, trade openness, inflation rate, long-term interest rate, labor force participation rate of women, and percentage of population aged 65 over).
22 I use the same criteria that I used in Figure 2.4 to make two groups of countries. See Table 2.5 in the additional Tables section. 40
Results This section examines the impact of different government expenditures on active labor market policies on the unemployment rate among OECD countries. Panel regression (both time and country fixed and random effects models are considered) and the difference-in-difference analysis provide the estimates of the overall effectiveness of active labor market policies in reducing the unemployment rate, and in increasing resilience to the economic crisis. For these analyses, I also examine the relationship between the unemployment rate and other relevant variables because ALMP is only one of the variables affecting the unemployment rate. Table 2.2 shows the overall effects of active labor market policies on the unemployment rate (Models 1 and 2), including the long-term unemployment rate (Models 3 and 4). The regression results in Models 1 and 2 of Table 2.2 show that an increase in spending on active labor market policies
(expenditure per unemployed worker as a percentage of GDP per capita) of 1 percent typically reduces the unemployment rate by around 0.06 percentage points (Models 1 and
2), and reduces the long-term unemployment rate by around 0.12 and 0.15 percentage points (Models 3 and 4).
However, passive labor market policies (expenditure per unemployed worker as a percentage of GDP per capita) do not have significant effects on the unemployment rate.
An increased output gap (deviation of actual GDP from potential GDP as % of potential
GDP) decreases the unemployment rate. To examine the robustness of these findings, I conducted additional analyses, expanding my scope to all available data from the OECD countries. The same effects appear, and I report these results in Table 2.8 (in the additional Tables section).
41
Unemployment Rate Long-Term Unemployment Rate Model 1 Model 2 Model 3 Model 4 Estimation Method FE RE FE RE ALMP Expenditure -0.0605*** -0.0629*** -0.124** -0.153*** (0.0170) (0.0172) (0.0477) (0.0484) PLMP Expenditure 0.00852 0.00590 0.00795 -0.0125 (0.0162) (0.0161) (0.0451) (0.0452) Tax wedge -0.0359 0.158*** -0.0464 0.495*** (0.0909) (0.0609) (0.253) (0.180) Output gap -0.394*** -0.353*** -1.198*** -1.032*** (0.0563) (0.0436) (0.157) (0.123) Manufacturing -0.111 -0.219*** 0.702** 0.626*** (0.117) (0.0757) (0.325) (0.217) Openness -0.00191 0.0214** 0.0327 0.0728** (0.0173) (0.0107) (0.0481) (0.0313) Union Density 0.0446 -0.0327 0.585*** -0.0376
42 (0.0742) (0.0329) (0.207) (0.100)
Inflation -0.167*** -0.160*** -0.245* -0.211 (0.0520) (0.0521) (0.145) (0.145) Long-term interest rates 0.757*** 0.710*** -0.505 -0.695** (0.114) (0.102) (0.317) (0.286) Women’s labor force participation rate 0.256*** 0.242*** -0.293* -0.230* (0.0587) (0.0454) (0.164) (0.129) Population ages 65 and above (% of total) -1.045*** -0.531*** -0.648 -1.253** (0.233) (0.176) (0.649) (0.510) Government Debt 0.0235** 0.0272*** 0.137*** 0.147*** (0.0117) (0.0102) (0.0326) (0.0287) Constant 4.225 -7.230* 28.06* 31.68** (5.455) (4.363) (15.18) (12.51) R-squared 0.0015 0.1128 0.0077 0.5755 Observations 251 251 250 250 Table 2.2. Unemployment Rate, Long-Term Unemployment Rate: Year 2001-2013
Although expenditure on active labor market policies appears to be effective in reducing unemployment rates, there is a concern about whether this policy is effective to overcome the economic crisis. That is, there is doubt about whether spending on active labor market policies helps to improve resilience for recovering from unemployment problems during adversity. To assess this resilience, I use a difference-in-difference analysis.
Tables 2.3 and 2.4 depict the effects of active labor market policies on unemployment rates by showing the difference of resilience between the two country groups (higher ALMP spending vs. lower ALMP spending) as time goes on. It summarizes the results of regressions reporting β1, β2, and β3 from equation 2. β1 (the coefficient of Higher ALMP countries) represents the overall difference between higher and lower spending on active labor market policies, β2 (the coefficient of Post Crisis) represents the overall trend between before and after the crisis for all countries, and β3
(the coefficient of Post Crisis * Higher ALMP countries) represents the main effect from a difference-in-difference analysis: the effect post-crisis of the higher spending on active labor market policies countries, controlling for other relevant variables effects. The results in Table 2.3 show that the level of β3 decreased from -1.07 (p<0.01) in 2009 to -
0.64 (p<0.01) in 2013. The difference in unemployment rates between higher ALMP and lower ALMP countries decreases over time from 2009 to 2013. The results in Table 2.4 show that the level of β3 decreased from -2.38 (p<0.01) in 2010 to -1.81 (p<0.01) in 2013.
The difference of long-term unemployment rates between higher ALMP and lower
ALMP countries also decreases over time, from 2009 to 2013.
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These results suggest that there was a gap in the unemployment and long-term unemployment rates between the two groups at the beginning of the economic crisis.
However, this gap decreased, as in Figure 2.3, due to the countries’ recovery efforts after the crisis. That is, resilient countries (those with higher spending on active labor market policies) had a smaller impact on the unemployment rate from the economic crisis, compared to less resilient countries (those with lower spending on active labor market policies). In summary, the analyses show that higher spending on active labor market policies is positively associated with a lower unemployment and lower long-term unemployment rate (support for hypotheses H1a and H1b), and that countries that spent more on active labor market policies before a crisis are more resilient (less change in unemployment and long-term unemployment rates) in the crisis (support for hypotheses
H2a and H2b).
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Model 1 Model 2 Model 3 Model 4 Model 5 Trends Over Time ≤2009 ≤ 2010 ≤ 2011 ≤ 2012 ≤ 2013 Higher ALMP Countries × Postcrisis -1.075*** -0.870*** -0.782*** -0.725*** -0.637*** (0.284) (0.249) (0.225) (0.215) (0.206) Higher ALMP Countries -0.268 -0.278 -0.303 -0.331 -0.375 (0.271) (0.262) (0.248) (0.243) (0.238) Postcrisis 2.980*** 1.791*** 0.954** 1.633*** 1.468*** (0.366) (0.395) (0.370) (0.364) (0.387) ALMP Expenditure -0.00741 -0.00546 -0.00563 -0.00753 -0.00986* (0.00702) (0.00667) (0.00623) (0.00602) (0.00576) PLMP Expenditure 0.0130** 0.0107* 0.0116** 0.0136*** 0.0150*** (0.00607) (0.00562) (0.00518) (0.00493) (0.00467)
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Tax wedge 0.0152 0.0140 0.0138 0.0119 0.0144 (0.0126) (0.0118) (0.0108) (0.0102) (0.00985) Output gap 0.0672* 0.0703* 0.0635* 0.0500 0.0611** (0.0380) (0.0361) (0.0339) (0.0325) (0.0303) Manufacturing -0.0189 -0.0275 -0.0319* -0.0402** -0.0480*** (0.0203) (0.0187) (0.0175) (0.0168) (0.0160) Openness -0.000804 0.000528 0.000436 0.000677 0.00105 (0.00280) (0.00263) (0.00239) (0.00223) (0.00209) Continued Table 2.3. Percentage Point Change in Unemployment Rate
Table 2.3 continued
Model 1 Model 2 Model 3 Model 4 Model 5 Trends Over Time ≤2009 ≤ 2010 ≤ 2011 ≤ 2012 ≤ 2013 Union Density -0.00549 -0.00311 -0.00340 -0.00292 -0.00223 (0.00434) (0.00397) (0.00358) (0.00336) (0.00314) Inflation 0.00252 -0.0275 -0.0270 -0.0488 -0.0461 (0.0379) (0.0336) (0.0316) (0.0300) (0.0291) Long-term interest rates 0.118 0.113 0.134* 0.188*** 0.140*** (0.0925) (0.0823) (0.0719) (0.0573) (0.0470) Women’s labor force participation rate 0.0429** 0.0332* 0.0353** 0.0333** 0.0313** (0.0183) (0.0172) (0.0157) (0.0149) (0.0141) 46 % of Population ages 65 and above 0.0837 0.0802* 0.0944** 0.122*** 0.119***
(0.0516) (0.0473) (0.0422) (0.0386) (0.0363) Government Debt -0.000893 -0.00259 -0.00314 -0.00499* -0.00597** (0.00361) (0.00332) (0.00296) (0.00274) (0.00254) Constant -5.724*** -4.738*** -5.093*** -5.310*** -4.803*** (1.884) (1.720) (1.544) (1.418) (1.324) Adj R-squared 0.5222 0.4923 0.4972 0.5027 0.4907 Observations 168 190 212 233 251
Model 1 Model 2 Model 3 Model 4 Model 5 Trends Over Time ≤2009 ≤ 2010 ≤ 2011 ≤ 2012 ≤ 2013 Higher ALMP Countries × Postcrisis -1.666 -2.377** -1.854** -1.877** -1.815** (1.080) (0.964) (0.897) (0.838) (0.798) Higher ALMP Countries 0.259 0.151 0.241 0.127 0.173 (1.030) (1.017) (0.989) (0.949) (0.920) Postcrisis 1.243 7.744*** 3.107** 1.581 2.409 (1.409) (1.545) (1.490) (1.434) (1.507) ALMP Expenditure -0.0379 -0.0318 -0.0228 -0.0177 -0.0248 (0.0285) (0.0277) (0.0265) (0.0251) (0.0238) PLMP Expenditure 0.0358 0.0310 0.0224 0.0200 0.0233 (0.0231) (0.0217) (0.0206) (0.0192) (0.0180)
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Tax wedge 0.0118 0.0262 0.0163 0.00940 -0.00103 (0.0479) (0.0457) (0.0429) (0.0398) (0.0381) Output gap -0.644*** -0.593*** -0.524*** -0.527*** -0.542*** (0.144) (0.140) (0.135) (0.127) (0.117) Manufacturing -0.0471 -0.0431 -0.0576 -0.0668 -0.0772 (0.0782) (0.0733) (0.0705) (0.0663) (0.0624) Openness -0.00238 -0.00194 0.00134 0.00279 0.00697 (0.0107) (0.0102) (0.00951) (0.00871) (0.00810) Continued Table 2.4. Percentage Point Change in Long Term Unemployment Rate
Table 2.4 Continued
Model 1 Model 2 Model 3 Model 4 Model 5 Trends Over Time ≤2009 ≤ 2010 ≤ 2011 ≤ 2012 ≤ 2013 Union Density -0.0254 -0.0200 -0.0158 -0.0108 -0.0121 (0.0167) (0.0155) (0.0144) (0.0132) (0.0122) Inflation 0.165 0.147 0.161 0.0982 0.0672 (0.144) (0.130) (0.126) (0.117) (0.112) Long-term interest rates 0.215 0.0691 -0.275 -0.213 -0.00914 (0.353) (0.320) (0.287) (0.223) (0.181) Women’s labor force participation rate 0.130* 0.111 0.0677 0.0547 0.0656 (0.0699) (0.0670) (0.0627) (0.0583) (0.0548) 48 % of Population ages 65 and above 0.106 0.0101 -0.0216 0.0197 0.105
(0.197) (0.185) (0.169) (0.151) (0.141) Government Debt 0.0161 0.0118 0.00517 0.000558 0.00146 (0.0138) (0.0129) (0.0118) (0.0107) (0.00980) Constant -14.01* -11.06 -5.194 -4.656 -7.377 (7.192) (6.702) (6.181) (5.553) (5.138) Adj R-squared 0.3201 0.4900 0.4588 0.4475 0.4478 Observations 166 188 210 231 249
Discussion Today’s economic problems are getting more complex and globalized than ever before, thus calling for governments to prepare for the worst possible economic outcomes. With this understanding, the government should not only focus on crisis management (Petak, 1985) but also take proactive steps to tackle the issues (Vernon,
1971). Personal and social costs will occur in the future as a result of our failure to adequately identify, evaluate, and develop the right policies with limited resources.
Building resiliency means that a country can bounce back to pre-crisis conditions and minimize the negative effects of the crisis, while preserving core competencies for the whole economy. The government can take the lead in providing safety nets for social and economic actors (corporations, individuals etc.), to actively engage in the economic and social transformation with fewer burdens (e.g. possible mass layoffs and isolated from society).
Policy options for addressing unemployment problems remain controversial. This study found out that active labor market policies are effective tools for reducing the unemployment rate, including long-term unemployment, by bolstering resilience to adversity (in this case, the economic crisis of 2008). Active labor market policies help the unemployed adapt to patterns of structural changes that are deeply integrated into shifts in the global division of labor. Like countries with lower spending on active labor market policies, those with higher spending also had a difficult time with the increasing unemployment rate due to the 2008 economic crisis. However, higher ALMP spending resulted in considerably higher recovery rates, while minimizing the negative consequences of the global recession.
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A more tailored set of active labor market policies will be needed to keep up with recent changes in the nature of unemployment problems (the combination of both structural and cyclical reasons). For instance, some sectors demanded employees during recovery periods, but they could not find the people that they want (Nie & Struby, 2011).
Skill mismatch issues should also be considered in government active labor market policies (Sahin, Song, Topa, & Violante, 2011).23
In this paper, I investigated the effect of government labor market policies on unemployment rates by extending the period, and found that active labor market policies were a reasonable way to reduce unemployment. Furthermore, I tried to measure resilience after experiencing the 2008 economic crisis, and discovered that active labor market policies were effective for generating resilience in the face of unexpected adversity. However, this study had some limitations. First, I could not include all OECD countries in my analysis due to limited available data in the OECD and World Bank dataset. Second, although I tried to include many important variables affecting unemployment in the analysis, based on previous studies, it is possible that I did not take into account some influential factors. Third, this study doesn’t consider the cost from government expenditure on labor market policies. The main source of government spending is tax revenue, but this study focused on the unemployment rate as a return on spending. So, I did not calculate the social cost from government labor market policies.
23 Sahin, Song, Topa, & Violante (2011) found that mismatch unemployment across industries contributed at most about 0.8 percentage points to the increase in the unemployment rate. It has declined starting in 2010, but still remains above its pre-recession levels. They also found that 1.4 percentage points of the recent (2011) surge in US unemployment can be attributed to occupational mismatch measured. 50
Despite these limitations, this paper still contributes to the literature related to the benefits of labor market policies.
In this paper, I only considered one aspect of resilience from the economic crisis, the unemployment rate. I demonstrated that active labor market policies are an effective policy tool for promoting resilience, while passive labor market policies do not affect unemployment rates. However, this difference might also come from the different characteristics of active and passive labor market policies. Passive labor market policies focus on maintaining purchasing ability in the absence of a job by providing income replacement. It is possible that passive labor market policies promote non-economic resilience. That is, even though passive labor market policies do little to reduce the unemployment rate, they may have an effect on overcoming an economic crisis. Further studies need to explore other characteristics of resilience from labor market policies, especially focusing on passive labor market policies (e.g., how labor marker policies affect economic growth).
Each country might require different combinations of strategies to address unemployment problems, because each country has its own distinct labor market and situation. Therefore, it is hard to provide one solution to the unemployment problem.
Even though each country has a different history, culture, and environment, the results of this study can shed light on reducing unexpected adversity in an uncertain society. As seen from the results of this study, countries with more active labor market policies showed better results in reducing unemployment rates, and proved more resilient than others. Government policy makers have to consider active labor market policies as effective tools for reducing unemployment rates and increasing resilience during an 51
unexpected crisis. It might take time for these tools to affect unemployment in countries with previously low spending on active labor market policies, because it takes time to change individual behavior through policy tools, not to mention changing the social environment (Bemelmans-Videc, 1998).
Reducing the unemployment rate is about more than simply putting people to work; it is about maintaining spending power and running the economy without disturbances. As tepid growth has become a ‘new normal’ in the global economy, we have to rethink how governments can keep up with citizens’ demands to assure their livelihood and quality of life (Petak, 1985). If we do not make significant progress in solving this problem, our contemporary problems will be much more severe than we thought. Government has to take a role as a safety net for the society by improving its policy tools like strengthening active labor market policies (OECD, 1994, 2006).
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Additional Tables
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Country Name ALMP Expenditure* Netherlands 74.03 Denmark 67.18 Sweden 38.28 Norway 33.59 Switzerland 29.12 France 27.48 Austria 26.86 Luxembourg 26.09 Germany 24.47 Belgium 21.12 Finland 20.37 Portugal 19.39 Italy 17.31 New Zealand 16.89 Spain 15.72 Hungary 14.59 United Kingdom 14.29 Australia 12.51 Canada 9.21 Japan 9.18 Czech Republic 5.81 United States 5.26 Slovak Republic 3.89 Median 19.39 * I calculate the average of ‘ALMP expenditure per unemployed worker as a percentage of GDP per capita’ from 2001 to 2007 (before 2008 crisis) of each country. Then I divide countries into two groups, split at the median: Higher ALMP (Gray), and Lower ALMP countries. Table 2.5. ALMP Expenditure per Unemployed Worker as a Percentage of GDP per Capita
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Higher ALMP Expenditure Countries Pre-Crisis Post-Crisis Variables N Mean SD Min Max N Mean SD Min Max Country 75 10.76 6.27 2.00 21.00 66 10.82 6.21 2.00 21.00 Year 75 2004 2 2001 2007 66 2011 2 2008 2013 Unemployment rate 75 5.95 2.31 2.10 11.10 66 6.08 2.03 2.60 10.40 Long-term unemployment rate 74 30.05 13.51 5.46 56.58 66 28.89 12.29 5.98 52.52 Change of unemployment rate 75 -0.01 0.69 -1.70 1.40 66 0.21 0.77 -1.20 2.60 Change of long term unemployment rate 73 0.10 3.27 -8.07 7.46 66 -0.24 3.77 -9.62 10.72 ALMP Expenditure 75 37.02 21.58 17.32 123.15 66 30.73 12.06 19.19 69.89 PLMP Expenditure 75 53.18 24.02 16.65 131.42 66 39.33 16.73 11.39 75.11 Tax wedge 75 43.45 9.19 22.11 57.10 66 42.18 8.89 21.86 56.09 Output gap (% of potential GDP) 70 0.52 1.49 -3.60 4.47 60 -0.01 2.56 -5.27 5.41
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Manufacturing (% of GDP) 75 17.50 4.90 8.01 27.64 66 15.21 5.12 5.44 25.30 Openness (Trade % of GDP) 75 100.95 55.27 50.13 322.13 66 119.59 74.58 49.57 352.90 Union Density 75 43.56 23.66 7.59 79.08 66 39.92 22.39 7.55 70.77 Inflation rate 75 2.21 2.31 -1.77 15.65 66 2.01 1.95 -5.39 10.91 Long-term interest rates 75 4.28 0.93 2.10 6.38 62 3.23 0.96 0.65 4.77 Labor force participation rate of women 75 68.94 7.45 53.59 78.66 66 71.74 6.33 58.88 79.86 Elderly population 75 15.79 1.29 13.58 19.53 66 16.84 1.72 13.92 20.68 (% of population over 65 old) Government Debt (% of GDP ) 73 49.42 21.58 3.67 104.53 65 48.83 23.10 4.79 100.85 Table 2.6. Descriptive Statistics of Higher ALMP Expenditure Countries by Pre-Post Crisis
Lower ALMP Expenditure Countries Pre-Crisis Post-Crisis Variables N Mean SD Min Max N Mean SD Min Max Country 75 6.37 3.49 1.00 12.00 71 6.44 3.46 1.00 12.00 Year 75 2004 2 2001 2007 71 2010 2 2008 2013 Unemployment rate 75 7.28 3.51 3.70 19.30 71 9.19 4.74 4.00 26.60 Long-term unemployment rate 75 31.73 18.43 5.97 73.12 71 34.63 17.11 4.33 66.58 Change of unemployment rate 75 -0.25 0.84 -3.50 1.30 71 0.71 1.36 -1.40 6.60 Change of long term unemployment rate 75 -0.24 2.75 -7.48 9.41 71 1.25 5.07 -19.04 12.83 ALMP Expenditure 75 11.81 6.11 2.57 31.45 71 9.67 3.84 2.92 18.88 PLMP Expenditure 75 18.92 10.44 3.27 44.99 71 17.25 8.95 5.56 41.68
56 Tax wedge 75 35.23 8.90 19.36 55.80 71 35.44 8.94 15.87 54.53
Output gap (% of potential GDP) 75 0.28 2.92 -8.01 6.85 71 -0.52 3.07 -7.02 7.79 Manufacturing (% of GDP) 68 17.93 4.46 10.77 26.16 68 15.71 4.92 7.58 25.96 Openness (Trade % of GDP) 75 69.37 37.75 20.26 166.38 71 76.39 46.37 24.77 179.90 Union Density 75 21.68 5.41 11.48 34.08 71 20.04 6.43 10.55 36.28 Inflation rate 75 3.03 2.34 -1.71 11.21 71 1.74 1.88 -2.16 6.19 Long-term interest rates 73 4.86 1.63 1.00 8.55 71 4.54 1.93 0.84 10.55 Labor force participation rate of women 75 65.25 6.72 50.73 74.36 71 67.10 7.21 51.07 76.26 Elderly population 75 14.60 2.60 11.42 20.82 71 16.03 3.14 12.03 24.15 (% of population over 65 old) Government Debt (% of GDP) 64 50.41 26.53 12.65 145.15 71 70.09 41.11 18.28 195.99 Table 2.7. Descriptive Statistics of Lower ALMP Expenditure Countries by Pre-Post Crisis
Unemployment Rate Long-Term Unemployment Rate (1) (3) (3) (4) Estimation Method FE RE FE RE ALMP Expenditure -0.0436** -0.0569*** -0.159*** -0.170*** (0.0168) (0.0170) (0.0467) (0.0469) PLMP Expenditure -0.00327 0.00175 -0.0276 -0.00665 (0.0161) (0.0160) (0.0448) (0.0441) Tax wedge -0.0713 0.142** -0.0295 0.504*** (0.0907) (0.0566) (0.251) (0.163) Output gap -0.333*** -0.351*** -0.946*** -1.052*** (0.0423) (0.0407) (0.117) (0.112) Manufacturing -0.245*** -0.178** 0.456** 0.608*** (0.0789) (0.0699) (0.219) (0.196) Openness 0.0323** 0.0182* 0.0868** 0.0728*** (0.0132) (0.00934) (0.0366) (0.0268) Union Density 0.0999 -0.0339 0.448** -0.0833 (0.0674) (0.0310) (0.187) (0.0915) Inflation -0.157*** -0.180*** -0.153 -0.165 (0.0502) (0.0512) (0.139) (0.140) Long-term interest rates 0.684*** 0.674*** -0.450* -0.481* (0.0963) (0.0968) (0.267) (0.266) Women’s labor force participation 0.329*** 0.224*** -0.0693 -0.230* rate (0.0499) (0.0449) (0.138) (0.125) Population ages 65 and above -0.716*** -0.469*** -1.262** -1.287*** (% of total) (0.211) (0.163) (0.585) (0.462) Government Debt 0.0237** 0.0176* 0.169*** 0.148*** (0.0102) (0.00951) (0.0283) (0.0264) Constant -5.536 -5.527 26.51* 33.16*** (5.104) (4.150) (14.13) (11.66) R-squared 0.0004 0.1929 0.0347 0.6118 Observations 266 266 265 265 Table 2.8. Unemployment Rate, Long-Term Unemployment Rate
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Chapter 3 : Effects of Government Spending on Research Workforce Development:
Focusing on Behaviors and Productivity of Biomedical Researchers
Abstract Governments invest in research activities to develop knowledge and to help develop and maintain a highly-skilled research workforce. From 1998 to 2003, there was huge US government spending on biomedical R&D through the National Institutes of Health (NIH); the government doubled NIH funding during this time. Since 2003, annual NIH funding has increased slowly. In this context, this study compares the productivity and behavior of postdoctoral researchers in biomedical science before, during, and after the NIH double funding period, especially in terms of workforce diversity (i.e. immigration and citizenship status). Using a difference-in-difference analysis of the 1995, 2001, 2003 and 2008 Survey of Doctoral Recipients (SDR) data, the study aims to understand how funding availability affects researchers’ behaviors, productivity, and workforce diversity.
Keywords: government funding, research policy, workforce development, diversity, productivity
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Introduction To support workforce development, increase productivity, and build resilience through technological advancement, the US government has been investing substantially in research and development (R&D). The total government spending on R&D has increased every year, and it is estimated to be around $146 billion in 2016 (Office of
Management and Budget, 2016). R&D investment is one of the important functions of government for supplying public goods (Anomaly, 2015). This spending covers everything from basic research (in which the private sector invests less) to applied and development-level research, in a wide range of fields. The benefits of the resulting discoveries often extend to all people. These government efforts also make it possible to keep up with the pace of technological change by providing better equipment, advanced facilities, and improved workforces (Hur, Ghaffarzadegan, & Hawley, 2015).
Technological advancement is one of the crucial elements in workforce development, as it is considered a key to enhanced workforce productivity and innovation (Solow, 1957;
Romer, 1990).
In terms of research productivity, researchers can work in better professional environments given more funding, and do high-risk and long-term research with advanced equipment (Mill, 2001; Laudel & Gläser, 2012; Zerhouni, 2006). These changed environments give them more opportunity and increase their intellectual challenges, and are expected to raise productivity. For example, increased government funding lets researchers focus on their research activities rather than on writing grant proposals (Alberts, Kirschner, Tilghman, & Varmus, 2014; Diaz, 2012; Zerhouni, 2006).
Similarly, they can improve their professional development through conferences,
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collaboration, and formal training (e.g. postdoctoral research programs) without worrying too much about job security (Alberts, 2013; Bloch, Graversen, & Pedersen, 2014;
Ghaffarzadegan, Hawley, & Desai, 2014; van Arensbergen, van der Weijden, & van den
Besselaar, 2014). Government funding thus potentially helps researchers to be more knowledgeable and productive individuals (Ghaffarzadegan et al., 2014). Some of these effects, however, might not be positive or might have unforeseen consequences for research productivity and workforce development. For instance, more available funding might also increase institutional pressure on researchers to apply for more grants, which could actually increase the time spent on writing grant applications (Teitelbaum, 2008;
Larson, Ghaffarzadegan, & Diaz, 2012). Similarly, with increased job security due to increased funding, postdocs may be less motivated to move on to long-term research positions (Ghaffarzadegan, Hawley, Larson, & Xue, 2014).
From 1998 to 2003, there was huge government spending on R&D through the
National Institutes of Health (NIH).24 The US government doubled NIH funding in 1998-
2003. Since then, the gross amount of funding has increased, but its value has actually decreased in inflation-adjusted constant dollars.25 This phenomenon has caused concern in the biomedical science field, and there have been calls to increase NIH funding beyond
24 US government funding is “distributed to research institutions and individuals through several government organizations, such as the National Institutes of Health (NIH) and the National Science Foundation (NSF)” (Hur, Ghaffarzadegan, & Hawley, 2015, p. 1). Among them, NIH funding is more concentrated among biomedical research than other fields due to NIH’s mission. NIH funding is the most important source for biomedical science community. 25 Johnson, Judith A. Brief History of NIH Funding: Fact Sheet. Washington D.C. (2013), available at http://www.fas.org/sgp/crs/misc/R43341.pdf. “NIH funding adjusted for inflation (in constant 2003 dollars) using the Biomedical Research and Development Price Index (BRDPI). It shows that the purchasing power of NIH funding (non-American Recovery and Reinvestment Act) peaked in FY2003 (the last year of the five-year doubling period) and has steadily declined in the years since. By FY2008, funding had dropped below the FY2002 level. In constant 2003 dollars, FY2013 funding was 22% lower than the FY2003 level” (p. 2). 60
inflation levels. Understanding the effects of NIH funding on biomedical researchers better allows for a more informed decision about increasing funding. Like their colleagues in other fields, biomedical researchers are not homogenous, especially in terms of the different citizenship status of the researchers (Ghaffarzadegan et al., 2014).
These characteristics affect their individual decision-making (Lan, 2012, 2013).
This study only evaluates postdoctoral researchers in the biomedical fields, in order to reduce the variations in researchers’ motivations and behaviors. Considering the workforce pipeline, postdocs represent the future research engine in biomedical fields.
So, it is important to look at their productivity and behavior. In this study, I use the double NIH funding period (1998-2003) as a unique natural experimental opportunity to analyze these trends. Specifically, I consider biomedical researchers’ workforce diversity
(i.e. immigration and citizenship status) and compare their productivity and behavior before, during, and after 1998-2003. To do so, I use a difference-in-difference analysis of the 1995, 2001, 2003 and 2008 Survey of Doctoral Recipients (SDR) data.
NIH Funding for Maintaining Resilience in Biomedical Fields The scientific research enterprise is highly sensitive to the availability of human capital. In scientific research, creative and knowledgeable individuals who master their fields are required to articulate complex problems and offer innovative solutions, in order to advance research. Educated individuals with high research skills and capabilities are thus important resources in this research environment. However, there is no single well– defined process that can lead individuals to be productive research scientists. Supportive research environments (e.g., research funding and a developed research infrastructure)
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and individual research efforts are both important. The US government recognizes the importance of these research environments and provides funding for research institutions and individuals (Hur et al., 2015). Accordingly, the government is invested in developing high-skilled and knowledgeable researchers. However, evaluating the specific effects of government funding is challenging, because the research outcomes are difficult to define
(Hawley, 2014). Nevertheless, there is growing demand to analyze government efforts to educate and train human capital.
According to Loscalzo (2006), “funding for biomedical research in the United
States has fueled discoveries that have advanced our understanding of human disease, led to novel and effective diagnostic tools and therapies, and made our research enterprise an international paragon” (p. 1665). As a core biomedical research agency, the NIH has been taking the role of advancing and improving the health of people in every corner of
America and the world (NIH, 2015). NIH funding is used for infrastructure investment and workforce development in biomedical fields, such as building and operating research centers, training, and salary compensation for researchers (Loscalzo, 2006). Furthermore,
NIH funding allows researchers to enhance their knowledge and be productive by utilizing the newest and most advanced technologies and methods. That is, NIH funding results in improving research facilities while fostering workforce development in biomedical research.
From this perspective, NIH funding improves resilience to changes in the research environment such as technology innovation. Funded researchers can better adapt to advancing technology and pursue more advanced research. In this context, resilience is
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defined as 1) ability to bounce back to the previous status in the face of adversity
(Holling, 1973; Pimm, 1984), 2) the ability to absorb changes (Holling, 1973; Martin &
Sunley, 2015), and 3) a robust transition-enhancing internal capacity to adapt to those changes (Marcos & Macaulay, 2008; Martin, 2012). The concept of resilience is not confined to overcoming challenges and changes but includes adapting to a new environment with the opportunities for developing skills, abilities, and innovation. From the standpoint of the biomedical field, resilient researchers can maintain their research activities without discontinuity or losing capacity, and can make innovations by improving their knowledge and learning advanced technologies and methods. Like making medicine, biomedical research requires many requirements to achieve both progress and innovation. Maintaining the adaptability of the biomedical field to a rapidly changing and highly competitive environment thus requires strategic policy tools, and sophisticated policy options, including funding.
Many other countries also recognize the importance of the biomedical field, and have begun to implement policies to attract highly-skilled biomedical researchers to their countries (Ghaffarzadegan et al., 2014). For example, China’s investments in infrastructure and workforce development include as much as $308.5 billion over 5 years for biomedical sciences (Atkinson, 2014; Atkinson, Ezell, Giddings, Stewart, & Andes,
2012). Singapore, Taiwan, and South Korea also continue to increase their investments in the biomedical field (Atkinson et al., 2012). Given this competition, the US is at risk of falling behind as an innovator. Recognizing the need for increasing NIH funding, the US government made a decision to increase it in 1997 (Smith, 2006). The funding was doubled from $13.6 billion to $27.1 billion from 1998-2003. This funding was expected 63
to provide advanced and improved research infrastructure, so biomedical researchers could better develop their professional skills and increase their productivity (Lawler,
1997; Mervis, 1997).
Figure 3.1 shows the ratio of NIH funding to all other federal research funding in the US. Before 1998 the ratio was around 0.5. However, the ratio grew to almost 1.0 in
2003.26 In this period, the average grant size increased from $294,859 in 1998 to
$316,841 in 2003, giving more spending flexibility to principal investigators. The number of awards was also increased by 38%. In the same time period, the number of applications for research project grants (RPG) in NIH also increased by 44%, to 34,710 in
2003, maintaining an overall success rate (the percentage of grant applications funded) of around 30% in this time period.27
26 In 1998, the amount of money that the NIH received divided by that received by all other research and development agencies (except defense) was 0.57: if all others received $1, NIH received 57 cents. In 2003, for every $1 that all other agencies received, the NIH received 98 cents. This means that they grew a lot in comparison to other agencies. 27 See Figure 3.3 in the additional Figures and Tables section: Trends in Research Project Grants, FY1995- FY2015. 64
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
1998 1991 1992 1993 1994 1995 1996 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1990 Source: https://report.nih.gov/nihdatabook Figure 3.1. Ratio of NIH Budget to Other Federal Research Spending
In 2003, total NIH funding was $27 billion. Since then, the budget has continued to increase by the rate of growth has slowed. In fact, after taking inflation into account, the value of the NIH budget has actually been decreasing. As seen in Table 3.1, the average grant size (inflation-adjusted) during this period decreased by 7% in 2008 and by
2% in 2015, giving less spending to principal investigators. The number of awards has also decreased by 9% in 2008 and by 8% in 2015. Similarly, after 2003 the overall success rate decreased to around 27% in 2008 and 40% in 2015. This phenomenon caused concern in the biomedical field, prompting demands to increase NIH funding beyond inflation levels. Before deciding to increase funding, however, we need to understand the effects of NIH funding on researchers better: how researchers use these funding opportunities and what the outcomes are from NIH funding.
65
FY1998 FY 2003 FY 2008 FY2015 NIH Budget (in millions) 13,675 27,167 29,607 30,311 NIH Budget (in millions): Constant 1998 13,675 24,066 22,415 20,846 Research Project Grants: Applications 24,151 34,710 43,467 52,190 Research Project Grants: Awards 7,518 10,393 9,460 9,540 Research Project Grants: Success rates 31% 29.9% 21.8% 18% Average Grant Size: Current $ $294,859 $378,045 $409,571 $477,786 Average Grant Size: Constant 1998 $ $294,859 $334,899 $310,075 $328,580 Source: https://report.nih.gov/nihdatabook Table 3.1. Major NIH Funding Measures: 1998-2015
Workforce in Biomedical Fields
Postdoctoral Researchers: Training Position as Future Research Engine
Government funding aims to support the research enterprise by maintaining and increasing the supply of the workforce (Ghaffarzadegan et al., 2014). Government funding is awarded to principal investigators (PIs), who hire a large pool of capable researchers. Many of these researchers are postdocs, as postdoctoral training is a popular method of research workforce development. From a workforce-development perspective, postdoctoral training functions as a gateway to a scientific research career. A postdoc is a person who has a doctoral degree with a temporary employment appointment. They represent an important element of the biomedical workforce, and are considered to be between the early and established career stage (National Postdoctoral Association, 2014).
The postdoc position also provides important opportunities for new scholars, in terms of their career development (Ghaffarzadegan et al., 2014). During postdoc periods, 66
researchers complete training and develop professional skills, including publishing research and writing grant proposals, so they can be better independent researchers in their future research careers (Garrison, Gerbi, & Kincade, 2003). With the increasing number of postdocs, however, there are questions about their productivity (Schillebeeckx,
Maricque, & Lewis, 2013).
Some PhDs take a postdoc position to increase their competitiveness in the labor market (Sauermann & Roach, 2016). There are limited numbers of tenure-track faculty positions and full time non-academic positions (e.g., researchers in corporate R&D sectors) available in the labor market (Cyranoski, Gilbert, Ledford, Nayar, & Yahia,
2011; Powell, 2015; Zumeta, 1984, 1985). A postdoc is considered a good training investment for PhDs who are pursuing either type of position (Cyranoski et al., 2011;
National Postdoctoral Association, 2014). As it is more difficult to get a job than in the past, more people want to take postdocs as stepping stones to their desired jobs
(Ghaffarzadegan et al., 2014; Sauermann & Roach, 2016; Stephan, 2005, 2012, 2013).
Some people also use postdoc positions to reduce the risk of wasting time in their careers
(Davis, 2009; Ghaffarzadegan et al., 2014; Stephan, 2013). More funding increases the demand for postdocs, while less funding decreases the demand (Sauermann & Roach,
2016). PhDs can stay in their postdoc positions longer in strong funding situations, but shorter in weak funding situations. Given the importance of postdocs in the research workforce, their characteristics, and funding’s effects on holding postdoc positions. I propose to investigate the following hypotheses:
Hypothesis 1a: More funding results in longer postdoc durations. 67
Hypothesis 1b: Less funding results in shorter postdoc durations.
In addition to the benefits identified above, postdocs can make more research connections and gain research experience (Bonetta, 2010; Gentile, Nancy, Jolly, & Dial,
1989; Pierre, 2016). By working with supervisors and other researchers, as well as department staff and students, postdocs can improve their team learning and leadership skills (Hur et al., 2015; Kolb, 1984). Similarly, by collaborating with other researchers, postdocs can learn how to manage research projects and gain more professional knowledge from their PIs and team members (Ghaffarzadegan et al., 2014; Levy et al.,
1988; Ross, Greco-Sanders, & Laudenslager, 2016; Schillebeeckx et al., 2013). Within a supportive and well-funded environment, postdocs can take higher-risk initiatives (Mill,
2001; Laudel & Gläser, 2012; Zerhouni, 2006). Furthermore, they often have opportunities to use well-equipped and advanced laboratories, and to attend more conferences or workshops to improve their research (Bonetta, 2010).
Therefore, I expect that government funding might result in positive effects not only for the research team but also for the postdocs themselves. With government funding, the performance of the research team might be improved through collaborative work with postdocs and other researchers (Scribner & Beach, 1993). Postdocs can improve their research abilities and show higher productivity, such as an increase in their number of publications (Olivieri & Rowlands, 2006; Steiner, Lanphear, Curtis, & Vu,
2002). Similarly, by building networks to exchange knowledge with other researchers, postdocs can generate new ideas for enhancing the biomedical field (Olivieri, 2006).
Therefore, research funding can increase productivity, as well as creating better-prepared 68
and more experienced job applicants (Laudel & Gläser, 2012; Lerner, 1996; van
Arensbergen et al., 2014; Bloch et al., 2014). Accordingly, I propose the following hypotheses:
Hypothesis 2a: More funding increases the average productivity of postdocs.
Hypothesis 2b: Less funding decreases the average productivity of postdocs.
As more funding increases the demand for postdocs, it is also possible that more funding creates more postdoc job openings and weaker competition for a given position
(Sauermann & Roach, 2016). That is, less productive and less qualified people may be hired as postdocs. Thus, I can propose these competing hypotheses:
Hypothesis 2c: More funding decreases average productivity of postdocs.
Hypothesis 2d: Less funding increases average productivity of postdocs.
Workforce Diversity in Biomedical Fields: Asymmetric Effects of Funding As a result of the increase in funding, the number of PhD students, researchers, and postdocs has increased (Diaz et al. 2012; Teitelbaum 2008). Increased funding brought more attention to US biomedical research, attracting many young competent researchers, including international researchers. With more funding, therefore, the supply of the US workforce has increased, but the inclusion of international researchers makes the postdoc population more heterogeneous (Davis, 2009; Sauermann & Roach, 2016).
International postdocs’ different preferences, incentives, institutions, and conditions of 69
citizenship status might prompt asymmetric behaviors in response to the same changes.
With this understanding, this study analyzes how postdocs react to the changes in funding by considering different citizenship categories: US citizens, permanent residents (green card holders), and temporary residents (visa holders).
The choices to pursue a postdoc position may vary depending on the candidate’s characteristics, background, and preferences (Bound, Turner, & Walsh, 2009;
Ghaffarzadegan et al., 2014; Lan, 2013). Different citizenship statuses reflect these differences, and impact individual application decisions (Hur et al., 2015; Lan, 2012,
2013). With the increasing difficulty of finding permanent jobs or faculty positions, the duration and number of postdoctoral positions has increased (Davis, 2009;
Ghaffarzadegan et al., 2014; Stephan, 2013). Many people take postdoc positions because other employment is not available or because they could not get their desired jobs
(Sauermann & Roach, 2016). A postdoc position is the one of the options for non-US citizens who want to stay in the US (Sauermann & Roach, 2016), but unlike citizens and permanent residents, temporary residents have limits on the number of years they can stay in the US (Hur et al., 2015).
Based on these understandings, I expect that the funding level affects postdoc duration, especially among US citizens and permanent residents. Therefore, I propose the following hypotheses:
Hypothesis 3a: For US citizens and international permanent residents, more
funding results in longer postdoc durations.
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Hypothesis 3b: For US citizens and international permanent residents, less
funding results in shorter postdoc durations.
According to a report from the National Science Foundation, 75% of international
PhD recipients (both permanent residents and temporary residents) plan to stay in the US.
Once they make this decision, they are highly motivated to gain better professional opportunities and benefits, such as by using well-equipped advanced laboratories and other resources. According to the 2013 National Survey of College Graduates (NSCG),
36.8% of doctoral degree holders chose ‘scientific or professional infrastructure’ as the most important reason for coming to the US. 25.7% and 20.6% of doctoral degree holders, respectively, cited ‘educational opportunities, and ‘job or economic opportunities.’ As seen in these survey results, many doctoral degree holders believe that they can get training and improve their professional skills and knowledge by using advanced resources.
With these experiences, non-US citizen students think that they can find jobs and take advantage of economic opportunities in the US. Developed research environments and job opportunities from investments are attractive factors to international doctoral degree holders. Both permanent residents and temporary residents take a risk by staying in the US, with different potential costs for each individual (Hur et al., 2015). While permanent residents don’t have duration restrictions on their stays in the US, they are often more motivated than citizens to get a permanent job so they can support their families. Of course, different researchers have different preferences and incentives for their activities. Given the same opportunities and different environmental restrictions, this 71
raises the question of how government funding has different effects on productivity.
Thus, I propose the following competing hypotheses:
H4a: International permanent residents and temporary residents have higher
productivity.
H4b: International permanent residents and temporary residents have lower
productivity.
Data and Measurement
My analysis is based on data from the 1995, 2001, 2003 and 2008 waves of the
Survey of Doctoral Recipients (SDR), conducted by the NSF. The SDR is a nationally representative sample of all Ph.D. graduates in hard and social sciences living in the US, collected in response to the National Research Council's demand for data on occupational outcomes of academic training. For the purpose of my analysis, I use data from current postdoctoral researchers in these datasets. I code the following fields as related to biomedical sciences: biochemistry and biophysics, biology, cell and molecular biology, animal and plant genetics, microbiological sciences and immunology, nutritional sciences, human and animal pharmacology, human and animal physiology and pathology, and zoology.28
28 Garrison, H. H., & Ngo, K. (2011) Education and employment of biological and medical scientists: Data from national surveys. Available: http://report.nih.gov/investigators_and_trainees/ACD_BWF/pdf/FASEB_ PPT-Slide_Set.ppt Accessed on 2 February 2017. 72
Method The literature lacks a consensus on the effects of government spending on workforce development, especially in regards to the productivity and behavior of postdocs. By dividing the period based on a specific event, this study can shed light on workforce productivity over time. Since the doubled NIH funding started in 1998 and ended in 2003, I compare results by classifying data before (1995), during (2001, 2003), and after (2008) the doubled funding. As the NIH has been taking a lead role of advancing and improving the health of people, more funding is expected to increase resources that ultimately improve the productivity of scientists in the biomedical sciences.
I conduct two rounds of analyses. In the first round, I look at the whole sample of postdoctoral researchers, regardless of workforce diversity (i.e., their citizenship status).
In the second round of analysis, I run separate models for different citizenship statuses:
US citizens, permanent residents (green card holders), and temporary residents (visa holders). Heterogeneities in preferences based on different citizenship status might cause researchers to behave differently and affect their productivity (Bound et al., 2009;
Ghaffarzadegan et al., 2014; Hur et al., 2015; Lan, 2013). The increase in funding in biomedical fields is expected to increase the productivity of biomedical scientists more than scientists in other fields. I compare the productivity and behavior of postdoctoral researchers in biomedical fields to that of postdoctoral researchers in non-biomedical fields using a classic difference-in-difference analysis.
Specifically, I run the following model with four dependent variables: 73
푌푖푡 = 훽0 + 훽1퐵푖표푚푒푑푖푐푎푙퐹푖푒푙푑푖푡 + 훽2퐷표푢푏푙푖푛푔퐹푢푛푑푖푛푔푖푡 +
훽3퐵푖표푚푒푑푖푐푎푙퐹푖푒푙푑푖푡 × 퐷표푢푏푙푖푛푔퐹푢푛푑푖푛푔푖푡 + 푘푖푡 + 휀푖푡 (1)
In this equation, 푌푖푡 is the outcome of interest for individual i at time t. The dummy variable BiomedicalField captures possible differences between the treatment
(biomedical postdocs) and comparison groups (non-biomedical postdocs).29 The variable
DoublingFunding is equal to 1 for the second time period (2001 and 2003), and equal to zero for year 1995. When I compare outcomes during and after the doubling policy, the variable DoublingFunding is equal to 1 for the second time period (2008), and equal to zero for years 2001 and 2003. The variable k represents other control variables (age, gender, race, marital status, children, working hours, research activity, institutional rank, and graduation year). Finally, the duration of the postdoctoral career is added as a control variable when I use conference papers and publications as dependent variables. In a difference-in-difference analysis, the coefficient of the interaction term (β3) represents the effect.
I use ordinary least-square regression for the first two dependent variables and test the first hypotheses (More (or less) funding results in longer (or shorter) postdoc durations) and third hypotheses (For US citizens and international permanent residents, more (or less) funding results in longer (or shorter) postdoc durations). To test the second hypotheses (More (or less) funding decreases (or increases) average productivity
29 Scientists in the comparison group are less likely to receive NIH grants than biomedical scientists, and their grants tend to be smaller since they do not need labs. 74
of postdocs) and fourth hypotheses (International permanent residents and temporary residents have higher (or lower) productivity), I perform negative binomial regressions because the measure of productivity (number of publications, number of conference papers) is non-negative integers with a skewed distribution (there are many 0 or 1 values) with only positive values.
Dependent Variable I use four outcome measures. The first measure is time in the latest postdoc. This variable is calculated by using the starting month and year of the current postdoctoral job and the time that the survey is conducted. The second measure is time since graduation.
Time since graduation is calculated by using the awarding month and year of the respondent’s US PhD and the time that the survey is conducted. The third measure is the number of (co)authored articles accepted for publication during the last five years.
Journal publications are a common standard for professional success among researchers.
The SDR system provides the number of (co)authored articles accepted for publication for each respondent. Survey respondents were asked the following question: "How many articles (co)authored by you have been accepted for publication since survey month and year?” The fourth measure is the number of papers presented at conferences during the last five years. Conference papers are not published papers, but most people revise conference papers for publication. They also represent the activities of researchers with other people in the same fields. The SDR system provides the number of (co)authored papers accepted for conferences, measured with the following question: "How many papers have you authored or co-authored for presentation at regional, national or international conferences?”
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Other Variables Apart from the aforementioned four variables, I also consider other factors
(individual and organizational factors) that affect the productivity of biomedical researchers, and include these as control variables: age, gender, race/ethnicity, marital status, children, working hours, research activity, cohort, and the rank of the doctoral degree awarding institution. SDR provides the respondents’ race and gender information.
The race is categorized into White, Black, Asian, Hispanic, and other races. Research activity is a dummy variable that is set to be one for respondents that pick a basic or applied research study as their work activity, and is set to zero for the ones that announce a focus on other activities.30 I use the 1994 Carnegie classification code to define rank of the doctoral degree awarding institution. Postsecondary education institutions can be classified as follows: Research University I, Research University II, Doctorate Granting I,
Doctorate Granting II, and others (Master’s Universities and Art College I, II;
Baccalaureate College I, II; Associate of Arts College; Professional Schools and
Specialized Institutions).31 I include this measure because of the assertion that higher-
30 Work activities on basic or applied research occupy at least 10 percent of their time. 31 According to Carnegie Classification Definitions: Research Universities I: These institutions offer a full range of baccalaureate programs, are committed to graduate education through the doctorate, and give high priority to research. They award 50 or more doctoral degrees each year. In addition, they receive annually $40 million or more in federal support. Research Universities II: These institutions offer a full range of baccalaureate programs, are committed to graduate education through the doctorate, and give high priority to research. They award 50 or more doctoral degrees each year. In addition, they receive annually between $15.5 million and $40 million in federal support. Doctoral Universities I: In addition to offering a full range of baccalaureate programs, the mission of these institutions includes a commitment to graduate education through the doctorate. They award at least 40 doctoral degrees annually in five or more disciplines. Doctoral Universities II: In addition to offering a full range of baccalaureate programs, the mission of these institutions includes a commitment to graduate education through the doctorate. They award annually 76
ranking university postdoctoral researchers have a better chance to contact and work with other professors in their university or at other universities. Furthermore, the culture of the academy forces those who aspire to a scholarly career to engage in publication (Olivieri
& Rowlands, 2006). I show summary statistics for the main variables in Table 3.4 (in the additional Figures and Tables section).
Results
Policy Effects on the Whole Population
Table 3.2 presents the estimated coefficients of how doubling NIH funding affected the whole population of postdoctoral researchers, by comparing results before-during and during-after the 1998-2003 funding policy. It summarizes the results of regressions by reporting β1, β2, and β3 from equation 1. β1 (the coefficient of Biomedical Field) represents the overall difference between the treatment group and the control group, β2
(the coefficient of Doubling Funding Period) represents the overall trend for all fields, and β3 (the coefficient of Doubling Funding Period*Biomedical Field) represents the main effect from a difference-in-difference analysis: the effect of doubling funding for the treatment group after controlling for secular and between-field effects. Compared with non-biomedical researchers, as shown in Table 3.2, time in the latest postdoc significantly changes before and during the double funding policy in Model 1. The estimation results reveal that postdoctoral researchers in biomedical fields stayed around
at least 10 doctoral degrees in three or more disciplines or 20 or more doctoral degrees in one or more disciplines. 77
four more months (p <.05) longer than non-biomedical researchers. Time in the latest postdoc also significantly changed during and after the funding policy, as shown in
Model 4. Compared with non-biomedical researchers, the time since graduation of doctoral recipients in biomedical fields decreased by approximately five months compared to non-biomedical fields (p<.1) after the double funding policy. The numbers of conference papers and publications per person did not significantly change, regardless of the funding policy.
In summary, the analyses show that time in the recent postdoc position comparably increased after the double funding policy, with no observable effects on productivity.
This can support hypothesis 1a (More funding results in longer postdoc durations), but cannot support hypothesis 2a (More funding increase average productivity of postdocs) or hypothesis 2b (Less funding decrease average productivity of postdocs). I show all results of these models (from Model 1 to Model 8 in Table 3.2) in Tables 3.5-3.6, in the additional Figures and Tables section. To examine the robustness of my findings, I conduct additional analyses with the same data reported in the supporting information
(Table 3.9 in the additional Figures and Tables section). I limit the data points to individuals who have government funding. Similar effects appear: time since graduation increases in treatment groups during the funding policy, and time since graduation decreases in treatment groups after the funding policy, with no significant change in conference papers and publications.
78
Time in Latest Postdoc Time since Graduation Published Articles Conference Papers
Before During Before During Before During Before During Change of pre, during and post vs. vs. vs. vs. vs. vs. vs. vs. of Doubling Funding During After During After During After During After
Model 1 Model 2 Model 3 Mode 4 Model 5 Model 6 Model 7 Model 8
ALL
Biomedical Field (β1) -0.98 3.12** 4.47*** 7.48*** -0.22*** -0.18*** -0.34*** -0.33***
(1.40) (1.26) -1.72 -1.52 (0.04) (0.04) (0.04) (0.04)
Doubling Funding (β2) 10.70*** 31.03*** -2.22 8.45*** 0.16** 0.08 0.03 0.17**
79 (2.47) (2.39) -1.49 -1.50 (0.07) (0.08) (0.08) (0.08)
Difference in Difference (β3) 4.32** 1.02 3.01 -4.84* 0.027 -0.04 -0.02 -0.06
(1.87) (2.12) -2.29 -2.57 (0.06) (0.07) (0.06) (0.07)
0.19 0.26 0.41 0.45 -10669.70 -9127.06 -11439.02 -9796.37 Adj R-squared / Log likelihood Observations 4,007 3,392 4,007 3,392 4,007 3,392 4,007 3,392 *** p<0.01, ** p<0.05, * p<0.1 Note: Standard errors are presented in parentheses below coefficient estimates. DV stands for dependent variable. Control variables include age, gender, race, marriage, children, working hours, research activity, cohorts, time in the postdoc (only when DV is conference papers or published articles), and institutional rank of the organization where researchers got their first US S&E or health PhD. Data source: 1995, 2001, 2003, and 2008 Survey of Doctorate Recipients Table 3.2. Difference-in-Difference estimates for the Before (1995), During (2001,2003), After (2008) Doubling Funding Effect
Diversity of Workforce: Effects on Different Citizen Groups
Table 3.3 summarizes the main results of the analysis for postdocs in different citizenship groups. I control for a wide range of individual and organizational variables, as described in the method section. Firstly, I look at the effects of the change in funding on the time in the current postdoc (Models 1 and 2). As depicted, the time in the current postdoc increased for US citizens by about six months (p <.05) during the funding policy.
The time in the current postdoc among temporary residents (visa holders) decreased by about seven months (p <.05) during the funding policy and decreased by about four months (p <.1) after the funding policy. Secondly, I analyze the change in time since graduation of postdocs (Models 3 and 4). This variable decreased for US citizens after the funding policy, by approximately eight months (p <.05). Overall, the analysis shows that US citizens waited longer in the pipeline (i.e., in postdoc positions) during the doubled funding period. However, the time since graduation of postdocs decreased after the funding policy. Thirdly, in terms of the number of publications and the number of conference papers, permanent residents are the only ones that showed a significant change related to the changed funding policy (Models 5 and 6). They showed a significant positive change during funding (p <.05) and a negative change after funding
(p <.1). The estimated effects mean effects of the doubled funding among permanent residents in the biomedical group are 0.29 and -0.37, respectively.
In summary, in regards to the effects of changing funding policies on postdoctoral researchers, my analyses show that changes in funding affected changes in postdoctoral behavior differently by citizenship status:
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1) US citizens stay longer in postdoctoral positions given increased funding. After funding decreased, however, US citizens shortened their time since graduation, supporting hypothesis H3a for US citizens only. Temporary residents (visa holders) decreased the duration of their postdoctoral positions during the funding policy, but extended time since graduation after funding decreased.
2) More funding increased the number of publications and conference papers for permanent residents, with no significant effect for other groups (support for hypothesis
H4a for permanent residents only). However, less funding decreased the number of publications and conference papers for permanent residents, with no significant effect on other groups (support for hypothesis H4b for permanent residents only).
Similar effects can be found in the sensitivity analysis reported in the supporting information (Table 3.10 in the additional Figures and Tables section), which shows an increase in the number of both publications and conference papers for permanent residents and a shorter time since graduation for US citizens during funding policy.
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Time in Latest Postdoc Time since Graduation Published Articles Conference Papers
Before During Before During Before During During Before Change of pre, during and post of Doubling vs. vs. vs. vs. vs. vs. vs. vs. Funding During After During After During After After During Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
US
Biomedical Field (β1) -2.62 3.48** 4.82** 8.81*** -0.20*** -0.24*** -0.34*** -0.40***
(1.81) (1.63) (2.16) (1.93) (0.05) (0.05) (0.05) (0.05)
Doubling Funding (β2) 7.76** 31.97*** -4.63** 9.47*** 0.13 0.052 0.084 0.17*
(3.07) (3.09) (1.90) (2.04) (0.08) (0.09) (0.09) (0.10)
Difference in Difference (β3) 6.18** -0.29 3.51 -7.91** -0.03 0.032 -0.0728 -0.02
(2.42) (2.85) (2.88) (3.37) (0.06) (0.08) (0.07) (0.09) Adj R-squared / Log likelihood 0.20 0.27 0.44 0.48 -7762.46 -6304.62 -8314.32 -6827.70
Observations 2,945 2,381 2,945 2,381 2,945 2,381 2,945 2,381
82
GREEN CARD HOLDER
Biomedical Field (β1) 1.13 3.09 0.73 6.30 -0.26*** 0.06 -0.42*** -0.06
(2.05) (3.47) (2.76) (3.88) (0.09) (0.13) (0.10) (0.12)
Doubling Funding (β2) 21.66*** 31.82*** 8.98*** 12.79*** -0.06 0.19 -0.63*** 0.08
(4.05) (4.94) (2.87) (3.82) (0.19) (0.20) (0.21) (0.18)
Difference in Difference (β3) 3.06 4.67 5.16 -1.59 0.29** -0.37* 0.35** -0.32*
(3.12) (5.48) (4.15) (6.10) (0.14) (0.20) (0.15) (0.19) Adj R-squared / Log likelihood 0.20 0.25 0.26 0.43 -1415.92 -947.98 -1496.97 -955.26
Observations 524 352 524 352 524 352 524 352 Continued Table 3.3. Difference-in-Difference Estimates for the Before (1995), During (2001,2003), After (2008) Doubling Funding Effect by Citizenship Status
Table 3.3 continued
Time in Latest Postdoc Time since Graduation Published Articles Conference Papers Before During Before During Before During During Before Change of pre, during and post of Doubling vs. vs. vs. vs. vs. vs. vs. vs. Funding During After During After During After After During
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
VISA CARD HOLDER
Biomedical Field (β1) 8.13*** 1.08 0.57 2.37 -0.23 -0.06 -0.08 -0.19* (2.36) (1.56) (2.83) (1.89) (0.15) (0.10) (0.15) (0.10)
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Doubling Funding (β2) -0.54 21.10*** 3.04 2.81* 0.84** 0.19 0.88*** 0.07 (5.95) (4.70) (1.89) (1.49) (0.34) (0.34) (0.34) (0.31)
Difference in Difference (β3) -6.94** 4.25* 1.72 1.65 0.15 -0.08 -0.18 -0.10
(2.83) (2.43) (3.39) (2.96) (0.17) (0.16) (0.17) (0.16)
Adj R-squared / Log likelihood 0.14 0.17 0.11 0.11 -1433.43 -1822.11 -1563.42 -1944.50 Observations 538 659 538 659 538 659 538 659 *** p<0.01, ** p<0.05, * p<0.1 Note: Standard errors are presented in parentheses below coefficient estimates. DV stands for dependent variable. Control variables include age, gender, race, marriage, children, working hours, research activity, cohorts, time in the postdoc (only when DV is conference papers or published articles), and institutional rank of the organization where researchers got their first US S&E or health PhD. Data source: 1995, 2001, 2003, and 2008 Survey of Doctorate Recipients
Discussion and Conclusion Governments invest in research activities to build resilience to the effects of changes in research environments, such as technological advancement, by developing knowledge, maintaining productivity, and developing talent in the research workforce.
However, did the government achieve its desired results by doubling NIH funding from
1998-2003? Starting with this question, this study examines the change of behavior (time in postdoc, time since graduation) and productivity (number of publications, number of conference papers) of postdoctoral researchers in response to the change in NIH funding.
I emphasize the postdocs’ career characteristics, holding positions and training opportunities. This study finds that 1) US citizens stayed in postdocs longer and temporary residents stayed in postdocs shorter, while permanent resident were more productive in a more-funded environment. It also finds that 2) US citizens shortened and temporary residents extended their time since graduation, while permanent residents were less productive in a less-funded environment. This study aims to improve our understanding of workforce diversity in biomedical sciences by showing the heterogeneous behavior and productivity responses to the change in research environment. In order to understand the federal government’s impact, I look specifically at biomedical researchers as an example. But this analysis is also relevant to bigger questions about the role of research funding on outcomes that matter to the Congress, the
President, and the public.
Having a supportive research environment is important to all researchers, especially to biomedical researchers who work on enhancing our understanding of human
84 diseases. Government funding helps these researchers make better experiments with more advanced equipment, and facilitates the development of advanced technology by recruiting qualified researchers (Hur et al., 2015). Researchers’ activities are motivated by whether the work environment is supportive to increase their knowledge and skills
(Van Iddekinge, Roth, Putka, & Lanivich, 2011). The US government also recognizes the importance of workforce development in biomedical sciences to prepare for global competition. With increasing competition among countries, supportive research environments for technology innovation become key elements to increase the productivity of researchers (Foster & Rosenzweig, 1996; Jacobs & Hawley, 2009; Levy
& Murnane, 2004). Through NIH funding, therefore, the US government tries to provide advanced and improved research infrastructure in biomedical fields to enhance productivity and workforce development (Lawler, 1997; Mervis, 1997). By doing so, researchers have more opportunities to develop their professional skills, and it is expected that this will increase productivity in the biomedical sciences. This government funding affects researchers’ behavior and productivity in the biomedical sciences. That is, the
NIH funding plays a role in building resilient workforce development. To accomplish these goals, the US government doubled NIH funding from $13.6 billion to $27.1 billion from 1998-2003.
Since the doubled funding policy has ended, however, the NIH budget has actually decreased after accounting for inflation. As seen in the results of this study, a change of funding environment affects the behavior and productivity of postdocs differently, depending on their motivations and the incentives of their position. In
85 particular, the productivity of permanent residents was significantly changed by the changed funding environment (Hur et al., 2015). One possibility is that the motivation for intellectual challenge changes with changes in funding. This motivation decreases among permanent residents as inflation-adjusted NIH funding decreases (see Figure 3.4 in the additional Figures and Table section). Having an intellectual challenge is important to all researchers, especially to biomedical researchers. Government funding can enhance this challenge by allowing experiments with advanced equipment, funding advanced technology, and recruiting more qualified researchers (Hur et al., 2015). Improving resilience through these activities would increase the researchers’ motivation, which leads to productivity (Holland, 1973; Kuder, 1977; Mount, Barrick, Scullen, & Rounds,
2005; Van Iddekinge et al., 2011). This motivation can be increased with training and opportunities for utilizing advanced facilities and methods, which potentially enhance researchers’ career and improve their professional knowledge (Mill, 2001).
A supportive research environment with high-tech laboratories is an especially attractive factor for foreign biomedical science researchers (Garrison, Stith, & Gerbi,
2005). The US biomedical science research environment has been maintaining a good infrastructure through equipment using advanced technology. This great environment attract many talented foreign researchers to the US, who have contributed greatly to the development of biomedical sciences. For continuous development in biomedical fields, therefore, the US government has to invest more money to form better research environments, not only for training domestic scholars but also for attracting talented young scholars from around the world. Accordingly, it is necessary to establish better
86 research workforce development policies to allow continuous progress in biomedical sciences at US institutions (National Research Council, 2005).
Although I try to analyze the detailed impacts of the NIH funding policy on the workforce in the biomedical field, there are some limitations to this study. First, I cannot rule out all potential variables affecting the behaviors and productivity of postdoctoral researchers. Second, in terms of productivity, I do not consider the quality of productivity because of limited information in the data. Third, I only focused on government spending on the workforce development in the short term, not the long term. Despite these limitations, this study contributes to the current literature of workforce development by investigating the importance of government funding in biomedical workforce development. By using individual records from graduates in the US, this study also provides detail on the both the behavior and productivity of the research workforce by considering one element of workforce diversity. Producing a supportive research environment is important not only for improving and maintaining an excellent biomedical workforce in the US, but also for solving public health problems (NIH, 2015). NIH has played the most important role in shaping the biomedical sciences research environment.
By understanding the heterogeneous behavior and productivity of the biomedical science community, policy makers should create ways to make government funding more effective and productive.
87
Additional Figures and Tables
88
$35,000 $32,311 $29,607 $30,000 $27,167 $25,000 $22,501 $20,000 $20,957 $13,675 $22,230 $15,000 $11,300 $12,786
Dollars (Millions) Dollars $10,000 NIH Appropriation in Current $5,000 Constant $ (1995) $0 1995 1998 2003 2008 2016
Source: https://report.nih.gov/nihdatabook Figure 3.2. Trends in NIH Funding FY1995-FY2016
89
60,000 35%
50,000 30%
25%
40,000 Success Success Rate 20% 30,000 15%
20,000 (%)
10% Applications Applications Awards /
10,000 5%
0 0%
Applications Awards Success Rate (%)
Source: https://report.nih.gov/nihdatabook Figure 3.3. Trends in Research Project Grants FY1995-FY2015
90
4.00
3.90
3.80
3.70
3.60
3.50
3.40
3.30 2003 2010 2013
US Citizens Permanent residents Temporary residents
Note: Motivation on intellectual challenge is measure by whether researchers treat and behave their job with having intellectual challenge or not. This is related to questionnaire that was asked to measure respondent’s perceptions of job expectation and importance: “when you think about a job, how important intellectual challenge to you”. Response options for importance on the intellectual challenge on your job include “very important (4)”, “somewhat important (3)”, “somewhat unimportant (2)”, and “not important at all (1)”. Data source: 1995, 2010, and 2013 Survey of Doctorate Recipients Figure 3.4. Motivation on Intellectual Challenge: Postdocs
91
Before & During During &After Variables N Mean SD Min Max N Mean SD Min Max Published article 4,013 5.350 5.846 0 96 3,398 5.401 6.451 0 96 Conference papers 4,013 6.390 6.883 0 96 3,398 6.514 7.209 0 96 Time Since Graduation 4,025 43.647 45.888 10 544 3,398 44.939 45.066 10 544 Time in Latest Postdoc 4,013 27.028 31.863 0 483 3,398 28.495 31.866 0 483 Time in Latest Postdoc2 4,013 1745.519 9096.372 0 233,289 3,398 1,827.137 9,800.369 0 233,289 Biomedical fields (no = 0, yes = 1) 4,025 0.401 0.490 0 1 3,398 0.356 0.479 0 1 Funding Periods 4,025 0.553 0.497 0 1 3,398 0.345 0.475 0 1 Age 4,025 34.966 5.854 24 72 3,398 35.103 6.078 23 73 Female (no = 0, yes = 1) 4,025 0.420 0.494 0 1 3,398 0.459 0.498 0 1 Race: White 4,025 0.584 0.493 0 1 3,398 0.532 0.499 0 1 Black 4,025 0.046 0.209 0 1 3,398 0.057 0.233 0 1 Asian 4,025 0.278 0.448 0 1 3,398 0.295 0.456 0 1 Hispanic 4,025 0.077 0.266 0 1 3,398 0.092 0.290 0 1 92 Other 4,025 0.016 0.124 0 1 3,398 0.024 0.153 0 1 Citizen Type: US citizens 4,024 0.736 0.441 0 1 3,398 0.702 0.457 0 1 Green card holders 4,024 0.130 0.337 0 1 3,398 0.104 0.305 0 1 Visa card holders 4,024 0.134 0.341 0 1 3,398 0.194 0.396 0 1 Married (no = 0, yes = 1) 4,025 0.625 0.484 0 1 3,398 0.608 0.488 0 1 Children (no = 0, yes = 1) 4,025 0.368 0.482 0 1 3,398 0.363 0.481 0 1 Working Hours 4,013 49.390 10.925 1 96 3,398 48.775 11.223 1 96 Research Activity (no = 0, yes = 1) 4,025 0.864 0.343 0 1 3,398 0.851 0.356 0 1 Continued Table 3.4. Descriptive Statistics
Table 3.4 continued Before & During During &After Variables N Mean SD Min Max N Mean SD Min Max University Rank: Research University I 4,020 0.765 0.424 0 1 3,392 0.751 0.433 0 1 Research University II 4,020 0.088 0.283 0 1 3,392 0.087 0.281 0 1 Doctorate Granting I 4,020 0.040 0.197 0 1 3,392 0.050 0.217 0 1 Doctorate Granting II 4,020 0.032 0.176 0 1 3,392 0.032 0.175 0 1 Others 4,020 0.075 0.264 0 1 3,392 0.082 0.274 0 1 Graduation Year: before 1991 4,025 0.126 0.332 0 1 3,398 0.024 0.153 0 1 1991 4,025 0.060 0.238 0 1 3,398 0.006 0.080 0 1 1992 4,025 0.091 0.288 0 1 3,398 0.009 0.095 0 1 1993 4,025 0.129 0.335 0 1 3,398 0.012 0.109 0 1 1993 4,025 0.088 0.284 0 1 3,398 0.017 0.128 0 1 1995 4,025 0.016 0.127 0 1 3,398 0.020 0.141 0 1 1996 4,025 0.031 0.174 0 1 3,398 0.038 0.192 0 1 1997 4,025 0.052 0.222 0 1 3,398 0.064 0.245 0 1 93 1998 4,025 0.081 0.274 0 1 3,398 0.100 0.301 0 1 1999 4,025 0.115 0.319 0 1 3,398 0.140 0.347 0 1 2000 4,025 0.097 0.297 0 1 3,398 0.120 0.325 0 1 2001 4,025 0.066 0.248 0 1 3,398 0.089 0.285 0 1 2002 4,025 0.046 0.211 0 1 3,398 0.070 0.255 0 1 2003 3,398 0.026 0.161 0 1 2004 3,398 0.041 0.199 0 1 2005 3,398 0.063 0.242 0 1 2006 3,398 0.098 0.297 0 1 2007 3,398 0.062 0.241 0 1
ALL US GREEN CARD HOLDER VISA CARD HOLDER Before During Before During Before During Before During vs. vs. vs. vs. vs. vs. vs. vs. During After During After During After During After DV: Time in Latest Postdoc Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Biomedical Field (β1) -0.975 3.118** -2.622 3.480** 1.129 3.090 8.125*** 1.084 (1.401) (1.257) (1.807) (1.630) (2.049) (3.471) (2.361) (1.559) Doubling Funding (β2) 10.70*** 31.03*** 7.764** 31.97*** 21.66*** 31.82*** -0.544 21.10*** (2.470) (2.385) (3.065) (3.093) (4.052) (4.943) (5.952) (4.700) Difference in Difference (β3) 4.324** 1.017 6.177** -0.293 3.058 4.669 -6.944** 4.254* (1.870) (2.120) (2.417) (2.845) (3.120) (5.477) (2.830) (2.430) Age 1.389*** 0.771*** 1.625*** 0.937*** 0.0176 -0.0527 0.489*** 0.234* (0.0929) (0.102) (0.115) (0.130) (0.175) (0.317) (0.167) (0.141) Female -3.116*** -3.116*** -3.983*** -4.437*** -1.576 1.855 -1.927 -1.185 (0.948) (0.976) (1.229) (1.314) (1.613) (2.592) (1.280) (1.089) Race: Black -6.671*** -3.943* -6.434** -4.648* -1.689 13.75* 1.218 -4.169 (ref: White) (2.210) (2.093) (2.649) (2.592) (5.739) (7.145) (4.059) (3.102) Asian -1.384 0.0322 -1.288 -0.0453 0.627 1.269 -0.868 -0.219 94 (1.222) (1.216) (1.739) (1.780) (1.925) (2.847) (1.375) (1.268)
Hispanic -0.734 2.501 0.119 4.164* -0.975 -6.688 -1.998 -0.406 (1.760) (1.699) (2.203) (2.186) (4.197) (5.222) (2.364) (2.116) Other -1.696 -2.345 -2.256 -2.217 1.007 1.809 -2.946 (3.704) (3.133) (4.276) (3.614) (17.31) (7.988) (6.653) Married 0.163 -1.318 0.747 -1.311 -2.732 -4.432 -1.341 -0.709 (1.103) (1.149) (1.394) (1.509) (2.168) (3.570) (1.512) (1.284) Children 0.252 0.451 0.689 1.113 2.883 -0.108 -1.115 0.499 (1.141) (1.199) (1.495) (1.623) (1.803) (2.966) (1.576) (1.375) Working Hours 0.0522 -0.0475 0.0323 -0.0984 0.0600 0.0835 0.102* 0.0790 (0.0444) (0.0452) (0.0583) (0.0613) (0.0720) (0.114) (0.0576) (0.0485) Research Activity -0.609 0.765 -0.668 0.622 2.206 2.489 1.668 2.485 (1.383) (1.377) (1.789) (1.834) (2.503) (3.805) (1.807) (1.520) Continued Table 3.5. Difference-in-Difference Estimates: Time in Latest Postdoc
Table 3.5 continued ALL US GREEN CARD HOLDER VISA CARD HOLDER Before During Before During Before During Before During vs. vs. vs. vs. vs. vs. vs. vs. During After During After During After During After DV: Time in Latest Postdoc Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 University Rank: Research University II -1.764 -1.197 -2.651 -1.408 -0.910 1.078 -0.383 -0.457 (ref: Research University I) (1.627) (1.709) (2.209) (2.442) (2.570) (4.075) (1.882) (1.670) Doctorate Granting I -3.008 0.797 -3.389 0.587 -0.254 0.544 -6.010* -2.530 (2.329) (2.221) (3.010) (2.947) (3.819) (5.768) (3.158) (2.524) Doctorate Granting II -3.300 -0.774 -4.405 -1.689 0.540 18.35 -1.901 -0.794 (2.590) (2.728) (3.433) (3.678) (4.405) (13.27) (3.028) (2.484) Others -1.801 -0.186 -3.105 -1.587 0.0627 5.018 0.602 0.293 (1.775) (1.767) (2.344) (2.372) (2.502) (4.140) (2.621) (2.033) Graduation Year: 1991 -14.71*** -19.71*** -14.46*** -19.54** -9.551** -18.18 0.160 (ref: before 1991) (2.310) (6.674) (2.870) (7.687) (3.856) (25.57) (4.635) 1992 -18.07*** -41.39*** -18.50*** -38.93*** -13.94*** -28.53* 0.974 -3.090 (2.054) (5.927) (2.607) (7.159) (3.139) (16.37) (4.321) (18.50) 95 1993 -23.04*** -31.89*** -22.97*** -33.85*** -20.56*** 4.671 -3.317 10.67 (1.910) (5.413) (2.424) (6.665) (3.047) (14.51) (3.894) (18.54) 1994 -24.67*** -32.32*** -24.35*** -32.72*** -22.23*** 8.827 -9.334** 9.399 (2.129) (4.959) (2.746) (6.144) (3.350) (14.17) (4.006) (15.16) 1995 -26.36*** -42.79*** -25.76*** -42.48*** -25.96*** -16.15 9.669 19.26 (4.344) (4.765) (5.306) (5.721) (7.345) (14.55) (10.16) (15.20) 1996 -18.22*** -36.19*** -16.77*** -35.70*** -19.13*** -6.004 18.14** 28.41** (3.619) (4.209) (4.472) (5.087) (6.336) (13.52) (7.881) (13.91) 1997 -22.91*** -44.02*** -21.89*** -43.56*** -29.21*** -21.43 14.14** 23.27* (3.297) (3.994) (4.156) (4.853) (5.543) (13.28) (6.968) (13.42) 1998 -24.53*** -45.15*** -21.92*** -43.05*** -29.87*** -20.88 5.987 15.48 (3.116) (3.880) (3.880) (4.690) (5.444) (13.26) (6.829) (13.35) 1999 -29.77*** -51.49*** -27.89*** -50.27*** -37.75*** -29.92** 4.556 13.64 (3.029) (3.826) (3.755) (4.606) (5.352) (13.30) (6.770) (13.31) Continued
Table 3.5 continued ALL US GREEN CARD HOLDER VISA CARD HOLDER Before During Before During Before During Before During vs. vs. vs. vs. vs. vs. vs. vs. During After During After During After During After DV: Time in Latest Postdoc Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Graduation Year: 2000 -33.16*** -54.52*** -31.60*** -53.36*** -40.26*** -31.56** 2.477 11.91 (ref: before 1991) (3.096) (3.869) (3.857) (4.685) (5.454) (13.26) (6.787) (13.32) 2001 -31.55*** -54.60*** -30.16*** -54.14*** -34.58*** -31.35** 2.888 12.41 (3.238) (3.909) (4.074) (4.742) (6.390) (13.54) (6.818) (13.32) 2002 -35.63*** -58.99*** -34.15*** -58.44*** -42.63*** -34.26** -0.257 8.107 (3.459) (4.039) (4.447) (4.995) (6.123) (13.52) (6.877) (13.34) 2003 -65.99*** -64.75*** -41.94*** 3.750 (5.005) (6.552) (13.89) (14.49) 2004 -71.94*** -71.18*** -53.81*** 3.209 (4.744) (5.943) (14.53) (14.29) 2005 -78.00*** -77.26*** -61.19*** -2.195 (4.615) (5.811) (14.19) (14.20) 96 2006 -84.12*** -83.71*** -66.67*** -9.770 (4.529) (5.607) (14.47) (14.17) 2007 -87.91*** -87.48*** -64.59*** -13.97 (4.749) (6.036) (14.72) (14.21) Citizen Type: Green card holders -2.712* -1.906 (ref: US citizens) (1.567) (1.678) Visa card holders -3.024** -1.132 (1.487) (1.375) Constant -4.552 50.84*** -10.68* 46.96*** 31.84*** 45.70** -2.148 -4.607 (4.886) (6.692) (6.162) (8.442) (8.848) (21.24) (7.893) (14.72) Adj R-squared 0.1932 0.2620 0.1995 0.2688 0.1964 0.2458 0.1403 0.1726 Observations 4,007 3,392 2,945 2,381 524 352 538 659 *** p<0.01, ** p<0.05, * p<0.1, Note: Standard errors are presented in parentheses below coefficient estimates. Data source: 1995, 2001, 2003, and 2008 Survey of Doctorate Recipients
ALL US GREEN CARD HOLDER VISA CARD HOLDER Before During Before During Before During Before During vs. vs. vs. vs. vs. vs. vs. vs. During After During After During After During After DV: Time since Graduation Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Biomedical Field (β1) 4.467*** 7.480*** 4.818** 8.806*** 0.730 6.303 0.572 2.370 (1.720) (1.523) (2.157) (1.927) (2.762) (3.881) (2.827) (1.894) Doubling Funding (β2) -2.218 8.451*** -4.634** 9.468*** 8.976*** 12.79*** 3.038 2.809* (1.487) (1.501) (1.896) (2.036) (2.868) (3.817) (1.890) (1.485) Difference in Difference (β3) 3.009 -4.843* 3.507 -7.913** 5.156 -1.586 1.715 1.649 (2.293) (2.567) (2.881) (3.366) (4.149) (6.097) (3.388) (2.958) Age 4.838*** 4.775*** 5.331*** 5.165*** 2.443*** 4.260*** 1.405*** 1.409*** (0.0999) (0.0997) (0.118) (0.120) (0.217) (0.306) (0.191) (0.162) Female -6.058*** -4.984*** -9.441*** -8.195*** -0.770 -4.193 0.885 0.569 (1.162) (1.188) (1.459) (1.555) (2.174) (3.041) (1.538) (1.322) Race: Black -8.536*** -8.527*** -5.022 -4.178 -11.93 -29.72*** -8.434* -9.502** (ref: White) (2.719) (2.550) (3.168) (3.081) (7.614) (8.233) (4.878) (3.762) Asian 1.311 3.137** 5.026** 6.692*** 0.282 3.181 -0.949 -3.095**
97 (1.499) (1.479) (2.076) (2.104) (2.553) (3.241) (1.647) (1.535)
Hispanic -6.624*** -4.038* -5.378** -2.761 3.503 3.724 -4.718* -4.643* (2.161) (2.066) (2.624) (2.588) (5.593) (5.992) (2.821) (2.563) Other -11.21** -11.61*** -11.55** -11.57*** 3.137 -2.399 -2.257 (4.550) (3.817) (5.107) (4.293) (23.42) (9.608) (8.121) Married 2.487* 0.0489 3.899** 1.192 -5.171* -10.85*** -1.173 -0.225 (1.357) (1.401) (1.667) (1.794) (2.908) (4.147) (1.808) (1.556) Children -1.950 -1.288 -0.772 1.238 1.528 -2.471 0.0532 0.504 (1.397) (1.451) (1.777) (1.912) (2.424) (3.478) (1.867) (1.656) Working Hours 0.0220 -0.00348 -0.0598 -0.0715 0.209** 0.119 0.139** 0.0927 (0.0545) (0.0551) (0.0696) (0.0728) (0.0967) (0.134) (0.0688) (0.0587) Research Activity -4.382*** -5.591*** -4.233** -6.591*** 4.293 4.291 0.241 -0.0882 (1.699) (1.676) (2.135) (2.178) (3.345) (4.421) (2.170) (1.849) Continued Table 3.6. Difference-in-Difference Estimates: Time Since Graduation
Table 3.6 continued ALL US GREEN CARD HOLDER VISA CARD HOLDER Before During Before During Before During Before During vs. vs. vs. vs. vs. vs. vs. vs. During After During After During After During After DV: Time since Graduation Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 University Rank: Research University II -0.989 -1.209 -1.392 -1.339 -1.751 1.075 -0.168 -0.594 (ref: Research University I) (1.996) (2.079) (2.634) (2.894) (3.376) (4.616) (2.246) (1.999) Doctorate Granting I -7.296** -3.344 -8.237** -5.745 -1.544 0.881 -7.093* -5.103* (2.864) (2.706) (3.599) (3.500) (5.140) (6.689) (3.801) (3.084) Doctorate Granting II -10.24*** -9.256*** -10.94*** -11.38*** -3.407 -4.969 -4.974 -2.229 (3.180) (3.318) (4.099) (4.354) (5.901) (15.60) (3.636) (3.030) Others -11.10*** -10.92*** -13.06*** -13.15*** -7.265** -7.813 -6.790** -3.278 (2.173) (2.146) (2.793) (2.807) (3.348) (4.874) (3.150) (2.479) Citizen Type: Green card holders -14.93*** -5.469*** (ref: US citizens) (1.915) (2.038) Visa card holders -14.74*** -15.29*** (1.805) (1.647)
98 Constant -116.1*** -114.8*** -128.6*** -125.4*** -64.30*** -109.7*** -25.99*** -20.38***
(5.143) (5.115) (6.340) (6.447) (10.32) (14.67) (7.658) (6.666) Adj R-squared 0.4057 0.4484 0.4399 0.4801 0.2613 0.4268 0.1100 0.1141 Observations 4,007 3,392 2,945 2,381 524 352 538 659 *** p<0.01, ** p<0.05, * p<0.1, Note: Standard errors are presented in parentheses below coefficient estimates. Data source: 1995, 2001, 2003, and 2008 Survey of Doctorate Recipients
ALL US GREEN CARD VISA CARD HOLDER HOLDER Before During Before During Before During Before During vs. vs. vs. vs. vs. vs. vs. vs. During After During After During After During After DV: Published Articles Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Biomedical Field (β1) -0.216*** -0.177*** -0.199*** -0.239*** -0.259*** 0.0609 -0.225 -0.0558 (0.0411) (0.0404) (0.0480) (0.0466) (0.0925) (0.129) (0.146) (0.104) Doubling Funding (β2) 0.159** 0.0836 0.132 0.0517 -0.0558 0.193 0.836** 0.191 (0.0737) (0.0809) (0.0831) (0.0937) (0.188) (0.199) (0.341) (0.338) Difference in Difference (β3) 0.0270 -0.0422 -0.0340 0.0320 0.288** -0.367* 0.145 -0.0802 (0.0551) (0.0681) (0.0645) (0.0814) (0.143) (0.201) (0.174) (0.164) Age -0.0202*** -0.0230*** -0.0188*** -0.0259*** -0.0146* 0.00657 -0.044*** -0.026*** (0.00288) (0.00342) (0.00324) (0.00388) (0.00805) (0.0118) (0.0104) (0.00983) Female -0.286*** -0.257*** -0.314*** -0.252*** -0.257*** -0.228** -0.249*** -0.244*** (0.0281) (0.0315) (0.0331) (0.0380) (0.0746) (0.0962) (0.0771) (0.0730) Race: Black -0.424*** -0.422*** -0.382*** -0.431*** -0.992*** -0.768** -0.477* -0.342 (ref: White) (0.0700) (0.0715) (0.0755) (0.0793) (0.319) (0.299) (0.261) (0.214)
99 Asian -0.0251 -0.0527 0.0206 -0.0330 -0.124 -0.0696 -0.0967 -0.132
(0.0356) (0.0391) (0.0465) (0.0516) (0.0876) (0.105) (0.0812) (0.0846) Hispanic -0.0593 -0.00140 -0.0917 -0.0505 0.249 -0.0365 -0.0354 0.136 (0.0521) (0.0542) (0.0594) (0.0625) (0.189) (0.193) (0.142) (0.141) Other -0.0504 -0.119 -0.0117 -0.0785 -18.80 -0.735 -0.772 (0.110) (0.103) (0.114) (0.105) (6,937) (0.519) (0.478) Married 0.0909*** 0.0121 0.0484 -0.0389 0.226** 0.111 0.232*** 0.128 (0.0325) (0.0370) (0.0373) (0.0435) (0.0988) (0.133) (0.0881) (0.0848) Children -0.0801** -0.0160 -0.0935** -0.0397 -0.0548 0.0528 -0.0280 -0.0107 (0.0338) (0.0389) (0.0404) (0.0473) (0.0811) (0.109) (0.0926) (0.0908) Working Hours 0.00917*** 0.00936*** 0.0103*** 0.0111*** 0.00498 0.00610 0.00752** 0.00687** (0.00135) (0.00148) (0.00160) (0.00180) (0.00335) (0.00424) (0.00355) (0.00329) Research Activity 0.370*** 0.347*** 0.418*** 0.411*** 0.430*** 0.243* 0.150 0.232** (0.0419) (0.0451) (0.0491) (0.0537) (0.123) (0.145) (0.108) (0.103) Continued Table 3.7. Difference-in-Difference Estimates: Published Articles
Table 3.7 continued ALL US GREEN CARD VISA CARD HOLDER HOLDER Before During Before During Before During Before During vs. vs. vs. vs. vs. vs. vs. vs. During After During After During After During After DV: Published Articles Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Time in Latest Postdoc 0.00540*** 0.00372*** 0.00650*** 0.00554*** -0.00321 -0.00726* 0.00626 0.0124 (0.000922) (0.00105) (0.00103) (0.00120) (0.00465) (0.00417) (0.00519) (0.00773) Time in Latest Postdoc2 -9.57e-06*** -7.99e-06** -1.24e-05*** -1.23e-05*** 5.19e-05 6.22e-05** -6.18e-05 -0.00024* (3.08e-06) (3.65e-06) (3.30e-06) (3.92e-06) (4.95e-05) (3.02e-05) (6.10e-05) (0.000130) University Rank: Research 0.0350 0.0619 0.0232 0.0576 -0.0333 0.120 0.174 0.0226 University II (ref: Research University I) (0.0477) (0.0549) (0.0591) (0.0705) (0.116) (0.149) (0.112) (0.112) Doctorate Granting I 0.0141 -0.150** 0.0453 -0.201** -0.268 0.00575 0.174 0.0262 (0.0688) (0.0732) (0.0803) (0.0877) (0.183) (0.219) (0.187) (0.167) Doctorate Granting II -0.217*** -0.198** -0.197** -0.193* -0.343 -0.841 -0.150 -0.110 (0.0794) (0.0898) (0.0953) (0.108) (0.215) (0.550) (0.189) (0.170)
100 Others -0.0268 -0.119** -0.0195 -0.0990 0.0523 0.0206 -0.218 -0.273**
(0.0531) (0.0581) (0.0638) (0.0697) (0.115) (0.156) (0.161) (0.139) Graduation Year: 1991 -0.0127 -0.595*** 0.0107 -0.726*** -0.167 1.272 0.160 (ref: before 1991) (0.0675) (0.225) (0.0761) (0.234) (0.176) (0.916) (0.275) 1992 -0.0183 -0.440** -0.0309 -0.559** -0.0207 0.748 -0.0481 -1.763 (0.0607) (0.200) (0.0701) (0.217) (0.146) (0.632) (0.259) (1.213) 1993 -0.163*** 0.134 -0.107 0.158 -0.408*** 0.877 -0.110 -0.919 (0.0581) (0.175) (0.0670) (0.192) (0.147) (0.560) (0.232) (1.181) 1994 -0.322*** -0.103 -0.293*** -0.187 -0.446*** 0.909 -0.381 -0.879 (0.0661) (0.165) (0.0775) (0.182) (0.161) (0.567) (0.242) (0.948) 1995 -0.215* -0.236 -0.0955 -0.225 -0.709** 0.501 -0.734 -1.104 (0.130) (0.160) (0.144) (0.171) (0.356) (0.589) (0.583) (0.954) 1996 -0.0644 -0.0896 0.0830 -0.0908 -0.248 1.017* -1.796*** -2.136** (0.107) (0.143) (0.120) (0.155) (0.293) (0.537) (0.491) (0.893) 1997 -0.254** -0.280** -0.128 -0.276* -0.574** 0.730 -0.805** -1.330 (0.0994) (0.137) (0.114) (0.149) (0.264) (0.538) (0.397) (0.836) 1998 -0.306*** -0.338** -0.261** -0.416*** -0.379 0.867 -0.819** -1.313 (0.0947) (0.134) (0.108) (0.144) (0.260) (0.540) (0.388) (0.829) Continued
Table 3.7 continued ALL US GREEN CARD VISA CARD HOLDER HOLDER Before During Before During Before During Before During vs. vs. vs. vs. vs. vs. vs. vs. During After During After During After During After DV: Published Articles Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Graduation Year: 1999 -0.273*** -0.335** -0.177* -0.360** -0.462* 0.769 -0.935** -1.413* (ref: before 1991) (0.0930) (0.132) (0.105) (0.142) (0.261) (0.542) (0.385) (0.827) 2000 -0.230** -0.299** -0.113 -0.304** -0.288 0.891* -1.028*** -1.521* (0.0953) (0.134) (0.108) (0.145) (0.264) (0.536) (0.387) (0.828) 2001 -0.236** -0.332** -0.141 -0.361** -0.415 0.807 -0.895** -1.408* (0.0992) (0.135) (0.113) (0.147) (0.309) (0.551) (0.389) (0.829) 2002 -0.382*** -0.457*** -0.394*** -0.594*** -0.477 0.846 -0.853** -1.334 (0.107) (0.140) (0.126) (0.154) (0.297) (0.555) (0.389) (0.827) 2003 -0.0916 -0.120 0.962* -1.156 (0.167) (0.193) (0.572) (0.909) 2004 -0.266 -0.313* 0.815 -1.243
101 (0.162) (0.180) (0.602) (0.900)
2005 -0.276* -0.265 0.966* -1.540* (0.158) (0.176) (0.585) (0.898) 2006 -0.279* -0.398** 0.965 -1.290 (0.157) (0.173) (0.593) (0.897) 2007 -0.434*** -0.405** 0.485 -1.668* (0.164) (0.184) (0.600) (0.902) Citizen Type: Green card holders 0.161*** 0.0970* (ref: US citizens) (0.0458) (0.0541) Visa card holders 0.0704 0.110** (0.0436) (0.0438) Constant 1.704*** 2.083*** 1.527*** 2.092*** 2.094*** 0.218 2.724*** 3.438*** (0.148) (0.226) (0.169) (0.256) (0.414) (0.810) (0.484) (0.936) Pseudo R2 0.0247 0.0216 0.0286 0.0288 0.0359 0.0224 0.0256 0.0209 Log likelihood -10669.704 -9127.0639 -7762.4599 -6304.6214 -1415.9218 -947.9806 -1433.434 -1822.109 Observations 4,007 3,392 2,945 2,381 524 352 538 659 *** p<0.01, ** p<0.05, * p<0.1, Note: Standard errors are presented in parentheses below coefficient estimates. Data source: 1995, 2001, 2003, and 2008 Survey of Doctorate Recipients
ALL US GREEN CARD HOLDER VISA CARD HOLDER During vs. After Before During Before During Before During Before During vs. vs. vs. vs. vs. vs. vs. vs. During After During After During After During After DV: Conference Papers Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Biomedical Field (β1) -0.336*** -0.331*** -0.335*** -0.400*** -0.418*** -0.0579 -0.0788 -0.187* (0.0428) (0.0424) (0.0505) (0.0510) (0.0960) (0.122) (0.145) (0.100) Doubling Funding (β2) 0.0329 0.168** 0.0844 0.172* -0.634*** 0.0799 0.880*** 0.0680 (0.0766) (0.0843) (0.0864) (0.102) (0.206) (0.181) (0.339) (0.310) Difference in Difference (β3) -0.0177 -0.0610 -0.0728 -0.0156 0.352** -0.323* -0.181 -0.0994 (0.0575) (0.0715) (0.0678) (0.0888) (0.149) (0.194) (0.173) (0.156) Age -0.00267 0.000567 -0.00427 -0.00164 0.00897 0.0229** -0.000168 0.00457 (0.00294) (0.00344) (0.00334) (0.00401) (0.00814) (0.0113) (0.0105) (0.00908) Female -0.245*** -0.183*** -0.243*** -0.170*** -0.292*** -0.198** -0.218*** -0.169**
102 (0.0290) (0.0327) (0.0343) (0.0407) (0.0772) (0.0921) (0.0781) (0.0691) Race: Black -0.250*** -0.254*** -0.241*** -0.256*** -0.609** -0.421 -0.133 -0.250 (ref: White) (0.0693) (0.0713) (0.0758) (0.0822) (0.288) (0.259) (0.246) (0.197) Asian -0.149*** -0.206*** -0.119** -0.140** -0.153* -0.184* -0.313*** -0.375*** (0.0371) (0.0403) (0.0487) (0.0555) (0.0900) (0.0987) (0.0828) (0.0802) Hispanic -0.0626 0.00299 -0.0900 -0.0372 0.302 0.355** -0.176 -0.0137 (0.0536) (0.0559) (0.0615) (0.0671) (0.194) (0.171) (0.142) (0.133) Other -0.0515 -0.0952 -0.00865 -0.0492 -18.61 -0.546 -0.676 (0.114) (0.106) (0.120) (0.112) (4,431) (0.491) (0.435) Married 0.0909*** -0.00912 0.0839** -0.0187 0.0365 0.0851 0.136 -0.0128 (0.0337) (0.0386) (0.0389) (0.0470) (0.102) (0.125) (0.0909) (0.0815) Children -0.0469 -0.0212 -0.0562 -0.0430 -0.0611 -0.000778 -0.00473 0.0536 (0.0348) (0.0402) (0.0418) (0.0505) (0.0842) (0.102) (0.0950) (0.0870) Working Hours 0.0107*** 0.0108*** 0.0125*** 0.0132*** 0.00319 0.00445 0.00840** 0.00734** (0.00138) (0.00154) (0.00165) (0.00194) (0.00347) (0.00391) (0.00360) (0.00312) Research Activity 0.193*** 0.258*** 0.210*** 0.316*** 0.0335 -0.00763 0.198* 0.207** (0.0422) (0.0459) (0.0497) (0.0564) (0.119) (0.132) (0.110) (0.0970) Continued Table 3.8. Difference-in-Difference Estimates: Conference Papers
Table 3.8 continued ALL US GREEN CARD HOLDER VISA CARD HOLDER During vs. After Before vs. During vs. Before vs. During vs. Before vs. During vs. Before vs. During vs. During After During After During After During After DV: Conference Papers Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Time in Latest Postdoc 0.00344*** 0.00177* 0.00415*** 0.00244* 0.00216 -0.00650* 0.00395 0.0199*** (0.000995) (0.00107) (0.00112) (0.00126) (0.00511) (0.00380) (0.00522) (0.00732) Time in Latest Postdoc2 -7.71e-06** -5.93e-06 -8.92e-06** -7.98e-06** -2.98e-05 6.92e-05** -4.82e-05 -0.000335*** (3.43e-06) (3.67e-06) (3.74e-06) (4.07e-06) (5.90e-05) (2.72e-05) (6.08e-05) (0.000124) University Rank: Research 0.174*** 0.170*** 0.198*** 0.193*** 0.321*** 0.228 -0.0284 0.0113 University II (ref: Research University I) (0.0487) (0.0563) (0.0606) (0.0746) (0.118) (0.140) (0.113) (0.105) Doctorate Granting I 0.0634 -0.0124 0.0283 -0.0677 -0.0102 -0.124 0.335* 0.293* (0.0705) (0.0739) (0.0834) (0.0912) (0.179) (0.208) (0.187) (0.156) Doctorate Granting II -0.123 -0.0880 -0.0981 -0.0905 -0.0604 -0.673 -0.264 -0.0424 (0.0802) (0.0915) (0.0968) (0.114) (0.213) (0.500) (0.187) (0.156) Others 0.0128 -0.0645 0.0141 -0.0421 0.176 0.0378 -0.135 -0.239*
103 (0.0548) (0.0598) (0.0659) (0.0742) (0.121) (0.146) (0.161) (0.131) Graduation Year: 1991 0.0148 -0.349 0.0311 -0.471* -0.219 1.576* 0.136
(ref: before 1991) (0.0710) (0.235) (0.0810) (0.252) (0.179) (0.871) (0.284) 1992 0.0224 -0.228 0.0167 -0.351 -0.193 1.056* 0.247 -0.345 (0.0642) (0.208) (0.0749) (0.234) (0.152) (0.615) (0.268) (1.152) 1993 0.0834 0.180 0.158** 0.157 -0.510*** 0.750 0.399* -0.990 (0.0605) (0.183) (0.0703) (0.210) (0.151) (0.555) (0.238) (1.235) 1994 -0.0146 0.353** 0.00905 0.240 -0.418** 0.986* 0.186 0.541 (0.0688) (0.170) (0.0811) (0.196) (0.167) (0.549) (0.248) (0.935) 1995 0.0607 0.0859 0.0756 -0.00269 -0.286 1.064* 0.250 0.330 (0.135) (0.165) (0.150) (0.183) (0.375) (0.570) (0.584) (0.941) 1996 -0.0722 -0.0155 -0.0510 -0.114 0.218 1.564*** -1.562*** -1.324 (0.113) (0.148) (0.127) (0.165) (0.308) (0.522) (0.492) (0.887) 1997 0.0636 0.138 0.117 0.0876 -0.0568 1.365*** -0.475 -0.497 (0.103) (0.140) (0.118) (0.158) (0.280) (0.523) (0.405) (0.839) 1998 0.108 0.171 0.0771 0.0334 0.227 1.621*** -0.281 -0.296 (0.0976) (0.136) (0.111) (0.152) (0.276) (0.525) (0.396) (0.831) Continued
Table 3.8 continued ALL US GREEN CARD HOLDER VISA CARD HOLDER During vs. After Before vs. During vs. Before vs. During vs. Before vs. During vs. Before vs. During vs. During After During After During After During After DV: Conference Papers Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Graduation Year: 1999 0.152 0.165 0.185* 0.0954 -0.116 1.223** -0.344 -0.332 (ref: before 1991) (0.0965) (0.135) (0.109) (0.151) (0.281) (0.525) (0.394) (0.830) 2000 0.0661 0.0867 0.126 0.0417 0.0186 1.397*** -0.598 -0.590 (0.0986) (0.137) (0.112) (0.153) (0.286) (0.522) (0.394) (0.830) 2001 0.112 0.0826 0.164 0.0203 -0.0738 1.324** -0.468 -0.494 (0.102) (0.138) (0.118) (0.155) (0.324) (0.538) (0.397) (0.832) 2002 -0.121 -0.0481 -0.177 -0.202 0.0175 1.437*** -0.521 -0.481 (0.111) (0.142) (0.131) (0.162) (0.314) (0.536) (0.399) (0.832) 2003 -0.154 -0.182 0.950* -0.257 (0.173) (0.207) (0.557) (0.903) 2004 -0.0764 -0.179 1.295** -0.363 (0.166) (0.192) (0.584) (0.894)
104 2005 0.0418 -0.0538 1.536*** -0.403 (0.161) (0.186) (0.570) (0.890)
2006 -0.0927 -0.200 1.368** -0.471 (0.160) (0.182) (0.578) (0.889) 2007 -0.0688 -0.170 1.133* -0.443 (0.168) (0.195) (0.581) (0.892) Citizen Type: Green card 0.200*** 0.0416 holders (ref: US citizens) (0.0478) (0.0565) Visa card holders 0.145*** 0.187*** (0.0445) (0.0447) Constant 1.268*** 1.174*** 1.160*** 1.166*** 2.090*** -0.380 1.175** 1.817** (0.153) (0.232) (0.177) (0.271) (0.424) (0.798) (0.483) (0.922) Pseudo R2 0.0170 0.0157 0.0186 0.0169 0.0338 0.0341 0.0245 0.0259 Log likelihood -11439.019 -9796.3711 -8314.3164 -6827.6994 -1496.9667 -955.2557 -1563.4246 -1944.4955 Observations 4,007 3,392 2,945 2,381 524 352 538 659 *** p<0.01, ** p<0.05, * p<0.1, Note: Standard errors are presented in parentheses below coefficient estimates. Data source: 1995, 2001, 2003, and 2008 Survey of Doctorate Recipients
Time in Latest Postdoc Time since Graduation Published Articles Conference Papers
Change of pre, during Before During Before During Before During Before During and post of vs. vs. vs. vs. vs. vs. vs. vs. Doubling Funding During After During After During After During After
Model 1 Model 2 Model 3 Mode 4 Model 5 Model 6 Model 7 Model 8
ALL
Biomedical Field (β1) 0.22 1.78 1.69 6.33*** -0.26*** -0.21*** -0.41*** 0.14
(1.53) (1.27) (1.90) (1.57) (0.05) (0.05) (0.05) (0.11)
Doubling Funding (β2) 18.50*** 21.32*** -3.77** 6.75*** 0.20** 0.03 0.10 -0.35***
105
(2.86) (2.68) (1.67) (1.66) (0.09) (0.10) (0.10) (0.05)
Difference in Difference (β3) 1.24 1.38 5.00** -4.66* 0.03 -0.03 0.04 -0.04
(2.04) (2.19) (2.53) (2.70) (0.07) (0.08) (0.07) (0.09)
0.18 0.21 0.37 0.34 -7253.97 -6004.38 -7698.25 -6354.05 Adj R-squared / Log likelihood Observations 2,671 2,186 2,671 2,186 2,671 2,186 2,671 2,186 Note: Standard errors are presented in parentheses below coefficient estimates. DV stands for dependent variable. Control variables include age, gender, race, marriage, children, working hours, research activity, cohorts, time in the postdoc (only when DV is conference papers or published articles), and institutional rank of the organization where researchers got their first US S&E or health PhD. Data source: 1995, 2001, 2003, and 2008 Survey of Doctorate Recipients Table 3.9. Difference-in-Difference Estimates within Only Government-Funded Postdocs
Time in Latest Postdoc Time since Graduation Published Articles Conference Papers Before During Before During Before During Before During Change of pre, during and post of vs. vs. vs. vs. vs. vs. vs. vs. Doubling Funding During After During After During After During After Model 1 Model 2 Model 3 Mode 4 Model 5 Model 6 Model 7 Model 8 US
Biomedical Field (β1) -1.40 2.07 2.52 7.01*** -0.22*** -0.28*** -0.40*** 0.14 (1.92) (1.59) (2.35) (1.94) (0.06) (0.05) (0.06) (0.13)
Doubling Funding (β2) 15.79*** 20.20*** -4.90** 6.48*** 0.21** 0.01 0.17 -0.41*** (3.49) (3.42) (2.10) (2.21) (0.10) (0.12) (0.11) (0.06)
Difference in Difference (β3) 2.95 -0.00 4.65 -6.25* -0.07 -0.02 -0.04 0.01 (2.57) (2.84) (3.13) (3.45) (0.08) (0.10) (0.08) (0.10) Adj R-squared / Log likelihood 0.18 0.21 0.40 0.38 -5367.64 -4287.59 -5675.76 -4571.29 Observations 1,992 1,573 1,992 1,573 1,992 1,573 1,992 1,573 106 GREEN CARD HOLDER
Biomedical Field (β1) 3.19 -0.27 -0.069 7.135 -0.38*** 0.0189 -0.59*** 0.28 (2.57) (4.01) (3.24) (4.39) (0.11) (0.17) (0.12) (0.24)
Doubling Funding (β2) 28.06*** 23.31*** 5.201 12.46*** -0.145 0.251 -0.58** -0.06 (5.13) (5.88) (3.36) (4.50) (0.23) (0.27) (0.25) (0.16)
Difference in Difference (β3) -2.24 5.68 7.608 -5.598 0.370** -0.323 0.50*** -0.15 (3.95) (6.44) (4.83) (7.08) (0.18) (0.27) (0.18) (0.26) Adj R-squared / Log likelihood 0.24 0.19 0.22 0.26 -961.36 -599.92 -999.56 -585.63 Observations 349 220 349 220 349 220 349 220 Continued Table 3.10. Difference-in-Difference Estimates by Citizenship Status within Only Government-Funded Postdocs
Table 3.10 continued Time in Latest Postdoc Time since Graduation Published Articles Conference Papers Before During Before During Before During Before During Change of pre, during and post of vs. vs. vs. vs. vs. vs. vs. vs. Doubling Funding During After During After During After During After Model 1 Model 2 Model 3 Mode 4 Model 5 Model 6 Model 7 Model 8 VISA CARD HOLDER
Biomedical Field (β1) 11.92*** 0.05 -0.97 2.84 -0.37** 0.01 0.02 -0.31 (3.33) (2.04) (3.43) (2.27) (0.18) (0.13) (0.18) (0.41)
Doubling Funding (β2) -4.36 24.74*** 1.92 3.58* 0.46 0.14 1.29** -0.15 (10.91) (6.44) (2.40) (1.85) (0.51) (0.44) (0.52) (0.12)
Difference in Difference (β3) -10.84*** 6.38* 3.10 1.79 0.362* 0.04 -0.25 -0.23 (3.95) (3.28) (4.05) (3.64) (0.21) (0.21) (0.21) (0.19) Adj R-squared / Log likelihood 0.13 0.20 0.09 0.09 -876.46 -1071.00 -960.90 -1141.19 Observations 330 393 330 393 330 393 330 393
107 *** p<0.01, ** p<0.05, * p<0.1 Note: Standard errors are presented in parentheses below coefficient estimates. DV stands for dependent variable. Control
variables include age, gender, race, marriage, children, working hours, research activity, cohorts, time in the postdoc (only when DV is conference papers or published articles), and institutional rank of the organization where researchers got their first US S&E or health PhD. Data source: 1995, 2001, 2003, and 2008 Survey of Doctorate Recipients
Chapter 4 : Turnover Behavior among U.S. Government Employees
Abstract This study explores some underlying causes of government-related, job-status trends in response to anticipated surges in the number of government employees who will become eligible for retirement. Specifically, this study examines the effects of (i) discrepancy between employees’ expectations and actual job satisfaction, (ii) job-education mismatches, and (iii) work-related training (provided to employees). Employee turnover is defined as (i) leaving the government or (ii) changing a job (or employer) within the government. This research is based on 2003, 2006, 2010, and 2013 data from the National Survey of College Graduates (NSCG), which was conducted by the National Science Foundation (NSF). This study finds that greater discrepancies between employee expectation and satisfaction—based on intellectual challenge and opportunity for advancement—yield a greater likelihood that an employee will leave government employment or move to a different government job. Additionally, work-related training is (i) negatively related with the likelihood that employees leave their government jobs and (ii) positively related to job-associated mobility within the government. Moreover, a job- education mismatch is positively related to job-associated mobility within the government. The implications of these findings (for strengthening government resiliency) are discussed.
Keywords: turnover, organizational resilience, job expectation, job satisfaction, job- education-mismatch, work-related training
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Introduction The U.S. government is starting to experience a human resources (HR) dilemma: while the turnover rate of government employees decreased during the Great Recession
(14.6% in 2008), it has increased since that time (e.g., approximately 18.3% in 2010 and
18.1% in 2015) (BLS, 2016; Government Accountability Office, 2014) and there will be a growing surge in the number of senior employees who are eligible for retirement (Cho
& Lewis, 2012; Government Accountability Office, 2014; Lewis & Cho, 2011; Tobias,
2001). Turnover effects vary based on origin: voluntary turnover occurs when the departure is the employee’s decision and involuntary turnover occurs when the employer decides to terminate an employee. Voluntary (vs. involuntary) turnover has more negative organizational effects (Barrick & Mount, 1991; Griffeth, Hom, Gaertner, 2000;
Shaw, Park, & Kim, 2013) via the collapse of employee morale and levels of organizational performance, among other factors. The three main reasons for voluntary turnover are ‘pay or promotion opportunity’ (38.2%), ‘working conditions and environment’ (31.9%), and changes in career or professional interests (29.9%).32
Voluntary turnover rates, within the U.S. government, have been rising (e.g., from 5.8% in 2010 to 7.7% in 2015),33 34 which can significantly affect associated workforce development, HR management, and organizational resiliencies in the public sectors (Kim
2005; Lee & Whitford, 2008; Whitford & Lee, 2015).
32 2013 National Survey of College Graduates (NSCG) 33 See Figure 4.2 in the additional Figures and Tables section. 34 “Total separations include quits, layoffs and discharges, and other separations. Total separations is referred to as turnover. Quits are generally voluntary separations initiated by the employee. Therefore, the quits rate can serve as a measure of workers’ willingness or ability to leave jobs” (BLS, 2016). 109
Turnover can yield diverse outcomes for individuals and organizations. Positive employee outcomes include new opportunities for job advancement and the acquisition of new skills (Wynen et al., 2013); organizational benefits are associated with the replacement of poor-performing employees (Wynen et al., 2013; Park, Ofori-Dankwa, &
Bishop, 1994; Williams & Livingstone, 1994). However, turnover generally brings negative results to organizations (e.g., the loss of experienced employees, costs of recruiting and training new employees, and disruptions in service delivery) (Cascio,
1982; Grissom, Nicholson-Crotty, & Keiser, 2012; Kellough & Osuna, 1995; Lambert &
Hogan, 2009). Low turnover is positively related to organizational resilience because resilient organizations have systems and abilities for promoting employees and thus minimizing turnovers (Reivich & Shatté, 2002); therefore, government-associated turnovers are related to organizational abilities to maintain and manage HR-related issues
(by using adaptive, proactive, and reactive strategies) (Sutcliffe & Vogus, 2003).
Thus, it is necessary to take steps to effectively manage turnover. Previous papers have found that several individual and organizational factors affect employee-generated, turnover-related decisions (Wynen & Lee, 2014); they include demographics, personal characteristics, job satisfaction levels, and perceptions of management and the workplace environment (Cotton & Tuttle, 1986; Kellough & Osuna, 1995; Lambert, Hogan, &
Barton, 2001; Lewis, 1991; Mobley, Griffeth, Hand, & Meglino, 1979).
In order to retain employees with scarce talents and enhance their capacities, organizations adopt human resource management (HRM) strategies that include (i) careful hiring, (ii) allocating employees to correct positions, and (iii) the support of work- related training (Hatum, 2010; Moynihan & Landuyt, 2008). Furthermore, the theory of 110
person-environment fit (P-E fit) argues that better matches between employees and their environments can improve organizational performances and resiliencies and promote the prosperity of employees via higher motivational levels (Beer, Spector, Lawrence, Mills,
& Walton, 1984; Turnbull, Blyton, & Turnbull, 1992). This study utilizes data from the
2003, 2006, 2010, and 2013 National Survey of College Graduates (NSCG) and seeks to understand the turnover of government employees by taking a closer look at how HRM strategies and diverse employee-related factors (e.g., differences between perceived expectations and actual job satisfaction levels, job-education mismatches, and work- related training) affect turnover—including employees leaving the government and changing jobs and employers (i.e., agencies and departments) within the government using logistic regression analysis.
This study compares two dataset over different time periods (from 2003 to 2006 and 2010 to 2013) to examine differences between the two groups. My analysis is based on employees’ actual turnover, which distinguishes my study from previous studies that used turnover intentions. Due to the costs and time needed to track employees’ behaviors, many studies use turnover intention as a proxy for actual turnover (Cho & Lewis, 2012;
Kim & Fernandez, 2017). Turnover intention is related with actual turnover (Dalton,
Johnson, & Daily, 1999; Cho & Lewis, 2012) but does not represent actual turnover perfectly (Jung, 2010; Kirschenbaum & Weisberg, 1990; Lee & Mowday, 1987).
However, the use of NSCG data allows me to identify actual turnover.
This study considers not only employee job satisfaction but also perceived expectations (while on the job). Furthermore, this study makes a contribution to turnover studies by providing broad insight of turnover in the public sector via consideration of 111
federal, state, and local government employees. Additionally, it focuses not only on employees leaving the government but also on mobility within the government (via job and employer changes).
Managing Voluntary Turnover and Organizational Resilience Several factors determine whether employees stay with their current jobs or organizations (Miner, 2015). Interestingly, federal, state, and local governments have similar employee-turnover rates (Kim & Fernandez, 2017; Wynen et al., 2013).
According to the Bureau of Labor Statistics (BLS) of the US Department of Labor, in
2015 the federal government turnover rate was 16.9% and the state/local government turnover rate was 18.3%.35 Employees who decide to stay in government will potentially change jobs and employers (i.e., agencies or departments) within the government over time (Figure 4.1), based on pay and promotion opportunities, changes in career and professional interests, job locations, and working conditions.36
35 The voluntary turnover rate for the federal government was 5.5% and the state/local government was 9.0% (See Figure 4.3-4.4 in the additional Figures and Tables section). 36 The two main reasons for changing jobs are pay/promotion opportunities (34%) and changing career or professional interests (30%). The two main reasons for changing employers are job location (23%) and working conditions (21%). The two main reasons for changing jobs and employers are changes in career or professional interests (41%) and job location (13%). 112
Figure 4.1. Overview of the Mobility of Government Employees
As previously mentioned, turnover decisions have significant effects on employees and organizations (Finkelstein & Hambrick, 1990), which can be positive or
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negative. When I explore voluntary turnovers, I see that organizations often perform better following departures of poor performers (via recruitments of new, energetic, high performers) (Kellough & Osuna, 1995; Meier & Hicklin, 2007; Park et al., 1994). This personnel flow creates an environment that enables people and organizations to perform better—with more advancement and promotion opportunities (Meier & Hicklin, 2008;
O’Toole & Meier, 2003; Pitt, Marvel, & Fernandez, 2011). Turnover makes organizations more stable, flexible, and able to realize more long-term benefits
(Campbell, Im, & Jeong, 2014).
Nevertheless, organizations have visible and invisible turnover-related costs.
Visible costs (e.g., advertising, recruiting, hiring, selection, education, and training) are associated with the replacement of retiring and “voluntary turnover” employees (Abbasi
& Hollman, 2000; Boushey & Glynn, 2012; Cho & Lewis, 2012; Kim, 2005; Meier &
Hicklin, 2007; Selden & Moynihan, 2000). Invisible and unquantifiable costs (e.g., losses of institutional knowledge and associated memories) are (i) associated with letting trained and experienced employees leave (Carley, 1992; Government Accountability Office,
2014) and (ii) often exacerbated by the amount of time that it takes to fill such voids with new employees (Cascio, 1982; Kellough & Osuna, 1995; Lee & Whitford, 2008; Lewis
& Park, 1989)—particularly when they must become accustomed to new environments and systems of organization. Furthermore, workplaces with significant turnover often
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precipitate employee dissatisfaction and can diminish neutral competence37 in the public sector (Haas & Wright, 1989; Lewis, 1991).
A rough estimate of turnover cost in the private sector is approximately 150 percent of an employee’s annual compensation package (Pitt et al., 2011; Schlesinger &
Heskett, 1991)—or between one and two times an individual’s salary (Kepner-Tregoe
Business Issues Research Group, 1999, p. 3; Selden & Moynihan, 2000). While there are no such estimates associated with government employment, it is evident that turnover is
(i) costly for the government, (ii) generating increasing concern, and (iii) requiring more attention to manage it effectively (Bertelli, 2007; Lee & Whitford, 2008; Price, 1977).
Of course, external turnovers (i.e., leaving an organization) are quite different from internal turnovers (i.e., changing jobs within an organization). Internal turnover is generally considered to be positive when organizations (i) are able to optimize the abilities of their employees, (ii) have a need for experienced and highly skilled employees
(Kim & Fernandez, 2017; Wynen et al., 2013), and (iii) have such opportunities for employees (e.g., to enhance their skills, advance, be promoted, and network) (Moynihan
& Pandey, 2007; Wynen et al., 2013). However, when internal turnover is extreme, it is considered to be detrimental because transient job and work environments are associated with higher turnover costs (Lambert & Hogan, 2009).
37 Kaufman (1956) defines neutral competence as the “ability to do the work of government expertly, and to do it according to explicit, objective standards rather than to personal or party or other obligations and loyalties” (p. 55). 115
Thus, the management of internal and external turnover is associated with organizational resilience in both stable and turbulent eras—especially when organizations are anticipating a decreasing number of experienced employees due to retirement (Cho &
Lewis, 2012; Government Accountability Office, 2014; Lewis & Cho, 2011; Tobias,
2001). “Organizational resilience” is hard to narrowly define (Gibson & Terrant, 2010) but is generally considered to be an ability to (i) absorb adversity (Dutton, Frost, Worline,
Lilius, & Kanov, 2002; Gittell, Cameron, Lim, & Rivas, 2006; Horne, 1997; Horne &
Orr, 1998; Mallak, 1998; Robb, 2000; Rudolph & Repenning, 2002) or (ii) transform adversity into an opportunity for a successful future (Coutu, 2002; Freeman, Hirschhorn,
& Maltz, 2004; Jamrog et al., 2006; Lengnick-Hall & Beck, 2003, 2005; Weick, 1988).
Organizational resilience is associated with well-attuned HR management during times of adversity (e.g., via the monitoring of changes in internal and external environments and preparing for unexpected future events) (Vogus & Sutcliffe, 2007) in order to address unanticipated events and manage continuous environmental changes that affect employees (Mallak, 1998). Thus, organizational resilience is (i) considered to be important in both turbulent and stable times and (ii) supported by efforts that help organizations to (a) identify associated risks (within and outside the organization) and (b) build and develop strategies (Weick & Sutcliffe, 2011).
Therefore, it is important to acquire a better understanding of employees’ decision-making processes that affect internal and external turnovers in order to enable organizations to retain skilled employees, better manage turnover, and motivate employees (e.g., via work, performance, and loyalty). Otherwise, the negative effects of
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turnover (e.g. shortages of certain skills) could be emphasized in the public sector (which must compete for talent with the private sector) due to government-associated budget constraints and increases in the number of retiring workers (Government Accountability
Office, 2014). In fact, government human capital leaders are currently contemplating the issue of turnover as they focus on how to make the government more resilient via improvements in government HR systems. Successful HRM practice can affect the behavior of employees, make the government more resilient, and promote strategic workforce development and management.
Human Resource Management Practices Certain HRM practices can enhance the government’s ability to use the public workforce more effectively by managing turnover (Arthur, 1994; Gould Williams, 2004;
Mobley, 1977; Moynihan & Landuyt, 2008; Osterman, 1987; Selden & Moynihan, 2000;
Shaw, Delery, Jenkins, & Gupta, 1998). For example, as P-E fit theory has argued, a better fit between the characteristics of employees and their jobs (and work environments) is related to positive outcomes (e.g., low turnover, high job satisfaction, and high performance) (Galletta, Portoghese, Penna, Battistelli, & Saiani, 2011; Kristof,
1996; Kristof-Brown & Guay, 2011; Ostroff & Schulte, 2007; Schneider, Smith, &
Goldstein, 2000; Wynen et al., 2013). This study considers the relationship between (i) organizational policies and environment and (ii) the characteristics of employees
(Muchinsky & Morrow, 1980; Moynihan & Landuyt, 2008; Schneider et al., 2000;
Wynen et al., 2013) via the selection of three important factors affecting turnover: (i) discrepancies between employees’ expectations and actual job satisfaction (associated
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with intellectual challenge and opportunity for advancement), ii) job-education mismatch, and (iii) work-related training (provided to employees).
Job Discrepancy
“Job discrepancy” is defined here as a difference between what people value, need, expect, desire, or have and what they actually experience, perceive, or feel in their employment roles; it is used by researchers and practitioners in a variety of domains, such as motivation theories (Chang, Johnson, & Lord, 2009; Edwards & Parry, 1993;
Porter & Steers, 1973). According to Vroom’s (1964) expectancy theory and Locke’s
(1968) goal-setting theory, the behaviors of individuals are motivated by their expectations and goals. For example, when employees experience significant discrepancies between their desired and actual roles, they are more likely to quit their current jobs (Porter & Steers, 1973; Wanous, Poland, Premack, & Davis, 1992).
Furthermore, the size of the discrepancy is an important determinant of turnover.
Since the relationship between job discrepancy and employee behavior is so large, it is necessary to find (and use) a better measurement of job discrepancy (i.e., beyond job satisfaction and its effect on employees’ behaviors) (Brown, Venkatesh, Kuruzovich, &
Massey, 2008).
Discrepancy between Perceived Job Expectation and Actual Job Satisfaction
As previously discussed, the extent of employee turnover is affected by differences between job-associated expectations and satisfaction levels; thus, employees
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are affected by more than the rewards generated from their jobs and organizations (Cho
& Lewis, 2012). Job satisfaction is one of the most important topics in organizational study because it affects the employee’s job performance and decisions about job mobility. Previous studies have found that employees who have lower job satisfaction are more likely to experience turnover (Carsten & Spector, 1987; Cotton & Tuttle, 1986;
Lambert et al., 2001; Mobley et al., 1979; Porter & Steers, 1973; Pitt et al., 2011; Griffeth et al., 2000; Amah, 2009). Among several types of satisfaction, the satisfaction of intellectual challenge and interest (associated with a principal job) is most highly related with employee behavior (Holland, 1973; Kuder, 1977; Mount et al., 2005). Also, the activity of employees (e.g., turnover and performance) is motivated by whether work environments enable further acquisitions of knowledge and skills (Van Iddekinge et al.,
2011). Mill (2001) finds that intellectual challenge is related to organizational loyalty
(Kim, 2005). Furthermore, employees are more likely to be satisfied with (and stay in) their organizations when they have greater interest in their work (Van Iddekinge et al.,
2011).
Satisfaction with opportunities for advancement can also significantly affect turnover decisions; indeed, these two factors are negatively related (Cotton & Tuttle,
1986; Griffeth, Hom, & Gaertner, 2000; Porter & Steers, 1973; Spector, 1985; Pitt et al.,
2011). When employees are not satisfied with their opportunities for advancement or promotion, they are more likely to exhibit lower organizational commitment, less job involvement, and a greater desire to leave their organizations (Johnston & Johnson,
1993). Government employees do tend to believe that (i) governmental advancement processes are more transparent and fair and (ii) they will thus experience a greater 119
number of advancement opportunities (vs. at private and nonprofit organizations) (Pitt et al., 2011). Since managers consider advancement satisfaction as a significant tool for preventing turnover (Pitt et al., 2011), it is appropriate to consider this as an important indicator of turnover in this study.
Job satisfaction is defined as the affective response, but perceived job expectation is defined as the level of importance of employees towards organizational and work- related factors (Cranny, Smith, & Stone, 1992; Locke, 1976), which elicit different responses from different individuals (e.g., some employees value intellectual challenges more than job security and others prioritize advancement opportunities). Thus, job importance signifies perceived, job-related expectations vs. experiences (i.e., it signifies the desirability of associated organizational and work-related factors). When I compare job expectation and job satisfaction, I can determine the discrepancy between employees’ perceived expectations and actual job satisfaction (e.g., via comparisons of actual outcomes and anticipated and desired outcomes).38 This factor shows how employees fit with their work environments and thus follows the theory of P-E fit. Employees make turnover-based decisions via consideration of discrepancies between perceived expectations and actual job satisfaction levels (e.g., based on intellectual challenges and advancement opportunities). This study hypothesizes that when government employees
38 For example, if an individual considers the intellectual challenge on the job as very important (4 Point Likert scale: not important at all = 1, somewhat unimportant = 2, somewhat important = 3, very important = 4) and this person is very satisfied (4 Point Likert scale: Very dissatisfied = 1, somewhat dissatisfied = 2, somewhat satisfied = 3, very satisfied = 4) with the intellectual challenge on the job, the discrepancy is zero (4 minus 4). However, if this person feels somewhat satisfied, the discrepancy is 1 (4 minus 3). I can get the value of discrepancy from 3 to -3 (7 Point Likert scale: -3 = Absolutely appropriate, -2 = Inappropriate, -1= Slightly appropriate, 0 = Neutral, 1 = Slightly inappropriate, 2 = Inappropriate, 3 = Absolutely inappropriate). 120
possess a better fit with their work environments, they are less likely to (i) leave their government jobs and (ii) transition to other government jobs or employers.
Hypothesis 1: Higher appropriateness (i.e., lower discrepancy between perceived
expectation and actual job satisfaction) is negatively associated with turnover.
o Hypothesis 1a: Higher appropriateness is negatively associated with
voluntary leave from the government.
o Hypothesis 1b: Higher appropriateness is negatively associated with
mobility within the government.
Job-Education Mismatch
For individual and organizational development and success, it is important to identify good alignments between job candidates and the associated organizations.
Employees who possess certain characteristics (e.g., abilities, education levels, values, and goals) that are in alignment with organizational characteristics are more productive, perform better, have higher job satisfaction levels, and are associated with less turnover
(Bright, 2008; Perry, Hondeghem, & Wise, 2010; Steijn, 2008; Vandenabeele, 2009;
Wright & Pandey, 2008). Thus, while there are many organizational factors that affect employees (e.g., job-education mismatch), when the degree of job-education mismatch is low, employees can more effectively utilize their knowledge and skills (e.g., acquired from universities and training programs) to increase their job satisfaction and performance levels (Allen &Van Der Velden, 2001).
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Indeed, a mismatch between an employee’s job and his or her level of education can have negative effects on his or her job satisfaction levels (Johnson & Johnson, 2000;
Vila & Garcia-Mora, 2005); thus, the identification of good matches in this regard can significantly affect job satisfaction levels and thus employee behavior (Allen &Van Der
Velden, 2001; Rodriguez-Pose & Vilalta-Bufi, 2005). The degree of mismatch between an employee’s education level and job-related demands will generally affect his or her turnover-related decision-making. This study hypothesizes that employees who have higher mismatches (i.e., between their jobs and education levels) are more likely to leave their government jobs and transition to other government jobs or employers (as the theory of P-E fit has indicated).
Hypothesis 2: Higher job-education mismatch is positively associated with
turnover.
o Hypothesis 2a: Higher job-education mismatch is positively associated with
voluntary leave from the government.
o Hypothesis 2b: Higher job-education mismatch is positively associated with
mobility within the government.
Work-related Training
Approximately 59 percent of government employees pursue training “to improve skills or knowledge in my current occupation field” and 19 percent of government
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employees pursue training “for licensure/certification in my current occupation fields.”39
Thus, most employees pursue training in order to improve and broaden their skillsets and knowledge in their current occupational fields or areas by attending seminars, conferences, training workshops, and/or certificate licensing programs. Employees can, via training, acquire more information related to their fields and jobs and thus pursue professional development. Training is positively related to on-the-job performance (Aw
& Tan, 1995; Batt, 2002; Tan & Batra, 1995) and the provision of training opportunities is negatively related with turnover (Hequet, 1993; Huselid, 1995; OECD, 1993; Royalty,
1996; Selden & Moynihan, 2000; Shaw Delery et al., 1998). When employees pursue training opportunities, they can reduce work-related uncertainties (e.g., via enhanced communication skills) and this is therefore negatively related to turnover (Scot et al.,
1999). Employees also often believe that they are important organizational members when they are offered training by their organizations (Curry, McCarragher, & Dellmann-
Jenkins, 2005; Dawley, Andrews, & Becklew, 2008). Furthermore, employees often express more loyalty when they have opportunities to express their voices and exchange ideas with other organizational members (Lee & Whitford, 2008). Indeed, employees who pursue work-related training can better identify mutual interests (i.e., between themselves and their employers) and apply this knowledge to present-day and future tasks
(Kim, 2005; Sherman & Bohlander, 1992; Wright & Davis, 2003). Thus, work-related training provided to employees can increase job satisfaction levels via enhanced work-
39 National Survey of College Graduates (NSCG) from 2003, 2006, 2008, 2010, 2013. 123
related motivation levels and organizational commitment (Bradley, Petrescu, &
Simmons, 2004); therefore, this is related to less turnover.
The specific effect of training on turnover is inconclusive; however, it is argued that (i) training exerts less effect on turnover (vs. other factors) (Wynen & de Beeck,
2014) and (ii) a small number of training opportunities does not affect turnover (Selden &
Moynihan, 2000). Furthermore, there are arguments about a reverse relationship between training and turnover (Moynihan & Landuyt, 2008). For example, since training opportunities can enhance job-related skills and abilities, they can be associated with turnover (since these employees may become more attractive to other organizations) (Ito,
2003; Lee & Witford, 2008; Selden & Moynihan, 2000). I believe that both lines of reasoning offer strong arguments.
Thus, work-related training can affect turnover. This study considers the relationship between an organization’s training policies and turnover and hypothesizes that employees who undertake work-related training are less likely to leave the government and transition to other jobs or employers within the government.
Hypothesis 3: Work-related training reduces turnover.
o Hypothesis 3a: Work-related training reduces voluntary leave from the
government.
o Hypothesis 3b: Work-related training reduces mobility within the
government.
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Demographic Factors
Age
Demographic characteristics also affect employees’ turnover decisions; therefore,
I include the demographic characteristics of employees, with a focus at the individual level (Pitt et al., 2011). Age is negatively related to turnover (e.g., younger employees are more likely to turnover than older employees) (Cho & Lewis, 2012; Jung, 2010;
Kellough & Osuna, 1995; Pitts et al., 2011; Lewis, 1991; Lewis & Park, 1989). Younger workers have fewer family members and thus fewer associated financial responsibilities
(vs. older workers) (Kellough & Osuna, 1995; Lewis, 1991; Meyer, Beville, Magedanz,
& Hackert, 1979). Therefore, they exhibit greater tendencies to change their career paths
(Pitt et al., 2011). Older people have more risk-averse behavior (Moynihan & Landuyt,
2008) and care more about some of the relatively unique benefits (e.g., pensions) associated with their government jobs (Lewis & Frank, 2002; Pitt et al., 2011; Cohen,
Blake, & Goodman, 2016; Ippolito, 1987; O’Reilly, Chatman, & Caldwell, 1991).
Therefore, older (vs. younger) employees are less likely to turnover statistics.
Race/Ethnicity and Gender
The behavior of minority employees is also important (Boyne, Poole, & Jenkins,
1999; Selden & Selden, 2001; Weaver, 2012; Wynen et al., 2013); however, the effect of race on turnover has been inconclusive. Some of the literature has suggested that non- whites (vs. whites) are less likely to turnover because they are more risk-averse and prefer working in the government (Pitt et al., 2011). However, Kellough & Osuna (1995) find that there is no significant effect of race on turnover. In terms of the effect of gender
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on turnover, there are conflicting debates. Diverse studies have concluded that female employees might potentially be (i) less likely to turnover in the public sector (Choi,
2009), (ii) more likely to turnover in the public sector (Lewis & Park, 1998; Valcour &
Tolbert, 2003), or (iii) there may be no effects (Dowding & John, 2008). Thus, I included a gender variable in my analysis.
Length of Service in the Government and Wage
Tenure has been negatively related to turnover (Cotton & Tuttle, 1986; Lambert et al., 2001; Lewis, 1991; Sørensen, 2000). Specifically, employees who are in the earlier stages of employment have a greater tendency to turnover (vs. longer-term employees)
(Lewis, 1991). In the early stages of employment, employees (i) earn less money (Farber,
1999), (ii) do not know their coworkers well, (iii) are not familiar with their work and organizational environments (Sørensen, 2000), and (iv) often tend to not fully express and exchange opinions in their organizations, which is all related to less organizational commitment and loyalty (Cohen et al., 2016; Ippolito, 1987). Thus, early-stage employees need time to become accustomed to their work environments. However, employees nearing retirement care more about their long-term needs, such as pension benefits; therefore, they contribute less to turnover statistics (Cohen et al., 2016). As expected, higher wages are negatively related to turnover because these employees have less incentive to find other jobs and employers (Cotton & Tuttle, 1986; Lambert et al.,
2001; Shaw et al., 1998; Smith, 2005).
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Data and Measurement This analysis is based on National Science Foundation (NSF) NSCG data from
2003, 2006, 2010, and 2013 and the dataset is a representative sample of U.S. college graduates over these timeframes.40 The datasets have been anonymized and made available for research purposes (upon request) by the NSF. This study compares two dataset over the time periods from 2003 to 2006 and from 2010 to 2013. For the purpose of my analysis, I utilize data that focuses on individuals who were (i) employed by the government (federal, state, or local government) and drew a salary in 2003 or 2010 and
(ii) employed in any job sector (i.e., government or non-government) during the 2006 or
2013 survey.
Method This study measures the extent that certain HRM strategies (e.g., discrepancies between perceived expectations and actual job satisfaction, job-education mismatch, and work-related training) affect the likelihood of turnover. First, I compare two groups: employees who left the government and those who remained employed by the government. Since the dependent variable (used in this analysis) is binary, I perform logistic regression analysis. The model can be expressed as follows:
Yit = α+ β1Discrepancyit-1 + β2Mismatchit-1 + β3Trainingit-1 + β4kit-1 + ɛit (1)
40 NSCG data generally cover all types of college graduates (i.e., graduates with bachelor’s, master’s, and doctoral degrees as well as professionals. 127
where Yit is individual i’s turnover status at time t (1 = left the government, 0 = remained employed by the government), Discrepancyit-1 is the discrepancy between employee perceived expectation and the actual level of job satisfaction (e.g., based on intellectual challenge and opportunity for advancement) of individual i at time t-1,
Mismatchit-1 is the job-education mismatch of individual i at time t-1, Trainingit-1 is the work-related training experience of individual i at time t-1, kit-1 consists of a vector of demographic, socioeconomic, and organizational characteristics of individual i at time t-1
(i.e., age, race/ethnicity, gender, length of service in the government, log wage, supervisor, marriage, children, job field, employer sizes, employer locations, and government type). Of particular interest is the coefficient on the indicator variable of (i) intellectual challenge and opportunity for advancement (for how much individuals experience discrepancies between perceived expectations and actual job satisfaction)
(Discrepancyit-1), (ii) job-education mismatch (Mismatchit-1), and (iii) whether they have received work-related training (Trainingit-1) in the period prior to the one under observation (for turnover).
Second, I utilize a multinomial logistic regression model to estimate the role of factors affecting government employee turnover choices (i.e., change job, change employer, or change job and employer). This multinomial logistic regression model is generally effective when the dependent variable is composed of various categories with multiple choices and is thus utilized in this study to estimate the effect of individual variables on the probability of choosing a type of alternative turnover (associated with the government). In my study, mobility choices will be an outcome variable that consists of
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diverse, employment-related categories: (i) same job and same employer (agency or department), (ii) different job and same employer, (iii) same job and different employer, and (iv) different job and different employer. The probability that a government employee will choose one type of mobility is restricted (i.e., between zero and one). The model can be expressed as follows:
Pr [푌=푀표푏푖푙푖푡푦] log = α+ β1Discrpancyit-1 + β2Mismatchit-1 + β3Trainingit-1 + β4kit-1 + ɛit (2) Pr [푌=푀표푏푖푙푖푡푦′]
where Mobility’ is the reference status (same job & same employer)
Dependent Variables
The measure of external turnover (i.e., leaving the government) and internal turnover (i.e., mobility within the government) is based on employees’ actual turnover levels. Even though turnover intention has a positive relation to actual turnover (Dalton,
Johnson, & Daily, 1999; Steel & Ovalle, 1984; Tett & Meyer, 1993), stronger results are generated from actual turnover data (vs. turnover intention). I am interested in voluntary turnover; thus, I excluded involuntary turnover.41 The NSCG focuses on employees who were working during the survey period; they were asked the following question: “During these two time periods (from the previous survey date to the current survey), were you working for…”42 43 They were given the following response options: (i) same employer
41 Employees were asked the following question: “Why did you change your employer or your job?” Among the answers, I excluded respondents who chose “laid off or job terminated” or “retired” as a reason for employer and job changes. 42 Educational institution (four-year college/universities and two-year college/pre-college institution), business/industry (for-profit, self-employed, and non-profit), and government (federal, state/local). 43 In this analysis, the previous survey date is 2003 or 2010 and the current survey is 2006 or 2013. 129
and in same type of job, (ii) same employer but in different type of job, (iii) different employer but in same type of job, and (iv) different employer and in a different type of job.
Turnover variables were constructed based on the categories. First, a dummy variable indicates voluntary leave from the government. This dummy was set to one if the respondents were not working in the government and zero if the respondents were working in the government. I found that around 10.1 percent of employees left the government in 2006 (and 6.8 percent in 2013). This percentage is similar to overall voluntary turnover percentages in the U.S. government (i.e., 8.9 percent in 2006 and 7.1 percent in 2013).44 Second, categorical variables indicate all kinds of turnover within the government for multinomial logistic regression. When employees remained with the government over consecutive survey periods, their statuses were configured to certain categorical variables (e.g., one if respondents were working for the same employer and in the same type of job, two if respondents were working for the same employer but in a different type of job, three if respondents were working for a different employer but in the same type of job, and four if respondents were working for a different employer and in a different type of job).45
44 The Bureau of Labor Statistics (BLS) of the US Department of Labor. (2016, January 12) Economic news release: Job openings and labor turnover (monthly). Retrieved Feb 9, 2016, from www. Bls.gov/news.release/pdf/jolts.pdf See Figure 4.2 in the additional Figures and Tables section. 45 Same employer and in same type of job (86.1 percent in 2006 and 86.8 percent in 2013), same employer but in different type of job (10.3 percent in 2006 and 10.6 percent in 2013), different employer but in same type of job (2.4 percent in 2006 and 1.9 percent in 2013), and different employer and in a different type of job (1.1 percent in 2006 and 0.7 percent in 2013). 130
Independent Variables
To examine factors that affect actual turnover, I include three important components: HRM strategies and demographic factors. HRM strategies are divided into three categories: (i) discrepancy between perceived expectation and actual job satisfaction, (ii) job-education mismatch, and (iii) work-related training provided to employees. First, discrepancy between perceived expectation and actual job satisfaction is related to a questionnaire that was designed to measure respondents’ perceptions of certain job-related factors. For example, respondents were asked to respond to survey questions: (i) “When you think about a job, how important are such factors as intellectual challenge and opportunity for advancement to you” and (ii) “Rate your satisfaction with your job’s intellectual challenges and opportunity for advancement.” When I focused on satisfaction level, I could identify discrepancies between employees’ perceived expectations and their actual job satisfaction levels.46 Second, job-education mismatch is the level of disconnect between an individual’s education and his or her principal job.
Respondents were again asked to respond to survey questions, rate the extent to which
46 Response options for the importance of job-related intellectual challenges include “very important” (63.8 percent), “somewhat important” (33.7 percent), “somewhat unimportant” (2.2 percent), and “not important at all” (0.4 percent). Response options for satisfaction with intellectual challenges at work include “very satisfied” (41.7 percent), “somewhat satisfied” (43.4 percent), “somewhat dissatisfied” (11.0 percent), and “very dissatisfied” (4.0 percent). Respondents also rated the discrepancy between perceived expectation and actual job satisfaction on the factor of intellectual challenge: “absolutely appropriate” (0.1 percent), “appropriate” (0.7 percent), “slightly” (10.1 percent), “neutral” (51.8 percent), “slightly inappropriate” (27.3 percent), “inappropriate” (7.4 percent), and “absolutely inappropriate” (2.6 percent). Response options for importance of the opportunity for advancement at your job include “very important” (47.5 percent), “somewhat important” (44.2 percent), “somewhat unimportant” (6.9 percent), and “not important at all” (1.4 percent). Response options for satisfaction associated with the opportunity for advancement at your job include “very satisfied” (19.6 percent), “somewhat satisfied” (44.7 percent), “somewhat dissatisfied” (25.1 percent), and “very dissatisfied” (10.7 percent). Respondents rated the discrepancy between perceived expectation and actual job satisfaction based on the factor of opportunity for advancement: “absolutely appropriate” (0.4 percent), “appropriate” (1.9 percent), “slightly” (10.0 percent), “neutral” (35.7 percent), “slightly inappropriate” (30.9 percent), “inappropriate” (14.8 percent), and “absolutely inappropriate” (6.3 percent) (NSCG, 2010). 131
their current work is related to their degrees, and offered three categories: not related, somewhat related, and closely related. Third, queries on employee training focused on whether respondents had work-related training or not.
I consider five demographic factors as important: age, race/ethnicity, gender, length of service, and wages (while employed by the government). Survey respondents were asked to provide their ages and I divided their responses into five categories: under the age of 30, 30-39, 40-49, 50-59, and 60 and over (Pitt et al., 2011). I could then see the different characteristics of each age group in the analysis. The NSCG provides race and ethnicity (i.e., white, black, Asian, Hispanic, and other race types) and gender information for respondents. Length of government service (tenure) was calculated by using the start year, duration of time (in months) with the current government employer
(and associated job), and the survey year and month. The calculated work duration was categorized as follows: under five years, between five and 15 years, and over 15 years.47
Annual salaries are also included in the NSCG. I then calculated annual log wages with these data.
I also considered other factors that affect the turnover behaviors of government employees and included them as control variables: professional meetings,48 supervisor,49
47 Lewis (1991) finds that turnover behavior is different from work duration (Pitt, Marvel, & Fernandez, 2011) 48 Employees were asked to respond on the survey about whether they had attended a professional meeting in the past year or not. 49 Employees were asked the following question: “Did you supervise the work of others as part of your principal job?” A dummy variable has been set to one if respondents supervise the work of others and zero if respondents are not supervising the work of others. 132
marriage, children, job field,50 employer size,51 employer location,52 and government type
(i.e., federal, state, or local). Summary statistics for the main variables are included in
Table 4.3 (in the additional Figures and Tables section). I test multicollinearity using the variance inflation factor (VIF). The mean VIF equals 1.92. These values indicate that no collinearity exists between variables.
Results Tables 4.1 and 4.2 examine discrepancies in (i) the aforementioned HRM strategies (i.e., discrepancy between perceived expectation and actual job satisfaction, job-education mismatch, and work-related training) and (ii) demographic and other factors that affect the turnover patterns of two types of government employees over the time periods from 2003 to 2006 (Table 4.1) and 2010 to 2013 (Table 4.2). The coefficients were transformed into odd ratios.53
50 Employees were asked the following question: “On which two activities did you work the most hours during a typical week on this job?” The selections were: finance, basic research, applied research, development knowledge, design of equipment, computer programming, employee relations, managing people, production, operations, professional services, sales, marketing, quality management, teaching, and other work activities. 51 Employer sizes have been categorized by the number of employees: 1-99, 100-4,999, and over 5,000. 52 Employees were asked to identify their region of employment (i.e., Northeast, Midwest, Southwest, and West). 53 The results were transformed into odd ratios (Aldrich & Nelson, 1984; Crown, 1998). “Odds ratios represent the change in the odds of the dependent variables happening while holding the other variables constant. If an odds ratio is greater than 1, this means that a one-unit increase in the independent variable (holding all other variables constant) makes a higher score on the dependent variable more likely. An odds ratio between 0 and 1 means that a one-unit increase in the independent variable (holding all other variables constant) is associated with a lower score on the dependent variable” (Nesbit & Reingold, 2011, p. 72) 133
Human Resource Management Practices
The left portions of Tables 4.1 and 4.2 provide logistic regression estimates of whether government employees left the government.54 Estimation results (based on the
2010-2013 data) reveal that certain significant discrepancies are positively related to leaving the government: perceived expectation vs. job satisfaction levels of intellectual challenge and opportunity for advancement (Table 4.2). Every additional intellectual challenge-associated discrepancy increases an employee’s odds of leaving the government by 0.176 (Table 4.2). Furthermore, every additional discrepancy associated with opportunity for advancement increases an employee’s odds of leaving the government by 0.291 (Table 4.2). These results support the finding that (i) intellectual challenge is associated with employee behavior (Holland, 1973; Kuder, 1977; Mount et al., 2005) and (ii) the provision of sufficient advancement opportunities is a key method for retaining government employees (Pitt et al., 2011). These two results support hypothesis (H1a): Higher appropriateness is negatively associated with voluntary leave from the government in the 2010-2013 dataset. This study also reveals that recent government employees (2010-2013 dataset) tend to be prioritizing intellectual challenges and opportunities for advancement when they decide to leave the government (by comparing 2003-2006 dataset).
Employee training does appear to mitigate such behavior (i.e., of leaving the government); according to 2003-2006 dataset, government employee participating in work-related training were 0.57 times less likely to leave government (Table 4.1). These
54 See Tables 4.4 and 4.5 in the additional Figures and Tables for the full analysis. 134
results support the finding that training opportunities is negatively related with turnover
(Scot et al., 1999; Selden & Moynihan, 2000). Work-related training might support connections between employees and employers and thus help generate successful tasks and future outcomes (Kim, 2005; Sherman & Bohlander, 1992; Wright & Davis, 2003).
However, work-related training did not mitigate the number of employees leaving their government jobs, according to the 2010-2013 dataset. Thus, I can support the hypothesis
(H3a): Work-related training reduces voluntary leave from the government, based on results from the 2003-2006 dataset.
The right portions of Tables 4.1 and 4.2 show the relative likelihood of mobility within the government via the utilization of a multinomial logistic regression.55 In this analysis, I focus on employees who didn’t change jobs or employers in the base category and compared them with employees who did make a change (via changing jobs, employers, or jobs and employers). Higher discrepancies associated with intellectual challenges and opportunities for advancement are positively related to mobility within the government in both datasets (Tables 4.1 and 4.2). Government employees who changed jobs, within the same employer, were affected by discrepancies between job-related expectations and actual job satisfaction in the context of intellectual challenge and opportunity for advancement (according to the 2010-2013 dataset). Increasing intellectual challenge-related discrepancies increased the odds by 0.175 (to reference category) of an employee changing jobs with the same employer (Table 4.2). Furthermore, government employees who changed employers without changing jobs also experienced higher
55 See Tables 4.4 and 4.5 in the additional Figures and Tables for the full analysis. 135
intellectual challenge-related discrepancies (according to both datasets). Every additional discrepancy, associated with intellectual challenge, increases an employee’s odds of changing an employer but remaining in the same type of government job by 0.351 in the
2003-2006 dataset (Table 4.1) and by 1.733 in the 2010-2013 dataset (Table 4.2).
Government employees who changed jobs and employers experienced greater discrepancies associated with opportunity for advancement over both timeframes. Every additional discrepancy associated with opportunity for advancement increases an employee’s odds of changing his or her government job and employer by 0.618 in the
2003-2006 dataset and 1.998 in the 2010-2013 dataset. As in the logistic regression estimates (Tables 4.1 and 4.2), discrepancies associated with intellectual challenge and opportunity for advancement are positively related to mobility within the government. I can thus support the hypothesis (H1b) with these results: Higher appropriateness is negatively associated with mobility within the government.
Discrepancy effects on intellectual challenge and opportunity for advancement are associated with different types of job-related mobility within the government. For example, employees who experience more intellectual challenge-related discrepancies are likely to either change jobs or change employers (i.e., change his or her job but remain with the same employer or change his or her employer but remain in the same type of job), according to the 2010-2013 datasets; also, employees who experience higher discrepancies on opportunity for advancement are likely to change jobs and change employers. The effects of the two variables on mobility within the government increase in the 2010-2013 dataset in comparison with the 2003-2006 dataset. These results support
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the finding that intellectual challenge and advancement opportunity are related to employee behavior (Holland, 1973; Kuder, 1977; Mount et al., 2005; Pitt et al., 2011).
Work-related training is positively related to mobility within the government (i.e., changing jobs with the same employer in the 2003-2006 dataset and changing employers but maintaining the same types of jobs in the 2010-2013 dataset). In Table 4.2, the odds of a changing an employer (while maintaining the same government job) were 1.937 times greater (in comparison with departing government employees who did not participate in work-related training). Thus, these data do not support the hypothesis
(H3b): Work-related training reduces mobility within the government. Work-related training does enable employees to communicate with other employees (Lee & Whitford,
2008) and improve their skillsets. These factors can thus enhance the abilities of employees to find other jobs and employers (Ito, 2003; Selden & Moynihan, 2000).
Job-education mismatch is positively related to changing jobs with the same government employer. The odds of an employee changing jobs with the same employer when he or she has a higher job-education mismatch were 0.993 times in the 2003-2006 dataset and 0.678 times in the 2010-2013 dataset (which are greater than the odds of an employee changing jobs with the same government employer when he or she has a higher job-education match). Having somewhat job-education mismatches were also positively related to job changes with the same employer (0.465 times in the 2003-2006 dataset and
0.575 times in the 2010-2013 dataset). This result supports the notion that complementary organizational and individual characteristics can affect such behavioral changes (Bright,
2008; Perry et al., 2010; Steijn, 2008; Vandenabeele, 2009; Wright & Pandey, 2008).
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These two results support the hypothesis (H2b): Higher job-education mismatch is positively associated with mobility within the government.
Demographic Factors
Age is negatively related to turnover. Older employees vs. younger employees
(from 20 to 29 years of age) are less likely to leave the government (according to both datasets). In the 2010-2013 dataset, the odds that government employees would leave their jobs in their 30s, 40s, 50s, and over 60 years of age were 0.411, 0.265, 0.264, and
0.451 times lower than the odds of an employee leaving his or her government job in his or her 20s. This research also considered job mobility within the government and concluded that older employees are generally less likely to make any such changes (in both datasets). The length of service, associated with such jobs, is also negatively related to turnover in both datasets and supports existing findings (Doering & Rhodes, 1996;
Kellough & Osuna, 1995; Lewis, 1991; Moynihan & Landduyt, 2008; Muchinsky &
Morrow, 1980). Furthermore, senior government employees are less likely to (i) leave the government and (ii) change their work-related statuses within the government in comparison with junior government employees (with work durations less than five years).
The results for race and ethnicity show that Hispanics have more risk aversion, prefer to work in the government, and departed their government jobs less than whites in the 2003-
2006 dataset (Pitt et al., 2011). In the 2010-2013 dataset, Asians (vs. whites) are more likely to change their jobs or employers. In the 2003-2006 dataset, male (vs. female) employees are less likely to leave their government jobs and change jobs with same
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employer. In the 2010-2013 dataset, however, there no differences between females and males associated with turnover behavior.
In the 2010-2013 dataset, employees who attained higher levels of education (e.g., doctoral and professional degrees) were more likely to leave their government jobs and change jobs within the government (vs. employees who had exclusively attained bachelor’s degrees). These results support the finding that more highly educated employees are more likely to change jobs (Cotton & Tuttle, 1986; Moynihan & Landuyt,
2008). There is no difference in turnover behavior between employees who had supervisory roles vs. those who did not in the 2003-2006 dataset. However, employees in supervisory roles were more likely to leave their government jobs and change employers within the government (vs. employees in non-supervisory roles), according to the 2010-
2013 dataset. The odds of a government employee in a supervisory role leaving the government were 0.561 times greater (vs. the odds of an employee leaving government who was in a non-supervisory role) in the 2010-2013 dataset.
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Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer HRM practices Discrepancy opportunity for advance 1.026 1.060 0.989 1.618*** (0.0639) (0.0682) (0.126) (0.252) Discrepancy intellectual challenge 1.040 1.029 1.351*** 1.117 (0.0523) (0.0551) (0.143) (0.161) Mismatch: Somewhat related 0.904 1.465*** 0.754 1.319 (Ref: Closely Related) (0.109) (0.172) (0.188) (0.418) Not related 0.979 1.993*** 0.301** 1.459 (0.159) (0.308) (0.149) (0.595) Training 0.570*** 1.454*** 1.386 1.178 (0.0644) (0.197) (0.402) (0.396) 140 Log salary 0.705*** 1.179 0.891 0.742
(0.0495) (0.141) (0.147) (0.148) Male 0.791** 0.766** 1.013 1.268 (0.0816) (0.0829) (0.218) (0.372) Race: Black 1.227 1.082 1.026 0.853 (ref: White) (0.189) (0.178) (0.346) (0.375) Asian 1.208 0.795 0.860 0.163* (0.195) (0.152) (0.326) (0.166) Hispanic 1.417** 1.100 0.854 0.721 (0.233) (0.201) (0.322) (0.396) Other 0.529* 1.059 2.711** 1.838 (0.189) (0.296) (1.066) (1.027) Continued Table 4.1. Logit and Multinomial Estimates of Different Type of Turnover of Government Employees: 2003- 2006 Cohorts
Table 4.1 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Age group: 30-39 0.824 0.792 1.689 1.134 (ref: 20-29) (0.151) (0.170) (0.642) (0.565) 40-49 0.558*** 0.732 0.832 0.695 (0.106) (0.157) (0.341) (0.369) 50-59 0.482*** 0.361*** 0.759 0.578 (0.0959) (0.0837) (0.328) (0.324) Over 60 0.575** 0.189*** 0.520 1.86e-07 (0.154) (0.0755) (0.361) (0.000187) Supervisor 1.102 1.346*** 0.811 0.659 (0.120) (0.154) (0.185) (0.218) Degree Type: Masters 1.059 1.349*** 1.042 1.577 141 (ref: Bachelor) (0.118) (0.149) (0.245) (0.467)
Doctorate 2.207** 1.183 0.854 5.31e-07 (0.808) (0.650) (0.925) (0.00122) Professional 1.341 0.744 2.338** 1.164 (0.252) (0.201) (0.824) (0.764) Work length: From 5 to 15 years 0.710*** 0.721*** 0.420*** 0.557* (ref: below 5 years) (0.0804) (0.0853) (0.0976) (0.173) Over 15 years 0.585*** 0.621*** 0.0985*** 0.201*** (0.0879) (0.0893) (0.0524) (0.113) Local/State government 1.123 0.734** 1.201 0.841 (0.165) (0.108) (0.383) (0.339) Constant 11.41*** 0.0226*** 0.267 0.816 (9.473) (0.0305) (0.513) (1.934) Observations 5,105 4,592 4,592 4,592 Pseudo R-squared 0.0793 0.0913 Note: Multinomial logit models estimated with “same job in same employer’ as the base group. Relative risk ratio shown. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer HRM practices Discrepancy opportunity for advance 1.291*** 1.031 1.130 2.998*** (0.128) (0.0902) (0.215) (1.028) Discrepancy intellectual challenge 1.176** 1.175** 1.733*** 1.024 (0.0961) (0.0849) (0.301) (0.299) Mismatch: Somewhat related 0.847 1.575*** 0.176*** 0.492 (Ref: Closely Related) (0.181) (0.248) (0.110) (0.322) Not related 1.196 1.678** 0.734 0.241 (0.321) (0.385) (0.400) (0.281) Training 0.869 1.027 2.937* 0.335* (0.179) (0.183) (1.673) (0.200) 142 Log salary 0.518*** 1.095 0.468* 1.556
(0.0730) (0.204) (0.190) (1.135) Male 0.929 0.802 1.430 0.594 (0.165) (0.117) (0.497) (0.344) Race: Black 1.239 0.958 0.660 1.029 (ref: White) (0.319) (0.207) (0.378) (0.923) Asian 1.348 0.555** 2.298* 0.669 (0.338) (0.152) (1.086) (0.773) Hispanic 0.647 0.964 1.140 1.419 (0.204) (0.213) (0.611) (1.074) Other 0.481 0.947 0.607 1.207 (0.265) (0.343) (0.647) (1.424) Continued Table 4.2. Logit and Multinomial Estimates of Different Type of Turnover of Government Employees: 2010- 2013 Cohorts
Table 4.2 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Age group: 30-39 0.411*** 0.845 1.613 1.515 (ref: 20-29) (0.106) (0.216) (0.905) (1.766) 40-49 0.265*** 0.583** 1.021 0.648 (0.0789) (0.158) (0.637) (0.800) 50-59 0.264*** 0.335*** 0.561 0.812 (0.0799) (0.0969) (0.383) (1.030) Over 60 0.451** 0.160*** 3.71e-07 1.26e-06 (0.160) (0.0746) (0.000264) (0.00109) Supervisor 1.561** 1.536*** 1.950* 2.068 (0.302) (0.242) (0.707) (1.252) Degree Type: Masters 1.337 1.359** 1.691 1.446 143 (ref: Bachelor) (0.251) (0.207) (0.602) (0.855)
Doctorate 3.408** 1.288 5.995 8.80e-07 (1.882) (0.773) (7.094) (0.00206) Professional 1.997** 2.145** 0.641 0.788 (0.669) (0.670) (0.535) (0.991) Work length: From 5 to 15 years 0.498*** 0.630*** 0.300*** 0.147** (ref: below 5 years) (0.104) (0.103) (0.123) (0.120) Over 15 years 0.437*** 0.775 0.0776** 0.276 (0.132) (0.163) (0.0815) (0.247) Local/State government 1.558* 0.717 0.846 0.189 (0.399) (0.155) (0.424) (0.221) Constant 73.12*** 0.0747 44.32 0.000444 (120.0) (0.156) (199.4) (0.00359) Observations 2,621 2,444 2,444 2,444 Pseudo R-squared 0.1567 0.1259 Note: Multinomial logit models estimated with “same job in same employer’ as the base group. Relative risk ratio shown. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Discussion and Conclusion This study analyzed how certain discrepancies (e.g., between perceived expectations and job satisfaction levels) affect government-related turnover statistics via a comparison of two datasets: from 2003 to 2006 and 2010 to 2013. This study sought to identify and utilize a better measurement of employee behavior (vs. maintaining an exclusive focus on job satisfaction)56 and finds that (i) recent government employees (i.e., from 2010 to 2013) experienced higher discrepancies between perceived and actual job satisfaction in association with intellectual challenge and opportunity for advancement
(vs. employees in the 2003 to 2006 dataset) and that (ii) they were more likely to leave government employment or move to a different government job. Additionally, work- related training is negatively related with the likelihood that an employee will leave his or her government job only in the 2003-2006 dataset; additionally, it is positively related with job mobility within the government in both timeframes. Moreover, job-education mismatch is positively related to an employee’s mobility within the government in both timeframes.
The U.S. government is recruiting from an increasingly complex society and it is important to better understand some of the significant, job-related transitions of government employees in order to prepare for impending changes (McKinsey, 2012;
Wynen et al., 2013)—especially with the expected surge in the number of senior employees who are becoming eligible for retirement (Hatum, 2010). Furthermore, it is important to explore the turnover of employees who are not only leaving the government
56 This study also analyzes how job satisfaction affects the mobility of government employees (See Table 4.6-4.7 in the additional Figures and Tables section). 144
but also changing jobs within the government. Government employers might help fill such gaps (i.e., shortages of experienced employees) with employees who are already working for the government; associated employees could thereby acquire opportunities to enhance their skills and advance their careers (Wynen et al., 2013). Furthermore, a motivated workforce results in an effective workforce, which benefits customers and—in the case of government organizations—citizens. Thus, it is important to understand the mobilization of employees and role of HRM practices (e.g., to help managers better manage employees) (Pitt et al., 2011). A strategic HRM system can affect employee behavior and enhance organizational resilience (Lengnick-Hall, Beck, & Lengnick-Hall,
2011).
According to attraction-selection-attrition (ASA) theory (Scheneider, 1987), job candidates first identify and assess attractive jobs and subsequently apply (when appropriate). Organizations then proceed with identifying candidates who fit the goals and interests of their organizations. While this process can be quite rigorous within some organizations, new hires will often leave their organizations or jobs if they receive insufficient training or their interests are not aligned with their work (Van Iddekinge et al., 2011). This study utilizes ASA theory to generate two workforce development policy and management objectives. First, it is important to properly understand the effects of discrepancies between (i) perceived expectations and (a) job satisfaction levels and (b) work-related training (for employees) and (ii) job-education mismatches (for managing turnover). According to Mill (2001), intellectual challenges can be generated via training.
Thus, it is important to support work-related training for government employees. Second,
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according to the Bureau of Labor Statistics (BLS), the workforce participation rate has continued to decline over the years (Toossi, 2013). However, there are more young employees entering the labor market; thus, there should be more focus on understanding how they learn to ensure the effectiveness of their work-related training. Additionally, organizations can begin to mitigate turnover rates during hiring stages by prioritizing the identification of effective job-education matches.
Furthermore, an increasing amount of associated literature is examining the determinants of turnover and addressing estimates associated with career mobility (i.e., leaving the government or changing jobs within the government). This particular study is intended to better understand the turnover of government employees by examining how
HRM practices affect turnover. Higher discrepancies between perceived expectations and actual job satisfaction levels associated with the aforementioned factors (intellectual challenge, opportunity for advancement, lack of work-related training, and job-education mismatch) can significantly affect voluntary turnover (i.e., leaving the government and transferring within the government). For example, some employees have high expectations in relation to certain factors associated with their jobs, find that their job satisfaction levels are less than expected, and are discouraged. Thus, a higher discrepancy between perceived expectation and actual job satisfaction affects labor outcomes (e.g., turnover and performance).
While other papers focus on job satisfaction within associated turnover analyses, I add the concept of “expectation on the job” and use the variable of discrepancy between perceived expectation and actual job satisfaction. I then analyze the actual turnover of
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government employees (without utilizing turnover intention). Furthermore, this study covers all government (i.e., federal, state, and local) employees. However, this study has certain limitations, such as (i) time gaps between occurrences of actual turnover and turnover associated with the disparate variables and (ii) collections of NSCG data biennially or triennially (In this analysis, a three-year time gap exists.). I tried to minimize this issue by selecting people who are employed during both survey timeframes but we were not able to control this perfectly. For example, as see in Figure 4.2, the voluntary turnover rate in the U.S. government decreased during the 2008 recession; however, I could not control this factor. Second, I was not able to control external factors affecting turnover (e.g., economic condition and unemployment rate); this also occurred in other studies (Lee & Jimenez, 2011; Moynihan & Landduyt, 2008).
The results of this study suggest the importance of HRM strategies for managing turnover (Government Accountability Office, 2014). Based on the 2003-2006 and 2010-
2013 datasets, this study finds that management policies should be modified to meet the modern-day needs of employees in order to make the government more resilient within changing work environments. Organizational leaders must manage disparate foreseen and unforeseen factors that affect turnover (Reivich & Shatté, 2002). Further studies should consider how to better manage myriad job-related factors, such as (i) discrepancies between perceived expectations and actual job satisfaction levels, (ii) tools for improving job satisfaction, (iii) the inflow of private-sector workforces into the government, and (iv) the fact that supervisors (vs. non-supervisors) are currently more likely to leave the government. The loss of managers is related to the loss of organizational leaders and this
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affects subordinate employees. Thus, it is important to know why supervisors are leaving the government.
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Additional Figures and Tables
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20 18 16 14 12 10 8 6
Turnover rate (%) 4 2 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Year
Total Turnover Voluntary Turnover
Source: United States Department of Labor. Bureau of labor Statics Figure 4.2. U.S Government Turnover Rate (Federal, State/Local Government)
10 20
8 18
6 16
4 14
Turnover rate (%) Turnover rate (%) 2 12
0 10 2011 2012 2013 2014 2015 2011 2012 2013 2014 2015 Year Year
Federal State/Local Federal State/Local
Source: United States Department of Labor. Source: United States Department of Labor. Bureau of labor Statics Bureau of labor Statics Figure 4.3. Total Turnover Figure 4.4. Voluntary Rate by Government Turnover Rate by Level Government Level
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2003-2006 2010-2013
Variables N Mean SD Min Max N Mean SD Min Max
Leave the Government 5,105 0.100 0.301 0 1 2,621 0.068 0.251 0 1
Same Job Same Employer within Government 4,592 0.861 0.346 0 1 2,444 0.868 0.338 0 1
Different Job Same Employer within Government 4,592 0.103 0.303 0 1 2,444 0.106 0.308 0 1
Different Job Same Employer within Government 4,592 0.024 0.152 0 1 2,444 0.018 0.134 0 1
Different Job Different Employer within 4,592 0.013 0.112 0 1 2,444 0.007 0.083 0 1 Government
Gap Opportunity for Advancement 5,105 0.373 0.868 -3 3 2,621 0.377 0.888 -3 3
Gap Intellectual Challenge 5,105 0.636 1.084 -3 3 2,621 0.643 1.122 -3 3
151 Mismatch: Closely Related 5,105 0.613 0.487 0 1 2,621 0.620 0.486 0 1
Somewhat related 5,105 0.262 0.440 0 1 2,621 0.260 0.439 0 1
Not related 5,105 0.125 0.331 0 1 2,621 0.120 0.325 0 1
Training (no = 0, yes = 1) 5,105 0.766 0.423 0 1 2,621 0.776 0.417 0 1
Professional Meeting (no = 0, yes = 1) 5,105 0.591 0.492 0 1 2,621 0.452 0.498 0 1
Log (salary) 5,105 10.943 0.599 4 13 2,621 11.140 0.531 5 13
Male (no = 0, yes = 1) 5,105 0.631 0.483 0 1 2,621 0.592 0.492 0 1 Continued Table 4.3. Descriptive Statistics
Table 4.3 continued 2003-2006 2010-2013
Variables N Mean SD Min Max N Mean SD Min Max
Race: White 5,105 0.674 0.469 0 1 2,621 0.602 0.490 0 1
Black 5,105 0.109 0.312 0 1 2,621 0.135 0.342 0 1
Asian 5,105 0.103 0.304 0 1 2,621 0.118 0.322 0 1
Hispanic 5,105 0.082 0.275 0 1 2,621 0.111 0.314 0 1
Other 5,105 0.032 0.175 0 1 2,621 0.035 0.183 0 1
Age group: 20-29 5,105 0.060 0.237 0 1 2,621 0.078 0.268 0 1
30-39 5,105 0.238 0.426 0 1 2,621 0.254 0.435 0 1
152 40-49 5,105 0.352 0.478 0 1 2,621 0.293 0.455 0 1
50-59 5,105 0.293 0.455 0 1 2,621 0.289 0.453 0 1
Over 60 5,105 0.058 0.233 0 1 2,621 0.086 0.280 0 1
Married (no = 0, yes = 1) 5,105 0.733 0.442 0 1 2,621 0.698 0.459 0 1
Children (no = 0, yes = 1) 5,105 0.546 0.498 0 1 2,621 0.527 0.499 0 1
Supervisor (no = 0, yes = 1) 5,105 0.451 0.498 0 1 2,621 0.390 0.488 0 1 Continued
Table 4.3 continued 2003-2006 2010-2013
Variables N Mean SD Min Max N Mean SD Min Max
Degree Type: Bachelor 5,105 0.586 0.493 0 1 2,621 0.537 0.499 0 1
Masters 5,105 0.329 0.470 0 1 2,621 0.384 0.486 0 1
Doctorate 5,105 0.013 0.113 0 1 2,621 0.014 0.118 0 1
Professional 5,105 0.072 0.259 0 1 2,621 0.065 0.246 0 1
Work length: below 5 years 5,105 0.406 0.491 0 1 2,621 0.444 0.497 0 1
From 5 to 15 years 5,105 0.335 0.472 0 1 2,621 0.340 0.474 0 1
Over 15 years 5,105 0.259 0.438 0 1 2,621 0.216 0.411 0 1
153 Employer size: # of Employees 1-99 5,105 0.061 0.240 0 1 2,621 0.062 0.242 0 1
100-4,999 5,105 0.343 0.475 0 1 2,621 0.307 0.461 0 1
Over 5,000 5,105 0.596 0.491 0 1 2,621 0.631 0.483 0 1
Work activity: finance 5,105 0.047 0.211 0 1 2,621 0.061 0.240 0 1
Basic research 5,105 0.033 0.179 0 1 2,621 0.039 0.193 0 1
Apld. research 5,105 0.082 0.275 0 1 2,621 0.122 0.327 0 1
Dev.-knowledge 5,105 0.042 0.200 0 1 2,621 0.038 0.191 0 1
Design of equipment 5,105 0.047 0.213 0 1 2,621 0.042 0.200 0 1 Continued
Table 4.3 continued 2003-2006 2010-2013
Variables N Mean SD Min Max N Mean SD Min Max
Work activity: finance 5,105 0.047 0.211 0 1 2,621 0.061 0.240 0 1
Computer programming 5,105 0.117 0.322 0 1 2,621 0.047 0.212 0 1
Employee relations 5,105 0.024 0.152 0 1 2,621 0.022 0.147 0 1
Managing people 5,105 0.249 0.432 0 1 2,621 0.266 0.442 0 1
Production, operations 5,105 0.041 0.198 0 1 2,621 0.036 0.186 0 1
Prof. services 5,105 0.182 0.386 0 1 2,621 0.159 0.366 0 1
Sales, marketing 5,105 0.028 0.164 0 1 2,621 0.034 0.180 0 1
154 Quality management 5,105 0.025 0.158 0 1 2,621 0.029 0.167 0 1
Teaching 5,105 0.015 0.122 0 1 2,621 0.017 0.130 0 1
Other work activity 5,105 0.068 0.251 0 1 2,621 0.089 0.284 0 1
Employer Location: Northeast 5,105 0.156 0.362 0 1 2,621 0.146 0.353 0 1
Midwest 5,105 0.158 0.365 0 1 2,621 0.160 0.367 0 1
South 5,105 0.395 0.489 0 1 2,621 0.399 0.490 0 1
West 5,105 0.291 0.454 0 1 2,621 0.296 0.456 0 1
Local/State government (federal = 0, local/state = 1) 5,105 0.594 0.491 0 1 2,621 0.551 0.497 0 1
Data from National Survey of College Graduates by NSF.
Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer HRM practices Discrepancy opportunity for advance 1.026 1.060 0.989 1.618*** (0.0639) (0.0682) (0.126) (0.252) Discrepancy intellectual challenge 1.040 1.029 1.351*** 1.117 (0.0523) (0.0551) (0.143) (0.161) Mismatch: Somewhat related 0.904 1.465*** 0.754 1.319 (Ref: Closely Related) (0.109) (0.172) (0.188) (0.418) Not related 0.979 1.993*** 0.301** 1.459 (0.159) (0.308) (0.149) (0.595) Training 0.570*** 1.454*** 1.386 1.178 (0.0644) (0.197) (0.402) (0.396) Professional meeting 1.164 0.849 1.224 0.792
155 (0.128) (0.0936) (0.286) (0.236) Log salary 0.705*** 1.179 0.891 0.742 (0.0495) (0.141) (0.147) (0.148) Male 0.791** 0.766** 1.013 1.268 (0.0816) (0.0829) (0.218) (0.372) Race: Black 1.227 1.082 1.026 0.853 (ref: White) (0.189) (0.178) (0.346) (0.375) Asian 1.208 0.795 0.860 0.163* (0.195) (0.152) (0.326) (0.166) Hispanic 1.417** 1.100 0.854 0.721 (0.233) (0.201) (0.322) (0.396) Other 0.529* 1.059 2.711** 1.838 (0.189) (0.296) (1.066) (1.027) Continued Table 4.4. Logit and Multinomial Estimates of Different Type of Turnover of Government Employees: 2003-2006 Cohorts
Table 4.4 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Age group: 30-39 0.824 0.792 1.689 1.134 (ref: 20-29) (0.151) (0.170) (0.642) (0.565) 40-49 0.558*** 0.732 0.832 0.695 (0.106) (0.157) (0.341) (0.369) 50-59 0.482*** 0.361*** 0.759 0.578 (0.0959) (0.0837) (0.328) (0.324) Over 60 0.575** 0.189*** 0.520 1.86e-07 (0.154) (0.0755) (0.361) (0.000187) Married 0.978 1.059 0.902 0.814 (0.118) (0.137) (0.224) (0.271)
156 Children 1.046 0.916 0.838 0.987 (0.118) (0.107) (0.199) (0.314)
Supervisor 1.102 1.346*** 0.811 0.659 (0.120) (0.154) (0.185) (0.218) Degree Type: Masters 1.059 1.349*** 1.042 1.577 (ref: Bachelor) (0.118) (0.149) (0.245) (0.467) Doctorate 2.207** 1.183 0.854 5.31e-07 (0.808) (0.650) (0.925) (0.00122) Professional 1.341 0.744 2.338** 1.164 (0.252) (0.201) (0.824) (0.764) Work length: From 5 to 15 years 0.710*** 0.721*** 0.420*** 0.557* (ref: below 5 years) (0.0804) (0.0853) (0.0976) (0.173) Over 15 years 0.585*** 0.621*** 0.0985*** 0.201*** (0.0879) (0.0893) (0.0524) (0.113) Continued
Table 4.4 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer # of Employees: 100-4,999 0.907 1.382 0.575* 1.371 (ref: 1-99) (0.164) (0.387) (0.190) (0.876) Over 5,000 0.696* 1.552 0.424** 1.281 (0.140) (0.452) (0.163) (0.879) Work: Basic research 1.012 0.915 0.828 1.306 (Ref: finance) (0.392) (0.305) (0.574) (0.976) Apld. research 1.125 0.531** 0.794 0.559 (0.358) (0.152) (0.438) (0.410) Dev.-knowledge 2.168** 0.511* 1.228 0.302 (0.717) (0.179) (0.757) (0.343)
157 Design of equipment 1.081 0.933 0.894 0.290 (0.400) (0.280) (0.575) (0.332)
Computer programming 1.251 0.544** 0.664 0.553 (0.369) (0.142) (0.363) (0.371) Employee relations 1.695 0.968 0.971 0.513 (0.652) (0.338) (0.733) (0.591) Managing people 1.079 0.952 0.858 0.975 (0.304) (0.215) (0.419) (0.575) Production, operations 1.853* 0.594 1.450 0.880 (0.622) (0.209) (0.894) (0.705) Prof. services 1.786** 0.661 0.582 0.672 (0.498) (0.167) (0.294) (0.422) Sales, marketing 0.875 0.716 1.697 0.311 (0.361) (0.260) (1.057) (0.355) Quality management 1.672 0.695 2.02e-07 0.415 (0.647) (0.266) (0.000254) (0.476) Continued
Table 4.4 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Work: Teaching 5.089*** 0.661 1.18e-07 0.786 (Ref: finance) (1.847) (0.347) (0.000204) (0.905) Other work activity 0.960 0.886 0.613 0.629 (0.319) (0.241) (0.389) (0.440) Employer Location: Midwest 1.086 1.267 0.761 0.842 (Ref: Northeast) (0.183) (0.239) (0.296) (0.382) South 0.951 1.111 1.208 0.827 (0.140) (0.180) (0.377) (0.315)
158 West 0.968 1.122 0.847 0.566 (0.149) (0.190) (0.283) (0.248)
Local/State government 1.123 0.734** 1.201 0.841 (0.165) (0.108) (0.383) (0.339) Constant 11.41*** 0.0226*** 0.267 0.816 (9.473) (0.0305) (0.513) (1.934) Observations 5,105 4,592 4,592 4,592 Pseudo R-squared 0.0793 0.0913 Note: Multinomial logit models estimated with “same job in same employer’ as the base group. Relative risk ratio shown. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer HRM practices Discrepancy opportunity for advance 1.291*** 1.031 1.130 2.998*** (0.128) (0.0902) (0.215) (1.028) Discrepancy intellectual challenge 1.176** 1.175** 1.733*** 1.024 (0.0961) (0.0849) (0.301) (0.299) Mismatch: Somewhat related 0.847 1.575*** 0.176*** 0.492 (Ref: Closely Related) (0.181) (0.248) (0.110) (0.322) Not related 1.196 1.678** 0.734 0.241 (0.321) (0.385) (0.400) (0.281) Training 0.869 1.027 2.937* 0.335* (0.179) (0.183) (1.673) (0.200) Professional meeting 1.028 1.239 1.504 1.018
159 (0.187) (0.182) (0.511) (0.578) Log salary 0.518*** 1.095 0.468* 1.556 (0.0730) (0.204) (0.190) (1.135) Male 0.929 0.802 1.430 0.594 (0.165) (0.117) (0.497) (0.344) Race: Black 1.239 0.958 0.660 1.029 (ref: White) (0.319) (0.207) (0.378) (0.923) Asian 1.348 0.555** 2.298* 0.669 (0.338) (0.152) (1.086) (0.773) Hispanic 0.647 0.964 1.140 1.419 (0.204) (0.213) (0.611) (1.074) Other 0.481 0.947 0.607 1.207 (0.265) (0.343) (0.647) (1.424) Continued Table 4.5. Logit and Multinomial Estimates of Different Type of Turnover of Government Employees: 2010-2013 Cohorts
Table 4.5 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Age group: 30-39 0.411*** 0.845 1.613 1.515 (ref: 20-29) (0.106) (0.216) (0.905) (1.766) 40-49 0.265*** 0.583** 1.021 0.648 (0.0789) (0.158) (0.637) (0.800) 50-59 0.264*** 0.335*** 0.561 0.812 (0.0799) (0.0969) (0.383) (1.030) Over 60 0.451** 0.160*** 3.71e-07 1.26e-06 (0.160) (0.0746) (0.000264) (0.00109) Married 1.253 1.224 1.142 0.614 (0.258) (0.214) (0.475) (0.408)
160 Children 0.963 1.020 0.926 1.954 (0.187) (0.162) (0.355) (1.252)
Supervisor 1.561** 1.536*** 1.950* 2.068 (0.302) (0.242) (0.707) (1.252) Degree Type: Masters 1.337 1.359** 1.691 1.446 (ref: Bachelor) (0.251) (0.207) (0.602) (0.855) Doctorate 3.408** 1.288 5.995 8.80e-07 (1.882) (0.773) (7.094) (0.00206) Professional 1.997** 2.145** 0.641 0.788 (0.669) (0.670) (0.535) (0.991) Work length: From 5 to 15 years 0.498*** 0.630*** 0.300*** 0.147** (ref: below 5 years) (0.104) (0.103) (0.123) (0.120) Over 15 years 0.437*** 0.775 0.0776** 0.276 (0.132) (0.163) (0.0815) (0.247) Continued
Table 4.5 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer # of Employees: 100-4,999 0.768 1.083 0.366* 0.478 (ref: 1-99) (0.220) (0.358) (0.195) (0.422) Over 5,000 0.713 0.986 0.369 0.0630** (0.229) (0.352) (0.224) (0.0856) Age group: 30-39 0.411*** 0.845 1.613 1.515 (ref: 20-29) (0.106) (0.216) (0.905) (1.766) 40-49 0.265*** 0.583** 1.021 0.648 (0.0789) (0.158) (0.637) (0.800)
161 50-59 0.264*** 0.335*** 0.561 0.812 (0.0799) (0.0969) (0.383) (1.030)
Over 60 0.451** 0.160*** 3.71e-07 1.26e-06 (0.160) (0.0746) (0.000264) (0.00109) Married 1.253 1.224 1.142 0.614 (0.258) (0.214) (0.475) (0.408) Children 0.963 1.020 0.926 1.954 (0.187) (0.162) (0.355) (1.252) Supervisor 1.561** 1.536*** 1.950* 2.068 (0.302) (0.242) (0.707) (1.252) Degree Type: Masters 1.337 1.359** 1.691 1.446 (ref: Bachelor) (0.251) (0.207) (0.602) (0.855) Doctorate 3.408** 1.288 5.995 8.80e-07 (1.882) (0.773) (7.094) (0.00206) Professional 1.997** 2.145** 0.641 0.788 (0.669) (0.670) (0.535) (0.991) Continued
Table 4.5 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Work length: From 5 to 15 years 0.498*** 0.630*** 0.300*** 0.147** (ref: below 5 years) (0.104) (0.103) (0.123) (0.120) Over 15 years 0.437*** 0.775 0.0776** 0.276 (0.132) (0.163) (0.0815) (0.247) # of Employees: 100-4,999 0.768 1.083 0.366* 0.478 (ref: 1-99) (0.220) (0.358) (0.195) (0.422) Over 5,000 0.713 0.986 0.369 0.0630** (0.229) (0.352) (0.224) (0.0856) Work: Basic research 3.331** 1.046 1.84e-07 5.42e-07 (Ref: finance) (1.981) (0.422) (0.000192) (0.000793)
162 Apld. research 2.828** 0.485** 0.496 0.551 (1.496) (0.162) (0.408) (0.818)
Dev.-knowledge 4.240** 1.288 0.511 4.71e-07 (2.518) (0.496) (0.616) (0.000736) Design of equipment 2.134 0.812 0.627 6.97e-07 (1.438) (0.336) (0.633) (0.00106) Computer programming 1.834 0.798 0.956 1.932 (1.181) (0.325) (0.929) (2.933) Employee relations 0.537 0.990 1.256 1.899 (0.607) (0.432) (1.540) (2.936) Managing people 2.086 0.605* 0.672 0.640 (1.072) (0.171) (0.481) (0.759) Production, operations 2.129 0.647 1.313 7.89e-07 (1.412) (0.307) (1.342) (0.00131) Prof. services 2.496* 0.460** 0.935 1.026 (1.298) (0.152) (0.697) (1.309) Continued
Table 4.5 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Work: Sales, marketing 1.633 0.223** 2.663 4.44e-07 (Ref: finance) (1.073) (0.145) (2.260) (0.000732) Quality management 0.442 0.705 1.118 6.814 (0.501) (0.322) (1.348) (9.093) Teaching 6.861*** 0.647 0.919 4.573 (4.285) (0.433) (1.234) (7.459) Other work activity 1.907 1.200 1.087 1.489 (1.067) (0.375) (0.886) (1.945) Employer Location: Midwest 0.979 1.365 1.963 11.94** (Ref: Northeast) (0.316) (0.340) (1.266) (14.91)
163 South 1.242 0.986 1.620 2.841 (0.333) (0.218) (0.965) (3.529)
West 1.364 0.985 1.131 5.042 (0.378) (0.230) (0.699) (6.290) Local/State government 1.558* 0.717 0.846 0.189 (0.399) (0.155) (0.424) (0.221) Constant 73.12*** 0.0747 44.32 0.000444 (120.0) (0.156) (199.4) (0.00359) Observations 2,621 2,444 2,444 2,444 Pseudo R-squared 0.1567 0.1259 Note: Multinomial logit models estimated with “same job in same employer’ as the base group. Relative risk ratio shown. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Job satisfaction 0.761*** 0.878* 0.640*** 0.619*** (0.0535) (0.0675) (0.0944) (0.111) Mismatch: Somewhat related 0.883 1.458*** 0.739 1.378 (Ref: Closely Related) (0.107) (0.171) (0.184) (0.435) Not related 0.922 1.985*** 0.293** 1.627 (0.150) (0.306) (0.144) (0.657) Training 0.575*** 1.459*** 1.412 1.092 (0.0651) (0.198) (0.410) (0.362) Professional meeting 1.181 0.855 1.277 0.823
164 (0.130) (0.0943) (0.299) (0.243) Log salary 0.719*** 1.187 0.905 0.734
(0.0511) (0.143) (0.149) (0.144) Male 0.778** 0.758** 0.971 1.166 (0.0803) (0.0820) (0.208) (0.341) Race: Black 1.213 1.090 1.086 0.899 (ref: White) (0.186) (0.178) (0.366) (0.390) Asian 1.211 0.805 0.908 0.183* (0.195) (0.153) (0.343) (0.187) Hispanic 1.430** 1.117 0.950 0.788 (0.234) (0.203) (0.356) (0.429) Other 0.520* 1.053 2.823*** 1.857 (0.186) (0.295) (1.108) (1.032) Continued Table 4.6. Logit and Multinomial Estimates of Different Type of Turnover of Government Employees: 2003-2006 Cohorts
Table 4.6 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Age group: 30-39 0.807 0.784 1.624 1.135 (ref: 20-29) (0.148) (0.168) (0.617) (0.563) 40-49 0.543*** 0.723 0.806 0.689 (0.103) (0.156) (0.330) (0.364) 50-59 0.471*** 0.358*** 0.751 0.557 (0.0939) (0.0828) (0.325) (0.311) Over 60 0.586** 0.189*** 0.522 1.66e-07 (0.157) (0.0753) (0.361) (0.000175) Married 0.995 1.061 0.887 0.806 (0.120) (0.137) (0.220) (0.268)
165 Children 1.056 0.916 0.854 0.948 (0.119) (0.107) (0.203) (0.302)
Supervisor 1.111 1.349*** 0.811 0.633 (0.121) (0.154) (0.185) (0.209) Degree Type: Masters 1.035 1.346*** 1.030 1.579 (ref: Bachelor) (0.116) (0.148) (0.242) (0.468) Doctorate 2.105** 1.149 0.772 5.12e-07 (0.772) (0.633) (0.834) (0.00118) Professional 1.318 0.742 2.278** 1.213 (0.249) (0.200) (0.808) (0.798) Work length: From 5 to 15 years 0.706*** 0.722*** 0.432*** 0.559* (ref: below 5 years) (0.0801) (0.0853) (0.0998) (0.173) Over 15 years 0.592*** 0.623*** 0.104*** 0.210*** (0.0889) (0.0894) (0.0555) (0.117) Continued
Table 4.6 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer # of Employees: 100-4,999 0.897 1.375 0.590 1.446 (ref: 1-99) (0.163) (0.385) (0.195) (0.932) Over 5,000 0.679* 1.538 0.420** 1.367 (0.137) (0.448) (0.161) (0.945) Work: Basic research 1.021 0.911 0.899 1.249 (Ref: finance) (0.395) (0.303) (0.623) (0.934) Apld. research 1.146 0.537** 0.884 0.590
166 (0.365) (0.153) (0.490) (0.432) Dev.-knowledge 2.228** 0.511* 1.322 0.303
(0.738) (0.179) (0.816) (0.344) Design of equipment 1.113 0.937 0.945 0.298 (0.412) (0.281) (0.609) (0.340) Computer programming 1.269 0.544** 0.709 0.534 (0.374) (0.142) (0.389) (0.357) Employee relations 1.797 0.982 1.035 0.563 (0.691) (0.344) (0.782) (0.648) Managing people 1.105 0.958 0.927 1.030 (0.312) (0.217) (0.455) (0.607) Production, operations 1.878* 0.598 1.554 0.972 (0.632) (0.210) (0.960) (0.771) Prof. services 1.809** 0.660 0.598 0.645 (0.505) (0.167) (0.303) (0.405) Continued
Table 4.6 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Work: Sales, marketing 0.894 0.722 1.715 0.324 (Ref: finance) (0.369) (0.262) (1.072) (0.370) Quality management 1.635 0.697 2.13e-07 0.449 (0.634) (0.267) (0.000264) (0.513) Teaching 5.506*** 0.677 1.42e-07 0.841 (2.007) (0.355) (0.000246) (0.965) Other work activity 0.959 0.889 0.634 0.682
167 (0.319) (0.242) (0.404) (0.475) Employer Location: Midwest 1.085 1.252 0.742 0.775
(Ref: Northeast) (0.183) (0.236) (0.288) (0.351) South 0.964 1.109 1.222 0.803 (0.142) (0.180) (0.382) (0.305) West 0.986 1.122 0.851 0.568 (0.152) (0.190) (0.285) (0.247) Local/State government 1.099 0.730** 1.206 0.851 (0.162) (0.107) (0.385) (0.342) Constant 23.96*** 0.0345** 1.134 6.427 (20.45) (0.0467) (2.210) (15.13) Observations 5,105 4,592 4,592 4,592 Pseudo R2 0.0833 0.0895
Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Job satisfaction 0.550*** 0.927 0.484*** 0.158*** (0.0633) (0.0993) (0.112) (0.0568) Mismatch: Somewhat related 0.827 1.584*** 0.166*** 0.444 (Ref: Closely Related) (0.177) (0.250) (0.104) (0.307) Not related 1.179 1.715** 0.655 0.191 (0.316) (0.393) (0.358) (0.250) Training 0.847 0.996 2.680* 0.277** (0.174) (0.176) (1.529) (0.173) Professional meeting 1.028 1.245 1.502 0.967
168 (0.187) (0.182) (0.512) (0.569) Log salary 0.517*** 1.063 0.459* 1.786
(0.0744) (0.197) (0.190) (1.478) Male 0.912 0.794 1.414 0.400 (0.162) (0.116) (0.490) (0.239) Race: Black 1.373 1.005 0.837 0.965 (ref: White) (0.350) (0.215) (0.468) (0.889) Asian 1.350 0.570** 2.336* 0.584 (0.339) (0.156) (1.105) (0.705) Hispanic 0.707 0.994 1.243 1.758 (0.222) (0.220) (0.658) (1.360) Other 0.475 0.928 0.468 1.387 (0.261) (0.335) (0.498) (1.628) Continued Table 4.7. Logit and Multinomial Estimates of Different Type of Turnover of Government Employees: 2010-2013 Cohorts
Table 4.7 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Age group: 30-39 0.418*** 0.844 1.612 1.690 (ref: 20-29) (0.108) (0.215) (0.897) (2.047) 40-49 0.267*** 0.579** 1.045 0.501 (0.0793) (0.156) (0.651) (0.635) 50-59 0.265*** 0.335*** 0.575 0.852 (0.0801) (0.0966) (0.388) (1.097) Over 60 0.443** 0.160*** 7.21e-08 6.11e-07 (0.157) (0.0744) (0.000101) (0.000934) Married 1.301 1.209 1.215 0.828
169 (0.269) (0.211) (0.509) (0.575) Children 0.946 1.033 0.875 1.508
(0.184) (0.164) (0.333) (1.006) Supervisor 1.651** 1.507*** 1.939* 1.849 (0.323) (0.237) (0.703) (1.122) Degree Type: Masters 1.380* 1.383** 1.750 1.680 (ref: Bachelor) (0.259) (0.209) (0.625) (1.065) Doctorate 3.399** 1.409 5.875 2.00e-06 (1.906) (0.841) (6.955) (0.00741) Professional 1.997** 2.183** 0.760 0.933 (0.667) (0.680) (0.636) (1.201) Work length: From 5 to 15 years 0.501*** 0.642*** 0.301*** 0.127** (ref: below 5 years) (0.105) (0.105) (0.123) (0.108) Over 15 years 0.434*** 0.794 0.0730** 0.197* (0.131) (0.167) (0.0770) (0.194) Continued
Table 4.7 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer # of Employees: 100-4,999 0.796 1.085 0.338** 0.486 (ref: 1-99) (0.228) (0.358) (0.180) (0.451) Over 5,000 0.712 0.990 0.330* 0.0651* (0.229) (0.352) (0.201) (0.0927) Work: Basic research 3.173* 1.038 4.63e-08 1.03e-07 (Ref: finance) (1.905) (0.416) (0.000103) (0.000248) Apld. research 2.926** 0.493** 0.542 0.472 (1.564) (0.164) (0.445) (0.737) Dev.-knowledge 5.035*** 1.313 0.628 2.76e-07 (3.010) (0.505) (0.753) (0.000621) Design of equipment 2.357 0.842 0.783 4.31e-07
170 (1.595) (0.347) (0.777) (0.00101) Computer programming 2.108 0.807 1.112 3.485 (1.365) (0.328) (1.082) (5.441) Employee relations 0.589 0.991 1.410 3.465 (0.668) (0.432) (1.730) (5.614) Managing people 2.198 0.609* 0.696 0.887 (1.141) (0.172) (0.498) (1.079) Production, operations 2.704 0.666 2.287 7.45e-07 (1.795) (0.315) (2.284) (0.00159) Prof. services 2.638* 0.458** 0.899 1.127 (1.386) (0.151) (0.674) (1.496) Continued
Table 4.7 continued Logit Estimates Multinomial Logit Estimates Left Government Change Job Same Job Change Job Same Employer Change Employer Change Employer Work: Sales, marketing 1.746 0.229** 3.006 2.37e-07 (Ref: finance) (1.154) (0.148) (2.538) (0.000650) Quality management 0.464 0.695 1.080 8.479 (0.526) (0.317) (1.302) (12.15) Teaching 7.377*** 0.625 1.102 4.456 (4.641) (0.419) (1.462) (7.710) Other work activity 1.957 1.212 1.092 1.951 (1.111) (0.378) (0.893) (2.657) Employer Location: Midwest 0.906 1.326 1.730 7.422* (Ref: Northeast) (0.293) (0.329) (1.112) (9.014) South 1.146 0.972 1.563 1.511
171 (0.308) (0.215) (0.935) (1.843) West 1.313 0.961 1.150 3.345 (0.363) (0.223) (0.711) (4.075) Local/State government 1.438 0.739 0.815 0.154 (0.371) (0.160) (0.414) (0.185) Constant 676.7*** 0.152 1,172 0.116 (1,160) (0.314) (5,388) (1.050) Observations 2,621 2,444 2,444 2,444 Pseudo R2 0.1622 0.1264 Note: Multinomial logit models estimated with “same job in same employer’ as the base group. Relative risk ratio shown. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Chapter 5 : Discussion
This chapter is composed of three sections. In the first section, I review the findings from
Chapters 2-4, each of which analyzes a different aspect of effective human resource management policy and government expenditures for workforce development. In the second section, I provide a broader discussion about the role of government in workforce development, along with the dissertation’s specific contributions and implications. Finally, in the third section I identify the limitations of this dissertation and suggest future research for policy makers and scholars, based on my findings about workforce development.
Summary
Workforce development is considered an important way to increase a nation’s capabilities and competitiveness. In recognition of this fact, the US government has consistently monitored and invested in workforce development. Human resource management policy plays a significant role in this process, and its history goes hand in hand with the history of government- driven workforce development. In particular, the concept of resilience is important in situations with economic and social uncertainty. This dissertation uses resilience to analyze three case studies of how human resource management policy and government spending affect workforce development, from both macro and micro perspectives. 172
Chapter 2 presented macro perspectives on workforce development by evaluating how different government expenditures on labor market policies affect OECD unemployment rates.
Using data from 2001 to 2013, I examined how government labor market policies (especially active labor market policies) help increase resilience, as measured by changes in the unemployment rate, toward the adversity of the 2008 economic crisis. My analysis shows that higher spending on active labor market policies is positively associated with a lower unemployment rate and a lower long-term unemployment rate (supporting hypotheses H1a and
H1b), and that countries that spent more on active labor market policies before a crisis are more resilient (less change in unemployment and long-term unemployment rates) during the crisis.
Next, Chapter 3 showed a micro perspective on workforce development by examining the productivity and behavior of biomedical postdocs in the US by considering workforce diversity
(i.e. immigration and citizenship status) and comparing behavior before, during, and after NIH doubled its funding (1998-2003). I measured different behaviors and productivity levels to explore how the change of funding has affected different cohorts based on their characteristics and unique situations. Specifically, I found that US citizens stayed for longer and temporary residents stayed for a shorter time in postdoc positions, while permanent residents were more productive under the more supportive funding environment. Likewise, I found that US citizens shortened and temporary residents extended their time since graduation, while permanent residents were less productive under the less supportive funding environment.
Chapter 4 also provides a micro perspective of workforce development by focusing on the turnover of US government employees. This chapter explored some underlying causes of government-related job-status trends by taking a closer look at how human resource management strategies (the difference between perceived expectation and actual satisfaction on the job, job- 173
education mismatch, and work-related training) affect turnover (either changing departments or leaving government work altogether). I found that among recent government employees (2010-
2013 cohort), those who have a higher discrepancy between perceived expectation and actual job satisfaction, in terms of intellectual challenge and opportunity for advancement, are more likely to leave government employment or move to a different government job. Additionally, work- related training was negatively correlated to the likelihood for employees to leave their government jobs only in the 2003-2006 cohort, and positively related to employees’ mobility between jobs within the government among both cohorts. Moreover, job-education mismatch is positively related to an employee’s mobility within the government for both cohorts.
Overall, these analyses provide a perspective on policy and management response toward workforce development using both macro-level (OECD countries) and micro-level (US postdoctoral researchers and government employees) approaches. I evaluated governments’ efforts in workforce development by understanding how human resource management policy and government spending works (how much money has been spent), and how the government changed its human resource management policy to promote resilient workforce development.
Contributions and Implications
To acquire, develop, and retain a workforce capable of responding to emerging challenges, a strategic approach is important. With the current social and economic uncertainty, the notion of workforce development is expanding and the role of government in workforce development is also increasing. Under these circumstances, it is essential to have a comprehensive and strategic perspective about workforce development, to adapt well to the changes. A strategic workforce development plan should not be limited to one specific group or 174
area, but cover both employees and employers across organizations. Along with this broad perspective on workforce development, the workforce environment should be changed to motivate people to work, improve their skills, achieve their organizational goals, and meet workers’ expectations. Based on these understandings, this dissertation evaluates government efforts for workforce development in terms of resilience.
Previous studies failed to bridge workforce development, resilience, and human resource management policy. To fill this gap this dissertation links the three topics, and improves our understanding of the role of government in resilient workforce development through human resource management policies and government spending. Another contribution of this dissertation is an enhanced understanding of workforce development in the public administration literature. Government-driven workforce development has long been considered important in public policy and political science scholarship. I confirm the similar importance of workforce development issues in public administration by focusing on the role of government. By evaluating human resource management policies, this work increases the awareness of workforce development in public administration.
Specifically, this dissertation provides more information on how government human resource management policies contribute to solving the complex, multilevel workforce development challenges that the US and other countries are currently facing. The study in
Chapter 2 emphasizes the government’s role in providing safety nets for social and economic actors (corporations, individuals, etc.), so they can actively engage in the economic and social transformation with fewer burdens (e.g. possible mass layoffs and isolation from society). In particular, this study shows that government has to choose the right policy options for promoting resilient workforce development with limited resources. This research expands our 175
understanding of how government can reduce personal and social costs by taking proactive steps to tackle relevant issues (Vernon, 1971).
Chapter 3 examines resilient government activities for keeping up with changes and improving research environments, focusing on biomedical science. The primary policy tool for the federal government to improve a desired outcome is supplying more resources. This research enhances our understanding of the relationship between research output and changes in federal government expenditures. It also helps clarify bigger questions about the role of research funding in outcomes that matter to the Congress, the President, and the public. Finally, the study in
Chapter 4 emphasizes the modification of human resource management policies to meet the modern-day needs of employees, in order to make the government more resilient within changing work environments. It does so by comparing a cohort of government employees from
2003 to 2006 to one from 2010 to 2013. This study improves our understanding of organizational resilience by examining how government human resource management policies affect government employees’ turnover. From this dissertation, therefore, policy makers and scholars can get a better understanding about resilient workforce development and government human resource management policy in today’s rapidly changing environment.
Limitation and Future Research
This dissertation improves our understanding of how government responds to and prepares for significant (and sometimes unexpected) social and economic crises, such as increasing global competition, technological innovation, and demographic shifts. However, it has two important limitations. First, this dissertation only covers part of workforce development.
Many scholars and policy makers are interested in how human resource management policies 176
affect workforce development. As this interest expands, so does the range of potential analysis.
However, this dissertation only treats three topics relevant to workforce development, and the objects of analysis are limited to OECD countries, US postdoctoral researchers in biomedical science, and the US government. With these topics, I only focus on the effects of government human resource management policies on unemployment rates, the productivity and behavior of researchers, and the career mobility of government employees. I also only consider a limited number of human resource management policies to analyze resilient workforce development.
Second, this dissertation only looks at the supply side of government policy. I only focus on the government initiatives to improve resilient workforce development under economic and social uncertainty, but I do not cover the individual and social costs from government policy interventions for individual behaviors.
To bypass these limitations, in future work I will expand my range of analysis on workforce development, by considering more diverse objectives and using longer data records.
In Chapter 2, for example, I can consider more outcome variables such as the duration of unemployment. I can also include more nations, not just OECD countries. In Chapter 3, I can do similar analyses with all researchers, not only postdoctoral researchers. I can also track their long-term productivity and career development under less/more supportive funding environments. In Chapter 4, I can analyze government employees’ satisfaction or wage changes when they do change departments or leave the public sector. I can also compare the mobility of government employees with the mobility of employees in non-government sectors, such as business or non-profit organizations. Finally, in a future study I plan to evaluate the possible costs to individuals and society of government human resource management policies for resilient workforce development. 177
Today, there are several workforce challenges from global competition, technological innovation, and demographic shifts. Under this economic and social uncertainty, the government plays a vital role in managing risk to maintain a resilient workforce. Government human resource management policies can be used strategically to help overcome current challenges.
This dissertation can significantly enhance policy makers’ understanding of how this process works, and how it might affect individual behaviors and productivity.
178
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