Generational Differences in Training, Job Satisfaction, and Intent to Leave Among the State Governmental Public Health Agency Workforce

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GENERATIONAL DIFFERENCES IN TRAINING, JOB SATISFACTION, AND INTENT TO LEAVE AMONG THE STATE GOVERNMENTAL PUBLIC HEALTH AGENCY WORKFORCE

Brian Christopher Castrucci

A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Public Health in the Department of Health Policy and Management in the Gillings School of Global Public Health.

Chapel Hill 2018

Approved by:

Leah M. Devlin

Edward L. Baker

Sandra B. Greene

Asheley Cockrell Skinner

Hugh Tilson

ABSTRACT

Brian Christopher Castrucci: Generational Differences in Training, Job Satisfaction, and Intent to Leave among the State Governmental Public Health Agency Workforce (Under the direction of Leah M. Devlin)

“Entitled,” “lazy,” “narcissistic,” “glued to their phones,” “high maintenance in the workplace.” These descriptors of Millennials come from a brief sampling of blogs from trusted publications like Inc. and Entrepreneur. In a 2013 cover of Time Magazine,

Millennials were deemed the “Me Me Me Generation” with the subheading “Millennials are lazy, entitled narcissists who still live with their parents.” However, for the state governmental public health agency workforce, no previous studies have examined this negative Millennial narrative. This work tackles three separate, but related research questions to provide the first ever insight on generational differences in the state governmental public health agency workforce.

The analyses completed here yield many findings. The key messages that most directly inform change include:

 Millennials working in state governmental public health agencies have

better attitudes toward workplace training compared to other generations

 Millennials working in state governmental public health agencies have

greater odds of self-identified training needs compared to other

generations

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 Millennials working in state governmental public health agencies are just

as satisfied with their jobs as other generations

 Millennials working in state governmental public health agencies are

considering other workplaces compared to other generations

 Millennials working in state governmental public health agencies have at

least equal, if not better, workplace attitudes and experiences compared to

other generations

This discordance between the popular impressions of Millennials and information presented here highlights the presence of a destructive Millennial myth narrative.

Persistence of this myth has the potential to alienate Millennial staff and allows for a more aligned effort to skill building in the workforce, which could potentially yield cost and time savings.

This work informs a plan for change that must undo a narrative about Millennials in the existing state governmental public health agency workforce that is simply not supported by the evidence. This will require deliberate steps to undo this narrative and and provide leaders in state governmental public health agencies with solid steps to increase engagement and retention among this group.

To Ellen, my wife, who bought low. To my children, Evan and Chloe.

ACKNOWLEDGEMENTS

One thing you know when you see a turtle on a fence post, you know it didn’t get there alone. This dissertation is my fence post, and I certainly didn’t get here alone. I thank my advisor, Leah Devlin, for her encouragement. She was the nagging voice in my head that I needed to get this done with competing priorities at work. Ed Baker and Hugh Tilson both encouraged me from before I started the program through completion. I benefited from their wisdom and tough love” throughout this process. Sandra Greene and Asheley Cockrell Skinner were outstanding committee members whose insightful feedback and knowledge of how to traverse this weird journey proved extremely valuable.

None of this would have been possible without the support of the de Beaumont

Foundation, where I have worked for the past six years and am currently the chief executive officer (CEO). Particular thanks are owed to James B. Sprague, MD, the Foundation’s founding and current chairman and founding CEO. Dr. Sprague encouraged me to pursue this degree and wrote the policy to provide the financial support that made this degree possible. I wouldn’t be here without Dr. Sprague’s support. The data I used for this dissertation also came from a much larger project approved by the Foundation’s board. This dissertation is just one example of the impact of the Public Health Workforce Interests and Needs Survey has had on the fields of governmental public health and workforce research. Lastly, as we often say after Foundation events, “Thank you, Pete.”

Of course, I would never have finished this dissertation with the support of my wife,

Ellen, and children, Evan and Chloe. Evan and Chloe generally left me alone for a couple hours every week for class, though making occasional appearances. When I first met my wife in

2002, I was in a doctoral program that I would never finish. Ellen has been the person by my

side who knows when I need a hug and when I need a swift kick. She has spoken truth to spouse throughout this process and our entire relationship. For this, I will be ever grateful.

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TABLE OF CONTENTS

LIST OF TABLES ...... xiii

CHAPTER 1: INTRODUCTION ...... 1

Statement of the issue...... 1

Significance ...... 2

Background ...... 3

Approach ...... 7

Institutional Review Board and other Ethical Considerations ...... 7

Challenges with Generational Research ...... 8

How to Read This Dissertation ...... 9

CHAPTER 2: GENERATIONAL DIFFERENCES IN ATTITUDES TOWARD TRAINING SUPPORT AND SELF-IDENTIFIED TRAINING NEEDS IN A NATIONAL SAMPLE OF STATE GOVERNMENTAL PUBLIC HEALTH AGENCY EMPLOYEES ...... 12

Background ...... 12

Millennials and Training ...... 14

Research Questions and Implications ...... 14

Methods ...... 16

Data Sources ...... 16

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Measures ...... 18

Generations...... 18

Measuring a Learning Culture ...... 18

Training Gaps ...... 19

Demographic and Workforce Characteristics ...... 19

Statistical Analysis ...... 20

Results ...... 20

Generational Differences in the Perceived Support for Training in State Governmental Public Health Agencies ...... 21

Differences in the Training Gaps among the State Governmental Public Health Agency Workforce by Generation ...... 21

Discussion ...... 23

Tables ...... 25

CHAPTER 3: MILLENNIAL SUPPORT & SATISFACTION: GENERATIONAL DIFFERENCES IN ORGANIZATIONAL SUPPORT, SUPERVISORY SUPPORT, AND JOB SATISFACTION AMONG THE STATE PUBLIC HEALTH AGENCY WORKFORCE ...... 31

Background ...... 31

Generational Theory and Job Satisfaction ...... 32

Research Questions and Implications ...... 35

Methods ...... 36

Data Sources ...... 36

Measures ...... 39

Generations...... 39

Organizational and Supervisory Support ...... 39

Job Satisfaction ...... 40

Demographic and Workforce Characteristics ...... 41

Statistical Analysis ...... 41

Results ...... 42

Discussion ...... 43

Tables ...... 45

CHAPTER 4: AN EXPLORATION OF MILLENNIALS’ REASONS FOR ENTERING AND INTENT TO LEAVE THE STATE GOVERNMENTAL PUBLIC HEALTH AGENCY WORKFORCE ...... 48

Background ...... 48

Nonprofits v. Government: The Changing Perceptions of Government ...... 50

Research Questions and Implications ...... 52

Methods ...... 53

Data Sources ...... 53

Measures ...... 55

Initial Reasons for Entering Public Health ...... 55

Workplace Environment: Supervisory Support, Organizational Support, and Employee Engagement ...... 56

Satisfaction...... 57

Intentions to Leave Organization ...... 57

Statistical Analysis ...... 58

Results ...... 58

Intrinsic Motivators ...... 59

Extrinsic Motivators ...... 60

Supervisory Support ...... 60

Organizational Support ...... 61

Employee Engagement ...... 61

Job, Organization, Pay, and Job Security Satisfaction ...... 62

Discussion ...... 64

Tables ...... 67

CHAPTER 5: A PLAN FOR CHANGE ...... 75

Destructive millennial myth narrative ...... 75

Defining the Audience ...... 77

DIssemination of Key Findings ...... 78

Generating Practical Guidance and implementing the findings ...... 81

informing future ph wins administrations ...... 81

REFERENCES ...... 83

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LIST OF TABLES

Table

1.1. Four Generations in the Workforce ...... 6

1.2. Research Questions by Chapter Title ...... 10 2.1. Demographic and Workforce Characteristics of the State Governmental Public Health Agency Workforce by Generation ...... 25 2.2. Attitudes toward Training among the State Governmental Public Health Agency Workforce by Generation ...... 26

2.3. Adjusted Odds Ratios (OR) of Positive Attitudes toward Training* ...... 27 2.4. Training Needs among the State Governmental Public Health Agency Workforce by Generation ...... 28 2.5. Adjusted Odds Ratios (OR) of Being Unable to Perform/Beginner at Key Workforce Skills ...... 29 3.1. Association between Supervisory Support and Being Somewhat or Very Satisfied with One’s Job by Generation ...... 45 3.2. Association between Organizational Support and Being Somewhat or Very Satisfied with One’s Job by Generation ...... 46 3.3. Results of Multivariate Logistic Regression on Supervisory Support and Organizational Support as Correlates of Job Satisfaction ...... 47 4.1. Intentions to Leave among State Governmental Public Health Agency Workforce by Generation ...... 67 4.2. Reasons for Entering Public Health among the State Governmental Public Health Agency Workforce by Generation and Intention to Leave ...... 68 4.3. Supervisory Support among the State Governmental Public Health Agency Workforce by Generation and Intention to Leave .... 70 4.4. Organizational Support among the State Governmental Public Health Agency Workforce by Generation and Intention to Leave .... 71 4.5. Employee Engagement among the State Governmental Public Health Agency Workforce by Generation and Intention to Leave .... 72 4.6. Job, Organization, Pay, Job Security Satisfaction among the State Governmental Public Health Agency Workforce by

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Generation and Intent to Leave ...... 73

4.7. Adjusted Odds of Intention to Leave by Generation ...... 74

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CHAPTER 1: INTRODUCTION

STATEMENT OF THE ISSUE

This is a time of unprecedented health transformation. Disparities of all types

(rural, age, race, gender) are steady, if not increasing.(Akinboro et al., 2016; Jennings &

Gaither, 2015; Owen, Goldstein, Clayton, & Segars, 2013; Plescia & Emmanuel, 2014;

Stanbury & Rosenman, 2014) Climate change,(Bowles, Butler, & Morisetti, 2015;

Machalaba et al., 2015; Patz, Frumkin, Holloway, Vimont, & Haines, 2014) opioids,(Dowell, Haegerich, & Chou, 2016; Schmidt, Haddox, Nielsen, Wakeland, &

Fitzgerald, 2015; Shei et al., 2015) and mental health (Bernardini-Zambrini, 2014; de

Jong et al., 2015; Saraceno & Zullino, 2013; Stewart-Brown, 2015) are new, emerging, and challenging problems. However, new science and technologies bring exciting opportunities to improve health. Tackling these problems and leveraging these technologies requires that the state governmental public health workforce be vibrant, diverse, and competent in the skills necessary to maintain and increase life expectancy.

It is against this backdrop that generational analyses can inform workforce recruitment and retention.

For the first time, there are multiple generations existing simultaneously in the US workforce with Baby Boomers, Generation X, and Millennials accounting for approximately 95% of the current workforce.(Zemke, Raines, Filipczak, & American

Management, 2000) By the first quarter of 2015, Millennials became the largest

1 generation in the workforce (53.5 million) followed by Generation X (52.7 million) and

the Baby Boomers (44.6 million).(Fry, 2015)

Analysis of the 2014 Public Health Workforce Interests and Needs Survey (PH

WINS) confirms an impending exodus from the state governmental public health agency

workforce.(Sellers et al., 2015) Nearly 40% of the state governmental public health

agency workforce reports an intention to leave by 2020.(Liss-Levinson, Bharthapudi,

Leider, & Sellers, 2015) One fifth of the workforce reports an intention to leave in the

next year.(Liss-Levinson et al., 2015) The replacement workforce will need to come

from the Millennial generation. Failing to understand the generational dynamics in the

state governmental public health agency workforce and tailor appropriate workforce policies and strategies to recruit, support, and retain Millennials could exacerbate a

serious problem of a weakened public health system that potentially jeopardizes many

of the gains in life expectancy achieved through the previous century.(Centers for

Disease Control and Prevention, 2011)

Understanding this new generation of workers and predicting how this generation

may or may not successfully interact with the state governmental public health

infrastructure is central to crafting effective and impactful public health workforce policy

and interventions. However, little is known about Millennials in the state governmental

public health agency workforce.

SIGNIFICANCE

Maintaining a well prepared workforce in governmental public health agencies

has been a long held policy concern.(K. M. Gebbie & Turnock, 2006) There is a clear

gap in our understanding of how generational influences may be impacting the state

2 governmental public health agency workforce. The national trends are clear, but how

these population trends apply to the state governmental public health agency workforce is unknown. Without an understanding of the generational dynamics impacting the state governmental public health agency workforce, critical information is unavailable to workforce planners. These analyses will provide important information that could be applied to tailor and hone workforce recruitment and retention strategies for Millennials throughout state governmental public health agencies nationwide.

BACKGROUND

The term generation is used in two distinct ways. When used in the context of a family, the term is meant to count the years between the birth of each parent and child.

Generations defined in this way are intensely personal to the individual family and have no external generalizability. The term generation is also used in a societal context to describe people born in a particular year or group of years. Elwood D. Carlson in The

Lucky Few: Between the Greatest Generation and the Baby Boom identified seven US generations.(Carlson, 2008) These are:

 The New Worlders (born from 1871-1889)

 The Hard Timers (born from 1890-1908)

 The Good Warriors (born from 1909-1928)

 The Lucky Few (born from 1929-1945)

 The Baby Boomers (born from 1946-1964)

 Generation X (born from 1965-1982)

 The New Boomers (born from 1983-2001)

3 While Carson’s calls the newest generation the New Boomers, they are more often

referred to as the Millennial generation.(Howe & Strauss, 2009)

Each generation is shaped differently. Generation members share temporal and

age-related events such as starting school, entering the workforce, having children, and

retiring. While historical events occur simultaneously for multiple generations, each is

experiencing these events at different developmental stages with young adulthood

being a particularly impressionable developmental stage.(Baltes, Reese, & Lipsitt, 1980;

Duncan & Agronick, 1995; Noble & Schewe, 2003) Different social, cultural, political experiences contribute to this identity.(Delli Carpini, 1989; Drew, 2015; Webb-Morgan,

2012) For example, the Greatest Generation (born prior to 1928), who would be from

88 years to 100 years of age in 2015,(Pew Research, 2014) were shaped by the Great

Depression as children and then experienced World War II either as combatants or through experiences within the war’s home front. These experiences had a universal impact on those being born, growing, maturing, and living in this era.

Millennials have been shaped by the proliferation of the World Wide Web and wireless communication/computing increasing their access to unlimited, global-wide information. For example, at the end of 1995, 0.4% of the world’s population used the internet.(Internet World Stats, 2015) This proportion increased to 5.8% by 2000 and to

42.4% by 2014.(Internet World Stats, 2015) Between 2004 and 2008, cellular phone ownership among adolescents increased 58% to near equal proportions to

adults.(Lenhart, 2009) However, Millennials have also been affected by the events of

September 11th, the housing bust and the Great Recession, which delayed entry into

careers and first jobs.

4 Each generation has characteristics that it believes make it unique. In 2010, the

Pew Research Center found 61% of Millennials, roughly half of all Generation Xers, and

approximately 60% of Baby Boomers believed their generation has a unique and distinctive identity.(Pew Research, 2010) Among this group, respondents were asked about what makes their generation unique. Nearly a quarter of Millennials reported that their use of technology is what makes the generation distinctive.(Pew Research, 2010)

It is integral to their academic, social, economic, and personal lives. For example, 75% of Millennials have a profile on a social networking site compared to 50% of Generation

X and 30% of Baby Boomers.(Pew Research, 2010)

This engagement with social media has contributed to the “Look at Me” moniker often ascribed to the Millennials. Comparatively, those born before the advent of the

Worldwide Web, email, and social networks must adapt to new technologies in order to use them. Given their life long exposure to technology, Millennials excel at multitasking, filtering information, and responding to visual stimulation. However, they may struggle in terms of face-to-face communication and reading non-verbal cues, though this can be learned later in life. While a strong work ethic (Generation Xers: 11%, Baby Boomers:

17%) and being respectful (Generation Xers: 5%, Baby Boomers: 14%) were cited as key characteristics of past generations, these were not determined to be unique characteristics of Millennials while popular culture and clothes were.(Pew Research,

2010)

Generational characteristics and differences have been a part of workforce research since industrialization. However, unlike previous generational transitions, today, people are living longer, healthier lives.(Collins, 2003) Therefore, as Baby

5 Boomers are reaching the traditional retirement age, many are continuing to work well into their 60s and 70s, if not longer. This longevity has implications for workforce advancement and entry for younger generations.(Collins, 2003) This means that members of three generations will be in the workforce for a longer duration than in the past. Therefore, the implications of generation on the workforce takes on greater importance. In The Trophy Kids Grow Up: How the Millennial Generation is Shaking Up the Workplace, Alsop identified the following traits for each of the four generations currently in the US workforce.(Alsop, 2008)

Table 1.1. Four Generations in the Workforce Millennials Gen Xers Baby Boomers Traditionalists

Traits Entitled, Self-reliant, Workaholic, Patriotic, optimistic, civic adaptable, idealistic, dependable, minded, close cynical, competitive, conformist, parental distrusts loyal, respects involvement, authority, materialistic, authority, values work-life resourceful, seeks rigid, socially balance, entrepreneurial, personal and financially impatient, technology fulfillment, conservative, multitasking, savvy values titles solid work team oriented and the corner ethic office

While the impact the multigenerational workforce and specifically the impact of

Millennials has been studied in the health sciences – nursing,(Pardue & Morgan, 2008;

Sherman, 2006; Skiba, 2005) medicine,(Brown & Schafer, 2009; Platt, 2010) and

physician assistants (Danielsen, Lathrop, & Arbet, 2012; Lopes & Delellis, 2013) – and

other fields like hospitality (Chen & Choi, 2008; Gursoy, Maier, & Chi, 2008) and the

building trades,(Real, Mitnick, & Maloney, 2010) there are no studies addressing these

6 issues in the state governmental public health agency workforce. A brief search of the

National Library of Medicine using PubMed found no articles on generational differences in the state governmental public health agency workforce. A search using the terms “Millennials” and “Public Health” yielded 26 articles. There were several articles that addressed the challenges of educating Millennials with articles focusing on practice primarily addressed either nursing or medicine. There was only one article that addressed the in-place workforce, and it addressed the health professions, generally.

To further explore the availability of literature, a second search was performed using the terms “Millennials” and “Workforce.” Sixteen articles were returned as a result of that search. All related to clinical medicine or healthcare. This demonstrates the significant gap in the existing literature and the need for empirical research.

APPROACH

PH WINS is first nationally representative sample of the state governmental public health agency workforce. It was fielded in 37 states in the Fall 2014. The PH

WINS data are the only available data that can be applied to generational questions in the state governmental public health agency workforce. This research will capitalize on this novel, innovative dataset to explore generational differences in the state governmental public health agency workforce for the first time. This work tackles three separate, but related research questions to provide the first ever insight on generational differences in the state governmental public health agency workforce.

Institutional Review Board and other Ethical Considerations

The PH WINS survey was reviewed by the Chesapeake Institutional Review

Board and determined to be exempt. The research includes analyses of this existing,

7 secondary dataset. The analyses pose no threat to the confidentiality of the participants

and have no ethical implications.

The de Beaumont Foundation created, funds, and owns the PH WINS data. As a

de Beaumont Foundation employee, access is available to variables commonly

redacted in the public use file. These variables, which include the respondent’s state,

can potentially lead to confidentiality issues given the limited numbers in some cells.

However, to further ensure no potential ethical concerns, the public use research dataset will be used for these analyses.

CHALLENGES WITH GENERATIONAL RESEARCH

There is an inherent limitation when using cross sectional data to explore generational differences that has been previously documented.(Hobcraft, Menken, &

Preston, 1985) When using cross sectional data, period, age, and generation are occurring simultaneously and are, therefore, inherently intertwined.(Yang & Land, 2006,

2008) Therefore, the independent effects of each variable are indistinguishable. While cross sectional data controls holds period constant across the sample, the effects of age and generation are still confounded.(Costa & McCrae, 1982) Due to the age/generation effect, it is unclear as to whether an identified relationship is primarily an age effect – variation due to physiological growth, progression through developmental stages, and accumulation of experience – or a generational effect – variation due to the shared experiences of the same age-group at the same period. Therefore, while a relationship between generation and a specific outcome variable may be identified using cross sectional data, it is not clear if that relationship will persist over time – a generational effect.

8 Due to this limitation, caution is required when interpreting findings. However,

the impact of this limitation is somewhat moderated in this work for several reasons.

First, generational theory provides a framework to understand workforce differences in

state governmental public health agencies. As Millennials continue to enter the

workforce through the end of 2020, the results of this research will have immediate

implications for workforce policy and strategy. Second, the repeated cross sectional

data needed for a hierarchical age-period-cohort model is not currently available in the field of public health.(Fienberg & Mason, 1985)

HOW TO READ THIS DISSERTATION

This dissertation uses the “three manuscripts” option, a first for a DrPH dissertation in this program. Therefore, information for which a reader may be searching may not be in the traditional location. In this option, Chapters 2 through 4 each represent one of the three manuscripts. Chapter 5 will present the Plan for

Change. The literature review is included as the Background section in each of the

manuscripts, as is typical in the preparation of peer reviewed manuscripts. A separate

literature review was prepared for each manuscript that focuses on framing the research

questions to be addressed. However, a traditional chapter titled literature review is not

included.

The questions to be answered through this research are included in each

chapter. Within each chapter in the Background section there is a subheading for

Research Questions and Implications. This is where the research questions are presented and discussed. To assist the reader, Table 1.2 includes the research questions by chapter.

9 Table 1.2.Research Questions by Chapter Title Chapter Title Research Question(s)

Generational Differences in Attitudes toward Training Research Question 1. Are there Support and Self-Identified Training Needs in a National generational differences in the perceived Sample of State Governmental Public Health Agency support for training in state governmental Employees public health agencies?

Research Question 2. Are there differences in the training gaps among the state governmental public health agency workforce by generation?

Millennial Support & Satisfaction: Generational Research Question 1. Are there differences Differences in Organizational Support, Supervisory in the relationship between supervisory Support, and Job Satisfaction among the State Public support and organizational support and job Health Agency Workforce satisfaction across the three generational cohorts that account for at least five percent of the state governmental public health agency workforce?

An Exploration of Millennials’ Reasons for Entering and Research Question 1. Are there differences Intent to Leave the State Governmental Public Health in the motivations for entering the field of Agency Workforce state governmental public health by generation?

Research Question 2. Are there generation differences in the influence of employee motivation for entering public health, workplace perceptions, supervisory relationships, employment satisfaction, and employee engagement on intentions to leave state governmental public health agency workforce for reasons other than retirement?

This approach (1) was best for the data set proposed, (2) allowed for comprehensive analyses that best supported a thorough Plan for Change with an increased likelihood of implementation, and (3) would facilitate publication post- dissertation defense in an area of public health inquiry without any empirical studies.

Chapters 2, 3, and 4 are intended to be viewed as separate manuscripts that could potentially simultaneously appear in one issue of a single peer-reviewed journal.

10 As these are three separate but related manuscripts using the same data set, there are also some natural redundancies that the reader will note between chapters. Each manuscript includes a specific Methods section that includes Data Sources, Measures, and Statistical Analysis. The analyses presented in each chapter employ secondary data analyses of existing quantitative data. As these data are already collected, a thorough description of the data is included in each manuscript in the Data Sources section of the Methods. Each of the three manuscripts explores generational differences, so the operationalized definition of the generations is presented in each manuscript. The Measures sections located in the Methods in each chapter separately provide the definitions of other key variables used in the analyses. The data analysis plan is included in the Methods section under the heading Statistical Analysis.

CHAPTER 2: GENERATIONAL DIFFERENCES IN ATTITUDES TOWARD TRAINING SUPPORT AND SELF-IDENTIFIED TRAINING NEEDS IN A NATIONAL SAMPLE OF STATE GOVERNMENTAL PUBLIC HEALTH AGENCY EMPLOYEES

BACKGROUND

For proper and effective function, the governmental public health enterprise requires an adequate supply of well-trained, skilled public health professionals.(Baker et al., 2005) However, this supply is in jeopardy. A recent analysis of the Public Health

Workforce Interests and Needs Survey (PH WINS) found that 18% of all state governmental public health workers plan to leave within the next twelve months and nearly 40% plan to leave by 2020.(Liss-Levinson et al., 2015) These data highlight the recruitment and retention challenge facing the state governmental public health agency workforce. Similar to other disciplines, but possibly even of greater importance given the diversity of the occupations within the broader field of public health, employee training can address skill and knowledge gaps in order to strengthen the technical, scientific, managerial and leadership competence of public health professionals and ensure the optimal operation of state and local health departments.(Goldstein, 1989)

Training may also be a way to promote organizational commitment, especially among younger workers.

Currently, the majority of the US workforce is comprised of workers from one of three generations – Baby Boomers, Generation X, and Millennials.(Fry, 2015) Each has been shaped by shared social, cultural, political experiences that are now co-existing simultaneously in the workforce.(Delli Carpini, 1989; Drew, 2015; Webb-Morgan, 2012)

12 Generational theory posits that the unique identity of each group influences its values,

expectations, and norms both in and out of the workplace.(Howe & Strauss, 2009)

Failure to recognize these differences and tailor workplace interactions–both

supervisory and peer-to-peer–appropriately can lead to dissatisfaction and turnover.

Entry of Millennials into the state governmental public health agency workforce is

necessary for continued operation. Therefore, recruitment and retention of this group

should be a priority for those concerned with workforce research and policy.

Millennials’ concern for finding organizations that are socially responsible or

provide personal fulfillment over salaries aligns nicely with employment at state

governmental public health agencies.(Macky et al., 2008; Rawlins, Indvik, & Johnson,

2008) However, the bureaucratic structures through which governments accomplish

their work may be more off-putting to Millennials.(Gore, 1993; Kristof-Brown,

Zimmerman, & Johnson, 2005; Merton, 1940) For those Millennials who enter to the

state governmental public health agency workforce, it will be important to promote

organizational commitment, which is defined as an individual's psychological attachment

to an organization.(Lee, Carswell, & Allen, 2000; Meyer & Allen, 1997; Mowday, Steers,

& Porter, 1979) Organizational attachment has been shown to be a better predictor of turnover than job satisfaction and employees with strong organizational attachment perform better than employees without lesser attachment.(Mowday, Porter, & Dubin,

1974; Porter, Steers, Mowday, & Boulian, 1974) In developing organizational commitment, past studies have emphasized the importance of training and development, especially when employees are new to an organization.(Arnold & Davey,

1999; Tannenbaum, Mathieu, Salas, & Cannon-Bowers, 1991)

13 Millennials and Training

Millennials have higher expectations regarding training and development in

organizations than previous generations.(Rawlins et al., 2008; Sturges, Guest, Conway,

& Davey, 2002) This interest in training may be due to a perceived lack of job security.

Millennials may take a more proactive approach toward their own security by enhancing their employability in the labor market believing that organizational security are rare in today’s workplace.(Macky et al., 2008; Tomlinson, 2007) This may elevate the contribution of training and professional development in the creation of organizational attachment among Millennials.

It is also important to consider that generations have different preferred learning styles. While classroom style learning may be a preference for Baby Boomers, younger generations prefer on the job learning that maximizes peer-to-peer sharing.(Deal, 2007)

This may be especially true for Millennials who draw satisfaction from working with members of a team as an extension of the group-based learning to which they were often exposed during their education.(Alsop, 2008; Howe & Strauss, 2009)

Research Questions and Implications

The literature on training needs among the public health workforce is well developed. Managerial, leadership, and policy development skills are often cited as in need of development.(Honoré, 2014; Lichtveld & Cioffi, 2003) Significant effort has been made to define competencies for public health generally and for specific disciplines or degree types.(Allegrante, Moon, Auld, & Gebbie, 2001; Barry, Allegrante,

Lamarre, Auld, & Taub, 2009; Calhoun, Ramiah, Weist, & Shortell, 2008; Kristine

Gebbie & Merrill, 2002; K. Gebbie, Merrill, & Tilson, 2002; K. M. Gebbie & Qureshi,

2002; Markenson, DiMaggio, & Redlener, 2005) However, the lack of awareness of

14 generational differences in perceived organizational support for training and training

needs is a critical gap in workforce development and may represent a missed opportunity to tailor training more appropriately and support retention efforts.

The present study seeks to contribute to the empirical literature on generational

differences in perceptions of support for training and specific training gaps in the context of governmental state public health agencies. Specifically, this study seeks to address two specific research questions:

Research Question 1. Are there generational differences in the perceived

support for training in state governmental public

health agencies?

Research Question 2. Are there differences in the training gaps among the

state governmental public health agency workforce by

generation?

This will be the first study to explore the generational differences in perceived organizational and supervisory support for training and self-identified training gaps in the state governmental public health agency workforce. These findings can be used by human resource leaders, supervisors and managers in state governmental public health agencies to improve the culture of training that exists in state governmental public health agencies and inform the development of specifically targeted trainings to

generational groups with different needs, if necessary. These findings also may be valuable to federal funders of training, schools and programs of public health, and national training networks who either support or are tasked with improving the quality of

the state governmental public health agency workforce.

15 METHODS

Data Sources

Data used in the analyses for this article were drawn from the 2014 Public Health

Workforce Interests and Needs Survey (PH WINS) – specifically, the nationally

representative sample of central office employees of state health agencies (SHAs) in

the United States.(Sellers et al., 2015) Developed by the de Beaumont Foundation in

partnership with the Association of State and Territorial Health Officials (ASTHO), PH

WINS is the largest state governmental public health agency workforce survey of its

kind. PH WINS is the only national survey of the state governmental public health

agency workforce that collects individual-level data. As such, it is the first and only

national data source that allows for investigation of generational differences in the state

governmental public health agency.

The methods used in the creation of PH WINS have been described in detail previously.(Leider, Bharthapudi, Pineau, Liu, & Harper, 2015) To summarize, the purpose of PH WINS was to collect individual worker perspectives across all disciplines and geographic regions. The development of PH WINS began in 2013, with a

consensus-building process among 31 public health stakeholders representing an array

of disciplines.(Kaufman et al., 2014) A technical expert panel was convened to guide

the sampling methodology, instrument creation, and protocols for survey fielding and

administration.(Sellers et al., 2015)

When developing the instrument, existing and/or validated measures were

incorporated when possible. Items used in PH WINS were adapted from the 2009

National Assessment of Epidemiology Capacity,(Council of State and Territorial

Epidemiologists, 2009) the US Office of Personnel Management Annual Employee

16 Survey,(U. S. Office of Personnel Management, 2008) the US Office of Personnel

Management Federal Employee Viewpoint Survey,(U. S. Office of Personnel

Management, 2012) the Centers for Disease Control and Prevention Technical

Assistance and Service Improvement Initiative: Project Officer Survey, the Public Health

Foundation Public Health Workforce Survey,(Centers for Disease Control and

Prevention, 2013) and the Job in General Scale (abridged).(Balzer et al., 2000) The

instrument adapted and used several items from Boulton et al.'s public health workforce

taxonomy to ask respondents about occupational classification, program area, degrees

and certifications, work setting, and demographics.(Boulton et al., 2014) The research

team drafted new questions when appropriate existing items could not be identified.

Cognitive interviews were conducted, and the instrument was pretested with three groups of public health practitioners at the state and local levels. The finalized survey was administered online in fall 2014. After pretesting and preliminary psychometric analysis (also explained in depth in previous a previous publication), the instrument was fielded among 37 states from September to December 2014.(Leider et al., 2015) The survey was confidential; contact information was retained only to ascertain whether a

potential respondent had indeed responded. No contact information is associated with

responses in final PH WINS data sets.

The national sampling frame of state public health employees was stratified on

the basis of 5 geographic (paired HHS) regions using employee lists provided by each participating state and stratified with the state as the lowest stratum variable before selection of a random sample within each state. The complex sampling methodology

for PH WINS has been outlined elsewhere.(Leider et al., 2015) A total of 40,091 survey

invitations were distributed via electronic mail to health agency employees in 37

17 participating states; 19,171 responded for a raw response rate of 48%. After adjusting

for noncentral office staff, nonpermanent employee status, undeliverable e-mail

addresses, and those who were no longer in their position, the response rate was 46%

(n=10,246). A nationally representative data set of central office staff, defined as

permanent employees who work in the central office of the SHA as opposed to having

been assigned to local or regional offices, was constructed. A set of weights was

calculated using balanced repeated replication to account for differential nonresponse

and demographic characteristics.

Measures

Generations

There is no one source that defines the generations. Various authors use slightly

different cut points to define each generation. The generation definitions developed by the Pew Research Center were used for this study.(Pew Research, 2014) These are also consistent with the definition used in the Federal Employee Viewpoints Survey.

Using this framework, the birth year cut points are:

 Millennials: 1981 and after

 Generation X: 1965-1980

 Baby Boomers: 1946-1964

 Silent Generation: 1928-1945

Only generations comprising five percent or more of the state public health agency workforce were included in the analysis eliminating the Silent Generation.

Measuring a Learning Culture

Respondents were presented with four statements regarding training in their

organizational context. For each statement, respondents were asked to rate their

18 agreement on a 5-point Likert scale ranging from strongly disagree to strongly agree.

The statements presented were, “Supervisors/team leaders in my work unit support employee development,” “My training needs are assessed,” “Employees have sufficient training to fully utilize technology needs,” and “Employees learn from one another as

they do their work.”

Training Gaps

Respondents were asked to assess 18 different skills, which have been

presented previously.(Sellers et al., 2015) For each skill, respondents were asked to

rate the importance of the skill on a 5-point Likert scale ranging from very important to

not very important. Separately, respondents were asked to assess their ability to

perform the same 18 skills on a 5-point Likert scale ranging from unable to perform to

expert. Training needs were assessed by examining the reported ability to perform a

skill among those who identified the skill as somewhat or very important.

Demographic and Workforce Characteristics

Other independent variables included in the analysis are the respondent’s

supervisory status (nonsupervisory, team leader, supervisor, manager, executive), gender (male, female), race (White, Black or African American, Native Hawaiian or other Pacific Islander, Asian, American Indian or Alaska Native, two or more races),

Hispanic origin (yes, no), degree earned (associate’s, bachelor’s, graduate’s degree), and pairwise Department of Health and Human Services geographic region (New

England and Atlantic, Mid-Atlantic and Great Lakes, South, Mountain/Midwest, West).

Educational attainment, having a public health degree (any level), job classification, and program area were also collected.

19 Statistical Analysis

All analyses for this research were conducted in Stata Version 13 (StataCorp.

2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP). To

account for the complex sampling design, survey commands included in SPSS were

used to produce weighted estimates using balanced repeated replication.

Bivariate analyses were conducted to explore generational differences in

demographic and other characteristics as well as attitudes toward training needs and assessed training needs. Logistic regression models will be estimated for each training need to determine the association between generation and specific training needs while adjusting for the influence of other variables.

RESULTS

In 2014, Millennials accounted for 13.2% (95% CI 12.3%-14.2%) of the state

governmental public health agency workforce, Generation X accounted for 36.5% (95%

CI 35.6%-37.3%), and Baby Boomers accounted for 50.3% (95% CI 49.1%-51.5%).

There was minimal variation in the demographics of the state governmental public

health agency workforce by generation (Table 2.1). Those reporting Hispanic

race/ethnicity increased with each subsequent generation. The major difference

between Millennials and the other generations was in education. Of Millennials, 30.9%

(95% CI 27.5%-34.6%) had a degree in public health compared to 19.5% (95% CI

18.0%-21.0%) of Generation and 11.4% (95% CI 10.3%-12.6%) of Baby Boomers. The

proportion of Millennials with a bachelor’s or master’s degree was also greater than

among Generation X or Baby Boomers.

20 Generational Differences in the Perceived Support for Training in State

Governmental Public Health Agencies

More than half of all respondents, regardless of generation, agreed that

“supervisors/team leaders in my work unit support employee development” and

“employees learn from one another as they do their work.”(Table 2.2) A greater proportion of Millennials “strongly agreed” with these statements compared to respondents from the other generations. Fewer than half of the respondents from

Generation X and Baby Boomers “strongly agreed” or “agreed” that their training needs

are assessed and that employees have sufficient training to fully utilize technology

needs. However, more than half of Millennials “strongly agreed” or “agreed” to the

same statements. After adjusting for other factors, Millennials had more positive

attitudes toward workplace training than did either Generation X or Baby Boomers. The

odds of holding positive attitudes did not differ between Generation X and Baby

Boomers.

Differences in the Training Gaps among the State Governmental Public Health

Agency Workforce by Generation

Regardless of generation, “influencing policy development” was the most

commonly cited training gap (respondent indicated high importance but low skill) (Table

2.4). Even among Baby Boomers, more than a quarter of respondents indicated

“influencing policy development” as a training gap. Generally, more than third of

Millennial respondents noted training needs for five skills:

 “influencing policy development,”

 “understanding the relationship between a new policy and many types of public

health problems,”

21  “collaborating with diverse communities to identify and solve health problems,”

 “ensuring that programs are managed within the current and forecasted budget

constraints,” and

 “preparing a program budget with justification.”

More than a third of Generation X respondents identified “influencing policy

development” as a training gap, but the proportion did not exceed a third of respondents for any other training need. There was no one training gap identified by one third or more of Baby Boomers.

Of the 18 skills assessed, the proportion of Millennials and Generation X identifying the item as a training gap was similar for three:

 ‘addressing the needs of diverse populations in a culturally sensitive way,’

 “applying evidence-based approaches to solve public health issues,” and

 “applying quality improvement concepts in my work.”

A greater proportion of Millennials had training gaps compared to Baby Boomers for all skills. There were four items for which there were no differences by generation:

 “finding evidence on public health efforts that work,”

 “interpreting public health data to answer questions,”

 “communicating ideas and information in a way that different audiences can

understand,” and

 “gathering reliable information to answer questions.”

After adjusting for other factors, the odds of being unable to perform or being a beginner at a skill were consistently lower for Baby Boomers compared to Millennials

(Table 2.5). However, the same was not found for Generation X. For six of the 18 skills

22 assessed, there were no differences in the odds of being unable to perform or being a

beginner at a skill between Millennials and Generation X.

DISCUSSION

In the state governmental public health agency workforce, Millennials generally

had more positive workplace attitudes toward training than did Generation X or Baby

Boomers. This is consistent with previous literature.(Costanza, Badger, Fraser, Severt,

& Gade, 2012; Kowske, Rasch, & Wiley, 2010; Zabel, Biermeier-Hanson, Baltes, Early,

& Shepard, 2017) The positive attitudes toward training among Millennials working in

state governmental public health agencies could be a leverage point when marketing

positions to Millennials. Previous research has found that Millennials value development more than previous generations.(Gallup, 2016; Sayers, 2007) These data suggest that these opportunities may exist in the governmental public health setting and should be shared when recruiting staff.

Informing policy was the most prominent training gaps regardless of generation.

This finding should punctuate the need for increased skill development in this area for the entire workforce. Skills for which there were no differences in the odds of being a beginner or unable to perform between Millennials and Generation X – assessing the broad array of factors that influence specific public health problems, finding evidence on public health efforts that work, addressing the needs of diverse populations in a

culturally sensitive way, applying quality improvement concepts in my work,

communicating ideas and information in a way that different audiences can understand, gathering reliable information to answer questions – may be a result of the greater public health training that Millennials received compared to Generation X. More than

30% of Millennials had a degree in public health compared to less than 20% of

23 Generation X. An even lower proportion of Baby Boomers had degrees in public health, but longer service could explain why a difference persisted between Millennials and

Baby Boomers. This finding has implications for public health training. Generally, there will be an increased demand for training among both Millennials and Generation X.

This may be especially true for those competencies that are commonly addressed in academic public health training.

Despite broad perceptions, there are no empirical studies that explore Millennials compared to Generation X or Baby Boomers in the state governmental health agency workforce. While this analysis is limited by potential confounding by age and experience, it is the first contribution to the literature that explores this group. Future research is needed in the field of public health workforce development to determine if these initial findings persist at different measurement points over time. These findings do highlight the positive attitudes that Millennials have to the training they are receiving in the state governmental public health agency workforce and underscore the need for policy training throughout the workforce regardless of generation.

24 TABLES

Table 2.1. Demographic and Workforce Characteristics of the State Governmental Public Health Agency Workforce by Generation Millennials Generation X Baby Boomers % (95% CI) % (95% CI) % (95% CI) Gender Male 25.3 (22.7-28.0) 26.2 (8.97-10.26) 29.76 (13.70-16.20) Female 74.7 (72.0-77.3) 73.8 (26.32-27.80) 70.24 (33.71-36.68) Race/Ethnicity American Indian or Alaskan Native 0.4 (0.2-0.7) 0.4 (0.2-0.8) 0.6 (0.0-0.8) Asian 5.5 (3.9-7.8) 5.7 (5.0-6.5) 3.6 (3.0-4.4) Black or African American 11.3 (9.7-13.2) 16.3 (14.9-17.7) 11.8 (10.2-13.6) Hispanic or Latino 10.6 (8.7-13.0) 8.1 (7.4-8.9) 4.8 (4.2-5.5) Native Hawaiian or other Pacific Islander 0.3 (0.1-0.5) 0.5 (0.1-1.6) 0.1 (0.00-0.3) White 65.1 (61.1-69.0) 63.5 (62.2-64.7) 75.53 (73.5-77.5) Two or more races 6.76 (5.2-8.8) 5.5 (4.8-6.4) 3.5 (2.9-4.2) Educational attainment No College Degree 9.8 (8.1-11.7) 13.3 (12.2-14.5) 17.5 (16.1-19.0) Associates 4.5 (3.6-5.5) 9.5 (8.5-10.5) 11.1 (9.4-13.0) Bachelors 36.4 (32.6-40.3) 28.0 (25.1-30.8) 32.9 (31.3-34.7) Masters 45.6 (42.8-48.5) 34.7 (32.7-36.7) 28.3 (26.7-30.1) Doctoral 3.8 (2.6-5.6) 9.6 (8.3-11.1) 10.1 (8.9-11.5) Any degree in public health 30.9 (27.5-34.6) 19.5 (18.0-21.0) 11.4 (10.3-12.6) Job Classification Administrative 22.3 (18.9-26.1) 28.6 (25.4-32.1) 29.3 (27.5-31.2) Clinical and lab 13.5 (11.2-16.1) 12.5 (11.0-14.2) 16.2 (15.0-17.4) Public health science 44.5 (39.4-49.7) 43.9 (41.7-46.1) 38.9 (36.4-41.5) Social service and all other 19.8 (16.4-23.7) 14.9 (13.4-16.6) 15.6 (14.5-16.8) Program area Chronic disease and injury 6.4 (5.1-8.0) 6.1 (5.3-7.1) 4.2 (3.6-4.9) Communicable disease 14.6 (11.5-18.3) 8.3 (7.0-9.7) 8.0 (7.1-8.9) Environmental health 10.2 (8.5-12.1) 10.1 (8.9-11.3) 10.7 (9.8-11.8) Maternal and child health 10.4 (8.5-12.6) 8.8 (7.6-10.3) 9.7 (8.2-11.3) All hazards 3.5 (2.3-5.3) 4.1 (3.3-5.0) 3.6 (3.1-4.2) Other Program Areas 55.0 (51.5-58.3) 62.6 (60.8-64.4) 63.8 (62.2-65.4)

25 Table 2.2. Attitudes toward Training among the State Governmental Public Health Agency Workforce by Generation Millennials Generation X Baby Boomers % (95% CI) % (95% CI) % (95% CI) Supervisors/team leaders in my work unit support employee development Strongly disagree 2.9 (1.7-4.7) 4.2 (3.2-5.4) 3.54 (3.1-4.0) Disagree 7.9 (5.6-11.1) 7.3 (6.5-8.1) 9.07 (8.3-10.0) Neither agree nor disagree 13.0 (11.4-14.8) 17.4 (15.9-19.0) 19.05 (17.6-20.6) Agree 42.1 (39.3-44.9) 45.3 (43.5-47.1) 45.84 (43.7-48.0) Strongly agree 34.1 (31.1-37.1) 25.9 (23.9-28.1) 22.5 (20.2-25.0) My training needs are assessed Strongly disagree 4.4 (3.1-6.4) 6.1 (5.4-6.8) 5.8 (5.3-6.4) Disagree 18.0 (15.3-21.1) 21.7 (20.0-23.5) 22.1 (20.5-23.9) Neither agree nor disagree 23.1 (21.0-25.5) 27.7 (25.8-29.7) 29.6 (28.1-3.1) Agree 39.8 (36.7-43.0) 35.1 (33.2-36.9) 34.4 (32.6-36.2) Strongly agree 14.6 (12.2-17.5) 9.5 (8.3-10.8) 8.1 (7.4-8.8) Employees have sufficient training to fully utilize technology needs Strongly disagree 6.2 (4.8-8.0) 5.4 (4.7-6.3) 5.4 (4.8-6.0) Disagree 18.3 (15.8-21.2) 22.7 (20.9-24.6) 22.3 (20.8-23.8) Neither agree nor disagree 20.6 (17.0-24.6) 23.7 (22.3-25.3) 24.3 (23.1-25.6) Agree 41.0 (37.2-44.9) 38.7 (36.8-40.6) 40.1 (38.3-41.8) Strongly agree 13.9 (11.2-17.2) 9.4 (8.2-10.8) 8.0 (7.2-8.8) Employees learn from one another as they do their work Strongly disagree 1.0 (0.5-1.7) 2.3 (1.8-3.1) 1.4 (1.0-1.8) Disagree 4.9 (3.5-6.7) 4.3 (3.5-5.2) 4.7 (4.0-5.6) Neither agree nor disagree 10.0 (7.8-12.8) 13.7 (12.2-15.4) 11.7 (10.5-12.9) Agree 53.2 (48.4-57.9) 56.1 (53.3-58.9) 59.2 (57.3-61.1) Strongly agree 31.0 (27.2-35.0) 23.5 (21.4-25.9) 23.0 (21.7-24.4)

26 Table 2.3. Adjusted Odds Ratios (OR) of Positive Attitudes toward Training* OR 95% CI Low Limit High Limit Supervisors/team leaders in my work unit support employee development Millennials 1.00 ------Generation X 0.71 0.59 0.85 Boomers 0.58 0.46 0.73 My training needs are assessed Millennials 1.00 ------Generation X 0.63 0.51 0.78 Boomers 0.55 0.46 0.65 Employees have sufficient training to fully utilize technology needs Millennials 1.00 ------Generation X 0.79 0.66 0.94 Boomers 0.76 0.62 0.93 Employees learn from one another as they do their work Millennials 1.00 ------Generation X 0.66 0.49 0.90 Boomers 0.70 0.52 0.95 *Each variable listed represents a separate model. Models adjusted for gender, race/ethnicity, region, supervisory status, educational attainment, any public health degree, job classification, and program area. Bold suggests significant findings.

27 Table 2.4. Training Needs among the State Governmental Public Health Agency Workforce by Generation Millennials Generation X Baby Boomers % (95% CI) % (95% CI) % (95% CI) Influencing policy development High Importance/Low Skill 41.0 (37.7-44.5) 33.6 (31.6-35.7) 27.4 (25.6-29.2) High Importance/High Skill 36.6 (33.1-40.3) 53.7 (51.2-56.1) 61.3 (58.7-63.9) Understanding the relationship between a new policy and many types of public health problems High Importance/Low Skill 38.6 (35.7-41.5) 28.5 (26.0-31.2) 24.6 (23.0-26.3) High Importance/High Skill 48.5 (45.8-51.1) 61.8 (60.2-63.4) 67.2 (65.2-69.2) Preparing a program budget with justification High Importance/Low Skill 32.3 (27.3-37.7) 23.5 (21.6-25.6) 20.6 (19.3-22.0) High Importance/High Skill 40.9 (36.6-45.4) 59.9 (57.9-61.9) 63.6 (61.9-65.2) Assessing the broad array of factors that influence specific public health problems High Importance/Low Skill 28.8 (25.4-32.4) 24.7 (22.1, 27.6) 22.8 (21.3-24.3) High Importance/High Skill 58.0 (53.5-62.5) 65.1 (62.8, 67.3) 67.1 (65.6-68.7) Collaborating with diverse communities to identify and solve health problems High Importance/Low Skill 33.5 (30.9-36.3) 22.0 (19.6-24.5) 20.3 (18.0-22.7) High Importance/High Skill 52.9 (48.4-57.4) 64.5 (62.2-66.7) 67.2 (64.6-69.7) Finding evidence on public health efforts that work High Importance/Low Skill 24.0 (20.5-27.7) 22.7 (20.5-24.9) 21.1 (19.3-23.0) High Importance/High Skill 62.2 (57.1-67.1) 66.3 (64.5-68.1) 68.6 (66.48-70.8) Ensuring that programs are managed within the current and forecasted budget constraints High Importance/Low Skill 32.4 (27.9-37.3) 23.5 (21.7-25.4) 18.2 (16.3-20.2) High Importance/High Skill 47.9 (43.2-52.7) 65.8 (62.8-68.8) 73.6 (72.2, 75.0) Anticipating the changes in your environment High Importance/Low Skill 28.9 (25.7-32.3) 22.0 (19.1-25.2) 18.8 (16.9-20.8) High Importance/High Skill 59.3 (55.1-63.4) 69.5 (66.7-72.3) 75.0 (73.2-76.7) Addressing the needs of diverse populations in a culturally sensitive way High Importance/Low Skill 25.2 (21.9-28.7) 21.1 (18.9-23.6) 19.2 (17.6-21.0) High Importance/High Skill 63.2 (59.4-66.9) 68.9 (66.3-71.5) 72.8 (71.0-74.6) Applying evidence-based approaches to solve public health issues High Importance/Low Skill 24.4 (21.7-27.3) 20.89 (18.5-23.5) 19.5 (17.3-21.9) High Importance/High Skill 66.2 (63.0-69.3) 71.19 (68.2-74.0) 72.5 (70.0-74.9) Applying quality improvement concepts in my work High Importance/Low Skill 26.3 (22.8-30.2) 22.2 (19.2-25.6) 18.4 (17.1-19.8) High Importance/High Skill 66.5 (63.0-69.9) 71.6 (68.7-74.3) 77.1 (75.8-78.3) Interpreting public health data to answer questions High Importance/Low Skill 17.2 (14.5-20.5) 18.1 (16.2-20.3) 18.13 (16.9-19.4) High Importance/High Skill 73.6 (69.0-77.7) 74.1 (72.1-76.0) 74.50 (72.5-76.4) Engage partners outside your health department to collaborate on projects High Importance/Low Skill 24.5 (20.6-28.8) 17.1 (15.3-19.1) 14.7 (12.6-7.1) High Importance/High Skill 62.9 (58.3-67.2) 75.3 (73.0-77.4) 77.3 (74.6-79.8) Managing change in response to dynamic, evolving circumstances High Importance/Low Skil 21.3 (18.9-24.0) 16.0 (14.1-18.0) 14.1 (12.9-15.4) High Importance/High Skill 73.5 (70.4-76.3) 79.5 (77.1-8.6) 80.9 (79.6-82.1) Communicating in a way that persuades others to act High Importance/Low Skill 22.8 (19.7-26.1) 16.1 (14.3-18.1) 12.0 (11.0-13.2) High Importance/High Skill 70.1 (67.0-72.9) 79.3 (77.0-81.4) 84.8 (83.6-85.9) Engaging staff within your health department to collaborate on projects High Importance/Low Skill 21.8 (18.5-25.5) 13.76 (12.4-15.2) 13.15 (11.8-14.7) High Importance/High Skill 70.5 (66.9-73.8) 80.86 (79.3-82.4) 81.40 (80.3-82.5) Communicating ideas and information in a way that different audiences can understand High Importance/Low Skill 11.9 (10.0-14.2) 9.9 (8.7-11.3) 9.9 (8.6-1.1) High Importance/High Skill 84.2 (81.3-86.6) 87.2 (85.8-88.5) 87.2 (85.9-88.3) Gathering reliable information to answer questions High Importance/Low Skill 9.1 (6.6-12.4) 7.1 (5.6-8.9) 6.3 (5.3-7.3) High Importance/High Skill 88.8 (85.7-91.4) 91.5 (89.9-92.8) 92.5 (91.5-93.3)

28 Table 2.5. Adjusted Odds Ratios (OR) of Being Unable to Perform/Beginner at Key Workforce Skills OR 95% CI Low Limit High Limit Influencing policy development Millennials 1.00 ------Generation X 0.75 0.59 0.95 Boomers 0.57 0.46 0.71 Understanding the relationship between a new policy and many types of public health problems Millennials 1.00 ------Generation X 0.61 0.50 0.75 Boomers 0.49 0.41 0.60 Preparing a program budget with justification Millennials 1.00 ------Generation X 0.79 0.63 0.98 Boomers 0.65 0.54 0.78 Assessing the broad array of factors that influence specific public health problems Millennials 1.00 ------Generation X 0.81 0.65 1.01 Boomers 0.68 0.55 0.86 Collaborating with diverse communities to identify and solve health problems Millennials 1.00 ------Generation X 0.61 0.49 0.76 Boomers 0.52 0.41 0.66 Finding evidence on public health efforts that work Millennials 1.00 ------Generation X 0.82 0.62 1.07 Boomers 0.68 0.51 0.90 Ensuring that programs are managed within the current and forecasted budget constraints Millennials 1.00 ------Generation X 0.79 0.65 0.96 Boomers 0.53 0.42 0.66 Anticipating the changes in your environment Millennials 1.00 ------Generation X 0.73 0.59 0.90 Boomers 0.53 0.43 0.72 Addressing the needs of diverse populations in a culturally sensitive way Millennials 1.00 ------Generation X 0.82 0.66 1.00 Boomers 0.63 0.49 0.80 Applying evidence-based approaches to solve public health issues Millennials 1.00 ------Generation X 0.76 0.62 0.93 Boomers 0.66 0.47 0.89 Applying quality improvement concepts in my work Millennials 1.00 ------Generation X 0.90 0.73 1.10 Boomers 0.67 0.53 0.84 Interpreting public health data to answer questions Millennials 1.00 ------Generation X 0.77 0.61 0.97 Boomers 0.68 0.54 0.85 Engage partners outside your health department to collaborate on projects Millennials 1.00 ------

29 Generation X 0.66 0.50 0.87 Boomers 0.50 0.38 0.65 Managing change in response to dynamic, evolving circumstances Millennials 1.00 ------Generation X 0.78 0.63 0.98 Boomers 0.66 0.54 0.81 Communicating in a way that persuades others to act Millennials 1.00 ------Generation X 0.72 0.56 0.91 Boomers 0.47 0.38 0.59 Engaging staff within your health department to collaborate on projects Millennials 1.00 ------Generation X 0.59 0.45 0.76 Boomers 0.50 0.40 0.64 Communicating ideas and information in a way that different audiences can understand Millennials 1.00 ------Generation X 0.82 0.64 1.05 Boomers 0.71 0.55 0.92 Gathering reliable information to answer questions Millennials 1.00 ------Generation X 0.71 0.45 1.13 Boomers 0.59 0.39 0.88 Note: Each variable listed represents a separate model. Models are limited to those responding Somewhat/Very Important to the modeled variable. Models adjusted for gender, race/ethnicity, region, supervisory status, educational attainment, any public health degree, job classification, and program area. Bold suggests significant findings.

CHAPTER 3: MILLENNIAL SUPPORT & SATISFACTION: GENERATIONAL DIFFERENCES IN ORGANIZATIONAL SUPPORT, SUPERVISORY SUPPORT, AND JOB SATISFACTION AMONG THE STATE PUBLIC HEALTH AGENCY WORKFORCE

BACKGROUND

Workers and their workplaces exist in a symbiotic relationship in which the thoughts and behaviors of each influence the other.(Brief & Weiss, 2002; Mathieu &

Zajac, 1990) Job satisfaction is one of the most studied aspects of this relationship and has been linked to nearly all important organizational outcomes.(Judge, Thoresen,

Bono, & Patton, 2001; Lu, Barriball, Zhang, & While, 2012) Studies have shown that workers with high levels of job satisfaction are more productive and motivated, perform better at their jobs, and have greater organizational commitment and engagement.(Abelson & Baysinger, 1984; Judge et al., 2001; Lawler Iii & Porter, 1976)

Job satisfaction has also been linked to reduced absenteeism.(Hacket, 1989; Hom &

Kinicki, 2001) As a key predictor of employee turnover,(Carsten & Spector, 1987;

Mossholder, Settoon, & Henagan, 2005; Tett & Meyer, 1993) low job satisfaction can have significant costs to organizations to replace departing and orienting new employees.(Hellman, 1997; Schlesinger & Heskett, 1991)

While job satisfaction has been discussed in the empirical literature since the

1930s,(Hoppock, 1935) a 25-year systematic review of the public health workforce literature found limited information on job satisfaction in the state governmental public health agency workforce.(Hilliard & Boulton, 2012) Recent work by Harper et al. was among the first empirical studies to explore job satisfaction among the state government

31 public health agency workforce finding an association between organizational and

supervisory support and job satisfaction.(Harper, Castrucci, Bharthapudi, & Sellers,

2015) While this work was significant, it neglected to consider the impact of generational

identity on job satisfaction.

Generational Theory and Job Satisfaction

This is a time of significant generational transition as, for the first time, there are

multiple generations existing simultaneously in the U.S. workforce.(Zemke et al., 2000)

The three generations that comprise the majority of the workforce are the Baby

Boomers, Generation X, and the Millennials. By the first quarter of 2015, Millennials became the largest generation in the workforce (53.5 million) followed by Generation X

(52.7 million) and the Baby Boomers (44.6 million).(Fry, 2015) However, unlike in previous generational transitions, people are living longer, healthier lives.(Collins, 2003)

Therefore, as Baby Boomers are reaching the traditional retirement age many are continuing to work well into their 60s and 70s, if not longer.(Collins, 2003) For example, a 2010 American Association of Retired Persons survey of older Baby Boomers found that nearly 40% reported that they plan “to work until they drop.”(Love & Nannis, 2010)

This longevity has implications for workforce advancement and entry for younger generations. For example, more than half of all Millennials interviewed as part of The

Hartford’s 2013 Benefits for Tomorrow Study agreed that Baby Boomers who delay retirement prevent young employees from promotional opportunities and deny employment opportunities.(The Hartford, 2013)

Generational theory suggests that within each generational cohort, shared social, cultural, political experiences contribute to a unique identity.(Delli Carpini, 1989; Drew,

32 2015; Webb-Morgan, 2012) A generational cohort is a group of individuals similar in

age who have experienced the same historical events within the same time period.(Howe & Strauss, 2009; Ryder, 1965) Generation members share temporal and age-related events such as starting school, entering the workforce, having children, and retiring. While historical events occur simultaneously for multiple generations, each is experiencing these events at different developmental stages with young adulthood being a particularly impressionable developmental stage.(Baltes et al., 1980; Duncan &

Agronick, 1995; Noble & Schewe, 2003) Each of these three generations come from

very different backgrounds that shape their views and motivations and influence their

relationship with the workplace, including their perceptions of job satisfaction. These differences can positively contribute to the workforce through creative strengths and opportunities.(Lancaster & Stillman, 2009) For example, 9 in 10 Millennials who participated in The Hartford’s 2013 Benefits for Tomorrow Study agreed that Baby

Boomers bring substantial experience and knowledge to the workplace.(The Hartford,

2013) A similar proportion of Baby Boomers agreed that Millennials bring new skills and ideas to workplace.(The Hartford, 2013) However, about three quarters of Generation X

from the same study agreed that the “entitlement generation” is an appropriate

nickname for the Millennials.(The Hartford, 2013) This demonstrates the potential for

unpleasant conflict, mistrust, and miscommunication that can directly impact job

satisfaction.(Hankin, 2005; Lancaster & Stillman, 2009; Ruch, 2005)

Common among models and theories used to explain job satisfaction is the

concept that employees’ needs – whether they are recognition for contributions, training

opportunities, structured feedback, adequate training, or quality relationships with

33 coworkers and supervisors – are considered and addressed. This concept can be

divided into two separate themes – organizational support (training, communications,

workload) and supervisory support (respect, good relationships, working well with

individuals of different backgrounds).(Campbell, Fowles, & Weber, 2004; Crose, 1999;

Pitts, Marvel, & Fernandez, 2011; Rowden, 2002) The different value systems

(Kupperschmidt, 2000; Zemke et al., 2000) and life experiences of each generation

(Hicks & Hicks, 1999; Lancaster & Stillman, 2009; Zemke et al., 2000) can directly influence their perceptions of the workplace including reactions to organizational and supervisory support.(Lancaster & Stillman, 2009; Ruch, 2005)

Baby Boomers believe in paying their dues in an organization and a strong organizational commitment.(Hicks & Hicks, 1999; Howe & Strauss, 2009) They routinely sacrificed on behalf of their workplace with 50 or even 60-plus hour weeks, and they frequently advise young coworkers to work hard, demonstrate their dedication, and patiently wait their turn for promotions.(Chatman & Flynn, 2001; Hicks & Hicks,

1999; Howe & Strauss, 2009) They are the original workaholics who, even as young adults, had little notion of work-life balance.(McGuire, Todnem By, & Hutchings, 2007;

Stauffer, 1997)

Comparatively, Generation X is characterized by a lack of organizational loyalty and an unwillingness to sacrifice family for success.(Howe & Strauss, 2009; Lancaster

& Stillman, 2009) Unlike Baby Boomers, Generation X requires managers to earn respect rather than gain it by virtue of a title.(Tulgan, 1995) Moving even further from the notion of organizational commitment, empirical research indicates that Millennials do not develop organizational commitment as more senior workers have.(Howe & Strauss,

34 2009; Lancaster & Stillman, 2009) Instead, more than other generations, Millennials

develop commitment to individuals, especially supervisors with whom they develop

meaningful relationships.(Lockwood, 2009; Marston, 2010) They also expect a balance

between family and careers that previous generations have not.(Carless & Wintle, 2007;

Myers & Sadaghiani, 2010; Ng, Schweitzer, & Lyons, 2010; Wey Smola & Sutton, 2002)

In regard to supervisory relationships, Millennials’ interactions with supervisors

are a departure from the relationships Baby Boomers and Generation X. Millennials

expect open communication from their supervisors and managers, even about matters

normally reserved for more senior employees.(Gursoy et al., 2008; Hershatter &

Epstein, 2010) Empirical studies also found Millennials to be more impatient about

becoming recognized as valuable contributors.(Gursoy et al., 2008; Pew Research,

2010) Millennials expect communication with supervisors to be more frequent, more

positive, and more affirming than has been the case with prior generations. (Gursoy et al., 2008; Hershatter & Epstein, 2010; Hill, 2002)

Research Questions and Implications

Since 2008, most governmental public health agencies have experienced job

losses through a combination of layoffs and attrition.(Association of State and Territorial

Health Officials, 2011, 2014) An adequate supply of well-trained, skilled public health

professionals is essential for the effective operation of the governmental public health

enterprise.(Baker et al., 2005) To ensure that such a supply exists, strategies are

needed to address challenges to workforce retention. With fewer new graduates

choosing to work for state governmental public health departments, it will be critical to

35 retain those who opt to enter this workforce.(K. Gebbie et al., 2002; The Association of

Schools of Public Health Council of Public Health Practice Coordinators, 2000)

Job satisfaction has been shown to be a significant predictor of retention.(Irvine &

Evans, 1995; Tett & Meyer, 1993) While Harper et al. published the first study to

explore correlates of job satisfaction in the state governmental public health agency

workforce, given the generational differences regarding work values and attitudes, job

satisfaction may be perceived differently across the multigenerational state

governmental public health agency workforce.(Harper et al., 2015) Consequently,

understanding job satisfaction within each generational group may lead to increasing

clarity about strategies that could be implemented to promote retention among those

currently in the state governmental public health agency workforce, especially

Millennials. To inform the development of retention strategies, the present study seeks

to determine if there are differences in the relationship between supervisory support and

organizational support and job satisfaction across the three generational cohorts that

account for at least five percent of the state governmental public health agency

workforce.

METHODS

Data Sources

Data used in the analyses for this article were drawn from the 2014 Public Health

Workforce Interests and Needs Survey (PH WINS) – specifically, the nationally

representative sample of central office employees of state health agencies (SHAs) in

the United States. Developed by the de Beaumont Foundation in partnership with the

Association of State and Territorial Health Officials (ASTHO), PH WINS is the largest

36 public health workforce survey of its kind.(Sellers et al., 2015) PH WINS is the only

national survey of the public health workforce that collects individual-level data. As

such, it is the first and only national data source that allows for investigation of generational differences in the public health workforce.

The methods used in the creation of PH WINS have been described in detail previously.(Leider et al., 2015; Sellers et al., 2015) To summarize, the purpose of PH

WINS was to collect individual worker perspectives across all disciplines and

geographic regions. The development of PH WINS began in 2013, with a consensus-

building process among 31 public health stakeholders representing an array of

disciplines.(Kaufman et al., 2014) A technical expert panel was convened to guide the sampling methodology, instrument creation, and protocols for survey fielding and administration.(Sellers et al., 2015)

When developing the instrument, existing and/or validated measures were

incorporated when possible. Items used in PH WINS were adapted from the 2009

National Assessment of Epidemiology Capacity,(Council of State and Territorial

Epidemiologists, 2009) the US Office of Personnel Management Annual Employee

Survey,(U. S. Office of Personnel Management, 2008) the US Office of Personnel

Management Federal Employee Viewpoint Survey,(U. S. Office of Personnel

Management, 2012) the Centers for Disease Control and Prevention Technical

Assistance and Service Improvement Initiative: Project Officer Survey,(Centers for

Disease Control and Prevention, 2013), the Public Health Foundation Public Health

Workforce Survey,(Council on Linkages Between Academia Public Health, 2010) and

the Job in General Scale (abridged).(Balzer et al., 2000) The instrument adapted and

37 used several items from Boulton et al.'s public health workforce taxonomy to ask

respondents about occupational classification, program area, degrees and certifications,

work setting, and demographics.(Boulton et al., 2014) The research team drafted new

questions when appropriate existing items could not be identified. Cognitive interviews

were conducted, and the instrument was pretested with three groups of public health

practitioners at the state and local levels. The finalized survey was administered online

in fall 2014. After pretesting and preliminary psychometric analysis (also explained in

depth in previous a previous publication), the instrument was fielded among 37 states

from September to December 2014.(Leider et al., 2015) The survey was confidential;

contact information was retained only to ascertain whether a potential respondent had

indeed responded. No contact information is associated with responses in final PH

WINS data sets.

The national sampling frame of state public health employees was stratified on the basis of 5 geographic (paired HHS) regions using employee lists provided by each participating state and stratified with the state as the lowest stratum variable before selection of a random sample within each state. The complex sampling methodology

for PH WINS has been outlined elsewhere.(Leider et al., 2015) A total of 40,091 survey

invitations were distributed via electronic mail to health agency employees in 37

participating states; 19,171 responded for a raw response rate of 48%. After adjusting

for noncentral office staff, nonpermanent employee status, undeliverable e-mail

addresses, and those who were no longer in their position, the response rate was 46%

(n = 10,246). A nationally representative data set of central office staff, defined as

permanent employees who work in the central office of the SHA as opposed to having

38 been assigned to local or regional offices, was constructed. A set of weights was

calculated using balanced repeated replication to account for differential nonresponse

and demographic characteristics.

Measures

Generations

There is no one source that defines the generations. Various authors use slightly

different cut points to define each generation. The generation definitions developed by the Pew Research Center were used for this study (Pew Research, 2014). These are also consistent with the definition used in the Federal Employee Viewpoints Survey.

Using this framework, the birth year cut points are:

 Millennials: 1981 and after

 Generation X: 1965-1980

 Baby Boomers: 1946-1964

 Silent Generation: 1928-1945

Only generations comprising five percent or more of the state public health agency workforce were included in the analysis eliminating the Silent Generation.

Organizational and Supervisory Support

Previous factor analysis using PH WINS data defined questionnaire items that

comprised categories of organizational support and supervisory support.(Harper et al.,

2015; Liss-Levinson et al., 2015) Items included in organizational support were (1)

employees have sufficient training to fully utilize technology needed for their work, (2)

my training needs are assessed, (3) communication between senior leadership and

employees is good in my organization, (4) creativity and innovation are rewarded, (5)

39 my workload is reasonable, and (6) I recommend my organization as a good place to

work.

Items included in supervisory support included (1) my supervisor/leader treats

me with respect, (2) my supervisor and I have a good working relationship, (3) my

supervisor supports my need to balance work and family issues, (4) my supervisor/team

leader provides me with opportunities to demonstrate my leadership skills, (5)

supervisors/team leaders in my work unit support employee development, and (6)

supervisors/team leaders work well with employees of different backgrounds.

Items were assessed on a 5-point Likert scale ranging from strongly disagree to

strongly agree. The mean across all items in each category – organizational support

and supervisory support – were included in the regression models.

Job Satisfaction

Previous PH WINS analyses used the Bowling Green State University Job in

General (JIG) Scale (abridged) to measure job satisfaction.(Balzer et al., 2000) This

validated scale includes 8 descriptive words or phrases such as “makes me content”

and “better than most” to determine overall job satisfaction.(Balzer et al., 2000; Russell et al., 2004) Generally, a score above 27 signifies satisfaction with one's job and a score less than 22 signifies dissatisfaction.(Balzer et al., 2000) The JIG Scale

(abridged) is a global measure of job satisfaction that can be used to gauge the overall evaluative or affective judgment about one’s job. Given the complexity of the JIG Scale and the number of items required to compose the survey, analyses were done to determine the added predictive value of the JIG Scale versus the question, “Considering everything, how satisfied are you with your job?” Responses to this question ranged

40 from “very dissatisfied” to “very satisfied” on a 5-point Likert scale. Analyses found that

the single question was as effective in capturing job satisfaction as was the JIG Scale.

Therefore, unlike in the previous analyses,(Harper et al., 2015) the single question was

used to measure job satisfaction.

Demographic and Workforce Characteristics

Other independent variables included in the analysis are the respondent’s (1)

supervisory status (nonsupervisory, team leader, supervisor, manager, executive), (2) gender (male, female), (3) race (White, Black or African American, Native Hawaiian or other Pacific Islander, Asian, American Indian or Alaska Native, two or more races), (4)

Hispanic origin (yes, no), (5) degree earned (associate’s, bachelor’s, graduate’s degree), and (6) pairwise region (New England and Atlantic, Mid-Atlantic and Great

Lakes, South, Mountain/Midwest, West). Educational attainment, having a public health degree (any level), job classification, and program area were also collected.

Statistical Analysis

All analyses for this research were conducted in Stata Version 13 (StataCorp.

2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP). To

account for the complex sampling design, survey commands included in SPSS were

used to produce weighted estimates using balanced repeated replication.

Bivariate analyses were conducted to determine the association between

generation and each of the items in the organizational support and supervisory support

categories. Three separate logistic regression models, one for each generation, will be

estimated to explore the association between organizational support and supervisory

support and job satisfaction when adjusting for the influence of other variables. Job

41 satisfaction is dichotomized–very dissatisfied, somewhat dissatisfied, neither

dissatisfied nor satisfied versus somewhat satisfied, very satisfied–for use into the

logistics regression analyses.

RESULTS

Job satisfaction did not differ by generation. Among Millennials, 80.6% (95% CI

77.8%-83.2%) reported to be satisfied with their jobs compared to 78.8% (95% CI

76.5%-80.9%) among Generation X and 78.5% (95% CI 76.8%-80.2%) among Baby

Boomers. While there was no difference in overall job satisfaction, additional analyses sought to determine if supervisory support and/or organizational support impacted job satisfaction differently by generation.

In all generations, there was a positive association between supervisory support and job satisfaction (Table 3.1). Like with supervisory support, the pattern between organizational support and job satisfaction is clear and consistent across all generations

(Table 3.2). Generally, negative supervisory support measures were associated with lower job satisfaction scores than were negative organizational satisfaction scores. For example, average job satisfaction among those neither agreeing nor disagreeing with the supervisory measures for Millennials was 57.7% compared to 73.4% for organizational support. This general pattern was repeated for all generations.

Movement between agreement and strong agreement had a more pronounced impact for supervisory support variables than organizational support variables. For

Millennials, the average difference in job satisfaction between agreeing and strongly agreeing was 13.1 percentage points compared to 8.0 percentage points for organizational support. This pattern was consistent for all generations.

42 The logistic regression demonstrates the strong association between supervisory support and organizational support and job satisfaction.(Table 3.3) The strength of this association transcended generations.

DISCUSSION

Several past studies outside of public health have suggested that an organizational and supervisory support appealing to Millennials is different from

previous generations.(Alsop, 2008; Farrell & Hurt, 2014; Ferri-Reed, 2014a, 2014b;

Jerome, Scales, Whithem, & Quain, 2014) However, this study found no differences between supervisory support and organizational support and job satisfaction across the three generational cohorts that account for at least five percent of the state governmental public health agency workforce. Supervisory support and organizational support were found to be strong indicators of job satisfaction, which is consistent with past literature, across all generations actively working in the state governmental health agency workforce. The discordance between these results and the published literature may be the result of positive-results bias,(Dickersin, 1990) which leads to the overpublication of articles that rejected the null hypothesis presenting a unbalanced view of relationship.

While past studies outside of public health have suggested that a workplace and supervisory structure different by generations,(Alsop, 2008; Andersson, 2018; Farrell &

Hurt, 2014; Ferri-Reed, 2014a, 2014b; Gibson, Greenwood, & Murphy Jr, 2009; Jerome

et al., 2014; Sharp, 2015) such conclusions appear unwarranted in the state

governmental public health agency workforce. State governmental public health agency

leadership should pursue improvements in supervisory and organizational support, but

43 these can be general and do not require generational tailoring. In a resource limited environment, these data suggest that investments in supervisory support may yield a better return than organizational changes. However, these strategies do not require tailoring based on generation.

44 TABLES

Table 3.1. Association between Supervisory Support and Being Somewhat or Very Satisfied with One’s Job by Generation Millennials Generation X Baby Boomers % (95% CI) % (95% CI) % (95% CI) My supervisor/team leader treats me with respect Strongly disagree 34.1 (17.8,55.3) 26.4 (18.1,36.8) 26.3 (16.7,38.9) Disagree 29.1 (17.7,43.8) 36.5 (31.4,42.1) 37.6 (29.7,46.2) Neither agree/disagree 52.0 (35.4,68.2) 54.2 (45.6,62.6) 53.5 (47.5,59.4) Agree 73.2 (67.2,78.5) 78.3 (75.4,81.0) 81.9 (79.1,84.4) Strongly agree 92.2 (90.7,93.5) 92.1 (90.0,93.7) 91.6 (89.6,93.2) My supervisor and I have a good working relationship Strongly disagree 72.8 (38.0,92.1) 33.8 (22.9,46.8) 41.2 (28.1,55.7) Disagree 53.6 (30.3,75.4) 37.2 (24.8,51.5) 39.5 (30.3,49.5) Neither agree/disagree 45.1 (31.4,59.7) 53.4 (47.4,59.2) 44.7 (38.0,51.7) Agree 76.9 (71.2,81.7) 77.8 (75.0,80.3) 78.4 (76.1,80.5) Strongly agree 91.8 (86.4,95.2) 90.2 (87.3,92.5) 89.9 (87.8,91.7) My supervisor supports my need to balance work and family issues Strongly disagree 36.5 (11.4,72.0) 24.8 (16.9,34.8) 27.1 (18.5,37.9) Disagree 45.6 (28.4,63.9) 52.6 (41.0,63.9) 41.0 (31.6,51.2) Neither agree/disagree 58.0 (42.1,72.4) 58.3 (47.6,68.2) 55.4 (50.1,60.6) Agree 76.8 (72.5,80.6) 76.9 (74.5,79.2) 78.5 (76.8,80.1) Strongly agree 90.3 (86.6,93.1) 89.1 (86.7,91.1) 91.3 (89.4,92.9) My supervisor/team leader provides me with opportunities to demonstrate my leadership skills Strongly disagree 26.9 (15.7,42.3) 28.3 (23.2,34.0) 28.0 (19.7,38.1) Disagree 41.6 (33.3,50.4) 49.5 (41.0,58.0) 48.1 (41.9,54.4) Neither agree/disagree 72.3 (65.0,78.5) 68.6 (63.5,73.3) 68.5 (64.4,72.4) Agree 86.5 (82.2,89.9) 85.9 (84.2,87.5) 88.5 (86.6,90.1) Strongly agree 94.9 (91.4,97.0) 94.9 (93.0,96.4) 94.2 (92.5,95.4) Supervisors/team leaders in my work unit support employee development Strongly disagree 33.2 (15.8,57.0) 29.7 (22.9,37.6) 35.0 (25.4,46.0) Disagree 44.9 (28.9,62.1) 45.1 (40.2,50.1) 40.1 (35.9,44.5) Neither agree/disagree 55.7 (45.8,65.1) 64.3 (58.3,69.8) 66.8 (62.2,71.2) Agree 85.0 (79.9,89.0) 84.6 (83.0,86.0) 86.3 (83.5,88.8) Strongly agree 97.2 (95.3,98.3) 96.1 (94.4,97.3) 94.8 (92.0,96.7) Supervisors/team leaders work well with employees of different backgrounds Strongly disagree 22.5 (10.5,41.8) 29.8 (23.2,37.4) 31.0 (23.6,39.6) Disagree 47.0 (33.1,61.5) 49.0 (43.3,54.8) 47.9 (41.3,54.7) Neither agree/disagree 63.3 (53.6,72.0) 65.0 (59.5,70.2) 62.9 (59.5,66.3) Agree 83.5 (79.6,86.8) 85.6 (83.2,87.7) 86.0 (84.3,87.6) Strongly agree 94.0 (91.4,95.8) 92.9 (89.0,95.5) 94.1 (91.6,95.9)

Table 3.2. Association between Organizational Support and Being Somewhat or Very Satisfied with One’s Job by Generation Millennials Generation X Baby Boomers % (95% CI) % (95% CI) % (95% CI) Employees have sufficient training to utilize technology needed for their work Strongly disagree 40.2 (26.2,56.0) 41.0 (32.4,50.3) 44.9 (39.3,50.7) Disagree 69.7 (60.6,77.4) 69.3 (63.9,74.2) 69.4 (65.9,72.8) Neither agree/disagree 77.5 (70.9,83.0) 75.0 (71.8,78.0) 73.1 (68.5,77.3) Agree 87.8 (85.2,90.0) 88.9 (87.0,90.5) 87.7 (85.4,89.6) Strongly agree 96.9 (93.7,98.5) 93.8 (91.5,95.5) 97.0 (94.4,98.5) My training needs are assessed Strongly disagree 39.6 (22.9,59.2) 38.1 (31.7,45.0) 44.4 (38.6,50.3) Disagree 64.2 (50.9,75.6) 68.3 (63.8,72.5) 62.2 (58.3,65.9) Neither agree/disagree 74.3 (69.5,78.5) 76.8 (72.1,81.0) 78.5 (76.3,80.6) Agree 89.8 (86.5,92.3) 88.9 (87.1,90.5) 91.5 (89.1,93.4) Strongly agree 98.0 (94.7,99.2) 96.7 (93.3,98.4) 93.9 (88.0,97.0) Communication between senior leadership and employees is good in my organization Strongly disagree 53.7 (45.0,62.1) 45.7 (39.7,51.7) 45.6 (39.9,51.4) Disagree 69.3 (58.8,78.1) 69.7 (66.3,72.9) 66.2 (62.9,69.4) Neither agree/disagree 79.0 (73.8,83.4) 78.6 (75.7,81.2) 80.0 (76.1,83.4) Agree 90.2 (86.6,92.9) 93.1 (90.9,94.8) 93.0 (89.9,95.2) Strongly agree 97.4 (93.4,99.0) 96.2 (93.4,97.8) 95.8 (90.6,98.2) Creativity and innovation are rewarded Strongly disagree 38.9 (30.8,47.7) 35.8 (30.1,41.9) 38.7 (32.7,45.0) Disagree 58.4 (46.8,69.1) 64.1 (58.4,69.3) 63.4 (58.7,67.8) Neither agree/disagree 77.9 (73.9,81.4) 81.3 (79.3,83.2) 81.4 (78.4,84.1) Agree 93.7 (90.4,96.0) 93.7 (91.5,95.4) 94.6 (93.0,95.9) Strongly agree 98.3 (95.6,99.4) 98.5 (96.1,99.4) 95.2 (88.4,98.1) My workload is reasonable Strongly disagree 50.1 (35.2,65.0) 44.5 (36.4,53.0) 51.9 (43.1,60.6) Disagree 58.9 (46.4,70.4) 69.2 (64.5,73.5) 69.8 (66.9,72.5) Neither agree/disagree 70.1 (62.2,76.9) 71.4 (63.7,78.0) 66.3 (61.8,70.5) Agree 85.2 (81.8,88.0) 84.9 (83.3,86.4) 86.3 (83.6,88.6) Strongly agree 95.8 (92.4,97.7) 93.2 (89.3,95.7) 92.6 (88.8,95.1) I recommend my organization as a good place to work Strongly disagree 22.3 (9.6,43.8) 26.4 (18.7,36.0) 24.5 (19.1,30.9) Disagree 39.3 (27.3,52.8) 38.5 (32.7,44.7) 41.9 (37.1,46.7) Neither agree/disagree 61.7 (53.8,68.9) 63.6 (59.1,67.9) 65.2 (61.3,68.9) Agree 90.6 (87.6,92.9) 92.1 (89.8,94.0) 92.5 (90.6,94.0) Strongly agree 98.9 (97.3,99.6) 98.2 (96.9,98.9) 98.5 (97.5,99.1)

Table 3.3. Results of Multivariate Logistic Regression on Supervisory Support and Organizational Support as Correlates of Job Satisfaction Millennials Generation X Baby Boomers OR 95% CI OR 95% CI OR 95% CI Supervisory Support 7.13 4.44, 11.43 5.67 4.68, 6.87 5.84 4.98, 6.86 Organizational Support 6.32 4.47, 8.97 6.19 5.12, 7.50 6.53 5.72, 7.46 Note: Separate models were run for each generation. Models adjusted for supervisory status, gender, race, degree earned, and region.

CHAPTER 4: AN EXPLORATION OF MILLENNIALS’ REASONS FOR ENTERING AND INTENT TO LEAVE THE STATE GOVERNMENTAL PUBLIC HEALTH AGENCY WORKFORCE

BACKGROUND

Amongst their many traits, a key characteristic of Millennials is civic mindedness.

Howe and Strauss note that “No other adult peer group possesses anything close to their upbeat, high-achieving, team-playing, and civic-minded reputation.”(Howe &

Strauss, 2009) This civic spirit of the Millennials can be seen in their political and social engagement throughout their college years. Millennials first entered college in 2000, and, by 2006, more than half of all college freshmen reported that within the last year they had participated (frequently or occasionally) in organized demonstrations, an all- time high.(Perry & Buckwalter, 2010) In 2007, 10.9% of respondents reported participating in local, state, or national political campaigns, which was greater than in previous decades though fell short of pre-1970 levels.(Perry & Buckwalter, 2010) Given this high-civic mindedness, public service – particularly in governmental public health – should be a fit for employment.(Ng & Gossett, 2013) However, civic-minded, Millennials possess other characteristics that are incongruous with government service.(Ng &

Gossett, 2013)

Federal and state governments throughout the United States can be characterized as large, top-down bureaucratic organizations.(Gore, 1993) The style of these agencies has been slow and cumbersome with rigid standard operating

procedures, vertical chains of command, and standardized services.(Gore, 1993) While

48 built for reliability and adequacy of performance, government bureaucracies have created cultures with an over-concern with strict adherence to regulation thereby limiting creativity and promoting a culture of timidity, conservatism, and rigidity.(Merton, 1940)

Scheider’s Attraction-Selection-Attrition framework of organizational attraction suggests that individuals select organizations based on alignment with their own individual characteristics.(Schneider, 1987) While Millennials may be service-minded and altruistic, changing perceptions of public service and current generational characteristics limit the potential fit between Millennials and state public health agencies.(Kristof-Brown et al., 2005) This presents a significant challenge for recruiting and retaining

Millennials.

Previous research has found that Millennials place the greatest importance on

individualistic aspects of a job.(Ng et al., 2010) While they may have realistic

expectations of their first job and salary, rapid advancement and the development of

new skills was an expectation along with ensuring a meaningful and satisfying life

outside of work.(Hershatter & Epstein, 2010; McGuire, Todnem By, & Hutchings, 2007b;

Myers & Sadaghiani, 2010; Ng et al., 2010) Millennials desire flexibility in their

workplace to find their own solutions and place their focus on achieving

outcomes.(Gursoy, Maier, & Chi, 2008b; Hershatter & Epstein, 2010; Reisenwitz & Iyer,

2009; Solnet & Kralj, 2011) A study published in the Harvard Business Review found

that nearly 90% of all Millennials reported that having a flexible work schedule is

important.(Hewlett, Sherbin, & Sumberg, 2009) Telework in an important component of

that flexibility.(Hershatter & Epstein, 2010) Eighty percent of Fortune’s “100 Best

Companies to Work For” allow telecommuting for at least 20 hours per week,("100 best

49 companies to work for," 2009) which demonstrates a broader workplace pivot to accommodate this interest.(Hershatter & Epstein, 2010) Further, Millennials see workplaces as an interconnected set of nodes rather than the typical chain-of-command hierarchy associated with governmental bureaucracies. Hershatter and Epstein note

that “the Millennial expectation [is that] of a flat hierarchy and access to senior

leadership...[O]rganizational tensions may occur when new Millennial hires circumvent

the system and go immediately to the top to vent their frustration, vet their ideas, and

build relationships.”(Hershatter & Epstein, 2010) Governmental agencies and their rigid

vertical hierarchies are not designed to integrate the needs of the Millennials and

successive generations.(Behrens, 2009; Bennett, Pitt, & Price, 2012; Hershatter &

Epstein, 2010) Millennials are attracted to environments, often in contrast to

bureaucratic organizations, that prioritize collaboration and involvement in decision-

making, increased interaction, and, overall, less formality.(Crumpacker & Crumpacker,

2007; Lancaster & Stillman, 2009; Lester, Standifer, Schultz, & Windsor, 2012)

Nonprofits v. Government: The Changing Perceptions of Government

Another challenge to the governmental sector – including governmental public

health – is the changing perception of service and government among Millennials as

they become more attracted to nonprofit organizations over government.(Rosen-Carole

et al., 2015) In a Brookings study of graduating college seniors, nonprofits – not

government – were seen as the best place to work to help people and make a

difference.(Light, 2003) Nearly three-quarters of the respondents indicated that

nonprofits were best at helping people compared to just 16% who said

government.(Light, 2003) This is compounded by the perception that finding

50 government employment is difficult and confusing. Just slightly more than one-quarter

of respondents said finding a job in government would not be difficult compared to more

than 60% for nonprofits.(Light, 2003) Similarly, graduating seniors described the

governmental hiring process as confusing (63%) and slow (78%).(Light, 2003)

Governing Magazine reported that with more public services being provided by private

sector and nonprofit sector organizations, graduates are expanding their interests and

governments may be losing top candidates.(Maciag, 2013)

The impact of the perceptions of the government bureaucracy and confusion

around the governmental hiring along with changing perceptions of nonprofit and

government employment is apparent in an analysis of the 2013 National Association of

Colleges and Employers Student Survey which found that fewer than six percent of

graduating seniors reported that they planned to work in government immediately after

graduating, the lowest of any occupational sector for the fifth consecutive

year.(Partnership for Public Service, 2014) Further downstream, the proportion of the

Federal workforce that was under 30 years of age fell below seven percent in 2013, an

eight-year low, compared to 1975 when more than one-fifth of the federal workforce was under 30 years of age.(Feintzeig, 2014)

Without a greater influx of young people into the state governmental public health

agencies, not only will the state governmental public health agencies face crude

workforce shortages, but, also the technological savvy to carry agencies into a digital

future.(Feintzeig, 2014) If governmental public health agencies are to be successful in

recruiting talented Millennial employees, they will need to distinguish themselves from other competitors.

51 Given the potential challenges in recruiting Millennials to governmental state public health agencies, those who have chosen to work in governmental public health are an important group to understand to identify possible new recruitment strategies and ensure retention. It is likely that the Millennials already in government service could be the best recruitment tool that governmental public health has to attract more Millennials to governmental public health service.

Research Questions and Implications

There is one previous paper that explores intentions to leave the state governmental public health agency workforce.(Liss-Levinson et al., 2015) However, this previous work did not include sub-analyses by generation, which, given the unequal distribution of different generations within the workforce, may have underrepresented generationally-relevant differences. Therefore, this study seeks to contribute to the empirical literature on generational differences in governmental state public health agency employees’ self-reported reasons for entering the state governmental public health agency workforce and to explore generational differences in intentions to leave governmental state public health agencies for reasons other than retirement and, for those who are intending to leave, where they plan to continue their careers.

Specifically, this study seeks to address two specific research questions:

Research Question 1. Are there differences in the motivations for entering

the field of state governmental public health by

generation?

Research Question 2. Are there generation differences in the influence of

employee motivation for entering public health,

52 workplace perceptions, supervisory relationships,

employment satisfaction, and employee engagement

on intentions to leave state governmental public health

agency workforce for reasons other than retirement?

These findings can be used by human resource leaders, supervisors and managers in

state governmental public health agencies, and membership association leaders to craft generationally tailored retention strategies across the workforce, if warranted.

METHODS

Data Sources

Data used in the analyses for this article were drawn from the 2014 Public Health

Workforce Interests and Needs Survey (PH WINS) – specifically, the nationally representative sample of central office employees of state health agencies (SHAs) in the United States. Developed by the de Beaumont Foundation in partnership with the

Association of State and Territorial Health Officials (ASTHO), PH WINS is the largest state governmental public health agency workforce survey of its kind. PH WINS is the only national survey of the public health workforce that collects individual-level data. As such, it is the first and only national data source that allows for investigation of generational differences in the public health workforce.

The methods used in the creation of PH WINS have been described in detail previously.(Leider et al., 2015; Sellers et al., 2015a) To summarize, the purpose of PH

WINS was to collect individual worker perspectives across all disciplines and geographic regions. The development of PH WINS began in 2013, with a consensus- building process among 31 public health stakeholders representing an array of

53 disciplines.(Kaufman et al., 2014) A technical expert panel was convened to guide the sampling methodology, instrument creation, and protocols for survey fielding and administration.(Sellers et al., 2015a)

When developing the instrument, existing and/or validated measures were

incorporated when possible. Items used in PH WINS were adapted from the 2009

National Assessment of Epidemiology Capacity,(Council of State and Territorial

Epidemiologists, 2009) the US Office of Personnel Management Annual Employee

Survey,(US Office of Personnel Management, 2008) the US Office of Personnel

Management Federal Employee Viewpoint Survey,(US Office of Personnel

Management, 2012) the Centers for Disease Control and Prevention Technical

Assistance and Service Improvement Initiative: Project Officer Survey,(Centers for

Disease Control and Prevention, Office of State, Tribal, Local and Territorial Support,

2013), the Public Health Foundation Public Health Workforce Survey,(Council on

Linkages Between Academia and Public Health Practice, 2010) and the Job in General

Scale (abridged).(Balzer et al., 2000) The instrument adapted and used several items from Boulton et al.'s public health workforce taxonomy to ask respondents about

occupational classification, program area, degrees and certifications, work setting, and

demographics.(Boulton et al., 2014) The research team drafted new questions when

appropriate existing items could not be identified. Cognitive interviews were conducted,

and the instrument was pretested with three groups of public health practitioners at the

state and local levels. The finalized survey was administered online in fall 2014. After

pretesting and preliminary psychometric analysis (also explained in depth in previous a

previous publication), the instrument was fielded among 37 states from September to

54 December 2014.(Leider et al., 2015) The survey was confidential; contact information

was retained only to ascertain whether a potential respondent had indeed responded.

No contact information is associated with responses in final PH WINS data sets.