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Where will your path lead? Military services, career paths, and life course outcomes: implications for social mobility

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Where will your path lead? Military services, career paths, and life course outcomes: implications for social mobility

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WHERE WILL YOUR PATH LEAD?
MILITARY SERVICES, CAREER PATHS, AND LIFE COURSE OUTCOMES:
IMPLICATIONS FOR SOCIAL MOBILITY

by

Ruoqing Rachelle Wang-Cendejas

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
(SOCIOLOGY)

August 2021

Copyright 2021 Ruoqing Rachelle Wang-Cendejas
ii

DEDICATION
To the military community in the United States.

iii

TABLE OF CONTENTS
DEDICATION ii
LIST OF TABLES v
LIST OF FIGURES vi
ABSTRACT vii
CHAPTER 1 INTRODUCTION 1
Military as a Viable Career Option 1
A Modified Conceptual Model of Social Stratification 3
Military Service in the All-Volunteer Force Era 4
Contributions 5
Organization 8
CHAPTER 2 A HISTORICAL OVERVIEW OF MILITARY SERVICE IN THE UNITED
STATES 10
A Tradition of Citizen-Solders 10
A Military in 20
th
Century America 12
The All-Volunteer Force Era 13
Conclusion 29
CHAPTER 3 MILITARY SERVICES, EMPLOYMENT PATTERNS, AND CAREER
PATHWAYS 30
Introduction 30
Motivation – Maturity, Experience, and Education 31
Career Pathways 33
Data and Methods 40
Results 45
Social Correlates 52
Limitations 55
Conclusion 58
CHAPTER 4 CLUSTER MEMBERSHIP AND SOCIAL CORRELATES 60
Introduction 60
Social Correlates and Hypotheses 61
Modeling Military Employment Patterns 69
Results 70
Conclusion 82
CHAPTER 5 MILITARY INCOME PREMIUM 85
Introduction 85
Literature Review 86
Measures and Methods 99
iv

Descriptive Statistics 101
Results 102
Discussions 110
Conclusion 112
CHAPTER 6 CONCLUSION 114
REFERENCES 125
APPENDIX 151

v

LIST OF TABLES
Table 2.1 Geographic Distribution of Military Recruits, by Service, 2012 17
Table 2.2 A Comparison of Educational Benefits 19
Table 2.3 Military Occupational Specialties, by Race, 2018 22
Table 3.1 Substitution Cost Matrix 44
Table 3.2 Weighted Means and Proportions by Clusters (N = 710) 56
Table 4.1 Weighted Multinomial Logistic Regression Results (N = 710) 72
Table 4.2 Hypotheses and Results 78
Table 4.3 Average Change in Predicted Probabilities (N = 710) 79
Table 5.1 Regression Results: Log of Annual Income on Socioeconomic Backgrounds and
Measures of Military Service 104

Table 5.2 Logistic Regression Results: Whether Received Technical or Vocational Training
During Military Service 106

Table 5.3 Logistic Regression Results: Whether Received Technical or Vocational Training
During Military Service 108

Table 5.4 Regression Results: Log of Annual Income on Socioeconomic Backgrounds, Human
Capital, and Military Indicator 109

vi

LIST OF FIGURES
Figure 1.1 A Modified Conceptual Model of Social Stratification 3
Figure 2.1 Enlisted and Civilian Men and Women by Age, 2018 16
Figure 2.2 Distributions of Specialized Training Acquired During Initial Military Contract (A)
and Additional Formal Education Acquired Post Military Service (B), 2015 21

Figure 2.3 Military Occupational Specialties, by Gender, 2018 22
Figure 3.1 Sample Employment Patterns and Career Pathways 34
Figure 3.2 Employment Status Distribution [NLSY79] (N = 710) 46
Figure 3.3 Medoids of A Six-Cluster Solution [NLSY79] (N = 710) 48
Figure 3.4 Employment Sequences, by Cluster [NLSY79] (N = 710) 50
Figure 5.1 Income Profile over 15 Years, by Military Career Pathways 102
Figure A1 Medoids from a Five-cluster Solution 152
Figure A2 Medoids from a Seven-cluster Solution 152
Figure A3 Silhouette Width of the Six-cluster Solution 153

vii

ABSTRACT
This study examines how military service shapes, reshapes, and moderates the patterns of
socioeconomic mobility over the life course in the United States. Based on data from 27 rounds of
the 1979 National Longitudinal Study of Youth (1979 – 2016), this dissertation starts with a
construction of a typology of discrete employment patterns over 15 years since All-Volunteer
Force (AVF) military personnel’s first enlistment, explicating the timing and sequencing of
military service, educational attainment, and post-service employment, using sequence analysis
and clustering solution. The result shows six distinct military-related pathways, from a life-time
military career, to three pathways differed by the lengths of military services (Late Discharge,
Midway Discharge, and Early Discharge), to two outliers, one with multiple statuses and one with
re-enlistment. This dissertation also describes how these pathways are associated with social
correlates, such as gender, race/ethnicity, family experiences, and attitudes and expectations with
multinomial logistic regressions and found that women are more likely to be associated with early
discharge and midway discharge pathways, and African Americans are more likely to be
associated with a lifetime service career and later discharge pathways. Based on four sets of
multivariate regressions, this dissertation further examines the long-term effects of military service
on education attainments and mid-life income. The results show that service members differ from
nonveteran civilians with respect to human capital accumulation. Not only do service members
benefit from having received occupational training during service, they but also are more likely to
have acquired vocational training as civilians following service. These differences in human
capital accumulation explain the positive effect of being a Black service member or a
socioeconomically disadvantaged service member on higher annual income, providing support for
the human capital perspective. However, military income premium is only partially explained by
viii

human capital accumulation. The social capital perspective and screening and signaling
perspective receive limited support. Overall, various degrees of military income premium are
associated with selected military career pathways, and such a premium is limited to less
socioeconomically advantaged service members. These findings have significant policy
implications in recruitment, retention, benefits, and programs in the All-Volunteer Force era.

1

There are those who choose a different path in life, a path of selflessness. Service, the path
that leads to freedom. Where will your path lead?
U.S. Army, 2018
CHAPTER 1
INTRODUCTION

When I met Tyler, a Major in the U.S. Army, he had just graduated with a master’s
degree in cybersecurity in computer science. With advanced trainings in cyber command, Tyler
works as an intelligence officer at the Special Forces. Tyler’s path to a rewarding military career
was like no others. He grew up in the South-Central region of Houston, where widespread
unemployment, poverty, and crime dominated the neighborhood during his child- and young
adulthood. Cutting off the undesirable social ties, Tyler joined the military upon graduating from
high school and served two tours of duty in Afghanistan. With rigorous military training and
generous educational benefits, Tyler received postsecondary and graduate education upon
honorable discharge and is pursuing a lifetime military career. Tyler now lives a fulfilling life
with his wife and baby girl in North Carolina.
Military as a Viable Career Option
Movement up the socioeconomic ladder is generally best achieved through higher
education (Furstenberg 2008). Attending college and receiving advanced degrees prepare one for
a career of opportunities and pave the ways for upward socioeconomic mobility; these options
are the least accessible to those most in need of their benefits. Young people, especially those of
less advantaged backgrounds, now face more academic, informational, and financial obstacles to
enter college and complete higher education (Bennett and Lutz 2009; Bennett and Xie 2003;
Kahlenberg 1996). Alternatively, entering the labor market, finding a job, and working for pay is
an option that many take upon high school graduation. Like Tyler, many also choose to join the
military as a pathway of status attainment and social mobility (Wilmonth and Londone 2013).

2

Military service therefore serves as a transition to civilian labor market or a lifetime career
(Kleykamp 2013). Enlisting in the military and becoming a service member is a less understood
pathway to economic independence and socioeconomic attainment (Bennett and McDonald
2013). The timing and sequencing of major life-course events, such as education, employment,
and family, is more flexible when military service is considered. This dissertation explores the
life-course trajectories of military personnel in contemporary America and examines how these
trajectories and their associated outcomes diverge, based on individual and social characteristics.
Like the ways that postsecondary education transforms lives and betters socioeconomic
outcomes, military service produces promising results. Service members obtain marketable skills
through military training (physical, technical, and vocational) and enjoy generous educational
benefits (e.g., the GI Bill) with an honorable discharge, developing a lifetime military career or
receiving competitive wages in the civilian labor market (Bailey and Sykes 2018; Teachman and
Tedrow 2007; Wilmonth and Londone 2013). Military service thus facilitates social mobility by
offering a viable career path. By providing means for service members to raise their own
socioeconomic status, military service also helps their family to acquire a more advantaged life.
Based on a modified conceptual model for social stratification proposed and theorized by this
work, this dissertation answers the following research questions: In what ways has military
transformed the lives of service members? Specifically, in what ways has military (re)shaped the
employment patterns and career pathways of military personnel? How are social correlates
associated with various pathways among service members? Is military a conduit to education
during and post-service? How do service-connected training and education opportunities impact
employment trajectories of service members? And how do military career pathways modify the
processes of income attainment? Does a “Military Income Premium” exist in contemporary

3

America? Whether and how military service affects an individual’s income and the likelihood of
obtaining additional human capital – one determinant of future income – has implications for
movement up the socioeconomic ladder (Martorell, Miller, Daugherty, and Borgschulte 2013).
This dissertation explicates how military service shapes, reshapes, and moderates the patterns of
career pathways and income mobility over the life course.
A Modified Conceptual Model of Social Stratification
This dissertation adapts a modified conceptual model of social stratification built upon
the life-course perspective and status attainment perspective. The model is theorized as follows:
an early-life participation in a social institution, in this case, the military, shapes the processes of
later-life socioeconomic attainment, which lead to disparities in life-course outcomes of selected
groups with various individual and social characteristics. The impacts of military service on
socioeconomic attainment are therefore two-fold: first, participation in the military may
exacerbate, ameliorate, or have no moderating effect on individual characteristics and
circumstances (“origin”) that impact the choices of pathways to status attainment (“destination”).
In addition, participation in the military may (re)shape the educational, occupational, and other
socioeconomic statuses that influence socioeconomic outcomes.

Figure 1.1 A Modified Conceptual Model of Social Stratification.
As it is seen in Figure 1.1, the effects of individual characteristics and circumstances have
long-lasting effects on socioeconomic statuses and outcomes throughout the life course (hollow

4

arrow), but such effects can be moderated by various pathways to status attainment, such as
higher education, military service, and civilian employment, placing individuals on different life-
course trajectories that produce divergent later-life outcomes (Blau and Duncan 1967; Ishida,
Muller, and Ridge 1995; Wilmoth and London 2013). The diverse pathways therefore result in
within-group differences among service members and veterans and between-group differences
between military personnel and civilians (MacLean and Kleykamp 2016).
Almost all conclusions and implications on life course outcomes and mobility are based
on studies of the civilian population; they are at best incomplete and at worst misleading. This
dissertation fills this void by considering the military population in the All-Volunteer-Force
(AVF) era using the modified conceptual model of social stratification. To better understand the
different patterns of reaching socioeconomic status, I map out discrete and distinct career
pathways, based on major life events, such as school enrollment, military enlistment, and/or
labor market participation, using sequence analysis and clustering solution. I then examine the
social correlates that are associated with each military-related life-course trajectory, identifying
the macro- and micro-factors that facilitate or impede the processes of status attainment. I further
compare the total annual income of military personnel and the one of their civilian counterparts
at their professional midlife. This dissertation delivers empirical data and analyses on the
organizations and operations of the U.S. military as well as the life-course experiences and
outcomes of active-duty personnel and veterans, crystalizing the long-term impacts of military
service.
Military Service in the All-Volunteer Force Era
Enlistees in the all-volunteer force enter a highly structured setting and anticipate various
personal growth. Service members are secure in knowing that by serving the country, their

5

material needs are guaranteed, such as livable wages and income, subsistence and housing
allowances, education benefits, and trainings and skills, and the last of which is highly
transferrable and valuable in the civilian labor market (MacLean 2017; Kelty, Kelykamp, and
Segal 2010; Mangum and Ball 1989). Previous research has shown that service members in early
AVF era have received higher earnings on average than their civilian counterparts with
comparable educational attainment, but the effects of military service on socioeconomic
attainment varies by backgrounds
1
(Angrist 1998). The employment patterns and career
pathways that produce divergent incomes are yet to be examined during the AVF era; explicating
the timing, ordering, and contexts of military service, education attainment (concurrently during
military service or additionally during civilian employment), and labor market participation
provide new insights on military personnel’s life course experiences and outcomes.
Contributions
A baseline contribution of this dissertation is to describe U.S. military personnel’s long-
term employment patterns and associated incomes, following their career pathways for more
than a decade after the first enlistment. Military service happens early in life and likely interrupts
one’s normal life progression (Segal 1986; Segal and Segal 2004); it temporarily or, sometimes,
permanently takes individuals from educational institutions and/or the civilian labor market and
summons them into an organized life. Military service, thus, often stops the accumulation of
human capital, civilian work experience, and job tenure, which happen early in the life course for
civilians (Edwards 2016). Such disruptions do not coincide with the culturally prescribed
timetable and, therefore, has negative consequences for subsequent life-course trajectories and
outcomes (Wilmonth and London 2013).

1
Upon discharge, African American veterans and less advantaged veterans earn more than nonveterans, though the
income premium associated with the military service tends to dissipate over time (Teachman and Tedrow 2007).

6

Yet military service has beneficial effects on later-life labor market experiences for
various reasons. Military work is valued (MacLean 2017); such experiences substitute for
civilian work experiences in selected occupations (Magnum and Ball 1987, 1989). Military
teaches “social skills,” such as discipline, punctuality, and leadership, which are carried over to
civilian jobs (Furstenberg, Rumbaut, and Settersten 2005). Service members also receive
physical and technical trainings during active duty or acquire additional education after
discharge, an opportunity that they may not have had without military service (Teachman and
Tedrow 2004, 2007). Recognizing that income is largely shaped by human capital accumulation
and long-term employment and career, I set the observation window to cover 15 years of
employment since the service members’ first enlistment. Drawing on date from 27 rounds of the
1979 National Longitudinal Study of Youth (1979 – 2016), I describe the military, education,
and work experiences of active service members and veterans throughout their professional
midlife.
In this dissertation, I consider life course career patterns holistically, allowing me to
answer the following questions: Is military service a temporary status, such as a transition from
high school to higher education and eventually to the civilian labor market? Or is it a permanent
destination, chosen by a selected group of specific individual and social characteristics?
Moreover, is volunteer enlistment primarily driven by generous educational benefits from the
military and positive prospects in the labor market or by considerable employment stability and
substantial lifetime benefits in a military career?
Another contribution of this dissertation is to examine and evaluate how individual social
correlates are associated with different career pathways and corresponding income. These social
correlates include gender, race/ethnicity, family experiences and backgrounds, and attitudes and

7

expectations prior to enlistment. I argue that selected characteristics differentiate the choices of
career pathways, and different pathways to social attainment lead to differential income mobility
patterns over the life course. Which group constructs and experiences what career pattern is
crucial to understand the socioeconomic consequences among 1) service members, 2) service
members and veterans, and 3) military personnel and their civilian counterparts.
Military service in the AVF era is a planned intervention in the life course for men and
women; there is yet a comprehensive understanding on what it means for their socioeconomic
outcomes (Teachman 2013). In sum, this dissertation explores the mechanisms of how military
serves as a potential vehicle for social movement of selected groups of Americans. I paint a more
complete picture of military personnel’s life-course experiences by describing their unique
employment patterns and career pathways. The analytical methods, including sequence analysis,
clustering solution, propensity score matching, and multivariate regressions, build on and
enhance the existing quantitative modeling of the processes of career establishment and
development. The results show that the effects of military service on service members’ and
veterans’ employment and earnings vary by individual and family characteristics, socioeconomic
conditions leading to military enlistment, the military experience itself (e.g., enlistment age,
contract length, occupational training, etc.), and the transition and reception to later civilian life.
This dissertation contributes to the literature in military sociology, labor studies, and social
stratification by linking military services, educational attainment, and career choices to a broader
concept of socioeconomic attainment. This dissertation also informs future policies; how military
service affects education, work experience, and earnings has profound implications for the
design and implementation of military policies related to recruiting, retention, and reward in the
United States.

8

Organization
Chapter 2 provides a historical overview of the establishment and development of the
U.S. military starting from the 19
th
century and arriving at the current AVF era. It delivers
detailed background information on various military modal patterns – age of first enlistment,
duration of initial contract, proportion of renewal and re-up, types of military training, and more
importantly, opportunities of post-discharge education and prospects of civilian employment, all
of which impacts the choice of establishing a military-related career. Moreover, this chapter
discusses the potential negative consequences associated with military service, including
physical and psychological injuries and family disruptions. The potential positive and negative
outcomes of military service set up a debate on the overall impact of serving.
Chapter 3 generates the counts and modal patterns of various military-related pathways. I
construct a typology of discrete and distinct employment patterns over 15 years since AVF
military personnel’s first enlistment, explicating the timing and sequencing of military service,
educational attainment, and post-service employment. Based on sequence analysis and clustering
solution, I present six representing military employment patterns and describe diverse
demographic characteristics associated with these career clusters.
Chapter 4 examines how career pathways are associated with social correlates, such as
demographic characteristics, human capital, family background and experiences, and attitudes
and expectations prior to enlistment, based on multinomial logistic models. The results indicate
that unique social correlates are associated with distinctive career pathways among service
members in contemporary America. I offer various explanations of the heterogeneity in how
service members structure and optimize military service, education, and civilian employment and

9

theorize typical pathways based on gender, race/ethnicity, socioeconomic status (SES), and other
personal and family characteristics.
Chapter 5 discusses five theoretical perspectives in examining the variation in income
associated with unique pathways among service members. These frameworks include: the life
course perspective, the human capital perspective, the social capital perspective, the status
attainment perspective, the screening and signaling perspective, and the selectivity perspective. I
identify a compatible group of non-veteran civilians to each military group/military career cluster
and compare the income of military personnel at their professional midlife with their civilian
counterparts using multivariate regressions. Specifically, I test whether military status have a
positive effect on incomes net of various characteristics (“a military income premium”) and
examine the role of human capital and its impacts on the military premium if it exists.
Chapter 6 concludes. It reviews the major takeaways from this dissertation. I summarize
the main findings of each analytical chapter and discuss the broader implications of the study.

10

CHAPTER 2
A HISTORICAL OVERVIEW OF MILITARY SERVICE IN THE UNITED STATES

18.2 million Americans living today have served in the military – 1.3 million are in active
(82.3% enlisted and 17.7% officers) and 803,000 are in reserve (Dependent of Defense 2018).
The current U.S. military is drastically different from the military two centuries ago, when
militia was the selectively recruited, or from the military of 50 years ago, when conscription was
widely practiced. The military, in other words, has gone through significant structural,
demographic, and policy changes; it has transitioned from a part-time activity and a privileged
service in the Colonial Era to a full-time occupation and a lifetime profession in the
contemporary era (Segal and Segal 2004). The armed forces have become a major factor in the
America occupational structure and labor force (MacLean 2017; Segal and Segal 2004). This
chapter provides a historical overview of the military service; it discusses, in detail, the
recruitment, separation, and personal characteristics of the All-Volunteer Force (AVF).
Moreover, it summarizes potential negative consequences associated with military service,
setting up a debate on the positive and negative impacts of serving in the AVF era.
A Tradition of Citizen-Solders
An informal military was formed during the Colonial Era of the 16
th
and 17
th
Century.
Colonies heavily relied on local militias to fight the Indians. Specifically, each of the thirteen
colonies enacted laws for a compulsory militia organization (except for Pennsylvania), but
service was reserved for able-bodied, free White men from age 16 to 60 (Boucher 1973; Stewart
2005). All social classes were represented, although most of the early participations were granted
to landowners (Stewart 2005). Blacks were only allowed to serve during selected periods of
crisis (Kestnbaum 2000). Each member of the militia was obligated to receive a few days of

11

combat training at his county or town each year, holding himself in readiness for call of duty or
other emergencies (Stewart 2005). A part-time citizen army allowed men to live and work in
their home communities unless needed for military service. During the Colonial Era, military
service was not a common profession, but a privilege extended to a selected group of White men.
The popularity of obligatory military service among the propertied class slowly eroded.
By the end of 1700s, many service-eligible men started shirking their military duties by paying a
fine (Kelty and Segal 2007). Less advantaged men, who did not have sufficient means to buy
their way out, reminded in service (Boucher 1973). Soldiers with less education and lower social
class filled up the ranks of the militia. Following the increasing conflicts with the Indians and
foreign nations, the part-time service model was replaced by a small standing army with formal
military recruitment in the early 18
th
Century.
A true American military was created during the American Revolution between 1765 and
1783. The first recruitment was selected from several jurisdictions and was sent among the
thirteen colonies and later states. The military force composed exclusively of men, recruited
volunteers, occasionally practiced conscription, and called up the militia during wartime
2
.
Thousands of Black soldiers fought along with White soldiers in integrated units in the American
Revolutionary War
3
, though they held lower status than their White counterparts. The Congress
authorized the service of Black men in the Union forces during the Civil War in segregated units.
Starting in the 1800s, the early American military slowly evolved into a professional
force with generally recognized standards of training, discipline, and doctrine (Stewart 2005).
Besides basic combat training, military education was further advanced to include specialties of

2
This model continued till the mid-20
th
century.
3
This feature did not repeat until after World War II.

12

science and engineering with the establishment of the U.S. Military Academy at West Point,
providing dedicated technical education and producing skilled military technicians and engineers
(Stewart 2005). These engineers, many of whom were officers in the military, developed
professional codes of standards, behaviors, and ethnics that were essential to any profession.
Whether they remained in the military for a long-term career or fulfilled their obligation and left
the service, they had contributed to the maturity and professionalism of the military (Stewart
2005). The U.S. Military before the 20
th
century has transformed from a group of citizen-soldiers
towards an institution of armed, disciplined, and trained personnel with combat, technical, and
vocational skills.
A Military in 20
th
Century America
Along with the transformation from a part-time service to a full-time profession, the U.S.
military maintained its practice of reserve and conscription throughout most of the 20
th
Century.
During World War I, 3 million men served, and the military demobilized and maintained a small
number of service members after the war (Segal and Segal 2004). During World War II, a
national conscription was implemented (Kelty and Segal 2007). 75 percent of men age 18-24
served in the military; 35 percent of men age 25-29 and over 25 percent of men age 30-34
entered the force (Segal and Segal 2004). By the end of the war, more than 50 million young
men had registered for service, and 16 million men had served on active duty at some point
(Teachman and Tedrow 2004; Segal 1989). Women, for the first time, participated in military-
related work voluntarily. More than a quarter of a million women served in various female
auxiliary military corps (Segal and Segal 2004). African Americans also served, though they
could only serve in separated, all-Black units. Other racial-ethnic minorities served during World
War II as well; their presence was limited, working in less prestigious support roles (Kelty and

13

Segal 2007). Though selected groups, who were not allowed to serve before, had working
opportunities during the war, the conscription created a selection bias that affected the post-war
civilian employment of those who served and who did not. Employers required registration
papers and classification documents to show compliance with the law. Veterans who had met
their military obligations and received an honorable discharge were appealing to employers;
those who did not serve were deemed unqualified and therefore less attractive (Kleykamp 2006).
A selection bias surfaced, and veterans were preferred job candidates because of the
conscription.
Nearly 5.8 million men and women served in the U.S. Armed Forces between 1950 and
1953. Because of a male-only draft during the Korean War, around 1.8 million men served.
Moreover, the military was desegregated. White and minority soldiers fought side by side with
an increased presence of African American men
4
. The male-only draft was again implemented,
drafting 2.7 million Americans, which represents 9.7% of the generation (Segal 1989). The
Selective Service System favored White men of high social class, who avoided going to war, and
drafted the poor during early years of the war
5
, leaving less advantaged men serving on the front
lines (Kelty and Segal 2007). Men who received college education avoided being drafted and
entered the labor market as professionals upon graduation; men who went overseas and fought in
the war reentered the civilian world and received additional education. Many veterans from the
Korean War and the Vietnam War benefited from the earliest GI Bill
6
, developed different
employment pathways, and saw positive life-course outcomes (Teachman 2005).

4
Black recruits accounted for one-quarter of the new recruits (Bogard 1969). All-black units were abolished, and the
army was racially integrated by 1954.
5
Blacks were overrepresented among the poor.
6
A comparison of various versions of the GI Bill is discussed in a later section.

14

The All-Volunteer Force Era
The U.S. military ended conscription and established an All-Volunteer Force in 1973.
The AVF operates on open market dynamics, relying on individuals’ will and call to service
(Kelty, Kelykamp, and Segal 2010). Since the initiation of the AVF, the demographic profile of
the U.S. military has changed drastically; the military is more diverse and more educated than
any other eras. A significant number of women, minorities, Southerners, and youth from rural
communities have joined the force (Kelty and Segal 2013). Female representation increased from
2 percent of the enlisted forces and 8 percent of the officer corps in 1973 to 16 percent and 21
percent, respectively, in 2017 (Reynolds and Shendruk 2018). The military has also been
successfully drawing African Americans; their representation climbed from 14% in the early
1970s (Kleykamp 2007) to 20% in late 2010s (Reynolds and Shendruk 2018). The share of
Hispanic service members has also risen rapidly in recent decades, from 2% in 1975 to 15.5% in
2017 (Reynolds and Shendruk 2018). Military service has become a viable and accepted choice
of transitioning to adulthood for women and for racial-ethnic minorities (Kelty and Segal 2013).
In terms of social class, the all-volunteer force is composed of personnel drawn from a broad
middle range of the socioeconomic distribution, with a mean socioeconomic status somewhat
below that of the broader society (Kelty and Segal 2013; Bachman et al. 2000). Youth with less
advantaged background are more likely to enlist. Currently, the military comprises 1.30 million
active-duty members (82.3% enlisted and 17.7% officer), who serve in the various military
branches and functions (Department of Defense 2018).
Recruitment and length of service
The armed forces favor youth over experience; almost half of the enlisted men are under
age 25, so are more than half of enlisted women. Each year, the military recruits about 200,000

15

people, of whom 98 percent have at least a high school diploma or equivalent, and commissions
15,000 to 20,000 officers, of whom 94 percent have at least a college degree (Segal and Segal
2004). Increasing numbers of enlisted personnel join the military with some college education,
and one of the major motivations for young people to enlist is to receive formal education that
may not be available to them otherwise and obtain additional technical and/or professional
education during service or after discharge.
Most service members leave the service within a decade. In the All-Volunteer Force, new
recruits sign up for a preset length of service – a contract that varies with service, over time, and
across individuals. A typical initial contract includes three to six years of active service
(Department of Defense 2018). Most turnover occurs when people reach the end of their initial
contract, at which point service members separate from the military and enter the civilian sector.
The rate of separation falls sharply after eight years of service towards the end of the second
contract, and service members who remain in the military tend to serve till they retire
(Department of Defense 2018). Only a minority of first-time soldiers “re-up” for more tours, and
a much smaller minority remain in service for a full military career of 20 years or more
7
. The
average length of service for enlisted personnel rose from 5.8 years at the beginning of AVF to a
peak of 7.5 in mid-1990s, and now stands under 6 years (Department of Defense 2018). While
the All-Volunteer Force has become more career-orientated than the draft-era force, most service
members, except for the Air Force
8
, do not make the military a lifetime career. While the civilian

7
Less than 10 percentage of separations were retirements in most years (Segal and Segal 2004).
8
About 30% of the Air Force separations have been retirements since the 1980s (Segal and Segal 2004).

16

labor market consists a large share of workers age 50 or older, the active military workforce is
concentrated in a much younger age group
9
(Segal and Segal 2004).

Figure 2.1 Enlisted and Civilian Men and Women by Age, 2018. Department of Defense, Population Representation
in the Military Services, FY2017.
Geographic distribution
In recent peacetime time, almost 90 percent of the active-duty personnel, about 1.14
million people, have been stationed domestically, and just over 10 percent have been stationed
overseas
10
. Comparing with other advanced foreign nations, the United States has more of its
military personnel deployed outside the homeland. Domestic military facilities are concentrated
on the West Coast and in the South. California and Texas have the largest military population,
and Southerners are overrepresented among new recruits (Department of Defense 2018).

9
In 2018, 45.6% of active-duty members are 25 years or younger, 21.1% are 26 to 30 years, 14.9% are 31 to 35
years, 10.5% are 36 to 40 years, and 8% are 41 years or older.
10
The percentages change during distinct wartimes.

17

Moreover, military personnel move often – about one-third move and relocate to a new residence
every year, compared with 15 percent of their civilian counterparts (Segal and Segal 2004)

Table 2.1 Geographic Distribution of Military Recruits, by Service, 2012. U.S. Census Bureau, "Estimates of the
Population by Selected Age Groups and Sex for the United States and States: July 1, 2012"
(www.census.gov/popest/states/files/ST-EST2013-AS2012.txt).
Education
Military service provides various kinds of education, systematically managing the
amount and type of trainings that service members obtain. Substantial educational opportunities
are provided through provisions for acquiring postsecondary education, known as the “GI Bill of
Rights.” The original bill allowed World War II veterans to go back to school and receive
additional education upon returning home. With a generous package, WWII veterans were
entitled to fully covered higher education up to four years, should they choose to do so (Radford
2009). The Bill did not specify a cap on tuition and fees, which shortened the distance between
some of the most prestigious and most expensive institutions and disadvantaged veterans if they
were academically qualified for admission. Almost 8 million individuals used the original GI Bill
and increased their human capital, which better facilitated their re-entry into the civilian labor
market (Veterans Admission 1977).

18

The terms and conditions of the GI Bill has varied greatly with each major war event in
which the U.S. has participated. The amended GI Bill following the end of Korean War applied
to those who served in the military during times of peace and times of war (Altschuler and
Blumin 2009). But no version of GI Bill between the first and the current one is as generous as
the original one posted initially in 1944 (Altschuler and Blumin 2009). Nevertheless, with the
educational benefits offered by the GI Bill, millions of veterans have chosen the pathway from
high school to military then to higher education before rejoining the civilian labor market.
Currently, there are two GI Bills that provide benefits and supports to veterans, the
Montgomery GI Bill, which was enacted in 1984, and the Post-911 GI Bill, which was enacted in
2008. Unlike the original GI Bill, the Montgomery Bill has a cap for education expenses, which
includes tuition, fees, books, room and board, and other related expenses. The cost of higher
education has gone up significantly over the years; with a fixed amount of support, the value of
the Montgomery Bill declines as a result (Bennett and McDonald 2013). To compare, the
original GI Bill covered tuition and fees fully, regardless of public or private institutions, but the
Montgomery Bill covers 100% of tuition for four years at public institutions and only 47% of
tuition for four years at private institutions (Bennett and McDonald 2013). Service members
likely pay out of pocket or take on student loans to pay for higher education fully.
The Post-911 GI Bill is created for those who served after September 11, 2001. As the
macroeconomic conditions fluctuated and the price level rose in the early 21
st
Century, the
eroding value of the Montgomery Bill trigged a newer round of amendments to the bill, aiming
to make postsecondary education more affordable for service members and veterans. The Post-
911 Bill still has a limit on the coverage of tuition and fees, but veterans can use their benefits for

19

longer periods of time, compared to that of the Montgomery Bill. The details of each version of
the GI Bill are shown in the Table 2.2.
All U.S. military personnel receive combat and professional trainings during service and
are eligible to acquire education after honorable discharge (Bennett and McDonald 2013; Hexter
and El-Khawas 1988). Physical trainings are provided to all enlistees, and occupational trainings
are provided based on military specialties (discussed below). As it is shown in Figure 2.2, most
technical and professional trainings are concentrated during the first 4 years of service (Panel A),
and formal education is mostly obtained immediately following discharge (Panel B).
Using the GI Bill, many veterans go back to school after separation from the military.
38.1% of enlisted men and 15% of officers acquired additional education post service (Edward
2016). 23.8 percent of male Gulf War II (September 2001 – present) veterans ages 25 to 34 were
enrolled in school on full-time or part-time basis, compared with 10.3 percent of civilian men in
that age group
11
(Congressional Budget Office 2018). With the generous education benefits
provided by the GI Bill, slightly more veterans than civilians (36 percent vs. 32 percent) had a
college or a more advanced degree by ages 35 to 44 (Congressional Budget Office 2018).

11
30.1 percent of male Gulf War II veterans ages 22 to 24 were enrolled in school, compared with 30.8 percent of
civilian men in that age group; 14.5 percent of male Gulf War II veterans ages 35 to 44 were enrolled in school,
compared with 30.8 percent of civilian men in that age group.

20

Table 2.2 A Comparison of Educational Benefits.

21

Figure 2.2 Distributions of Specialized Training Acquired During Initial Military Contract (A) and Additional
Formal Education Acquired Post Military Service (B), 2015. Congressional Budget Office.
Specialties
Almost half of active-duty personnel are trained in technical specialties (49%) with a
concentration in electronic, electrical, and mechanic equipment operation and repairs (30%).
Others are in administrative specialties (35%), and only a minority is in combat specialties
(17%). More female service members are in functional support and administration specialties,
medical and dental specialties, and service and supply handing specialties. Black service
members are more likely to specialize in functional support/service and administration than
Whites, who are more likely to specialize in electronic, electrical, or mechanical engineering
(Segal and Segal 2004). In general, Blacks are less likely to be in career-enhancing tactical
operation specialties than their White counterparts.

22

Figure 2.3 Military Occupational Specialties, by Gender, 2018. Department of Defense, Population Representation
in the Military Services, FY2017.

Table 2.3 Military Occupational Specialties, by Race, 2018. Department of Defense, Population Representation in
the Military Services, FY2017.
Post-service employment
Approximately 200,000 Service members re-enter the civilian world each year (Bureau of
Labor Statistics 2019). The end of the conscription and the initiation of the AVF marked the first
time that the armed forces entered the labor market in competition with civilian employers (Segal

23

and Segal 2004). 49.2 percent of all veterans were employed in 2018, which was below the
figure of 65.5 percent for the nonveteran civilian population (Bureau of Labor Statistics 2019).
Veterans who served more recently had higher labor market participation post service: among
veterans serving in Gulf War era II (September 2001 – present), 80.9 percent were employed,
while 78.6 percent of those who served in Gulf War era I (August 1990 – August 2001) were
employed. Veterans served earlier had lower labor market participation. 20.9 percent of veterans
from the Vietnam era, the Korean War, and World War II were employed, most of whom are of
retirement age (Bureau of Labor Statistics 2019).
Income
Military service is an attractive alternative to gain economic independence and
socioeconomic attainment with a lifelong development. Because military service and higher
education happen at similar points in life (Teachman, Call, and Segal 1993), junior enlistees tend
to be young and have relatively low levels of educational attainment, making them less
competitive in the civilian labor market (Kelty and Segal 2013). Military service, as a full-time
occupation, pays a compensating wage differential to service members on active duty (Martorell,
Miller, Daugherty, and Borgschute 2013), providing an entry level pay and benefits that are
better than the one of entry level jobs in the service sector (Bennett and McDonald 2013). For a
new enlistee (known as E-1 with less than 2 years of service) stationed in the Los Angeles
County without dependent, a military compensation based on the 2017 pay scale starts with a
monthly stipend of $1,599 (in 2010 U.S. dollars) and additional allowances for food, housing,
medical, and education, a total amount of $4,000 a month or $47,990 a year (Federal Pay 2019).
Military, therefore, not only offers base earnings that are comparable, if not better, to those that
high school graduates obtain in the civilian labor market, but also the standard of living offered

24

by the military through various benefits is substantially higher (O’Brien 2008). The combined
rewards and benefits serve as major incentives for individuals with limited education and work
experience to enlist. Military compensation increases as the service length increases, as the
service rank advances (e.g., commissioned officers receive higher basic pay then enlisted
personnel), and/or as the dependence status changes (e.g., service members with dependents
receive more basic allowance of housing). For service members seeking a lifetime career in the
military, military wages are competitive or superior to those found in the civilian labor market,
and military guarantees improvements in human capital through skills training (Bennett and
McDonald 2013; Martorell, Miller, Daugherty, and Borgschute 2013). A military career provides
lifelong development and advancement through training, education, leadership, and economic
rewards, which have transformed the life course of service members physically, emotionally,
intellectually, and financially, as well as those to whom service members are linked (Bennett and
McDonald 2013).
Negative consequences of military service
Though military services may be associated with upward mobility in terms of education,
employment, and income, there are potential negative consequences of military service, such as
injuries of physical and psychological wellbeing and deterioration of personal and familial
relationships, which may lead to suboptimal consequences in career trajectories over the life
course. This section discusses military-related health and family outcomes that civilians do not
often encounter in the life course.
Health Military service could positively impact health outcomes. Orderly lifestyle and
physical training encourage the development of healthy habits. Social distinction, recognition,
and admiration also provide a sense of self-esteem among service members (MacLean 2012).

25

However, it could also negatively impact health outcomes due to training- and combat-related
injuries (e.g., death, disability, mental health issues), exposure to hazardous conditions (e.g.,
chemicals, radiation, etc.), or development of detrimental habits (e.g., smoking, alcoholism, and
substance abuse). Health trajectories directly and indirectly shape life-course trajectories –
undesirable injuries and behaviors potentially impact education attainment, employment
opportunities, occupation choices, income, and access to health care during and after service. For
those who have experienced physical and psychological effects of military service may
experience net adverse impacts overall (Prigerson, Maciejewski, and Rosenheck 2002; Wilmoth
and London 2013).
Physical Health During both peacetime and wartime, service members can be physically
injuries during trainings, in combat, and as a result of sexual assault; service-related physical
injuries, therefore, serve as negative turning points in the life course. During AVF era, military
personnel were more likely to have brain injuries because of accidents during peacetime, but
they were more likely suffer brain injuries because of combat (e.g., explosion-related incidents)
during wartime (Wilmoth and London 2013). Reduced brain function and cognitive performance
are the most common results of veterans who have experienced brain and nervous system
injuries; such injuries could limit cognition, speech, and movement, which are essential functions
for life activities. Without such, opportunities for learning, communicating, and working are
constrained, which may translate to lower well-being in general.
Besides brain injuries, service members tend to have higher rates of physical limitations
and disabilities than civilians (Wilmoth, London, and Parker 2010). Specifically, combat
veterans experience greater rates of disability and unemployment across the life course
(MacLean 2010). Higher levels of functional limitations, beyond chronic fatigue and pain, not

26

only have negative implications for family life but also for professional development, career
advancement, and economic well-being. Exposing to the risks of physical injured and disability,
service members may not enjoy military service as a mechanism for upward mobility but a
contact for material hardship and strain (Heflin, Wilmoth, and London 2012).
Sexual harassment and assaults have been identified in the military during AVF. Though
happened to both genders, female service members have experienced military sexual trauma,
including rape and sexual harassment, which they encounter at significantly greater rates than do
their male counterparts (Suris and Lind 2008). Such trauma further damages their later physical
and mental health, which may become a reason for their separation from the service.
Mental Health Besides negative physical health outcomes, service members may also
suffer from mental health problems as a consequence of serving the country. Depression, post-
traumatic stress disorder (PTSD), and substance abuse, as results of traumatic events such as
combat, sexual assault, and death of a close fellow soldier, are the most common mental health
diagnoses among current and formal military personnel (Wilmoth and London 2013). The
symptoms can be immediate or delayed and short-term or long-term. Depending on the situations
and triggers, some service members experience symptoms immediately after a traumatic event,
and others suffer some time later. One pervious study, for example, has shown that soldiers who
returned from Iraq during AVF had relatively low rates of PTSD, ranging 1 to 5% (Bliese et al.
2007). However, the figure increased to 3 to 8% after six months (Bliese et al. 2007). Another
study has shown that the mental health disparities gradually disappear between active-duty
veterans and non-veteran civilians after ten years of discharge (Wyman, Lemmon, and Teachman
2010). In terms of prevalence, nearly 20% of war veterans in Iraq and Afghanistan have been
identified with one or more mental health disorders (Tanielian and Jaycox 2008). Symptoms of

27

various disorders may drive away friends and relatives and damage social networks, which are
support systems to mentally injured service members. Depression and other mental health issues
last a significant period during the life course, which in turn may negatively impact daily life
activities, such as education, employment, and family life.
Deaths Service members die from various causes, such as training and non-training
accidents, illness, and suicide. One unique cause of death to military personnel during the 20
th

Century is death from hostile fire during combat. The numbers of soldiers killed during the more
recent wartimes (e.g., Iraq and Afghanistan) were much smaller comparing with earlier wars
(e.g., World War I&II, Korean War, and Vietnam War), over 4,400 had died during Operation
Iraqi Freedom and Operation New Dwan in Iraq, and more than 1,700 had died during Operation
Enduring Freedom in Afghanistan by the end of operations in 2014 (Department of Defense
2015). Besides combat-related death, military personnel are at higher risk of suicide. Emile
Durkheim shared in one of his pieces that French soldiers committed suicide at a higher rate than
the general French population, and the cause of suicide was likely the way that the soldiers were
trained and suppressed that they valued their own lives less than did non-veteran civilians
(Durkheim 1951). During the AVF era, service members, regretfully, have been more likely than
their civilian counterparts to committee suicide (MacLean 2013); an elevated risk of suicide-
related mortality is a negative factor participating in the armed forces. Together, combat-related
death and suicide have ultimate consequences, discontinuing regular life course progression and
stopping all life activities that accrue physical, emotional, and socioeconomic wellbeing.
Maladaptive Behaviors Service members may have poorer health outcomes because they
are more likely to engage in risky and maladaptive behaviors than their civilian counterparts.
Though suspended, the armed forces had provided troops with cigarettes and beers and, which

28

enabled smoking and drinking during service. Active-duty service members may initiate the use
as a means of treating stress, depression, and other mental health issues brought about by
training, combat, and separation. Some service members experience maladaptive coping
strategies during and after combat, such as increasing consumption of tobacco, alcohol, and
recreational substances. Pervious research has shown that veterans are more likely than non-
veteran civilians to develop problems with smoke, alcohol, and drug (Dobkin and Shabani 2009);
they are more likely to abuse substances if they were deployed to war zones (Wright, Carter, and
Cullen 2005). All such behaviors lead to worse health outcomes, which could act negatively on
life course activities and trajectories.
Family Life Military life can be very stressful on families. Long separations, frequent
relocations, inconsistent training schedules, and the toll of mental and physical injuries on both
the service member and the family can all add up over time, which lead to negative
consequences for the individuals and their career trajectories. Some recent research has
suggested that veterans are significantly more likely than non-veterans to have ever divorced,
and female active-duty service members have higher divorce rates than their male counterparts
(Karney and Crown 2007). Deployment and combat experience impacts marriages and families
negatively. Divorce rates are higher among both combat soldiers and veterans (Negrusa and
Negrusa 2014), and military families often experience higher levels of conflict and stress after
deployments and combats (Karney and Crown 2007); such strain translates to negative
consequences in career development among service members and their loved ones (Lowe, et al.
2012).
Overall, veterans who served on active duty have poorer self-rated health and, to a certain
extent, suboptimal family life, after controlling for socioeconomic status and health behaviors

29

(Teachman 2007, 2010). Though military service during AVF era comes with many advantages
(e.g., education opportunity, career development, and income mobility), adverse health and
family outcomes as results of service negatively impact life course trajectories and outcomes.
Conclusion
This chapter starts with a historical overview of the establishment and development of the
U.S. military. From an informal organization consisting of privileged citizen-soldiers before the
turn of the 20
th
century to a combat-focused force of mixed gender and groups participating in
major conflicts during the 20
th
century to a fully diverse all-volunteer force offering skill
development now, military services have transformed and become a viable career option in
contemporary America. The landscape of the military has changed significantly in demographics
with increasing presence of females, minorities, Southerners, and youth from the rural areas.
Moreover, with great flexibility in service lengths and excellent benefits in education, service
members and veterans have expanded opportunities to construct their life course by selecting
various career pathways based on education, service, and post-service employment. The training
received and education obtained during service and after discharge translate into valuable skills
and experiences in the civilian labor market. However, the employment patterns and career
pathways that produce divergent labor market outcomes are yet to be examined during the All-
Volunteer-Force era. The next chapter explicates the timing, ordering, and contexts of military
service, education attainment, and labor market participation and provides new insights on
military personnel’s life course experiences and outcomes.

30

CHAPTER 3
MILITARY SERVICES, EMPLOYMENT PATTERNS, AND CAREER PATHWAYS
Introduction
Career often starts with education in the United States. Some choose to enter the labor
market upon high school graduation, and others continue with postsecondary education before
starting employment. Entering the military is a viable option but a less understood pathway to a
professional career. As discussed in the previous chapter, the All-Volunteer Force (AVF) has
changed the way military is considered as a career option and therefore positioned in the labor
market; it is no longer a privilege that was made available to selected groups or an armed force
that concentrated on combat. Instead, the contemporary military service in the United States
focus on both physical enhancement and vocational development, offering new opportunities to
services members to construct desired life paths. Not only will service members receive combat
training but also occupational schooling in preparation of developing a rewarding career with the
military or in the civilian labor market. The timing, sequencing, and contexts of education,
service, and employment determine the life-course experiences and outcomes of military
personnel. This chapter explores the various career options of service members in the AVF era.
The goal of this chapter is to generate counts and modal patterns of various military-
related pathways to better understand the military population – active-duty service members and
veterans – that follow different career trajectories in the AVF era. It focuses on three research
questions, in what ways has military (re)shaped the employment patterns and career pathways of
military personnel? Is military a conduit to education during and post-service? How are various
military pathways different by individual and social characteristics? Using sequence analysis and
clustering solution, I construct a typology of discrete and distinct employment patterns over 15
years since military personnel’s first enlistment, explicating the timing and sequencing of

31

military service, educational attainment, and post-service employment. I then describe the
military respondents associated with each pathway, focusing on their demographic
characteristics, human capital, family background and experiences, and attitudes and
expectations towards military service, future employment, and family planning. The results of
this chapter provide up-to-date descriptions of military affairs in the United States.
Motivation – Maturity, Experience, and Education
In the United States, secondary education is typically acquired during adolescence. The
timing of post-secondary education, such as college and graduate education, varies. One can
receive higher education immediately following high school graduation; one can also delay
higher education and acquire it later in life (Edward 2016). Postponement is common nowadays
and needs not to undermine the pursuit of advanced degrees; a break from academic work allows
students to mature, acquire work experience, and, more often, accumulate necessary resources
for college (Niu and Tienda 2013). Joining the military serves all three purposes above. The
deliberate organizational socialization within the military aims at producing “mature and
disciplined bodies,” capable of carrying out military order and waging war on enemies (Godfrey,
Lilley, and Brewis 2012). Enlistees adopt many socially appreciated qualities, such as loyalty,
integrity, courage, determination, resilience, and commitment to duty that the military seeks to
promote (Bergman, Burdett, and Greenberg 2014). Such qualities become social capitals which
are valuable in finding suitable employments and receiving financial rewards in professional
lives (Cooper et al. 2018).
There are three perspectives that military work is attractive and valuable – military
experience as a signal, military skills as transferrable skills, and military work as substitutes
(Davis and Minnis 2016; MacLean 2017). Previous research has shown that military experiences

32

serve as a positive signal of competence and quality for employers in the civilian labor market;
an honorable discharge increases a veteran’s the likelihood of receiving interviews and offers,
relative to one with no military experience
12
(Figinski 2019). Moreover, service members bring
distinctive capabilities and valuable skills developed through real-world, high-pressure military
experiences, and the transability of such skills are robust in various occupations (Davis and
Minnis 2016; Mangum and Ball 1987, 1989). Military work experience is a key asset for
veterans, serving as a partial, if not complete, substitute for civilian work experience in
determining pay scales and steps in the civilian labor market (Boutelle 2016).
Military service facilitates the accumulation of human capital (Niu and Tienda 2013).
Service members exhibit a large variation in the timing and types of education across the life
course. Many receive formal education and/or informal training during military service, and
some acquire more education after discharge (Edward 2016; Teachman and Call 1996). Military
increases human capital through two primary channels: 1) technical training or occupational
education during military service, and 2) educational benefits (e.g., the GI Bill) of post-
secondary courses and degrees upon discharge (Dempsey and Schafer 2020). Whether and how
military service affects an individual’s likelihood of obtaining additional human capital – one
determinant of future income – has implications for movement up the socioeconomic ladder
13
.
Previous studies have considered the impacts of military on various socioeconomic
outcomes, such as returns to education (Angrist 1990, 1998; Griliches 1980; Griliches and
Mason 1972; Marcus 1984; Teachman 2005) and income and occupational mobility (Bailey and
Sykes 2018); the timing, ordering, and context of sequential life events have not been

12
Using a resume study, Figinski (2017) suggests that completed service in the military, relative to no military
experience, increases the probability of receiving a request for an interview by 19%.
13
A topic to be further explored in Chapter 5 of this dissertation.

33

systematically examined in the literature. This chapter answers the following research questions:
at what point(s) in the life course do military personnel receive higher education, separate from
the military, and/or enter the civilian labor market (e.g., lifetime service, early discharge, late
discharge, or multiple transitions)? Through what channels and in what forms are additional
human capital obtained (e.g., technical training and vocational education during military service
or additional schooling after discharge)? Do different subgroups of service members choose
different pathways? If so, in what ways? I explore these questions using a data reduction
technique of sequence analysis and unsupervised machine learning of clustering solution.
Career Pathways
Various career pathways lead to diverse socioeconomic attainments in the life course;
these pathways often involve three social institutions – postsecondary education, the civilian
labor market, and the military (Bennnett and McDonald 2013), as illustrated in Figure 3.1.
Embedded in these pathways are sequential choices built on previous statuses and associated
socioeconomic outcomes results from a sequence of actions.
Upon high school graduation, there are two common choices - continuing with higher
education or entering the labor market. Many youths attend colleges immediately after high
school and take their college credentials to the civilian labor market (Civilian Pathway 10).
Those with limited postsecondary education opportunities enter the civilian labor market and
secure employment and pay, based on lower credentials (Civilian Pathway 6) (Bennett and
McDonald 2013). These two civilian pathways are well examined in the literature; the
mechanisms among education, occupation, and income are well studied and understood but are
exclusive to the civilian noninstitutionalized population [CNP].

34

Figure 3.1 Sample Employment Patterns and Career Pathway.
Military offers an alternative career pathway upon high school graduation. Three factors
drive peacetime enlistment: 1) recruitment strategies, including pay and benefits, used by the
military; 2) macroeconomic opportunities and constraints faced by young men and women; and
3) individual characteristics and circumstances (Dempsey and Schafer 2020; Teachman, Call,
and Segal 1993). Joining the military expands individual’s career choices. Starting with
enlistment, military personnel have diverse employment statuses at various points in life, let it be
military service, education, or employment. These combinations of employment statuses create
divergent career pathways over the life course. Moreover, military service differs from the
civilian labor market. Should service members seek to accumulate additional human capital
during or after service, there is a built-in pathway to education via military occupational trainings
and the GI Bill, making available a number of education opportunities that are not found in the
labor market or are not readily accessible in the civilian context (Bennett and McDonald 2013;

35

Dempsey and Schafer 2020; Teachman 2005, 2007). To illustrate the employment patterns
among military personnel, three modal military pathways are introduced; they are
1) Lifetime military career (Military Pathway 1);
2) Military to civilian labor market (Military Pathway 2);
3) Military to higher education to the civilian labor market (Military pathway 3).
The current literature treats military career as a uniformed experience. I describe three
sample pathways to illustrate the diverse military-related career options in contemporary
America, providing background information on the ways that military (re)shapes the
employment patterns and career choices. This section discusses, in detail, the motivations and
justifications to enter, the terms and conditions to stay, and the potential benefits and outcomes
to accrue as a service member in the AVF Era.
Military pathway 1 – a lifetime military career
A lifetime service member enters the military at an early age and stays with the same
social institution over the life course. Though serving the country comes with promising rewards
and benefits, individuals must meet all selection criteria to pursue a military career, such as
educational prerequisite, physical requirement, legal status, and integrity restrictions. Some
selection principles create barriers for men and women to serve and retire with the military,
especially the ones from disadvantaged backgrounds (Kelty and Segal 2013). Accomplished
lifetime service members are, therefore, a selected group based on various characteristics.
There are standard educational prerequisites for prospective soldiers. High school
diploma is not strictly required for enlistment, but the current All-Volunteer Force enlists very
few who do not have a high school diploma or a General Educational Development (GED)

36

certificate. Prospective enlistees must pass the Armed Forces Qualification Test (AFQT) before
stepping a foot in the military. Selection into various military occupation specialties, such as
engineering, science, and technical occupations or combat specialties, are based on pre-
enlistment education level and the AFQT score (Kelty and Segal 2013). Therefore, military
service is a gated profession that individuals must be qualified for educationally.
Individuals must also meet the physical requirement to serve. The Army, for example,
has strict height, weight, and body-fat composition rules for new recruits. Allowable
measurements vary by age and gender, and each branch of the military has similar sets of rules
for perspective enlistees. Military recruits must also pass the Basic Training Physical Fitness
Test at the Boot Camp, including two minutes of push-ups, two minutes of sit-ups, and a timed,
two-mile run. Additional physical requirements must be met to proceed further with a military
career in selected branches and units. Military enlistees are thus a selected group with better
physical fitness, compared to their civilian counterparts (Edward 2016).
Citizenship/legal status is an important enlistment criterion. U.S. citizens, legal residents
(“green card” holders), and immigrants with valid visas for the entire period of their enlistment
are eligible to join the military. Undocumented immigrants may not enlist. Only U.S. citizens can
become commissioned officers or receive special security clearances for branches, such as
Intelligence, Nuclear Power, and Special Operations in the military, like Tyler from the opening
story. Non-citizens are not eligible to be commissioned, unless naturalized through other means.
The citizenship and legal status requirement limit the access to a long-term military career
among selected groups of racial minorities (Bennett and McDonald 2013). For noncitizens with
legal residency, the military could be a path to citizenship in exchange of their service (U.S.
Army 2018). Over a 15-year period between 2001 and 2015, more than 100,000 noncitizen

37

service members have been naturalized (USCIS 2016). Military service, therefore, becomes a
transformative experience for legal residents and immigrants, who become U.S. citizens and
enjoy full civil and political rights shared by all Americans. Moreover, citizenship guarantees
eligibility to be commissioned officers, a promising military career with abundant rewards and
benefits.
The military recruit individuals with good characters. Multiple minor criminal offenses or
a single felony conviction burns the bridge for military service (Bennett and McDonald 2013).
Because of the zero-tolerance policies, it is more selective for socioeconomically disadvantaged
students from poorer neighborhoods, who tend to translate maladaptive behavior and conduct
into criminal charges, to join the military and build a military career (Wald and Losen 2003).
A military career is not given. Although the enlistment standards are relatively low,
remaining in the military till retirement requires continuous upgrading of knowledge and skills
(Bennett and McDonald 2013). Service members received periodical evaluations – physically
and technically – throughout one’s military career; failure to complete necessary trainings and
courses leads to an early separation from the military. In other words, upskill is necessary for a
successful lifetime military career. For individuals who demonstrate continuous qualification and
progress during service, the military offers competitive earnings, substantial benefits, and skill
trainings (Bennett and McDonald 2013), which will be explored in detail in Chapter 5. A long-
term or a lifetime military career, therefore, attracts a selected group of Americans, whose
individual and socioeconomic characteristics differentiate them from other service members and
their civilian counterparts.
Military pathway 2 – military service to civilian labor market

38

A second military pathway involves two major social institutions – the military and the
civilian labor market. Besides meeting the military-entry selection criteria discussed above, a
service member enlists for a pre-determined period based on contact(s) and re-enters the civilian
labor market at one’s discretion. In other words, the All-Volunteer Force is built on labor market
dynamics; once enlisted, service members have options to renew the contract or leave the
military with skills acquired before serving a full term of 20 years (Kelty, Kleykamp, and Segal
2010). As discussed in Chapter 2, most who serve do so for a limited amount of time; a typical
initial contract includes three to six years of active service (Department of Defense 2018).
Moreover, military service often serves as a career transition between school and the civilian
workforce (Kelty and Segal 2013). Some military occupational specialties have no close
equivalent in the civilian workforce, but others are highly transferrable (Kleykamp 2009). More
than 50% of veterans transfer their military skills into civilian work (Davis and Minnis 2016;
Mangum and Ball 1987; 1989). Young veterans who enter the civilian labor force expect to have
acquired additional human capital in technical specialties (e.g., analytics, engineering,
administrative roles) and soft skills (e.g., discipline, punctuation, and leadership), with which
veterans achieve and sustain financial independence (Xie 1992; Kleykamp 2006, 2013).
However, military experience is not a one-size-fits-all experience; service members choose the
lengths of the service, and the type of work and the volume of training are different based on
various pathways constructed by service members. The experiences of service are vastly
different among service members, though one commonality among various military to labor
market pathways is that formal education and work experience are either substituted by military
experience, delayed till later in life, or forgone once and for all; upskills and/or reskills in
preparation of adaption to the civilian market are often needed upon discharge.

39

Military pathway 3 – military service to higher education to civilian labor market
A third military pathway involves all three major social institutions – the military,
postsecondary education, and the civilian labor market. Like Military Pathway 2, the enhanced
training and skills obtained during service likely predispose veterans to greater labor market
rewards (Kleykamp 2013). Unlike the previous pathway, service members often acquire
additional education after discharge. Instead of entering the civilian labor market immediately
after discharge, many veterans have gone back to school and received college and advanced
education, a direct mechanism for reskilling or upskilling. Post-service education has enhanced
the educational attainment, occupational status, and job perspective and stability of veterans of
World War II, the Korean War, the Vietnam War, and the AVF, though the effects differed by
socioeconomic backgrounds (Angrist 1993; Bailey and Skyes 2018; Bound and Turner 2002;
MacLean 2005, 2017). Overall, the military service has a built-in pathway to higher education
and a potential mobility strategy among contemporary veterans (Bennett and McDonald 2013).
As it is seen from the description above, military service leads to diverse career pathways
in the AVF era, providing additional employment options and occupational training. However, it
is not clear, first, who chooses which military-related pathway, second, which military pathway
is more sustainable/popular, and third, whether military serves as a conduit to education. This
chapter uncovers the modal patterns and proportions of population that follow various military
trajectories.
The remainder of this chapter describes the data sample and constructs 15 years of
military employment patterns since military personnel’s first enlistment, based on a data
reduction technique of sequence analysis and unsupervised machine learning of clustering
solution. Previous studies have focused on the impacts of military service on health, family, and

40

labor market outcomes (Teachman 2004, 2005, 2007, 2010; Teachman and Tedrow 2004, 2007,
2013, 2014; Teachman, Anderson, and Tedrow 2015). A few studies have looked at income
trajectories of veterans (Martorell, Miller, Daugherty, and Borgschute 2013; Teachman and Call
1996) and intergenerational mobility across generations (Bailey and Sykes 2018). No study, to
my knowledge, has looked at the long-term military employment patterns and career pathways
that produce divergent outcomes among service members. This study uses a longitudinal sample
of 27 rounds over a span of 37 years with information on military connectedness, career
choices/changes, and life course outcomes. To paint a fuller portrait of how military personnel
fare in the AVF era, I construct military career trajectories over time, explicating the experiences
during service and employments after discharge. The results of this study provide up-to-date
descriptions of military affairs in the United States.
Data and Methods
I use data from the 1979-2016 waves of the 1979 National Longitudinal Survey of Youth
[NLSY79], covering a cohort of men and women who served in the All-Volunteer-Force era.
The NLSY79 is a national representative sample of 12,686 individuals aged 14 to 21 in 1979.
Respondents are interviewed annually from Rounds 1 through 16 (1979-1994) and biannually
thereafter (1996-2016), with a retention rate above 71% (Bureau of Labor Statistics 2018). My
results describe the work experiences of military personnel throughout their young adulthood till
mid- and late-30s (ages 36-37 by the end of the observation period), allowing consideration of
the effects of military services and educational attainments on labor market outcomes into the
professional midlife.
My target population is military respondents who have at least 15 years of observations
since they joined the military for the first time. Respondents must have 1) a valid first enlistment

41

date, 2) a valid last interview date, 3) valid annual non-interview indicators, and 4) valid military
pay grades and dependence status to be potentially selected into the sample. Among military
respondents who are potentially in my target population, 2,028 respondents who had a valid date
on which they joined the military for the first time. I exclude military respondents who had less
than 15 years of observations since the date of their first enlistment till the date of the last
interview; the sample is reduced to 809 respondents. I further exclude military respondents who
did not have valid military pay grades and dependence status during active duty, the sample is
reduced to 710 respondents. After all exclusions, the sample contains 11,360 observations on 710
military respondents
1415
.
Imputation
Imputations are made for respondents who have missing observations, using employment
information reported in the adjacent years (Pessin 2018). The lagged then forward variables are

14
There are two major sources of sample selectivity. The first one is involuntary attrition of NLSY in 1985, which
resulted in a significant smaller group of military respondents since. The NLSY79 military sample originally
included 1,280 respondents in 1979, but the figure was reduced to 201 respondents in 1985 due to funding cutbacks.
Though more than 200 respondents reported that they were newly enlisted in any branch of the military between
1979 and 1984, the figure of new enlisted reduced to less than 100 in 1985 immediately after the funding cut and to
a single-digit figure after 1996. The second source is the way that the analytical window is defined and constructed –
a selection of military respondents who have at least 15 years of observations since they joined the military for the
first time. Comparing with the target population of 2,028 military respondents, the analytical sample of 710
respondents have an overrepresentation of African Americans (20% vs. 41%) and Hispanics (6% vs. 15%) and an
underrepresentation of Whites (74% vs. 45%), females (36% vs. 11%), and younger enlisted (age at first enlistment
< 20 years of age) (62% vs. 58%). Moreover, the respondents of the analytical sample have marginally more pre-
enlistment education and final education and marginally more educated parents. Marital status and sibship size are
comparable.
15
Originally, a longer observation window of 20 years since the first enlistment was selected to construct an analysis
on labor market outcomes into the professional mid-life (e.g., early to mid-40s). Such a window resulted in a
considerably small analytical sample of 385 military respondents, which did not provide enough statistical power for
meaningful clustering solutions. The 20-year window was then relaxed to a 15-year window to consider professional
work life in the late 30s, which resulted in a larger sample size and the current clusters. A 10-year window was
considered but later discarded. One major limitation is this shortened window would allow an analysis of
employment patterns and labor market outcomes in the early to mid-30s, which does not fit the framework of this
dissertation. Moreover, the current clustering solutions indicate several clusters with different military-departing
points - Later Discharge, Midway Discharge, and Early Discharge, and Life-time Military Service. Shortening the
observation window will not differentiate those pathways fully. For this reason, an analytical window of 15 years
since the first enlistment is appropriate.

42

used to impute missing employment statuses. Missing values constitute between 4% and 6% of
each of the 15 years of observations since the first enlistment.
Identifying common career pathways
I use optimal matching to describe respondents’ life-course employment patterns and
career pathways. First, I define a year-by-year status sequence for each military respondent based
on military, education, and employment status. Second, I create a dissimilarity matrix that
measures how different any two status sequences are from each other. Third, I use the
dissimilarity matrix to create clusters of sequences that have similar patterns. The clusters of
employment sequences represent common career pathways and become an outcome variable in
the subsequent analyses.
Year-by-year status sequences
I describe service members and veterans long-term career pathways by defining a year-
by-year status sequence for each respondent since the first enlistment. The NLSY79 contains
information that identifies each respondent’s military and employment status, based on the start
and the stop dates of military services, jobs, unemployment, and out-of-labor-force position
(Bureau of Labor Statistics 2019). It also contains information that identifies school enrollment
status, documenting whether a respondent is attending regular school, and military training
status, documenting whether a respondent was receiving technical or vocational training during
military service. I use the labor force status, school enrollment status, and military training status
to construct the employment sequences that begin at the first calendar year after enlistment and
last 15 years.

43

I classify the employment status of a military respondent as (1) Education (E); (2)
Military Service (M); (3) Civilian Labor Market Employment (L); (4) Unemployment (U); (5)
Out of the Labor Force (OOLF); (6) Training and Education during Civilian Employment (CE);
(7) Training and Education during Military Service (ME). All statuses are mutually exclusive;
one respondent cannot hold two statuses concurrently. I define the Education status (E) as full-
time enrollment in regular school; the Military status (M) as full-time enlistment in any branch of
the military; the Civilian Labor Market Employment status (L) as civilian employment of any
type; the Unemployed status (U) as out of employment (military service or civilian employment)
but actively looking for jobs; the Out of the Labor Force (OOLF) status as not actively looking
for employment; the Education and Training during Civilian Employment status (CE) as
enrolling in regular school while working in the civilian labor market; and the Education and
Training during Military Service status (ME) as receiving technical training or vocational
education while being on active service. Therefore, there are seven yearly employment statuses –
E, M, L, U, OOLF, CE, and ME, and they are mutually exclusive and collectively exhaustive of
the employment status space.
Constructing a dissimilarity matrix
With the specification of a status space above, I then calculate the substitution costs
between any two statuses, based on the assumption that the substitution cost is lower between
two statuses when they are more easily transitioning from one to the other (Aisenbrey and
Fasang 2010; Killewald and Zhuo 2019).
Under my research assumptions, military service and civilian labor force employment are
two viable career options. Either is closely associated with and therefore transitions well with
education. The transition from education to military service is easier, because of a lower level of

44

education requirement prior to enlistment (e.g., high school degree or GED) or a generous
package of educational benefits available after discharge. The substitution cost is lower between
education and military than between education and civilian employment. Moreover, military
service is considered a possible alternative to civilian employment, and military experiences may
substitute for labor market experiences; these two statuses, therefore, share the same substitution
costs to the Unemployment status and the Out of Labor Force status. The hybrid statuses, 1)
Education and Training during Military Service (ME) and 2) Education and Training during
Civilian Employment (CE), indicate additional human capital accumulation. These additional
training and education are valued by the military and in the civilian labor market; they transition
well with full-time education (ME or CE to E) and corresponding full-time employment status
(ME to M and CE to L, or vice versa). They are increasingly discrepant from unemployment and
OOLF. The Unemployment status and the Out of the Labor Force status do not have paid work
hours; they are similar to each other. However, the Unemployment status is relatively closer to
the Civilian Labor Market Employment status because unemployed respondents are still in the
labor market; they may be in between jobs and actively looking, so a link to employment is
maintained. Table 3.1 shows the custom substitution cost matrix.

Table 3.1 Substitution Cost Matrix.
Identifying clusters

45

I use cluster analysis to identify groups of respondents with similar status sequences.
Following Aisenbrey and Fasang (2010) and Kelliwald and Zhuo (2019), I use the Partitioning
Around Medoids (PAM) algorithm with six potential clusters. To generate a six-cluster solution,
the program selects six random medoids as seeds and partitions around these seeds. The
algorithm iterates by changing the selection of seeds till the selected seeds and associated
assignment of sequences to clusters minimizes the sum of dissimilarities from each sequence to
the seed of its assigned cluster (Kaufman and Rousseeuw 2008; Reddy and Vinzamuri 2013). A
medoid is the central data point in cluster; it is the least dissimilar from all other sequences
within the same cluster (Reddy and Vinzamuri 2013). Since clusters solutions are sensitive to the
initial random draw of seeds (Aisenbrey and Fasang 2010), I use an algorithm with one hundred
random starts and select the best fit. The result of the algorithm does not necessarily indicate that
there are exactly six military-related career pathways; based on the AVF sample provided by the
NLSY79 (1970 – 2016), six clusters is one reasonable solution based on model fit, statistical
convergence, and empirical interpretability. A discussion of solutions with different numbers of
clusters is presented in the Appendix.
Results
Employment patterns and career pathways

46

Figure 3.2 Employment Status Distribution [NLSY79] (N = 710)..
Figure 3.2 shows the distribution of the military respondents in each employment status
from the first-year enlistment to the fifteenth-year post-enlistment. At initial enlistment, 40% of
enlistees start with technical training or vocational education while serving (cranberry); 60% of
enlistees work in full-time military occupations (navy blue). The share of military respondents
who receive training or education while serving decreases gradually to 20% between the second
and fifth enlistment year then reduces steadily to approximately 10% at the 15
th
year since
enlistment. Opportunities for technical training and vocational education are generally provided
throughout the military career. The share of military respondents who hold full-time military
occupations expands to 70% by the end of first-year enlistment; it then falls progressively to
approximately 20% by the 11
th
year and stays around 20% throughout the remaining period. This
result confirm that a smaller percentage of service members remain in service for a long-term
military career (Segal and Segal 2004). Immediately following the first anniversary of
enlistment, military respondents start career transition between the military and the civilian labor

47

market, indicated by an expansion of the “Employment” status (forest green) at the one-year
mark. The share of civilian employment increases and consists of approximately 60% of all
respondents by the of the end of observation period. Additional education during civilian
employment (lavender) gains significant share, starting around the 5
th
year (4 years since the
beginning of transition into civilian employment); it expands to approximately 20% at the end of
the observation window. The shares of unemployment (orange) and OOLF (teal) never exceed
5%, and the share of full-time education is trivial. Education or training is either obtained during
military service or acquired after discharge and during civilian employment.
There are two highlights of examining the employment sequences of military personal
over a long period of time. First, there are substantial changes in employment statuses since the
first enlistment. Though some serve for life, a significant share of service members switches
paths after spending some time in the military. Second, military personnel receive substantial
training during the military career. Though most trainings concentrate at the beginning of the
enlistment, human capital accumulation continues throughout the service period. Upon
discharge, many veterans choose to acquire additional education while holding civilian
employment. The changes in career pathways and accumulation of human capital during
employment would be overlooked when the observation window ends only a few years after the
first enlistment.

48

Figure 3.3 Medoids of A Six-Cluster Solution [NLSY79] (N = 710).
Figure 3 shows six medoids, or centers of clusters, of the cluster solution. Each of the
medoid represents the least dissimilar sequence from all other sequences within the same cluster
(Reddy and Vinzamuri 2013); it is the best representation of each cluster and, in this case, each
career pathway. Twenty-three percent of the military respondents are in the Life-time Military
Service cluster (cluster 1), serving for at least 15 years since the first enlistment. Working full-
time in military occupations, the medoid of this cluster receives numerous spells of technical
training and vocational education, including one-year of initial training at the beginning of the
service and two additional training around the fifth and ninth year since the first enlistment.
Three percent of the sample is in the Re-enlistment cluster (cluster 6), with a medoid
characterized by 6 years of combined technical training and military occupation. Upon discharge,
the representing medoid acquires an additional year of education while working in the civilian
labor market then transitions into full-time civilian employment. It eventually re-enlists in the

49

military around the 9
th
year and serves for the remaining period. Seven percent of the sample is
in the Multiple Statuses cluster (cluster 5). The medoid sequence is approximately 4 years
military occupations, followed by 5 years of civilian employment before entering a multiyear
period of unemployment then OOLF; it eventually re-enters the civilian labor market.
The final three clusters are Late Discharge (22%), Midway Discharge (24%), and Early
Discharge (21%), differed by the lengths of military service. The medoid of the Late Discharge
cluster (cluster 2) is one year of initial training followed by 7 years of full-time military
occupations. Upon discharge, it acquires 1 year of education while working in the labor market
then starts its civilian employment approximately 10 years after the first enlistment. The medoid
of the Midway Discharge cluster (cluster 3) is one year of initial training before working full-
time in military occupations till the 5
th
year of enlistment, followed by 1 year of education then
civilian employment for the next decade. Since a typical initial contact lasts between three and
six years, the Midway Discharge cluster is also known as the “One Contract” cluster. The
medoid of the Early Discharge cluster (cluster 4) is one year of initial training before working
full-time in military occupations till the 3
rd
year of enlistment, followed by civilian employment
in all subsequent years.
Analyses with a short observation window covering only the first few years after the first
enlistment will not distinguish the Late Discharge, Midway Discharge, Early Discharge, and the
Life-time Military Service clusters. The above result shows the diverse employment patterns and
career choices of service members who eventually depart from the military and reenter the
civilian world.

50

Figure 3.4 Employment Sequences, by Cluster [NLSY79] (N = 710).
Figure 3.4 shows all employment sequences by cluster, with each row reflecting the
employment sequence of one military respondent. The clusters differ substantially in their
military service, educational attainment, and labor market experiences over 15 years since the
first enlistment. Initial technical training and vocational education is provided for many enlistees,
regardless of the length of military service and later employment statuses (e.g., cluster 4 – Early
Discharge, cluster 3 –Midway Discharge, and cluster 2 – Late Discharge). Military respondents,
who serve for life, tend to receive multiple spells of technical training and vocational education
during service (cluster 1 – Lifetime Military Service). Military respondents, who receive little
technical training or vocational education before re-enter the civilian labor market (cluster 5 -
Multiple Statuses), tend to experience numerous spells of unemployment and OOLF after
discharge. A selected group of service members are associated with many educational
opportunities in and out of the military (cluster 6 – Re-enlistment); these military respondents

51

receive numerous spells of training during service and acquire additional education after
discharge. The occurrence, timing, and order of employment statuses are heterogeneous across
clusters, confirming the variation in employment patterns and career pathways among military
personnel in the AVF Era indicated by the medoids in Figure 3.3.
Education - is military a conduit to education during and post-service?
Education benefits have become the most commonly cited reason for joining in the
military in the United States (McMurray 2007). The results of sequence analysis and clustering
solution support that military provides substantial training and education opportunities to service
members; it serves as a conduit to education in the AVF era. Though the timing, sequencing, and
context of education are different across various military-related career, it is well represented in
clusters (Figure 3.3) and sequences (Figure 3.4).
Initial Training. New enlistees from five out of six career pathways receive
approximately 1 to 2 years of technical training or vocational education at the beginning of the
service, preparing one for a specialized military occupation. As it is discussed in Chapter 2,
about half of active-duty personnel acquire specialized training, and such training concentrate in
the early years of the initial enlistment (Congressional Budget Office 2015). The research results
support the emphasis of education while serving.
Subsequent Training. Subsequent trainings are differentiated by timing and context. In
terms of timing, two pathways, Life-time Military Service Pathway (pathway 1) and Re-
enlistment Pathway (pathway 6), indicate that selected groups of service member receive
technical training and vocation education in addition to the initial training offered at the
beginning of service while serving. For lifetime service members, two additional episodes of

52

education are received throughout the career; for re-enlisted service members, one extended
episode of education is identified before the end of the initial contact. Though cluster solutions
(Figure 3.3) indicate discrete episodes of training and education, it is shown in the sequence
diagram (Figure 3.4) that education is offered on a continuous basis, supporting the expectation
that military sets up channels for continuous learning. In terms of context, three pathways, Late
Discharge Pathway (pathway 2), Midway Discharge Pathway (pathway 3), and Re-enlistment
Pathway (pathway 6), indicate additional human capital accumulation during civilian
employment. These episodes of training and education are short at approximately 1 to 2 years,
but they support the expectation that a sizable body of veteran students join their civilian
counterparts to receive further education after discharge. Though post-discharge education does
not constitute a distinct episode for the Early Discharge Pathway (pathway 4), the sequence
diagram (Figure 3.4) shows that selected veterans from this cluster choose to receive additional
education in various points during the life course. These results likely support the usage of
educational benefits provided by the GI Bill.
Overall, education is an important component of all employment patterns and career
pathways identified in the study; military, therefore, is a conduct to education in the All-
Volunteer Force Era.
Social Correlates
Following the sequence analysis and clustering solution, I describe the demographic
characteristics, human capital, family backgrounds and experiences, and attitudes and
expectations associated with each career cluster in the section below. Descriptive statistics are
shown in Table 3.2.

53

Demographic characteristics
Gender is binary variable measured as male or female. Race/ethnicity is a categorical
variable measured as Hispanic, non-Hispanic African American, and non-Hispanic respondents
of other races, which I designate as “non-Hispanic White” for simplicity
16
. Information on
gender and race/ethnicity are collected during the NLSY79 household screenings; I use the initial
assignment recorded in 1979. I also include the age of the first enlistment by two categories: less
than 20 years of age and between age 20 and 25
17
.
Human capital
Pre-enlistment education is measured by highest grade completed in the calendar year of
the first enlistment; there are four categories: no high school diploma (less than 12
th
grade), high
school diploma (12
th
grade), some college (one to three years of college), and college degree or
more (four or more years of college). Final education is measured by highest grade completed in
the last observation year, 15
th
year since the first enlistment; the categories are the same as the
ones for pre-enlistment education.
Family backgrounds and experiences
The pre-enlistment marital status of the respondent is measured in as married or not
married. For family backgrounds, I include region of residence (Northeast, North Central, South,
and West), urban/rural residence and sibship size (top coded at 4) at the first enlistment. I further
include respondent’s mother’s and father’s education attainment and employment status when

16
3% of the respondents in the “non-Hispanic White” category report a nonwhite, first ethnicity, and 4% have either
an unspecified first ethnicity or the first ethnicity is missing.
17
The minimum age for enlistment in the United States military is 17 (with parental consent) and 18 (without
parental consent), and almost all enlistment happens before age of 25. The cutoff point of 20 is selected because the
military reported an average enlistment age of just under 21 in 2018 (Department of Defense 2018).

54

the respondent was age 14. For respondents who did not live with their biological mother or
father at the age 14 but did live with a stepparent, I use the education and employment measures
of a stepparent, if available. Parental education is measured in the same categories as the
respondent’s education. The binary variable of parental employment is based on the employment
status when the respondent was 14, indicating if a parent was employed.
Attitudes and expectations
I include three indicator variables on respondents’ attitudes and expectations towards
military service, future employment, and family planning, each measured in 1979. I use two
categories to measure the approval attitude of an influential person (e.g., parent, stepparent, or
grandparent) to the respondent’s decision to join armed forces: approve or disapprove. The
respondents report their expectation to work at age 35 – yes or no/don’t’ know – and the number
of children desired, top coded at 4.
Social correlates of military employment patterns
Table 3.2 summarizes the weighted means and proportions, when appropriated, of
demographic characteristics, human capital, family background and experiences, and attitudes
and expectations associated with each cluster. Between cluster differences in average
characteristics are statistically significant for all variables, except for family attitude towards
military service, preference for employment at age 35, and the number of children desired.
Some salient features of the clusters include: Female military respondents comprise 11%
of the sample, and they are overrepresented in the Multiple Statuses group (26%). White
respondents (45%) and African American respondents (41%) comprise the majority of the
sample. There are more White service members (61%) who choose to re-enlist, and there are

55

more Black service members (60%) who hold multiple statuses and are in and out of the labor
market after discharge. Most enlistees enter the military before age 20 (58%) and have a high
school degree (73%). Less than one-fifth has some college education or a college degree; this
figure increases to 50% 15 years after the enlistment. The lifetime Service cluster have
disproportionately higher shares of college educated enlistees prior to enlistment (8%); the Late
Discharge cluster gain a significant share of college educated individuals at the end of the
observation window (from 2% to 27%). Most of the respondents are from the Southern states
(43%) and urban areas (58%) of the United States. Many of their mothers have a high school
degree (40%) and worked (54%) when the respondent was age 14. Comparing with mothers,
fathers are less educated (only 34% had a high school degree), but an overwhelm majority was
working (92%). Most service members received family approval to join the military (87%) and
expected to work at age 35 (94%). They expressed the desire to have children in the future, but
these three figures are not categorically or jointly significant; for this reason, these variables are
not included in further analyses in the subsequent chapters.
Limitations
The counts and modal patterns of military pathways better explicates the different career
trajectories followed by service members in the AVF Era. There are several data limitations.
First, the current sample does not differentiate the specialties of initial training (e.g., technical,
administrative, medical, etc.) and classifications of subsequent training (full-time college
education vs. part-time training). Such a limitation does not impact the result that military
supports continuous education in general.

56

Table 3.2 Weighted Means and Proportions by Clusters (N = 710).

57

Table 3.2 (Continued).
Second, the availability and the usage of the GI Bill is not available; however, the results
show that there is significant demand of post-service education among veterans (e.g., veterans
from the Midway and Late Discharge pathways), supporting the policies and practices to assist
veteran students in successfully complete further education.
Third, selectivity is a limitation of the current analysis. More than four fifths of the
original 1979 military sample were removed in 1985 due to funding cut, so many military
respondents who joined the military during earlier periods were not eligible to be considered for

58

a 15-year status sequence construction. When looking at the years that the military personnel
joined the military for the first time, the result shows that the respondents of the analytical
sample joined more often in the later years than the target sample. In other words, the analytical
sample represented a military population that is closer to the modern time, during which African
Americans and other minorities are overrepresented in the military. Moreover, female service
members represent about 16% of the current force. At 11%, my analytical sample is also closer
to that figure than the targeted sample. One possible impact of the sample selection is that the
sequence analysis and clustering solution would have similar, if not the same, sequences and
clusters, but in different proportions.
Conclusion
This chapter discusses the distinct employment patterns and career pathways of military
personnel in the All-Volunteer Force era. I begin the chapter with a topology of career pathways
involving military, education, and civilian employment. Focusing on employment patterns
following the first enlistment, I document the motivations and justifications to enter the military,
the terms and conditions to establish a lifetime career, and the potential benefits and outcomes
associated with various pathways. Based on the sequence analysis and clustering solution, I
identify six military employment patterns, including 1) lifetime military service, 2) early
discharge, 3) midway discharge, 4) late discharge, 5) multiple statuses, and 6) re-enlistment. One
major finding is that military serves as a conduit to education during and post service; human
capital accumulation continues throughout the life course of service members and veterans.
Moreover, service members and veterans from different career clusters have diverse
demographic characteristics, human capital, family background and experiences, and attitudes
and expectations. Descriptive results indicate that different pathways are associated with

59

difference experiences of military service, as well as different short- and long-term consequences
in the life course. Overall, my results explicate different aspects of military employment patterns
– whether to separate from the military once enlisted, when to separate and whether to return to
military, and how much training and education to obtain while working in the military or in the
civilian labor market. This chapter illustrates the sequencing of life events among military
personnel and sets the foundation for understanding their mid-life socioeconomic attainment and
later-life outcomes. The next chapter studies the associations between social correlates and
career clusters/pathways with multivariate analyses, examining hypotheses that theorize the
choices of selected demographic groups.

60

CHAPTER 4
CLUSTER MEMBERSHIP AND SOCIAL CORRELATES
Introduction
In the previous chapter, I identified six common military career pathways. The
representative employment patterns are: 1) lifetime military career with multiple spells of
trainings during service (Lifetime Military); 2) Military service lasting 10 years (Late
Discharge), 5 years (Midway Discharge), or 3 years (Early Discharge), followed by a transition
to the civilian labor market; 3) inconsistent employment statuses starting with military service
then a multiyear spell of unemployment and out of the labor force (Multiple Statuses); 4)
Military and civilian workforce contacts with an eventual re-enlistment (Re-enlistment). My
sequence analysis and clustering solution show great variability in employment patterns of AVF
service members since their first enlistment.
The previous chapter also shows that different demographic characteristics, human
capital, family background and experiences, and attitudes and expectations prior to enlistment
are associated with different career pathways. The patterns of service members’ career pathways
and how they vary with socioeconomic characteristics are only partially understood with mean
comparisons. Prior research has largely considered how veteran status alone is linked to various
labor market outcomes, concluding that the associations between social correlates and status
attainment vary by cohorts and socioeconomic characteristics (Baily and Sykes 2018; Edward
2016; Teachman 2004, 2007). Two important aspects are overlooked. First, as it is shown in
Chapter 3, service members in the AVF era have taken vastly different career pathways before
reaching a life-course destination. Some serve for life, some separate from the military and enter
the civilian labor market with skills acquired during temporary service, and some accumulate
additional human capital with generous educational benefits while working in the civilian

61

workforce. Different career pathways are likely associated with various subgroups of service
members, differentiated by early-life characteristics and circumstances (e.g., socioeconomic
status). Second, women, in general, have different mobility patterns than men, and the career
pathways of female service member in the AVF era have not been well unpacked due to data
limitations (Baily and Sykes 2018). The intertwined role of military service, education, and
family is a key element to better understand female status attainment and mobility patterns in
contemporary America. This chapter aims to examine the long-term employment patterns among
male and female military personnel, painting a more complete picture of the life course outcomes
among service members.
This chapter goes beyond mean comparisons and evaluates how conventional
socioeconomic predictors of employment are associated with different military pathways using
multinomial logit models. The results show that unique social correlates are more strongly
associated with distinctive career pathways among AVF service members. What characteristics
and traits predict these pathways can help us better understand heterogeneity in service
members’ experiences at the intersection of military service, education, and work that have long-
term consequences for their wage outcomes, which is further explored in the next chapter.
Social Correlates and Hypotheses
Social correlates may independently or jointly affect the likelihood of military service,
employment patterns, and career pathways among service members.
Effects of misogyny - is there a gendered pathway?
There is an increasing female representation in the All-Volunteer Force. Women
compose 16 percent of enlisted forces and 21 percent of officer corps in 2017 (Reynolds and

62

Shendruk 2018). Three general aspects that impact the female service members’ career paths and
life course outcomes are discussed; they are 1) gender beliefs, 2) work-family conflicts, and 3)
structural characteristics of military work. These three perspectives generate hypothesized
military pathways to be gendered; some pathways are male typed, and others are female typed.
The gender belief perspective highlights the ways that women’s gender role and
expectations about employment shape their life course trajectories (Damaske and Frech 2016).
Prior research has found that women have more traditional gender role attitudes than men
(Damaske and Frech 2016; Hynes and Clarkberg 2005), and they are therefore less likely to enter
nontraditional occupations or be employed at all. Military service is considered a male-
dominated occupation/career in the United States; a majority of service members are currently
male (Lofquist 2017). I expect that women are more reluctant to choose a military career, and if
a military career were chosen, female service members are more likely to exit the military and
enter paid workforce or unpaid household labor.
The work-family conflict perspective emphasizes the unequal division of labor between
males and females, Men tend to hold formal employment and shoulder breadwinning duties in
the labor market; women tend to perform informal housework and shoulder childrearing duties at
home with or without formal employment (Becker, 1981; Sayer 2010). Military service is known
as the “extreme work;” it generates significant structural, physical, psychological, and behavioral
tensions with family life, demanding substantial attention and energy from service members
(Wadsworth and Southwell 2011). I expect female service members to face more tension
between military work and family, shortening their military service or experiencing multiple
statuses in and out of employment.

63

Although the official ceiling on women’s military participation has been lifted with the
initiation of the all-volunteer force in the early 1970s (Kelty and Segal 2013), female service
members still face various structural obstacles that may impact their decisions to remain in or
separate from the military. First, the current ground combat exclusion bars women from serving
in certain combat units (Kamarck 2015), restricting the number of female enlistees and officers
and closing doors to some of the most prestigious positions in the military (Kelty and Segal
2013). This policy creates a potential ceiling in military occupations for female service members
and, therefore, impacts their decision to stay with the military and develop a lifetime career
(Stewart and Firestone 2001). Second, the all-volunteer force leads to a greater acceptance of
women in military roles; however, sexism exists, and female qualities and characteristics are
often overlooked (Snyder 2003). Physical violence and sexual harassment, resulted from military
culture of hypermasculinity and hostile attitude toward women, propel female service members
to leave the armed forces before reaching retirement (Downs 2017). With these two structural
hurdles mentioned above, I expect female service members to leave the armed forces after a
short period of service and transition to other employments rather than a military career, echoing
my expectation based on gender beliefs (Kelty and Segal 2013).
Hypothesis 1: A lifetime military career pathway is a male typed pathway; relative to
male service members, female service members will be less likely to enter a lifetime military
career.
Hypothesis 2: Military pathways with shorter service terms and multiple statues are
female typed pathways; relative to male service members, female service members, who
eventually separate from the military, are more likely to serve shorter terms or have multiple
statuses.

64

Effects of racism - is there a pathway more typical from service members of color?
The landscape of the U.S. labor market has changed drastically during the 1970s and
1980s. Many jobs in the manufacturing industry that did not require college education, provided
on-the-job training, and offered decent compensation were replaced by jobs in the finance,
service, and technology industry that demand higher education and advanced skills in the AVF
era (Collins and Mayer 2010). Finding a job that pays a livable wage, climbing a career ladder
that leads to higher positions, achieving financial independence that provides for the family, and
making a smooth life-course transition from high school to the labor market have been
increasingly difficult for socioeconomically disadvantaged groups, many of whom are racial
minorities (Zhou 1993). The growing mismatch of labor demand and labor supply has been
particularly harmful to the employment prospects of African Americans, Latinos, other less
advantaged persons, who become increasing aware of the difficulty of finding and securing
employment without advanced training (Holzer and Danziger 2001). Besides the conventional
labor market, military service serves as an alternative pathway from high school to
socioeconomic attainment. It provides a compensating wage differential to service members on
active duty (Martorell, Miller, Daugherty, and Borgschute 2013) and pays for food, housing,
medical, education, and other intangible benefits, such as occupational training and leadership
development (O’Brien 2008). These benefits are not readily available in the civilian labor
market. Moreover, military values diversity and equal opportunity. It treats racial monitories
more fairly with structured promotion and compensation; the pay gap between Whites and
minority service members is considerably narrower in the military than in the civilian labor
market (Booth and Segal 2005). I therefore expect African American and Hispanic service

65

members to remain in service for a longer period, preferring a later discharge or a lifetime
military career.
Hypothesis 3: Relative to white service members, service members of color are more
likely to serve longer terms or develop a military career with Later Discharge pathway and
Lifetime Career pathway.
Old habits die hard - is there a pathway more preferred by younger enlistees?
Almost all enlistments occur between age 18 and 24 (Teachman 1993), and this is the
typical age range of getting higher education and finding the first employment in the civilian
workforce. Service members who enter the military at earlier ages may be behind their civilian
peers in formal schooling; service members who enter the military at older ages may have
completed more education and being closer to their desired education level (Teachman 2005).
Since the overall educational attainment among the U.S. population has been rising (Bailey and
Sykes 2018), the overlap in ages at which college attendance and military service happen means
that younger enlistees may increasingly fall behind than their civilian counterparts. I expect
service members who enlist at earlier ages to treat military service as a transitory phrase; they are
more likely to enter the civilian labor market after shorter terms of services and catch up in
human capital accumulation upon discharge. Moreover, military is a significant event that
interrupts the normal life-course patterns (Wilmoth and London 2013). At younger ages,
individuals are less likely to have complete plans for the life course. At older ages, many
investments have been made in planning and structuring subsequent civilian life. Enlistees who
enter late may have formed families, found employments, and established career paths. Given
military tends to create severer disruptions for late entrants, I expect them to remain in the
military for an extended period, (re)establishing stability in life.

66

Hypothesis 4: Younger enlistees are more like to treat military as a transition and enter
the civilian labor market after shorter terms of service.
Hypothesis 5: Older enlistees are more like to use military as a turning point and serve
longer terms or turn military service into a career.
Not fitting in - are there career pathways stratified by pre-enlistment education?
Educational attainment during young adulthood has short-term implications for
employment and long-term implications for economic well-being, and the associations tend to be
positive (see Bonnie 2015 for a review). Regardless of active or reserve status, more than 80
percent of enlisted forces have high school diplomas and about 85 percent of officers have
college degrees (Department of Defense 2017). There are two possible associations between pre-
enlistment human capital and employment patterns among service members. On the one hand,
those who have less formal schooling before enlistment may treat military as a springboard for
educational attainment. I expect military to be a transitory step taken before assuming alternative
employment in the civilian labor market. On the other hand, selection into various military
occupations, such as engineering, science, and technical occupations, are based one’s education
level. Those who have higher education before enlistment may be qualified into specialized
military occupations or be pipelined into the officer corps (Kelty and Segal 2013), both of which
provide significant economic rewards in the long run (Martorell, Miller, Daugherty, and
Borgschute 2013). I expect service members with more prior education to develop a long-term
military career. There are likely other factors that jointly impact individual’s human capital and
labor supply; the net association between career pathways and pre-enlistment human capital is
empirical tested in this chapter.

67

Hypotheses 6: Service members with higher levels of pre-enlistment human capital are
more likely to serve longer terms or develop a military career.
Don’t have a choice - is there a pathway more typical among services members of lower
SES?
As it is seen in the Modified Conceptual Model of Social Stratification, socioeconomic
origins impact socioeconomic destinations (Blau and Duncan 1967; Gugushvili, Bukodi, and
Goldthorpe 2017). The processes for mid-life status attainment and late-life outcomes are
influenced by family socioeconomic status (Blau and Duncan 1967; Wilmoth and London 2013).
Parental education and employment often serve as a proxy for early life economic resources
which translate to better life course outcomes (Blau and Duncan 1967; Duncan, Featherman, and
Duncan 1972; Erola, Jalonene, and Lehti 2016; Killewald and Zhuo 2019). Individuals, who lack
such resources, would need a mechanism that generates pathways leading to better rewards.
Parental SES, therefore, attribute to children’s SES over the life course (Erola, Jalonene, and
Lehti 2016). Military service is serves as a mechanism that moderates potential negative impacts
of lower SES and reshapes the life courses by providing individuals with opportunity to receive
training, education, skills, and resources that they would not have had, putting them on different
and better life-course trajectories (Bennett and McDonald 2013; Teachman and Tedrow 2003,
2007). I expect military members of lower socioeconomic background (e.g., less educated and
unemployed parents) to treat military services as a social and economic resource generator and
therefore select more stable and consistent employments and pathways throughout the life
course, such as a lifetime military service or a later discharge.

68

Hypothesis 7: Service members of lower family SES are more likely than service
members of higher family SES to have stable and consistent employments throughout the life
course (e.g., a Later Discharge pathway or a Lifetime Military Career).
Other family characteristics
Other family circumstances also likely impact the decision to join the military (Wilmoth
and London 2013). Marriage and military retention are closely associated due to career
orientation, job satisfaction, and military compensation (Hull 2008). Married military personnel
tend to have higher career motivation and job performance than unmarried ones; they receive
promotion faster and have less job-related problems during service (Burnam et al., 1992; Hogan
and Seifert 2009; Orthner et al., 1992; Ryan and Bevilacque 1994). I expect married service
members to remain longer in the military.
Children from large families tend to receive few resources (Becker and Lewis 1973;
Becker and Tomes 1976; Steelman et al. 2000). If individuals demand more resources, such as
education, than what parents could provide, they are more likely to join the military. With
guaranteed (and required, if seeking a lifetime military career) development and accumulation in
human capital, I expect service members with more siblings to serve longer. Similarly, military
personnel from rural communities with constrained employment opportunities are more likely to
be drawn into the military disproportionately. Individuals from communities with a large military
presence where the culture of honor is preserved and present, such as the states in the South and
the West with higher concentration of military installations, are also more likely to enlist and
serve for longer terms.

69

Hypothesis 8: Relative to service members who are single, service members who are
married at enlistment are more like to remain in the military for a later discharge or a lifetime
military career.
Hypothesis 9: Service members who have more siblings are more likely to serve longer
terms or develop a lifetime military career.
Hypothesis 10: Service members from rural communities or states with a large military
presence (e.g., the South and the West) are more likely to serve for longer periods or develop a
lifetime military career.
Attitudes and expectations
Family attitudes and individual expectations toward military service, future employment,
and family formation are not individually or jointly significant when tested in Chapter 3.
Therefore, family approval, work expectation at age 35, and number of children desired are no
longer included in the forthcoming analysis.
Modeling Military Employment Patterns
The six clusters of employment status sequences from Chapter 3 represent the common
career pathways of military personnel in the All-Volunteer Force era. In this chapter, I apply
multinomial logit model to identify the associations between career pathways and social
correlates, including demographic characteristics, human capital, and family background and
experiences. My estimations are based on pre-enlistment traits, asking what early-life
characteristics and circumstances may predict the later-life employment pattern that service
members will follow.

70

I further generate the predicted probabilities of membership in each cluster from the
multinomial logistic regression, setting the value of each covariate to its reference category and
then changing the value to an alternative category (Kilelwald and Zhuo 2019), for example,
changing region of residence from Northeast (reference) to South. I then calculate the average
changes in predicted probabilities for each covariate by subtracting the predicted probability of
the reference from the predicted probability of an alternative and average the differences across
observations. The result indicates the average percentage change in predicted probability of
membership in a given cluster when a covariate changes from its reference to an alternative.
I estimate models on the military sample with all covariates described above. These
covariates include gender, race and ethnicity, and age at first enlistment; pre-enlistment
education; marital status, region of residence, urban/rural residence, and sibship size; mothers’
and fathers’ education and employment status when the respondent was age 14
1819
. The
descriptions of these variables are in Chapter 3.
Regression model
Base on the predictors described above, there are five corresponding models, each with a
different reference cluster, including Lifetime Military Service, Late Discharge, Midway
Discharge, Early Discharge, and Multiple Statuses. The basic model is

18
Upon checking the correlation between mother’s and father’s education and employment, pairwise, collinearity is
detected. Since status characteristics of parents, especially the ones of fathers, explain approximately half of
children's outcomes, and these explanations do not vary significantly during children's life course (Erola, Jalonene,
and Lehti 2016), father’s education and employment were selected into the model.
19
Upon testing the interaction terms between 1) mother’s and father’s education and employment and 2) gender of
the respondent (not shown), the results showed that father’s education and employment are more important for both
daughters and sons.

71

ln (
𝑃 ( 𝑐𝑙𝑢𝑠𝑡𝑒𝑟 = 𝑎𝑙𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑣𝑒 )
𝑃 ( 𝑐𝑙𝑢𝑠𝑡𝑒𝑟 = 𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 )
) = 𝛼 + 𝛽𝑋 + 𝜖 , 𝑤 ℎ𝑒𝑟𝑒 𝑋 𝑖𝑠 𝑎 𝑣𝑒𝑐𝑡𝑜𝑟 𝑜𝑓 𝑖𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠
20

Results
Table 4.1 shows the results of the multinomial logistic models. I start with a model
setting the Life-course Military Service pathway as a reference, a pathway that offers a
consistent employment since enlistment. The subsequent models (results not shown) use
alternative references with reduced employment consistency, from late discharge to midway
discharge to early discharge to multiple statues to re-enlistment. The Multiple Statuses pathway
and the Re-enlistment pathway are comparatively less stable and consistent. They both involve
numerous statuses in addition to military service, civilian employment, and related training and
education, and the sequencing of events is less conventional. I include these two pathways to
further examine the variation of associations between social correlates and military employment
patterns.
The results describe how changes in the predictors are associated with changes in the
odds of being in a selected cluster verses the reference. Positive coefficients indicate a positive
association with odds of membership in the selected cluster verses the reference. I focus on the
most robust social correlates that distinguish career choices among service members in the AVF
era.

20
X is a vector of independent variables, including gender, race, age group, pre-enlistment education, marital status,
region of residence, rural resident, sibship size, father’s education, and father’s employment when respondent was
14.

72

Table 4.1 Weighted Multinomial Logistic Regression Results (N = 710).

73

Table 4.1 (Continued).
Inter-cluster comparisons
All else equal, the relative log odds of being in the Midway Discharge cluster, Early
Discharge cluster, or Multiple Statuses cluster versus in the Lifetime Service cluster will increase
by 0.08, 0.18, or 1.64 if a service member is a female than male. In other words, female service
members have 1.08 times, 1.20 times, and 5.20 times greater odds than their male counterparts to
choose the Midway Discharge path, Early Discharge path, or Multiple Statuses path versus the
Lifetime Service path. Therefore, the expected risk of being in these clusters with shorter
services is higher for female service members. They are, thus, less likely to enter a lifetime
military career. Their preference for other career pathways is statistically significant, except the
Late Discharge or Re-enlistment pathway, for which there is no significant association. Should
female service members choose to separate from the military, they prefer shorter terms of
service. For a female service member, the odds of being in the Midway Discharge cluster, Early

74

Discharge cluster, or Multiple Statuses cluster versus in the Late Discharge cluster are 2.48
times, 2.75 times, and 6.69 times, as large as the odds for a male counterpart
21
. If separating from
the military, women tend to discharge early and pursue other career options. Moreover, there is
strong evidence that women are more like to experience multiple statuses, referenced to all other
pathways. Exclusion policies that prevent female service members from ascending in certain
military units or work-family conflicts that create strains for women in general are possible
explanations for shorter services or multiple statuses. Gender strongly distinguishes among
career clusters, increasing predicted probability of memberships in pathways associated with
shorter services or multiple statuses among female service members.
There is empirical evidence that minority service members are more likely to serve
longer terms or develop a military career, relative to their White counterparts. Black service
members are more likely to serve for life. For an African American service member, the odds of
being in the Early Discharge cluster, for example, versus in the Lifetime Service cluster is 56%
less than the odds of a White service member
22
, indicating the expected risk of being in the
Lifetime Service cluster is higher for African American service members. The odds of being in
the Late Discharge cluster or the Midway Discharge cluster versus in the Lifetime Service cluster
are also less for Blacks than Whites. Lifetime Service seems to be a viable career choice for
African American youth. Should Black service members choose to leave the service, they are
more like to serve longer terms and have a later discharge. Unexpected, Black service members
are also more likely to experience multiple statuses once separate from military; for African

21
The relative log odds of being in the Midway Discharge cluster, Early Discharge cluster, or Multiple Statuses
cluster versus in the Late Discharge cluster will increase by 0.91, 1.01, or 1.90, respectively, if a service member is a
female than male.
22
The relative log odds of being in the Early Discharge cluster, for example, versus in the Lifetime Service cluster
will decrease by 0.81 for an African American than White, so the relative risk ratios for being in the Early Discharge
cluster versus in the Lifetime Service for being African American than White is 0.44 (less than 1).

75

Americans, the odds of being in the Multiple Statuses cluster versus in the Lifetime Service
cluster or in the Late Discharge cluster are 2.03 times and 1.58 times as large as the odds for
Whites
23
. Military values equal opportunity, but statistical discrimination and stereotyping are
often found in the civilian labor market. The veteran status may not always serve as a positive
signal to employers, and Black veterans may end up with a prolonged job searching period.
Hispanic service members follow similar military employment patterns, except they do not tend
to experience multiple statuses once separated from the military. All else equal, White service
members are uniformly more like to be re-enlisted then minority service members.
The results on age at first enlistment is as expected. Relative to younger enlistees, older
enlistees are more likely to serve longer terms and have a later discharge if they choose to
separate from the military
24
. However, there is insufficient evidence that they are more likely to
develop a military career. The general results on longer service terms engage the argument that a
life-course, in general, is largely age graded, and older entrants tend to encounter more role
conflicts when military service interrupts the normal progression. To find more life balance and
re-establish stability, these enlistees are more like to remain in the military.
A positive association between levels of pre-enlistment human capital and length of
military service is generally found. In reference to the Lifetime Military Service pathway, less
educated service members are more likely to discharge early or experience multiple statues.
College educated service members are more likely to remain in the military; their odds of being
in the Late Discharge, Midway Discharge cluster, Early Discharge cluster, or Multiple Statuses

23
Relative log odds of being in the Multiple Statuses cluster versus in the Lifetime Service cluster or in the Late
Discharge cluster will increase by 0.71 or 0.46, respectively, if a service member is Black versus white.
24
The relative log odds of being in the Midway Discharge cluster or Early Discharge cluster versus in the Late
Discharge cluster will decrease by 1.10 and 0.89, respectively.

76

versus in the Lifetime Service cluster are 3 times, 4.95 times, 6.82 times, and 3.67 times as large
as the odds for service members with less pre-enlistment education
25
. One possible explanation is
that highly educated enlistees are likely to join the officer corps and develop a rewarding career
with the military. The other reference groups provide some more evidence on the association
between college education and later discharge, indicating a longer service. For enlistees with less
than a high school degree, they are more likely to discharge early or experience multiple statuses,
showing a lower expected probability of being a lifetime service member
26
. In general, service
members with more pre-enlistment education are more likely to remain in the military. For those
who were less educated before enlistment, military service may be a transition to alternative
pathways in life, for better or worse.
There is partial evidence that service members who were married at enlistment are more
like to remain in the military. Married service members are more likely to have a military career
than discharge halfway or re-enlist; their odds of being in the Midway Discharge cluster or Re-
enlistment cluster versus in the Lifetime service cluster will decrease by 43% and 40%
respectively
27
. Should service members choose to separate from the military, married service
members are more like to have a late discharge after a lengthy service than choosing other
options. Stability is likely a contributing factor to the result; married service members prefer a
stable employment for their families and themselves, extending their service or developing a
military career.

25
The relative log odds of being in the Late Discharge, Midway Discharge cluster, Early Discharge cluster, or
Multiple Statuses versus in the Lifetime Service cluster will all decrease significantly by 1.10, 1.60, 1.92, and 1.30,
respectively for college educated service members than their less educated counterparts.
26
The relative log odds of being in the Early Discharge cluster or Multiple Statuses cluster versus in the Lifetime
Service cluster will all increase by 1.12 or 1.25 for enlistees with less than a high school degree.
27
The relative log odds of being in the Midway Discharge cluster or Re-enlistment cluster versus in the Lifetime
service cluster will decrease by 0.55 or 0.52, respectively.

77

Family backgrounds, indicated by father’s education and employment status, are closely
associated with the employment stability of military personnel. There is evidence that service
members with less educated fathers are more likely to remain in the military. Those with
working fathers are more likely to separate from the military early; their odds of separation are
1.27 times as large as the odds of service members with unemployed fathers
28
. When a father
figure is missing, service members from single-mother households are consistently more likely
to have an extended service, serving for life or having a late discharge, comparing with all other
career pathways (numerical results not shown). Such an impact is only partially found when a
mother figure is missing (numerical results not shown). Missing a parent figure may indicate
financial constraint and household instability when a military respondent is young (at age 14). A
long-term military service provides stability with sizeable compensation and benefits.
Sibship size is not a significant factor distinguishing career clusters among service
members. However, in few instances, service members who have more siblings are more likely
to be re-enlisted (verse lifetime military service or late discharge); a one-unit increase in the
sibship size is associated with a 0.45 increase in the relative log odds of being in Re-enlistment
versus the Lifetime Service cluster, or a 0.49 increase in the relative log odds of being in Re-
enlistment versus the Late Discharge cluster. I recognize that some statistically significant
differences are likely due to chance, given the reduced number of pairwise comparisons.
Though coming from a rural or urban community does not differentiate service members
from lifetime service to other career options, except for the Early Discharge cluster, rural/urban
residence is a significant factor that differentiate the lengths of service, if separating from the

28
The relative log odds of being in the Early Discharge cluster versus in the Lifetime service cluster will increase by
0.24.

78

military. Having a rural background increases service length; service members with rural
background are less like to discharge earlier
29
. Military seems to provide a viable career pathway
for rural residents who may have faced limited employment opportunities in their home
communities. Though the military recruits a disproportionately from the South and the West,
region of residence does not distinguish the career cluster significantly, with a few exceptions for
the South. Service members from the Southern states are less likely to have multiple statuses in
reference to keep a lifetime military career and are less likely to discharge early in reference to
have a late discharge. They are more likely to be re-enlisted. These service members likely gain
favor towards the military as a reliable employer and a viable career option, given the large
military presence in the Southern states. Table 4.2 summarizes the hypotheses and the results.
Intra-cluster comparisons
Table 4.3 shows the average change in predicted probabilities of membership in each
cluster, changing the value of a covariate from its reference to an alternative. I focus on social
correlates that statistically significantly change the odds of membership in a cluster relative to at
least two references. There are numerous minor but significant differences displayed in Table
4.3. In this discussion of results, I am going to focus on six most important takeaways.
First, Lifetime Career path is largely male typed, and Early Discharge pathway and
Multiple Statuses pathway are largely female typed. All else equal, being a female versus a male
is associated with higher odds of membership in the Early Discharge cluster and Multiple
Statuses cluster, increasing the predicted probabilities, on average, by 9 percent and 12 percent.
Therefore, military career pathways are gendered.

29
The relative log odds of being in the Midway Discharge cluster, Early Discharge cluster, or Multiple Statuses
cluster versus in the Later Discharge cluster will decrease by 0.48, 0.52, and 0.64, respectively,

79

Table 4.2 Hypotheses and Results.

80

Table 4.3 Average Change in Predicted Probabilities (N = 710).
Second, Late Discharge pathway and the Multiple Statuses pathway are more typical for
service members of color. Being African American as opposite to White is associated with
greater odds of membership in the Late Discharge cluster and the Multiple Statuses cluster with
an average increase of the predicted probabilities by 9 percent and 4 percent. There is a limited
association between being African American and being a lifetime service member with, on
average, an increased predicted probability of 2 percent. Hispanic service members have similar
patterns as African American service members, except they do not have heightened odds in
membership in the Multiple Statuses cluster.
Third, age at first enlistment discriminates the length of military services; older enlistees
prefer military pathways with longer services. Entering the military at older ages are associated

81

smaller probabilities of membership in the Midway Discharge cluster and the Early Discharge
cluster, decreasing the predicted probabilities of membership in these two clusters by 10 percent
and 12 percent, on average. They serve longer terms, reducing possible interruptions in the life
course.
Forth, there is a differential effect of pre-enlistment education on selection of career
pathways. Net of other characteristics, having no high school degree is associated with
membership in the Early Discharge group and the Multiple Statuses group. Having some college
versus is associated with membership in the Midway Discharge group. Having a college or an
advanced degree is associated with membership in the Lifetime Service group
30
. Less pre-
enlistment education could indicate limited resources and opportunities at a younger age;
however, it could also indicate limited motivation and determination towards structured learning,
such as military training and way of life. On the contrary, having college or advanced degree
before enlistment indicates commitment to learning, which is valued and rewarded in the
military. The level of pre-enlistment education partially predicts the length of military service.
Fifth, service members’ employment patterns are closely related to family socioeconomic
status, indicated by parental education and employment. Having a father without a high school
degree is associated with an average increase of 3 percent in the predicated probability of
membership in the Lifetime Service cluster; having a father who was employed is associated
with an average increase of 6 percent in the predicted probability in the Early Discharge cluster.

30
Net of other characteristics, having no high school degree versus holding a high school diploma is associated with
average increases of 13 and 5 percent in the predicted probabilities of membership in the Early Discharge group and
the Multiple Statuses group. Having some college versus a high school degree is associated with an average increase
of 8 percent in the Midway Discharge group. Having a college or an advanced degree versus a high school degree is
associated with an average increase of 14 percent in the Lifetime Service group and an average decrease of 15
percent in the Early Discharge group.

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Moreover, having no father figure is associated with higher odds of membership of the Lifetime
Service cluster. Difficult early life circumstances, proxied by less parental education or no
parental presence, propel an individual to serve longer terms, fitting the Turning Point hypothesis
that the military potentially removes service members from a constrained environment to one
with more opportunities.
Sixth, being married at the time of enlistment is associated with higher odds of
membership in the Lifetime Service group and lower odds of membership in the Re-enlistment
group
31
. Coming from the Southern states, comparing to the Northeastern States, is associated
with an increased predicted probability of being in the Re-enlistment group
32
. Have an urban
background, comparing to a rural background, is associated with an increased predicted
probability of being in the Early Discharge group. Selected family characteristics therefore
impact the choice of enlisting in the military.
Conclusion
With an exception that only a few social correlates that I considered differentiate the
members of Late Discharge cluster and Midway Discharge cluster from those of other clusters
33
,
my results from the multinomial regression analyses indicate numerous key pre-enlistment
individual and family characteristics that likely impact career choices of service members in the
All-Volunteer Force.

31
Being married at the time of enlistment is associated with higher odds of membership in the Lifetime Service
group and lower odds of membership in the Re-enlistment group with an average increase in the predicted
probability of membership in the Lifetime Service cluster of 4 percent and an average decrease in the predicted
probability of membership in the Re-enlistment cluster of 5 percent
32
The intersection of the Culture of Honor in the South and the Culture of Honor in the military likely impacts the
perception and choice of enlisting.
33
Being White and young and having less educated parents are positively associated with membership in the
Midway Discharge cluster. In contrast, being non-White and old(er) and having more educated parents are positively
associated with membership in the Late Discharge cluster.

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The Lifetime Service cluster shows some heterogeneity in the socioeconomic statuses of
its member. Some service members appear to be disadvantaged. Being a minority, having a
father without a high school degree or missing a parent figure are positively associated with
membership in this cluster. However, enlisting at older ages, having a college degree or more, or
being married are also positively associated with this cluster. Thus, a lifetime military service
tends to attract individuals with various sources of instability in the past or individuals with more
structured roles prior to enlistment; both sets of individuals desire a career with employment
consistency and financial stability.
The Early Discharge cluster show socioeconomic advantage in many aspects. Being
White, coming from an urban community, and having a father with an employment are positively
associated with membership in this cluster. At the same time, being a female, young, and less
educated are also positively associated with membership in this cluster. For service members in
this cluster, joining the military is a step taken to explore various employment options before
assuming alternative adult roles in the civilian labor market. The Multiple Statuses cluster, on the
contrary, show socioeconomic disadvantage in many aspects. Being female, African American,
and less educated are positively associated with the membership in this cluster. Racial minorities
are disproportionately represented in the labor force whose work is nonstandard and/or
precarious (Johnson and Johnson 2005; Western and Pettit 2005); therefore, military did not
serve as a contingency that (re)shapes the processes of status attainment for better. On the
contrary, military service seems to be a disruption in the life-course patterns for these service
members without high family status.
Being White, single, less educated, having more siblings, and coming from the South and
rural communities are positively associated with membership in the Re-enlistment cluster.

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Members of this cluster seem to have had limited family resources and employment
opportunities during early life. Instead of getting married and forming a family early like some of
their peers with a similar background, these members explore various life domains and obtain
substantial training and education from the military. Having tried out both military occupation
and civilian work, members of this cluster return to the military.
Research that considers veteran status alone (e.g., Bailey and Sykes 2018) does not
differentiate the diverge pathways upon discharge, creating a knowledge gap on how social
correlates prior to enlistment distinguish among subsequent employment patterns. This chapter
paints a more complete picture on military personnel’s long-term employment patterns and
career pathways following their first enlistment, demonstrating heterogeneity in how service
members structure and optimize military service, education, and civilian employment.

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CHAPTER 5
MILITARY INCOME PREMIUM
Introduction
The previous chapter examined the associations between various social correlates and the
long-term employment patterns among service members in the All-Volunteer Force era. One
major takeaway is that not only military pathways are heterogenous but also gender-, race-, and
SES-based. How such heterogeneity in military career pathways leads to variation in later life
outcomes is explored in this chapter.
Despite the comprehensive studies of the impacts of military service on wartime
veterans’ wages, earnings, and occupational statuses (Angrist 1990; Teachman 2004, 2005;
Teachman and Call 1996; Teachman and Tedrow 2004), the knowledge about the socioeconomic
consequences of serving in the armed forces during peacetime is limited (Angrist 1993; Bailey
and Sykes 2018; Teachman 2007; Teachman and Tedrow 2007). It is shown that there are
limited leverages on either income or occupational mobility among service members once
socioeconomic background is controlled (Teachman and Tedrow 2003; Bailey and Sykes 2018).
What is not well understood are, first, whether AVF service members obtained more or less
education and earned higher or lower income than their non-veteran civilians across career
pathways, and second, whether later-life achieved statuses, superior or inferior, vary by early life
ascriptive characteristics. Based on the clustering solutions identified in Chapter 3 and social
correlates examined in Chapter 4, this chapter further examines the income differentials
associated with various military pathways.
Several distinct, yet well connected, theoretical perspectives guide this chapter in
examining the typology of unique career pathways and the variation in income associated with

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these pathways among service members. These frameworks include: the life course perspective,
the human capital perspective, the social capital perspective, the status attainment perspective,
the screening and signaling perspective, and the selectivity perspective. The interruption of a
normal life progression, the enhancement of human capital, the acquisition of social and culture
capital, and a certification of an honorable discharge moderates the impacts of the early-life
characteristics and circumstances and further influence the employment patterns and career
pathways that produce later-life status attainment and socioeconomic outcomes. Moreover, the
impacts of military service are not likely to be uniform across various pathways within the same
cohort of service members; this chapter further unpacks the differential impacts by pathway and
compares the results with the ones of non-veteran civilians.
Literature Review
The life course perspective
The life course perspective is an overarching perspective that guides this dissertation. It
highlights how major life events happening in specific historical, structural, social, and cultural
contexts interact with individual biographies to shape the pathways in life (Elder 1985).
Specifically, it focuses on life-long development of individuals and concerns the timing,
sequencing, trajectories, and transitions of life events, based on human choices within a larger
structure of opportunities and constraints (Elder 1985; Elder, Johnson, and Crosnoe, 2003).
Getting an education, finding a job, and forming a family, to name a few, are all major life events
on which life course perspective represents. Social scientists have studied and theorized the
mechanisms shaping these events, but almost exclusively among the civilian non-
institutionalized population [CNP]. Military service often changes the timing, sequencing, and
environment of major life events, and its impact has far-reaching impacts on service members,

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discussed in the Chapter 2. It fits the framework of life course perspective and should be an
addition to its theorization.
Military and paramilitary personnel, however, are a far less studied population. Joining
the military is a unique and demanding life event that reshapes the pathways and outcomes of
active-duty service members and veterans. Though all principles of life course perspective are
applied in this dissertation, two specific corollary hypotheses are tested in this chapter, and they
have broad implications in understanding the long-term impacts of military service on life-course
outcomes – 1) the Life-course Disruption Hypothesis, and 2) the Military as a Turing Point
Hypothesis (Wilmoth and London 2013). Military service is often associated with familial,
educational, and employment discontinuities in the life-course trajectories. These two hypotheses
suggest mechanisms associated with various discontinuities and make plausible inferences about
income differentials.
Life-course disruption hypothesis Military service may interrupt one’s normal
progression in life (Segal 1986); it may temporarily or, sometimes, permanently take individuals
from family responsibilities, educational institutions, and/or the civilian labor market and
summon them into an organized life. Because many social institutions are age-graded, age is an
important factor in impacting decisions (Hogan and Astone 1986). At younger ages, life-course
trajectories are less well formed (e.g., limited education and labor market experiences), and role
transitions are more ambiguous and fluid (Rindfuss et al. 1987; Teachman and Tedrow 2004).
Enlistees who enter the military early have more opportunities to catch up in human capital
accumulation after a short service, mitigating the disruptive effects over time. In fact, most who
serve do so for a limited period only; military is likely a transitory disruption between school and
civilian work roles for early entrants. At older ages, many investments have been made in

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planning and structuring subsequent civilian life. Enlistees who enter late have likely formed
families, found employments, established career paths, and made other long-term investments.
They experience more disruptions in family, parenting, and employment trajectories, negatively
impacting subsequent outcomes (Teachman, Call, and Segal 1993). The Life-course Disruption
Hypothesis suggests that interruptions in the normal life-course patterns, conditioned on the age
of enlistment and historical context, likely diverge subsequent pathways and associated
outcomes; the most detrimental consequences of military service likely accrue to individuals
whose life progressions are most severely interrupted (Elder 1986; Teachman and Tedrow 2004).
Therefore, it is expected that military service that occurs at older ages is more likely to have
detrimental effects on income than military service at younger ages.
Military as a turning point hypothesis Military service may also serve as a turning point,
redirecting the life-course of individuals (Wilmoth and London 2013). For youth from
socioeconomically disadvantaged backgrounds, military service is a practical route out of the
difficult early-life circumstances (Kelty, Kleykamp, and Segal 2010), cutting off undesirable ties,
limiting maladaptive behaviors, and arresting the process of cumulative disadvantage (see
DiPrete and Eirich (2006) for a review). Serving in the military also provide individuals with
opportunity to receive training, education, skills, and resources that they would not have had
before, putting them on different and opportunity-enhancing life-course trajectories (Bennett and
McDonald 2013; Browning, Lopreato, and Poston 1973; MacLean 2017; Sampson and Laud
1996). For these reasons, military service has been a turning point for many disadvantaged men
who served in selected wartimes. Veterans from the WWII cohort with the lowest levels of
educational attainment appear to have enjoyed labor market premiums (Teachman and Tedrow
2004); the Korean War also changed the course of lives of disadvantaged veterans toward a

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brighter future – equal occupational achievements with non-veterans (Teachman 2005). More
recent studies have also argued that the military provides a counterfactual environment in which
service members of color face less discrimination than they do in the civilian market (Lundquist
2008). It is therefore expected that socioeconomically disadvantaged individuals are more likely
to receive a higher military income premium than more advantaged groups, regardless of career
pathways.
The human capital perspective
Work experiences The economic model of human capital (Becker 1993) has shown that
competitive wage and income are based on the marginal productivity (education and skills) and
labor market experiences (Becker 1981). Among the CNP, who follow the conventional higher
education-labor market participation-socioeconomic outcome pathway, workers acquire skills
through formal schooling, occupational education, and/or on-the-job trainings. Employers prefer
workers with more work experiences and transferrable skills, and these workers are less likely to
be unemployed or out of the labor market and more likely to receive higher wages and enjoy
higher earnings. Military service stops the accumulation of civilian work experience and job
tenure (Teachman and Tedrow 2004, 2007). Instead, service members work in military
occupational specialty (MOS) and accumulate military work experiences. Some MOS have more
direct transferability to the civilian workforce, such as health care, telecommunication,
engineering, science, and technology, preparing veterans for a smooth transition to post-service
employments (Kleykamp 2013; MacLean 2017). Some MOS are less transferrable, such as the
combat specialty, with only limited civilian equivalents (MacLean 2017). Net of the
transferability of military occupational skills, military work experiences substitute for civilian
work experiences to a certain extent; the positive effect of military experience becomes less

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positive over a veteran’s life course if one were to separate from the service (Teachman and
Tedrow 2007). Therefore, veterans who selected pathways with earlier discharge may not have
accumulated substantial work experience and accrue less income over the life course.
Training and education Training for civilian pursuits is a highlight of military
recruitment in the All-Volunteer Force era. Human capital is developed and accumulated but in
different forms in the military. Military provides training to various occupation specialties, and
these trainings may enhance civilian labor market skills (Teachman and Tedrow 2004, 2007).
Various educational benefits during and after military services (e.g., the GI Bill) allow veterans
to go back to school and receive formal and informal higher education. Hence, individuals may
enter the military to obtain skills that eventually open doors and expand paths in the civilian
labor market. Previous research has shown that knowledge, skills, certificates, and degrees
obtained from the military are valued in the civilian context (Mangum and Ball 1989), and there
is an income premium associated with military training for veterans of different cohorts (Bryant
and Wilhite 1990) and by different socioeconomic backgrounds (Bailey and Sykes 2018;
Teachman and Tedrow 2004, 2007). The GI Bill has broadened the educational opportunities and
economic rewards (Xie 1992). Additional formal education and vocational training acquired after
discharge translate into higher income and better socioeconomic outcomes (e.g.,
homeownership) for service members with shorter tours (Fredland and Little 1985; Martorell,
Miller, Daugherty, and Borgschute 2013). Therefore, training received in the military and
education obtained after discharge facilitate service members in finding employment and
establishing a career in the civilian labor market. Career pathways that start with military service
may diverge over time with various spells of training and education in and out of the military,

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and the ones that permit more human capital accumulation provides service members more
leverages in receiving high income in the later life.
Service length connecting work experience and education Service members work in the
military for different lengths of time. Some serve for a few years to qualify for specific benefits,
and others serve for a lifetime. The length of service is consequential for financial rewards and
other socioeconomic outcomes in the military and in the civilian markets (Teachman and Tedrow
2004, 2007). When a service member chooses to leave the military, he or she tends to have the
largest deficiency in civilian work experience and job tenure, compared with their civilian
counterparts of the same age. Leaving a fulltime military occupation that pays a compensating
wage differential, service members tend to experience a sharp income gap immediately
following discharge. However, many veterans serve short tours of less than 5 years and leave the
military in their mid-20s (Kelty and Segal 2013). Should they choose to go back to school and
get more education or return to the civilian labor market and start accumulating work experience,
these veterans have the chance to close or even reverse the income gap between nonveterans and
themselves over time (Berger and Hirsch 1983; Xie 1992). Military service represents only a
fraction in career; veterans with shorter services continue with their civilian market portfolios.
The income gap between veterans and nonveterans may not close quickly but last for
years (Light 2005). Starting with different levels of labor market activities, non-veteran civilians
and service members, who are out of the labor market temporarily, tend to have a different
earning profile and end up making less than continuous, active workers (Light and McGarry
1998; Teachman and Tedrow 2007). Previous studies have shown that the income penalty for
veterans lasts for more than a decade (Teachman 2003). Though the income gap between
veterans and nonveterans become less negative over time as veterans establish civilian career

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pathways and accumulate various forms of human capital, the effect of military service may not
be transitory but long-lasting.
The human capital perspective emphasizes on the impact of military service on
marketable skills and focuses on the importance of the gain and loss of experiences (MacLean
2017). The extent to which military work experiences and occupational skills are recognized in
the civilian labor market shapes the post-service employment patterns. Training received in the
military and education acquired after discharge help service members make a smooth transition
to the civilian workforce and expand their career pathways, diversifying the processes of
socioeconomic attainment and outcomes of such processes.
The status attainment perspective
The status attainment perspective focuses on one’s position, class, or prestige in the
society and the processes from which these attainments are reached (Blau and Duncan 1967;
Duncan, Featherman, and Duncan 1972; Featherman and Hauser 1978). The traditional model
separates the impacts of ascribed status and the impacts of achieved status on socioeconomic
outcomes (Blau and Duncan 1967). Based on one’s ascribed status, such as family origin and
parental status, and achieved attributes, such as educational attainment and occupational status,
individuals are allocated to a unidimensional social hierarchy (Treiman 1977; Treiman and
Ganzeboom 1990). This hierarchy could be conceptualized as narrowly as occupational prestige
(Duncan 1961) or as broadly as social standing (Blau and Duncan 1967), incorporating various
evaluations of socioeconomic outcome and statuses. Substantial aspects of status attainment have
been examined among the CNP.

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The social attainment perspective can also be applied to understand the impacts of
military service in such a way that military service is considered a contingency that shapes and
modifies the processes of status attainment (Teachman and Tedrow 2004). It alters the returns of
various ascribed and achieves statuses, for better or worse, among individuals of different
socioeconomic backgrounds (Angrist 1998; Bailey and Sykes 2018). For those who had a rough
start in life, military service may be a change agent against the resource poor and information
deprived environment; it provides them with equal opportunities by creating structures,
organizing activities, and utilizing resources that are not available to them before. Previous
research has shown that less advantaged men tend to benefit more from time spent in the military
(for example, Angrist 1998). Military service changes the playing field of less educationally and
economically advantaged veterans and moderates the potential negative impacts of
socioeconomic background on subsequent social attainment. For example, military services
enhanced occupational status, job stability, and economic well-being for WWII veterans who had
a delinquent past (Sampson and Laub 1996).
Like the Military as a Turing Point Hypothesis, the status attainment perspective supports
that military services represents a change in direction in life. Different from the Life-course
Disruption Hypothesis that looks at continuities and discontinuities of the life sequence indexed
by age, the status attainment perspective places the change in a broader context of social
stratification – through military service, individuals overcome the difficulties and limitations
associated with their origin and the past (Sampson and Laub 1996) or depart from structural
disadvantages of discrimination and poverty (Seeborg 1994). The term, “bridging environment,”
has been used to describe the lessons, such as work discipline, time management, and life
responsibilities, taught by the military, which are attributes and attitudes not often identified

94

among disadvantaged men (Gade et al. 1991). These characteristics translate into opportunities in
the civilian sector, bridge less advantaged military personnel into the mainstream society, and
enable them to further their socioeconomic attainment (Browning, Lopreato, and Poston 1973;
Kleykamp 2009; Smith, Marsh, and Segal 2010). Though employment patterns and career
pathways are impacted by differences in early-life characteristics and circumstances, they can be
(re)shaped by military service, a status equalizing and enhancing platform among the
disadvantaged.
The social capital perspective
Like human capital, social capital may provide opportunities in life; it connects one party
to another and exchanges information among them (Coleman 1988; Granovetter 1973). Coleman
(1988) argues that social capital comes in three forms – obligations and expectations,
information channels, and social forms, which all serve as “resources for action” and facilitate an
integration of social structure into a rational actor framework (Coleman 1988). Granovetter
(1973) argues that not all social capital and ties have the same strength, and weak ties tend to
reach broader populations than strong ones. People to whom one is weakly connected are more
likely to move in different circles and receive more information from various sources
(Granovetter 1973, 1985). Overall, social capital stands as a universal mediating variable
between family background, human capital, work experience, on the one side, and life course
outcomes, on the other (DiMaggio and Mohr 1985).
Labor market outcomes emphasize the exchanges of social capital. Existing research in
the CNP has shown that social networks, such as contacts and referrals, provide low cost, high
quality information for workers (Briton and Kariya 1998). Jobs found through friends, family,
and acquaintance are closely associated with higher income and job satisfaction (Huffman and

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Torres 2002; Stainback et al. 2010). Social capital, such as networks and ties, facilitate civilian
workers in the job-seeking process (Hagan 2004, 2006a, 2006b; Menjívar 2000).
Social capital exists but operates differently in the military setting. Service members, who
depart from civilian life and join the military, lose civilian social capital that provide useful
information on the public. Working on a military base or a designated military community tend
to isolate service members, who slowly lose knowledge of the local labor market (Teachman and
Tedrow 2004, 2007). The labor demand and supply change constantly based on macroeconomic
fluctuations and microeconomics conditions. Without the most up-to-date information on the
local market and employers, service members may take longer to research for employment and
secure the best matching job, should they choose to leave the military. The argument is similar to
the one applied in the Human Capital perspective – the deficiency in social capital is the largest
immediately following discharge. However, the gap in social capital may gradually close after
veterans start working in the civilian labor market and reestablishing the networks supporting
socioeconomic attainment. The effect of military service on finding employment and
transitioning to a civilian career becomes less negative over time.
A recent extension of social capital goes beyond social networks and entails social
resources, such as social skills and catered assistance (Teachman and Tedrow 2004). Military
provides “soft social capital” that is highly applicable in the civilian context. It teaches social
lessons such as motivation, attitude, reliability, and teamwork in a bureaucratic environment
(Kleykamp 2009). Besides human capital, such as work experience and occupational skills, these
cognitive and social interactive skills are contributing factors to a successful employment,
fostering the establishment of a civilian career. Social training is therefore an important element
of military social capital.

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Military also generates its one-of-a-kind social resources that may not be fully available
in the civilian context. Military communities have social networks, dedicating to providing
useful information on the military occupations, resources, and the broad public. For example,
military contractors, such as Blackwater USA, Huntington Ingalls Industries, Booz Allen
Hamilton, to name a few, work with service members to transition out of the military and
provide them with abundant employment opportunities in the civilian labor market. These
military-connected civilian works attract veterans with specialized military occupational skills.
Instead of searching for employment upon discharge, some service members are sought by
military contractors. Moreover, the current integration of civilian contract work with military
units provides a taste of employment in the civilian world, serving as a trial for service members
to better understand civilian work. The development of hybrid employment potentially affects
service members’ employment trajectories (Kelty and Segal 2007), pulling them away from the
military and engaging them with contracting agencies in the civilian world. Research has shown
that an increasing number of military personnel have elected to leave for jobs with these
military-connected agencies after more than ten years of service (Burgess 2008). Social capital
generated within the military help veterans match with potential employers in the civilian
workforce, assisting them in securing fitting employment opportunities and establishing viable
career pathways.
The social capital perspective emphasizes the social skills and networks that are
supporting factors in life-course development and social status attainment, integrating and
extending these two theoretical perspectives. Military (re)shapes the life-course trajectories by
removing individuals from negative social networks that may encourage maladaptive behaviors
and providing them with positive social networks that teach lessons and provide information,

97

leading to different but achievable processes in socioeconomic attainment. Starting with the
military, service members acquire social resources and employ them in various life-course
pathways. If the military’s key role in affecting labor market processes operates through
enhancing social capital, including social resources and social skills, I anticipate a stronger
association between labor market rewards and less advantaged, less educated, and minority
service members.
The screening and signaling perspective
Like schooling, military service serves as a screening device to employers, signaling
characters, productivity, and employability, which tend not to be available on job applications
(de Tray 1982; Xie 1992). Individuals must have good moral characters and pass a series of
educational, mental, and physical screenings to be qualified for enlistment. Once enlisted, service
members must continuously meet minimal standards of training and performance to remain in
the military or receive an honorable discharge. An honorable discharge, therefore, indicates
veterans with a high level of mental and physical readiness for work in a structured environment
(de Tray 1982) and certifies veterans with occupation-specific training and disciplined work
ethic (Kleykamp 2009). These positive signals on personal characters and growth are typically
not available for non-veteran civilians. On the contrary, a premature separation, (e.g., in year 1 or
2 after the first enlistment, before the end of the initial contract) may be a signal of “poor
quality” or “poor performance” (Martorell, Miller, Daugherty, and Borgschute 2013). If either
indicates other unobservable problems in the labor market, a service member with an early
discharge is less employable in either the military or the civilian context. I anticipate employers
use longer service length as a positive signal, and military personnel who have a later discharge
has higher income than their civilian counterparts.

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Because of the differences in the quality of schooling and level of educational attainment
among disadvantaged background individuals and the advantaged (Berger and Hirsch 1983),
service members may benefit more from military status. Using military experience as a screening
tool, employers may understand that less advantaged veterans are more selective than their
civilian peers and, therefore, treat them differently than non-veterans in hiring practices and
wage setting (Kleykamp 2013). Employers may also feel military, a highly structured social
institution, instills strength, alters attitudes, changes behaviors, and enhances skills of those who
serve (Kleykamp 2013). Personal growth tends to be more advantageous for socioeconomically
disadvantaged individuals, which may attribute to preferential treatment for veterans. Treating
veteran status as a screening device, employers rewarded Vietnam veterans with limited
education with a wage premium (De Tray 1982). I anticipate the employers use military service
as a positive signal for socioeconomically disadvantaged individuals, and these service members
have higher income than nonveteran civilians of similar background.
The military is perceived to provide more equal opportunities to minorities than the
civilian labor market (Segal, Bachman, and Dowdell 1978). The racial pay gaps are much
smaller, so more productive minorities disproportionately choose to enter the military than enter
the civilian labor market (Kleykamp 2009). If racial-ethnic minorities positively self-select into
the military, veteran status helps counteract negative stereotypes and serves as a strong indicator
of employability. Previous research has shown that military positively selects African
Americans, providing them with positive returns to military service based on characteristics that
make them more employable in the civilian labor market (Mare and Winship 1984). Black
veterans with administrative experiences, for example, were treated more favorably than their

99

civilian counterparts (Kleykamp 2009). I anticipate that minority service members are preferred
by employers, and they receive higher income than minority non-veterans.
The screening and signaling perspective provides an additional explanation that military
service, especially for racial minority and socioeconomically disadvantaged service members,
may moderate the early life characteristics and circumstance and alter the processes of mid-life
status attainment and pathways to late-life outcomes.
The selectivity perspective
The selectivity perspective argues that service members make different decisions in
constructing the life-course, because of preexisting differences in social correlates, such as
demographic characteristics, family backgrounds, parental statuses, human capital prior to
enlistment, and other factors, such as physical health at first enlistment, associated with
subsequent choices in life (see Wolf et al. 2013 for review). These differences dictate the
occurrence, timing, and ordering of selected life events, with which the employment patterns and
career pathways are constructed over the life course.
Whether military service is more akin to a temporary absence from the labor market or to
the accumulation of transferable skill from military training or post-service education and how
these military-related benefits translate into labor market rewards have not been fully studied.
This dissertation aims to fill this gap in knowledge.
Measures and Methods
The main outcome variable I use in this analysis is the total annual compensation
(“annual income”). For military respondents, a comprehensive measure of military income is
used, including the annual income provided by the NLSY79 plus the basic allowance for

100

subsistence [BAS] and the allowance for housing [BAH] provided by the Department of
Defense, taking into consideration of military paygrade and child credit. For non-veteran civilian
respondents, the annual income is based on the individual annual wages and salaries. To be
compatible, the outcome variable refers to a period 15 years following the first enlistment for
military personnel and 15 years following the first employment for non-veteran civilians,
reflecting a long-tern economic well-being of respondents during the AVF era. Income is
expressed in 2010 dollars and as logarithms. Two educational outcome variables are considered;
they indicate 1) whether the respondent received technical or vocational training while in the
military, which only applies to the military connected sample, and 2) whether the respondent
received additional education or training while in the civilian labor force, which applies to both
the military and the civilian samples, except the group of lifetime service members. The
differences in human capital accumulation may explain a differential military income premium
across various career pathways or by selected socioeconomic groups.
The major independent variable is an indicator of military service, defining military
personnel in each career pathway as either a service member on active duty or a veteran who
served time in the armed forces. Matched nonveteran civilians are, therefore, not military
connected.
A sample of 1,420 respondents consisting of a treatment group of 710 military personnel
identified in Chapter 3 and a comparison group of 710 nonveteran civilians are included in the
analysis. Propensity Score Matching (PSM) is used to identify a comparison group that
resembles each of the six military pathways (Rosenbaum and Rubin 1983)
34
. Measures of social

34
Rosenbaum and Rubin (1983) has shown that propensity score matching is sufficient to remove selection bias by
including potential confounders in the construction of the scalar. The approach allows me to examine whether the
treatment group – military respondents – and the comparison group – non-veteran civilians – are balanced in terms

101

correlates, including demographic characteristics and family backgrounds and experiences are
used as covariates in the PSM model. These variables include gender, race and ethnicity, marital
status, urban/rural residence, sibship size, father’s education and employment when the
respondent was age 14. I examine whether military personnel and their nonveteran civilian
counterparts earn similar incomes across career pathways once social correlates are controlled.
Descriptive Statistics
Across pathways with various lengths of service, there is a large return to being in the
military in the years of active duty. As it is shown in Figure 5.1, the estimated returns remain
positive and trend up with time. In addition, some military personnel, from selected pathways,
earn more than others. Lifetime service members (panel 1) make the most by the end of their 15
th

year service, and their income profile seems to be flattened out as these service members are
approaching military retirement. Individuals who have held multiple statuses make the least
(panel 5). Late dischargers and midway dischargers (panel 2 and panel 3) both experience a dip
in income immediately after the separation from the service, but both profiles show significant
upward trends as the life course unfolds. The difference between these two profiles is that late
dischargers have a constant upward trend, and midway dischargers have a jump after the dip then
a slight upward trend. Early dischargers (panel 4) also experience a dip in income right after
discharge; their income then increase over a 5-year period before flattening out. The income
profile of the Re-enlistment group (panel 6) starts at a high level, goes through two dips at first
military separation (around year 5) and after re-enlistment (after year 10), and ends with an
upward trend. Due to limited sample size of the re-enlistment cluster, the result for this group

of observed covariates, a similar process of what would be accomplished by randomization in randomized controlled
trials. Bias reduction and effect adjustment are achieved.

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should be interpreted with caution. The dissimilarity of the income profiles show that different
military pathways produce vastly different economic outcomes for service members in the AVF
era.

Figure 5.1 Income Profile over 15 Years, by Military Career Pathways.
Results
Does military service influence income net of socioeconomic background?
In the first set of regressions, I regress the annual income on military service and the
socioeconomic characteristics by pathway. For each pathway, there are two models. Model 1
tests the main effects of military service net of background characteristics. Model 2 includes
several interaction terms, including interactions between military service and 1) gender, 2) race
and ethnicity, 3) less educated, and 4) father’s employment status when the respondent was 14. I
defined less educated as respondents who had less than a high school degree at the time of
enlistment and father’s employment status as “employed” if father was employed when the

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respondent was age 14. These interactions allow me to determine whether the association
between military service and income are constant between gender, across education level, and
among individuals of different socioeconomic statuses. Table 5.1 shows the regression results.
With respect to the effects of socioeconomic characteristics, the results are consistent
with previous research: individuals with more education earn more. Similarly, individuals who
are male, White, and who have an employed father at age 14 earn more. Compared with older
individuals, younger ones have lower annual income.
The effect of military service increased the annual income of military personnel from the
Lifetime Service pathway and the Late Discharge pathway. Compared to non-veteran civilians,
lifetime service members have annual incomes that are 8 percent higher and late discharged
veterans have annual income that are 4 percent higher. On the contrary, the effect of military
service decreased the annual income of military personnel from Multiple Statuses Pathway
35
.
Veterans from this pathway have annual incomes that are 15 percent lower. One possible
explanation is that the frequent transitions in and out of different statuses create additional
interruptions to the life course and fragment eligible work experiences, which results in an
income penalty. There is no significant difference between the income of military personnel from
the Early Discharge pathway and its civilian counterparts, partially indicating that military serves
as a transition to civilian labor market among veterans of this group; the temporary interruption
does not result in differentiations between these veterans and non-veteran civilians. Thus, some
veterans, who have lengthy associations with the military, enjoy an income premium net of
socioeconomic backgrounds.

35
Being female, African American, and less educated are positively associated with the membership in the Multiple
Statuses cluster.

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Table 5.1 Regression Results: Log of Annual Income on Socioeconomic Backgrounds
and Measures of Military Service.

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The effect of military service increased the annual income of military personnel from the
Lifetime Service pathway and the Late Discharge pathway. Compared to non-veteran civilians,
lifetime service members have annual incomes that are 8 percent higher and late discharged
veterans have annual income that are 4 percent higher. On the contrary, the effect of military
service decreased the annual income of military personnel from Multiple Statuses Pathway
36
.
Veterans from this pathway have annual incomes that are 15 percent lower. One possible
explanation is that the frequent transitions in and out of different statuses create additional
interruptions to the life course and fragment eligible work experiences, which resulted in an
income penalty. There is no significant difference between the income of military personnel from
the Early Discharge Pathway and its civilian counterparts, partially indicating that military serves
as a transition to civilian labor market among veterans of this group; the temporary interruption
does not result in differentiations between these veterans and non-veteran civilians. Thus, some
veterans, who have lengthy associations with the military, enjoy an income premium net of
socioeconomic background.
Model 2 tests the whether the effects of military service differentiate by gender and/or
socioeconomical backgrounds. When the interaction terms are included, the main effect of
military service become insignificant for the Lifetime Service pathway and the Late Discharge
pathway, indicating that the positive effect of military service on income premium is limited to
disadvantaged individuals. However, only Blacks and less socioeconomic advantaged service
members from these two pathways receive the income premium; female service members do not
appear to reap positive benefits of military service by serving lengthy terms. Moreover, female

36
Being female, African American, and less educated are positively associated with the membership in the Multiple
Statuses cluster.

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service members encounter an income penalty for an early separation, either having an Early
Discharge or Multiple Statuses. The inclusion of the interaction terms reduces the main effect to
insignificant in these two pathways.
These results provide partial support for the status attainment perspective and the social
capital perspective for minorities and service members with less advantaged background. I also
tested the life-course perspective by including interaction terms between military service and age
at enlistment (early vs. late). These interactions did not reach the acceptable statistical
significance across the pathways. Therefore, the results do not provide support that the effects of
military service on income vary by age.
Human capital accumulation
In the second set of regressions, I test whether the military income premium associated
with selected career pathways is due to human capital accumulation during service, examining
the relationship between characteristics of service members and technical and vocational training
received. Table 5.2 shows the regression results.

Table 5.2 Logistic Regression Results: Whether Received Technical or Vocational Training During Military Service.
The results indicate that service members, who enlisted at younger ages across all
pathways, except the multiple statuses pathway, are more likely to have received technical and

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professional training. Black service members, who have chosen the Early Discharge pathway or
the Multiple Statuses pathway, are less likely to receive training. On the contrary, they are more
likely to receive training when they choose the Lifetime Military pathway or the Late Discharge
pathway. Thus, these results partially support that human capital accumulation during service
serves as a reason for Black service members to receive higher income.
In the third set of regressions, I test whether a military income premium, if it exists, is
associated with human capital accumulation post service, examining the relationship between
socioeconomic background and additional education, including formal schooling and
occupational training post discharge. This set of analyses exclude the military respondents from
the Lifetime Military pathway and their matched civilian counterparts by design. Since the GI
Bill has provided service members with generous education benefits, I anticipate a positive
impact of being a veteran on human capital accumulation upon discharge. The results in table 5.3
indicate that service members who enlisted at younger ages and had a midway discharge (“One
Contract” pathway) and service members who have more educated parents and had a late
discharge are also more likely to receive more education in the civilian context. Thus, some
military premium is associated with post-service schooling and training as an indirect result of
military service. Black veterans from the Midway Discharge pathway and White veterans from
the Re-enlist pathway were more likely than their corresponding counterparts to acquire more
civilian education
37
, but Black veterans from the Early Discharge pathway and Multiple Statuses
pathway are less likely to receive additional civilian education. Thus, there is some evidence that
post-service education is implicated in military income premiums associated with race.

37
The result of the Re-enlistment group may be an artifact due to limited sample size.

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Table 5.3 Logistic Regression Results: Whether Received Technical or Vocational Training During Military Service.
Overall, human capital investments partially explain the results found on annual income.
The very strong relationship between being a Black service member who has a late discharge and
received technical and vocational training during and post service, to a certain extent, helps
explain these veterans have higher annual incomes than nonveteran civilians. On the contrary,
the fact that being a Black service member who has an early discharge or multiple statuses and
received minimal training during and/or post service may be a reason that these veterans received
lower annual income than their civilian counterparts.
I further explored the above possibility in a fourth set of regressions by regressing the
income variable on socioeconomic backgrounds, age at first enlistment, human capital, and
indicator of military service. The human capital variables include received training during
military service and received education in the civilian context. Table 5.4 shows the regression
results.

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Table 5.4 Regression Results: Log of Annual Income on Socioeconomic Backgrounds, Human Capital, and Military
Indicator.
Technical and vocational training received during service significantly increases annual
income of service members in the Lifetime Military pathway and the Late Discharge pathway by
12 percent and 9 percent respectively. Additional civilian education acquired post service
increases the annual income of service members of the Midway Discharge pathway by 10
percent. The inclusion of the two human capital variables reduces the main effect of military
service to insignificant, but it does not change the significance of the coefficient for the
interaction between military service and being Black. These results suggest that differences in
human capital accumulation during and post military service partially explain the reason the
selected Black service members who have served for life or who have served at least one
contract before separation receive higher annual income.
To better understand the intersectionality among military service, gender, and race, I
included an interaction term of military, female, and Black. None of the regression results (not
shown) on the interaction between military and Black women reached an acceptable significant

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level. However, the coefficient of the interaction term from the Lifetime Service pathway is
positive, indicating that Black female service members who serve for life would have higher
annual income than their civilian counterpart, implying a potential gendered military income
premium for long-term service members.
Discussions
This chapter aims to understand whether an income premium is associated with selected
military pathways and the reasons of such a premium. Was it because certain military pathways
provide more opportunities for human capital accumulation that boosted their labor market
returns? Or was it because selected military pathways served as a bridging environment that
increased social capital applicable in the civilian context? My analyses indicate that there is no
single perspective that fully explicates the results, and there is some support for each perspective.
There are a few major takeaways of this chapter. First, lifetime service members and late
dischargers have the highest later life income 15 years after their first enlistment. This result
provides partial support to the life-course perspective; though age at enlistment was not found to
be an influential factor, less interruptions in the life course – a uniform or a long-term military
career – rewards service members with additional income.
Second, female service members are less able than male service members to convert their
military trainings and experiences into the civilian labor market. As it is seen, female service
members tend to have an early separation and be panelized in later life income for such a
decision. As it is discussed in Chapter 2 and Chapter 4, female service members have higher risk
of experiencing sexual harassments and assaults during service than their male counterparts.
Military sexual trauma therefore likely has economic consequences for female service members.

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Third, African Americans and less socioeconomically advantaged service members
received a military income premium by serving longer terms (e.g., lifetime service or late
discharge). Though socioeconomic backgrounds explain away the main effect of military
service, less educated, less advantaged, and minorities likely enjoy later life economic reward by
offering long-term services to their country, supporting the goal of the military to award long-
term service member with generous support in general. Moreover, military service moderates the
potential negative impacts of early life characteristics on subsequent processes and outcomes;
this result fits the social attainment perspective.
Fourth, Black service members receive more technical and professional training during
service than other service members when they choose the Lifetime Military pathway or the Late
Discharge pathway, and such differences in human capital accumulation explain their higher
professional mid-life income, providing support for the human capital perspective. Moreover,
there is a strong relationship between being a Black service member who has a later discharge
and received technical and vocational training after discharge, and this result shows that some
military income premium is associated with post-service schooling and training, serving as
additional support for human capital perspective.
Fifth, the result that Black service members, who have served for life or who have served
at least one contract before separation, receive higher annual income is robust. The differences in
human capital accumulation do not fully explain this premium. It is possible that social capital,
such as social networks or social lessons, associated with military service increases the income
of Black service members. Military service therefore provides a bridge environment for selected
service members. Moreover, it is possible that civilian employers treat an honorable discharge
from the military as a screen device or a signal for employability, which leads to positive job

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prospects and higher income for Black service members. Therefore, the social capital perspective
and screening and signaling perspective receive limited support.
Several limitations of the study are acknowledged. One limitation is that the current
analyses considered the long-term consequences of military service – service members’
economic well-being during the professional midlife. The short-term consequences of military
service are likely different from those of the long-term, due to, for example, variation in human
capital accumulation and more broadly differences in life event transitions. Moreover, the short-
term outcomes may moderate long-term outcomes because life-long development is an important
component of the life course perspective. Another limitation is that selectivity is controlled to an
extent in the analyses. Since service members are selected into service based on health-related
characteristics partially, superior health status may help explain military income premium
associated with the lifetime service pathway and later discharge pathways. Though the PSM
approach controls for selected socioeconomic covariates, the data unfortunately does not provide
any leverage on modeling the selection process based on health-related factors. It is, however,
likely that long-term service members have better physical health as results of training and
having to pass physical tests throughout the military career.
Conclusion
Service members differ from nonveteran civilians with respect to human capital
accumulation. Not only do service members benefit from having received occupational training
during service, they but also are more likely to have acquired vocational training as civilian
following service. African American service members are more likely to have received
additional education than civilians. These differences in human capital accumulation explain the
positive effect of being a Black service member on higher annual income, providing support for

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the human capital perspective. After taking into consideration the differences in human capital
investments, Black service members receive higher annual income than other non-veteran
Blacks. Military income premium is only partially explained by human capital accumulation.
The social capital perspective receives limited support. Military experience likely partially
substitutes for work experience in the civilian labor market; employers may also use military
status as a screening device to channel veterans into higher-paying jobs, though those
assumptions are not directly tested in the analyses.
Overall, various degrees of military income premium are associated with selected
military career paths and is limited to less advantaged service members. Moreover, variation in
income is largely driven by differences in human capital investment and socioeconomic
background.

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CHAPTER 6
CONCLUSION
Research on military service has a long and rich history in sociology. The American
Soldier (Stouffer et al., 1949) implemented a quantitative lens to empirically study the adaptation
and adjustment of military life; it continuously shapes military sociology and social stratification
today. Subsequence studies examined the important role military has played in studying social
institutions, occupational structures, and social cohesion. Yet seventy years after the publication
of The American Solider, the ways that military service shapes life course trajectories and
mobility patterns are yet to be fully understood. This dissertation links military service with
trajectories and outcomes of service members through a life-course perspective, constructing
typical military career pathways, explaining the associations between social correlates and the
pathways, and demonstrating military as an upward income mobility mechanism in
contemporary America.
This chapter refocuses on the life-course perspective and reviews important findings of
the study, highlighting the connections between the principles and the applications in military
research. Moreover, it discusses future research issues that life-course approach supports in
investigating the impacts of military service under a broad perspective of social stratification.
The first principle of life-course perspective focuses on lifelong development (Elder,
1985; Elder, Johnson, and Crosnoe, 2003). Training (combat, technical, and professional) and
operational experiences (peacekeeping, support, and career development) that military service
offers have short-term impacts on immediate outcomes and long-term impacts on developmental
processes. The effects are far reaching beyond the active-duty period, leading into a life-long
military career or a transition into civilian life. The educational, social, and financial capital

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accrued during and/or after services continuously impacts lifelong development of service
members.
The second principle of life-course perspective focuses on temporal influence of military
service. Different time periods and associated historical events constitute the various cohorts.
During the All-Volunteer Force era, the macroeconomic environment has changed drastically;
opportunities in major domains of life, such as education, employment, and career, are more
constrained compared with earlier periods. Limited presence of wars and substantial offering of
education credit diversify the pathways that individuals take to reach different destinations in
life. Moreover, the disproportion presence of military personnel and families create opportunities
for intergenerational, familial, community-based influence to enter and stay with the military,
linking lives through time and space. Time and space of the life-course perspective suggest an
inter- and intra-generational perspective of understanding the impacts of military services in
contemporary America.
The third principle of the life-course perspective focuses on timing in life (Elder, 1985).
A selected group of young individuals join the military, and the chronological, biological, and
social age that one enlists has profound impacts on the life progression and outcomes. Younger
enlistees from the previous wars were more likely to benefit from the service than those who
entered at a later age in terms of employment, income, and family life (Teachman and Tedrow
2003, 2007); similar results are found in the All-Volunteer Force era. Such differential effects are
found due to amplification of advantages and disadvantages associated with continuities and
disruptions at different points in life. Timing and therefore sequencing of events are therefore
significant markers of life-course perspective.

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The fourth principle of the life-course perspective focuses on human agency, with which
humans makes choices and construct their life pathways within systems of opportunities and
constraints. Decisions on military service, education attainment, occupation choice, and career
fulfillment construct one’s military experience and post-discharge life. The overall decision one
makes to enter a military way of life and pursue a military career has implications for their
socioeconomic wellbeing and that of their families on a micro level and, more broadly, for that
of their fellow soldiers and the military institution as a whole on a macro level. This dissertation
explicates the human agency within the life course or intra-generationally.
The fifth and last principle of the life-course perspective focuses on trajectories and
transitions, emphasizing the diversity in pathways and journeys. As it is seen in this dissertation,
trajectories are represented by various dynamic pathways, based on training, education, and
employment, have been identified among service members, and the construction and
development of these pathways are associated with early life circumstances and military
involvements, which link to later life experiences in adulthood. Transitions are represented by
turning points in life, which change one’s default direction and move one onto a different course
in life. Military service is one such mechanism that can offset early-life disadvantage as a
positive turning point; however, it can also lead to negative turning points through combat-
related injuries and disruption in family life. Trajectories and transitions are important aspects of
the life-course perspectives to understand the strengths and limitations of military service and its
impacts on the paths and outcomes of individual lives.
Much research on military service has considered it to be unidimensional, and thus there
is only one military path. On the contrary, military exerts influence by altering an individual’s
life chances and choices in myriad ways (Street and Hoffman 2013). As it is seen in Chapter 3 of

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this dissertation, there are distinct military pathways based on the ordering and sequencing of life
events. Based on 27 rounds of data from the 1979 National Longitudinal Survey of Youth
[NLSY79] (1979 – 2016), this chapter started with a construction of a typology of discrete and
distinct employment patterns over 15 years since military personnel’s first enlistment in the AVF
era, explicating the timing and sequencing of military service, educational attainment, and post-
service employment. Patterns of effects across the life course are often complex, which
highlights the importance of considering life-course trajectories well beyond the first few years
of military service. What may seem to be a null effect of military service at one point in life may
be very different at earlier or later points in time. A window of 15 years since the first enlistment
allows consideration of the effects of military services and associated educational attainments on
labor market outcomes into the professional mid-life.
Clustering solution shows that though all military personnel start with military service in
this analysis, their pathways diverge, and military service is multidimensional, corresponding to
the human agency aspect of the life-course perspective that individuals make choices and
construct their pathways within the military institution. Moreover, the divergent career pathways
also correspond to the trajectories and transitions of the life-course perspective, emphasizing the
diversity in career journeys.
Different events happen in different orders and at different times lead to diverse military
career pathways; timing of an event is therefore a significant factor to consider. Differences in
the timing for returning to civilian life between non-career veterans and lifetime career service
members are factors that complicate the understanding service-related influence on later-life
outcomes. For example, all military personnel (e.g., veterans or active-duty members), may face
service-related injuries and mortality, which may distinguish lifetime career holders, who survive

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into later life, in unique ways from early dischargers and non-veteran civilians in general. The
timing for returning is therefore confounded with choices that individuals make. For this reason,
selectivity is a limitation of the study.
Another potential source of selectivity is the construction of the analytical sample.
Comparing with the target population of 2,028 military respondents, the analytical sample of 710
respondents have an overrepresentation of African Americans (20% vs. 41%) and Hispanics (6%
vs. 15%) and an underrepresentation of Whites (74% vs. 45%), females (36% vs. 11%), and
younger enlistees (age at first enlistment < 20 years of age) (62% vs. 58%). It is acknowledged in
Chapter 3 that more than four fifths of the original 1979 military sample were removed in 1985
due to funding cut, so many military respondents who joined the military during earlier periods
were not eligible to be considered for a 15-year status sequence construction. To further
investigate, the years that the military personnel joined the military for the first time were
verified, and the result shows that the respondents of the analytical sample joined more often in
the later years than the target sample. Therefore, the analytical sample represented a military
population that is closer to the modern time, during which African Americans and other
minorities are overrepresented in the military. Moreover, female service members represent
about 16% of the current force. At 11%, my analytical sample is also closer to that figure than
the targeted sample. One possible impact of the sample selection is that the sequence analysis
and clustering solution would have similar, if not the same sequences and clusters, but in
different proportions. Such a difference would not significantly change the results of the study.
One major finding of Chapter 3 supports the claim that military serves as a conduit to
education in the All-Volunteer Force era by providing various training and education
opportunities to service members. Though the timing, sequencing, and context of education are

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different across various military-related careers, it is well represented in all clusters, echoing that
education benefits are the most cited reason for enlisting in the United States (McMurray 2007).
This result also corresponds to the lifelong development aspect of the life-course perspective that
education is offered on a continuous basis, supporting the expectation that military sets up
channels for continuous learning.
Following the sequence analysis and clustering solution, descriptive statistics show that
service members and veterans from different career clusters have diverse demographic
characteristics, human capital, family background and experiences, and attitudes and
expectations, leading to the analyses of understanding the association between social correlates
and career choices.
As it is seen in Chapter 3, military service influences education attainment, employment
experiences, and career development, though the strength and direction of the influence is shaped
by unique circumstances associated with an individual’s young and middle adulthood. Chapter 4
of this dissertation discussed the characteristics of men and women that lead them to enlist in the
military and how these characteristics have directed their career pathways. One major takeaway
from multinomial logistic regression is that women are more likely to be associated with Early
Discharge and Multiple Statuses pathways. Gender belief perspectives highlights that women’s
gender role and expectations about employment shape their life course trajectories (Damaske and
Frech 2016). The hypermasculine culture of the military often devalues feminine qualities and
characteristics, and this devaluation leads to violence against women, which propels them to
leave the military after a short period of service. Moreover, the work-family conflict perspective
also explains the female service members’ early exit; they face more tension between family life
and the extreme work of military. Ceilings in military occupations for female service members

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further restrict their career development with the military. Therefore, for female service
members, military service tends to lead to a transition to other adult occupational, familial, and
educational roles rather than a transition to a military career.
Another potential reason that female service members have early separation from the
military is the higher risk of sexual harassment and assault during service. Though not directly
explored in this study, previous research has found that younger age, low education, and junior
rank are associated with increased risk for assault in the military (Suris and Lind 2008); these
characteristics align exactly with the ones associated with the Early Discharge pathway. The fact
that female serve members are more likely to leave the military early (e.g., no more than one
contact) is a significant finding; not only sexual trauma damages the physical and psychological
well-being of female serve members and veterans, which may propel them to separate from the
service early, it but also has indirect economic consequence, such as subsequent career choices
and life-course outcomes. The economic consequence is further explored in Chapter 5.
Service members of color are more likely to be associated with Lifetime Military Career
pathway and Late Discharge pathway. Joining the military is a transition to an adult military role,
rather than a step taken prior to assuming alternative adult roles in the civilian labor market. The
All-Volunteer Force has been one that is blind to demographic differences among enlistees; it
treats racial monitories more fairly with structured promotion and compensation comparing with
the civilian labor market (Booth and Segal 2005; Martorell, Miller, Daugherty, and Borgschute
2013). Service members of color therefore remain in the military for a longer period, shaping
later life-course outcomes through service.
Furthermore, service members’ employment patterns are closely related to family
socioeconomic status. There is an overrepresentation of individuals of less affluent strata in the

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All-Volunteer Force (Kelty and Segal 2013). Difficult early life circumstances, proxied by less
parental education or no parental presence, propel an individual to serve longer terms, fitting the
Turning Point hypothesis that the military potentially removes service members from a
constrained environment to one with more opportunities. In other words, military service serves
as a mechanism that moderates potential negative impacts of lower SES and redirects the life
courses by providing individuals with opportunity to receive training, education, skills, and
resources that they would not have had, putting them on different and better life-course
trajectories (Bennett and McDonald 2013). Moreover, like the way that higher education opens
doors to opportunities that are otherwise unavailable, military service paves ways to
opportunities for socioeconomic achievement unlikely to be otherwise accessible to
disadvantaged individuals. Those opportunities arrest the process of cumulative disadvantage
from early life, creating a bridging environment to success.
Overall, Chapter 4 shows that intra-cohort variation emerges as service-related risks and
benefits intersect with varying demographic and socioeconomic backgrounds (e.g., gender, race
and ethnicity, age at enlistment, family status, etc.).
It is important to recognize that though military service often occupies a short period of
time in the life course, its impact is long-lasting. This dissertation provides one opportunity to
comprehend the ways in which military service structures the life course and discusses the
possible mechanisms that lead to different outcomes. As it is seen in Chapter 5, training and
education received during and after military affects subsequent income, which supports the life-
long development principle. Moreover, Black service members receive more technical and
professional training during and post service than other service members when they choose long-
term service pathways, and such differences in human capital accumulation explain their higher

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professional mid-life income, providing support for the human capital perspective. One result
that is not fully explained by differences in human capital investment and socioeconomic
background is the military income premium received by Black service members; social capital
perspective and screening and signaling perspective receive modest support.
In conclusion, the effects of military service on individuals depend on the socioeconomic
circumstance that they belonged, and more importantly, the pathways that they took. Military
service can be a bridge to a better life, such as one with a stable career, generous economic
reward, and social recognition. However, it can also disrupt the regular life progression through
physical and psychological injuries, such as deaths and stress disorders, which have detrimental
effects on health and earning potentials. In general, serving in the military tends to have positive
effects on the socioeconomic achievement of non-Whites and on the socioeconomically
disadvantaged.
Understanding the needs and selections of various segments of service members have
policy implications in recruitment, retention, and reward, helping the military select and screen
applications and, more importantly, better catering the service, programs, and benefits to
enlistees. One major contribution of this work is that it examined the interaction between
military service and the life course trajectories of woman, minorities, and individuals of
disadvantaged backgrounds, echoing the increasing presence of women, Blacks, and less
socioeconomically advantaged in the military in the All-Volunteer Force era. One limitation is
that those characteristics studies did not capture the motivations to join the military directly,
especially the linkages between these characteristics and the aspirations to serve verse the
aspirations to select other competing activities, such as pursuing higher education or entering the
civilian labor market. With human agency being one of the principles of the life-course

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perspective, the role of attitudes and values towards military is crucial in determining the
behavior, and reciprocally, the role of military is as crucial in shaping and reshaping life course
experiences.
Much of the life-course perspective is implemented when studying service members from
older cohorts, such as those who served during World War II, the Korean War, and the Vietnam
War. This dissertation focuses on the impacts and implications of military services on the All-
Volunteer Force, providing leverage on the effects of the changing landscape of military service
and associated opportunities and benefits. The current exploration calls on the principles of
lifelong development, temporal influence, timing in life, human agency, and trajectories. There is
yet more to explore, based on life-course perspective. One such research agenda is to understand
the linkage between military service and family life-course events through an intergenerational
perspective. Another research agenda is to understand the factors that influence the adaptive and
maladaptive transitions into the military and from military to civilian life through the lens of
transitions. A third research agenda is to further examine the temporal influence of military
service by comparing cohorts from various historical periods, though the data to support
examinations of consequences of military service across different eras remain limited. Last but
not least, social networks created and amended by military service deserve attention in research,
because military links lives through time and space and generates social capital that shapes
outcomes beyond education and employment. The broader social context therefore matters
during veterans’ period of service and for their trajectories and outcomes after discharge, in
terms of, for example, public support for military operations, policy support for meeting needs of
service members and veterans, and civil support for providing employment and educational

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opportunities (Wilmonth and Londone 2013). Overall, the possibilities and opportunities of
future research are unlimited.
In sum, military service is an important component of the life course for many American
men and women. As a matter of fact, military is the single largest employer of young men in the
United States; it is not an isolated event that one chooses to transition to adulthood but exerts a
long-lasting influence on young adults through different means (e.g., training, education,
network, employment, career, etc.), especially those from educationally and economically
disadvantaged backgrounds. Given its transformative potential, military service serves as a
contingency that moderates early life circumstances and therefore modifies later life outcomes.
Yet there is still little understanding of what such service means in terms of education,
employment, and socioeconomic attainment; this dissertation offers one glimpse into the
mechanisms that link those factors and outcomes, with which policy and funding decisions are
based. A better understanding of the factors that shape trajectories contributes to the theoretical
foundations and empirical models concerning human development throughout the life span. How
military service plays out in the interaction among service members, the military institution, and
the society provides a blueprint for the ways that social context and stratification influence
contemporary individual and collective choices and experiences.

125

REFERENCES
Abbott, Abbott and Alexandra Hrycak. 1990. “Measuring Resemblance in Sequence Data: An
Optimal Matching Analysis of Musicians’ Careers.” American Journal of Sociology
96:144–185.
Altschuler, Glenn C. and Stuart M. Blumin. 2009. The GI Bill: A New Deal for Veterans. New
York: Oxford University Press.
Anderson, Elijah. 1999. Code of the Street: Decency, Violence, and the Moral Life of the Inner
City. New York: W. W. Norton.
Angrist, Joshua D. 1990. “Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from
Social Security Administrative Records.” American Economic Review 80(3):313–336.
_____. “The effect of Veterans Benefits on Education and Earnings.” Industrial and Labor
Relations Review 46:637–652.
_____. 1998. “Estimating the Labor Market Impact of Voluntary Military Service Using Social
Security Data on Military Applicants.” Econometrica 66(2):249-288.
Angrist, Joshua D., and Alan B. Krueger, 1994. “Why Do World War II Veterans Earn More
than Nonveterans?” Journal of Labor Economics 12(1):74–97.
Backman, Jerald G., David Segal, Peter Freedman-Doan, and Patrick O’Malley. 2000. “Who
Chooses Military Service? Correlates of Propensity and Enlistment in the US Armed
Forces.” Military Psychology 12(1):1-30. doi: 10.1207/S15327876MP1201_1.

126

Bailey, Amy. 2013. “Military Employment and Spatial Mobility across the Life Course.” Pp.
185-199 in Life-course Perspectives on Military Service, edited by J. M. Wilthom and A.
S. London. New York: Routledge.
Bailey, Amy, and Brian Sykes. 2018. “Veteran Status, Income, and Intergenerational Mobility
across Three Cohorts of American Men.” Population Research and Policy Review
37:539-568.
Becker, Gary S. 1981. A Treatise on the Family. Cambridge, MA: Harvard University Press.
_____. 1993. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to
Education. Chicago, IL: University of Chicago Press.
Becker. Gary S. and H. Gregg Lewis. 1973. “On the Interaction Between the Quantity and
Quality of Children.” Journal of Political Economy 81: S278-S288.
Becker, Gary S., and Nigel Tomes. 1976. “Child Endowments and the Quantity and Quality of
Children.” Journal of Political Economy 84(4): S143-S162.
Ben-Akiva, Moshe, and Steven R. Lerman. 1985. Discrete Choice Analysis: Theory and
Application to Travel Demand. Cambridge, MA: MIT Press.
Bennett, Pamela R., and Amy Lutz. 2009. “How African American Is the Net Black Advantage?
Differences in College Attendance among Immigrant Blacks, Native Blacks, and
Whites.” Sociology of Education 82(1):70-99.

127

Bennett, Pamela R., and Katrina B. McDonald. 2013. “Military Service as a Pathway to Early
Socioeconomic Achievement for Disadvantaged Groups.” Pp. 119-143 in in Life-course
Perspectives on Military Service, edited by J. M. Wilthom and A. S. London. New York,
NY: Routledge.
Bennett, Pamela R., and Yu Xie. 2003. “Revisiting Racial Differences in College Attendance:
The Role of Historically Black Colleges and Universities.” American Sociological
Review 68(4):567-580.
Berger, Mark C., and Barry T. Hirsch. 1983. “The Civilian Earning Earnings and Experience of
Vietnam-era Veterans.” Journal of Human Resources 28:455-479.
_____. 1985. “Veteran Status as a Screening Device during the Vietnam Era.” Social Science
Quarterly 66(1):79-89.
Bergman, Beverly P., Burdett, Howard J., and Neil Greenberg. 2014. “Service life and beyond—
Institution or culture?” The RUSI Journal 159: 60–68
Billingsley, Sunnee. 2018. “Women’s Career Mobility and Childbearing in Sweden.” Stockholm
Research Reports in Demography 6:1-35. doi:10.17045/sthlmuni.5928994.v1.
Blau, Peter M., and Otis D. Duncan. 1967. The American Occupational Structure. John Wiley
and Sons, New York.
Bliese, Paul D., Kathleen M. Wright, Amy B. Adler, Jeffrey L. Thomas, and Charles W. Hoge.
2007. “Timing of Postcombat Mental Health Assessments.” Psychological Services 4(3):
141 – 148.

128

Bloome, Deirdre, and Bruce Western. 2011. “Cohort change and racial differences in educational
and income mobility.” Social Forces 90(2):375–395.
Blumenstiel, Jan Eric, and Tobias Gummer. 2015. “Prävention, Korrektur oder beides? Drei
Wege zur Reduzierung von Nonresponse Bias mit Propensity Scores.” Pp. 13–44 in
Nonresponse bias: Qualitätssicherung sozialwissenschaftlicher Umfragen, edited by C.
Wolf and J. Schupp. Wiesbaden: VS Verlag.
Bogart, Leo. 1969. Social Research and the Desegregation of the U.S. Army. Chicago, IL:
Markham.
Bonnie, Richard J. 2015. “Education and Employment.” Pp. 123-170 in Investing in the Health
and Well-being of Young Adults, edited by R. J. Bonnie, C. Stroud, and H. Breiner.
Washington, DC: National Academies Press.
Booth, Bradford H., and David R. Segal. 2005. “Bringing in the Soldiers Back In: Implications
of Inclusion of Military Personnel for Labor Market Research on Race, Class, and
Gender.” Race, Gender & Class 12(1): 34-57.
Boucher, Ronald L. 1973. “The Colonial Militia as a Social Institution: Salem, Massachusetts
1764-1775.” Military Affairs 27(4) (December): 125-130.
Bound, John, and Sarah Turner. 2002. “Going to War and Going to College: Did World War II
and the G.I. Bill Increase Educational Attainment for Returning Veterans?” Journal of
Labor Economics 20:784–815.
Boutelle, Clif. 2016. Veterans Bring Key Skill Assets to the Civilian Workforce. Bowling Green,
OH: Society for Industrial and Organizational Psychology, SIOP Public Relations.

129

Brewster, Karin L., and Ronald R. Rindfuss. 2000. “Fertility and Women’s Employment in
Industrialized Nations.” Annual Review of Sociology 26(1):271–296.
Briton, Mary, and Takehilo Kariya. 1998. “Institutional Embeddedness in Japanese Labor
Markets.” Pp. 181-207 in the New Institutionalism in Sociology. Edited by M. C. Brinton
and V. Nee. New York, NY: Russell Sage Foundation.
Browning, Harley L., Sally C. Lopeato, and Dudley L. Poston. 1973. “Income and Veteran
Status – Variations among Mexican Americans, Blacks, and Anglos.” American
Sociological Review 38(1):74-85.
Bryant, Richard, and Al Wilhite. 1990. “Military Experience and Training Effects on Civilian
Wages.” Journal of Applied Economics 22(1): 69-81.
Bureau of Labor Statistics. 2019. Employment Situation of Veterans – 2018. Retrieved
December 10, 2019 (https://www.bls.gov/news.release/pdf/vet.pdf).
Burgess, Lisa. 2008. “$150,000 Bones Offered for Some Special Forces.” Stars and Stripes.
Retrieved October 17, 2019 (https://www.stripes.com/news/150-000-bonus-offered-for-
some-special-forces-1.75636).
Burnam, M. Audrey, Lisa S. Meredith, Cathy D. Sherbourne, R. Burciaga Valdez, and Georges
Vernez. 1992. Army Families and Soldier Readiness. Santa Monica, CA: RAND
Corporation.
Cohen, Jere, Rebecca L. Warner, and David R. Sega. 1995. “Military Service and Educational
Attainment in the All-Volunteer Force.” Social Science Quarterly 76(1):88-104.

130

Coleman, James S. 1988. “Social Capital in the Creation of Human Capital.” American Journal
of Sociology 94: S95-S120.
Collins, Jane, and Victoria Mayer. 2010. Both Hands Tied: Welfare Reform and the Race to the
Bottom of the Low- Wage Labor Market. Chicago, IL: University of Chicago Press.
Cooper, Linda, Nick Caddick, Lauren Godier, Alex Cooper, and Matt Fossey. 2018. “Transition
from the Military into Civilian Life: An Exploration of Cultural Competence.” Armed
Forces & Society 44(1), 156–177. https://doi.org/10.1177/0095327X16675965
D’Agostino, Ralph B. 1998. “Propensity Score Methods for Bias Reduction for the Comparison
of a Treatment to a Non-randomized Control Group.” Statistics in Medicine 17:2265–
2281.
Damaske, Sarah, and Adrianne Frech. 2016. “Women’s Work Pathways across the Life Course.”
Demography 53:365-391.
Davis, Valerie E., and Sarah E. Minnis. 2017. “Military Veterans’ Transferrable Skills: An HRD
Practitioner Dilemma.” Advances in Developing Human Resources 19(1), 6–13.
https://doi.org/10.1177/1523422316682961
Davis-Kean, Pamela E. 2005. “The Influence of Parent Education and Family Income on Child
Achievement: The Indirect Role of Parental Expectations and the Home Environment.”
Journal of Family Psychology 19(2): 294-304. doi: dx.doi.org/10.1037/0893-
3200.19.2.294
Dempsey, Jason, and Amy Schafer. 2020. “Veteran Pathways to Employment: Hurdles and
Opportunities.” Center for a New American Security Reports 1–22.

131

Department of Defense. 2018. 2018 Demographics Profile of the Military Community.
Washington, D.C.: Department of Defense. Retrieved October 15, 2019
(https://www.militaryonesource.mil/reports-and-surveys/infographics/active-duty-
member-and-family-demographics).
_____. 2011. “Military Casualty Status.” Washington, DC: U.S. Department of Defense.
Retrieved November 10, 2020, http://www.defense.gov/news/casualty2015.pdf
Department of Veterans Affairs. 2019. Education and Training. Washington, D.C.: Department
of Veterans Affairs. Retrieved October 16, 2019
(https://www.benefits.va.gov/gibill/post911_transfer.asp).
De Tray, Dennis N. 1982. ‘‘Veteran status as a screening device.” American Economic Review
72:133–142.
DiMaggio, Paul, and John Mohr. 1985. “Cultural Capital, Educational Attainment, and Marital
Selection.” American Journal of Sociology 90(6):1231-1261.
DiPrete, Thomas A., and Gregory M. Eirich. 2006. “Cumulative Advantage as a Mechanism for
Inequality: A Review of Theoretical and Empirical Developments. Annual Review of
Sociology 32:271-297. doi: 10.1146/annurev.soc.32.061604.123127
Dobkin, Carlos, and Reza Shabani. 2009. “The Health Effect of Military Service.” Economic
Inquiry 47(1):69 – 80.
Domencich, Tom A., and Daniel L. McFadden. 1975. Urban Travel Demand: A Behavioral
Analysis. Amsterdam: North-Holland.

132

Downs, Kiersten H. 2017. “‘Beautifully Awful’: A Feminist Ethnography of Women Veterans'
Experiences with Transition from Military Service.” Ph.D. dissertation. Department of
Anthropology, University of South Florida, Tampa, Florida.
Durkheim, Emile. 1951. Suicide: A Study in Sociology. Glencoe, IL: Free Press.
Duncan, Otis D. 1961. “A socioeconomic index for all occupations.” Pp. 109-138 in Occupations
and Social Status, edited by A. J. Reiss, Jr. Glencoe, IL: Free Press.
Duncan, Otis, D., David Featherman, and Beverly Duncan. 1972. Socioeconomic Background
and Achievement. New York, NY: Academic Press.
Elder, Glen H. 1986. “Military Times and Turning Points in Men’s Lives.” Developmental
Psychology 22(2):233-245.
_____. 1985. Life Course Dynamics: Trajectories and Transitions, 1968 – 1980. Ithaca, NY:
Cornell University Press.
Elder, Glen H., Monica Kirkpatrick Johnson, and Robert Crosnoe. 2003. “The Emergence and
Development of Life Course Theory.” Pp. 3–19 in Handbook of the Life Course, edited
by Jeylan T. Mortimer, and Michael J. Shanahan. Springer: New York.
Erola, Jani, Sanni Jalonene, and Hannu Lehti. 2016. “Parental Education, Class and Income over
Early Life Course and Children's Achievement.” Research in Social Stratification and
Mobility 44:33-43.
Featherman, David L., and Robert M. Hauser. 1978. Opportunity and Change. New York, NY:
Academic Press.

133

Fernandez, Roberto M., and Nancy Weinberg. 1997. “Sifting and Sorting: Personal Contacts and
Hiring in a Retail Bank.” American Sociological Review 62:883-902.
Fredland, J. Eric, and Roger D. Little. 1985. “Socioeconomic Status of World War II Veterans
by Race: An Empirical Test of the Bridging Hypothesis.” Social Science Quarterly
66:533-551.
Fredland, J. Eric, Cutis Gilroy. L., Roger D. Little, and W. S. Sellman. 1996. Professionals on
the Front Line: Two Decades of the All-Volunteer Force. Washington, DC: Brassey’s.
Furstenberg, Frank F. 2008. “The Intersections of Social Class and the Transition to Adulthood.”
New Directions for Child and Adolescent Development 119: 1–10.
https://doi.org/10.1002/cd.205.
Gade, Paul A., Hyder Lakhani, and Melvin Kimmel. 1991. “Military Service: A Good Place to
Start.” Military Psychology 3: 251–267.
Goldin, Claudia, and Lawrence F. Katz. 2008. Race Between Education and Technology.
Cambridge MA: The Belknap Press of Harvard University Press.
Giddens, Anthony. 2007. Europe in the Golden Age. Cambridge, MA: Polity Press.
Godfrey, Richard, Simon Lilley, and Joanna Brewis. 2012. “Biceps, Bitches and Borgs: Reading
Jarhead’s Representation of the Construction of the (Masculine) Military Body.”
Organization Studies 33, 541–562.
Granovetter, Mark. 1995. Getting a Job: A Study of Contacts and Careers. 2nd Edition. Chicago,
IL: Chicago University Press.

134

_____. 1985. “Economic Action and Social Structure: The Problem of Embeddedness.”
American Journal of Sociology 9(3):481-510.
_____. 1973. “The Strength of Weak Ties.” American Journal of Sociology 78:360-80.
Griliches, Zvi. 1980. “Schooling Interruption, Work While in School and the Returns from
Schooling.” Scandinavian Journal of Economics 82(2):291–303.
Griliches, Zvi and William M. Mason. 1972. “Education, Income, and Ability.” Journal of
Political Economy 80(3):S74–S103.
Groves, Robert. M. 2006. “Nonresponse Rates and Nonresponse Bias in Household Surveys.”
Public Opinion Quarterly 70(5):646–675.
Gugushvili, Alexi, Erzsébet Bukodi, and John H. Goldthorpe. 2017. “The Direct Effect of Social
Origins on Social Mobility Chances: ‘Glass Floors’ and ‘Glass Ceilings’ in Britain.”
European Sociological Review 33(2):305–316. doi: doi.org/10.1093/esr/jcx043.
Gummer, Tobias, and Joss Roβmann. 2019. “The Effects of Propensity Score Weighting on
Attrition Biases in Attitudinal, Behavioral, and Socio-demographic Variables in a Short-
term Web-based Panel Survey.” International Journal of Social Research Methodology
22(1):81-95.
Hagan, Jacqueline Maria. 2006a. “Social Membership and Immigrant Integration across National
Contexts.” Social Forces 85(2):631-642.
_____. 2006b. “Negotiating Social Membership in the Contemporary World.” Social Forces
85(2):631-642.

135

_____. 2004. “Contextualizing Immigrant Labor Market Incorporation: Legal, Demographic, and
Economic Dimensions.” Work and Occupations 31(4):407-423.
Halpin, Brendan. 2016. "SILHOUETTE: Stata Module to Calculate and Graph Silhouette Width
for Cluster Analysis.” Statistical Software Components S458165, Department of
Economics, Boston College. Retrieved on November 4, 2019
(https://ideas.repec.org/c/boc/bocode/s458165.html)
Hauser, Robert M. 2010. “Intergenerational Mobility in the United States: Measures,
differentials, and Trends.” Center for Demography and Ecology Working Paper 98-12.
Department of Sociology, The University of Wisconsin-Madison. Retrieved on
November 4, 2019
(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9293&rep=rep1&type=pd
f).
Hattiangadi, Anita U., Aline O. Quester, SgtMaj USMC Ret. Gart Lee, Diana D. Lien, Ian D.
MacLeod, David L. Reese, and Robrt W. Shuford. 2005. Non-Citizens in Today’s
Military: Final Report. Alexandria, VA: Center for Naval Analyses.
Heflin, Colleen, Janet Wilmoth, and Andrew S. London. 2021. “Veteran Status and Material
Hardship: the Moderating Influence of Disability.” Social Service Review 86(1):119 –
142.
Hensher, David A., and Lester W. Johnson. 1981. Applied Discrete-Choice Modeling. London,
England: Croom Helm.
Hexter, Holly, and Elaine El-Khawas. 1988. Joining Forces: The Military's Impact on College
Enrollments. Washington, DC: American Council on Education.

136

Hirsch, Barry T., and Stephen L. Mehay. 2003. “Evaluating the Labor Market Performance of
Veterans Using a Matched Comparison Group Design.” Journal of Human Resources
38(3):673-700.
Hirvonen, Lalaina. 2008. “Intergenerational Earnings Mobility Among Daughters and Sons:
Evidence from Sweden and a Comparison with the United States.” The American Journal
of Economics and Sociology 67(5):777-826.
Hogan, Dennis P, and Nan Marie Astone, 1986. “The Transition to Adulthood.” Annual Review
of Sociology 12:109-130.
Hogan Paul F., and Rita Furst Seifert. 2009. “Marriage and the Military: Evidence That Those
Who Serve Marry Earlier and Divorce Earlier.” Armed Forces & Society 36(3):420-438.
Hoge, Charles W, Carl A. Castro, Stephen C. Messer, Dennis McGurk, Dave I. Cotting, and
Robert L. Koffman. 2004. “Combat Duty in Iraq and Afghanistan, Mental Health
Problems, and Barriers to Care.” New England Journal of Medicine 351(1):13-22.
doi: 10.1056/NEJMoa040603
Holzer, Harry, and Sheldon Danziger. 2001. “Are Jobs Available for Disadvantaged Workers in
Urban Areas? Pp. 496-538 in Urban Inequality: Evidence from Four Cities, edited by A.
O’Connor, C. Tilly, and L. Bobo. New York, NY: Russel Sage Foundation.
Huffman, Matt L., and Lisa Torres. 2002. “It’s Not Only ‘Who You Know’ that Matters: Gender,
Personal Contacts, and Job Lead Quality.” Gender and Society 16(6):793-813.

137

Hull, Emily L. 2008. Military Service and Marriage: A Review of Research. National Healthy
Marriage Resource Center. Retrieved on October 16, 2019
(http://www.healthymarriageinfo.org/docs/reviewmmilitarylife.pdf).
Ishida, Hirosh, Müller, Walter., and Ridge, John M. 1995. “Class Origin, Class Destination, and
Education: A Cross-national Study of Ten Industrial Nations.” The American Journal of
Sociology 101(1):145–193. https://doi.org/10.1086/230701
Jantti, Markus, Bernt Bratsberg, Knut Roed, Oddbjorn Raaum, Robin Naylor, Eva Osterbacka,
Anders Bjorklund, and Tor Eriksson. 2006. “American Exceptionalism in a New Light: A
Comparison of Intergenerational Earnings Mobility in the Nordic Countries, the United
Kingdom, and the United States.” Discussion paper no. 1938. Institute for the Study of
Labor (IZA), Bonn.
Johnson, Gloria J., and W. Roy Johnson. 2005. “Cumulative Disadvantage Theory and
Contingent Work among African Americans.” African American Research Perspectives
11(1):151-168.
Kaufman, Leonard, and Peter J. Rousseeuw. 2008. “Monothetic Analysis (Program MONA).”
Pp. 280-311 in Finding Groups in Data: An Introduction to Cluster Analysis, edited by
Leonard Kaufman and Peter J. Rousseeuw. Hoboken, NJ: John Wiley & Sons.
Kahlenberg, Richard D. 1996. “Class-Based Affirmative Action.” California Law Review 84(4):
1037-1099.
Kalleberg, Arne. 2011. Good Jobs, Bad Jobs: the Rise of Polarized and Precarious Employment
Systems in the United States, 1970s to 2000s. New York, NY: Russell Sage Foundation.

138

Kamarck, Kristy N. 2019. “Diversity, Inclusion, and Equal Opportunity in the Armed Services:
Background and Issues for Congress.” CRS Report R44321. Washington, DC:
Congressional Research Service.
_____. 2015. “Women in combat: Issues for Congress.” CRS Report R42075. Washington, DC:
Congressional Research Service.
Karney, Benjamin R., and John S. Crown. 2007. Families Under Stress: An Assessment of Data,
Theory, and Research on Marriage and Divorce in the Military. Santa Monica, CA: Rand
Corporation.
Kelty, Ryan, Meredith Kleykamp, and David R. Segal. 2010. “The Military and Transition to
Adulthood.” Future Child 20(1):181-207.
Kelty, Ryan, and David R. Segal. 2013. The Military as a Transforming Influence. Integration
into or Isolation from Normal Adult Roles? Pp. 19-47 in Life-Course Perspectives on
Military Service, edited by J. M. Wilmoth and A. S. London. New York, NY: Routledge.
_____. 2007. “The Civilianization of the U.S. Military: Army and Navy Case Studies of the
Effects of Civilian Integration on Military Personnel.” Pp. 213-240 in Private Military
and Security Companies: Chances, Problems, Pitfalls, and Prospects, edited by T. Jager
and G. K. Wiesbaden, Germany: VS Verlag fur Sozialwissenschaften.
Kestnbaum, Meyer. 2000. “Citizenship and Compulsory Military Service: The Revolutionary
Origins of Conscription in the United States.” Armed Forces & Society 27(1):7-36.
Killewald, Alexandra, and Xiaolin Zhuo. 2019. “U.S. Mothers’ Long-Term Employment
Patterns.” Demography 56:285-320.

139

Kleykamp, Meredith. 2013. Labor market outcomes among veterans and military spouses. Pp.
144-164 in Life-Course Perspectives on Military Service, edited by J. M. Wilmoth and A.
S. London. New York, NY: Routledge.
_____. 2009. “A Great Place to Start? The Effect of Prior Military Service on Hiring.” Armed
Forces & Society 35(2):266-285.
_____. 2006. “College, Jobs, or the Military? Enlistment during a Time of War.” Social Science
Quarterly 87:272–290.
Lee, Sunghee. 2006. ‘Propensity Score Adjustment as a Weighting Scheme for Volunteer Panel
Web Surveys.” Journal of Official Statistics 22(2):329-349.
Light, Audrey. 2005. “Job Mobility and Wage Growth.” Monthly Labor Review 128(2):33-39.
Light, Audrey, and Kathleen McGarry. 1998. “Job Change Patterns and the Wages of Young
Men.” Review of Economics and Statistics 80(2):276-286.
Little, Roderick J., and Sonya Vartivarian. 2005. “Does Weighting for Nonresponse Increase the
Variance of Survey Means?” Survey Methodology 31(2):161–168.
Lowe, Kendra N, Katharine S. Adams, Blaine L. Browne, and Kerry T. Hinkle. 2012. “Impact of
military deployment on family relationships.” Journal of Family Studies18(1):17-27.
MacLean, Alair. 2017. “Skills Mismatch? Military Service, Combat Occupations, and Civilian
Earnings.” Sociological Perspectives 60(2):229–250.
https://doi.org/10.1177/0731121416632011

140

_____. 2013. “A Matter of Life and Death: Military Service and Health.” Pp. 200-220 in Life-
course Perspectives on Military Service, edited by J. M. Wilthom and A. S. London. New
York: Routledge.
______. 2010. “The Things They Carry: Combat, Disability, and Unemployment among U.S.
Men.” American Sociological Review 75(4):563–585.
_____. 2005. “Lessons from the Cold War: Military Service and College Education.” Sociology
of Education 78(3):250-266.
MacLean, Alair, and Meredith Kleykamp. 2016. “Income Inequality and the Veteran
Experience.” The Annals of the American Academy 663:99-116.
McMurray, Andrew J. 2007. “College Students, the GI Bill, and the Proliferation of Online
Learning: A history of learning and Contemporary Challenges.” Internet and Higher
Education 10:143-150. doi:10.1016/j.iheduc.2007.03.002
Mangum, Stephen L., and David E. Ball. 1989. “The Transferability of Military-Provided
Occupational Training in the Post-Draft Era.” ILR Review 42(2):230-245.
_____. 1987. “Military Skill Training – Some Evidence of Transferability.” Armed Forces &
Society 13(3):425 – 441.
Marcus, Richard D. 1984. “Measuring the Rate of Return to Interrupted Schooling.” Journal of
Educational Statistics 9(4):295–310.
Mare, Robert D., and Christopher Winship. 1984. “The Paradox of Lessening Racial Inequality
and Joblessness among Black Youth – Enrollment, Enlistment, and Employment, 1964 –
1981.” American Sociological Review 49(1):39-55.

141

Martorell, Paco, Trye Miller, Lindsay Daugherty, and Mark Borgschulte. 2013. “Effects of
Military Service on Earnings and Education, Revisited.” RAND National Security
Research Division. Santa Monica, CA. Retrieved on November 4, 2019
(https://www.rand.org/pubs/research_reports/RR342.html).
McFadden, Dale. 1981. “Econometric Models of Probabilistic Choice.” Pp. 198-272 in
Structural Analysis of Discrete Data with Econometric Applications, edited by C.F.
Manski and D. McFadden. Cambridge, MA: MIT Press.
_____. 1978. “Modeling the Choice of Residential Location.” Transportation Research Record
673:72-77.
_____. 1974. “Conditional Logit Analysis of Qualitative Choice Behavior.” Pp. 105-142 in
Frontiers in Econometrics, edited by P. Zarembka. New York, NY: Academic Press,
McGinn, Kathleen L., Mayra Ruiz Castro, and Elizabeth Long Lingo. 2018. “Learning from
Mum: Cross-national Evidence Linking Maternal Employment and Adult Children’s
Outcomes.” Working, Employment, and Society 33(3):374-400. doi:
doi.org/10.1177/0950017018760167.
Menjivar, Cecilia. 2000. Fragmented Ties: Salvadoran Immigrant Networks in America.
Berkeley, CA: University of California Press.
Negrusa, Brighita, and Sebastian Negrusa. 2014. “Home Front: Post-deployment Mental Health
and Divorces.” Demography 51:895–916.

142

Nilsen, Oivind A., Kjell Vaage, Ariild Arkvik, and Karl Jacobsen. 2008. “Estimates of
Intergenerational Elasticities Based on Lifetime Earnings.” Discussion Paper no. 3709.
Institute for the Study of Labor (IZA), Bonn.
Niu, Sunny, and Marta Tienda. 2013. “Delayed enrollment and College Plans: is There a
Postponement Penalty?” Journal of Higher Education 84(1):1–26.
https://doi.org/10.1353/jhe.2013.0007
Obama, Barack. 2009. Remarks by the President to the Hispanic Chamber of Commerce on
Complete and Competitive American Education, March 10. The White House.
Washington, DC: The White House, Office of the Press Secretary. Retrieved on
November 4, 2019 (https://obamawhitehouse.archives.gov/the-press-office/remarks-
president-united-states-hispanic-chamber-commerce).
O’Brien, Marco. 2008. “5 Miliary Myths – BUSTED!” Military.com. Retrieved December 3,
2009 (http:// www.military.com/Recruiting/Content/0,13898,02262008-military-
myths.htm).
Orthner, Dennis K., Gary L. Bowen, Laura I. Zimmerman, and Kathrine A. Short. 1992. “Young
Single Soldiers and Relationships (Technical Report).” Alexandria, VA: U.S. Army
Research Institute.
Prigerson, Holly G., Paul K. Maciejewski, and Robert A. Rosenheck. 2002. “Population
Attributable Fractions of Psychiatric Disorders and Behavioral Outcomes Associated
with Combat Exposure among US Men.” American Journal of Public Health 92(1):59–
63.

143

Raaum, Oddbjorn, Bernt Bratsberg, Knut Roed, Eva Osterbacka, Tor Eriksson, Markus Jantti,
and Robin A. Naylor. 2007. “Marital Sorting, Household Labor Supply, and
Intergenerational Earnings Mobility Across Countries.” The B.E. Journal of Economic
Analysis and Policy 7(2):1-49.
Radford, Alexandra Walton. 2009. Military Service Members and Veterans in Higher Education:
What the New GI Bill May Mean for Postsecondary Institutions. Washington, DC:
American Council on Education.
Reddy, Chandan K. and Bhanukiran Vinzamur. 2013. “A survey of partitional and hierarchical
clustering algorithms. Pp. 87—110 in Data clustering: Algorithms and Applications,
edited by C. C. Aggarwal and C. K. Reddy. Boca Raton, FL: CRC Press.
Reynolds, George M., and Amanda Shendruk. 2018. Demographics of the U.S. Military. Council
on Foreign Relations. Washington DC: Council on Foreign Relations. Retrieved on
November 4, 2019 (https://www.cfr.org/article/demographics-us-military).
Rindfuss, Ronald R., C. Gray Swicegood, and Rachel A. Rosenfeld. 1987. “Disorder in the life
course: how common and does it matter?” American Sociological Review 52:785–801.
Rohwer, Götz. 1997. TDA: User’s Manual. Rostock, Germany: Max-Planck-Institut für
Bildungsforschung.
Rose, Elaina. 2007. “Siblings and Soldiers: Family Background and Military Service in the All-
Volunteer Era.” Unpublished manuscript.
Rosenbaum, James E. 2001. Beyond College for All: Career Paths for the Forgotten Half. New
York, NY: Russel Sage Foundation.

144

Rosenbaum, Paul R., and Donald B. Rubin. 1984. “Reducing Bias in Observational Studies
Using Subclassification on the Propensity Score.” Journal of the American Statistical
Association 79: 516–524.
_____. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal
Effects.” Biometrika 70:41–55.
Rousseeuw, Peter J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of
Cluster Analysis.” Journal of Computational and Applied Mathematics 20:53–65.
Ryan, Francis J., and Joseph J. Bevilacqua. 1964. “The Military Family: An Asset or a
Liability?” Military Medicine 29:956-959.
Sassen, Saskia. 1990. “Economic Restructuring and the American City.” Annual Review of
Sociology 16:465-490.
Sayer, Liana C. 2010. “Trends in Housework.” Pp. 19–38 in Dividing the Domestic: Men,
Women, and Household Work in Cross-National Perspective, edited by T. Judith and D.
Sonja. Palo Alto, PA: Stanford University Press.
Seeborg, Michael C. 1994. “Race, Poverty, and Enlistment: Some Evidence from the National
Longitudinal Survey of Youth.” The Journal of Economics 20:15-24.
Segal, David R. 1989. Recruiting for Uncle Sam: Citizenship and Military Manpower Policy.
Lawrence, KS: University Press of Kansas.
Segal, Mady W. 1986. “The Military and the Family as Greedy Institutions.” Armed Forces &
Society 13(1):9-38.

145

Segal, David R., Jerald G. Bachman, and Faye Dowdell. 1978. “Military Service for Female and
Black Youth: A Perceived Mobility Opportunity.” Youth & Society 10(2):127–34.
doi:10.1177/0044118X7801000203.
Segal, David. R. and Mady W. Segal. 2004. “America’s Military Population.” Population
Bulletin 59(4):3–40.
Smith, Irving, Kris Marsh, and David R. Segal. 2010. “The World War II Veteran Advantage? A
Lifetime Cross-Sectional Study of Social Status Attainment.” Armed Forces & Society
38(1):5-26.
Snyder, R. Claire. 2003. “The Citizen-Soldier Tradition and Gender Integration of the U.S.
Military.” Armed Forces & Society 29(2):185-204.
Stanley, Marcus. 2003. “College Education and the Midcentury GI Bills.” Quarterly Journal of
Economics 118(2):671-708.
Steelman, Lara Carr, Brian Powell, Regina Werum, and Scott Carter. 2002. “Reconsidering the
Effects of Sibling Configuration: Recent Advances and Challenges.” Annual Review of
Sociology 28:243-69.
Stewart, Richard W. 2005. American Military History. Washington, D.C.: Center of Military
History United States Army.
Stewart, James B., and Juanita M. Firestone. 2001. “Looking for a Few Good Men: Predicting
Patterns of Retention, Promotion, and Accession of Minority and Women Officers.” Pp.
231-256 in Managing Diversity in the Military, edited by M. R. Dansby, J. B. Stewart,
and S. C. Webb. New Brunswick, NJ: Transaction Publishers.

146

Stouffer, Samuel, A., Arthur A. Lumsdaine, Marion Harper Lumsdaine, Robbin M. Williams Jr.,
M. Brewster Smith, Irving L. Janis, Shirley A. Star, and Leonard S. Cottrell Jr. 1949a.
The American Solider, Vol 1: Adjustment during Army Life. Princeton, NJ: Princeton
University Press.
_____. 1949b. The American Solider, Vol 1: Combat and Its Aftermath. Princeton, NJ: Princeton
University Press.
Stovel, Katherine, Michael Savage, and Peter Bearman. 1996. “Ascription into Achievement:
Models of Career Systems at Lloyds Bank 1890-1970.” American Journal of Sociology
102(2):358-99.
Street, Debra, and Jessica Hoffman. 2013. “Military Service, Social Policy, and Later-Life
Financial and Health Security.” Pp. 221-242 in Life-course Perspectives on Military
Service, edited by J. M. Wilthom and A. S. London. New York: Routledge.
Studer, Matthias, Gilbert Ritschard, Alexis Gabadinho, and Nicolas S. Müller. 2011.
“Discrepancy Analysis of State Sequences.” Sociological Methods & Research 40:471–
510.
Suris, Alina, Linda Lind, T. Michael Ashner, Patricia D. Orman, and Frederick Etty. 2004.
“Sexual Assault in Women Veterans: An Examination of PTSD Risk Health Care
Utilization, and Cost of Care. Psychosomatic Medicine 66(5):749–756.
Tanielian, Terri L., and Lisa H. Jaycox. 2008. Invisible Wounds of War: Psychological and
Cognitive Injuries, Their Consequences, and Services to Assist Recovery. Santa Monica,
CA: RAND.

147

Teachman, Jay. 2010. “Are Veterans Healthier? Military Service and Health at Age 40 in the
All-volunteer Era.” Social Science Research 40:326-335.
_____. 2007. “Military Service and Educational Attainment in the All-Volunteer Era.” Sociology
of Education 80:359-374.
_____. 2005. “Military Service in the Vietnam Era and Educational Attainment.” Sociology of
Education 78:50-68.
_____. 2004. “Military Service during the Vietnam Era: Were There Consequences for
Subsequent Civilian Earnings?” Social Forces 83:709-730.
Teachman, Jay, Carter Anderson, and Lucky Tedrow. 2015. “The Relationship between Military
Service and Childbearing for Men and Women.” Sociological Perspectives 58:595-608.
Teachman, Jay, and Lucky Tedrow. 2014. “Delinquent Behavior, the Transition to Adulthood,
and the Likelihood of Military Enlistment.” Social Science Research 45:46-55.
_____. 2013. “Veteran Status and Body Weight: A Longitudinal Fixed-effects Approach.”
Population Research and Policy Review 32:199-220.
_____. 2007. “Joining Up: Did Military Service in the Early All Volunteer Era Affect
Subsequent Civilian Income?” Social Science Research 36:1447-1474.
_____. 2004. “Wages, Earnings, and Occupational Status: Did World War II Veterans Receive a
Premium?” Social Science Research 33:581-605.
Teachman, Jay, and Vaughn Call. 1996. “The Effect of Military Service on Educational,
Occupational and Income Attainment.” Social Science Research 25:1-31.

148

Theodore F. Figinski, 2019. “Do Employers Favor those with Military Experience in the U.S.
Reserve Forces? Evidence from a Field Experiment.” Defense and Peace Economics
30(2):213-226.
Trappmann, Mark, Tobias Gramlich, and Alexander Mosthaf. 2015. “The effect of events
between waves on panel attrition.” Survey Research Methods 9(1):31–43.
Treiman, Donald J. 1977. Occupational Prestige in Comparative Perspective. New York, NY:
Academic Press.
Treiman, Donald J., and Patricia A. Roos. 1983. “Sex and earnings in industrial society: A nine-
nation comparison.” American Journal of Sociology 89:612-650.
Tyson, Ann Scott. 2006. “Repeat Iraq Tours Raise Risk of PTSD, Army Finds.” Washington
Post 20:A19.
U.S. Citizenship and Immigration Services. 2016. Naturalization through Military Service Fact
Sheet. Washington, DC: U.S. Citizenship and Immigration Services. Retrieved on
November 4, 2019 (https://www.uscis.gov/news/fact-sheets/naturalization-through-
military-service-fact-sheet).
U.S. Department of Education. 2018. The Condition of Education 2018. Washington, DC: U.S.
Department of Education. Retrieved on November 4, 2019
(https://nces.ed.gov/pubs2018/2018144.pdf).
U.S. Department of Veterans Affairs. 1977. VA History in Brief. Washington, DC: U.S.
Department of Veterans Affairs. Retrieved November 4, 2019
(https://www.va.gov/opa/publications/archives/docs/history_in_brief.pdf).

149

Wadsworth, Shelley MacDermid, and Kenona Southwell. 2011. “Military Families: Extreme
Work and Extreme ‘Work-Family.’” The ANNALS of the American Academy of Political
and Social Science 638(1):163–183. https://doi.org/10.1177/0002716211416445.
Wald, Johanna, and Daniel J. Losen. 2003. “Defining and Re-Directing a School to Prison
Pipeline.” New Directions for Youth Development 99: 9-15.
Western, Bruce, and Becky Pettit. 2005. “Black-White Wage Inequality, Employment Rates, and
Incarceration.” American Journal of Sociology 111(2):553-578.
Wilmoth, Janet M. Andrew S. London, and Wendy Parker. 2010. “Military Service and Men’s
Health Trajectories in Later Life.” Journal of Gerontology: Social Sciences 56(6): 744 –
755.
Wilson, William Julius. 2011. “The Declining Significance of Race: Revisited & Revised.”
Daedalus 140(2): 55-69.
_____. 1978. The Declining Significance of Race; Blacks and Changing American Institutions.
Chicago, IL: University of Chicago Press.
Wolf, Douglas Alan, Coady Wing, and Leonard Michael Lopoo. 2013. “Methodological
Problems in Determining the Consequences of Military Service.” Pp. 254-274 in Life-
Course Perspectives on Military Service, edited by J. M. Wilmoth and A. S. London.
New York, NY: Routledge.
Wright, John Paul, David E. Carter, and Francis T. Cullen. 2005. “A Life-Course Analysis of
Military Service in Vietnam.” Journal of Research in Crime and Delinquency 42(1): 55 –
83.

150

Wyman, Mira, Megan Lemmon, and Jay D, Teachman. 2010. “Non-Combat Military Service in
the United States and its Effects of Depressive Symptoms among Men.” Social Service
Research 40: 695 – 703.
Xie, Yu. 1992. “The Socioeconomic Status of Young Male Veterans, 1964-1984.” Social
Science Quarterly 73(2):379-396.
Zhou, Ming. 1993. “Underemployment and Economic Disparities among Minority Groups.”
Population Research and Policy Review 12(2): 139-157.

151

APPENDIX
PAM ALGORITHM AND CLUSTERING SOLUTIONS

I used Partition Around Medoid (PAM) algorithm (also known as K-medoids clustering)
to identify a solution with six career clusters. The result does not necessarily indicate that there
are exactly six military-related career pathways; based on the AVF sample provided by the
NLSY79 (1979 – 2016), six clusters is one reasonable solution. This section provides
justifications of the selection of a six-cluster solution based on two criteria: first, empirically,
solutions of different numbers of clusters do not provide additional information on the variation
of career pathways; second, statistically, the six-cluster solution has high between-cluster
distances and low within-cluster distances.
Empirical Justifications
I applied the same data reduction procedure and identified two more solutions, one with
five clusters and another with seven clusters. Figures A1 and A2 show the medoids from these
two solutions. Either solution characterizes the major military-related career pathways
differently. The five-cluster solution contains the following pathways: Early Discharge (cluster
4, 24%), Midway Discharge (cluster 3, 39%), Lifetime Military (cluster 2, 12%; cluster 5, 18%),
and Multiple Statuses (7%). The seven-cluster solution
38
contains two pathways (clusters 1 and
2) that do not include vocational training during service; they are similar to the Multiple Statuses
pathway from the six-cluster solution as one would experience multiple episodes of
unemployment and/or OOLF at some point in the life course. One highlight of the seven-cluster
solution is that with additional human capital accumulated during the civilian employment

38
Multiple Statues with OOLF, 7%; Multiple Statues with Additional Human Capital Accumulation, 4%; One-
contract with Additional Human Capital Accumulation, 4%; Early Discharge, 38%; Later Discharge, 10%, Midway
Discharge, 17%; Lifetime Service, 20%.

152

(cluster 2), one is less likely to experience an extended period of inactivity outside of the labor
force. Therefore, service members’ choices to accumulate additional human capital post
discharge impact the later-life employment and outcomes. Solutions with more or less clusters do
not yield additional information to the selected solution of six-clusters; the general employment
patterns and career pathways are the same.

Figure A1 Medoids from the Give-cluster Solution.

Figure A2 Medoids from a Seven-cluster Solution.
Statistical Justifications
Silhouette width is an indicator of adequacy and efficiency of a classifier (Halpin 2016;
Rousseeuw 1987). I applied the Silhouette statistics to measure the quality of solutions with

153

different number of clusters. The Silhouette statistics indexes how well sequences are placed in
clusters (Halpin 2016; Rousseeuw 1987). It is calculated as
ℎ
𝑖 =
𝑏 𝑖 −𝑎 𝑖 max ( 𝑏 𝑖 ,𝑎 𝑖 )
,
where ai is the mean distance of sequence i to sequences of the same cluster, and bi is the mean
distance of sequence i to the next nearest cluster (Halpin 2016). The values of Silhouette width
range between -1 and 1, with more positive values indicating better classification. When
sequences are properly grouped and clusters are fittingly formed, the between-cluster distances
are high, and the within-cluster distances are lower; the Silhouette widths are closer to 1. When
sequences are mis-assigned as being nearer to the center of another cluster then their own, the
silhouette widths are negative (Halpin 2016).

Figure A3 Silhouette width of the six-cluster solution.

154

The six-cluster solution identified in this study has high inter-cluster dissimilarity and
low intra-cluster dissimilarity. As it is seen in the Figure A3, the silhouette width of each cluster
is positive and approaches 1. A small number of sequences from Employment, Training and
Education during Military Service, Training and Education during Civilian Employment, and
OOLF have negative silhouette statistics; the leakages are results of misclassification in the
cluster solution. The six-cluster solution has small leakages, which shows that it is a statistically
acceptable solution.
Overall, the six-cluster is a suitable solution.

Abstract (if available)

Abstract This study examines how military service shapes, reshapes, and moderates the patterns of socioeconomic mobility over the life course in the United States. Based on data from 27 rounds of the 1979 National Longitudinal Study of Youth (1979 – 2016), this dissertation starts with a construction of a typology of discrete employment patterns over 15 years since All-Volunteer Force (AVF) military personnel’s first enlistment, explicating the timing and sequencing of military service, educational attainment, and post-service employment, using sequence analysis and clustering solution. The result shows six distinct military-related pathways, from a life-time military career, to three pathways differed by the lengths of military services (Late Discharge, Midway Discharge, and Early Discharge), to two outliers, one with multiple statuses and one with re-enlistment. This dissertation also describes how these pathways are associated with social correlates, such as gender, race/ethnicity, family experiences, and attitudes and expectations with multinomial logistic regressions and found that women are more likely to be associated with early discharge and midway discharge pathways, and African Americans are more likely to be associated with a lifetime service career and later discharge pathways. Based on four sets of multivariate regressions, this dissertation further examines the long-term effects of military service on education attainments and mid-life income. The results show that service members differ from nonveteran civilians with respect to human capital accumulation. Not only do service members benefit from having received occupational training during service, they but also are more likely to have acquired vocational training as civilians following service. These differences in human capital accumulation explain the positive effect of being a Black service member or a socioeconomically disadvantaged service member on higher annual income, providing support for the human capital perspective. However, military income premium is only partially explained by human capital accumulation. The social capital perspective and screening and signaling perspective receive limited support. Overall, various degrees of military income premium are associated with selected military career pathways, and such a premium is limited to less socioeconomically advantaged service members. These findings have significant policy implications in recruitment, retention, benefits, and programs in the All-Volunteer Force era.

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Creator Wang-Cendejas, Ruoqing Rachelle (author)

Core Title Where will your path lead? Military services, career paths, and life course outcomes: implications for social mobility

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Sociology

Degree Conferral Date 2021-08

Publication Date 07/12/2021

Defense Date 06/14/2021

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