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Population Research and Policy Review

, Volume 37, Issue 4, pp 539–568 | Cite as

Veteran Status, Income, and Intergenerational Mobility Across Three Cohorts of American Men

  • Amy Kate BaileyEmail author
  • Bryan L. Sykes
Article

Abstract

Existing research linking prior military employment with labor market outcomes has focused on comparing the relative income of veterans and nonveterans. However, people who join the armed forces are uniquely selected from the broader population, and the form and direction of selectivity has shifted over time, with differential enlistment rates by race, region, and socioeconomic status. Understanding changes in the demographic composition of enlistees and veterans has significant import for the study of social mobility, particularly given changes in the occupational structure since the mid-twentieth century and wage stagnation well into the new millennium. Furthermore, labor market polarization and increases in educational attainment since WWII raise additional concerns about the social origins of military personnel and their occupational trajectories after discharge. Using data from the National Longitudinal Surveys, we investigate how social background is linked to both income and occupational mobility among veterans from three cohorts of American men: World War II, Vietnam, and the All-Volunteer Force. We find few benefits for veterans, for either income or intergenerational occupational mobility, once social background is controlled, suggesting that selection into the armed forces largely governs outcomes in the civilian labor market. Our findings have significant importance for understanding civilian labor market outcomes and trajectories of social mobility during distinct phases of military staffing.

Keywords

Military Veteran Social mobility Social capital Occupational mobility 

Introduction

Conventional wisdom is that the military is an engine of social mobility, allowing those who take up arms in defense of the nation to advance economically. However, it is unclear if veteran status greases the wheels of opportunity for all people or in all eras, raising questions about the relationship between veteran status and life-course trajectories. Do veterans earn higher wages than nonveterans? How have the social mobility prospects of veterans and nonveterans varied over time? Do disparities in social mobility stem from military experience or from compositional differences between men who do and do not join the military? This paper examines how veteran status is associated with income and intergenerational occupational mobility for three cohorts of American men. We focus on the way these changes differentially impact social mobility among blacks and whites, and whether the association is the same for men from higher- and lower-status families.

Comparing the statistical associations of veteran status across cohorts poses multiple challenges. The degree of State coercion involved in enlistment decisions, the educational and financial benefits available to veterans, and the value of skills gained on active duty vary over time (Barley 1998; Cohen et al. 1992; Collins et al. 2014). Disaggregating the associations of military service itself from paths leading young men into uniform have meant that the results of most research on life-course outcomes for veterans include a string of qualifications. At best, we can say that returns to being a veteran depend on military staffing policy, veterans’ benefits generosity, one’s pre-military biography, and the circumstances of each tour of duty (Angrist 1998; Cooney et al. 2003; Nam 1964; Sampson and Laub 1996; Schwartz 1986).1 A history of discriminatory policies—affecting selection into the military and the distribution of veterans’ benefits—means that black men have not enjoyed the same returns to military employment as whites (Katznelson 2005; Nalty 1986; Turner and Bound 2003).2

The likelihood of military service, and the demographic profiles, and social origins of active duty personnel, have changed substantially over the twentieth century (Carlson and Andress 2009; Wilmoth and London 2013). How veteran status is linked to labor market outcomes remains in question for four reasons. First, there is a lack of research that simultaneously links veterans’ and nonveterans’ occupational outcomes to their social origins, which is problematic for understanding how the military structures social mobility through occupational destinations. It could be that some service members would have been upwardly mobile even in the absence of military involvement (see Wolf et al. 2013 for a discussion on selection), or that changes in the composition of the veteran population shape the different occupational opportunities available to veterans after discharge. Second, analyzing trajectories of blacks and whites together risks glossing over racial dimensions of the armed forces as a stratifying institution. Historical racial segregation within the military and educational institutions may mean that cohorts of black enlistees are uniquely different from both White veterans and civilian African Americans (see Lutz (2013) for an extended discussion). Third, much extant scholarship on veteran socioeconomic status focuses solely on income, rather than occupational status (Angrist 1998; Angrist and Krueger 1994; Teachman 2004; Teachman and Tedrow 2004). Income and occupation capture different aspects of social stratification and inequality (van Leeuwen and Maas 2010), necessitating inquiries into both attainment processes. Finally, restricting analyses to a single cohort obscures the shifting role the military has played in the lives of American men.

In this paper, we analyze income and intergenerational occupational mobility for black and white men eligible to join the armed forces during three eras: World War II (WWII), the Vietnam draft, and the early All-Volunteer Force (AVF). We employ a unique series of longitudinal datasets that allow us to control for social origin factors in predicting labor market outcomes. We analyze whether and to what extent veteran status has changed over time, and how trajectories in status and income vary by race and educational attainment. Assessing income and mobility across three cohorts of American veterans highlights the role of military institutions in facilitating socioeconomic success across the life-course.

Inequality and Mobility in America

Classical social stratification research questions whether some form of inequality is necessary to solve problems of talent and motivation so that positions requiring the greatest training or talent receive the best rewards (Davis and Moore 1944). Since the mid-twentieth century, scholars have debated the mechanisms of occupational attainment and social mobility in industrialized countries (Featherman et al. 1975; Hauser and Grusky 1988a, b; Slomczynski and Krauze 1987; Hout 2015; Sobel et al. 1998).3 Structural mobility occurs when individuals experience upward movement due to occupational expansion and/or labor market transformations from one generation to the next (Featherman et al. 1975). Yet, since the late 1970s, research overwhelmingly suggests that social background reproduces educational and social inequalities for future generations. For instance, some researchers find that social background affects educational attainment through factors like motivation and achievement, which are linked to social capital (Hauser et al. 1983; Kim and Schneider 2005; Sewell and Hauser 1980; Teachman et al. 1997). Additionally, Lucas (2001) shows that economically advantaged parents maintain inequality by ensuring qualitative distinctions within and across educational transitions, suggesting that disadvantaged families experience less social mobility when education is universalized. Indeed, research demonstrates a decline in intergenerational income mobility over time (Levine and Mazumder 2002; Bloome and Western 2011).

Educational similarity across occupations, a status-linked characteristic, exhibits much more persistence over time than does income, suggesting that the processes governing social mobility may differ substantially from those affecting economic mobility (Hauser 2010). The links between social origins and adult occupational outcomes, however, vary by level of education (Hout 1988; Torche 2011), suggesting that mobility processes differ at various points along the educational distribution.

Mobility processes also vary by race. Over the latter half of the twentieth century, Black men experienced an increase in occupational stratification, while it decreased among other men (Hauser 2010). Educational mobility has also expanded for recent cohorts of Black Americans (Bloome and Western 2011). Black fathers, however, have a more difficult time conveying their occupational advantages to their sons than white fathers (Hauser et al. 2000), again suggesting a complex relationship between social origins, educational attainment, and occupational status.

Large-scale institutions like the military have the capacity to upend and reshape processes of status attainment. The enhanced training and skills obtained during service periods may predispose veterans to greater labor market rewards as a consequence of military enlistment (Kleykamp 2013). We seek to understand how social background matters for both income and occupational mobility across multiple cohorts of veterans, and also how the military’s role as a mediating institution might vary by race. We begin our inquiry with a review of military staffing policies and demographic change since WWII.

Military Staffing Policy and Demographic Change

Throughout U.S. history, the armed forces were staffed by a “skeleton crew” during times of peace, with wartime armies incorporating both volunteers and conscripts (Kelty and Segal 2013). Until the close of WWII, African Americans were underrepresented in the military due to a limited number of segregated units to which they were allowed entry. Additionally, Southerners of all races were disproportionately found unfit for service (Lutz 2013). Following WWII, veterans enjoyed generous benefits, including educational subsidies, homebuyer assistance, and preference in government hiring (Bound and Turner 2002; Chevan 1989; Hogan 1981; Katznelson 2005; Nalty 1986; Skocpol 1992; Turner and Bound 2003). Because a large proportion of American men served in WWII (Carlson and Andress 2009), these advantages were widely distributed, although black veterans were frequently denied benefits for which they were eligible (Katznelson 2005).4

Following WWII, the military underwent two major changes that affected the risk of service and the demographic profile of the standing army (Oi 1996): The Selective Service draft lottery and accelerated desegregation (Moskos and Butler 1996). These policy changes broadly distributed military risk among young men through the end of the Vietnam Era, although race- and class-based disparities in service assignments (and casualty rates) were widely publicized (Zeitlin et al. 1973), and many middle- and upper-class men avoided the armed forces altogether, remaining in college or receiving medical waivers (Appy 1993; Card and Lemieux 2001; Hogan 1981).

Selection into the military was revolutionized in 1973 when Congress authorized another major staffing policy change: the All-Volunteer Force. Recruitment since 1973 has disproportionately attracted Southerners, blacks, and young adults from rural communities (Fredland et al. 1996; Segal and Segal 2004), again stimulating rapid demographic change among active duty personnel. While the racial composition has shifted in recent years (Booth et al. 2007), blacks are now overrepresented in the military (Office of the Undersecretary of Defense, Personnel and Readiness 2014).

In the early AVF years, however, military pay lagged behind civilian wages, and Congress virtually eliminated educational benefits,5 making military service attractive only to those without viable employment or educational opportunities. In response to disappointing troop quality, Congress reinstated educational benefits in 1984 and institutionalized College Fund enlistment bonuses for highly qualified applicants.6 These efforts proved successful, and enlistee educational attainment and standardized test scores increased rapidly. High school graduation (or a GED) became nearly universal, and most recruits scored above the median on the standardized enlistment exam. Improvements in troop quality, however, are embedded in a landscape of rising educational attainment among the U.S. population, calling into question whether enlistee performance represents an increase in the relative or absolute human capital of enrolled personnel.

Veteran Status and Social Mobility

Veterans have historically received numerous economic, educational, and political benefits (Burk 1995; Chevan 1989; Slayer 2004; Skocpol 1992, 1997), and often enjoyed higher social status than nonveterans (Angrist 1998; Cooney et al. 2003; Nam 1964; Schwartz 1986). Yet, differences in social mobility between veterans and nonveterans may exist for several reasons. First, different kinds of people comprise the military and civilian populations during distinct periods. Compositional changes mean that selection mechanisms will operate strongly or weakly to produce differences between veterans and nonveterans (see Wolf et al. 2013) For instance, among the WWII cohort, military personnel were positively selected (Angrist and Krueger 1994), and military service and veterans’ benefits facilitated upward social mobility for disadvantaged men (Sampson and Laub 1996), suggesting that veteran status affects social mobility differently for different groups (see Turner and Bound 2003 on this point). Veterans from the WWII cohort with the lowest levels of educational attainment appear to have enjoyed the largest labor market premiums (Teachman and Tedrow 2004).

Similarly, during the Vietnam Era, student deferments allowed many middle- and upper-class men to remain in school, suggesting that veterans during this era may hail from lower class backgrounds (Appy 1993) and have completed less education, on average, than nonveterans (Teachman 2005). A consequence of this selectivity is that Vietnam Era veterans with low levels of education enjoyed a wage premium, unlike more highly educated men (Berger and Hirsch 1983); yet, comparative assessments of social mobility among this cohort of servicemen have not accounted for social origins and race (Hogan 1981).

A second reason social mobility differences may exist between veterans and nonveterans is due to military training and enhanced access to educational opportunities. Skills and training acquired during active duty may be transferrable to and valued in the civilian labor market, thereby influencing veteran occupational mobility in ways that are fundamentally different from nonveteran cohort members (Cohen et al. 1992; Kleykamp 2009; Magnum and Ball 1987, 1989). Research on the relationship between veteran status, occupational attainment, and economic outcomes generally finds that labor market opportunities declined over time. World War II veterans experienced better occupational outcomes than their nonveteran counterparts, while differences among Korean Era cohorts were small or neutral and Vietnam veterans fared worse in the formal labor market than similarly situated nonveterans (Cohen et al. 1986, 1992; Schwartz 1986; Teachman 2005; Villemez and Kasarda 1976). The material benefits available to veterans also reflect increasing demands for greater educational endowments in a skill-based labor market (Moskos and Butler 1996; Thirtle 2001). Investments in educational attainment during active duty and following separation are but one of the many military benefits (Bachman et al. 2001; Thirtle 2001). It is unclear whether active duty personnel and veterans from the AVF Era enjoyed broad socioeconomic mobility (Barley 1998; Xie 1992).

And of course, for many veterans, the military may result in long-term negative consequences. For example, veterans experience high rates of homelessness (US Department of Housing and Urban Development 2015), and those who endured combat carry physical and psychological scars that may substantially alter their occupational trajectories (MacLean 2010, 2013). Indeed, prior research has found lower earnings among some groups of veterans compared to similar civilian counterparts (Angrist 1998; Rosen and Taubman 1982), and the odds of poverty are significantly higher in households that include a disabled veteran than those that include a nondisabled veteran and those that include only nondisabled nonveterans (London et al. 2011).

Social Capital and Signaling

To the extent that veteran status facilitates improved labor market outcomes, understanding the mechanisms through which a vast institution like the armed forces affects occupational trajectories is important. Relationships, structures, and modes of interaction represent social capital that can be activated to achieve specific goals (Coleman 1988). These resources may be particularly effective when they operate within existing social networks and institutions (Lin 1999). Veteran status may accelerate access to better jobs when veterans leverage their social capital. As a “bridging” environment, the military builds social capital by allowing young adults from disadvantaged backgrounds to gain the skills needed to navigate complex, hierarchical organizations (Browning et al. 1973)—noncognitive job skills, such as compliance and punctuality, that men from more advantaged backgrounds acquire through extended educational careers. The military’s role as a bridging environment may be particularly important for black men, who may use armed forces experience to become acculturated into an occupational hierarchy dominated by whites (Browning et al. 1973; Cooney et al. 2003). If the military’s key role in affecting labor market processes operates through enhancing social capital at the individual level, we would anticipate measuring the strongest positive associations among poorly educated men and Blacks.

However, the military’s role may be more diffuse; veterans may benefit from broadly held perceptions based on group membership—a concept commonly referred to as symbolic capital (Bourdieu 1984). When applied to labor market processes, symbolic capital operates in a manner similar to attribution theory (Hewstone 1983), wherein employers attach value-laden characteristics to individuals based on their membership in specific status groups. Veteran status may signal to employers that the individual is likely to possess particular nontangible characteristics, based on membership in a socially defined group. If employers’ aggregated ideas about individuals become the basis for hiring or promotion (Aigner and Cain 1977; Blau 1984; Phelps 1972; Thurow 1975), then group membership may enhance or dampen the relationship between educational attainment and occupational outcomes.

Extant empirical work suggests that veteran status may operate in this manner. Americans tend to hold positive attitudes about veterans, despite strong beliefs that deployment may result in serious cognitive or psychological impairment (MacLean and Kleykamp 2014). Major media outlets portray the struggles of veterans as worthy of public sympathy and support (Kleykamp and Hipes 2015). By examining the association of group membership in the civilian labor market, De Tray (1982) finds that Vietnam veterans, particularly men with limited education, enjoyed a wage premium. Correspondence-based employment audits find that black veterans with administrative work experience in the armed forces, but not whites, receive a benefit in the civilian labor market (Kleykamp 2009), suggesting that the symbolic imprimatur associated with the military helped black men overcome negative stereotypes about black workers (Hogan 1981). If veteran status provides a positive signal to employers, black male veterans might be preferred in the hiring process over similar black male nonveterans. Whether this relationship is constant over time, and whether race mediates the relationship between education and veteran status, remains unknown.

Hypotheses

We posit three different hypotheses regarding the relationship between veteran status, social origins, and labor market outcomes. First, we expect that veteran status is positively related to income and occupational mobility (H1). Second, we expect educational investments gained during and after military experience will facilitate positive labor market outcomes through greater income and occupational mobility (H2). Third, we hypothesize that the interaction between veteran status and higher parental socioeconomic status results in greater income and occupational mobility (H3).

Data

We use three cohorts of men interviewed for the Bureau of Labor Statistics’ National Longitudinal Surveys: the Older Men Survey, representing the WWII generation; the Young Men Survey, representing the Vietnam cohort; and the National Longitudinal Survey of Youth—1979 (NLSY79), from the early AVF.7 The similarity in questions across cohorts, including military employment, social origins, education, income, and occupational outcomes, provides a unique opportunity to trace how veterans fare in the civilian labor market over time. Because of the small number of early-cohort respondents in other racial and ethnic categories, our analytic samples include only blacks and whites. Due to the small proportion of women who joined the military before the AVF—in 2015, fewer than 9% of all living U.S. veterans were women (Lofquist 2017)—and because we examine changes in the relationship between veteran status and mobility over time, analyses are restricted to men.

Measures and Methods

Occupational status is measured using Duncan’s (1961) socioeconomic index (SEI), an index based on the educational attainment and earned income of incumbents in an occupation. Occupational status for WWII Era and Vietnam Era cohorts utilizes 1950 census occupational codes. We calculate occupational status for the Volunteer cohort in 1998 using Stevens and Featherman’s (1981) typology based on 1970 census occupational codes. For 2008, we use a crosswalk based on the 2000 revised Census Occupational Codes and Hauser and Warren’s updated SEI (Frederick 2010), enabling consistent comparisons across decades. For the Volunteer cohort, in light of our expanded reliance on maternal occupations, we use the “total” SEI indices, which are intended to apply to both male and female workers. To facilitate comparison with prior research on the link between veteran status and income, we also examine whether veterans and nonveterans earn similar incomes, once education and social origins are controlled.

We conduct two nested series of multivariate OLS analyses predicting respondents’ total income and intergenerational occupational mobility by veteran status within each cohort. In the intergenerational mobility analyses, the difference between each respondent’s occupational status and that of his father serves as the outcome variable,8 calculated by subtracting father’s occupational status from the respondent’s. Positive values on the outcome variable indicate upward mobility, while negative values identify respondents with lower occupational status than their fathers. In a minority of cases—fewer than 5% for the Vietnam Era cohort, and between 35% (for black men) and 17% (for white men) in the AVF Era—we use mother’s occupation to calculate intergenerational mobility. In these cases, we use a dichotomous flag variable to indicate this variation.

To identify conditional effects of veteran status, net of family background and individual characteristics, we conduct a series of nested multivariate OLS regressions. The first model, which tests Hypothesis 1, explores whether the relationship between veteran status and intergenerational mobility, or veteran status and income, is modified by a variety of social background characteristics (Hout 2015). We include information on family structure, parental education, parental immigration status, and adolescent residential location. These measures include farm and urban residence at age 14, and whether respondents have a foreign-born parent and lived with both parents at age 14. We also include a dichotomous flag to identify missing parental information. Also included are controls for southern residence, adult family structure, unreported parental occupation, use of maternal SEI (see Beller 2009 on maternal social background effects on social mobility), and respondent’s age. Family structure incorporates the number of children each respondent has at his final interview, and whether he is married.9 Information on background characteristics were recorded during the first wave of data collection for each cohort. Adult residence and family structure are captured using the final year of data collection.

The second model, which tests Hypothesis 2, adds the highest grade completed by each respondent at the time of his final interview, and whether he reported obtaining additional years of schooling or vocational training after joining the armed forces. In the final series of regressions, where we test Hypothesis 3, we interact veteran status with the highest grade completed and with the quartile of his parents’ occupational status. These interactions allow us to determine whether the associations between veteran status and occupational outcomes are constant across the educational distribution and for men from different social class backgrounds. We use nonveterans from the lowest educational quartile as the omitted category.

Results

Table 1 displays descriptive information about our sample. The WWII cohort includes men born between 1907 and 1921, first interviewed in 1966, at ages 45 through 59. These men were in their late teens through early thirties when the U.S. entered WWII, and a large share of the sample are veterans: 33% of blacks and 46% of whites.10 We use data from the first interview, in 1966, and the second, in 1967, which provides information on veteran status.11
Table 1

Veteran status and social background characteristics: weighted percentages

 

World War II

Vietnam Era

AVF (1998)

AVF (2008)

Veteran

NonVet

Veteran

NonVet

Veteran

NonVet

Veteran

NonVet

White men

Not HS grad

42.4

60.3

8.9

12.0

4.8

11.0

2.5

8.5

HS graduate

31.1

23.1

35.8

27.9

46.4

42.0

44.7

42.5

Some college

12.0

7.9

29.6

19.7

30.7

18.2

31.5

18.9

College+

14.5

8.8

25.7

40.3

18.0

28.8

21.3

30.1

Total

45.5

54.5

40.0

60.0

15.9

84.1

15.2

84.8

Median parent high grade

8th

8th

11th

12th

12th

12th

12th

12th

Percent parent HS graduates

28.5

20.1

46.5

48.9

64.9

70.6

63.6

70.7

Med. parent SEI

18

14

27

34

27

31

25

31

Unweighted Ns

1496

1965

1582

1079

367

1768

315

1553

Black men

Not HS grad

67.4

87.7

17.0

39.3

0.4

17.4

0.4

14.8

HS graduate

22.2

8.0

37.3

31.7

58.7

51.8

51.8

52.9

Some college

6.7

2.0

35.2

15.2

31.0

18.0

33.0

18.4

College+

3.7

2.4

10.4

13.9

9.9

12.8

14.8

13.9

 Total

32.9

67.1

35.1

65.9

22.1

77.9

22.5

77.5

 Median parent high grade

7th

6th

8th

8th

12th

11th

12th

11th

Percent parent HS graduates

14.3

12.8

18.2

16.4

56.2

43.1

55.0

43.0

Med. parent SEI

14

14

14

14

21

21

21

21

Unweighted Ns

975

422

274

535

259

1057

239

969

Vietnam Era cohort members were born between 1942 and 1952, first interviewed in 1966, when they were aged 14 through 24, and re-interviewed regularly through 1981. They became eligible for military enlistment between 1963 and 1970, placing them at risk of the draft and deployment to Vietnam. We use social background variables recorded during the first interview, questions about the timing of military participation and education and vocational training from multiple interviews, and occupational status, educational attainment, and adult family structure reported at the 1981 interview. Respondents who remained in the sample through 1981 would have been aged 29 through 39 at their last interview. A large fraction report being veterans—40% of whites and 35% of blacks. Veterans from this cohort also indicated whether they enlisted voluntarily or were drafted, allowing us to control for voluntary versus compulsory entry into the military.

The AVF sample members were aged 14 through 21 in 1979, born between 1958 and 1965, and became eligible to join the armed forces following the transition to the AVF. Comparatively fewer sample members from this cohort are veterans – 22% of blacks and 16% of whites. Volunteer cohort respondents are still being interviewed biennially. We identify family background characteristics using the initial 1979 interview, and veteran status, educational attainment, occupational status, and adult family structure based on subsequent interviews. We measure income and intergenerational mobility at two time-points—the 1998 interview, when respondents were aged 33 through 40 (middle adulthood, which roughly corresponds to the ages of the Vietnam cohort at the time of their last interview), and the 2008 interview, when they were 43 through 50 (later adulthood, with ages overlapping with many members of the World War II cohort).

Differences in the proportion of black and white men who are veterans are statistically significant between all cohorts, and decline sharply over time. The racial distribution of the veteran population also shifts significantly. Among the WWII and Vietnam Era cohorts, a greater share of whites than blacks are veterans. During the AVF Era, a larger fraction of black men are veterans compared to whites.

The shifting educational distribution across cohorts reflects the overall increase in schooling among the American population, as well as persistent racial disparities in educational attainment. Among the WWII cohort, racial differences are particularly stark, and overall years of schooling low by contemporary standards. Men in later cohorts have completed many more years of schooling than have those from earlier generations, meaning the labor queues in which veterans compete for employment are composed of workers with vastly different human capital profiles over time. The relative levels of education completed by veterans and nonveterans also shift. Among the World War II cohort, white veterans had completed more schooling on average than nonveterans, while for later cohorts, white veterans are more heavily concentrated in the middle ranges of the educational distribution than are white nonveterans. Among black men, veterans are consistently more highly educated, on average, than are black nonveterans. For each cohort, within-race differences in education by veteran status are statistically significant. Within each cohort, white men enjoy a significant educational advantage over black men with similar military histories.

Veterans’ and nonveterans’ social backgrounds vary significantly between races and across cohorts. Within all cohorts, white sample members report that their parents had completed more schooling, and held higher-status jobs than was true for blacks. White veterans’ higher social origins compared to nonveterans erode following WWII and are lower for both the Vietnam Era and the AVF cohort. For black men, veterans have higher social origins than do nonveterans for all three cohorts, a relationship that strengthens over time. However, we see evidence of persistent labor market inequalities, with black parents mired in low-status jobs despite increasing levels of educational attainment.

Table 2 presents bivariate regression results. Veteran status appears to have strong, positive associations with both income and intergenerational occupational mobility for black and white men from the WWII generation. However, these associations are inconsistent among members of the Vietnam and AVF cohorts. For both black and white men who were at risk of military involvement during the Vietnam Era, being a veteran had no systematic relationship with either income or occupational mobility. Net of their father’s occupational status, black and white AVF veterans in later life (2008) gained occupational status, compared to similar nonveterans; yet, in midlife (1998), white AVF veterans experienced no measureable gains in occupational mobility over nonveterans. Black veterans of the AVF Era enjoyed higher average incomes than black men without military experience, although that advantage had evaporated by later adulthood. White AVF veterans suffered an income penalty in both middle and later adulthood.
Table 2

Bivariate OLS regression predicting income and intergenerational mobility among three cohorts

 

World War II

Vietnam Era

All-Volunteer force

(1998)

(2008)

Predicting income

Black men

Veteran status

1513.072***

810.836

4881.162**

2137.863

(232.057)

(636.182)

(1619.797)

(3098.687)

Constant

4680.878***

13,136.900***

26,813.622***

42,461.095***

(127.542)

(368.818)

(822.499)

(1593.729)

R 2

0.030

0.002

0.008

0.000

N

1397

845

1148

998

White men

  Veteran status

1423.944***

194.809

− 6967.419**

− 10276.529*

(265.517)

(536.004)

(2599.261)

(4458.603)

Constant

8997.064***

22,313.384***

47,720.653***

74,132.873***

(174.565)

(342.348)

(1081.329)

(1850.046)

R 2

0.008

0.000

0.003

0.003

N

3461

2694

2057

1725

Predicting intergenerational mobility

Black men

Veteran status

3.920***

0.741

− 0.464

2.577*

(1.095)

(1.681)

(1.345)

(1.230)

Constant

2.817***

14.019***

7.548***

9.284***

(0.602)

(0.970)

(0.650)

(0.622)

R 2

0.009

0.002

0.000

0.004

N

1396

865

1169

1022

White men

Veteran status

5.495***

1.628

2.266

5.963***

(0.910)

(1.175)

(1.427)

(1.361)

Constant

8.323***

8.665***

3.508***

2.608***

(0.598)

(0.750)

(0.576)

(0.561)

R 2

0.010

0.001

0.001

0.011

N

3460

2746

2026

1724

World War II Era Cohort

Table 3 presents the results from multivariate OLS regression equations assessing the relationship between veteran status and income, and veteran status and social mobility. We address questions of income first. For the WWII cohort, being a veteran (Hypothesis 1) initially appears to be associated with higher income for white men but not for African American men, as shown in the results from Model 1. However, in Model 2 (testing Hypothesis 2), which includes educational attainment and social background, the coefficient for being a veteran fails to achieve the threshold for statistical significance for either white or black men. This result suggests that the initial positive association between veteran status and income for this cohort was because white veterans from the WWII Era obtained more formal schooling than similar nonveterans. There is no income advantage, however, for white or black men who increased their level of human capital subsequent to joining the military, and veterans do not experience additional income returns to more years of completed schooling. Once education is accounted for, there is no remaining association between veteran status and income.
Table 3

OLS models predicting income and intergenerational mobility by race, WWII cohort

 

White men

Black men

Income

Intergenerational mobility

Income

Intergenerational mobility

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Veteran

0.120***

0.057

− 0.006

4.491***

2.174*

− 6.642**

0.088

− 0.005

0.032

0.899

− 1.760

− 8.250**

(0.030)

(0.030)

(0.091)

(0.942)

(1.048)

(2.538)

(0.054)

(0.059)

(0.149)

(1.135)

(1.229)

(2.873)

Military education

 

0.014

0.012

 

1.785

1.927#

 

0.127

0.129

 

3.957*

2.450

 

(0.038)

(0.039)

 

(1.312)

(1.080)

 

(0.089)

(0.092)

 

(1.849)

(1.773)

Highest grade completed

 

0.072***

0.073***

 

2.041***

2.844***

 

0.049***

0.049***

 

1.280***

1.144***

(0.004)

(0.005)

(0.145)

(0.152)

(0.007)

(0.008)

(0.144)

(0.153)

Veteran * highest grade completed

  

− 0.007

  

0.675**

  

− 0.001

  

1.036***

(0.008)

(0.224)

(0.014)

(0.276)

Parental education

0.031***

0.006

0.003

− 0.191

− 0.920***

0.131

0.022**

0.012

0.012

− 0.207

− 0.482**

− 0.268

(0.004)

(0.041)

(0.004)

 (0.134)

(0.140)

(0.116)

 (0.008)

(0.008)

(0.008)

 (0.177)

(0.174)

(0.163)

Father’s SEI: 2nd quartile

  

− 0.165***

  

− 5.175***

  

0.094

  

− 6.398***

(0.051)

(1.409)

(0.087)

(1.665)

Veteran * 2nd quartile

  

0.189**

  

2.599

  

− 0.174

  

0.990

(0.072)

(1.993)

(0.138)

(2.664)

Father’s SEI: 3rd quartile

  

− 0.017

  

− 10.216***

  

− 0.103

  

− 9.265***

(0.070)

(1.405)

(0.088)

(1.687)

Veteran * 3rd quartile

  

0.162*

  

1.129

  

0.062

  

0.340

(0.070)

(1.944)

(0.150)

(2.895)

Father’s SEI: highest quartile

  

0.008

  

− 44.322***

  

− 0.031

  

− 16.554***

(0.054)

(1.454)

(0.088)

(1.688)

Veteran * highest quartile

  

0.186*

  

− 1.121

  

− 0.005

  

− 4.970

(0.074)

(2.062)

(0.134)

(2.580)

Foreign-born parent

0.076*

0.077**

0.079**

0.087

0.126

− 1.207

0.373

0.304

0.317

7.723

5.978

8.707#

(0.030)

(0.030)

(0.030)

(1.050)

(1.020)

(0.829)

(0.236)

(0.232)

(0.232)

(4.950)

(4.811)

(4.503)

Constant

8.481***

7.839***

7.917***

13.188*

− 5.504

− 16.106**

9.540***

8.861***

8.842***

26.935***

9.122

12.468#

(0.192)

(0.183)

(0.187)

(6.253)

(6.209)

(5.193)

(0.324)

(0.331)

(0.334)

(6.730)

(6.798)

(6.410)

R 2

0.165

0.232

0.233

0.100

0.152

0.445

0.210

0.241

0.245

0.112

0.166

0.263

N

3357

3357

3357

3454

3454

3454

1361

1361

1361

1389

1389

1389

Models control for age, southern residence, southern birth, and missing values on parental metrics. Also included in the models are whether the respondent lived with both parents until age 15, and whether he lived on a farm or in an urban area at age 15, and whether he was married and the number of children reported at the 1966 interview

***p < 0.001, **p < 0.01, *p < 0.05, #p < 0.10

Model 3, testing Hypothesis 3, helps to solve that puzzle. We include three dichotomous variables identifying the quartile for father’s occupational status, with the lowest-status fathers serving as the reference category. We also interact veteran status with each of these quartiles. In these model specifications, then, the coefficient for being a veteran should be interpreted as the average (logged) income difference between veterans and nonveterans among men whose fathers worked the lowest-status jobs. The coefficients for each occupational status quartile identify the main effects for men without a history in the armed forces, and the interaction term specifies the (logged) income advantage (or disadvantage) accruing to veterans whose father held jobs in that status range, compared to nonveterans whose fathers had similar status. Among white men in the WWII Era, we find that on average, white veterans significantly out-earn white nonveterans whose fathers held jobs with similar occupational status. This holds for all white men except those whose fathers held the lowest-status jobs. Black veterans do not earn higher average pay than black nonveterans, regardless of their social origins.

Models 4 through 6 test the same set of hypotheses, this time focused on the level of intergenerational mobility. White veterans from the WWII Era, but not blacks, initially appear to reap greater occupational mobility, as evidenced by Model 4. Processes of institutional selection—which restricted entry among men with low levels of aptitude and human capital—may partially explain this finding, but another possibility is that veterans gained additional education and training through their military service and benefits. In Model 5, which tests this scenario, white veterans retain a mobility advantage, although including educational metrics suggests that intergenerational mobility is largely driven by human capital acquisition, and that both black and white men benefit. Post-military education is associated with similar levels of occupational status as is schooling obtained prior to enlistment. Model 6 allows us to identify whether the relationship between social origins and intergenerational mobility varies by veteran status. As expected, we see less intergenerational mobility among men whose fathers held higher-status occupations, due to the more limited opportunities for upward mobility for this group. There are no systematic differences, however, in intergenerational mobility between veterans and nonveterans of either race among men from the World War II cohort. Both black and white veterans from the lowest-status households experience less intergenerational mobility than nonveterans.

Vietnam Era Cohort

Multivariate regression results for the Vietnam Era cohort are presented in Table 4. In all modeling specifications, it appears that for both black and white men from the Vietnam Era, average incomes are equivalent for veterans and nonveterans. Black veterans’ income does not rise as rapidly with additional years of education as does income for black nonveterans, as evidenced in Model 3.
Table 4

OLS models predicting income and intergenerational mobility by race, Vietnam Era cohort

 

White men

Black men

Income

Intergenerational mobility

Income

Intergenerational mobility

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Veteran

0.019

0.004

0.390

2.364#

4.476*

− 4.542

− 0.006

− 0.107

0.494

1.223

− 4.586

− 19.497*

(0.036)

(0.062)

(0.188)

(1.268)

(2.010)

(5.000)

(0.075)

(0.107)

(0.355)

(2.178)

(2.926)

(9.135)

Drafted

− 0.039

− 0.042

− 0.045

− 3.797

− 3.524

− 2.282

0.195#

0.186#

0.164

− 2.725

− 3.304

− 3.431

(0.066)

(0.063)

(0.064)

(2.319)

(2.187)

(1.697)

(0.108)

(0.105)

(0.109)

(3.133)

(2.866)

(2.810)

Highest grade completed

 

0.089***

0.087***

 

4.209***

5.221***

 

0.078***

0.089***

 

3.804***

3.502***

(0.007)

(0.008)

(0.228)

(0.214)

(0.012)

(0.013)

(0.319)

(0.332)

Veteran * highest grade

  

− 0.001

  

0.152

  

− 0.057*

  

1.267#

(0.014)

(0.364)

(0.027)

(0.701)

Military education

 

0.053

0.038

 

− 1.039

0.798

 

0.052

0.097

 

3.807

3.358

(0.064)

(0.065)

(2.193)

(1.722)

(0.112)

(0.117)

(3.069)

(3.017)

Parental education

0.030***

0.003

− 0.004

− 1.352***

− 2.660***

− 0.329*

0.040***

0.017#

0.017#

0.124

− 0.984***

− 0.395

(0.005)

(0.005)

(0.006)

(0.184)

(0.187)

(0.156)

(0.009)

(0.010)

(0.010)

(0.271)

(0.265)

(0.258)

Parental SEI: 2nd Quartile

  

0.139*

  

− 7.392***

  

− 0.098

  

− 4.039

(0.065)

(1.736)

(0.108)

(2.757)

Veteran * 2nd quartile

  

− 0.098

  

1.711

  

0.136

  

− 4.590

(0.091)

(2.436)

(0.176)

(4.506)

Parental SEI: 3rd quartile

  

0.166*

  

− 31.664***

  

− 0.071

  

− 10.601***

(0.067)

(1.778)

(0.106)

(2.724)

Veteran * 3rd quartile

  

− 0.065

  

2.610

  

0.159

  

0.833

(0.093)

(2.468)

(0.177)

(4.553)

Parental SEI: highest quartile

  

0.162*

  

− 55.744***

  

− 0.080

  

− 22.858***

(0.070)

(1.851)

(0.117)

(2.999)

Veteran * highest quartile

  

0.066

  

5.671*

  

0.067

  

2.040

(0.096)

(2.553)

(0.175)

(4.500)

Foreign-born parent

0.086

− 0.006

− 0.008

4.129

− 0.369

0.399

0.135

0.002

− 0.055

6.156

− 0.415

− 0.820

(0.074)

(0.072)

(0.072)

(2.590)

(2.442)

(1.894)

(0.572)

(0.556)

(0.558)

(16.649)

(15.216)

(14.403)

Intercept

8.571***

7.772***

7.758***

8.135

− 30.283***

− 49. 543***

8.493***

7.665***

7.567***

14.387*

− 26.096***

− 14.778*

(0.130)

(0.140)

(0.159)

(4.566)

(4.791)

(4.241)

(0.226)

(0.252)

(0.277)

(6.556)

(6.865)

(7.106)

R 2

0.080

0.140

0.148

0.085

0.195

0.521

0.129

0.180

0.187

0.022

0.204

0.297

N

2457

2455

2448

2512

2512

2512

750

750

750

760

760

760

*p < 0.05, **p < 0.01, ***p < 0.00, #p < 0.10

Models control for age, southern residence, southern birth, and missing values on parental metrics. Also included in the models are whether the respondent lived with both parents until age 15, and whether he lived on a farm or in an urban area at age 15, and whether he was married and the number of children reported at the 1966 interview

The relationship between veteran status and intergenerational occupational mobility is substantially more complicated. The unrestricted model suggests that white veterans might experience greater social mobility than nonveterans (p < 0.10). Introducing educational attainment nearly doubles the size of the “veteran” coefficient and increases its level of statistical significance to a more conventional threshold (p < 0.05). Including additional measures of social class origins—parental occupational status—leaves the relationship between veteran status and intergenerational mobility indistinguishable from zero, except for men whose parents held the highest-status jobs (Model 6). For these white men, being a veteran is linked with enhanced mobility opportunities. Among black men, in the fully loaded model (Model 6), the coefficient for veteran—which here identifies the comparison between veterans and nonveterans whose parents’ occupations were in the lowest occupational status quartile—is large and negative. Only veterans from the lowest-status families experience a mobility penalty. We see, however, from the interaction between veteran status and educational attainment, that black Vietnam Era veterans experienced a larger intergenerational mobility benefit for each additional year of education than was true of black nonveterans.

All-Volunteer Force Cohort

Results for the regression analyses using the AVF cohort are presented in Tables 5 and 6. The regressions in Table 5 use data collected in 1998, when respondents would have been in middle adulthood—ages 33 to 40—similar in age as the men from the Vietnam cohort. Among white men, the results clearly identify that those who obtain higher levels of education, and whose parents are highly educated and hold jobs with high levels of prestige, earn more money than do other men. White men who begin life with these social class advantages had less “room” to advance in the occupational hierarchy and we observe a negative association on intergenerational mobility for them. These relationships do not vary by veteran status, and there is no independent relationship between being a veteran and labor market outcomes (Models 1–6). The coefficients interacting veteran status with educational attainment, and with parents’ occupational prestige, are statistically indistinguishable from zero. There also appears to be no measurable association between obtaining additional education or training after having joined the armed forces and occupational outcomes.
Table 5

OLS models predicting income and intergenerational mobility in middle adulthood by race, AVF cohort 1998

 

White men

Black men

Income

Intergenerational mobility

Income

Intergenerational mobility

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Veteran

− 0.014

− 0.075

0.073

0.829

1.240

− 4.342

0.187*

0.266

1.033

0.919

1.246

− 17.696*

(0.053)

(0.117)

(0.348)

(1.335)

(2.901)

(7.145)

(0.077)

(0.136)

(0.578)

(1.240)

(2.179)

(8.286)

Highest grade completed

 

0.098***

0.095***

 

3.428***

4.477***

 

0.146***

0.148***

 

3.013***

(0.257)

3.187***

(0.009)

(0.009)

(0.205)

(0.184)

(0.016)

(0.018)

(0.018)

(0.255)

Veteran * highest grade

  

− 0.006

  

0.200

  

− 0.051

  

1.501*

(0.026)

(0.538)

(0.044)

(0.636)

Military education

 

0.072

0.079

 

− 0.516

− 0.979

 

− 0.122

− 0.077

 

− 1.072

− 0.492

(0.125)

(0.126)

(3.115)

(2.624)

(0.151)

(0.153)

(2.423)

(2.240)

Parental education

0.059***

0.025***

0.017*

− 1.652***

− 2.837***

− 0.837***

0.014

− 0.008

− 0.013

− 0.786***

− 1.226***

− 0.633***

(0.006)

(− 0.007)

(0.008)

(0.156)

(0.163)

(0.152)

(0.011)

(0.011)

(0.011)

(0.173)

(0.168)

(0.156)

Parental SEI: 2nd quartile

  

0.072

  

− 2.876*

  

− 0.048

  

− 1.707

(0.069)

(1.361)

(0.135)

(1.870)

Veteran * 2nd quartile

  

− 0.106

  

2.629

  

− 0.092

  

− 2.177

(0.146)

(2.941)

(0.219)

(3.092)

Parental SEI: 3rd quartile

  

0.187**

  

− 12.108***

  

0.082

  

− 6.520***

(0.070)

(1.382)

(0.137)

(1.901)

Veteran * 3rd quartile

  

− 0.179

  

0.369

  

− 0.103

  

3.283

(0.143)

(2.855)

(0.220)

(3.133)

Parental SEI: highest quartile

  

0.172*

  

− 36.668***

  

0.195

  

− 19.559***

(0.077)

(1.518)

(0.140)

(1.952)

Veteran *highest quartile

  

0.023

  

5.894

  

− 0.346

  

− 3.822

(0.166)

(3.344)

(0.215)

(3.107)

Foreign-born parent

0.043

0.020

0.015

− 1.850

− 2.684

− 1.164

0.820

0.703

0.690

− 16.175*

− 18.249**

− 12.056*

(0.099)

(0.096)

(0.096)

(2.450)

(2.297)

(1.920)

(0.451)

(0.437)

(0.438)

(6.712)

(6.358)

(5.781)

Constant

8.816***

7.987***

8.042***

18.021***

− 11.340*

− 39.918***

9.511***

7.925***

7.883***

15.716**

− 16.890**

− 23.011***

(0.181)

(0.191)

(0.206)

(4.466)

(4.556)

(4.080)

(0.318)

(0.356)

(0.379)

(5.012)

(5.517)

(5.329)

R 2

0.137

0.188

0.193

0.163

0.265

0.490

0.121

0.179

0.186

0.194

0.280

0.415

N

2041

2041

2041

2017

2017

2017

1133

1133

1133

1165

1165

1165

Models control for missing information on parental education and occupational status, as well as the use of mother’s characteristics for either measure. Also included in the model are marital status, number of children, and whether the respondent lived in the census-defined southern region in 1998. Family of origin characteristics, reported at age 14, include having lived with both parents, southern residence, and living in an urban area or on a farm

*p < 0.05, **p < 0.01, ***p < 0.00

Table 6

OLS models predicting income and intergenerational mobility in late adulthood by race, AVF cohort 2008

 

White men

Black men

Income

Intergenerational mobility

Income

Intergenerational mobility

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Veteran

− 0.057

− 0.150

0.543

3.032*

0.374

− 11.917*

− 0.017

0.212

0.405

3.461**

5.629*

− 3.619

(0.058)

(0.150)

(0.358)

(1.191)

(3.106)

(5.726)

(0.085)

(0.166)

(0.567)

(1.102)

(2.188)

(6.481)

Highest grade completed

 

0.124***

0.125***

 

1.436***

2.633***

 

0.195***

0.189***

 

2.214***

2.565***

(0.009)

(0.010)

(0.189)

(0.151)

(0.017)

(0.020)

(0.227)

(0.224)

Veteran * highest grade

  

− 0.047

  

0.650

  

− 0.015

  

0.605

(0.025)

(0.408)

(0.042)

(0.485)

Military education

 

0.070

0.085

 

2.739

3.376

 

− 0.337

− 0.320

 

− 3.318

− 2.529

(0.157)

(0.158)

(3.269)

(2.411)

(0.179)

(0.181)

(2.364)

(2.097)

Parental education

0.056***

0.013

0.003

− 2.315***

− 2.810***

− 0.797***

0.043***

0.009

0.001

− 0.933***

− 1.309***

− 0.717***

(0.007)

(0.007)

(0.008)

(0.142)

(0.154)

(0.124)

(0.012)

(0.012)

(0.012)

(0.154)

(0.153)

(0.136)

Parental SEI: 2nd quartile

  

0.104

  

− 3.369**

  

− 0.018

  

− 3.951*

(0.074)

(1.162)

(0.144)

(1.615)

Veteran * 2nd quartile

  

− 0.169

  

1.349

  

0.108

  

0.610

(0.151)

(2.360)

(0.230)

(2.606)

Parental SEI: 3rd quartile

  

0.194**

  

− 11.611***

  

0.278

  

− 5.619***

(0.074)

(1.157)

(0.148)

(1.657)

Veteran * 3rd quartile

  

− 0.145

  

− 1.219

  

− 0.043

  

1.797

(0.149)

(2.341)

(0.231)

(2.625)

Parental SEI: highest quartile

  

0.211**

  

− 38.817***

  

0.371*

  

− 21.136***

(0.081)

(1.261)

(0.152)

(1.704)

Veteran * highest quartile

  

0.182

  

1.451

  

0.111

  

− 0.693

(0.176)

(2.778)

(0.229)

(2.620)

Foreign-born parent

0.025

− 0.017

− 0.036

0.537

0.180

0.946

0.507

0.411

0.369

− 12.971*

− 13.014*

− 6.937

(0.110)

(0.104)

(0.104)

(2.262)

(2.226)

(1.633)

(0.439)

(0.414)

(0.415)

(6.114)

(5.855)

(5.117)

Constant

9.615***

8.486***

8.488***

29.326***

16.535***

− 12.745***

9.356***

7.244***

7.224***

15.075**

− 8.616

− 16.295***

(0.201)

(0.207)

(0.224)

(4.136)

(4.424)

(3.491)

(0.355)

(0.384)

(0.412)

(4.464)

(4.924)

(4.606)

R 2

0.097

0.190

0.199

0.271

0.296

0.624

0.078

0.184

0.197

0.266

0.330

0.499

N

1718

1718

1718

1717

1717

1717

994

994

994

1018

1018

1018

Models control for missing information on parental education and occupational status, as well as the use of mother’s characteristics for either measure. Also included in the model are marital status, number of children, and whether the respondent lived in the census-defined southern region in 1998. Family of origin characteristics, reported at age 14, include having lived with both parents, southern residence, and living in an urban area or on a farm

*p < 0.05, **p < 0.01, ***p < 0.00

Among black men, parental characteristics appear to be less important in predicting income in middle adulthood than is true among white members of the sample. In middle adulthood, black AVF veterans likely earn more because they obtain more education than do black men who do not join the military. The question remains whether this social fact is a function of the military excluding the most poorly educated men or whether more highly educated black men are more likely to join the armed forces.

The negative relationship between black men’s veteran status and intergenerational occupational mobility does not emerge until the complete model (Model 6), when the term interacting veteran status with educational attainment was introduced. Because this is observed only for the main effects of veteran status, and not for any of the predictors interacting veteran status with parents’ occupational status, it suggests that intergenerational mobility is retarded only for those veterans from the lowest social origins. The term interacting veteran status with educational attainment has positive and significant association. This finding indicates that black men who are veterans are better able to translate their formal education into upward mobility than similar nonveterans; perhaps the additional institutional imprimatur provided by the armed forces allows black veterans to circumvent a measure of racial bias in the civilian labor market.

Following the AVF cohort later into their careers yields a substantially different story. Table 6 uses data on the AVF cohort from 2008, when they were in later adulthood, aged 43–50, similar in age to the younger members of the World War II cohort. We find no relationship between veteran status and income for white or black men. Income for both groups appears to be linked to the respondent’s years of schooling, and his parents having a higher-status occupation. The magnitude of these relationships was unaffected by whether a man was a veteran or not.

Similar to the findings for other cohorts, veterans in the AVF Era did not enjoy increased intergenerational occupational mobility when compared to similar nonveterans. For white men, being a veteran appeared to retard the level of occupational advancement relative to one’s parents among men from the lowest social class backgrounds. For black men, the association was neutral regardless of parents’ occupational status.

In general, then, we find no support for our first hypothesis—that veteran status is positively related to income and occupational mobility. Additionally, we find no benefit from educational investments due to military experience, as predicted by hypothesis 2, for any cohort. Finally, hypothesis 3—that the interaction between veteran status and parental socioeconomic status results in greater occupational rewards—is valid only for higher-status white men in the WWII Era. In fact, for black men in the Vietnam Era and in middle adulthood among AVF cohort members, and for white AVF veterans in later adulthood, veteran status is associated with poorer levels of intergenerational mobility—but only among those men from the lowest social origins. Among black men, this penalty may be slightly ameliorated by greater returns to education among veterans. No such amplification is observed for whites.

Discussion

This paper has examined the association between veteran status and labor market outcomes for three cohorts of black and white men. Changes in occupational structure, educational expansion, and military staffing policy during the mid-twentieth century led us to anticipate larger effects of social background on income and occupational mobility among veterans. We find that among white men, few associations between veteran status and occupational outcomes survive the inclusion of educational attainment and parents’ occupational status. For the WWII cohort, these associations are isolated among men from higher social class backgrounds but seem to be positive. The only link between veteran status and occupational outcomes for white men from the Vietnam Era accrues to the most advantaged men. Among the AVF cohort, white veterans seem to fare the same as white nonveterans in the civilian labor market, regardless of their social class backgrounds.

Black veterans from the WWII cohort enjoy greater returns to their education than do nonveterans. Black veterans from the Vietnam cohort and the AVF Era during middle adulthood (1998) appear to translate higher levels of education into greater intergenerational occupational mobility. There are no income advantages, however, and mobility benefits evaporate for the AVF cohort by later adulthood (2008). Changes in the labor market since the turn of the millennium have not resulted in greater income or intergenerational occupational mobility for Black veterans.

Our results differ from those of earlier scholarly work on the effects of veteran status and labor market outcomes for three key reasons. First, we focus on both income and occupational status, rather than income alone. Prior research has tended to focus on income differentials between veterans and nonveterans (Angrist 1998; Berger and Hirsch 1983; Lopreato and Poston 1977; Martindale and Poston 1979; Rosen and Taubman 1982; Teachman 2004), and scholarship separately examining income and occupational status has found that military experience differently affects these measures (Fredland and Little 1985; Elman and O’Rand 2004). Second, we account for the possibility that the associations between prior military employment and labor market outcomes vary across social class background. Once we controlled for parental education and occupational status, labor market benefits for veterans become increasingly attenuated. Finally, we focus on intergenerational mobility, incorporating social background characteristics as well as parental occupational status. While some prior work (see, for example, Fredland and Little 1985; Little and Fredland 1979) incorporated measures of social background, this scholarship is limited to single cohort studies (Martindale and Poston 1979 is an exception), or includes only one or two measures of each respondent’s childhood environment. We have compared three cohorts and included almost a dozen social background characteristics that potentially affect social mobility (Hout 2015).

Some prior scholarship (Angrist 1998; Hirsch and Mehay 2003) finds modest benefits for black veterans in terms of earnings. Our work supports earlier findings that among cohorts of men whose careers were nearly completed when major Civil Rights Legislation was enacted during the 1960s (i.e., the WWII cohort), veteran status benefitted whites substantially more than it did blacks (Berger and Hirsch 1983; Fredland and Little 1985; Little and Fredland 1979), with advantages accruing to men from the most advantaged backgrounds. Our findings contradict scholarship finding larger benefits for black veterans compared to whites (Martindale and Poston 1979).

Revisiting the questions of whether being a veteran increases an individual’s social capital, or whether the symbolic capital afforded to veterans has positive associations in the civilian labor market, our findings suggest that in most cases, the answer to both is “no.” At least, we do not find clear and consistent labor market advantages in terms of intergenerational occupational mobility. It may be that these forms of capital operate through higher rates of employment. It may also be that the military’s primary role in advancing the economic futures of disadvantaged men lies in its ability to provide access to more normative forms of human capital in the labor market. These forms might include greater opportunities for educational attainment, or extended employment for workers who lack post-secondary education and labor market experience. The provision of access to advanced education may, then, be reflected in our findings. It is worthy of note, however, that the reduced returns to education obtained in adulthood identified by Elman and O’Rand (2004) do not appear to apply to male veterans from these cohorts. That in itself may suggest a material benefit of military experience.

The current project has several limitations that restrict the applicability of these findings. First, it fails to disaggregate the effects of veteran status between officers and enlisted men. Given higher social origins among officers compared to enlisted personnel,12 veteran status may have disparate impacts depending on rank. The current project also fails to account for factors that may affect the linkage between military service and labor market outcomes. Chief among these are selection into the military (Wolf et al. 2013), the number of years an individual spent in the military, preferential hiring practices in employment processes, or the acquisition of skills related to military occupation. Including measures for length of service, highest rank achieved, or military occupation could alter the results of these analyses.

The current models also capture only the associations between measured variables. Unmeasured variables may help to explain the weak relationship between veteran status and labor market outcomes. Although it is difficult to identify a pattern based on the current results, an unobserved process may be at play. We considered using an instrumental variable (Wolf et al. 2013), but due to variation in the pre-military characteristics captured across cohorts, we were unable to identify a logical instrument that was available for all cohorts to tease out unobserved causes.

Conclusion

Social inequality does not occur in a vacuum. The WWII cohort faced a rapidly expanding industrial base and ample opportunities for access to high-paying—if primarily blue-collar—employment. Those from the Vietnam cohort entered the labor market when opportunities for advancement into white-collar jobs were increasing, and likely faced an array of opportunities, including many higher-status options. Young men who came of age during the 1970s and 1980s faced a harsh labor market, plagued by high unemployment, a shrinking pool of family wage jobs, and an increase in downward mobility. For men most likely to enter the military during that era—blacks, rural Americans, and those whose parents have blue-collar jobs—the picture was particularly bleak. Differences in the opportunity structure are certainly reflected in the overall patterning of differences between cohorts.

These results indicate that veteran status has little impact on income or mobility outcomes. Those that do exist vary by race and cohort, but not as much by human capital benefits obtained through the military. Labor market outcomes are driven by educational attainment and social background, and with a few notable exceptions, a history of military experience does not override that basic fact. The life-course impacts of veteran status for AVF Era veterans remain understudied, despite the salience of military service among populations of great interest to stratification researchers: blacks and white men from working class backgrounds. Perhaps the current research, and the important questions it leaves unanswered, will encourage scholars to examine the durable impacts of one of the largest institutions in the United States today.

Footnotes

  1. 1.

    See MacLean and Elder (2007) for an excellent discussion. See also Wolf et al. (2013).

  2. 2.

    Given our focus on how veteran outcomes in the civilian labor market are moderated by social class origins, we emphasize the military’s role in providing access to employment and training opportunities.

  3. 3.

    Although social mobility is rarely defined, sociologists generally conceive of this term as upward or downward “movement” in social class, status, or occupation within and across generations (Westoff et al. 1960). Payne and Payne (1983, p. 72) observe: “Despite this operational dependence on occupation, the terms ‘social mobility’ and ‘occupational mobility’ are used synonymously” and that “strictly speaking mobility is measured in an occupational dimension.”

  4. 4.

    See Nam (1964) for an analysis suggesting the GI Bill minimally affected aggregate educational attainment. See also Bennett and McDonald (2013), Bound and Turner (2002), Turner and Bound (2003) and Teachman and Tedrow (2004).

  5. 5.

    A limited “employer-matching” program, the Veterans’ Educational Assistance Program, provided support during the early Volunteer years.

  6. 6.

    The Montgomery GI Bill was an employer-matching program, requiring registration upon enlistment, and participation throughout the first year of employment. Tuition assistance for part-time study while on active duty was more broadly available (Bureau of Labor Statistics 2007; Thirtle 2001). See Bennett and McDonald (2013) for more details.

  7. 7.

    Because of the conceptual challenges associated with identifying labor market outcomes for later career, working class male workers following the financial collapse of 2008, we do not extend our analyses for the AVF cohort past 2008.

  8. 8.

    For information on using a change variable as a dependent variable in regression analysis, see Allison (1990).

  9. 9.

    Marital status may be an outcome of greater occupational mobility, rather than a cause. However, it has been used as a predictor in prior research on veterans (for example, Martindale and Poston 1979).

  10. 10.

    Veteran percentages for all cohorts reflect weighted totals. Restricting this sample to those who were aged 25 or younger in 1941 increases weighted percentages to 67% for white men and 48% for black men.

  11. 11.

    The 1966 Older Men’s sample had 5200 respondents. By 1967, 60 of those men had died, and another 216 were lost to follow-up, leaving 4924 men in the sample.

  12. 12.

    Many of these differences are likely captured by variables already incorporated, particularly educational attainment.

Notes

Funding

Funding was funded by University of Washington’s Graduate School, Harry Bridges Center for Labor Studies, West Coast Poverty Center and Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant no: 5 T32 HD 7163-31). We are also grateful for computing support from the University of Washington's Center for Studies in Demography and Ecology.

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© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of SociologyUniversity of Illinois at ChicagoChicagoUSA
  2. 2.Department of Criminology, Law & SocietyUniversity of California - IrvineIrvineUSA

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