The subjective underemployment experience of post-9/11 veterans after transition to civilian work

Abstract

BACKGROUND:

Underemployment is a challenge for the civilian workforce and a particular risk for veterans as they transition from military service to civilian employment. Workers’ economic and demographic characteristics factor into underemployment risk. Veterans may be at greater risk due to specific economic and demographic factors, transitional factors (e.g., geographic relocation), and characteristics of their military service (e.g., military skill alignment with civilian jobs).

OBJECTIVES:

Describe underemployment experiences in employed post-9/11 veterans three years after their military transition to the civilian workforce.

METHODS:

The current study uses self-reported underemployment experience data from a longitudinal study of transitioning veterans. This study compares average perceptions of veteran underemployment experiences by specific groups (e.g., by race, gender, and paygrade) using analysis of variance and logistic regression.

RESULTS:

Veterans reported underemployment in their current jobs based on a perceived mismatch between the skills, education, and/or leadership experience they gained during military service.

CONCLUSIONS:

Veterans who were enlisted rank, identified as non-White, completed a bachelor’s degree, and indicated PTSD symptoms reported higher pervasive underemployment. Intervention implications for the results, such as employer and veteran employment supports, are discussed.

Keywords

Veterans transition underemployment subjective experience military

1 Introduction

Underemployment is the involuntary discrepancy between a worker’s skills, pay, or time and a work-er’s desired employment. Underemployment is animportant indicator of economic, as well as individual well-being [1]. While unemployment rates are universally recognized as a measure of labor force strength and economic well-being, less attention is given to underemployment as a metric of labor market distress. However, underemployment is associated with many of the same negative consequences as unemployment such as poor psychological health and negative affect [2, 3], lower job satisfaction, lower job retention, and greater work stress [4 –6].

1.1 Defining and measuring underemployment

Underemployment is a multidimensional construct that has suffered from inconsistent definition and operationalization [1 , 8]. Underemployment may include both objective job characteristics (e.g., person-job fit mismatch) and the subjective interpretation of the employment experience (e.g., feeling underutilized in comparison to co-workers). Underemployment is influenced by and impacts multiple factors: worker attributes (e.g., education and training, work history) [1], organizations (e.g., job type; inadequate utilization of worker skills) [9], and labor market forces (e.g., economic recession, incentivizing education as a job entry requirement) [10 –12].

The complexities and multidimensionality of the construct have made measurement difficult. Indeed, underemployment measures often focus on only one or two dimensions at a time and focus on objective rather than subjective underemployment [1]. Subjective feelings of underemployment have been linked to negative well-being outcomes, including boredom, depression, job-related stress, job dissatisfaction, and higher turnover [13 –15].

1.2 Underemployment rate

Understanding the prevalence of underemployment in the United States is complicated by how the construct is defined and measured. The U.S. Bureau of Labor Statistics (BLS) currently only captures one dimension of underemployment, part-time work, by calculating the difference between two unemployment measures: the number of part-time workers who would rather have full-time jobs and the number of individuals who are unemployed and marginally attached to the workplace. During the administration of the survey in the present study (May 2019), using this estimation method, the BLS estimate of underemployment was 2.5% [16]. This is likely a conservative appraisal because the BLS estimate does not take into account other forms of underemployment such as overqualification or underpayment. When considering other forms of underemployment, some estimates indicate that underemployment is prevalent in one-third of the current workforce [17 –19].

1.3 Veteran underemployment

Underemployment risk have been well-documen-ted in the general population. Some risk factors for underemployment include being female, younger or older, being from underrepresented racial and ethnic groups, and having physical disabilities (8, 22–27). However, there is limited empirical research examining underemployment specifically among veterans after transition from military service to civilian life. Risk of underemployment among veterans is probable because underemployment has historically been high among workers during transitions, including college graduates, laid-off workers, or those re-employed in new careers [28, 29]. A white paper on veterans who used the ZipRecruiter job search platform found that underemployment among veterans was 15.6% higher than among non-veteran job seekers [27]. However, this study relied on a convenience sample of ZipRecruiter users and is not a representative sample based on known demographic biases of the platform’s users [27].

Veterans face the underemployment risk factors already mentioned for the general population (e.g., age, gender, disability). Several factors unique to military service may also increase the risk of underemployment among veterans. Veterans often need to translate their military service and skills for relevance in the civilian workforce. A primary example is combat veterans who learned and mastered weaponry or fighting tactics which are not directly translatable to civilian occupations [28 –30]. Transition may also make veterans susceptible to underemployment as veterans may need to replace income immediately or gain experience in the civilian workforce. This need may lead veterans to take a suboptimal job immediately after transitioning from military service [27].

Another contributing factor to veteran underemployment is that veterans, upon entering the civilian workforce, may be more likely to take a step back in seniority [31]. Another white paper comparing veterans and non-veterans on the LinkedIn platform, found that underemployment was higher among veterans in comparison to non-veterans [31] even though the veterans were more likely to have a graduate degree and more work experience than non-veterans. Interestingly, veterans in the study were more likely to stay in the job longer and more likely to be promoted. However, similar to the ZipRecruiter study, this study also used a convenience sample of platform users. For instance, veterans surveyed were more likely to be officers (∼50% of the sample) which is substantially higher than the percentage of officers in the military (15%; [31]. Hence, this study’s findings should be interpreted with caution given it is not based on a representative sample of veterans.

Despite these limitations, these findings seem to indicate that underemployment may be a transitional, rather than a permanent, state of employment for many veterans [12, 32]. Even when veterans are successful in obtaining employment, many do not immediately find a job that best fits their needs or interests, which can result in leaving or switching jobs later [33]. Studies have noted that only half of veterans stayed in their first post-separation job with many leaving within the first year [33, 34]. Veteran turnover from their first civilian job may be related to struggles navigating and adjusting to the civilian workforce or the perceived meaningfulness of the work. This low retention may again suggest veteran underemployment is a result of being unable to apply their skills and abilities or to receive benefits and pay commensurate with their experience [33].

1.4 Current study

This current study utilizes one wave of data collected from The Veterans Metrics Initiative (TVMI) to describe underemployment in employed post-9/11 veterans three years after military transition to the civilian workforce. Specifically, this current study provides a description of veterans’ underemployment experiences. This study also presents underemployment experiences as a function of different subgroups (e.g., gender, race and ethnicity, paygrade, current salary, and combat occupation). To our knowledge, this is the first study to utilize a subjective measure to understand underemployment experiences in the larger veteran population.

2 Methods

2.1 Participants

The joint Department of Veterans Affairs/De-partment of Defense Identity Repository database (VADIR) was used to identify a sample of transitioning veterans who served in the active component (i.e., Air Force, Army, Marines, Navy) or who were activated National Guard and Reserve service members in the 90 days prior to survey administration in September 2016 (n = 48,965). The survey was then administered at six-month intervals from November 2016 until May 2019. Detailed information regarding the study procedure and the sample have been previously published [35]. Almost 20% of the eligible population (n = 9,566) provided complete data at Wave 1. The majority of respondents were male (82%; n = 6,734), White Non-Hispanic (64%; n = 5,215), and from enlisted ranks (77%; n = 7,365). The current study uses the Wave 6 sample (n = 4,057) which included veterans who reported on underemployment at almost 3 years post-separation. The weighted sample varies by missing data on the covariate.

2.2 Measures

2.2.1 Demographic background information

At Wave 1, veterans reported on a series of questions about their demographic background including, gender, age, highest level of education, paygrade, race/ethnicity, and military occupation (i.e., combat arms, combat support, and service support). Concurrent and completed education, age, and probable posttraumatic stress disorder (PTSD) symptoms were used from Wave 6 to align with the questions on underemployment experiences. Probable PTSD symptoms were assessed using the abbreviated eight-item version of the PTSD Checklist for DSM-5 (PCL-5) [36]. Response options are on a five-point scale ranging from 0 = not at all to 4 = extremely. A sum total of the items was computed and a cutoff of 19 or more was used as the criteria for probable PTSD.

2.2.2 Underemployment experience

Veterans’ subjective experience regarding their underemployment was assessed during Wave 6 of the survey, approximately three years after separation from active duty. The survey adapted a previous measure of perceived underemployment [37]. Employed veterans were asked to indicate their agreement with the following three statements: “Given my skills, I should be in a better job than my current job”; “Given my education, I should be in a better job than my current job”; and “Given my leadership experience, I should be in a better job than my current job”. Veterans were asked to rate each of these statements on a five-point scale from 1 = strongly disagree to 5 = strongly agree.

2.3 Data analytic approach

The underemployment experience variable was collected at Wave 6 after identifying the need for a subjective measure of underemployment to be included in the survey. This current study compares average perceptions of veteran underemployment experiences by specific groups using analysis of variance with Stata 15. A description of underemployment experiences by questions is reported. Data were weighted to adjust for demographic information known in the population (i.e., branch, paygrade, and gender) and were multiplied by the non-response weights at Wave 6. Weighted descriptive statistics of gender, paygrade, race/ethnicity, military occupation, age group, concurrent education level, and indicated PTSD symptoms, are displayed in Table 1.

Table 1
Descriptive Statistics for the Analytic Sample

Weighted % (Standard Error) Design Effect

Gender

Female 13.2% (0.6%) 1.11

Male 86.8% (0.6%) 1.11

Paygrade

E1 to E4 40.1% (0.9%) 1.32

E5 to E6 29.1% (0.8%) 1.16

E7 to E9 13.3% (0.5%) 1.04

W1 to W5 1.1% (0.6%) 1.08

O1 to O3 7.3% (0.4%) 0.85

O4 to O7 9.2% (0.4%) 0.85

Race/Ethnicity

White Non-Hispanic 66.6% (0.8%) 1.29

Black Non-Hispanic 9.8% (0.5%) 1.33

Hispanic 13.9% (0.6%) 1.33

Asian, HPI/Other 9.7% (0.5%) 1.37

Military Occupation

Combat Arms 23.6% (0.7%) 1.20

Combat Support 40.0% (0.9%) 1.23

Service Support 36.3% (0.8%) 1.21

Wave 6 Education

Less than a Bachelor’s Degree 59.5% (0.8%) 1.15

Bachelor’s Degree or Higher 40.5% (0.8%) 1.15

Wave 6 Age Groups

18 to 24 7.3% (0.5%) 1.55

25 to 34 53.5% (0.9%) 1.21

35 to 54 36.9% (0.8%) 1.15

55 and older 2.5% (0.2%) 1.00

Wave 6 Probable PTSD Symptoms 6.7% (0.5%) 1.29

Underemployment Mean (SE)

Given my leadership experience, I should be in a better job 3.56 (0.02) 1.18

Given my skills, I should be in a better job 3.41 (0.02) 1.17

Given my education, I should be in a better job 3.26 (0.02) 1.16

	Weighted % (Standard Error)	Design Effect
Gender
Female	13.2% (0.6%)	1.11
Male	86.8% (0.6%)	1.11
Paygrade
E1 to E4	40.1% (0.9%)	1.32
E5 to E6	29.1% (0.8%)	1.16
E7 to E9	13.3% (0.5%)	1.04
W1 to W5	1.1% (0.6%)	1.08
O1 to O3	7.3% (0.4%)	0.85
O4 to O7	9.2% (0.4%)	0.85
Race/Ethnicity
White Non-Hispanic	66.6% (0.8%)	1.29
Black Non-Hispanic	9.8% (0.5%)	1.33
Hispanic	13.9% (0.6%)	1.33
Asian, HPI/Other	9.7% (0.5%)	1.37
Military Occupation
Combat Arms	23.6% (0.7%)	1.20
Combat Support	40.0% (0.9%)	1.23
Service Support	36.3% (0.8%)	1.21
Wave 6 Education
Less than a Bachelor’s Degree	59.5% (0.8%)	1.15
Bachelor’s Degree or Higher	40.5% (0.8%)	1.15
Wave 6 Age Groups
18 to 24	7.3% (0.5%)	1.55
25 to 34	53.5% (0.9%)	1.21
35 to 54	36.9% (0.8%)	1.15
55 and older	2.5% (0.2%)	1.00
Wave 6 Probable PTSD Symptoms	6.7% (0.5%)	1.29
Underemployment	Mean (SE)
Given my leadership experience, I should be in a better job	3.56 (0.02)	1.18
Given my skills, I should be in a better job	3.41 (0.02)	1.17
Given my education, I should be in a better job	3.26 (0.02)	1.16

Note. HPI = Hawaiian Pacific Islander. Weighted descriptive results - gender, paygrade (n = 37,604), race/ethnicity (n = 37,455), military occupation, education (n = 37,598), and age (n = 36,952).

Finally, this study used a logistic regression to examine the probability of agreement to all three types of underemployment (i.e., believed they were underemployed because of their leadership, skills, and education) compared to disagreement to all three types of underemployment. Veterans were dichotomously coded based on their agreement with each underemployment statement.

3 Results

3.1 Descriptive findings

Three years after separating from active duty service or deactivating from the National Guard or Reserves, a majority of the veterans were employed and completed the underemployment questions at Wave 6 (n = 4,107). The average ratings for underemployment (i.e., given my [...] I should be in a better job than my current job) were as follows: (1) leadership experience (M = 3.56, SE = 0.02), (2) skills (M = 3.41, SE = 0.02), and (3) education (M = 3.26, SE = 0.02). Skewness values for underemployment for leadership (–0.57), skills (–0.38), and education (–0.23) suggest symmetrical normal distributions. Correlations between the three types of underemployment were high: skills and education (r = 0.79), skills and leadership (r = 0.83), and leadership and education (r = 0.81). Due to the high correlations between dependent variables and violations of the assumption of multicollinearity, MANOVA analysis was not conducted. However, to determine if there were subgroups where specific differences in the types of underemployment existed further analysis was conducted. Descriptive statistics are displayed in Table 2 for each of the following subgroup analyses.

Table 2
Analysis of Covariance between Group Means

Underemployment: Given my skills Mean (SE) Underemployment: Given my education Mean (SE) Underemployment: Given my leadership experience Mean (SE)

Gender

Female 3.47 (1.25) 3.31 (1.26) 3.48 (1.25)

Male 3.40 (1.20) 3.25 (1.18) 3.57 (1.17)

Paygrade ^* ^*

E1 to E4 3.53 (1.16) 3.30 (1.13) 3.58 (1.10)

E5 to E6 3.45 (1.16) 3.32 (1.15) 3.62 (1.13)

E7 to E9 3.48 (1.23) 3.32 (1.23) 3.71 (1.20)

W1 to W5 3.10 (1.20) 2.90 (1.23) 3.13 (1.32)

O1 to O3 3.20 (1.29) 3.13 (1.29) 3.45 (1.32)

O4 to O7 2.88 (1.31) 2.90 (1.37) 3.17 (1.41)

Race/Ethnicity ^* ^*

White Non-Hispanic 3.30 (1.21) 3.17 (1.20) 3.48 (1.19)

Black Non-Hispanic 3.78 (1.17) 3.59 (1.21) 3.83 (1.16)

Hispanic 3.59 (1.18) 3.35 (1.14) 3.69 (1.13)

Asian, HPI/Other 3.54 (1.14) 3.37 (1.15) 3.62 (1.14)

Military Occupation ^*

Combat Arms 3.31 (1.22) 3.23 (1.17) 3.62 (1.18)

Combat Support 3.37 (1.22) 3.24 (1.20) 3.58 (1.16)

Service Support 3.31 (1.27) 3.29 (1.20) 3.49 (1.20)

Wave 6 Probable PTSD Symptoms ^* ^* ^*

Does not meet criteria 3.37 (1.20) 3.23 (1.19) 3.52 (1.18)

Meets criteria 4.02 (1.07) 3.69 (1.18) 4.10 (1.09)

Wave 6 Age Groups ^* ^* ^*

18 to 24 3.50 (1.06) 3.32 (0.97) 3.46 (1.00)

25 to 33 3.48 (1.19) 3.27 (1.16) 3.58 (1.13)

35 to 54 3.32 (1.25) 3.24 (1.26) 3.54 (1.25)

55 to highest 3.07 (1.30) 3.04 (1.31) 3.42 (1.35)

Wave 6 Education ^* ^* ^*

Less than a Bachelor’s Degree 3.52 (1.15) 3.21 (1.10) 3.61 (1.11)

Bachelor’s Degree or Higher 3.26 (1.28) 3.33 (1.32) 3.48 (1.28)

	Underemployment: Given my skills Mean (SE)	Underemployment: Given my education Mean (SE)	Underemployment: Given my leadership experience Mean (SE)
Gender
Female	3.47 (1.25)	3.31 (1.26)	3.48 (1.25)
Male	3.40 (1.20)	3.25 (1.18)	3.57 (1.17)
Paygrade	^*	^*
E1 to E4	3.53 (1.16)	3.30 (1.13)	3.58 (1.10)
E5 to E6	3.45 (1.16)	3.32 (1.15)	3.62 (1.13)
E7 to E9	3.48 (1.23)	3.32 (1.23)	3.71 (1.20)
W1 to W5	3.10 (1.20)	2.90 (1.23)	3.13 (1.32)
O1 to O3	3.20 (1.29)	3.13 (1.29)	3.45 (1.32)
O4 to O7	2.88 (1.31)	2.90 (1.37)	3.17 (1.41)
Race/Ethnicity	^*	^*
White Non-Hispanic	3.30 (1.21)	3.17 (1.20)	3.48 (1.19)
Black Non-Hispanic	3.78 (1.17)	3.59 (1.21)	3.83 (1.16)
Hispanic	3.59 (1.18)	3.35 (1.14)	3.69 (1.13)
Asian, HPI/Other	3.54 (1.14)	3.37 (1.15)	3.62 (1.14)
Military Occupation	^*
Combat Arms	3.31 (1.22)	3.23 (1.17)	3.62 (1.18)
Combat Support	3.37 (1.22)	3.24 (1.20)	3.58 (1.16)
Service Support	3.31 (1.27)	3.29 (1.20)	3.49 (1.20)
Wave 6 Probable PTSD Symptoms	^*	^*	^*
Does not meet criteria	3.37 (1.20)	3.23 (1.19)	3.52 (1.18)
Meets criteria	4.02 (1.07)	3.69 (1.18)	4.10 (1.09)
Wave 6 Age Groups	^*	^*	^*
18 to 24	3.50 (1.06)	3.32 (0.97)	3.46 (1.00)
25 to 33	3.48 (1.19)	3.27 (1.16)	3.58 (1.13)
35 to 54	3.32 (1.25)	3.24 (1.26)	3.54 (1.25)
55 to highest	3.07 (1.30)	3.04 (1.31)	3.42 (1.35)
Wave 6 Education	^*	^*	^*
Less than a Bachelor’s Degree	3.52 (1.15)	3.21 (1.10)	3.61 (1.11)
Bachelor’s Degree or Higher	3.26 (1.28)	3.33 (1.32)	3.48 (1.28)

Note. HPI = Hawaiian Pacific Islander. SE = standard error. Weighted ANOVA results- gender, paygrade (n = 37,604), Race/ethnicity (n = 37,455), military occupation, education (n = 37,598), age (n = 36,952).

3.2 Subgroup findings

3.2.1 Gender

A series of ANOVAs were conducted to determine the mean group differences in underemployment types (i.e., leadership, skills, and education). There were no significant differences between males and females in their underemployment due to their skills (F(1, 4059) = 1.28, p = 0.26), education (F(1, 4059) = 1.42, p = 0.23), or leadership experience (F(1, 4059) = 2.41, p = 0.12).

3.2.2 Race/Ethnicity

There were significant differences between race/ethnicity groups in their perceptions of underemployment due to their skills (F(3, 4040) = 25.23, p < 0.001), education (F(3, 4040) = 17.76, p < 0.001), or leadership experience (F(3, 4040) = 14.00, p < 0.001). Post hoc comparisons using t-test with the Bonferroni correction indicated that the mean scores for White Non-Hispanic veterans and each minority group (i.e., Black Non-Hispanic, Hispanic, and Other Non-Hispanic) were significantly different in underemployment due to skills. Education was also significant between White Non-Hispanic veterans and each minority group (i.e., Black Non-Hispanic, Hispanic, and Other Non-Hispanic). In addition, Black Non-Hispanic veterans’ reports of underemployment due to education were significantly different than Hispanic veterans’ reports. For underemployment due to leadership experiences, there were significant differences between White Non-Hispanic veterans, Black Non-Hispanic veterans, and Hispanic veteran groups. Black Non-Hispanic veterans were more likely to indicate underemployment given their leadership experience.

3.2.3 Paygrade

There were significant differences by paygrade in reported underemployment due to skills (F(5, 4055) =21.35, p < 0.001), education (F(5, 4055) = 9.75, p < 0.001), and leadership experience (F(5, 4055) =12.37, p < 0.001). A post hoc t-test with Bonferroni correction yielded significant differences for underemployment due to their skills between E1 to E4 and officers (i.e., O1 to O3 and O4 to O7); E5 to E6 and officers; E7 to E9 and officers; and O1 to O3 and O4 to O7. There were differences between E1 to E4, E5 to E6, E7 to E9, O1 to O3, and O4 to O7 for underemployment due to education. For underemployment due to leadership skills, O4 to O7 were significantly different than E1 to E4, E5 to E6, E7 to E9, and O1 to O3. In addition, E7 to E9 were significantly different than warrant officers, and O1 to O3.

3.2.4 Military occupation

There were no significant differences in underemployment based on military occupation due to their skills (F(2, 4054) = 1.45, p = 0.24) or education (F(2, 4054) = 1.08, p = 0.34). However, there was a significant difference in military occupation and underemployment due to leadership experience (F(2, 4054) = 3.67, p < 0.05). A Bonferroni post hoc analysis yielded significant differences between combat arms and service support military occupations. Veterans with combat arms occupations rated underemployment due to leadership experience on average higher than service support military occupations.

3.2.5 Age

There were significant differences between the age of the veteran and their underemployment due to their skills almost three years after discharge (F(3, 3989) = 8.02, p < 0.001). Bonferroni post hoc analysis yielded significant differences between the 18 to 24-year-old group and veterans 55 years and older; and between the 25 to 34-year-old group and 35 to 54-year-old group, and veterans 55 years and older. However, there were not significant differences in underemployment due to education (F(3, 3989) = 1.84, p = 0.14) and leadership experience (F(3, 3989) = 1.54, p = 0.20).

3.2.6 Concurrent Education Level

There were significant differences in underemployment by education level for those with a bache-lor’s degree or higher due to their skills (F(1, 4058) = 43.52, p < 0.001), education (F(1, 4058) =10.26, p < 0.001), and leadership experience (F(1, 4058) = 12.80, p < 0.001). Veterans with less than a bachelor’s degree reported higher underemployment means in skills and leadership experience than veterans with a bachelor’s degree or higher.

3.3 Findings by categorizing types of underemployment

Next, the study determined the percentage of veterans who perceived underemployment across all three types of underemployment (i.e., skills, education, and leadership experience). Responses of “somewhat agree” and “strongly agree” were recoded to agreement (1 = yes) and the responses of “strongly disagree”, “somewhat disagree”, and “neither agree nor disagree” were recoded to disagreement (0 = no) for each of the three underemployment items. Approximately 62% (weighted) of veterans reported that they somewhat or strongly agreed with being underemployed in at least one area (i.e., skills, education, or leadership experience). The majority of veterans (56%) believed they should be in a better job due to their leadership experience. Slightly fewer veterans reported they believed they should be in a better job given their skills (49%) or given their education (41%).

Next, we combined responses across the three types of underemployment. Thirty-six percent of veterans strongly disagreed, somewhat disagreed, or neither agreed nor disagreed to all three underemployment items. Thirty-four percent of veterans agreed to being underemployed in all three types. Ten percent of veterans reported that they should be in a better job due to a combination of both their leadership experience and skills. Nine percent reported they should be in a better job solely based on their leadership experience. A smaller proportion of respondents indicated they should be in a better job due to their leadership and education (3%), skills only (3%), or skills and education (1%).

Utilizing a logistic regression, this study examined the probability of agreement to all three types of underemployment (i.e., skills, education, and leadership experience) compared to disagreement on all three types of underemployment. Warrant officers and senior ranking officers (O4 to O7) were less likely to report underemployment in all three types compared to junior enlisted veterans (E1 to E4). African American, Hispanic, and veterans who indicated more than one race were 74%, 40%, and 91% respectively more likely to report underemployment in skills, education, and leadership experiences compared to White Non-Hispanic veterans. Veterans who completed their bachelor’s degree by Wave 6 were 46% more likely to report underemployment in all three types, and veterans who screened for possible PTSD symptoms were 2.76 times more likely to report underemployment in all three types compared to veterans who did not screen positive for PTSD symptoms. Table 3 displays the findings from the logistic regression analysis comparing all three underemployment types to no perceptions of underemployment types.

Table 3
Logistic Regression Analysis Comparing All Three Underemployment Types to No Perceptions of Underemployment Types

OR [Lower CI, Upper CI]

Male 1.02 [0.80, 1.30]

Paygrade E1 to E4 (reference group)

E5 to E6 1.08 [0.87, 1.34]

E7 to E9 1.26 [0.96, 1.66]

W1 to W5 0.48 [0.24, 0.97]^*

O1 to O3 0.82 [0.59, 1.13]

O4 to O7 0.81 [0.69, 0.95]^*

White Non-Hispanic (reference group)

Black Non-Hispanic 1.74 [1.3, 2.34]^***

Hispanic 1.40 [1.08, 1.82]^*

Asian HPI Non-Hispanic 1.52 [0.98, 2.34]

More than One Race Non-Hispanic 1.91 [1.26, 2.9]^*

Other Race non-Hispanic 0.64 [0.25, 1.65]

Service Support Occupation

Combat Arms Occupation 1.15 [0.92, 1.44]

Combat Support Occupation 1.06 [0.88, 1.29]

Wave 6 Completed a Bachelor’s Degree 1.46 [1.19, 1.8]^*

Wave 6 Probable PTSD symptoms 2.76 [1.92, 3.95]^***

	OR [Lower CI, Upper CI]
Male	1.02 [0.80, 1.30]
Paygrade E1 to E4 (reference group)
E5 to E6	1.08 [0.87, 1.34]
E7 to E9	1.26 [0.96, 1.66]
W1 to W5	0.48 [0.24, 0.97]^*
O1 to O3	0.82 [0.59, 1.13]
O4 to O7	0.81 [0.69, 0.95]^*
White Non-Hispanic (reference group)
Black Non-Hispanic	1.74 [1.3, 2.34]^***
Hispanic	1.40 [1.08, 1.82]^*
Asian HPI Non-Hispanic	1.52 [0.98, 2.34]
More than One Race Non-Hispanic	1.91 [1.26, 2.9]^***
Other Race non-Hispanic	0.64 [0.25, 1.65]
Service Support Occupation
Combat Arms Occupation	1.15 [0.92, 1.44]
Combat Support Occupation	1.06 [0.88, 1.29]
Wave 6 Completed a Bachelor’s Degree	1.46 [1.19, 1.8]^***
Wave 6 Probable PTSD symptoms	2.76 [1.92, 3.95]^***

Note. HPI = Hawaiian Pacific Islander. OR = Odds Ratio. Weighted estimates n = 26,260; unweighted n = 2,864. This analysis only includes two groups (all three types of underemployment [i.e., skills, education, and leadership experience) compared to no perceptions of underemployment).

4 Discussion

This study provided a description of veteran underemployment experiences after transition to the civilian workforce and tested the average differences between specific groups. There are several main findings as a result of this study. First, the majority of veterans report being underemployed even three years after transition. This indicates the need for proactive efforts to offer employment programs and resources to transitioning veterans focused on career planning, resume writing, interviewing, networking, and translation of military experience to civilian careers. In doing so, veterans may be better able to articulate the linkage of their education, skills, and leadership experience into civilian employment opportunities.

Second, the majority of veterans, regardless of most demographic characteristics, agreed that their leadership experience should have positioned them to be in a better job than their current job. This finding related to leadership experience aligns with the fact that veterans have numerous opportunities to develop their leadership skills while in the military. Perhaps those leadership experiences are not always fully understood or recognized by civilian employers who traditionally consider education level and civilian work experience. Therefore, a two-pronged approach focused on veterans and employers may be required to help veterans communicate their leadership experience and manage their employment expectations and to aid employers in understanding the skills veterans gain during military service and how they translate to civilian leadership roles.

Third, differences among subgroups need to be addressed. For instance, veterans who discharged at junior enlisted paygrades were more likely to report underemployment due to their skills and education. White Non-Hispanic male veterans, and those with higher paygrades when exiting the military were less likely to report being underemployed. Veterans in combat arms occupations described leadership experience as a primary reason for underemployment. These findings suggest the need to target services toward specific subgroups to help reduce underemployment. For example, veterans with combat occupations may need more assistance in translating their leadership experiences to civilian occupations. More research is needed to understand the specific skills gained in the military and how these skills influence the jobs veterans seek after discharge.

This study provides a glimpse into veterans’ post-transition experiences as they relate to underemployment nearly three-years after military transition. However, there were a few limitations to this study. First, this analysis is concurrent. Second, this study only examined subjective experiences of underemployment. Therefore, whether a veteran who feels underemployed is actually underemployed by more objective characteristics (e.g., pay, educational requirement for a job) is not known. However, empirical evidence indicates that the perception of underemployment contributes to negative outcomes (e.g., lower job satisfaction and retention).

An additional wave of the longitudinal survey at four years post-transition is occurring now which will allow for a greater understanding of how underemployment beliefs change over time. This additional survey is collecting data that may be used to look at objective indicators of underemployment (e.g., salary, education, military occupation). The same data can also be used (1) to examine how objective indicators and the subjective experience of underemployment impacts well-being outcomes, (2) to compare how subjective and objective measures capture underemployment experiences, (3) to understand the influence of COVID-19 on veteran underemployment [38], and (4) to examine how different groups of veterans experience underemployment, especially veterans with physical disabilities [39]. The research team also hopes to gather data on the experiences of veterans’ partners in a future wave to understand more about how spouses and partners experience underemployment [40].

5 Conclusion

The present study extends the limited body of literature on veteran underemployment. These findings contribute to the understanding of how veterans experience underemployment and suggest areas where veteran programs and services can more effectively target veterans’ transition needs. Existing research has pointed to the need to help veterans translate their skills. Our findings reinforce and indicate that such programs could further target, in particular, the translation of leadership skills that veterans acquire in the military into civilian language. Veteran employment programs may also seek to collaborate with employers to help them better understand the leadership skills veterans obtain through military service and how these skills can be an asset in the workplace.

Footnotes

Acknowledgments

The Clearinghouse for Military Family Readiness at Penn State is the result of a partnership funded by the Department of Defense between the Office of the Deputy Assistant Secretary of Defense for Military Community and Family Policy and the USDA’s National Institute of Food and Agriculture through a cooperative agreement with the Pennsylvania State University. This work leverages funds by the USDA’s National Institute of Food and Agriculture and Hatch Appropriations.

The Veterans Metrics Initiative (TVMI) research was managed by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), and it was collaboratively sponsored by the Bob Woodruff Foundation, Health Net Federal Services, HJF, Lockheed Martin Corporation, Marge and Philip Odeen, May and Stanley Smith Charitable Trust, National Endowment for the Humanities, Northrop Grumman, Prudential, Robert R. McCormick Foundation, Rumsfeld Foundation, Schultz Family Foundation, The Heinz Endowments, U.S. Department of Veterans Affairs Health Services Research and Development Service, Walmart Foundation, and Wounded Warrior Project, Inc. The opinions and assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of any of the sponsor organizations listed.

Conflict of interest

None to report.

References

McKee-Ryan

, Harvey

. “I have a job, but...”: A review of underemployment. Journal of Management. 2011;37: 962–96. https://doi.org/10.1177/0149206311398134.

Johnson

, Johnson

. Perceived overqualification and health: A longitudinal analysis. The Journal of Social Psychology. 1999;139:14–28. https://doi.org/10.1080/00224549909598358.

Thompson

, Shea

, Sikora

, Perrewé

, Ferris

. Rethinking underemployment and overqualification in organizations: The not so ugly truth. Business Horizons. 2013;56:113–21. https://doi.org/10.1016/j.bushor.2012.09.009.

Benedict

, Gayatridevi

, Velayudhan

. Perceived overqualification, job satisfaction, somatization and job stress of MNC executives. Journal of the Indian Academy of Applied Psychology. 2009;35:283–9.

Lobene

, Meade

, Pond

. Perceived overqualification: A multi-source investigation of psychological predisposition and contextual triggers. The Journal of Psychology. 2015;149:684–710. https://doi.org/10.1080/00223980.2014.967654.

Maynard

, Brondolo

, Connelly

, Sauer

. I’m too good for this job: Narcissism’s role in the experience of overqualification: Narcissism and overqualification. Applied Psychology. 2015;64:208–32. https://doi.org/10.1111/apps.12031.

Allan

, Douglass

, Duffy

, McCarty

. Meaningful work as a moderator of the relation between work stress and meaning in life. Journal of Career Assessment. 2016;24:429–40. https://doi.org/10.1177/1069072715599357.

Allan

, Tay

, Sterling

. Construction and validation of the Subjective Underemployment Scales (SUS). Journal of Vocational Behavior. 2017;99:93–106. https://doi.org/10.1016/j.jvb.2017.01.001.

Jensen

, Slack

. Underemployment in America: Measurement and evidence. American Journal of Community Psychology. 2003;32:21–31. https://doi.org/10.1023/A:1025686621578.

10.

Erdogan

, Bauer

, Peiró

, Truxillo

. Overqualified employees: Making the best of a potentially bad situation for individuals and organizations. Ind Organ Psychol. 2011;4:215–32. https://doi.org/10.1111/j.1754-9434.2011.01330.x.

11.

Erdogan

, Bauer

. Perceived overqualification and its outcomes: The moderating role of empowerment. Journal of Applied Psychology. 2009;94:557–65. https://doi.org/10.1037/a0013528.

12.

Wilkins

, Wooden

. Economic Approaches to Studying Underemployment. In: Maynard DC, Feldman DC, editors. Underemployment: Psychological, Economic, and Social Challenges, New York, NY: Springer; 2011, pp. 13–34. https://doi.org/10.1007/978-1-4419-9413-4_2.

13.

Jones-Johnson

, Johnson

. Subjective underemployment and psychosocial stress: The role of perceived social and supervisor support. The Journal of Social Psychology. 1992;132:11–21. https://doi.org/10.1080/00224545.1992.9924684.

14.

Khan

, Morrow

. Objective and subjective underemployment relationships to job satisfaction. Journal of Business Research. 1991;22:211–8. https://doi.org/10.1016/0148-2963(91)90002-F.

15.

Watt

, Hargis

. Boredom proneness: Its relationship with subjective underemployment, perceived organizational support, and job performance. J Bus Psychol. 2010;25:163–74. https://doi.org/10.1007/s10869-009-9138-9.

16.

BLS. Table A-15. Alternative measures of labor under utilization 2020. https://www.bls.gov/news.release/empsit.t15.htm (accessed October 12, 2020).

17.

Green

, McIntosh

. Is there a genuine under-utilization of skills amongst the over-qualified? Applied Economics. 2007;39:427–39. https://doi.org/10.1080/00036840500427700.

18.

Green

, Zhu

. Overqualification, job dissatisfaction, and increasing dispersion in the returns to graduate education. Oxford Economic Papers. 2010;62:740–63. https://doi.org/10.1093/oep/gpq002.

19.

McGuinness

, Pouliakas

, Redmond

. How useful Is the concept of skills mismatch? 2017:33.

20.

Das-Munshi

, Leavey

, Stansfeld

, Prince

. Migration, social mobility and common mental disorders: critical review of the literature and meta-analysis. Ethnicity & Health. 2012;17:17–53. https://doi.org/10.1080/13557858.2011.632816.

21.

Golden

. Irregular work scheduling and its consequences. SSRN Journal 2015. https://doi.org/10.2139/ssrn.2597172.

22.

Konrad

, Moore

, Ng

ESW

, Doherty

, Breward

. Temporary work, underemployment and workplace accommodations: Relationship to well-being for workers with disabilities. Brit J Manage. 2013;24:367–82. https://doi.org/10.1111/j.1467-8551.2011.00809.x.

23.

Maynard

, Joseph

, Maynard

. Underemployment, job attitudes, and turnover intentions. J Organiz Behav. 2006;27:509–36. https://doi.org/10.1002/job.389.

24.

Slack

, Jensen

. Underemployment among minorities and immigrants. In: Maynard DC, Feldman DC, editors. Underemployment: Psychological, Economic, and Social Challenges, New York, NY: Springer; 2011, pp. 127–43. https://doi.org/10.1007/978-1-4419-9413-4_7.

25.

Abel

, Deitz

. Underemployment in the early careers of college graduates following the Great Recession. 2016:51.

26.

Mousteri

, Daly

, Delaney

. Underemployment and psychological distress: Propensity score and fixed effects estimates from two large UK samples. Social Science & Medicine. 2020;244:112641. https://doi.org/10.1016/j.socscimed.2019.112641.

27.

Barrera

, Carter

. Challenges on the home front: Under employment hits veterans hard. 2018:21.

28.

Ainspan

, Penk

, Kearney

. Psychosocial approaches to improving the military-to-civilian transition process. Psychological Services. 2018;15:129–34. https://doi.org/10.1037/ser0000259.

29.

MacLean

, Kleykamp

. Income inequality and the veteran experience. The ANNALS of the American Academy of Political and Social Science. 2016;663:99–116. https://doi.org/10.1177/0002716215596964.

30.

Prokos

, Cabage

. Women military veterans, disability, and employment. Armed Forces & Society. 2017;43:346–67. https://doi.org/10.1177/0095327X15610743.

31.

LinkedIn. LinkedIn veteran opportunity report 2019. https://socialimpact.linkedin.com/content/dam/me/linkedinforgood/en-us/resources/veterans/LinkedIn-Veteran-Opportunity-Report.pdf (accessed October 12, 2020).

32.

Farber

. Alternative and part-time employment arrangements as a response to job loss. National Bureau of Economic Research 1999. https://www.nber.org/papers/w7002.pdf (accessed September 8, 2020).

33.

IVMF & VetAdvisor. Veteran job retention survey summary 2014. https://ivmf.syracuse.edu/wp-content/uploads/2016/10/VetAdvisor-ReportFINAL-Single-pages.pdf (accessed October 12, 2020).

34.

U.S. Chamber of Commerce. Veterans in the workplace: Understanding the challenges and creating long-term opportunities for veteran employees 2016. https://www.uschamberfoundation.org/sites/default/files/Veterans%20in%20the%20Workplace_0.pdf (accessed October 12, 2020).

35.

Vogt

, Perkins

, Copeland

, Finley

, Jamieson

, Booth

, et al. The Veterans Metrics Initiative study of US veterans’ experiences during their transition from military service. BMJ Open. 2018;8:e34–e34. https://doi.org/10.1136/bmjopen-2017-020734.

36.

Price

, Szafranski

, van Stolk-Cooke

, Gros

. Investigation of abbreviated 4 and 8 item versions of the PTSD Checklist 5. Psychiatry Research. 2016;239:124–30. https://doi.org/10.1016/j.psychres.2016.03.014.

37.

Crouter

, Davis

, Updegraff

, Delgado

, Fortner

. Mexican American fathers’ occupational conditions: Links to family members’ psychological adjustment. J Marriage and Family. 2006;68:843–58. https://doi.org/10.1111/j.1741-3737.2006.00299.x.

38.

Kaur

, Goyal

. Individual, interpersonal and economic challenges of underemployment in the wake of COVID-19. WOR. 2020;67:21–8. https://doi.org/10.3233/WOR-203249.

39.

Gewurtz

, Premji

, Holness

. The experiences of workers who do not successfully return to work following a work-related injury. WOR. 2019;61:537–49. https://doi.org/10.3233/WOR-182824.

40.

DaLomba

, Greer

, Cruz

, Harris

, King

, Laurel

, et al. The experiences of active duty military spouses with advanced degrees in maintaining and advancing their careers. Work. 2021;68:387–98. https://doi.org/10.3233/WOR-203380.