Transitioning from military service to civilian employment is a significant life change for veterans. Unfortunately, many veterans experience challenges in this transition, with a substantial percentage leaving their first post-military job within the first two years (Institute for Veterans and Military Families & VetAdvisor, 2014). Obstacles include limited job opportunities in desired locations and difficulty in finding roles that effectively utilize their military skills and education (Berglass & Harrell, 2012; Curry Hall et al., 2014; Keeling et al., 2018). This mismatch can lead veterans to initially accept positions that don’t fully align with their capabilities or long-term career goals (Kintzle & Castro, 2018; Lepage, 2020).
The primary reason veterans cite for leaving their first post-military employment is to pursue better opportunities (IVMF & VetAdvisor, 2014). Other significant factors include limited career advancement, unfulfilling work, inadequate compensation and benefits, and poor alignment between job requirements and veteran skills. Key predictors of job stability for veterans are job alignment—how well the job matches career preferences and military training, and the extent to which veterans can leverage their skills—and the organizational culture’s similarity to the military (IVMF & VetAdvisor, 2014). When asked what would have encouraged them to stay in their initial civilian job, veterans prioritized higher salaries and benefits, alongside increased opportunities for career progression and professional growth (IVMF & VetAdvisor, 2014). These findings underscore the critical need for defined career paths for veterans in civilian workplaces to improve retention. Career advancement is deeply ingrained in military culture, where structured progression is the norm (Hunter-Johnson et al., 2020; King, 2012; Lepage, 2020).
Understanding Career Advancement Theories
Career advancement, the process of achieving new professional goals, is evaluated through both objective measures like salary increases and promotions, and subjective measures such as career satisfaction (Ng et al., 2005). Two primary theoretical frameworks help explain career mobility: contest-mobility and sponsored-mobility.
Contest-mobility suggests that career success is an open competition where the most skilled and diligent individuals rise to the top. From this perspective, career advancement is a continuous effort requiring innovation and improvement (Ng et al., 2005). Activities like professional development and active job searching demonstrate this proactive approach. Individual attributes crucial for success in this model include human capital—education, personal experiences, and professional skills. Indicators of human capital investment are hours worked, job and organizational tenure, work experience, education level, career planning, political acumen, and social capital (e.g., mentorships, professional networks). Investing in human capital through ongoing learning and skill development is a strong predictor of career success and increased workplace value (Johnson & Eby, 2011; Ng et al., 2005; Seibert et al., 2001).
Sponsored-mobility theory challenges the idea of equal opportunity, arguing that not everyone starts from the same position (Ng et al., 2005). This theory posits that “elites”—individuals with influence due to wealth, privilege, or skills—act as gatekeepers to success. Social connections to these elites, rather than individual merit alone, often dictate career progression. Organizational sponsorship, referring to the support organizations provide for employee advancement, and sociodemographic factors are key indicators in this model. Support from supervisors, skill-building opportunities, and organizational resources reflect career advancement potential within this framework.
Sociodemographic factors frequently studied in relation to career advancement include gender, race, marital status, age, and personality traits. Factors like being married or previously married (Cobb-Clark & Dunlop, 1999), being older (Johnson & Eby, 2011; Judge et al., 1995), having a non-employed spouse (Kirchmeyer, 1998), and coming from an upper-class background (Useem & Karabel, 1986) have been positively linked to career advancement. Conversely, being female or non-White is often negatively correlated with career progression (Johnson & Eby, 2011; Ng et al., 2005).
Another perspective on career development is offered by Super’s theory (1957, 1980), which outlines five career stages: growth, exploration, establishment, maintenance, and disengagement. This model is dynamic, allowing individuals to cycle through stages rather than progress linearly. The growth stage involves initial exposure to different occupations and self-concept development. Exploration focuses on establishing vocational identity through career exploration and work experiences, refining self-concept further. The establishment stage is marked by seeking role stability and career advancement (Patton & McMahon, 2006). Maintenance involves preserving self-concept and job position, which can include changes in roles or organizations while maintaining one’s career level (Patton & McMahon, 2006). Disengagement, the final stage, involves planning for retirement. Super (1980) emphasized that career development is individualized and lifelong, not strictly age-based. Transitioning veterans are primarily in the exploration, establishment, and maintenance stages.
The Role of Employment Programs in Enhancing Veteran Outcomes
Human and social capital are crucial for career advancement (Eby et al., 2003; McArdle et al., 2007; Ng et al., 2005). Numerous programs, such as Onward to Opportunity and Corporate America Supports You/VetJobs, aim to build these assets for veterans seeking employment or career progression. However, the effectiveness of these programs in driving career advancement remains largely unverified (Keeling et al., 2018; Mathematica, 2014). It’s possible that specific components within these programs are key to helping veterans assess their situations and develop effective career transition strategies, ultimately leading to greater employment success. For instance, programs that assist veterans in identifying and translating military skills to civilian job requirements can lead to better job alignment and faster career advancement. Similarly, veterans initially in underemployment who utilize these programs may experience quicker promotions as they learn to better articulate their skills to employers. Furthermore, teaching veterans to leverage and expand their professional networks can broaden their job search and enable them to be more selective and competitive, leading to higher-paying positions, promotions, and greater job satisfaction (Eby et al., 2003; Scandura, 1992; Schulker, 2017; Wayne et al., 1999).
Identifying Effective Employment Program Components
This study sought to identify specific components of veteran employment programs that are most effective in promoting career advancement, defined as either leaving a job for a better opportunity or receiving a promotion. While many programs exist, their effectiveness is often undocumented (Keeling et al., 2018; Mathematica, 2014). This research aimed to pinpoint program components that lead to improved employment outcomes for veterans.
To address the lack of evidence, a qualitative coding approach, adapted from common components analysis (CCA; Morgan et al., 2018), was used to identify common elements across various veteran employment programs. CCA, initially introduced by Rosenzweig (1936) and also known as common elements (Chorpita et al., 2007), common components (Kaminski et al., 2008), and common factors (Rotheram-Borus et al., 2009), aims to identify program commonalities and assess their impact on intended outcomes (Chorpita et al., 2007). Traditional CCA relies on programs evaluated through randomized controlled trials (RCTs). This study’s adapted CCA diverges by including programs without RCT-based empirical support. Given the rapid development of veteran support programs, many lack rigorous evaluation data. The adapted CCA controls for predictors of program use, matches program users with non-users, and determines which common components are linked to positive outcome changes, such as promotions or better job opportunities.
This adapted approach allows for the analysis of common components in veteran programs lacking robust empirical validation. The first phase identified common components across employment-focused programs. The study focused on two types: (a) content components—the knowledge or skills taught, and (b) process components—the methods of content delivery (e.g., online, in-person, individual or group instruction). The second phase analyzed the correlation between exposure to these components and career advancement outcomes. The current study examined how veterans’ engagement with various employment program components, within the first three months post-separation from active duty, was associated with career advancement (leaving for a better job or promotion) within the following 6 to 12 months. The hypothesis was that engagement with specific program components would positively predict career advancement.
Methodology
Participants
The study sample was drawn from the US Department of Veterans Affairs and US Department of Defense Identity Repository (VADIR), representative of recently transitioned veterans. Eligibility included officers, warrant officers, or enlisted personnel separating from active-duty service (Army, Navy, Air Force, Marine Corps) within 90 days of data extraction, or National Guard or Reserve members deactivated after at least 180 days of active duty within 90 days of data extraction. Participants were required to have a US mailing address. Out of 48,965 veterans identified between May and September 2016, 9,566 completed the Wave 1 survey (20% response rate). Demographic details of the original sample are previously published (Rotheram-Borus et al., 2009). The majority were male (82%; n = 7,823), White Non-Hispanic (65%; n = 6,185), and from enlisted ranks (76%; n = 7,283). The current study subset included participants who completed career advancement outcome questions for the analyzed wave.
Procedures
Data collection was designed to protect veteran privacy by separating outreach and data collection between Veterans Affairs and ICF International, Inc. Surveys were conducted at roughly 6-month intervals starting in November 2016 (Wave 1, n = 9,566, 0–3 months post-separation; Wave 2, n = 7,200, 6–9 months post-separation; Wave 3, n = 7,201, 12–15 months post-separation). Most participants completed web-based surveys, with paper and phone options available. A $5 pre-incentive cash mailing and electronic gift codes ($20 for Wave 1, increasing by $5 each wave) for completed surveys, plus random drawings for $100 gift codes and small tokens of appreciation, were used to boost response rates. The study received human subjects protection approval from ICF International, Inc., and all participants provided informed consent (Protocol Number: 151636.0.000.00.000). Further study details, including participant characteristics and recruitment strategies, are available in Vogt et al. (2018).
This analysis aimed to determine if employment program component use correlated with career advancement. Veterans nominated programs used during their military-to-civilian transition. These programs were then coded for content and process components. Propensity score matching was used to strengthen the quasi-experimental design by adjusting for confounding variables (Braitman & Rosenbaum, 2002). Program use was considered the “treatment,” compared to non-use as the “control.” The analysis involved four key steps: (a) propensity score estimation, (b) using scores to adjust for confounders, (c) balance assessment to check for mean differences in propensity scores between groups, and (d) treatment effect estimation (Lanza et al., 2013).
Propensity scores, indicating the likelihood of a veteran participating in employment programs, were calculated using logistic regression. Covariates, identified as potential predictors of program use or career advancement, were included in the model (see Table 1). These included gender, paygrade, retirement status, race/ethnicity, marital status, discharge status, military occupation, combat exposure types, deployments, resilience, anxiety, depression, suicidal thoughts, PTSD symptoms, alcohol misuse, financial status, social support, and student status. These covariates were previously identified as predictors of program use (Aronson et al., 2019). Predicted probabilities (propensity scores) were saved and used as covariates in the outcome analysis, a “double robust” method to mitigate misspecification (D’Agostino, 1998; Kang & Schafer, 2007).
Table 1
Predictors of Veterans’ Use of Employment Programs (Veterans Completed Wave 2).
Note: n = 7,200; Service branch was omitted from the table: (Army 32%, Navy 19%, Air Force, 19%, Marine Corps, 17%, National Guard/Reserves (13%); Joined the National Guard/Reserves after discharge (16%). * p
Before matching in Wave 2 (Table 1), male veterans were less likely to use employment programs, while higher-ranking veterans (E5-E6, E7-E9, O1-O7) were significantly more likely to participate. Veterans who retired were also more likely to use programs, as were full-time and part-time students. Married veterans and those separated, widowed, or divorced were more likely to use programs compared to single veterans. Veterans with general discharges or those not yet discharged were less likely to participate. Veterans in combat support military occupations were more likely to use programs than those in service support roles.
Greedy Nearest Neighbor Matching was used to create matched samples. This method pairs treatment participants with control participants based on the closest propensity scores. Two-to-one matching with a 0.1 caliper was used, ensuring matches were within 0.10 standard deviations. This approach retains more of the sample for analysis. Rosenbaum and Rubin (1985) suggest a 0.10 caliper reduces 98% of covariate bias in normally distributed data. Propensity score quality was assessed by examining box plot overlap and mean difference of predicted probability estimates before and after matching. The initial probability estimate difference of 0.09 (slightly over half a standard deviation, SD = 0.14) reduced to 0.04 after matching. Predictor balance was reassessed using logistic regression. The final Wave 2 matched sample included 6,218 veterans, with 60% (n = 3,699) using at least one employment content component. Similar procedures were used for the Wave 3 matched sample (Table 2), resulting in a final sample of 5,908 veterans.
Table 2
Predictors of Veterans’ Use of Employment Programs (Veterans Completed Wave 3).
Note: n = 7,201; Service branch was omitted from the table: (Army 32%, Navy 19%, Air Force, 19%, Marine Corps, 18%, National Guard/Reserves (13%); Joined the National Guard/Reserves after discharge (17%); * p
Measures
Employment Program Use and Components
Veterans were asked to nominate employment programs used since discharge to aid their transition. Programs were broadly defined as activities addressing specific veteran needs from any organization (community, government, private, faith-based), including self-paced online programs or group sessions led by qualified professionals. Veterans could nominate up to two programs across seven types within the employment domain, totaling 14 possible nominations. These categories included online job databases, career fairs, resume writing/military skills translation, job placement assistance, career counseling, job training/certification, and other employment-related programs. Wave 1 yielded 914 employment program nominations. Programs with verified website URLs nominated by three or more veterans (n = 184) were coded. Over 2,781 unique nominations were made at baseline, making full coding infeasible. The “three or more nominations” criterion covered the majority of nominations, allowing 95.5% of Wave 1 participants to have at least one program coded, and 57% to have all nominations coded. For programs lacking full URLs, content-only codes were derived from the nomination question (e.g., a “resume writing” program was coded as offering resume writing content).
Nominated programs were coded for content and process components. Common content components included career planning, entrepreneurship, interviewing skills, job accommodations, job training/certification, networking conferences, resume writing, and translating military experience to civilian work. Process components (delivery methods) included self-paced online reading, direct instruction, rehearsal/role-playing, interactive tools, mentor/coach, social support/peer learning, and networking groups. Appendix A provides component definitions.
Leaving Job for Better Opportunity and Work Promotion
Veterans reported work and education/training changes since the previous survey (6-month intervals). The question was, “Since you completed the last survey, have you experienced any of the following changes related to your work or education/training activities? (Check all that apply).” Response options included job loss, leaving for a better opportunity, promotion, completing school/training, leaving school/training, difficulties in school/training, and other changes. This study focused on positive work changes: leaving for a better opportunity and receiving a promotion.
Qualitative Coding and Data Analysis
Program website pages were collected using BeamUsUp SEO web crawler software (Gomes, n.d), with screenshots for consistent coding. Trained coders used NVivo 11 (QSR International) to code content (skills/information taught) and process components (delivery/teaching methods). Morgan et al. (2018) provides detailed information on coding development and rationale. In summary, an adapted common components analysis was used due to the limited RCT evaluations of employment programs. The coding was based on a literature review of employment programs, themes from other CCA approaches (Rotheram-Borus et al., 2009), content/process codes from empirical CCA literature (Chorpita et al., 2013; Kaminski et al., 2008), and employment content from supplemental materials (Meyer, 2013).
Logistic regression was used to analyze component associations with leaving for a better opportunity or promotion. A robust covariate—the probability of employment program use for each content component—was included in each model, along with propensity scores. Stata 15.1 was used for all statistical analyses.
Results
Demographics are shown in Table 1. Content and process components were examined for leaving for a better opportunity and promotion at Waves 2 and 3. Wave 2 was 6-9 months post-separation, and Wave 3 was 12-15 months post-separation. Results are in Tables 3 and 4.
Table 3
Employment Program Use and the Odds Ratio of Leaving for a Better Opportunity.
Note: Leaving for a better opportunity at Wave 2 (6 to 9 months) post separation matched sample (n = 6,218); Leaving for a better opportunity at Wave 3 (12 to 15 months) post separation matched sample (n = 5,908). a These models were analyzed with all processes within one model; however, due to multicollinearity, follow-up analysis with the probability of employment use and individual process/content were added. * p
Table 4
Employment Program Use and the Odds Ratio of Promotion.
Note: Getting a promotion at Wave 2 (6 to 9 months) post separation matched sample (n = 6,218); Getting a promotion at Wave 3 (12 to 15) months post separation matched sample (n = 5,908); a These models were analyzed with all processes within one model; however, due to multicollinearity, follow-up analysis with the probability of employment use and individual process/content were added. * p
Content and Process Components Predicting Leaving for a Better Opportunity
Sixteen percent of veterans reported leaving for a better opportunity between Wave 1 and Wave 2 (6-9 months post-transition). Between Wave 2 and Wave 3 (12-15 months post-transition), 14.8% before matching and 15.3% after matching (n = 5,908) reported this outcome. Interviewing, resume writing, translating military skills, and career planning content components were significant predictors. Associated process components are detailed below for Waves 2 and 3.
Interviewing
Veterans using interviewing content delivered through direct instruction were 63% more likely at Wave 2 and twice as likely at Wave 3 to report leaving for a better opportunity. Those using rehearsal/role-playing or mentor/coach approaches were 41% and 38% more likely, respectively, to report this outcome at Wave 3.
Resume Writing
Veterans using online reading for resume writing were 74% more likely at Wave 2 and 64% more likely at Wave 3 to report leaving for a better job. Direct instruction in resume writing increased the likelihood by 18% at Wave 2 and 30% at Wave 3. Mentor/coach support in resume writing increased the likelihood by 30% at Wave 3. Conversely, interactive online resume writing tools were associated with a 24% decrease in leaving for a better opportunity by Wave 3.
Translating Military to Civilian Work
Direct instruction in translating military skills increased the likelihood of leaving for a better opportunity by 58% at Wave 3. Mentor/coach support in translation increased this likelihood by 67% at Wave 3.
Career Planning and Exploration
Direct instruction in career planning increased the likelihood of leaving for a better opportunity by 19% at Wave 2 and 53% at Wave 3. Interactive online tools for career planning increased this likelihood by 32% at Wave 2 and 31% at Wave 3. However, networking groups for career planning were associated with a 23% decrease in leaving for a better opportunity at Wave 3.
Content and Process Components Predicting Work Promotion
A smaller percentage of veterans received promotions: 14.5% (Wave 2) and 11.6% (Wave 3). No program components significantly predicted promotion at Wave 2. However, resume writing, translating military skills, career planning, and entrepreneurship content components were significant at Wave 3. Associated process components are detailed below for Wave 3.
Resume Writing
Online reading for resume writing increased the likelihood of promotion by 35% at Wave 3. Interactive online resume writing tools increased this likelihood by 22% at Wave 3.
Translating Military to Civilian Work
Direct instruction in translating military skills increased the likelihood of promotion by 37% at Wave 3. Networking conferences (with networking groups) increased this likelihood by 38%.
Career Planning and Exploration
Direct instruction in career planning increased the likelihood of promotion by 28% at Wave 3. Mentor/coach support increased this likelihood by 27%, and networking groups by 23% at Wave 3.
Entrepreneurship
Online reading for entrepreneurship increased the likelihood of promotion by 42% at Wave 3.
Discussion
This study investigated specific content and process components of employment programs impacting career advancement for Post-9/11 veterans in their first year post-separation. It advanced previous research by identifying program elements predicting veterans leaving for better opportunities and receiving promotions.
Many veterans utilize employment programs (Perkins et al., 2019), with over 60% in this study’s matched sample using at least one content component. Propensity score matching highlighted demographic groups needing additional support: males, single veterans, lower enlisted ranks, and those with general/other discharges.
The study identified effective program components for career advancement. Outcomes varied by advancement type (better job vs. promotion), component combination, and time since transition. Components significantly related to leaving for a better opportunity included: (a) interviewing (direct instruction, rehearsal/role play, mentor/coach); (b) resume writing (online reading, direct instruction, online tools, mentor/coach); (c) translating military skills (direct instruction, mentor/coach); and (d) career planning (direct instruction, online tools). Components associated with promotion included: (a) resume writing (online reading, online tools); (b) translating military skills (direct instruction); (c) networking conference; (d) career planning (direct instruction, mentor/coach, networking group); and (e) entrepreneurship (online reading).
Prior research using the same sample examined components associated with job attainment (Perkins et al., 2022). Common components across both job attainment and career advancement were career planning, resume writing, and interviewing skills, with entrepreneurship and virtual career fairs being unique to job attainment.
Similar to job attainment, not all delivery methods were equally effective for career advancement. Mentor/coach delivery remained significant across many content components (interviewing, resume writing, translating military skills). However, veterans may underutilize mentor/coach components due to availability, cost, or veteran preferences for less intensive delivery methods.
Conclusions
This research provides valuable insights for program developers, researchers, implementers, and policymakers. It identifies program usage patterns, specific component effectiveness, and links to job outcomes like better opportunities and promotions. Decision-makers can use this to target programming, address underserved groups, and understand effective components for specific outcomes. Implementing effective content and process components can maximize program impact and resource allocation. Program developers should also consider veteran transition timing, as component utility may vary over time. While no components predicted Wave 2 promotions, several were significant for Wave 3 promotions and for leaving for better opportunities at both waves, suggesting continued program use post-transition is beneficial. Consistent with prior research, increased support for at-risk veterans (male, lower enlisted, general/other discharge) who are less likely to use programs is crucial (Aronson et al., 2019; Perkins et al., 2022).
Limitations
Study strengths include longitudinal data collection from recently discharged veterans in real-world settings and the use of a robust covariate to account for program use likelihood. This methodology enhances confidence in results and mitigates selection bias.
Limitations include the lack of quality assessment for content and process components. Non-significant components might be due to poor quality, which this study couldn’t assess. The study also only examined the first year post-transition. Longer-term data is needed to assess cumulative component impact on career trajectories. Current findings offer an initial understanding of career advancement components, but a longer study duration is needed for a comprehensive view.
Implications for Future Research
Future research should evaluate the quality of veteran employment program components, as variable quality within broad categories like “career planning” may explain non-significant findings. The dynamic job market and economic factors also influence career advancement opportunities. Further research should explore the impact of continued program use over time on outcomes like salary and job retention, and identify optimal intervention timing.
This study advances the understanding of how specific program components link to successful veteran outcomes. These findings provide early evidence of employment program component effectiveness and can guide evidence-based programming decisions, ultimately enhancing career advancement services for transitioning veterans.
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