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The U.S. Army has several levers at its disposal to try to meet its recruiting mission, with resources jointly used for both Regular Army (RA) and U.S. Army Reserve (USAR) accessions. These resources differ in their cost per additional recruit produced and the lead time necessary to change individual resourcing levels and affect enlistments. The Army can also modify recruit eligibility policies to help it achieve its accession requirement within available resources. Recruiting resources and enlistment eligibility policies work together as a system to produce RA and USAR recruits, and understanding their interactions under varying requirements and environments enables decisionmakers to use their limited resources more effectively and efficiently to achieve the Army's accession requirements. The authors present a model-the Reserve Recruiting Resource Model (RRRM)-designed to optimize the resource levels and mix needed to achieve future USAR recruiting goals under changing enlisted accession requirements and recruiting environments and alternative eligibility policies for potential recruits. The model also enables comparison of alternative courses of action. This research builds on prior work by the RAND Arroyo Center on the effectiveness and lead times of alternative recruiting resources. In their results, the authors discuss using the RRRM to predict annual accessions from a specified baseline resourcing plan and provide several examples of how the tool can be used to assess potential recruiting resource and policy trade-offs or to prepare for alternative recruiting requirements via optimization of recruiting resources used for USAR recruiting.
Soldiers --- Supply and demand --- Forecasting. --- United States. --- Recruiting, enlistment, etc. --- Evaluation. --- United States
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The U.S. Department of Defense (DoD) places a high priority on promoting diversity, equity, and inclusion at all levels throughout the organization. Simultaneously, it is actively supporting the development of machine learning (ML) technologies to assist in decisionmaking for personnel management. There has been heightened concern about algorithmic bias in many non-DoD settings, whereby ML-assisted decisions have been found to perpetuate or, in some cases, exacerbate inequities. This report is an attempt to equip both policymakers and developers of ML algorithms for DoD with the tools and guidance necessary to avoid algorithmic bias when using ML to aid human decisions. The authors first provide an overview of DoD's equity priorities, which typically are centered on issues of representation and equal opportunity within personnel. They then provide a framework to enable ML developers to develop equitable tools. This framework emphasizes that there are inherent trade-offs to enforcing equity that must be considered when developing equitable ML algorithms. The authors enable the process of weighing these trade-offs by providing a software tool, called the RAND Algorithmic Equity Tool, that can be applied to common classification ML algorithms that are used to support binary decisions. This tool allows users to audit the equity properties of their algorithms, modify algorithms to attain equity priorities, and weigh the costs of attaining equity on other, non-equity priorities. The authors demonstrate this tool on a hypothetical ML algorithm used to influence promotion selection decisions, which serves as an instructive case study. The tool the team developed in the course of completing this research is available on GitHub. View the repository on GitHub
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Previous research has found that, compared with their civilian counterparts, military spouses are more likely to be unemployed or underemployed. This work is the second phase of a two-phase study to evaluate data on the Military Spouse Employment Partnership (MSEP) program for the Military Community and Family Policy (MCFP) office. The authors conducted a new query of job postings in 2016 from the online MSEP Career Portal to analyze the types of jobs that employers were posting on this portal and compared the geographic distribution of these jobs with the locations of spouses. Next, the authors conducted interviews with employers in 2016 who post these jobs on the portal and fielded a survey of spouses in 2019 who had recently used the MSEP Career Portal. Finally, they interviewed a subsample of these respondents in 2019 over the phone. Results showed that the MSEP Career Portal lists a range of jobs, but a limited percentage of them are telecommuting positions, which are often desirable for military spouses, and there are more jobs in the Northeast than in the other regions, which are home to more military spouses. The employers interviewed expressed a desire for more and better communication among MSEP stakeholders, and the spouses surveyed expressed some dissatisfaction with the quality of jobs available via the portal. The authors recommend that MCFP attend to increasing the number of jobs on the MSEP Career Portal that would be of interest to military spouses within their specific labor markets.
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The U.S. Army uses a variety of resources and tools to achieve its recruiting mission each year. In this report, the authors present results from an updated version of RAND Corporation's Recruiting Resource Model (RRM), a multipart statistical model that explores how trade-offs between key recruiting resources (bonuses, advertising, and recruiters) affect the Army's ability to achieve recruiting goals and the cost of doing so. They use the RRM to analyze the mix and level of resources required to meet the recruiting mission under alternative recruiting environments and recruit eligibility policies. The RRM was updated to include more recent data to analyze the relationship between resource inputs and recruiting outcomes while incorporating the use of digital advertising, which has become an increasingly important recruiting resource in recent years. Consistent with previous iterations of the model, the results indicate that television advertising and, to a lesser extent, recruiters have positive associations with contract production and that these inputs are relatively more cost-effective than bonuses. This research can help inform how the Army might move resources in a variety of recruiting environments. Making marginal changes along these lines in a purposeful manner over time—either broadly or at a more local level (as might be done in an experimental setting)—would be an appropriate first step in implementing the recommendations that arose from this research.
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Between 2000 and 2021, the U.S. Department of Defense diagnosed more than 444,300 service members as experiencing at least one traumatic brain injury (TBI) during their military service. The number of TBIs experienced, and their severity, can affect the trajectory of and prognosis for recovery. Much progress has been made in developing, implementing, and disseminating effective treatments for TBI, yet gaps remain in understanding the long-term care and support needs for veterans who have sustained one or more TBIs during their military service. This report presents the findings from a study commissioned by Wounded Warrior Project (WWP) and conducted by the RAND Corporation to identify the long-term outcomes of TBI for veterans serving since the terrorist attacks of September 11, 2001; the future needs of this population; effective treatments for TBI; and the availability of community-based resources. The information in this report can be used to better understand which approaches may offer the best care for veterans with TBI, as well as help inform the care and support offered to veterans and their caregivers.
Brain damage --- Brain damage --- Disabled veterans --- Veterans --- Patients --- Care --- Patients --- Rehabilitation --- Medical care --- Medical care
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