Retention Analysis Model (RAM) For Navy Manpower and Personnel Analysis
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Authors
Ahn, Sae Young (Tom)
Menichini, Amilcar
Tick, Simona
Subjects
Retention Modeling
Force Structure Modeling
Retention Bonus
Reenlistment Bonus
Logistic Regression
Retention Auction
Force Structure Modeling
Retention Bonus
Reenlistment Bonus
Logistic Regression
Retention Auction
Advisors
Date of Issue
2019-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
This report addresses deficiencies in our understanding of service members’ career trajectories. The insights generated will be used to construct more sophisticated and useful models of long run manpower projections, allowing complex simulations to predict the impact of personnel policy changes. This will allow Navy leadership to avoid unanticipated shocks to service member supply and quality. This report proceeds along two lines. First, we collect a dataset of Navy officers and examine their career trajectory, paying particular attention to their educational background and sociodemographic characteristics. Using long-term trend, as well as regression analysis, we find significant retention rate differences over the long run across gender, marital and dependent status, race, and education level. While the long run trends and regression results are illuminating, we should be wary of drawing definite conclusions about the innate ability or desire of officers to stay or separate based on these analyses. Without a formal model to distinguish between correlation and causation, we should recognize that the findings in this study primarily help direct our modeling efforts in subsequent years. Second, we provide an in-depth description of dynamic programming models, demonstrating their usefulness and internal consistency for predicting rational, forward-looking agents making choices that affect their future. We provide a detailed technical description of the model, defining value functions, Bellman’s equations, and other concepts necessary to program, estimate, solve, and simulate a dynamic programming model. We then propose the path forward to examine how service members in different communities may make different career choices.
Type
Technical Report
Description
Series/Report No
Department
Identifiers
NPS Report Number
NPS-GSBPP-19-006
Sponsors
OPNAV N81 Assessment Division
Funding
Format
65 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.