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This dissertation was motivated by concerns at the Air Force Recruiting Service that there was an imbalance in workloads among their enlisted accessions recruiters, which could negatively influence both recruiter morale and aggregate production of high-quality recruits. I used a mixed methods approach of interviews and predictive models in this study to identify key difficulty factors and predict the level of production from each recruiting zone. The interviews confirmed that some recruiters do perceive the difficulty imbalance as a problem, identified several recommendations from recruiters that could facilitate recruiting operations, and found sixteen factors which recruiters used to explain the variance in difficulty between recruiting zones. I used several datasets which represented these factors and additional factors identified in prior recruiting research to fit models which predict the quantity of high quality non-prior-service new enlisted contracts for recruiting offices and recruiting flights. I demonstrate that these models can be used to guide modifications to and predict the outcomes of various allocations of recruiters and goals, which can result in fairer recruiting zones and increase the aggregate number of high-quality recruits. I recommend the use of these models both to identify regions where the number of recruiters or the recruiting goal should be modified and also to monitor the performance of recruiting flights and offices. I also recommend more training for recruiters, both in the Recruiting School and on-the-job, and I recommend a reduction in the administrative burdens placed on recruiters. Together, these changes could balance regional variation in recruiting difficulty and lower the difficulty baseline for all recruiters.
Table of Contents
Chapter One
Introduction and Background
Chapter Two
Methodology
Chapter Three
Interview Findings
Chapter Four
Recruiting Factors and Data Sources
Chapter Five
Predictive Model Findings
Chapter Six
Recommendations
Appendix A
Informed Consent and Interview Protocol
Appendix B
Interview Codebook
Appendix C
Predictive Model Data Sources and Codebook
Appendix D
Correlation Matrices
Research conducted by
This document was submitted as a dissertation in September 2019 in partial fulfillment of the requirements of the doctoral degree in public policy analysis at the RAND Pardee Graduate School. The faculty committee that supervised and approved the dissertation consisted of Lawrence M. Hanser (Chair), Denis M. Agniel, and Malcolm Ree.
This publication is part of the RAND Corporation Dissertation series. Pardee RAND dissertations are produced by graduate fellows of the Pardee RAND Graduate School, the world's leading producer of Ph.D.'s in policy analysis. The dissertations are supervised, reviewed, and approved by a Pardee RAND faculty committee overseeing each dissertation.
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