Predicting the Effect of Marine Corps Selective Reenlistment Bonuses in the Post-9/11 Era: Integrating the Effects of Deployment Tempo

Abstract

This thesis explores the predictive effects of the Marine Corps Selective Reenlistment Bonus (SRB) on first-term retention while controlling for varying levels of deployment tempo. In order to successfully predict reenlistment decisions in the current era, the model must control for conditions that affect a Marine's choice to reenlist, none being more influential than deployments to Operation Iraqi/Enduring Freedom. Adding deployment tempo variables to the logit prediction model enables Marine Corps manpower planners to properly account for changing conditions in the "Long War." The results of this analysis find the increased deployment tempo in recent years has had a negative affect on reenlistments. To counter this effect the Marine Corps has steadily increased its SRB budget and subsequent SRB offers to all Marines. In order to improve the accuracy of reenlistment predictions, this thesis estimated a model with alternative indicators of deployment tempo. The model developed is parsimonious, yet predicts accurately. Validation results show that if the model was utilized to predict FY07 reenlistment rates, it would have average prediction errors of 12 percent for the 27 high-density MOSs, who make up nearly 61 percent of the first-term population.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2009
Accession Number
ADA496983

Entities

People

  • David Barber

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Cyber
  • Human Systems
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Aircrafts
  • Airframes
  • Artillery
  • Control Systems
  • Employment
  • Enlisted Personnel
  • Fixed Wing Aircraft
  • High Density
  • Management Personnel
  • Manpower
  • Marine Corps
  • Navigation
  • Organizational Structure
  • Personnel Management
  • Statistical Analysis
  • Unmanned Aerial Vehicles

Readers

  • Computational Modeling and Simulation
  • Naval Personnel Management