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 reenlistmentdecisions 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 thelogit 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 SRBoffers 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-termpopulation.
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