The objective of this thesis is to examine the returns to mobility of civilian personnel within the Department of Defense (DoD). This study employs panel data provided by the Defense Manpower Data Center (DMDC) and drawn from the Department of Defense Civilian Personnel Data Files. The dataset consisted of 21,143 personnel who were new hires in years 1994-1995. Between 1994-1995 and 2003, 3,267 (15.4%) employees were interstate migrants. The data were set up as an unbalanced panel with a total of 132,068 observations. This study uses ordinary least squares (OLS), probit and Heckman selection-correction techniques to explore two returns to mobility measures: compensation and promotion. Multivariate models were specified and estimated for each performance measure. The results indicated workers who migrate are more likely subsequently to be promoted. Migration is a strategic move for workers to advance and maximize their personal utility since migrants earn higher salaries than non-migrants. Females present no evidence of tied-mover effects, and pursue promotion and salary opportunities like males. Women promote faster than men, and women migrants increase their promotion rates even more. Females, however, earn lower salaries than males. The models also reveal that veterans earn lower salaries than non-veterans and have no significant advantages in promotion over their counterparts.

This thesis analyzes the factors that influence the decision of first-term Naval Veterans (NAVETs), who are eligible for reenlistment, to choose to affiliate with the Selected Reserve (SELRES). A model of the determinants of affiliation is specified and estimated using data on active Navy separations and Navy Reserve accessions during the period between 1990 and 2002. The data set analyzed the affiliation decisions of 388,637 NAVETs. Some of the features in the maximum likelihood logit model include the use of rating groups to determine differences in affiliation patterns by occupational categories, determining differences over various time periods, and looking at pay and unemployment rate elasticities across rating groups. Overall, NAVET affiliation in the SELRES is found to depend upon Reserve pay, unemployment rates, census region, gender, race, marital status, dependency status, age at time of separation from active duty, education, mental category, and Navy rating. More specifically, the findings indicate that technical ratings are more responsive to changes in pay than nontechnical ratings, while the unemployment elasticity indicates that affiliation increases with an increase in the unemployment rate. Various time periods were analyzed to determine if the drawdown years of the early 1990s differed from the rest of the sample, and to determine if differences existed during and after the 'dot.com' boom of the late 1990s. In both cases, models in the restricted period were found to be significantly different from the pooled period. Females, Blacks and Hispanics were more likely to affiliate, while those NAVETs who are married, have children, and are older are less likely to affiliate. Finally, there was a significant regional effect in the probability of affiliation.

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Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Anne M. Boylan, Dept. of History. Includes bibliographical references.