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Author: Publisher: ISBN: Category : Languages : en Pages : 78
Book Description
A logit model was applied to estimate the effect of selective reenlistment bonuses (SRBs) on the retention rates of Army Soldiers. The model was estimated separately by occupational group and by first (zone A), second (zone B) and third term (zone C) reenlistment decisions. An "annualized cost of leaving" (ACOL) variable was constructed to estimate the net financial returns to reenlisting in the Army compared to leaving for the civilian sector. The model was estimated using data on actual reenlistments from the period FYl99O tlirough FY2OOO. The effects of SRBs on reenlistments at Zones A, B, and C were estimated at three levels of occupational aggregation-all Army, CMF, and MOS. After out-of-sample testing, we re-specified and re-estimated the model. In general, the results for Zone A at all levels of occupational aggregation indicate that reenlistment bonuses have a positive and statistically significant effect on Zone A reenlistments. The magnitude of the effect varied by occupation, but a one-level increase in SRB at Zone A typically increases the reenlistment rate by three to seven percentage points, depending upon the occupation. The results for Zone B are also solid at both the CMF and MOS levels. Results for Zone C, where reenlistment rates are typically very high, were reasonably solid but not as good as the Zone A and B results. We were unable to obtain positive, statistically significant ACOL parameter estimates for a small number of occupation groups. Statistically significant effects for demographic control variables and labor market conditions were also obtained.
Author: Publisher: ISBN: Category : Languages : en Pages : 78
Book Description
A logit model was applied to estimate the effect of selective reenlistment bonuses (SRBs) on the retention rates of Army Soldiers. The model was estimated separately by occupational group and by first (zone A), second (zone B) and third term (zone C) reenlistment decisions. An "annualized cost of leaving" (ACOL) variable was constructed to estimate the net financial returns to reenlisting in the Army compared to leaving for the civilian sector. The model was estimated using data on actual reenlistments from the period FYl99O tlirough FY2OOO. The effects of SRBs on reenlistments at Zones A, B, and C were estimated at three levels of occupational aggregation-all Army, CMF, and MOS. After out-of-sample testing, we re-specified and re-estimated the model. In general, the results for Zone A at all levels of occupational aggregation indicate that reenlistment bonuses have a positive and statistically significant effect on Zone A reenlistments. The magnitude of the effect varied by occupation, but a one-level increase in SRB at Zone A typically increases the reenlistment rate by three to seven percentage points, depending upon the occupation. The results for Zone B are also solid at both the CMF and MOS levels. Results for Zone C, where reenlistment rates are typically very high, were reasonably solid but not as good as the Zone A and B results. We were unable to obtain positive, statistically significant ACOL parameter estimates for a small number of occupation groups. Statistically significant effects for demographic control variables and labor market conditions were also obtained.
Author: Stuart H. Rakoff Publisher: ISBN: Category : Job satisfaction Languages : en Pages : 138
Book Description
This project was designed to improve the understanding and modeling of the decisions, made each year by thousands of first-term soldiers, to reenlist in the Army or to leave for civilian jobs and school. A model of the reenlistment decision formulated from a decision-analytic perspective was developed, based on an extensive review of the literature in the areas of military personnel, job satisfaction and job change, and decision theory, as well as from focus groups conducted with first-term soldiers at Fort Benning, Georgia. A multicomponent decision-modeling approach incorporating attitudinal, normative, and affective predictors of reenlistment intent was then developed, along with a set of instruments to capture data on these components. Consistent with previous findings for an enlistment task, the analysis of the pilot test data indicated that the three components predicted reenlistment intent in the following rank order: affect, attitudinal, and normative. The results also suggest that the Army has available tools for influencing these reenlistment decisions that are much more varied than the limited set of mainly economic factors that are now predominant in these programs. Specifically, the affective component dominated the economic variables in predicting reenlistment intent for this limited sample of soldiers, and may be an important reenlistment program and policy lever in the future. Keywords: Military personnel, Retention.
Author: Paul J. Sticha Publisher: ISBN: Category : Continuing education Languages : en Pages : 100
Book Description
This evaluation of the Army Continuing Education System (ACES) considered the following programs: (a) Tuition Assistance (TA); (b) Functional Academic Skills Training (FAST; (c) Military Occupational Specialty Improvement Training (MOSIT); (d) Noncommissioned Officer (NCO) Leader Skill Enhancement Courses; and (e) the Armed Forces Classification Test (AFCT). The assessment of the effectiveness of these programs is based on their ability to enhance soldier performance and increase the prospects of promotion, as well as to reduce attrition and increase reenlistment. The evaluation data came from a longitudinal administrative database that tracked a three-year accession cohort over a six-year period and an NCO database including self-reported participation in ACES programs, promotion information, and observed performance ratings. The analysis was designed to separate effects of participant characteristics from the effects of the program, and to control for differences in the opportunity and propensity to participate in ACES. Participation in TA and FAST were associated with an increase in the probability of first term reenlistment FAST participation was also associated with lower first-term attrition. Participation in several ACES programs showed positive effects on measures of performance and promotion potential.
Author: Lenore A. Pienta Publisher: ISBN: Category : United States Languages : en Pages : 56
Book Description
This report analyzes the reenlistment part of the Reserve/National Guard force structure model. Analysis of the Defense Manpower Data Center's Reserve Component surveys, 1986, Reserve Components Common Personnel Data System, and civilian moonlighting wages predicted from the Bureau of the Census reveals that the U.S. Congress can increase reenlistment by increasing Reserve pay. Reenlistment can also be increased by assigning reservists to primary Military Occupational Specialties in which they are trained.
Author: Panel on Retirement Income Modeling Publisher: National Academies Press ISBN: 0309589533 Category : Social Science Languages : en Pages : 288
Book Description
This book brings together in one volume what researchers have learned about workers, employers, and retirees that is important for formulating retirement income policies. As the U.S. population ages, there is increasing uncertainty about the solvency of the Social Security and Medicare systems and the adequacy of private pensions to provide for people's retirement needs. The volume covers such critical behaviors as workers' decisions to retire, people's choices of saving over consumption, and employers' decisions about hiring older workers and providing pension and health care benefits. Also covered are trends in mortality, health status, and health care costs that are key to projecting the likely costs and effects of alternative retirement income security policies and a strategy for combining data and research knowledge into a policy modeling framework.
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Author: Stuart H. Rakoff
Author: U.S. Army Research Institute for the Behavioral and Social Sciences
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Author: Paul J. Sticha
Author: Lenore A. Pienta
Author: Patrick C. Mackin
Author: Joseph Zeidner
Author: Panel on Retirement Income Modeling
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