Estimation of Retention Parameters for the Prototype Officer Personnel Inventory, Cost and Compensation Model.

Abstract

This research estimated a multiperiod Annualized Cost of Leaving (ACOL-2) model that predicts officer career decisions as a function of economic, demographic, and Army personnel policy (e.g., military compensation) influences. The panel probit estimation yielded statistically significant pay but not unemployment effects. The research also found that fixed, unobserved preferences for military service significantly influence retention behavior. The estimation encompassed up to 13 consecutive annual decision points, with data taken from ARI's Officer Longitudinal Research Database, covering year groups 1979-1992. The retention parameter estimates were embedded in an Officer Personnel Inventory, Cost and Compensation (OPICC) Model. This PC-based prototype model was designed and developed to improve the Army's ability to effectively manage its officer force by providing policy makers with accurate information about the impact of policy changes, including promotion policy, compensation, and separation incentives. The OPICC model provides estimates of the impacts of policy and economic changes to the Officer Personnel Management Directorate inventory for a 6-year projection horizon. The prototype version does not contain a cost estimation capability. The model was validated by using it to predict actual historical behavior.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1995
Accession Number
ADA313541

Entities

People

  • Lee S. Mairs
  • Patrick G. Mackin
  • Paul F. Hogan

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Administrative Personnel
  • Air Force
  • Artillery
  • Databases
  • Economic Models
  • Employment
  • Enlisted Personnel
  • Inventory
  • Management Personnel
  • Manpower
  • Military Education
  • Military Personnel
  • Officer Personnel
  • Organizational Structure
  • Personnel Management
  • Social Sciences
  • Unemployment

Readers

  • Computational Modeling and Simulation
  • Naval Personnel Management