Using an Experimental Approach to Improving the Selective Reenlistment Bonus Program

Abstract

The Selective Reenlistment Bonus program is a powerful force management tool designed to increase retention in undermanned skills. This thesis analyzes many of the theoretical applications of alternative compensation methods, specifically auctions, signaling theory, and experimental economics; and explains how an economic experiment might be applied within the Department of Defense. This experimental approach presents service members a certain scenario which mirrors a choice they might face when posed with a retention decision. Economic experiments are an inexpensive way to make more informed personnel policy decisions. This thesis postulates that economic experiments are an excellent means to capture the human element in the decision-making process. Additionally, economic experiments provide another form of simulation to wind-tunnel test policy changes before implementing them across the services. The sample experiment discussed in this thesis combines the theoretical principles of both auction and signaling theory and provides a means to analyze concrete data for which the Department of Defense could use before actually conducting an auction of selective reenlistment bonuses.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2007
Accession Number
ADA473267

Entities

People

  • William J. Norton

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Business Administration
  • California
  • Compensation
  • Department Of Defense
  • Economics
  • Employment
  • Enlisted Personnel
  • Governments
  • Management Personnel
  • Military Personnel
  • Personnel Management
  • Recruiting
  • Reenlistment
  • Students
  • United States
  • Wind Tunnels

Readers

  • Naval Personnel Management
  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.