A Manager's Guide and Program Evaluation of Arbitration in the Federal Sector.

Abstract

This thesis contains an information guide to, and an economic evaluation of arbitration in the public sector. The research has resulted in the description of legal relationships between compulsory arbitration and employee's and manager's rights. It describes procedures to follow in selecting an arbitrator and discusses how to prepare and present an arbitration case. The economic evaluation defines specific costs and benefits and evaluates the effect of arbitration on wage and benefits of public employees. The conclusions provide managers with an evaluation of the strengths and weaknesses of arbitration in the federal sector and provide mid-level managers with a guide to the procedural steps up to and including the arbitration process. Strengths include those benefits derived such as protection of employee interest, political and social stability, and inferred public and private wage parity. Weaknesses are the unmeasurable cost to the tax payer resulting from the allocating of scarce public resources by non-representative third party arbitrators. Recommendations are made for further cost benefit analysis on subjects relating to arbitration in the federal sector. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1982
Accession Number
ADA127647

Entities

People

  • James Clifton Davis Iii

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Agreements
  • Awards
  • Bargaining
  • Cost Benefit Analysis
  • Costs
  • Economic Analysis
  • Employment
  • Fringe Benefits
  • Governments
  • Labor Unions
  • Law
  • Motivation
  • National Governments
  • Negotiations
  • Organizational Structure
  • Personnel Management
  • Test And Evaluation

Readers

  • Government and Public Administration Law.
  • Instructional Design and Training Evaluation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy