Download

Download eBook for Free

FormatFile SizeNotes
PDF file 1.1 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.

Purchase

Purchase Print Copy

 Format Price
Add to Cart Paperback102 pages $33.00

Research Questions

  1. What are the potential strengths and limitations of various methods of economic analysis, including cost-benefit analysis, business case analysis, and related metrics, such as the CPE, CPO, and CPU?
  2. Under what conditions are various methods and related metrics most feasible and useful?
  3. How can the Army identify cases in which metrics may lead to incorrect conclusions, especially for capability investment decisions?

This report presents the potential strengths and limitations of cost-effectiveness analysis (CEA) and related metrics for economic analysis—such as cost-per-unit (CPU), cost-per-effect (CPE), and cost-per-objective (CPO)—to inform the U.S. Army and other Department of Defense (DoD) communities about whether, when, and how to usefully employ them for capability investment decisions. The authors propose a framework, based on CEA, for conducting economic analysis, and they explore—both in theory and by using a notional example of a technology choice across multiple platforms with similar capabilities—how an analysis can become more difficult when problems are more complex or are depicted with greater realism. The authors discuss how complexity can increase as objectives become less clear cut, ancillary benefits and unintended consequences emerge, technologies become intertwined, boundaries change, and risk and uncertainty take hold, explaining how these features can affect economic analyses.

Key Findings

  • CEA and related metrics, such as CPE or CPO, tend to focus on meeting a specific objective, such as mission success. This attribute differentiates them from other types of metrics, such as CPU. CPE- and CPO-like metrics can account for the differences in the technologies' capabilities, support costs, and other less direct costs of technology employment.
  • CEA and related metrics, such as CPE or CPO, tend to be most feasible when (1) there is a single fixed and measurable objective, (2) there are relatively few (or minor) ancillary benefits and unintended consequences, (3) the technologies being chosen from can operate independently, (4) the context for achieving the objective is well understood by the decisionmaker and not highly variable, (5) the boundaries of the problem space and cost elements lack ambiguity, (6) the problem space and cost elements lack substantial risk or uncertainty, and (7) sufficient data and computational capacity are available for conducting the analysis given the representation of the problem at hand.
  • Conducting an economic analysis (and developing associated metrics) is most useful when the models used to represent the pursuit of the objective can incorporate sufficient realism to encompass the salient features of the problem space.
  • Making decisions based on a single metric can be risky, especially when the results of an economic analysis are highly sensitive to modeling decisions and assumptions; the results of a CEA are additionally sensitive to the definition of the objective.

Recommendations

Analysts should consider whether the economic analysis they plan to conduct is both feasible and useful

  • Analysts should consider whether they can feasibly meet the specification, computational, and data requirements for the type of economic analysis they intend to pursue.
  • Analysts should consider whether the feasible representation of the problem they have specified produces a useful economic analysis that can represent the salient features of the real-world problem.
  • Analysts should refrain from relying solely on CEA and related CPE or CPO metrics when (1) the real-world problem cannot be portrayed reasonably with either a single fixed and measurable objective or a limited set of comparable objectives, or (2) the real-world decision requires an assessment of net benefits.
  • Analysts should consider the risks of using a single metric—such as a CPE, CPO, or net benefit estimate—for decision support.

Future work on the cost effectiveness of military capabilities should consider limitations of various approaches and metrics when both devising and applying results of economic analyses

  • Analysts should consider whether and how changes in assumptions or the extent of realism in an economic analysis affect the overall conclusions.

Research conducted by

This research was prepared for the United States Army and conducted within the Strategy, Doctrine, and Resources Program of RAND Arroyo Center.

This report is part of the RAND research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.