| To help the analyst
present the results of economic analysis, six principles of communication
are identified and briefly described below.
- Use Clear and Transparent Language: As indicated above,
the ultimate objective of ISEGs economic analyses is to
provide information to decisionmakers. Therefore, all aspects
of the analysis should be presented in such a manner that the
decisionmaker can completely understand the results of the analysis
and the methods by which the results were derived. Analytical
results should be able to withstand close scrutiny by the decisionmakers
themselves and by external parties. Making the analytical results
clear helps achieve that goal. On June 1, 1998, President Clinton
issued a memorandum directing Federal executive departments
and agencies to use plain language in the development
of all Federal rulemaking activities. While the directive applies
specifically to the publication of FR notices (see Chapter
3), the spirit of the memorandum is to encourage clear, understandable
writing wherever possible.
- Identify Data Sources and Assumptions: The EA/EIA
document should always include a description of all data sources
and references used in the analysis, subject to confidentiality
constraints imposed by CBI agreements. This information should
be presented in such a way that decisionmakers and other analysts
can obtain these data and other source materials with relative
ease. In addition, the analyst should clearly identify all relevant
assumptions made in the course of the analysis.
- Describe the Modeling: For those not trained
in economics and other quantitative disciplines, econometric
and other economic models can often appear unapproachable. Therefore,
it is important that the analyst describe the modeling techniques
used in the analysis in clear and understandable terms. The
level of detail of such a discussion will depend on the technical
expertise of the audience. Analytical detail may be appropriate
for an audience of economists but not for the wide range of
disciplines typically represented in the workgroup or senior
management. The discussion needs to be targeted to the audience.
Providing the basic reasoning underlying the modeling framework
not only bolsters the credibility of the results among noneconomists,
but it also ensures that decision makers correctly interpret
the results of the model.
- Characterize Uncertainty: As discussed at length
in Section 7 of this manual, all EAs are subject to uncertainty.
Uncertainty may arise from data, models, or general lack of
information. When summarizing results for decisionmakers, the
analyst should identify uncertainties that could alter a decision
and explain the implications of plausible alternative assumptions.
- Make Alternatives Comparable: Because several
regulatory alternatives are often evaluated at different stages
in the regulatory process, the process of narrowing the field
to a preferred alternative requires some form of direct comparison.
The ideal way to communicate the full range of consequences
of a regulatory option is to convert all of these consequences
to a single metric. From the perspective of welfare economics,
the most sensible metric is monetary, because it provides a
platform for comparing the benefits of the action with its costs
(i.e., the benefits that society foregoes to obtain the benefits
of the regulation) in a manner that is consistent with the tradeoffs
that members of society make all the timethe exchange
of dollars for goods and services.
- Clearly Identify Nonmonetized and Nonquantified Effects:
Often some of the consequences of a regulatory action
are not able to be quantified or monetized. To the extent that
the regulatory action being analyzed is expected to result in
such costs or benefits, monetized benefits and costs may misrepresent
the full range of impacts of the regulation. Therefore, it is
important to clearly present the full range of benefits and
costs that cannot be quantified or monetized. One way to present
these is through a table structured similarly to Table 9-1.
Potential health effects of the regulatory action are identified,
a subset of those identified are shown to be quantified in the
analysis, and a subset of those quantified are shown to be monetized.
This presentation provides some sense of the extent and nature
of the omissions from the monetary estimate of benefits. This
may become critical information if the monetized costs exceed
the monetized benefits, in which case the decisionmakers must
evaluate whether the implicit value of the nonmonetized benefits
is high enough to favor regulatory action.