Welcome to the Data-Poor Real World: Incorporating Benefit–Cost Principles into Environmental Policymaking

DOIhttps://doi.org/10.1016/S0193-5895(07)23005-2
Date16 October 2007
Pages103-130
Published date16 October 2007
AuthorMark L. Plummer
WELCOME TO THE DATA-POOR
REAL WORLD: INCORPORATING
BENEFIT–COST PRINCIPLES
INTO ENVIRONMENTAL
POLICYMAKING
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Mark L. Plummer
ABSTRACT
Elegant multi-marketmodels and intricate discounting methods are difficult,
at times impossible to utilize in the real world because the necessary data
just are not available. While there is no perfect substitute for adequate data,
there are good ones that are capable of improving policy decisions. This
paper describes one such substitute by way of an example: the designation of
critical habitat under the Endangered Species Act for West Coast salmon
and steelhead. The example shows how a cost-effectiveness approach can
mitigate (to some extent) the effects of poor data on the monetary benefits
of regulatory actions.
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The views expressed in this paper are those of the author alone, and do not reflect the official
or other views of the National Marine Fisheries Service.
Research in Law and Economics, Volume 23, 103–130
Published by Elsevier Ltd.
ISSN: 0193-5895/doi:10.1016/S0193-5895(07)23005-2
103
Benefit–cost analysis is as natural to economists as breathing – which
sometimes gets us into trouble when we try to ply our trade in a data
vacuum. Elegant multi-market models and intricate discounting methods
are difficult, at times impossible to utilize in the real world because the
necessary data just are not available. When this is the case, the benefit–cost
analyst often proceeds by quantifying as many of the benefits and costs as
feasible; describing the non-monetized or even non-quantified impacts as
precisely as possible; and then using ‘‘professional judgment’’ to compare
the total benefits and costs.
1
As the non-monetized and non-quantified impacts grow in proportion to
the overall impacts, however, the analysis eventually reaches a point where
the substitution of ‘‘professional judgment’’ for data becomes untenable, or
at least awkward. Benefit–cost analysis becomes less useful as a means for
assisting decision makers in choosing the ‘‘best’’ among a set of alternatives.
At times, it can even become a game of divining benefits to be large enough
to justify the chosen alternative. In the data-poor real world, then, the
practice of benefit–cost often becomes a process of substituting analysts for
analysis.
While there is no perfect substitute for adequate data, there are good ones
other than professional judgment, or at least ones that are capable of
making policy decisions better if short of best. In this paper, I describe one
such substitute by way of an example. The substitute is what one might
call a hybrid of benefit–cost analysis and cost-effectiveness analysis, with a
dash of a hypothetical valuation experiment thrown in for good measure.
The example is drawn from my experience as an economist with the
National Marine Fisheries Service (NMFS), conducting economic analyses
of regulatory actions taken under, of all statutes, the Endangered Species
Act. While I make no claims that the framework outlined below is the
solution to all the benefit–cost analyst’s data problems (it certainly is not),
I offer it as an example of how benefit–cost analysis in the real world strives
to make lemonade out of the data lemons with which we are often forced
to work.
The paper begins with a brief overview of the Endangered Species Act
(ESA) and the opportunities (or lack thereof) for conducting economic
analyses of regulatory actions taken under that law. I then focus on a part of
the ESA – critical habitat designation – that in fact allows such analyses.The
final section covers in detail an example drawn from the ESA of how data
problems can be overcome (to some extent) to improve (but not optimize)
regulatory action.
MARK L. PLUMMER104

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