One More Thought on the Entergy Case and Cost-Benefit

Thomas McGarity

April 3, 2009

On Wednesday, April 1, the Supreme Court issued its ruling in Entergy vs. EPA, holding that it was permissible for EPA to use cost-benefit analysis as its method of regulatory analysis in devising a regulation on power plant water intake structures.  Member Scholar Amy Sinden blogged on the decision that day, here.  Member Scholar Thomas McGarity adds a thought:

One of the most significant problems with cost-benefit analysis is its tendency to "dwarf soft variables." These "soft variables" are things that have value to all of us but are not typically traded in markets and are therefore difficult to quantify in any rigorous way. A good example of a soft variable is the value of the aquatic organisms that are not directly consumed by humans but will, along with those that are consumed by humans, be destroyed under the technology that EPA approved under the cost-benefit test that it employed.

Professor Sinden makes it clear that EPA did not have a clue as to the value of the environmental benefit of preserving these organisms. In fact, its cost-benefit analysis simply "punted" and concluded that they were of "indeterminate value." Does that mean that they should be ignored? Of course not.

Justice Scalia's opinion contains a classic example of the tendency of decisionmakers to dwarf soft variables. In describing EPA's cost-benefit analysis, he refers to "the relatively meager financial benefits of" the regulations, which included "reduced impingement and entrainment of 1.4 billion aquatic organisms with annualized use-benefits of $83 million and non-use benefits of indeterminate value" as compared to "annual costs of $389 million."

How can Justice Scalia conclude that the benefits are "relatively meager" when a large aspect of the benefit calculus was "indeterminate"? Only by essentially ignoring those "non-use" benefits. The implicit message here is that if you can't put a number on it, it doesn't count.

That's what cost-benefit skeptics mean when we worry about "dwarfing soft variables."

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