The Problem of Unquantified Benefits

Abstract

Many of the objections raised against the use of cost-benefit analysis (CBA) to evaluate government regulation, especially in the environmental context, center around the difficulties involved in quantifying and monetizing regulatory benefits. These difficulties implicate deep theoretical issues that have spawned a massive literature spanning many decades. But the difficulties posed by quantification also raise a straightforward empirical question that has been largely ignored: how often and to what extent does the problem of unquantified benefits actually arise in the practice of CBA, and how often is it attributable to the more prosaic problem of inadequate data? This Article presents methods and results of an empirical study aimed at this question. The study examined forty-five CBA’s prepared by the United States Environmental Protection Agency (EPA) in connection with major final rules issued between 2002 and 2015. In 80% of the CBAs analyzed, EPA excluded categories of benefits that the agency itself described as either actually or potentially “important,” “significant,” or “substantial” because they were unquantifiable due to data limitations. In order to understand the implications of these findings for the debate about CBA more generally, this Article lays out an analytic framework for understanding the role that quantification plays in CBA, detailing how significant unquantified benefits constrain the kind of CBA that can be performed, precluding more formal varieties. These results suggest that in developing environmental rules, agencies are rarely ever able to legitimately conduct formal CBA of the sort called for in the relevant executive orders and guidance documents, and that even the informal varieties of CBA they can conduct will produce only limited conclusions at best. This suggests that the connection between CBA and its normative foundations in efficiency or welfare is even more tenuous than most of its defenders have assumed, and bolsters the case for alternative tools, like feasibility and health-based criteria, that set standards based on the information we have rather than the information we wish we had.