On Balance: Benefit-Cost Lessons Learned

An important lesson from the Trump days is how a robust cost-benefit analysis helps an agency both defend itself in court and guard against future rollbacks. But agencies must also ensure that they finalize any big policies in time to have the rules reviewed in court before the next transition, which means that there is no time to waste. These competing demands put huge pressures on agencies in a new administration. These lessons also highlight significant opportunities.


In a 2015 paper, Caroline Cecot and W. Kip Viscusi found that in almost 60 percent of cases, the court upheld the agency’s cost-benefit analysis in total—approving its scope, its assumptions, and its transparency. In about 40 percent of the cases, however, the court criticized at least some part of the cost-benefit analysis, sometimes aggressively, pointing out perceived flaws in the analysis. (Cecot & Viscusi 2015.) Cost-benefit analyses is not easy, and scholars have pointed out errors frequently made by agencies. (Nardinelli 2018.) Low-quality cost-benefit analysis could put the agency at risk of reversal. In fact, work by Christopher Carrigan, Jerry Ellig, and Zhoudan Xie (2020) found that low-quality analysis is associated with lower odds that a regulation will be upheld in court. 

The Trump era highlighted the importance of conducting high-quality cost-benefit analysis. Analyzing all Trump administration actions in court, Bethany Davis Noll (2021) found that the administration’s overall win rate over the four years was 23%, lower than the win rate of prior administrations. And many of the Trump administration’s losses had to do with rules that lacked a sound economic or scientific grounding. (Davis Noll 2021.) For example, in striking down the administration’s repeal of the Waste Prevention Rule, a rule designed to limit leaks at oil and gas facilities on federal land, the court explained that the agency had relied on a faulty model for estimating damages from greenhouse gas emissions, explaining that “[a]n agency simply cannot construct a model that confirms a preordained outcome while ignoring a model that reflects the best science available.” (California 2020.) A federal district court vacated an agency decision for relying on a model that did not account for higher sea levels (Center for Biological Diversity 2020), while the U.S. Court of Appeals for the Third Circuit faulted EPA for failing to “show its work” in the analysis supporting its action (Sierra Club 2020). Meanwhile, rules with a complete cost-benefit analysis fared better in court. For example, the U.S. Court of Appeals for the D.C. Circuit upheld a new firearms regulation, which was finalized after the 2017 mass shooting in Las Vega, Nevada, holding that the agency had adequately considered the cost incurred by owners of bump-stock devices. (Guedes 2020.) 

The Trump era also demonstrated that rules supported by a strong cost-benefit analysis are more difficult for later administrations to repeal or modify. Cecot (2019) predicted that, despite the Trump administration’s interest in rolling back recent rules, it was going to be difficult to repeal the Obama-era rules with a strong cost-benefit analysis (meaning analysis that was largely complete and relied on the best available scientific evidence at the time). And that is how things played out. Take the rollback of the Clean Car Standards, for example. The Trump administration was not able to finalize that rollback until 2020, and the economic basis for the rollback was lacking, as evidenced by a cost-benefit analysis which showed the rollback would have net costs to society at a 3% discount rate. The converse has also been true. For example, despite the abysmal win rate in court, the Trump administration was able to keep the 2015 Effluents Limitations Guidelines and the 2017 Chemical Disaster Rule from going into effect with rollbacks. Though both those Obama-era rules promised significant benefits, the agency had not produced a strong cost-benefit analysis showing monetized net benefits, instead relying largely on unquantified or unmonetized benefits that the Trump administration was able to dismiss. 

In light of these lessons, the task for the Biden administration is monumental. As Bethany Davis Noll and Richard Revesz explained in their 2019 paper, Regulation in Transition, significant rollback risks hang over a new presidency. And to avoid those risks, a new administration must work quickly to finalize its policies within the first few years. (Davis Noll & Revesz 2019.) 

Economists have identified ways to improve agency cost-benefit analysis. (Ellig & Fike 2016.) And we have seen this work before. When EPA first set out to monetize the health and welfare benefits associated with reducing air pollutants, its task was not easy. But the analyses have improved over the years due to developments in underlying studies, and the agency now routinely monetizes a wide variety of costs and benefits, even those that were once thought unquantifiable. (Revesz 2014.) Recent work by David A. Keiser, Catherine L. Kling, and Joseph S. Shapiro (2019) is an example of research that can guide the efforts needed to fortify benefits analyses in other areas, such as the benefits from improving water quality, a context where many large categories of benefits remain unquantified and unmonetized. (See also Moore, et al. 2017) As these lessons from the Trump administration show, this work is more urgent than ever. And it presents a real opportunity to support durable and net beneficial regulatory policy.

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