Weighing Regulatory Tradeoffs – And Keeping Thumbs Off The Scales!

For more than 40 years, presidential executive orders have directed agencies to analyze regulatory impacts before they issue new rules. The Biden administration recently released draft revisions to Office of Management and Budget (OMB) Circular A-4, which has governed that analysis since 2003. As it did in the Bush administration, OMB under Biden has sought public comment on the revisions, and convened an expert panel for peer review. In comments I filed with OMB, I noted that the proposed revisions contain some worthwhile updates, but I raised concerns that some of the guidance deviates from the best available current economic science. This is the first in a series of posts exploring those concerns. It focuses on understanding the distributional effects of regulation.

Regulatory impact analysis is grounded in widely accepted principles.

Both the requirement to conduct regulatory analysis and the tools for doing so have remained remarkably stable across different administrations. The 2003 Circular A-4 built on the Clinton administration’s “Economic Analysis of Federal Regulations Under Executive Order 12866,” which in turn had its basis in the “Regulatory Impact Analysis Guidance” published in the George H. W. Bush and Reagan administrations. These guidelines all direct agencies to 1) clearly articulate a compelling public problem, 2) identify alternative approaches to address the identified problem, and 3) conduct benefit-cost analysis to examine the social impacts of the different alternatives.

The practice of regulatory impact analysis has proven durable across different presidential administrations because it is grounded in objective principles, and provides nonpartisan information to policymakers on an important policy consideration: the efficiency of different approaches to achieving policy goals. Regulatory impact analysis informs policy decisions; it does not determine them. While policymakers obviously consider various factors—including legal constraints, political viability, distributional impacts, practicality, etc.—regulatory impact analysis offers them an economic lens to understand the welfare differences among alternative policies.

On whom do the benefits and costs fall?

This distinguished and bipartisan history doesn’t mean there’s no room for improving how regulatory impact analysis is conducted. In fact, an important weakness of the existing Circular A-4 is its lack of guidance on how to conduct distributional analysis to understand who is expected to receive regulatory benefits and who is likely to bear the costs. Benefit-cost analysis focuses on maximizing net benefits to society on the premise that winners could compensate losers, on the assumption that “if [such analysis] is consistently used to select policies offering the largest net benefits and there are no consistent losers, then it is likely that overall everyone will actually be made better off” (Weimer 2018, 2384).

Nevertheless, policy makers are reasonably interested in understanding not only how a regulation would affect the size of the pie (how efficient it is), but how the slices are allocated (distributional impacts). The Draft Circular expands upon the 2-paragraph discussion of distributional analysis in the 2003 Circular, and much of the proposed guidance is sound. Disaggregating regulatory effects on lower-income households may be particularly important for regulations that increase the costs of basic goods and services, like food or energy, or that disproportionately affect disadvantaged segments of society.

Agencies should not bury important judgments in quantitative estimates.

The Draft goes awry, however, in suggesting that agencies could apply a weighting scheme to adjust estimates of benefits and costs to account for the income groups to which they accrue. For example, on the intuition that money is more valuable to those who have less of it, the proposed weighting method would count $100 in regulatory benefits as $696 for a household earning one-quarter of the median income. It would count that same $100 in benefits as only $38 for a household earning twice the median income. There is no scientific basis for making such interpersonal comparisons of value, even when the goal is simply to understand the social welfare improvement of forcing high-income households to directly transfer money to low-income households. But the approach is even more suspect here because it assumes that a low-income household would value the regulatory benefit (such as an incremental improvement in air quality) more than a wealthy household would. That seems unrealistic when a struggling family would consider many regulatory goods to be luxuries and would almost certainly prefer dollars to put food on the table, buy their children new shoes, or otherwise improve their current conditions and future prospects.

My comment supports guidance for providing more transparent information on distributional effects but recommends that OMB draw the line at embedding value-laden weights in estimates of benefits and costs. (Other experts’ comments on the public record make similar points, including for example Beales 2023, Mannix 2023, Morgenstern et al. 2023, Stavins 2023, and Sullivan 2023, to highlight a few.) Policies aimed at allocating slices of the pie almost always make the pie smaller, and such decisions should not be obscured by scientific-sounding numbers that emerge from a black box. Instead, the revised Circular should direct agencies to present information on distributional impacts as transparently and clearly as the evidence allows and let policymakers to make normative policy decisions.

Future posts in this series will examine when regulation is necessary, how agencies should account for impacts that occur in different time periods and geographic areas, the extent to which analysis should reflect the fact that we humans don’t always behave the way simple textbook economic models assume, and important topics that are missing from the draft Circular.