In 2023, the U.S. Office of Management and Budget (OMB) issued guidance documents that specified new procedures for assessing prospective government regulations (Circular A-4) and economic policies more generally (Circular A-94). These revisions to long-standing guidance were not minor updates but shifted policy analyses from an efficiency-oriented perspective to a redistributive approach. OMB broadened the guidelines for reporting distributional consequences of policies and also specified how policy impacts on different income groups should be weighted. The weights assume that the social welfare function is governed by the sum of identical individual utility functions, each of which exhibits a substantial rate of diminishing marginal utility of income. The resulting weights provide a premium for households below the median-income level and a considerable penalty for those at higher-income levels. Application of the weights to property losses creates potentially substantial inefficiencies. If based on current empirical evidence on the income elasticity of the value of a statistical life rather than assuming that there is a complete offset of the weights, application of the weights to mortality risk valuation would generate inequities in protection.

Estimates of the elasticity of the marginal utility of income are necessary for determining distributional weights to correct for diminishing marginal utility of income, which is particularly important in light of increasing concern about accounting for distributional impacts in regulatory review. The elasticity is also necessary for computing the social discount rate using the Ramsey formula. Despite many attempts to estimate the elasticity of the marginal utility of income, considerable uncertainty exists about the magnitude of this key parameter. In this paper, we use meta-analysis of estimates of the elasticity from the US and UK to shed light on the appropriate elasticity values to use for both distributional weighting and discounting. Relying on our findings, we tentatively conclude that it is reasonable to base the social discount rate and distributional weights on an elasticity of 1.6, with lower- and upper-bound sensitivity testing at 1.2 and 2.0. This estimate results in distributional weights which appear plausible, and which we believe can contribute to a consensus on how to conduct distributional weighting. Moreover, the resulting social discount rate is within the range typically recommended when the Ramsey formula is used.

Benefit–cost analysis (BCA) is often viewed as measuring the efficiency of a policy independent of the distribution of its consequences. The role of distributional effects on policy choice is disputed; either: (a) the policy that maximizes net benefits should be selected and distributional concerns should be addressed through other measures, such as tax and transfer programs or (b) BCA should be supplemented with distributional analysis and decision-makers should weigh efficiency and distribution in policy choice. The separation of efficiency and distribution is misleading. The measure of efficiency depends on the numéraire chosen for the analysis, whether monetary values or some other good (unless individuals have the same rates of substitution between them). The choice of numéraire is not neutral; it can affect the ranking of policies by calculated net benefits. Alternative evaluation methods, such as BCA using a different numéraire, weighted BCA, or a social welfare function (SWF), may better integrate concerns about distribution and efficiency. The most appropriate numéraire, distributional weights, or SWFs cannot be measured or statistically estimated; it is a normative choice.

Some issues in the application of benefit–cost analysis (BCA) remain contentious. Although a strong conceptual case can be made for taking account of the marginal excess tax burden (METB) in conducting BCAs, it is usually excluded. Although a strong conceptual case can be made that BCA should not include distributional values, some analysts continue to advocate doing so. We discuss the cases for inclusion of the METB and the exclusion of distributional weights from what we refer to as “core” BCA, which we argue should be preserved as a protocol for assessing allocative efficiency. These issues are topical because a recent article in this journal recommends ignoring the METB on the grounds that desirable distributional effects offset its cost. We challenge the logic of this article and explain why it may encourage inefficient policies.

We study climate change policies using the novel pattern scaling approach of regional transient climate response in order to develop a regional economy–climate model under conditions of deep uncertainty. We associate welfare weights with regions and analyze cooperative outcomes derived by the social planner's solution at the regional scale. Recent literature indicates that damages are larger in low latitude (warmer) areas and are projected to become relatively even larger in low latitude areas than at temperate latitudes. Under deep uncertainty, robust control policies are more conservative regarding emissions and, when regional distributional weights are introduced, carbon taxes are lower in the relatively poorer region. Mild concerns for robustness are welfare improving for the poor region, while strong concerns have welfare cost for all regions. We show that increasing regional temperatures will increase resources devoted to learning, in order to reduce deep uncertainty.

Following the prototype of Mexico’s Progresa program, a number of countries in Latin America and the Caribbean have initiated conditional cash transfer (CCT) programs. More recently, countries in Sub-Saharan Africa (SSA) have followed suit. However, no comprehensive framework to carry out a cost-benefit analysis (CBA) exists. This paper presents such a CBA framework for CCTs which enables design features such as targeting and conditionality to be separately evaluated. The framework is applied to an evaluation of a CCT program for orphans and vulnerable children in Kenya. The role of conditionality in SSA and the need for distribution weights is discussed.