Conventionally, benefit-cost analysis focuses on economic efficiency, summing the values of a policy’s costs and benefits based on the preferences of those affected. There is widespread agreement that it should be supplemented with information on how the impacts are distributed across individuals with different characteristics, such as varying income levels, yet reviews of completed analyses suggest that such information is rarely provided. Decision-makers and other stakeholders typically want to know who is harmed, who is helped, and by how much. Responding to these questions requires first identifying the characteristics of individuals and impacts of most concern, which will vary depending on the policy and decision-making context. The next step involves examining how the benefits and costs are distributed across individuals grouped by the attribute(s) of concern, such as differing income levels or geographic locations. Assessing the distribution of costs is often more difficult than assessing the distribution of benefits such as reduced mortality or morbidity risks. If costs are borne directly by individuals and households, estimating this distribution may be relatively straightforward. If costs are instead borne initially by the government, industry, donors, or other organizations, assessing the effects on individuals involves additional steps. The analyst must estimate how these costs translate into changes in prices, wages, or returns to capital, then assess how these impacts are in turn distributed across members of different groups. The results should be reported in tables, charts, or graphics, and can be supplemented with standard inequality metrics, such as the Gini coefficient, concentration index, or Atkinson index. Benefit-cost analysis can also be conducted with distributional weights that reflect estimates of societal preferences, or social-welfare functions can be used to represent preferences for both the level and distribution of wellbeing