Practical issues in conducting distributional weighting in benefit-cost analysis

Abstract

A commonly expressed concern about distributional weighting in benefit-cost analysis is that the informational burden is too high and the practical challenges insurmountable. In this paper, we address this concern by conducting distributional weighting on a number of real-world examples, covering a range of different types of policy impacts. We uncover and explore a number of methodological issues that arise in the process of distributional weighting and provide a simplified set of steps that we believe can be implemented by practitioners with a wide range of expertise. We conduct sensitivity analysis and Monte Carlo simulation to test the robustness of our estimates of weighted net benefits to the various assumptions we make, and find that, in general, distributional weighting is no more vulnerable to modeling assumptions and parameter selection than unweighted benefit-cost analysis itself. We conclude that the concern about the practicability of distributional weighting is, at least in a range of important cases, unfounded.