New methodologies for distributional weighting in benefit-cost analysis

• In Event: New Methodologies for Distributional Weighting and Program Evaluation

Abstract

Distributional weighting of monetized costs and benefits in benefit cost analysis, to account for diminishing marginal utility of income, has received considerable attention in the past several years, with the pace of scholarly work accelerating and the Biden administration adopting distributional weighting in its revisions to Circular A-4, the primary guidance document for regulatory analysis in federal regulatory agencies. In essence, distributional weighting is a method of adjusting for the fact that a dollar is worth more to a poor person than to a wealthy person, and that, as a result, the same increase or decrease in welfare is represented in benefit-cost analysis by a smaller number of dollars when it accrues to a poor person than to a wealthy person, essentially biasing benefit cost analysis against the poor as a measure of welfare. And, while there is little likelihood that distributional weighting will be applied in the federal government under the current administration, there is no reason to believe that interest in weighting will wane in the long-term across the field of benefit-cost analysis. There is a well-established methodology for computing weights and a steadily increasing consensus on the appropriate range of the key weighting parameter, the income elasticity of marginal utility of income. What has been lacking is clear-cut guidelines for implementing weighting in the real-word. A number of easily surmountable technicalities have been addressed in recent work, but the primary concern remains lack of data on the distribution of income within affected populations and the incidence of costs and benefits across the income distribution. I show that it is possible to compute weighted net benefit in a wide range of contexts, using microdata, under plausible assumptions about population characteristics and incidence of costs and benefits. I apply the methodology to a set of real-world regulations which allow me to address the challenges involved in distributional weighting of tax burden, cost of compliance to firms, mortality and morbidity benefits of economy wide safety regulations, and highly targeted labor-market interventions. I conclude that distributional weighting is feasible in a range of challenging real-world settings.

Author

• Daniel James Acland, University of California, Berkeley
  Presenting Author