At last week’s Annual Conference of the Society for Benefit-Cost Analysis, one topic was all the rage: distributional issues. Much of this discussion stems from the new Circular A-4 produced by the Office of Information and Regulatory Affairs, which emphasized the importance of distributional analysis in federal economic analyses.
At its heart, benefit-cost analysis is not a tool designed for distributional analysis. It is focused on economic efficiency, helping policymakers understand whether a policy change will “grow” the proverbial economic pie rather than just slicing it differently.
A rationale economic theoreticians often appeal to when justifying the use of benefit-cost analysis in policymaking is the Kaldor-Hicks efficiency criterion. This “test” says that if a policy changes the amount of resources in the economy such that the “winners” can compensate the “losers,” that it is economically efficient.
Kaldor-Hicks has come under fire over the years. One of the criticisms lobbed at Kaldor-Hicks at the conference is that losers of a policy are rarely compensated through the policy change itself, so the test seems more like a theoretical exercise than a practical concern.
I personally have never understood the appeal of Kaldor-Hicks. I understand the intuitive appeal of a policy that makes everyone off without making anyone worse off, but I also think benefit-cost analysis offers an even more intuitive insight. It simply tells us how much aggregate welfare is increased by a policy.
Okay, “aggregate welfare” is kind of a vague term. What I really mean by this is the total amount and value of stuff that people want in the economy. And by “stuff,” we’re talking broadly: not only goods, but also services, free time, and environmental goods in the economy.
This is utilitarianism in practice, but utilitarianism as defined by how people value things in the economy. When we utilize the value of a statistical life in an analysis, we’re using estimates of how much people themselves are willing to reduce risk of death as a way to measure the intervention against taking away their own resources to finance these small reductions in risk of death.
Now what does this all mean for distributional analysis, or our analysis of who gets what after a policy change? Benefit-cost analysis isn’t at its heart designed to answer this question, but it is a valuable tool for doing that. Knowing how a policy will impact the economy then lets an economist understand who will be impacted by a policy.
In light of this discussion, here are a couple tips for incorporating distributional analysis into benefit-cost analysis.
Show distributional impacts of policies
Wherever possible, show who will be impacted by a policy. Sure, a tax that takes money from workers in order to pay administrators will be a transfer, but showing who will be impacted is valuable for a policymaker, especially someone interested in the impact on workers.
Apply distributional weights as sensitivity analysis
Distributional weights, or multiplying different people’s impacts by a certain amount, have been controversial in benefit-cost analysis. The reason for this is that they seem to apply a judgment by the analyst to the inherent “value” of impact to different groups of people. Analyst after analyst at the conference, though, put forth the same point: assuming multipliers of one to every group is making a judgment in itself. By applying distributional weights as a type of sensitivity analysis, like through use of break-even analysis, it can help a policymaker understand how much a policy impacts one group compared to another.
Allow distributional analysis to stand on its own
Don’t think that benefit-cost analysis can fully incorporate distributional analysis. At its heart, benefit-cost analysis is designed to measure aggregate welfare. Thinking equity analysis can be “smuggled” into benefit-cost analysis is covering up the insights of equity analysis. Presenting the insights of efficiency and equity analysis separately allows the policymaker to weigh the two concerns against one another and make the decision herself.
Distributional analysis is still a frontier of benefit-cost analysis. That doesn’t mean we can’t apply it and use it today. Good policy analysis requires good equity analysis, and distributional analysis drawn from insights in benefit-cost analysis is a powerful tool for helping policymakers understand the impact of public policy.