TaxProf Blog

Editor: Paul L. Caron, Dean
Pepperdine University School of Law

Monday, December 3, 2018

Raskolnikov Presents Distributional Ignorance At Georgetown

Raskolnikov (2018)Alex Raskolnikov (Columbia) presented Distributional Ignorance at Georgetown on Friday as part of its Law & Economics Workshop Series:

No modern government can ignore distribution with impunity, and the U.S. government is no exception. Yet in all likelihood, U.S. policymakers did ignore large and widespread distributional effects of their policies ranging from trade to immigration, competition, capital market regulation, environmental protection, and others. Although each of these policies affected many people, it just so happened that most of them likely disadvantaged a particular group of Americans consisting of low-skill, low-education, pre-retirement-age workers. These workers are the likely casualties of distributional ignorance. Not surprisingly, they are also some of the most ardent opponents of many U.S. policies adopted over the past several decades.

After reviewing emerging economics literature identifying significant unintended distributional effects of many U.S. government decisions, this paper explores theoretical and policy implications of distributional ignorance. On the theory side, recognizing distributional ignorance leads to several arguments in favor of considering distribution in the design of legal rules; offers new support for a more generous social safety net as a compensatory insurance against government-created risks of adverse economic outcomes, and heightens doubts about the foundational assumption of luck egalitarianism. On the policy side, distributional ignorance reinforces the need for ongoing policy responses to unintended distributional impacts of government policies. Just as Congress enacts budgets and economic regulations based on uncertain empirical foundations—even though its earlier enactments produced unintended results time and again—so should Congress adopt distributional adjustments properly calibrated to the magnitude and likelihood of unintended distributional impacts.

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