Sufficient Statistics for Welfare Analysis: A Bridge Between Structural and Reduced-Form Methods

The debate between "structural" and "reduced-form" approaches has generated substantial controversy in applied economics. This article reviews a recent literature in public economics that combines the advantages of reduced-form strategies -- transparent and credible identification -- with an important advantage of structural models -- the ability to make predictions about counterfactual outcomes and welfare. This recent work has developed formulas for the welfare consequences of various policies that are functions of high-level elasticities rather than deep primitives. These formulas provide theoretical guidance for the measurement of treatment effects using program evaluation methods. I present a general framework that shows how many policy questions can be answered by identifying a small set of sufficient statistics. I use this framework to synthesize the modern literature on taxation, social insurance, and behavioral welfare economics. Finally, I discuss topics in labor economics, industrial organization, and macroeconomics that can be tackled using the sufficient statistic approach.

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