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

Raj Chetty

Annual Review of Economics · 2009 · 654 citations · 88 references

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TL;DR

The debate between structural and reduced‑form approaches has generated substantial controversy in applied economics. The article reviews recent public‑economics literature that blends reduced‑form identification with structural counterfactual predictions, and proposes a general framework for answering policy questions using a small set of sufficient statistics. The framework derives welfare formulas from reduced‑form elasticities and shows how program‑evaluation methods can estimate the necessary sufficient statistics. Using this framework, the author synthesizes contemporary work on taxation, social insurance, and behavioral welfare economics and identifies macroeconomic, labor, development, and industrial‑organization problems amenable to the sufficient‑statistic approach.

Abstract

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 literature has developed formulas for the welfare consequences of various policies that are functions of reduced-form elasticities rather than structural primitives. I present a general framework that shows how many policy questions can be answered by estimating a small set of sufficient statistics using program-evaluation methods. I use this framework to synthesize the modern literature on taxation, social insurance, and behavioral welfare economics. Finally, I discuss problems in macroeconomics, labor, development, and industrial organization that could be tackled using the sufficient statistic approach.

References

88