Title

Are Sufficient Statistics Necessary? Nonparametric Measurement of Deadweight Loss from Unemployment Insurance

Upjohn Author ORCID Identifier

https://orcid.org/0000-0002-3372-7527

Publication Date

5-5-2021

Source

Journal of Labor Economics 39(52): S455-S506

Abstract

Central to the welfare analysis of income transfer programs is the deadweight loss associated with possible reforms. To aid analytical tractability, its measurement typically requires specifying a simplified model of behavior. We employ a complementary “decomposition” approach that compares the behavioral and mechanical components of a policy’s total impact on the government budget to study the deadweight loss of two unemployment insurance policies. Experimental and quasi-experimental estimates using state administrative data show that increasing the weekly benefit is more efficient (with a fiscal externality of 53 cents per dollar of mechanical transferred income) than reducing the program’s implicit earnings tax.

DOI

https://doi.org/10.1086/711594

Publisher

University of Chicago Press

Subject Areas

UNEMPLOYMENT, DISABILITY, and INCOME SUPPORT PROGRAMS; Unemployment insurance

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Citation

Lee, David S., Pauline Leung, Christopher J. O'Leary, Zhuan Pei, and Simon Quach. 2021. "Are Sufficient Statistics Necessary? Nonparametric Measurement of Deadweight Loss from Unemployment Insurance." Journal of Labor Economics 39(52): S455-S506. https://doi.org/https://doi.org/10.1086/711594