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

David S. Lee (), Pauline Leung (), Christopher O'Leary, Zhuan Pei and Simon Quach ()
Additional contact information
David S. Lee: Princeton University
Pauline Leung: Cornell University

No 12154, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: regression kink design; unemployment insurance; partial unemployment insurance; optimal unemployment insurance; sufficient statistics; deadweight loss; decomposition; behavioral and mechanical effects; fiscal externality (search for similar items in EconPapers)
JEL-codes: C14 C20 C31 H2 H23 J64 J65 J68 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2019-02
New Economics Papers: this item is included in nep-ias, nep-lab and nep-pbe
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Published - published in: Journal of Labor Economics, 2021, 39 (S2), S455-S506.

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Related works:
Journal Article: Are Sufficient Statistics Necessary? Nonparametric Measurement of Deadweight Loss from Unemployment Insurance (2021) Downloads
Working Paper: Are Sufficient Statistics Necessary? Nonparametric Measurement of Deadweight Loss from Unemployment Insurance (2019) Downloads
Working Paper: Are Sufficient Statistics Necessary? Nonparametric Measurement of Deadweight Loss from Unemployment Insurance (2019) Downloads
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