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

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

Working Papers from Princeton University. Economics Department.

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: U.S.; Northern America; Budget; Tax; Unemployment; Unemployment Insurance (search for similar items in EconPapers)
JEL-codes: C14 C20 C31 H2 H23 J64 J65 J68 (search for similar items in EconPapers)
Date: 2019-12
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http://www.princeton.edu/~davidlee/wp/w25574.pdf

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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