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

No 25574, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C14 C20 C31 H2 H23 J64 J65 J68 (search for similar items in EconPapers)
Date: 2019-02
New Economics Papers: this item is included in nep-ias
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Published as David S. Lee & Pauline Leung & Christopher J. O’Leary & Zhuan Pei & Simon Quach, 2021. "Are Sufficient Statistics Necessary? Nonparametric Measurement of Deadweight Loss from Unemployment Insurance," Journal of Labor Economics, vol 39(S2), pages S455-S506.

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