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

David S. Lee, Pauline Leung, Christopher J. O’Leary, Zhuan Pei and Simon Quach
Authors registered in the RePEc Author Service: Christopher J. O'Leary

Journal of Labor Economics, 2021, vol. 39, issue S2, 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.

Date: 2021
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Citations: View citations in EconPapers (16)

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