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

72 Pages Posted: 26 Feb 2019 Last revised: 19 May 2021

See all articles by David Lee

David Lee

Princeton University

Pauline Leung

Princeton University

Christopher J. O'Leary

W.E. Upjohn Institute for Employment Research

Zhuan Pei

W.E. Upjohn Institute for Employment Research

Simon Quach

Princeton University

Multiple version iconThere are 2 versions of this paper

Date Written: February 2019

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.

Suggested Citation

Lee, David and Leung, Pauline and O'Leary, Christopher J. and Pei, Zhuan and Quach, Simon, Are Sufficient Statistics Necessary? Nonparametric Measurement of Deadweight Loss from Unemployment Insurance (February 2019). NBER Working Paper No. w25574, Available at SSRN: https://ssrn.com/abstract=3341134

David Lee (Contact Author)

Princeton University ( email )

22 Chambers Street
Princeton, NJ 08544-0708
United States

Pauline Leung

Princeton University ( email )

Christopher J. O'Leary

W.E. Upjohn Institute for Employment Research ( email )

300 South Westnedge Avenue
Kalamazoo, MI 49007-4686
United States
269-343-5541 (Phone)
269-343-3308 (Fax)

Zhuan Pei

W.E. Upjohn Institute for Employment Research ( email )

300 South Westnedge Avenue
Kalamazoo, MI 49007-4686
United States

Simon Quach

Princeton University

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