Title

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

Author(s)

David S. Lee David Lee (Princeton University)

Pauline Leung Pauline Leung (Cornell University)

Christopher J. O'Leary Christopher O'Leary (W.E. Upjohn Institute for Employment Research)

Zhuan Pei Zhuan Pei (Cornell University)

Simon Quach Simon Quach (Princeton University)

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.

Creation Date

2019-12

Section URL ID

Paper Number

2019-3

URL

http://www.princeton.edu/~davidlee/wp/w25574.pdf

File Function

Jel

C14, C20, C31, H2, H23, J64, J65, J68

Keyword(s)

U.S., Northern America, Budget, Tax, Unemployment, Unemployment Insurance

Suppress

false

Series

13