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

A-Tier
Journal: Journal of Labor Economics
Year: 2021
Volume: 39
Issue: S2
Pages: S455 - S506

Authors (5)

David S. Lee (not in RePEc) Pauline Leung (not in RePEc) Christopher J. O’Leary (not in RePEc) Zhuan Pei (Cornell University) Simon Quach (University of Southern Califor...)

Score contribution per author:

0.804 = (α=2.01 / 5 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:ucp:jlabec:doi:10.1086/711594
Journal Field
Labor
Author Count
5
Added to Database
2026-01-26