Are sufficient statistics necessary? : nonparametric measurement of deadweight loss from unemployment insurance / David S. Lee (Princeton University and NBER), Pauline Leung (Cornell University), Christopher J. O'Leary (W.E. Upjohn Institute), Zhuan Pei (Cornell University and IZA), Simon Quach (Princeton University) ; IZA Institute of Labor Economics
VerfasserLee, David S. ; Sebald, Alexander ; O'Leary, Christopher J. ; Pei, Zhuan ; Quach, Simon
KörperschaftForschungsinstitut zur Zukunft der Arbeit
ErschienenBonn, Germany : IZA Institute of Labor Economics, February 2019
Elektronische Ressource
Umfang1 Online-Ressource (53 Seiten) : Diagramme
SerieDiscussion paper ; no. 12154
 Das Dokument ist öffentlich zugänglich im Rahmen des deutschen Urheberrechts.
Are sufficient statistics necessary? [0.88 mb]
Verfügbarkeit In meiner Bibliothek
Zusammenfassung (Englisch)

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 policys 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.