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Titel
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
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Elektronische Ressource
Umfang1 Online-Ressource (53 Seiten) : Diagramme
SerieDiscussion paper ; no. 12154
URNurn:nbn:de:hbz:5:2-181626 
Zugriffsbeschränkung
 Das Dokument ist öffentlich zugänglich im Rahmen des deutschen Urheberrechts.
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Are sufficient statistics necessary? [0.88 mb]
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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.