@TechReport{iza:izadps:dp12154,
            	author={Lee, David S. and Leung, Pauline and O'Leary, Christopher J. and Pei, Zhuan and Quach, Simon},
            	title={Are Sufficient Statistics Necessary? Nonparametric Measurement of Deadweight Loss from Unemployment Insurance},
            	year={2019},
	month={Feb},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={12154},
            	url={https://www.iza.org/publications/dp12154},
	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.},
	keywords={regression kink design;unemployment insurance;partial unemployment insurance;optimal unemployment insurance;sufficient statistics;deadweight loss;decomposition;behavioral and mechanical effects;fiscal externality},
            }