Wharton Pension Research Council Working Papers
 

Author(s)

Mingli Zhong

Document Type

Working Paper

Date of this Version

4-16-2021

Abstract

I theoretically analyze and empirically identify the optimal default savings rate in automatic enrollment retirement saving plans. I derive a formula for the optimal default as a function of sufficient statistics that can be empirically identified. I estimate individual adherence to the default using exogenous increases in the default rate of OregonSaves, the first state-sponsored auto-enrollment plan in the U.S. I also use survey data to infer the degree of undersaving if workers actively switch to a non-default rate. Combining estimates from administrative and survey data with the optimal default formula, I find the optimal default is 7% of income.

Keywords

state-sponsored retirement plans, automatic enrollment, optimal default saving rate, undersaving, inattention

JEL Code

D14, D60, D91, G51, H00

Working Paper Number

WP2020-01

Disclosure

Mingli Zhong is a postdoctoral fellow at the National Bureau of Economic Research (NBER).

Copyright/Permission Statement

The opinions and conclusions expressed herein are solely those of the author and do not represent the opinions or policy of SSA, any agency of the Federal Government, the MRRC, OregonSaves, NBER, or any other institutions with which I am affiliated. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the contents of this report. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply endorsement, recommendation, or favoring by the United States Government or any agency thereof. ©2021 Zhong. All rights reserved.

Acknowledgements

I am extremely grateful to Hanming Fang, Ben Lockwood, and Olivia S. Mitchell for their guidance and support, and to Mike Abito, Douglas Bernheim, John Chalmers, Taha Choukhmane, Leora Friedberg, Bill Gale, Judd Kessler, Ilyana Kuziemko, Corinne Low, Sendhil Mullainathan, James Poterba, Alex Rees-Jones, Jonathan Reuter, Katja Seim, Lin Shen, Jonathan Skinner, and Juuso Toikka for comments and discussions. I also thank participants at the 21st Annual SSA Research Consortium Meeting, 2020 Association for Public Policy Analysis Management (APPAM) Fall Research Conference, 2020 National Tax Association’s Annual Conference on Taxation, 2021 NBER aging program meeting, 2020 World Risk and Insurance Congress, seminars at the Federal Reserve Bank of Dallas, the Federal Reserve Bank of Philadelphia, RAND, Urban Institute, Vanguard, and Wharton for helpful comments. I thank many individuals from the OregonSaves program for numerous discussions and insights into the OS program, and Yong Yu as well as Wenliang Hou for excellent research assistance. This research was supported by a grant from the US Social Security Administration (SSA) to the Michigan Retirement Research Center (MRRC) as part of the Retirement Research Consortium (RRC). The research reported herein was also performed pursuant to grant RDR18000003 from the US Social Security Administration (SSA) funded as part of the Retirement and Disability Research Consortium. Support was also provided by the AARP; the Pew Foundation; Wharton’s Pension Research Council/Boettner Center; and the Quartet program at the University of Pennsylvania.

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Date Posted: 08 January 2020