Who is a passive saver under opt-in and auto-enrollment?

Gopi Shah Goda, Matthew R. Levy, Colleen Flaherty Manchester, Aaron Sojourner, Joshua Tasoff

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Defaults have been shown to have a powerful effect on retirement saving behavior yet there is limited research on who is most affected by defaults and whether this varies based on features of the choice environment. Using administrative data on employer-sponsored retirement accounts linked to survey data, we estimate the relationship between retirement saving choices and individual characteristics – long-term discounting, present bias, financial literacy, and exponential-growth bias – under two distinct choice environments: an opt-in regime and an auto-enrollment regime. Consistent with our conceptual model, we find that the determinants of following the default and contribution behavior are regime-specific. Under the opt-in regime, financial literacy plays an important role in predicting total contributions, active saving choices, and maxing out contributions in the tax-preferred account. In contrast, under the auto-enrollment regime, present bias is the most significant behavioral predictor of contribution behavior. A causal interpretation of the estimates suggests that auto-enrollment increases saving primarily among those with low financial literacy.

Original languageEnglish (US)
Pages (from-to)301-321
Number of pages21
JournalJournal of Economic Behavior and Organization
Volume173
DOIs
StatePublished - May 2020

Bibliographical note

Funding Information:
This research was supported by the U.S. Social Security Administration through grant #RRC08098400-09 to the National Bureau of Economic Research (NBER) as part of the SSA Retirement Research Consortium, by the TIAA Institute through grant #124652, and the Laura and John Arnold Foundation through a grant to the Stanford Institute for Economic Policy Research (SIEPR). The findings and conclusions expressed are solely those of the authors and do not represent the views of SSA, any agency of the Federal Government, the NBER, TIAA/TIAA Institute, the Laura and John Arnold Foundation, SIEPR or any other institution with which the authors are affiliated. We are grateful to Paula Gablenz and Konhee Chang for exceptional research assistance. ?2017 Goda, Levy, Manchester, Sojourner and Tasoff. All rights reserved.

Publisher Copyright:
© 2019

Keywords

  • Choice architecture
  • Defaults
  • Exponential-growth bias
  • Financial literacy
  • Household finance
  • Present bias
  • Procrastination
  • Retirement saving decisions

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