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Who is a Passive Saver Under Opt-In and Auto-Enrollment?

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

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.

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  • Gopi Shah Goda & Matthew R. Levy & Colleen Flaherty Manchester & Aaron Sojourner & Joshua Tasoff, 2019. "Who is a Passive Saver Under Opt-In and Auto-Enrollment?," NBER Working Papers 26078, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26078
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    Cited by:

    1. Mitchell, Olivia S. & Utkus, Stephen P., 2022. "Target-date funds and portfolio choice in 401(k) plans," Journal of Pension Economics and Finance, Cambridge University Press, vol. 21(4), pages 519-536, October.
    2. Lucas Goodman & Anita Mukherjee & Shanthi Ramnath, 2022. "Set it and Forget it? Financing Retirement in an Age of Defaults," Working Paper Series WP 2022-50, Federal Reserve Bank of Chicago.
    3. Marta Cota, 2023. "Extrapolative Income Expectations and Retirement Savings," CERGE-EI Working Papers wp751, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    4. John Beshears & Ruofei Guo & David Laibson & Brigitte C. Madrian & James J. Choi, 2023. "Automatic Enrollment with a 12% Default Contribution Rate," NBER Working Papers 31601, National Bureau of Economic Research, Inc.
    5. Derby, Elena & Mackie, Kathleen & Mortenson, Jacob, 2023. "Worker and spousal responses to automatic enrollment," Journal of Public Economics, Elsevier, vol. 223(C).
    6. Susanna Levantesi & Giulia Zacchia, 2021. "Machine Learning and Financial Literacy: An Exploration of Factors Influencing Financial Knowledge in Italy," JRFM, MDPI, vol. 14(3), pages 1-21, March.
    7. David Blanchett & Michael S. Finke & Jonathan Reuter, 2020. "Portfolio Delegation and 401(k) Plan Participant Responses to COVID-19," NBER Working Papers 27438, National Bureau of Economic Research, Inc.
    8. Keane, Claire & O'Malley, Seamus & Tuda, Dora, 2021. "The Distributional Impact of Pension Auto-enrolment," Papers WP707, Economic and Social Research Institute (ESRI).
    9. Bonsang, Eric & Costa-Font, Joan, 2020. "Behavioral regularities in old age planning," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 297-300.
    10. Goodman, Lucas & Mukherjee, Anita & Ramnath, Shanthi, 2023. "Set it and forget it? Financing retirement in an age of defaults," Journal of Financial Economics, Elsevier, vol. 148(1), pages 47-68.
    11. Gallego-Losada, Rocío & Montero-Navarro, Antonio & Rodríguez-Sánchez, José-Luis & González-Torres, Thais, 2022. "Retirement planning and financial literacy, at the crossroads. A bibliometric analysis," Finance Research Letters, Elsevier, vol. 44(C).

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    JEL classification:

    • J32 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Nonwage Labor Costs and Benefits; Retirement Plans; Private Pensions

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