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Predicting Retirement Savings Using Survey Measures of Exponential-Growth Bias and Present Bias

Author

Listed:
  • Shah Goda, Gopi

    () (Stanford University)

  • Levy, Matthew R.

    () (London School of Economics)

  • Flaherty Manchester, Colleen

    () (University of Minnesota)

  • Sojourner, Aaron

    () (University of Minnesota)

  • Tasoff, Joshua

    () (Claremont Graduate University)

Abstract

In a nationally-representative sample, we predict retirement savings using survey-based elicitations of exponential-growth bias (EGB) and present bias (PB). We find that EGB, the tendency to neglect compounding, and PB, the tendency to value the present over the future, are highly significant and economically meaningful predictors of retirement savings. These relationships hold controlling for cognitive ability, financial literacy, and a rich set of demographic controls. We address measurement error as a potential confound and explore mechanisms through which these biases may operate. Back of the envelope calculations suggest that eliminating EGB and PB would increase retirement savings by approximately 12 percent.

Suggested Citation

  • Shah Goda, Gopi & Levy, Matthew R. & Flaherty Manchester, Colleen & Sojourner, Aaron & Tasoff, Joshua, 2018. "Predicting Retirement Savings Using Survey Measures of Exponential-Growth Bias and Present Bias," IZA Discussion Papers 11762, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp11762
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    References listed on IDEAS

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    1. Gopi Shah Goda & Colleen Flaherty Manchester & Aaron Sojourner, 2012. "What Will My Account Really Be Worth? An Experiment on Exponential Growth Bias and Retirement Saving," NBER Working Papers 17927, National Bureau of Economic Research, Inc.
    2. David I. Laibson & Andrea Repetto & Jeremy Tobacman, 1998. "Self-Control and Saving for Retirement," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 29(1), pages 91-196.
    3. Goda, Gopi Shah & Manchester, Colleen Flaherty & Sojourner, Aaron J., 2014. "What will my account really be worth? Experimental evidence on how retirement income projections affect saving," Journal of Public Economics, Elsevier, vol. 119(C), pages 80-92.
    4. David Bradford & Charles Courtemanche & Garth Heutel & Patrick McAlvanah & Christopher Ruhm, 2017. "Time preferences and consumer behavior," Journal of Risk and Uncertainty, Springer, vol. 55(2), pages 119-145, December.
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    6. Lin Zhang, 2013. "Saving and retirement behavior under quasi-hyperbolic discounting," Journal of Economics, Springer, vol. 109(1), pages 57-71, May.
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    8. Hans-Martin Von Gaudecker, 2015. "How Does Household Portfolio Diversification Vary with Financial Literacy and Financial Advice?," Journal of Finance, American Finance Association, vol. 70(2), pages 489-507, April.
    9. Annamaria Lusardi & Pierre-Carl Michaud & Olivia S. Mitchell, 2017. "Optimal Financial Knowledge and Wealth Inequality," Journal of Political Economy, University of Chicago Press, vol. 125(2), pages 431-477.
    10. B. Douglas Bernheim & Jonathan Skinner & Steven Weinberg, 2001. "What Accounts for the Variation in Retirement Wealth among U.S. Households?," American Economic Review, American Economic Association, vol. 91(4), pages 832-857, September.
    11. John Ameriks & Joseph Briggs & Andrew Caplin & Matthew D. Shapiro & Christopher Tonetti, 2020. "Long-Term-Care Utility and Late-in-Life Saving," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2375-2451.
    12. Chuang, Yating & Schechter, Laura, 2015. "Stability of experimental and survey measures of risk, time, and social preferences: A review and some new results," Journal of Development Economics, Elsevier, vol. 117(C), pages 151-170.
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    Cited by:

    1. Goda, Gopi Shah & Levy, Matthew R. & Manchester, Colleen Flaherty & Sojourner, Aaron & Tasoff, Joshua, 2020. "Who is a passive saver under opt-in and auto-enrollment?," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 301-321.
    2. Victor Stango & Joanne Yoong & Jonathan Zinman, 2017. "Quicksand or Bedrock for Behavioral Economics? Assessing Foundational Empirical Questions," NBER Working Papers 23625, National Bureau of Economic Research, Inc.
    3. Colleen Flaherty Manchester, 2019. "Retirement plan type and worker mobility," IZA World of Labor, Institute of Labor Economics (IZA), pages 461-461, October.
    4. Matthew Olckers, 2020. "On Track for Retirement?," Papers 2005.01692, arXiv.org, revised Sep 2020.
    5. Banerjee, Ritwik & Bhattacharya, Joydeep & Majumdar, Priyama, 2020. "Exponential-Growth Prediction Bias and Compliance with Safety Measures in the Times of COVID-19," IZA Discussion Papers 13257, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    survey-based elicitations; financial literacy; present bias; quasi-hyperbolic discounting; exponential-growth bias; retirement savings; household finance;
    All these keywords.

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D15 - Microeconomics - - Household Behavior - - - Intertemporal Household Choice; Life Cycle Models and Saving

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