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A Laboratory Study of Nudge with Retirement Savings

Author

Listed:
  • Dina Tasneem
  • Marine de Montaignac
  • Jim Engle-Warnick
  • Audrey Azerot

Abstract

We report results from an on-line economics experiment that examines the effect of nudging retirement savings decisions. In the experiments, participants make decisions in a finitely repeated retirement savings game, in which income during working years is uncertain, and retire-ment age is known. Participants, who are household financial decision-makers, are nudged with automatic savings in each period of the game. We find that that the nudge simply replaced nat-ural decision-making observed in the absence of a nudge in this experiment, even to the extent that it resulted in nearly identical inferred decision rules. This surprising result highlights the unpredictability of the effect of nudging human behavior.

Suggested Citation

  • Dina Tasneem & Marine de Montaignac & Jim Engle-Warnick & Audrey Azerot, 2018. "A Laboratory Study of Nudge with Retirement Savings," CIRANO Working Papers 2018s-23, CIRANO.
    • Handle: RePEc:cir:cirwor:2018s-23
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    File URL: https://cirano.qc.ca/files/publications/2018s-23.pdf
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    References listed on IDEAS

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    1. Banks, James & Blundell, Richard & Tanner, Sarah, 1998. "Is There a Retirement-Savings Puzzle?," American Economic Review, American Economic Association, vol. 88(4), pages 769-788, September.
    2. Malcolm Hamilton, 2015. "Do Canadians Save Too Little?," C.D. Howe Institute Commentary, C.D. Howe Institute, issue 428, June.
    3. Enrica Carbone, 2006. "Understanding intertemporal choices," Applied Economics, Taylor & Francis Journals, vol. 38(8), pages 889-898.
    4. YiLi Chien & Paul Morris, 2018. "Many Americans Still Lack Retirement Savings," The Regional Economist, Federal Reserve Bank of St. Louis, vol. 26(1).
    5. Emma Aguila & Orazio Attanasio & Costas Meghir, 2011. "Changes in Consumption at Retirement: Evidence from Panel Data," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 1094-1099, August.
    6. John Karl Scholz & Ananth Seshadri & Surachai Khitatrakun, 2006. "Are Americans Saving "Optimally" for Retirement?," Journal of Political Economy, University of Chicago Press, vol. 114(4), pages 607-643, August.
    7. Carbone, Enrica & Duffy, John, 2014. "Lifecycle consumption plans, social learning and external habits: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 106(C), pages 413-427.
    8. T. Ballinger & Eric Hudson & Leonie Karkoviata & Nathaniel Wilcox, 2011. "Saving behavior and cognitive abilities," Experimental Economics, Springer;Economic Science Association, vol. 14(3), pages 349-374, September.
    9. repec:bla:scandj:v:94:y:1992:i:2:p:253-73 is not listed on IDEAS
    10. Enrica Carbone & John D. Hey, 2004. "The effect of unemployment on consumption: an experimental analysis," Economic Journal, Royal Economic Society, vol. 114(497), pages 660-683, July.
    11. Dina Tasneem & Jim Engle-Warnick, 2018. "Decision Rules for Precautionary and Retirement Savings," CIRANO Working Papers 2018s-22, CIRANO.
    12. T. Parker Ballinger & Michael G. Palumbo & Nathaniel T. Wilcox, 2003. "Precautionary saving and social learning across generations: an experiment," Economic Journal, Royal Economic Society, vol. 113(490), pages 920-947, October.
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    Cited by:

    1. Duffy, John & Li, Yue, 2019. "Lifecycle consumption under different income profiles: Evidence and theory," Journal of Economic Dynamics and Control, Elsevier, vol. 104(C), pages 74-94.

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    More about this item

    Keywords

    Precautionary Savings; Retirement Savings; Life-cycle Models; Dynamic Optimization; Decision Heuristics; Nudge; Choice Architecture;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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