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Investigation of the Convex Time Budget Experiment by Parameter Recovery Simulation

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  • Keigo Inukai
  • Yuta Shimodaira
  • Kohei Shiozawa

Abstract

The convex time budget (CTB) method is a widely used experimental technique for eliciting an individual’s time preference in intertemporal choice problems. This paper investigates the accuracy of the estimation of the discount factor parameter and the present bias parameter in the quasi-hyperbolic discounting utility function for the CTB experiment. In this paper, we use a simulation technique called “parameter recovery.” We found that the precision of present bias parameter estimation is poor within the scope of previously reported parameter estimates, making it difficult to detect the effect of present bias. Our results recommend against using a combination of the CTB experimental task and the quasi-hyperbolic discounting utility model to explore the effect of present bias. This paper contributes to addressing the replicability issue in experimental economics and highlights the importance of auditing the accuracy of parameter estimates before conducting an experiment.

Suggested Citation

  • Keigo Inukai & Yuta Shimodaira & Kohei Shiozawa, 2022. "Investigation of the Convex Time Budget Experiment by Parameter Recovery Simulation," ISER Discussion Paper 1185r, Institute of Social and Economic Research, Osaka University, revised Mar 2023.
  • Handle: RePEc:dpr:wpaper:1185r
    as

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    References listed on IDEAS

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    1. Joshua Blumenstock & Michael Callen & Tarek Ghani, 2018. "Why Do Defaults Affect Behavior? Experimental Evidence from Afghanistan," American Economic Review, American Economic Association, vol. 108(10), pages 2868-2901, October.
    2. Charles A. Holt & Susan K. Laury, 2002. "Risk Aversion and Incentive Effects," American Economic Review, American Economic Association, vol. 92(5), pages 1644-1655, December.
    3. Stephen L. Cheung, 2015. "Risk Preferences Are Not Time Preferences: On the Elicitation of Time Preference under Conditions of Risk: Comment," American Economic Review, American Economic Association, vol. 105(7), pages 2242-2260, July.
    4. David Laibson, 1997. "Golden Eggs and Hyperbolic Discounting," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 443-478.
    5. Cheung, Stephen L. & Tymula, Agnieszka & Wang, Xueting, 2021. "Quasi-Hyperbolic Present Bias: A Meta-Analysis," IZA Discussion Papers 14625, Institute of Labor Economics (IZA).
    6. Leandro S. Carvalho & Stephan Meier & Stephanie W. Wang, 2016. "Poverty and Economic Decision-Making: Evidence from Changes in Financial Resources at Payday," American Economic Review, American Economic Association, vol. 106(2), pages 260-284, February.
    7. Bin Miao & Songfa Zhong, 2015. "Risk Preferences Are Not Time Preferences: Separating Risk and Time Preference: Comment," American Economic Review, American Economic Association, vol. 105(7), pages 2272-2286, July.
    8. Keigo Inukai & Yuta Shimodaira & Kohei Shiozawa, 2022. "Revisiting CES utility functions for distributional preferences: Do people face the equality–efficiency trade-off?," ISER Discussion Paper 1195, Institute of Social and Economic Research, Osaka University.
    9. Andreoni, James & Kuhn, Michael A. & Sprenger, Charles, 2015. "Measuring time preferences: A comparison of experimental methods," Journal of Economic Behavior & Organization, Elsevier, vol. 116(C), pages 451-464.
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