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Exploring differences in empirical time preference rates for health: an application of meta‐regression

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  • Francis Asenso‐Boadi
  • Tim J. Peters
  • Joanna Coast

Abstract

Estimated time preference rates are extremely varied, with many rates being extremely high. Reviewing empirical studies without quantitative synthesis of their findings is largely unhelpful in determining how rates vary according to different factors. This study therefore explores the use of meta‐regression techniques to combine available evidence to draw reliable conclusions about the factors influencing empirical time preference rates. Papers reporting empirically derived time preference rates related to health and health‐care programmes were selected. Included were papers presenting all of: a mean time preference rate; information allowing derivation of standard errors; and one or more covariates. Appropriate data were derived from only eight of the 16 papers reporting empirical time preference rates. Meta‐regression indicated that there were statistically significant relationships between mean time preference rates and: (a) delay period on a log scale; (b) whether the outcome question related to a gain or a loss. There were a number of limitations related to the use of meta‐regression in this area, including difficulties in extracting appropriate data from the original studies, and the extent to which the original studies provide fully deliberated estimates of time preference. Copyright © 2007 John Wiley & Sons, Ltd.

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  • Francis Asenso‐Boadi & Tim J. Peters & Joanna Coast, 2008. "Exploring differences in empirical time preference rates for health: an application of meta‐regression," Health Economics, John Wiley & Sons, Ltd., vol. 17(2), pages 235-248, February.
    • Handle: RePEc:wly:hlthec:v:17:y:2008:i:2:p:235-248
    DOI: 10.1002/hec.1256
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    File URL: https://doi.org/10.1002/hec.1256
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    References listed on IDEAS

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    Cited by:

    1. Kenneth C. Lichtendahl & Samuel E. Bodily, 2012. "Multiplicative Utilities for Health and Consumption," Decision Analysis, INFORMS, vol. 9(4), pages 314-328, December.
    2. Dodd, Mark C., 2014. "Intertemporal discounting as a risk factor for high BMI: Evidence from Australia, 2008," Economics & Human Biology, Elsevier, vol. 12(C), pages 83-97.
    3. Mike Paulden & Karl Claxton, 2012. "Budget allocation and the revealed social rate of time preference for health," Health Economics, John Wiley & Sons, Ltd., vol. 21(5), pages 612-618, May.
    4. Erik Nord & Jose Luis Pinto & Jeff Richardson & Paul Menzel & Peter Ubel, 1999. "Incorporating societal concerns for fairness in numerical valuations of health programmes," Health Economics, John Wiley & Sons, Ltd., vol. 8(1), pages 25-39, February.
    5. Verity Watson & Frauke Becker & Esther de Bekker‐Grob, 2017. "Discrete Choice Experiment Response Rates: A Meta‐analysis," Health Economics, John Wiley & Sons, Ltd., vol. 26(6), pages 810-817, June.
    6. John P. A. Ioannidis & T. D. Stanley & Hristos Doucouliagos, 2017. "The Power of Bias in Economics Research," Economic Journal, Royal Economic Society, vol. 127(605), pages 236-265, October.
    7. repec:wly:econjl:v::y:2017:i:605:p:f236-f265 is not listed on IDEAS
    8. Maamar Sebri, 2014. "A meta-analysis of residential water demand studies," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 16(3), pages 499-520, June.

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